TechTalk - KPIT

a quarterly journal of KPIT Cummins Infosystems LimitedSensors,Signal Processing& ApplicationsThe World of SensorsListening to SignalsWatchdog SensorsStriking a Chord with CarsMaking ‘Sense’ out of DataSniffing for DangerA Voyage in ConcertVOL. 5, ISSUE 3 JULY - SEPT 2012

ColophonTechTalk@KPITCummins is a quarterly journal ofScience and Technology published byKPIT Cummins Infosystems Limited, Pune, India.Guest EditorialProf. Marika Domenica Di BenedettoDepartment of Electrical & Information EngineeringCenter of Excellence for Research DEWSUniversity of L’ AquilaVia G. Gronchi, 1867100 L'Aquila (Italy)Chief EditorDr. Vinay G. VaidyaCTO-Engineering, VPKPIT Cummins Infosystems Limited,Pune, Indiavinay.vaidya@kpitcummins.comEditorial and Review CommitteeAditi AthavaleChaitanya RajguruSudhakar SahPriti RanadiveSanjyot GindiKrishnan KuttyTarun KancharlaDesigned and Published byMind’sye Communication, Pune, IndiaContact : 9673005089Suggestions and Feedbackcrest@kpitcummins.comDisclaimerThe individual authors are solely responsiblefor infringement, if any.All views expressed in the articles are thoseof the individual authors and neither the companynor the editorial board either agree or disagree.The information presented here is only for giving anoverview of the topic.For Internal Circulation OnlyTechTalk@KPIT Cummins

ContentsEditorialGuest EditorialDr. Marika Domenica Di BenedettoEditorialDr. Vinay Vaidya23Profile of a ScientistArticlesDennis GaborMayurika ChatterjeeBook ReviewReinventing IndiaAditi AthavaleThe World of SensorsSudhakar SahListening to SignalsSanjyot GindiWatchdog Sensors - Sensors in Critical ApplicationsPrasad Pawar, Nazneen ShaikhStriking a Chord with Cars - Car-to-car CommunicationCharudatt PachorkarMaking ‘Sense’ out of Data - Statistical Modeling of Sensor DataTarun Kancharla2733410162228Sniffing for Danger - Sensors in Defense 34Sonali Kalmegh, Priti RanadiveA Voyage in Concert - Cars and SensorsChaitanya S. RajguruResearch Publications4047TechTalk@KPITCummins, Volume 5, Issue 3, 2012 1

Guest EditorialSensors as the Enabling Technology for Future ICT SystemsDr. Marika DomenicaDi BenedettoDepartment of Electricaland Information EngineeringCenter of Excellence forResearch DEWSUniversity of L’ AquilaVia G. Gronchi, 1867100 L'Aquila (Italy)The integration of the physical and biological world with the information world (cyber-physical andbiocyber-physical systems) has been considered for the last few years as a critical technology for futureapplications. It has been predicted that by 2025 there will be trillions of smart sensors serving billions ofpeople: more than 1,000 smart sensors per person! The potentials of this revolution are immense bothin commercial terms and in society welfare, but so are the challenges. Particularly interesting are thesocietal benefits that will come from the use of this technology: health and environment monitoring,personal security, water, oil and power distribution, and disaster recovery. The systems of the future willbe able to ‘close the loop,’ adapting control strategies on the fly to handle faults safely and reduceresource usage. Control strategies for such systems can be optimized based on aggregated data.Consider for example a hybrid vehicle that gains access to information about traffic patterns, learningthat most cars turn left at a particular intersection. Such information is not available without aggregation,and in fact is likely changing dynamically, but such information could be used to improve fuel efficiencyand reduce emissions. Vehicles will coordinate with each other and with roadways and airways, andcontrol strategies will improve efficiency and safety. Autonomous vehicles will opportunistically recruitsensor and communication resources that they encounter and adapt their behaviors accordingly,improving traffic flows. These integrated systems, enabled by the sensory swarm, also have greatpotential to improve our personal lives in many ways. Health monitoring will occur continually and in thebackground, identifying anomalies early enough to prevent major health problems. The adaptiveness ofsmart sensor systems will also make them much better suited for operation in uncertain and highlydynamic environments such as disaster recovery.We can now envision a world ten years hence where widely distributed sensor and actuator systems willmake it possible for humans to interact seamlessly with the distributed information world (the cloud)without the need of laptops and smart phones, where our lives will be more secure and better in manyaspects. However, there is a gap today that we need to fill in order to leverage the available technologyfor achieving that vision. For example, Wireless Sensor Networks (WSN) have been the object ofintense research for many years since Professor Kris Pister’s invention of the smart dust concept in thelate 1990’s at the University of California, Berkeley, and the publication of the famous report by theNational Academies: Embedded, Everywhere, A Research Agenda for Networked Systems ofEmbedded Computers, in 2001. Yet, even the creation of several companies around Berkeley such asCrossBow (recently acquired by Moog), Arch Rock (acquired by Cisco), and Dust Networks (still aprivate company) among others, has not been enough to spur economic growth of the area. Indeed thelack of platforms and of programming tools to facilitate the deployment of applications quickly andefficiently has been a primary cause for the lack of the much-anticipated market growth. In addition, thecost of these devices is still too high to make them pervasive.Thus, the major evolution of the sensor market has been in the area of wired sensors for applicationdomains such as industrial engineering and automotive. In particular, in automotive, we are at aninflection point, since novel applications like active safety and (semi-) autonomous driving dependcritically on the availability of advanced sensors such as radars, precise image and sound capture, as wellas digital signal processing algorithms. The car of the future will refuse to crash, thanks to sophisticatedcontrol strategies, rich sensor architectures, and wireless communication (cellular, Wi-Fi, and ad hocnetworks). For companies working in this domain, such as KPIT Cummins, mastering knowledge insensor technology is critical to innovate and to gain new markets. Mastering the technology requires thecapability of making architectural tradeoffs to reduce costs, while maintaining the amount of sensed datathat enables the application of automatic control.The collection of review and position papers of this TechTalk issue is an important contribution to raisingthe level of attention of the community, to sustain developments in this relevant area, and to make itpossible to reap the benefits of sensors. This issue is also going to be a useful tool for researchers andstudents in universities who are motivated by the study of industrially relevant and advanced technology.2TechTalk@KPITCummins, Volume 5, Issue 3, 2012

EditorialDr. Vinay G. VaidyaCTO - Engineering, VPKPIT Cummins InfosystemsLimited, Pune, IndiaImagine for a minute what would have happened if we never had a sense of taste. We would not haveknown the difference between the taste of chocolate and mud! Imagine another scenario in whichthe sense of touch was missing. This would have been very dangerous, since we would have failed tounderstand any animal bite including a snakebite. Our species would have been extinct long ago.Our every sensor improves knowledge of our surrounding and that is the basic function of any sensoreither natural or manmade.There is a general tendency to believe that in today's world, we have made a lot of progress and weknow almost everything that we need to know. Commonly we believe that there are 5 senses andthat's all one needs to have if one wants to understand the surrounding. Is this really true? Are weadequately equipped with all the senses that we need to get the best understanding of oursurroundings?When we started exploring the universe, Galileo gave us a telescope. That was an extension to oursense of vision. We mapped the universe through the optical telescope and we thought that is howthe universe looked like. However, in the 1930s Karl Jansky found some radiation coming from theMilky Way. His research eventually led to the development of the radio telescope. Our worldcompletely changed with the advent of radio wave sensing. Today our understanding of the universeis much better with Very Large Arrays (VLA) and with Very Large Base Arrays (VLBA) than merely theoptical telescope. Tomorrow we will have even better sensors and our understanding will be evenbetter.One of the classic examples of sensors is of the Voyager 1 and the Voyager 2 probes. NASA launchedVoyager 1 probe in Sept. 1977. Today after more than 34 years, it continues to send us signals. Thesignals from the Voyager one have slowed down quite a bit not because it is old but due to thedistance. Ray of light from the sun reaches earth in 8.32 minutes but the signal, traveling at the speedof light, from the Voyager 1 takes 16 hours to reach us! The Voyager 1 is now over 17.9 billion kmsaway from the Earth. It is the only manmade object that has gone that far. It has reached a region ofthe space where there is increased flow of charged particles coming from beyond our solar system.Sensors have made all this knowledge possible.Development of science and technology should ideally be used for the betterment of humankind. Sobe the case with sensors. The use of sensors is rapidly increasing in every field including in health care.Bio Sensor Networks are going to increase our ability to detect and treat diseases by multifold. In theprocess of developing and using such sensors, it is quite possible that we would hit upon anothersignal that could revolutionize our understanding of ourselves.The advancement of radio telescope has certainly taught us that there are signals beyond what ourfive senses can sense. The Voyager 1 journey is about to take us out of the solar system in the next 5years. We are sure that we will encounter new signals that would throw more light about the universeand its occupants. Such is the importance of sensors and the technology surrounding it.Please send your feedback to :vinay.vaidya@kpitcummins.comTechTalk@KPITCummins, Volume 5, Issue 3, 20123

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The World of SensorsAbout the AuthorSudhakar SahCREST,KPIT Cummins Infosystems Ltd.,Pune, India.Areas of InterestParallel Computing,Signal Processing,Mathematical AlgorithmsTechTalk@KPITCummins, Volume 5, Issue 3, 20125

61. IntroductionThe five human senses help us live a rich and fruitfullife. Using these senses, we interact with thesurrounding environment and live in a comfortablemanner. Similarly, in today's world of technology,there are huge numbers of sensors that make ourlife easier. We are so used to their presence in ourdaily lives that we do not even feel their presence.Remote controls of television sets, telephones,cameras, and X-ray machines are just somecommon examples, and the list is endless. Thereare many more applications of sensors in the areasof transport, engineering, medicine, agricultureand so on. In this article, we will discuss somefeatures of the sensors, types of sensors accordingto their operating principles, and a few casestudies.II. Features of SensorsEven though the five human senses are present in(almost) each one of us, we come across a lot ofvariation in these senses. Some people are able tosee distant objects very clearly. A few have theirsmell instincts especially strong as compared toothers. On similar lines, manmade sensors alsohave varying types of features like accuracy,precision, range, resolution, etc. These featureshelp us choose particular sensors according thedemands of the application.Accuracy and precision are typically confusedwith each other. Figure 1 explains this conceptwith the help of a dartboard example. Multipledarts are analogous to repetitive measurements.Accuracy refers to hitting the dart as close to thecenter as possible. The example dartboard in thetop right corner shows high accuracy, as darts arerepeatedly hitting close to the center. Additionally,most of the darts are hitting at the same location,which means high precision. The dartboard at theleft bottom shows that the darts are far from thecenter and all of them are hitting the board atdifferent locations, which is analogous to lowaccuracy and low precision respectively.Fig. 1. Accuracy vs. Precision.TechTalk@KPITCummins, Volume 5, Issue 3, 2012When a thermometer measures temperatures0 ofrom -20 C to 100 C, the difference in these twoextreme values is called the range of the sensors.Consider the example of a motion-sensing doorthat opens automatically when a movement occursin front of it. The minimum time duration for whichthe movement should occur in order to open thedoors is nothing but the sensitivity of the sensors.Air bags installed in a car are deployed based on theoutput of some sensors. Response time is the timetaken to pop up the airbag in case of an accident.Resolution can be best demonstrated by thesmallest measurable temperature difference in our0thermometer example, typically 0.1 C in a clinicalthermometer.III. Classification and Types of SensorsSensors are classified based on different aspects likeoutput signal, power requirement, properties, etc.Output SignalDigital sensors – Digital sensors provide digitaloutput as '0' or '1' or in some cases discrete digitaloutput [1]. For example, a switch is the bestexample of digital sensors that detects the status ofthe switch as 'on' or 'off'.Analog sensors – Analog sensors providecontinuous values based on the value of inputparameter. For example, a temperature sensordetects and produces temperature values within acontinuous range. However, its output value isdictated by the resolution of sensor.Power and Energy RequirementActive Sensor – Active sensors require externalpower for their operation. Photoconductive cellsand active RFID sensors are two examples of activesensors.Passive Sensor – Sensors that work without anydedicated power supply are called the passivesensors. Passive RFID, strain gauge, resistancetemperature detectors (RTD) are few examples ofpassive sensors.PropertySensors are also categorized based on the physicalquantity that they measure. Table 1 shows differentparameters that can be measured and the sensorsthat measure it [2] [3].

Table 1. Sensor Classification Based onPhysical Quantity Measured.Property Examples of SensorsPressure Fiber optic sensor, vacuum sensorFlowElectromagnetic sensor,differential pressure sensor,positional displacementsensor, thermal, mass flow sensorLevelDifferential pressure sensor,ultrasonic radio frequency sensor,radar, thermal displacement sensorProximity & Photoelectric sensor, capacitivedisplacement sensor, magnetic sensor,ultrasonic sensorBiosensors Resonant mirror,electrochemical sensorImageCharge coupled devices, CMOSGas and chemical Semiconductor, conductance,electrochemicalAcceleration Gyroscopes, accelerometersTypes of SensorsIn this section, we will have a look at the sensorsfrom the perspective of the means that are usedfor sensing [4].Electrical SensorsAs the names suggests, these sensors measure thequantity using electrical means. For example, radardetects physical characteristics of a target bysending an electromagnetic signal and receivingthe modified signal. Other electrical sensorsare voltmeter/ammeter, metal detector,magnetometer etc. Figure 2 (a) shows atachometer that works on the principle ofgeneration of electromotive force. Electromotiveforce is generated by rotation of a conductor thatcuts the magnetic flux.Thermal SensorsA thermal sensor uses temperature change as thebasis of measurement. For example, a simplethermometer used in households or in hospitalsmeasures the change in mercury level by variationin temperature. Simple thermal sensors includethermometer, thermocouple, etc.Mechanical SensorsMechanical sensors measure the physical quantityusing mechanical means. One example of suchsensors is gas flow sensor that measures the flowusing mechanical movement of a mechanical part.The output of such sensors is measured as electricaloutput. Some more mechanical sensors arepressure sensors, accelerometers, potentiometers,humidity sensors, etc. Figure 2(c) shows a soundsensor that converts the vibration due to soundsignal into electrical form, which conveys the soundcharacteristics.Optical SensorsOptical sensors detect the change in lightcharacteristics to measure the required quantity.Some examples of these sensors are photodetectors, infrared sensors, fiber optic sensors,interferometers, etc.Chemical SensorsChemical sensors use the change in chemicalcharacteristics to measure a quantity. For example,modern MEMS sensors change their property inpresence of alcohol traces. Such sensors can beutilized in detecting 'drink and drive' cases. Otherchemical sensors are oxygen sensors, carbonmonoxide detectors, radox electrodes etc. Figure2(d) shows a catalytic sensor that works on theprinciple of oxidation of a catalyst in presence of agas, which creates the imbalance in an electricalbridge circuit to produce an output voltage.IV. Sensor Signal ProcessingA computer or microcontroller is often used forprocessing the signals obtained from sensors.However, a computer cannot understand theelectrical signals generated by the sensors. Hence,the signal is converted to a form that a computercan understand. The complete system that servesthis purpose is known as 'Data Acquisition System'(DAQ) and it consists of the following blocks.lPhysical to electrical – Sensor converts thephysical quantity to corresponding electricalsignal. For example, temperature is converted toelectrical waveform for further analysis.Fig 2. Types of sensors (a) Tachometer (b) Temperature Sensors(c) Sound Sensor (d) Catalytic Sensor (e) Flow Rate Sensor (f) Rotary Encoder [2].lElectricalsignal to a suitable analog signal – Theoutput of sensor signal may contain noise. Theamplitude of signal may be too low to process.Hence, the signal needs further processing toimprove the quality of signal. This step mayinvolve filtering, attenuation, amplification, etc.,TechTalk@KPITCummins, Volume 5, Issue 3, 20127

dependent upon the amplitude of signal, amountof data, and type of data required.lAnalog to digital conversion (ADC) - The analogsignal is converted to digital form using Analog toDigital Converters (ADC) so that computers canfurther process it.V. Applications of SensorsFig 3. Airbag Accelerometer [2].A typical high-end car uses more than 100 sensorsand more than 60 electronic control units (ECU).However, the sensor usage is dominated by MicroElectro Mechanical Devices (MEMS). Forexample, air bag accelerometer (Fig. 3), and tirepressure management sensors (TPM) are used inautomobiles to deploy air bags and detect pressureinside the tires respectively. A recent proposal bythe U.S. National Highway TransportationAdministration (NHTSA) has suggested that twoto six sensors should be added to every car toactivate side-impact airbags [5]. Parking assistsystems use multiple ultrasonic sensors to detect ausable parking space. Inertial sensing system asshown in figure 4 uses a vast number of sensingfunctions to get information about vehicledynamics, navigation, safety, and steer-by-wireapplications. A fiber optic based pressure sensor(as shown in figure 5) developed by Optrand uses aglow plug to measure engine pressure giving betteraccuracy and range for the sensor [6]. Seatelectrodes in a car are used to determine thepresence of a driver in the car, which can be used inanti-braking system to avoid run-away of a carparked on a slope.Fig 5. Fiber Optic Pressure Sensor.(Source: Imago group Benelux:http://www.imagogroup-benelux.com/)The wireless sector is another important home forsensor applications. According to pioneer of MEMS,Janusz Bryzek [6], wireless sensors capture a $500million share of the current $40 billion sensormarket. Most of these sensors are activesensors adhering to the wireless standardrecommendation. They operate in the operatingvoltage range between 2V and 3.6V. Minimization ofpower consumption is the next major challenge ofwireless sensor industry.Mobile phones are very important application areasfor sensors. For example, new generation smartphones use a number of sensors like accelerometer,GPS, digital compass, gyroscope,etc. These sensorsprovide information of location that is useful insocial networking and navigation applications [7].Sensors are becoming an integral tool for healthpractitioners to monitor patients' health. The roleof sensors is becoming increasingly important, asmost of the instruments used in hospitals usesensors that range from simplest instruments likethermometer, stethoscope etc. to the complexinstruments like ECG (Electrocardiogram)machines, ultrasound scanners, nasal endoscopes,etc.Any discussion about applications of sensors wouldbe incomplete without mentioning the industrialapplications [8]. Industrial automation is incompleteand ineffective without sensors. Some of theindustrial applications, which prominently make useof sensors, are water metering, gas metering, fuellevel sensing, hydraulic lift position, speed sensing,etc.VI. Future and ConclusionFig 4: Concept of MEMS Inertial Sensor Cluster [5].Sensors have made their impact on many areas likeautomotive, medical, industrial, aerospace, andconsumer electronics. Further increase in sensor8 TechTalk@KPITCummins, Volume 5, Issue 3, 2012

usage is expected in more areas like safety,entertainment, government regulations, etc. Inaddition, new technological developments likeMEMS, microsystem technology, andnanotechnology are making the process of sensormanufacturing easier, reducing their size, andenabling them to be used in new application areas.The future of active and passive automotive safetysystems largely depends on the extensive use ofsensors. Many of these systems such as parkingassist, cruise control, etc. are already at the stageof production. There are two major challengesthat automotive industry needs to deal with. Thefirst is the reduction in number of sensors byintegrating the features of multiple sensors into asingle sensor. The second challenge is related tothe proper design of cars to accommodate thesensors that can be aesthetically accepted.Fig 6. Future GPS Sensor [9].Sensors are also an integral part of cell phones andthey are expected to grow significantly in future.For example, a new type of sensor called'altimeter' will be used in next generation smartphones. Altimeter can be used to detect theelevation. In future, combination of new sensorscan be used to customize cell phones andautomatically train them based on location andother information derived from sensors. Otherfuture sensors that would be used in mobiles aremood sensors, tiny GPS sensors (figure 6) and soon.Fig 7. Curved Image Sensors [10].Conventional cameras use a flat sensor but they arestill far from mimicking the human eye. Researchersat university of Illinois [10] are working on creatingan image sensor (figure 7) using silicon detectorsand electronics that conform to a curved surface.Similar to the working of human eye, all parts of thecurved sensor capture the image more accuratelyby being at the focal point of the camera lens.Sensor manufacturers will continue to facechallenges with respect to robustness, reliability,quality, and cost. Existing sensors will continue toserve the purpose because of their provenperformance and cost benefits. However, newsensors will slowly create their place into themarket by providing advanced features for futureapplications.References[1] “Types of sensors,” online article in 'Roboticsfor You', Available at:http://roboticsforyou.blogspot.in/2008/10/sensor-typesanalog-and-digital-sensors.html[2] “Sensors,” online article in Engineer's handbook, 2004,Available at:http://engineershandbook.com/Components/accelerometers.htm[3] Sensors Portal website -www.sensorsportal.com/HTML/Sensor.htm[4] Susan Kristoff, “An introduction to sensors,” Engineering@suit101, Mar, 20008 Available at: http://susankristoff.suite101.com/an-introduction-to-sensors-a48734[5] S. J. Prosser, “Automotive sensors: past,present and future,” IOPscience, Journal ofPhysics: Conference Series- 76 012001, 2007,Available at: http://iopscience.iop.org/17426596/76/1/012001[6] Roger Allan, “The future of sensors,” Electronic design,July 2004, Available at:http://electronicdesign.com/article/components/the-future-of-sensors8326[7] Arnold Kim, “New types of sensors in future mobilephones: altitude, emotion and more,” MacRumours Newsarticle, May 2011, Available at:http://www.macrumors.com/2011/05/20/new- types-ofsensors-in-future-mobile-phones-altitude-emotion-andmore/[8] “Industrial applications,” Hamlin, Position, and MovementSensors Solutions, Official website of Hamlin, Available at:http://www.hamlin.com/sensor-applicationsindustrial.cfm[9] Christopher Mims, "GPS receivers now small enough toattach to almost anything,” MIT Technology Review, May2011, Available at:http://www.technologyreview.com/blog/mimssbits/27134/[10] Kurt Kleiner, “Artificial eyeball does away with distortedimages,” New Scientist Tech, Aug 2008, Available at:http://www.newscientist.com/article/dn14477-artificialeyeball-does-away-with-distorted-images.htmlTechTalk@KPITCummins, Volume 5, Issue 3, 20129

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Listening to SignalsAbout the AuthorSanjyot GindiCREST,KPIT Cummins Infosystems Ltd.,Pune, India.Areas of InterestImage Processing,Computer VisionTechTalk@KPITCummins, Volume 5, Issue 3, 201211

I. IntroductionWhat are the first few things that come to mindwhen you hear the word 'Signal'? You may think oftraffic signals, cell phone signal, signal for indicatingright turn, radio AM/FM signal, a weak Wi-Fi signal,and so on. What is the common fundamental thingabout all these signals? All are indicators of someinformation. For instance, the color of the trafficsignal light conveys whether the vehicles shouldstop or go, a turn indicator indicates the intentionof a driver to take a turn, an AM/FM signal bringmusic to our radio receivers and a Wi-Fi signalsends and receives data from the Internet. Allthese are different manifestations of signals. Asignal in any form is used to convey informationabout the behavior or attributes of a phenomenon[1]. These phenomena could either beenvironmental such as climate, earthquakes,growth rate of microorganisms etc.; or they couldbe pertaining to humans. Examples of later includecommunication, health (such as ECG), orentertainment. The information from the signalmay further be used to predict future phenomenalike natural disasters, weather, and extent of anillness. However, the information from the signalshould be read accurately for this purpose.What is Signal Processing?Historically, the interpretation of signals in naturehas been the epicenter of the development ofhumankind. In the present day too, with theexplosion of information and technology, humanlife is surrounded with an infinite number of signals.Timely and accurate interpretation of all thesesignals is a daunting task. The field of signalprocessing deals with the generation, analysis,manipulation, and usage of (typically) electricalsignals. Electrical signals can be analog or digital. Ananalog signal is a continuously varying signal in timeand voltage levels. However, most systems todayoperate on digital signals rather than analog. Digitalsignals contain discrete voltage levels, which arepresent at specific instants of time. Hence, digitalsignals can be represented by limited number ofvoltage levels existing at specific time instants only.This provides a simpler system design and promisesflexibility in handling and processing of signals. Withthe advancement of technology, digital systemshave become more robust to noise attacks andcheaper too! Because of these advantages and manymore, the field of digital signals and processingoffers tremendous opportunities and applicationsdevelopment.Digital signal processing involves the study oftechniques to suit the application at hand. Forexample, a communication system would require asignal to be transmitted as quickly as possible. Asecurity system for defense application and amedical diagnostics system should ensure that thereis absolutely no loss of information. Video (or music)equipment requires that the output signal is pleasingto the eye (or ear), and a system on an embeddedboard would need the processing techniques to besimple with minimum memory requirements.Let us look at some basic terminology that iscommonly used in the field of signal processing. Wewill assume for the rest of the article that a signalnecessarily means an electrical signal, since most ofthe sensors used today contain transducers thatconvert a physical world signal to electrical.II. QuantizationSignals in nature are inherently analog. Hence, theyneed to be first converted to digital form. Further,they are processed, modified, and reconstructedback to their perceivable (analog) natural form.The conversion of a signal from analog to digitalform involves a process called as quantization. Insimple words, quantization is the process of makingthe best approximation of an analog signal, usingdiscrete and fixed voltage levels. Let us consider asimple analogy to describe quantization. Suppose12 TechTalk@KPITCummins, Volume 5, Issue 3, 2012

you want to buy a book for Rs. 300. There aremany ways in which this amount can be given, forexample, three currency notes of Rs. 100, or sixcurrency notes of Rs. 50 each, or thirty currencynotes of Rs. 10 each. In short, Rs. 300 can be paidusing 3 notes or 6 notes or 30 notes depending onthe lowest decimation of currency notes available.These three options are nothing but threedifferent representations of Rs. 300. In signalprocessing, this representation is done by theprocess of quantization. Quantization represents acontinuous analog signal in fixed number of levelsbased on the lowest voltage level desired. In signalprocessing terms, any voltage signal, which variesfrom 0V to 15V, may be represented by 16 voltagelevels (or more) depending on the lowest voltagevalue that is desired. If a system requires thatintermediate values of voltage (i.e. for example,0.5V, 12.3V, 10.34V) to be used, more number oflevels would be needed. Consequently, theaccuracy (or fidelity) of the representation will beincreased. However, this increase in the accuracyis at the cost of memory.III. SamplingApart from being continuous in value, analog signalsare also continuous in time. This means that ananalog signal has some voltage value for all instancesof time. In other words, the voltage value maychange every microsecond and nanosecond. Just asquantization represents a continuous voltage signalby a limited number of levels, sampling represents acontinuous time signal by a finite number of timesamples. For digital processing of a signal, it issampled at regular and fixed time instants. Figure 2shows the time sampling of a signal S taken at theinterval of one second. The continuous curve fromt=0 to t=13 is the original signal while the s-dotsrepresent the sampled signal.In figure 1, the sine wave is the analog signal thatcontains all voltage levels from 0V to 7V includingdecimal values. However, the quantized signal(shown as a staircase-like signal) approximatesthese intermediate decimal values to the nearestinteger. The output of quantization now contains asignal with eight discrete voltage levels, which, maylater be converted into the binary form.Voltage LevelTimeFig. 1. QuantizationFig. 2. Sampling of a Continuous Time SignalIV. Frequency TransformsTransforms provide an alternate way forrepresenting a signal. An analogy to transforms canbe drawn with language translation. Suppose thatyou are in a locality looking for a specific address.You decide to ask someone for directions. Youwould most likely call out to some person aroundand explain to him your requirement. If you were inIndia, you would most likely speak in Hindi orEnglish. Now imagine that you are in anothercountry, for example, in China or in Germany.Although the question you want to ask would be thesame, you would have to use a different language,Mandarin (Chinese) or German. Thus, theinformation is the same, only the language used isdifferent. Translation is an example of transform. InTechTalk@KPITCummins, Volume 5, Issue 3, 201213

signal processing, transforms are used to convertsignals in one domain to one in a different domain.Every signal, either analog or digital, containscertain frequencies. The input AC power voltagethat we receive from the electricity board is of50Hz, 230V. The signals that we usually see are inthe time domain. For example, an ECG signal seenon the instrument at a doctor's clinic depicts thevariations in the heart signals with time. However,a music equalizer shows the presence and changesin frequencies in the music being played. 'Bass' inmusical terms refers the sound of drums orpercussion instruments while 'treble' generallyrefers to string instruments. Bass is nothing but lowfrequencies in the music while treble refers to highfrequencies.According to the application at hand, we need torepresent signals in either the time domain or thefrequency domain. This need may arise to cater tothe design complexities, or simply to increase easeof operation by the end user (as in the case of amusic equalizer).A frequency transform is a tool to convert a signalexpressed as a function of time to a signalrepresented by its frequency components. JosephFourier (Fig. 3) formulated the mathematicalprinciple of converting a time domain signal tofrequency domain. This transform, known asFourier Transform, is the foundation of most ofsignal processing. Figure 4 shows the Fouriertransform of a rectangular pulse signal.It is possible to determine many characteristics of asignal from its frequency transform like bandwidth,Fig.3. Joseph Fourierfidelity and noise content etc. Bandwidth is therange of frequencies contained in the signal thatenables the engineer to design interfaces, signalfilters etc. Fidelity denotes sameness between asignal as it is sent and as it is received. Noise contentof a signal would determine the filtering techniques.The above signal properties become evident fromits frequency spectrum.Fig.4. Rectangular Pulse Signal andits Fourier Transform [2]Another type of transform called the 'WaveletTransform' is widely being used to analyze signals ina better way. Fourier transform gives thefrequencies present in the signal but not the time atwhich that frequency exists. A Wavelet transform,on the other hand, provides the information of thetime instance at which particular frequencies arepresent in a signal. Alternatively, with images, it tellsthe location of a particular frequency component inan image. Wavelets are used widely in imageprocessing for data compression, in seismology,astrophysics, protein analysis, etc.V. ConvolutionThe convolution integral is another basic buildingblock in signal processing. It is used to predict theoutput of the system from the properties of theinput signal. Consider an image filter system as anexample. What happens when we capturephotographs with a camera that has dust particleson the lens? The images that we obtain are not clear.The objects in the image are not seen properly dueto the presence of random particles all over theimage. These unwanted effects in an image (orotherwise) are called 'noise' (figure 5).14 TechTalk@KPITCummins, Volume 5, Issue 3, 2012

Fig. 5. Original Image and Noisy ImageIn practice, to view images clearly, a filter is appliedto the images to remove noise. The filter isdesigned such that the noise particles randomlydistributed throughout the image are completelyremoved and the clarity of the image goes up. Afilter is a mathematical formula, which whenapplied to the image, removes noise effects in theimage. The application of a filter to the input isdone through the process of convolution.Similarly, in a music filter, if low pass (bass) signalsare to be blocked, a high pass filtering operation isperformed on the signal. Talking in terms of thefrequency domain, this filter blocks lowfrequencies and passes only higher frequencies.However, to apply this operation to any inputsignal sample we have to operate in the timedomain. The application of the filterimplementation to an input signal in time domain isperformed using convolutionMathematically, convolution is performed by'flipping' one signal in time domain and thenadvancing it by one time instant at a time. The valueof one output sample is calculated at one time stepof advancing each. The output signal at any timeinstant is the sum of the products of input timesamples and the corresponding time samples ofimpulse response, at that particular step ofadvancing. Figure 6 shows the process ofconvolution.In signal processing, convolution is used to predictthe output of a system, to design a system (itstransfer function) depending on the properties ofthe output desired, or to determine the relation orcorrelation of two signals.Fig. 6. Convolution of Two SignalsVI. SummaryA few of the basic signal-processing techniques andterms have been described in this article. A typicalsignal-processing algorithm or implementationinvolves many other concepts based on these and alot more mathematics! In fact, signal processing iscompletely dependent on mathematics for itsusage. As mentioned earlier, Digital SignalProcessing (DSP) finds wide applications acrossdomains. Dedicated processors called 'DSP's aredeveloped specifically for implementing signalprocessingalgorithms to ensure fast and efficientruntime performance. Be it mixing music, diagnosisin the field of medicine, statistical modeling, imageprocessing, speech recognition, or automotivesensors and diagnostics, signal processing is at theheart of development of most of the applications.References1. Smith, Steven, “The Scientist and Engineer's Guide to Digital SignalProcessing,” California Technical Publishing, Oct 2003.2. “Fourier Transform,” Wikipedia: The Free Encyclopedia, WikimediaFoundation, Inc., 7 June 2012. Available at:http://en.wikipedia.org/wiki/Fourier_transformTechTalk@KPITCummins, Volume 5, Issue 3, 201215

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Watchdog SensorsSensors in Critical ApplicationsAbout the AuthorsPrasad PawarCREST,KPIT Cummins Infosystems Ltd.,Pune, India.Areas of InterestParallel Computing, Operating Systems,Algorithms, and SAN and Disaster RecoveryNazneen ShaikhIntegrated Enterprise Solutions,KPIT Cummins Infosystems Ltd.,Pune, India.Areas of InterestMobility and Web Development,Artificial Intelligence, and Green TechnologyTechTalk@KPITCummins, Volume 5, Issue 3, 201217

I. IntroductionTechnology is a double-edged sword. It has takenhuman beings to the moon, at the same time it hasalso abolished millions of people in hazards likefailure of nuclear reactors, fatal accidents,pollution etc. In addition to these manmadehazards, human beings cannot escape nature'swrath such as earthquakes, tsunamis, floods, anddroughts. It is becoming increasingly important tobe proactive and mitigate these dangers as early aspossible. Here, technology again is of big help inthe form of sensors. In the past few years, sensorsare increasingly used in such critical applications tostrengthen our ability to face the wrath and to giveus a warning about the oncoming dangers.In this article, we will take a glimpse into thesensors and signal processing technologies forcritical applications such as GIS and GPS sensorsfor monitoring various disasters, and portable gassensors for improving atmospheric pollutionmeasurements. Smart sensors are used for bettermonitoring in nuclear reactors, and nextgeneration sensors find uses in space. We will alsodiscuss these sensors in this article.II. Disaster MonitoringWhen we talk about nature, images of greenery,joy, and originality come to our mind. At the sametime, we cannot forget the other face of nature,which comes in the form of natural disasters.Natural disasters come in various forms likeearthquakes, tsunamis and volcanoes, storms,cyclones, floods, and droughts.Though we cannot stop an oncoming naturaldisaster, we can monitor or predict it in two ways:one is by analyzing the history of disasters, and theother is by using a combination of sensors.Typically, geographic information systems (GIS),the global positioning system (GPS), and remotesensing (RS) technologies (also called “3S”) areused for the disaster monitoring. GPS is a satellitebased navigation system, which provides theinformation on location and time in any weatherconditions, anywhere on the earth. GIS is a systemdesigned to capture, store, operate, analyze, andmaintain all types of geographical data. Moreover,it is possible to collect data on risky or unreachableareas using remote sensors.There are two aspects of using sensors disastermonitoring: one is early prediction of disaster likeearthquakesusing a technique like GPS, where we get the signaldata and the processing of this signal data gives earlyindication of earthquake. The other aspect is toidentify overall damage due to the disaster by acombination of GIS, GPS and remote sensing. HereGIS plays vital role to analyze data fetched byvarious remote sensors, GPS, digital camera, andmobile telephone and predict the estimation ofdisaster damages.There are some examples of remote sensors suchas IKONOS and QuickBird [1], which are used toobserve the areas affected by tsunamis andearthquakes. According to a case study fromDecember 2004, when several Asian countrieswere hit by a tsunami, IKONOS and QuickBirdimages accurately identified the damages inIndonesia, Thailand, and Sri Lanka [1].We have tools like airborne laser scanner or Lidar[1], which are used for risk assessment duringdisasters such as floods, high tides, and tsunamis.The combination of digital photography with digitalcameras and airborne laser scanner also gives us 3Dmeasurement and analysis.Sensors receive signals based on variousphysical factors like underground pressure andunderground rock movement. These signals aresent for further processing to analytical systems.Fig. 1. GPS Measurements in Disasters[Illustration: Matt Zang]As shown in figure 1, when two tectonic plates pusheach other below the sea surface, an earthquakeoccurs. A tsunami is generated when the upperplate lifts the ocean upwards. By the use of GPSdisplacement technique, the movement of a GPSground station with respect to a GPS satellite, andthus the magnitude of the earthquake and tsunamiare measured.18 TechTalk@KPITCummins, Volume 5, Issue 3, 2012

III. Sensors in Nuclear Power PlantsIn the year 2011, Japan was heavily impacted by anearthquake, not only in terms of deaths, but alsodue to damage to infrastructure including a nuclearpower plant. The nuclear power plant generateselectricity from steam, which is generated by theheat produced during a nuclear reaction. Thepower plant system consists of variouscomponents like nuclear reactors, steam turbines,generators, cooling systems, safety valves, andfeed water pumps, which make it very complex. Tohandle such a complex environment, aninstrumentation and control system is installed innuclear power plants. The instrumentation andcontrol system consists of various sensors such asneutron detectors, resistance temperaturedetectors (RTD), pressure and level sensors,thermocouples, capacitance cells, bellows, andforce-balance sensors [2]. The control systemautomates the operations of the plant by sensingbasic physical parameters, observingperformance, and articulating information by usingvarious sensors. Figure 2 shows the temperaturesensor installation, and figure 3 shows theconnection of pressure transmitter to the processpipe.Sensors continuously interact with the physicalchannels in the power plant to measuretemperature, pressure and flow as well as withcontrol, regulation, and safety components thatprocess the sensors' data. To speed up the dataprocessing, the data processing module [4] isimplemented on a high-performance computingserver. Dynamic analysis of nuclear plant sensorsand equipment uses AC signal analysis to determinehow sensors and equipment react to fast-changingevents such as temperature or pressure steps.The sensors used in nuclear power plants are beingupgraded to smart sensors. Smart sensors havefeature like self-diagnostic capabilities, memory,and wireless transmission of data. Fiber-opticsensors offer high bandwidth, no drift over time,and easy installation. However, since radiationaffects their performance, they cannot be used innuclear power plants. Wireless sensors are alreadyin use in such plants, but they are vulnerable tocyber-attacks. In addition, battery life of wirelesssensors is limited. Ultrasonic sensors offer accuracybetter than conventional flow sensors, but they arevery costly.Next-generation sensors include the latesttechnologies to increase accuracy, and redundancy.Such technologies include Johnson noisethermometers, solid-state neutron flux monitors,magnetic flow meters, hydrogen sensors, virtualsensors, and fuel mimic power monitors. Most ofthese sensors are at the research stage at the OakRidge National Laboratory, Ohio State University,and other research organizations and universities[5].IV. Pollution MonitoringFig. 2. Temperature Sensor Installation ina Nuclear Power Plant [3]Fig. 3. Pressure Sensing Line Connecting a PressureTransmitter to the Process Pipe [3]Atmospheric pollution measurement is an activeresearch area, and multiple technologies have beenproposed for it. Use of portable greenhouse gassensors to measure the atmospheric pollution isone such research topic. Let us have a look at aportable gas sensor in action by means of anexample.A sensor system has been developed recently inwhich a compact, low power, open-path lasersensor is combined with a robotic helicopter. It isoperated by a remote control device to measurethe three most important greenhouse gases in theatmosphere – carbon dioxide, methane, and watervapor [6]. A GPS system easily tracks the helicopter,TechTalk@KPITCummins, Volume 5, Issue 3, 201219

which is fitted with a laser based sensor near thenose, as shown in figure 4. The helicopter can beprogrammed to navigate in specific areas usingGPS co-ordinates to work in extreme conditions.microelectromechanical system (MEMS)technology (figure 6). It measures the velocity of airacross two tiny, resistive strips of platinum that areheated to 200°C. It uses the temperature differencein the cross section of these extremely sensitiveheated wires to determine the acoustic particlevelocity.Fig. 4. Robotic Helicopter with Attached PortableLaser Sensor [6]V. Aerospace and AeronauticalEngineeringSensor systems such as radar, navigation, and spacesystems have played a vital role in aeronautics andaerospace missions. Planar OptoelectronicTechnology ("POET") is a unique and patentedtechnology of ODIS Semiconductor Company. Itsupports creation of ICs containing active andpassive optical elements along with highperformanceanalog and digital elements (Figure5). Many such spacecraft sensors with POET areused in long-term missions due to less weight andless power consumption [7]. Such sensors aresecure, lightweight, reliable, and fast, and theyconsume less power.Fig. 6. Acoustic Vector Sensor [9]The real-time acoustic data signal is processed intoa proper format and is mapped to the timestampand location. The signal is first examined forrelevant signatures in the time domain and the timefrequencydomain. The signal is classified with thehelp of preset limits, and calibrated models areapplied to generate an appropriate output. Forimproving the precision of classification and theaccuracy of localization, multiple outputs are linkedand combined. The small size and weight of acousticvector sensors and their ability to detect a variety ofacoustic events from a variety of mountingplatforms enables them to be used in numerousapplications. These include gunshot localization,rocket, artillery & mortar (RAM) localization,passive radar for border/perimeter protection, andvideo camera cueing [9].VI. ConclusionFig. 5. Planar Optoelectronic Technology(image released by ODIS) [8]Since the invention of RADAR systems, sensorshave played an important role in the field ofaeronautics. A sound field can be describedcompletely in two dimensions by the scalar andvector value which can be measured using varioussensors. The movement of air is calledparticle velocity in acoustics. An acousticvector sensor, named Microflown is based onSensors are available in a wide variety, each havingdifferent capabilities of measurement and dataanalysis. Dealing with such diversity will be achallenge in the future. As mobility gainsimportance in today's society, future sensors needto be mobile and portable. The use of GIS sensorsand remote sensing has been a revolutionary steptowards natural disaster management. Nextgeneration sensors such as Johnson NoiseThermometers and solid-state neutron fluxmonitors will help monitor nuclear reactorseffectively. Portable gas sensors introduced by thestudents from the Princeton University andUniversity of Texas, Dallas will be highly useful formonitoring human-induced hazards such as20 TechTalk@KPITCummins, Volume 5, Issue 3, 2012

environmental pollution. Advanced sensors suchas POET and signal processing technologies canhelp mitigate disasters through better predictionsof future scenario. This will make a significantdifference in critical domains such as space andaeronautics by detection of danger zones andenabling protection measures and safealternatives.References[1] “Monitoring of disasters using remotesensing, GIS and GPS,” by Shunji Murai,Professor Emeritus, University of Tokyo.[2] “Management of life cycle and ageing atnuclear power plants: Improved I&CTECDOC-1402, ISBN 92–0–108804–3,ISSN 1011–4289, 2004.[3] “Aging management of instrumentation &control sensors in nuclear power plants,” byDr. H.M. Hashemian, October 2010.[4] “Nuclear power plant instrumentation andcontrol,” by Dr. H.M. Hashemian, 1984.[5] “Process sensors for nuclear power plants,”by Dr H. M. Hashemian, July 2010. Available:http://www.scitopics.com/Process_Sensors_for_Nuclear_Power_Plants.html[6] News article: “Portable gas sensors improveatmospheric pollution measurements,”Enhanced Online News, May 2012.Available:http://eon.businesswire.com/news/eon/20120501006692/en/Optics/CLEO/ozone[7] News article: “ODIS continues work withNASA phase II development contract,”Marketwire, May 2012. Available:http://www.sys-con.com/node/2267002[8]Company literature: “POET technology,”ODIS Inc. Available:http://www.opelinc.com/odis.html[8] Company literature: “POET technology,”ODIS Inc. Available:http://www.opelinc.com/odis.html[9] Company literature: “Acoustic vectorsensors,” Microflown Avisa, 2012.Available:Maintenance,” IAEA, Vienna, IAEA-http://www.microflown-avisa.com/acousticvector-sensorsDiscovery of Radio WavesRadiating from the Milky WayKarl Guthe Jansky joined Bell TelephoneLaboratories in 1928. At that time, Bell Labswanted to work on transatlanticradiotelephone service using short waves.For the same purpose, Karl Jansky was askedto study if there are any static sources,which can affect short waves i.e. waves oflength 10 to 20 meters. He built an antennato receive signals of frequency of 20.5 MHz(i.e. with the wavelength of 14.6 m). Afterstudying the signals received by thatantenna for more than 1 year, he observedthat there is a constant faint noisy elementgenerated from an unknown origin. Thelocation of maximum intensity of this signalwould always increase and decrease once aday. He initially thought that these radiosignals are originating from Sun. Afteranalyzing the signal for few more months, hefound that the period of repetition of thatsignal is slightly less than 24 hours, i.e. 23hours and 56 minutes. This period is theperiod of rotation with respect to rotationaround the center of galaxy. Hence, hediscovered that the signals are originatedfrom the Milky Way. He is considered as oneof the early contributors to the field ofradio astronomy.TechTalk@KPITCummins, Volume 5, Issue 3, 201221

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Striking a Chord with CarsCar-to-car CommunicationAbout the AuthorCharudatt PachorkarIntegrated Enterprise Solutions SBU,KPIT Cummins Infosystems Ltd.,Pune, India.Areas of InterestNetworks and Systems,Parallel ComputingTechTalk@KPITCummins, Volume 5, Issue 3, 201223

I. IntroductionCommunication between non-living objects, e.g.telecommunication, is around for more than onehundred years. Now, cars have also joined theleague of such objects. For more than ten years,car-to-car communication (C2CC) has been amajor topic of research in the scientific communityand an important development focus for theautomotive industry. Car-to-car communication,based entirely on wireless communicationprotocols, not only enables many new services forvehicles, but also provides various ways forimproving safety. Figures 1 and 2 illustrate C2CCoperation. Issues like traffic flow blockages,collisions, and emergency driver supportcan be easily addressed by C2CC. C2CC systemexchanges information among vehiclesas well as roadside infrastructure by wirelesscommunication. Information available in the carfrom its sensors and instruments can be quicklypassed to others in its vicinity by ad hoccommunication protocol [1]. This will help driversreact quickly in emergencies like sudden brakingby cars ahead.Along with the improvements, C2CC also facesmany challenges related to technology, protocols,and security. Due to high mobility of vehicles,network topology changes frequently. Thisdemands new approaches for data andcommunication security; high-speed real-timecommunication to guarantee reliability; andtrustworthy data transfer. This article will explainthe implementation of such communicationsystems, security issues, network protocols, andrecent developments in this field.Fig. 2. Vehicle to Infrastructure CommunicationII. ImplementationTo implement car-to-car communication, we needcomplete integration between the transceiver,global positioning system (GPS), and on-boarddiagnostics for each vehicle. A microcontroller canbe used to combine all these modules. Thetransceivers are based on the standard DedicatedShort Range Communication (DSRC). In the year2008, KPIT Cummins launched a DSRC-basedplatform for car-to-car and car-to-infrastructurecommunication. This software solution byKPIT Cummins runs on Freescale's awardwinning MPC5121E processor for telematicscommunication. It allows one- or two-waycommunication in the 5.8 GHz band. The carbattery provides the required power, thus avoidinguse of any external power supply. GPS is used todetermine the position of any node (say, in the car)and it uses serial communication protocol NMEA-0183. The on-board diagnostic port (OBD-II) isused to find the speed and acceleration of thevehicle. OBD-II is present on every vehicle, and hasself-diagnostic and self-reporting ability. Signalscoming from another vehicle go through thetransceiver to the microcontroller for furtherprocessing. The microcontroller serves as theinterface between all other components. A welldesignedaccident avoidance algorithm will decidewhether the car should brake to avoid collision.Fig. 1. Car-to-car CommunicationAn Ethernet controller ENC28J60 is used as theinterface between the microcontroller unit and theDSRC receiver. After receiving data from GPS andOBD-II, the microcontroller creates packets fortransmission to other vehicles. Each coordinatesent by GPS requires three bytes of storage, and24 TechTalk@KPITCummins, Volume 5, Issue 3, 2012

speed measured by OBD-II requires one byte ofstorage. This relatively small size of data resultsinto smaller size of packet. Hence, packets can besent quickly without using more computing powerfrom the microcontroller. To send any moreinformation, the packet can be extended to up to190 bytes. Packets are written sequentially into thereceive buffer of the Ethernet controller uponreception. This buffer stores data in FIFO (first-in,first-out) manner. To avoid processing of oldpackets, the microcontroller ignores data if thereare three newer packets that follow. After a vehiclereceives speed and location information fromanother car, it determines whether the other car isin front or back. If the other car is in front and thecalculation shows probability of collision, then themicrocontroller generates a warning message.Due to high vehicle speeds, the rate of informationtransfer is critical and time sensitive. Therefore,GPS should provide accurate positions.To get the real-time speed of the vehicle, OBD IIcaptures information from the vehicle speedsensor (VSS). This data is helpful fortroubleshooting problems and for monitoring theperformance of the car. These sensors aremounted either in the rear differential assembly orin the transmission case. VSS of most new vehiclesare similar to camshaft or crankshaft sensors.Signals from VSS are used in a number of tasks suchas variable assist power steering, electronic enginecontrol, and antilock braking. When signal fromone wheel abruptly varies from the others, acontrol module takes appropriate action byapplying traction control or brakes. Problems inVSS can be easily found out by using diagnostictrouble codes. For example, code 'P0500' indicatesmalfunctioning of sensors that capture the speedof vehicle [2].OBD II also assists in efficient fuel combustion byusing oxygen sensors. Whenever fuel is leftunburned after combustion because of less oxygen(known as 'rich mixture'), it causes pollution. Ifthere is excess supply of oxygen (known as 'leanmixture'), that produces nitrogen oxide pollutants.Therefore, it is necessary to maintain a proper airfuelratio. The oxygen sensor is a chemicalgenerator. It generates voltage signal between 0-1.1volts after comparing the air outside the engine andoxygen inside the exhaust manifold. For richmixture, it generates a voltage greater than 0.45 V,and for lean mixture, it generates voltage less than0.45 V [3].III. Security Issues and NetworkProtocolsWireless communication technology playsvital role in car-to-car communication. Dedicatedshort-range communications and WLAN aresome technologies selected for this purpose.Car-to-car communication requires high-speedcommunication with guaranteed low latency. This isa challenging task especially to avoid direct back-tobackcollision between vehicles. Another bigchallenge is to maintain security and reliability forhigh speed as well as for group communication.Most security standards include cryptographycalculations that increase latency in data transfer.Besides all this, contents of received messages haveto be verified within a short time. Availablestandards were not enough to satisfy all theseneeds, therefore a new approach was defined tofulfill these requirements. 802.11p is a revisedversion of 802.11 standards to add support forwireless car-to-car and car-to-infrastructurecommunication. The 802.11p amendmentproposes small modifications to the PHY layer andMAC layer in order to achieve a robust connectionand fast setup for moving vehicles. It operatesbetween 5.8 GHz and 5.9 GHz frequency bands. Itis used as groundwork for dedicated short-rangecommunication [4] [5].IV. Recent DevelopmentsCar-to-car communication is about to get its firstmajor real world test in the U.S.A. The Departmentof Transportation (DOT) awarded $14.9 million toUniversity of Michigan's Transportation ResearchInstitute. The university is already moving aheadTechTalk@KPITCummins, Volume 5, Issue 3, 201225

with the plan to put 3,000 short-range radioequipped cars on the road in Ann Arbor over thenext couple of years [6]. Cars will communicatewith each other and with roadside infrastructure.This test will generate prodigious data output. Byanalyzing this data, DOT's National HighwayTraffic Safety Administration will make a decisionon whether this technology can be approved ornot.Last year, Ford has announced plans to accelerateits work in the field of intelligent vehicledevelopment by increasing investment and taskforce. This task force consists of companyplanners, engineers, and scientists from around theworld with expertise in safety, infotainment, anddriver conveniences. Ford is also in discussion withother automakers and federal government tocreate a common language that ensures that allvehicles can talk to each other based on commoncommunication standards [7].A u d i a l r e a d y h a s c a r- t o - t r a f f i c - l i g h tcommunication system. Audi's 15 test carscommunicate with almost 150 traffic signals inIngolstadt, Germany and now they are looking forcar-to-car communication [8].V. ConclusionRecent developments in the automobile sectorand wireless technology have enabled car-to-carand car-to-infrastructure communication in thelast few years. C2CC can cut down pollution andfuel consumption by reducing idling at stoplights. Itcan help keep track of and avoid traffic jams, andmake online payment at fuel pumps and parkinggarages. It can also report the status of upcomingtraffic lights by analyzing speed of cars, which canimprove the flow of traffic. In some cases, thesystem can even take control of the vehicle andprevent it from crossing a stop signal to avoid acollision. The ongoing research in C2CC hasshown promising developments, which willimprove the functionality and safety of cars as wellas traffic systems.References[1] Stephan Eichler, Christoph Schroth, and JörgEberspächer, “Car-to-car communication,”Proceedings of the VDE-Kongress - Innovations forEurope, Aachen, Germany, October 2006.[2] Position/Speed Sensor. Available:http://www.autoshop101.com/forms/h36.pdf[3] Oxygen Sensor Information. Available:http://www.mr2.com/TEXT/O2_Sensor.html[4] Andreas Festag, Alban Hessler, Roberto Baldessari,Long Le, Wenhui Zhang, and Dirk Westhoff,“Vehicle to vehicle and road side sensorcommunication for enhanced road safety,” The 9thInternational Conference on Intelligent TutoringSystems - ITS'08, Montréal, Canada, June 2008.[5] Juraj Miček and Jan Kapitulik, “Car to carcommunication system,” Proceedings of theInternational Multiconference on Computer Scienceand Information Technology, pp. 627 – 631,October 2009.[6] Clay Dillow, “First big U.S. test of car-to-carcommunications planne.” Available:http://www.popsci.com/cars/article/2011-09/vehicle-vehicle-communications-gets-its-first-bigus-test-ann-arbor[7] Article, “Car-to-car communication: preventingaccidents in the future.” Available:http://social.ford.com/our-articles/cars/taurus/carto-car-communication-preventing-accidents-in-thefuture/[8] News report, “Audi shows off Travolution vehicleto-infrastructurecommunication system.” Available:http://www.engadget.com/2010/06/04/audi-showsoff-travolution-vehicle-to-infrastructurecommunicati/[9] Lars Wischhof, André Ebner, and Hermann Rohling,“Self-organizing traffic information system based oncar-to-car communication: prototypeimplementation,” International Workshop onIntelligent Transportation (WIT), March 2004.[10]Andreas Festag, Holger F¨ußler, HannesHartenstein, Amardeo Sarma, and Ralf Schmitz,“Fleetnet: bringing car to car communication intothe real world,” 11th World Congress on ITS,Nagoya, Japan, October 2004.[11]Alan Pierce, “Crash survival systems + Car-to-carcommunication = A next gen crash avoidancesystem.” Available:http://www.its.dot.gov/press/pdf/Car_to_Car_Communication.pdf26 TechTalk@KPITCummins, Volume 5, Issue 3, 2012

Scientist ProfileDennis Gabor“The future cannot be predicted,but futures can be invented. It wasman's ability to invent which hasmade human society what it is.”Dennis Gabor (1900-1979) was born in Budapest on June5, 1900 as the elder son of a mining company director. Hestudied electrical engineering in Budapest and laterfinished his academic education in Berlin with the awardof Doctorate of Engineering (Dr-Ing) in 1927. Hisdoctoral research was focused on the recording oftransients in electric circuits with cathode rayoscilloscope (CRO).Gabor, who was Jewish, left Germany in 1934 to escapeHitler's regime and started working at the developmentdepartment of 'British Thomson-Houston' in Rugby,England. There he worked on improvement of theelectron microscope. This work led him to conceive theidea of 'wavefront reconstruction'. This later came to beknown as 'Holography'. It was a method of usinginterference patterns in waves to record all informationproduced by an object reflecting or refracting the waves.However, its realization became possible only after theinvention of laser technology, which happened some 20odd years later. With laser technology coming into thelimelight, Gabor's holography technique got its longoverdue recognition, and he received the Nobel Prize inphysics in 1971.He also did some outstanding research in communicationtheory and signal processing. Results of this works areused even today. In 1946, Gabor published a seminalpaper in the proceedings of IEE (now IET) which hadlong-term impacts in the field of signal processing. In thispaper, He discussed the limits in representing discretesignals in both time and frequency domain, which laterformed the basis of proposition of the 'wavelet' transform.He also discussed the problems in practically applying theFourier Transform to sound waves. To overcome thoseproblems, he proposed that ideas from quantum physicsbe applied to the sound waves. In this proposition, heestablished the concepts of 'Gabor functions' and 'logons'.These reduced the uncertainty of simultaneouslocalization in both the time and frequency domains. Thediscussion and research work in this paper subsequentlyled Shannon to provide the theoretical basis for efficientsource and channel coding in 1948.In 1949, Gabor joined the Imperial College of Science andTechnology, London, as a reader in the ElectricalEngineering Department. Here he worked on a widerange of projects including electron microscope, plasmaphysics, signal compression, and information bandwidthcompression. In the year 1956, he was awarded with'Fellowship of the Royal Society' (FRS) as anacknowledgment for his outstanding work. Later in 1958,he was appointed to the Chair of Applied Electron Physicsat the same institute, the post he held until his retirementin 1967.In his later years, he got interested in social analysis, wherehe emphasized on reducing the gap between socialinstitutions and technology, thus prioritizing the aspects ofsocial invention. He published two books on this subject,'Innovations' in 1970 and 'Mature Society' in 1972.Gabor was awarded over 100 patents for his inventions inhis lifetime, his first being at the age of 11. He published awealth of seminal research papers. Although an electricalengineer by profession, his immense love for physics wasevident by his research work as well as by his academicpursuits that were mostly in the area of applied physics andelectronics. His ideas always proved to be way ahead oftime and were far reaching. Consequently, Gaborachieved acknowledged success and worldwide attentionduring his lifetime.About the AuthorMayurika ChatterjeeCREST,KPIT Cummins Infosystems Ltd.,Pune, India.Areas of InterestControl Systems &MechatronicsTechTalk@KPITCummins, Volume 5, Issue 3, 201227

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Making 'Sense' out of DataStatistical Modeling of Sensor DataAbout the AuthorTarun KancharlaCREST,KPIT Cummins Infosystems Ltd.,Pune, India.Areas of InterestSignal Processing,Image ProcessingTechTalk@KPITCummins, Volume 5, Issue 3, 201229

I. IntroductionDo you find anything common among populationstudies, weather forecast, insurance policies, andvideo surveillance? All of these activities requireprocessing of huge amounts of data. Additionally,the data do not necessarily follow any patterns, andare random in nature. In order to make ‘sense’ outof such data, 'statistical modeling' is used. Apartfrom the large volume and the absence of patterns,the data also include a lot of 'impurities' orunwanted components called 'noise'. Statisticalmodels prove to be very effective in obtainingmeaningful information from data containing noisecomponents [1]. In this article, we are going todiscuss statistical modeling of data obtained fromdifferent types of sensors. Some commonexamples include audio signals captured by amicrophone, images taken by a camera, and datacollected by parking sensors mounted on a car.II. Different Modeling ApproachesLet us consider the example of automaticallywatering a plant in a nursery. It depends on manyfactors like the humidity level, temperature,sunlight availability, etc. Various sensors are placedto measure each of these factors. The dataobtained from these sensors are highly variable.Hence, statistical models are generally used forthese kinds of systems, where there is a lot ofvariability. Such models are called 'Stochastic'models. As against this, let us take the example ofan automatic switch of a motor that pumps waterinto a water tank. In this case, a sensor is used todetermine the water level. If the water level isbelow a lower threshold, the motor isautomatically turned on. On the water levelcrossing a higher threshold, the motor isautomatically switched off. Here, we need not usea statistical model as the outcome is deterministicbased on the sensor input. Hence, the model usedis 'Deterministic' in nature. A statistical model is astochastic model that contains parameters, whichare unknown constants that need to be estimatedbased on assumptions about the model and theobserved data [2].III. Why Statistical Modeling?In this section, we discuss some examples thatexplain why statistical models are preferred overdeterministic models in some situations.lSuppose we want to measure rainfall with thehelp of a rain gauge to study its effect on crops.Here, the aim is to record an average estimate ofthe crop output but not how an individual plant isaffected. It is often sufficient to describe theaverage behavior of a process rather than explaineach sample obtained from the process. In thiscase, a simple regression model would besufficient.lRandomness is often introduced into a system toget a certain representativeness of the entiresample set. For example, to measure the surfacetemperature of an ocean, a few sensors placed atrandom locations are used.lSome physical processes can be more simplydescribed by a statistical model than by adeterministic model. For example, it is possiblein principle to capture the result of a roll of dicewith a deterministic model, taking into accountthe properties of the die, the method of rolling,initial angle, and so on. A very complex model isrequired to describe the simple outcome.Alternatively, you can describe the outcomequite simply as the result of a stochastic process,by assigning probabilities to individual outcomes.IV. Different Classes of StatisticalModelingStatistical models tackle the problem ofrandomness by assigning probability distributions tothe data obtained. Some common probabilitydistributions are as shown in figure 1. The processof estimating the parameters is called “fitting themodel.” There are different methods of obtainingthe parameters of a distribution like least squares,maximum likelihood, etc.30 TechTalk@KPITCummins, Volume 5, Issue 3, 2012

(a) Gaussian Distribution(b) Poisson DistributionFig. 1. Some Examples of Distributionsin Statistical Analysis (source: Wikipedia)Statistical models can be divided into a variety ofclasses [3]. Some of them are as listed below:Linear and Non-linear Models: If the output ofmodel is a linear function of the input parameters,then it is a linear model. Otherwise, it is a nonlinearmodel.Regression Models and Models withClassification Effects: These are usually used inprediction or forecasting. Classification models areused to classify a particular sample into differentcategories. An example of regression models is therelation between the wind speed and the currentoutput of a wind turbine. A plot is drawn of windspeed obtained by a speed sensor and currentoutput obtained by a current sensor. The relationbetween these input and output variablesrespectively can be obtained by fitting a curve,which is done by regression analysis.Fig. 2. A Sample Graph Plot Showing Output ofRegression AnalysisUnivariate and Multivariate Models: If we modelplant growth as a function of rainfall, it is a univariatemodel, because the output of the model is dependenton only one parameter, i.e. rainfall. However, if wemodel plant growth as a function of rainfall,temperature, nutrient input, etc., then it is amultivariate model, as it depends on multiple inputs.Fixed, Random and Mixed Models: Models in whichall the input parameters are fixed are called fixedmodels. Similarly, models in which all parameters arerandom are called random models. Mixed models, arethose models that have both fixed and random inputparameters. In the above example of plant growth,nutrient input is in the control of the farmer so it is afixed parameter, but rainfall and temperature arerandom parameters. Hence, if we use all threeparameters, then the model is a mixed model.Bayesian models: Bayesian models provide aframework to incorporate prior knowledge into themodel. These models are used for testing hypotheses.For example, say we need to classify if an objectbelongs to a particular category. We then test twohypotheses based on the model: one is the probabilitythat the object belongs to the category, and the other isthat the object does not belong to the category. Thehypothesis that has the greater probability is selectedas the correct hypothesis.V. Specific Examples of StatisticalModelingBiometrics: Biometric security is the science of usingphysical characteristics to identify a person. Differentmethods like iris recognition, finger print recognition,and face recognition are used for this purpose.Statistical signal processing methods are used for mostof the biometric applications because they give verygood identification accuracy. Let us discuss the processof biometric identification using the example of facerecognition.TechTalk@KPITCummins, Volume 5, Issue 3, 201231

In face recognition, the facial image of a person iscaptured using a camera and compared against adatabase of images. The camera (sensor) should begood enough to capture the facial features of a personproperly. Since the image size is large, it is very difficultto compare the images directly. We need to identifythe distinctive features like eyes, skin color, etc.,which are used to distinguish one image from another.This step is called 'feature extraction.' This can bedone using statistical methods like PCA (PrincipalComponent Analysis) or LDA (Linear DiscriminantAnalysis). Once the features are identified, we needto determine if the image of the person belongs to thedatabase or not. A wide variety of classificationalgorithms based on statistical models such as SupportVector Machine (SVM), regression techniques, etc.,are available in the literature. Figure 3 shows ageneralized block diagram of a face recognitionsystem. J. Mazanec and M. Melisek give a detailedanalysis on using PCA, LDA, and SVM for facerecognition in their paper [4]. With proper choice offeature extraction and classification techniques, onecan achieve accuracy as high as 95 to 96%.movement of limbs, direction of the movement, wordsuttered by a person, etc. This step is called the featureextraction step. The obtained features are used tobuild a model that describes the activity. The statisticalmodels that are used can be either ‘generative’ likeBayesian models, Hidden Markov models (HMM -these are models which give the probability ofoccurrence of a particular sequence of steps) etc., or‘discriminative’ like SVM, Linear regression, etc. Thespecific model is chosen based on the type of outputrequired. Generative models are used to analyze aparticular activity like detect anomaly in the gait of aperson etc. Discriminative models are used to classifythe type of activity, e.g. determining whether theactivity is 'sitting' or 'standing'. Based on the inputs, themodel gives the specific activity output.Fig. 4. Human Activity Detection SystemT. Choudhury et al [5] describe how different sensorsand statistical models are used to determine the humanactivities.Fig. 3. Face Recognition SystemHuman Activity Detection:Currently, there is an increasing trend of developingsystems that mimic the behavior of humans. To do thiseffectively, a system needs to understand the humanbehavior thoroughly. A variety of different sensors isused to collect data of individual actions like sitting,standing, running, walking, etc. There is a lot ofvariability in these activities. Let us consider anexample of walking. The variability in walking includesthe speed of walking, variability in movement ofhands, positioning of head, etc. Statistical models arevery helpful in modeling such behaviors.A block diagram of a typical activity recognitionsystem is shown in figure 4. The first step involvescapturing the sensor data. Different sensors likecameras, microphones (conversation activity),wearable sensors (a jacket which contains differenttypes of sensors), etc. are used to captureinformation. The second step is to extract relevantinformation from the signals obtained fromthese sensors. The information could be related toReferencesVI. SummaryStatistical modeling offers a way of gaining insight intoseemingly random data obtained from differentsensors of physical phenomena. The main advantage ofstatistical methods is that they can handle variability inthe observed data, for which they use probabilitydistributions. Currently, such methods are being usedin various fields like vision systems, security,biometrics, agriculture, and so on. In the near future,we will also see applications of statistical modeling inrobotics for mimicking humans, in medicine forpredicting the ailments of patents, and in geology forforecasting the eruption of volcanoes and predictingearthquakes.[1] Christoph Heinz and Bernhard Seeger, “Statistical Modeling of Sensor Data and its Application toOutlier Detection,” Technical Report 2006/07, University of Stuttgart.[2] Thamotharampillai Sutharssan, “An Introduction to Mathematical Modeling,”University ofGreenwich.[3] “SAS/STAT(R) 9.2 User's Guide, Second Edition.” Available: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#intromod_toc.htm[4] J. Mazanec, and M. Melisek, “Support Vector Machines, PCA and LDA in Face Recognition,”Journal of Electrical Engineering, pp. 203-209, vol. 59, no. 4, 2008.[5] T. Choudhury et al, “Towards Activity Databases: Using Sensor and Statistical Models to SummarizePeople's Lives,” IEEE Data Eng. Bull., vol. 29, no. 1, pp. 49-58, 2006.32 TechTalk@KPITCummins, Volume 5, Issue 3, 2012

BOOK REVIEWReinventing IndiaBy Dr. Raghunath Mashelkar, FRSDr. Raghunath Mashelkar is one of the mosteminent scientists and a visionary leader ofmodern India. 'Reinventing India' is a compilation ofsome of his most appreciated lectures andpublications. He talks about technology, culture,society, academia, and industry from theperspective of a truly patriotic Indian. The book is aculmination of his thoughts regarding the path tobuild an 'Innovative' India. All through the book, histhought leadership is evident.In the inaugural talk of the Indian NationalCongress in 2000, he proposed 'New Panchasheelfor New Millennium.' In this book, he elaborateson each of these 'five principles': children centerededucation, woman centered family, humancentered development, knowledge centeredsociety, and innovation centered India. Heunderlines the importance of innovation in thegrowth of India. It is also stated that knowledge isgrowing larger in size and complexity than everbefore; hence continuously updating theknowledge has gained far more importance now.Towards achieving the dream of building aninnovative culture in India, he proposes a newparadigm of 'More from Less for More'. He assertsthat if we aim at getting more performance usingfewer resources, for more number of people, thenwe will definitely march towards creating a systemof 'inclusive growth'. The book emphasizes therole of science and technology in inventing,designing, manufacturing, and distributing thetechnologies at prices affordable to even thepoorest of people in India.According to Dr. Mashelkar, the Indian mindset willplay an important role towards building of'Innovative India'. While comparing the Indianeducational system and culture with those in someof the developed and progressive countries, heasserts that the culture of 'questioning' needsserious attention especially in Indian society. Hethinks that by asking questions, information can beconverted into 'insights' and that takes one a longway on the path of innovation.While emphasizing knowledge, he does not ignorethe economics of the knowledge systems.According to him, 'research' converts money intoknowledge, whereas 'innovation' convertsknowledge into money. In the book, he elaborateshis viewpoints on creating an ecosystem that willnurture knowledge and money, research andinnovation. It is stated that there is a need ofcreating elegant and autonomous institutes where aculture of thinking, questioning, and applying shallbe nurtured. The book highlights some of theunique ecosystems where academia and industrycoherently exist and grow with the help of another.While creating awareness on Intellectual PropertyRights (IPR) for innovations, he emphasizes theneed of IPR for traditional knowledge systems,which exist in civilizations like India. He thinks thatthe knowledge and the values that are being createdand used relentlessly in societies like ours needseparate attention. He narrates an interesting chainof events that has led to the foundation of'Traditional Knowledge Database Library' and howit helped India get a number of patents based on hertraditional knowledge systems.The book, in its own insightful way, guides scientists,students, academicians, industries, policy makers,and all those who wish to build an 'Innovative' India.The book gives an inspiration, a view, and anoptimism that enlightens one's mind with energy,and kindles a sincere hope to see a bright future forIndia.About AuthorAditi AthavaleCREST,KPIT Cummins Infosystems Ltd.,Pune, India.Area of InterestsMulticore Programming,CryptographyTechTalk@KPITCummins, Volume 5, Issue 3, 201233

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Sniffing for DangerSensors in DefenseAbout the AuthorsSonali KalmeghIntegrated Enterprise Solutions,KPIT Cummins Infosystems Ltd.,Pune, India.Areas of InterestDevelopment using Microsoft Technologies.Priti RanadiveCREST,KPIT Cummins Infosystems Ltd.,Pune, India.Areas of InterestParallel computing, OS and RTOS,Embedded Systems, TRIZ.TechTalk@KPITCummins, Volume 5, Issue 3, 201235

I. IntroductionEvery nation needs to ensure safety, security, andprotection of its citizens. Science and technologyare playing an important role in this mission.Military and domestic government agencies aredriving the use of high tech solutions in the areas ofsurveillance and intelligence. Sensors and sensornetworks are the most critical part in any defenseapplications and systems. Such systems could beheterogeneous collection of sensors that collectdata, observe, and report about various dynamicchanges. In this article, we discuss different rangingsensors that are used in defense applications, theiroperating principles, advantages, anddisadvantages, followed by their applications.to detect physical features with a good resolution.LiDAR can detect any kind of objects, for example,rocks, aerosols, single molecules, metal or nonmetal,etc. With its ability to detect all types oftarget objects, LiDAR has found applications inother areas apart from defense. Some of theapplications in geology and archaeology are wellknown. Figure 1 shows a LiDAR used to scanbuildings.Sensors used in defense applications are usuallyrange sensors that can detect an object withoutactually getting in contact with the object [1] [2].There are two main types of remote sensingsensors: passive and active. Passive sensorsdetect natural radiations that are emitted orreflected by the object or surrounding areas.Reflected sunlight is the most common sourceof radiation measured by passive sensors.Examples of passive remote sensors includephotographic film, infrared sensors, chargecoupleddevices, and radiometers. Active sensors,on the other hand, emit energy in order to scanobjects and areas. The sensor then detects andmeasures the radiations that are reflected or backscatteredfrom the target. RADAR and LiDAR areexamples of active remote sensing where the timedelay between emission and return is measured,establishing the location, height, speed, anddirection of an object. In the remaining article, wewill look at different ranging sensors includingLiDAR, RADAR, SONAR, and GPR.II. LiDARLiDAR is an acronym for Light Detection AndRanging. It is an optical remote sensing method tomeasure distance from an object. A target object isilluminated using ultraviolet, visible or nearinfrared light. A laser beam is used along with thisFig. 1. LiDAR(Source: http://en.wikipedia.org/wiki/LIDAR)III. LADARLaser Radar (LADAR) is similar to LiDAR except itdoes not use any separate light source forilluminating the target object. It only uses a laserbeam to create a virtual image of a target area orobject. Additionally, a LADAR system uses aprocessor that looks for similarity between patternsof created scenes. The processor stores 3D targetfiles and compares the generated patternscontinuously with those in the database. A weaponequipped with a LADAR system processes thegenerated 3D images to find out if targets of interestare present in an area. If it finds any such target, theweapon hovers on it to collect more data.Eventually, a clearer 3D image of the target isgenerated to verify the target before an attack isdecided on. Once an attack is assigned, the weaponpositions itself to aim at the target in such a way thatthe attack results in maximum damage. Theresolution of LADAR is as high as 15 cm from adistance of 1000 meters. Hence, LADAR sensorsare used in surveillance to keep track of objects36 TechTalk@KPITCummins, Volume 5, Issue 3, 2012

from different angles, to verify the targets, and toposition for the best attack. Figure 2 shows apicture of the LADAR data that detect objects. Italso shows specific identified features with highdefinition as mentioned above.in the water. Passive sonar sensors do not emit anywaves.The major disadvantage of sonar sensors is that theycan harm marine animals [5]. Some marine animalssuch as whales and dolphins use echolocation orbiosonar systems to locate predators and prey. Thesignals from sonar transmitters are known toconfuse and interfere with the animals' feeding andmating functions.Fig. 2. LADAR Data Identifying Object Features(Source: http://defense-update.com/products/l/ladar.htm)Fig. 3. SONAR Principle(Source: http://en.wikipedia.org/wiki/Sonar)IV. SONARThe word ‘SONAR’ is an abbreviation for ‘SOund,NAvigation, and Ranging.’ Sonar is a technique thatuses sound waves usually underwater [3] [4]. It isused commonly in submarine navigation. Otheruses are for detecting objects and forcommunication. In a SONAR system, an electricpulse is converted to a sound wave using atransmitter. This sound wave is sent into water,and it is reflected back after hitting any object. Therebounded wave or echo is collected by atransducer, which converts the sound wave backto an electric signal. This signal goes through signalprocessing and amplification stages, and the finalsignal is displayed by the system. The distance ofthe object is calculated based on the time taken bythe sound wave to travel from the transmitter tothe object and back, given that the speed of soundin water is known. The process of transmission,reception, and signal processing is repeatedseveral times in one second. Figures 3 and 4demonstrate the operating principle of SONARsensors. Sonar sensors described above are calledactive sonar sensors. There are passive sonarsensors that listen for sounds generated by vesselsFig. 4. SONAR Illustration(Source: http://gralston1.home.mindspring.com/Sidescan.html)V. Satellite Laser RangefinderSatellite Laser Ranging (SLR) is a technique used todetermine the position of the earth and of satellitesrelative to each other. In an SLR system, the distanceof an object from a surface or location is calculatedusing a laser beam. It operates on the time-to-flightprinciple, in which a laser pulse is sent towards anobject, and the time taken by the laser beam totravel to-and-fro from the object is measured. Thistechnique cannot measure the distance to submillimeteraccuracy, since the speed of light is veryhigh. Hence, other techniques like triangulation areused. To improve security the laser beams arecoded to avoid jamming. It is also possible to find outwhether the object is moving towards or away fromthe rangefinder and at what speed, by using theDoppler Effect.TechTalk@KPITCummins, Volume 5, Issue 3, 201237

As shown in figure 5, rangefinders can also beintegrated with binoculars and used as handheldmilitary devices. Such devices have a good rangebetween 2 km to 25 km. Sometimes therangefinders can be combined with a digitalmagnetic compass to calculate the azimuth, lengthand the inclination of the objects. As an add-on, therangefinders can be equipped with wired orwireless communication interface to transmitmeasured data to remote central computers.Other add-on features may include night visionmodules along with the handheld range finder.Apart from handheld rangefinders, rangefindersthat are more powerful with range up to 25 km arealso used in defense. Such rangefinders are placedon a tripod or directly on a vehicle. Sometimes,rangefinders are integrated with thermal and nightvision and other vision systems that are mountedon guns.Laser rangefinders are not useful if the targetobjects are painted with laser-absorbing materialor if the target objects do not reflect laser light.This makes it difficult to use laser rangefinders todetect such objects.like pipes, cables, or landmines. GPR can alsodetermine the depth and make of the differentlayers of soil [6].A GPR system operates by sending outelectromagnetic pulses and detecting their echoes.A GPR system with a 500 MHz antenna can send out1 ns long pulses at a rate of 1 MHz. The radar ismoved across the surface to create an image, similarto that of a CT scan. This is known as the timeprofile. Depth is measures in terms of Two WayTravel Time (TWTT) rather than the true depth.TWTT is calculated based on the time taken by theelectromagnetic pulses to travel from the antennato the object and bounce back to the receiver. If thetime taken is longer, the object is farther away. TheGPR system can adjust a time window in steps of 6ns to 800 ns but is generally used only in the range of26 to 50 ns. Figure 6 shows the operating principleof GPR.Fig. 6. Operating Principle of GPR(Source: http://www.worksmartinc.net/)Fig. 5. Binocular Integrated with Laser Rangefinder(Source: http://en.wikipedia.org/wiki/Laser_rangefinder)VI. Surface and Ground PenetratingRadar (GPR)GPR is a non-destructive method of investigatingthe internal composition of materials such asrocks, bricks, and earth. It can also be used todetect metallic, non-metallic, and buried objectsGPRs have the advantages of high-resolutioncoverage of the surveyed area and detecting smallobjects. However, their limitations include slowacquisition of the data on difficult terrains, limiteddepth of penetration in materials with highelectrical conductivities, and disturbance fromenergies reflected by above-ground features likewalls, canopies, etc. In addition, the artifacts in thenear surface may scatter the transmitted energyand complicate the received signal and/or reducedepth of penetration.VII. Signal Processing TechniquesIn order to extract useful information from signalsobtained using any of the above-mentionedsensors, it is necessary to apply signal-processing38 TechTalk@KPITCummins, Volume 5, Issue 3, 2012

techniques on the obtained raw data. Previousarticles in this issue have mentioned someimportant signal-processing steps. Here, we shalllimit our discussion to signal processing related toGPR systems.Data received from a GPR system would consist ofreflection of ground surfaces; any electromagneticinterference due to the GPR itself; reflection bysubsurface of the targets; coupling induced due tothe transmitter and receiver antennae; or anyother reflections due to the underground nonhomogeneity.The signals from the reflections andcoupling are noise or 'clutter.' Clutter signals mightbe stronger than the reflections got from the targetof interest. Hence, it is necessary to use signalprocessingtechniques that reduce the clutter.There are several de-noising techniques used todiscern landmines from clutter. In general, the denoisingtechniques are of three types. The firstcategory is based on linear data analysis.Techniques in this category include the methods offactor analysis (FA), independent componentanalysis (ICA) [10], single value decomposition(SVD) [7], and blind source separation (BSS). Thesecond type is the maximum likelihood detectionmethod. The third type is combining waveletpackets with higher-order-statistics. Methodsmentioned in [8] [9] use of SVD, FA and Principalcomponent analysis (PCA). They mentionpromising performance results by using waveletpackets with higher-order-statistic directly appliedto raw GPR data. However, other techniquesrequire that the raw data is pre-processed usingsome pre-processing techniques. They alsomention that ICA gives the best performance foroverall signal processing [11].Explaining each of these methods in detail isoutside the scope of this article. However, we willbriefly discuss one important technique, viz. theindependent component analysis (ICA) technique.Independent Component Analysis (ICA) – In thismethod, non-linear data is represented linearlyassuming different components of the data arestatistically independent. This kind of datarepresentation extracts the essential structure ofthe data from the signal. Let us take an example ofthe 'cocktail party problem,' where there are twopersons speaking simultaneously in a room. Twomicrophones capture these speech signals. Eachrecorded speech signal would be a weighted sum ofthe original signals received from the two persons.The ICA technique allows us to extract informationbased on the independent components, thusallowing the two original source signals to beseparated from the mixed signal.VIII. ConclusionIn this article, we saw different sensors that are usedin defense applications and their operatingprinciples. Specifically, ranging sensors are used indefense applications. These sensors requiredevelopment of complex algorithms to identifydifferent types of features and data processing.These sensors also find applications in areas otherthan defense.References[1] Article, Available:http://en.wikibooks.org/wiki/Robotics/Sensors/Ranging_Sensors[2] Article, Available: http://www.acfr.usyd.edu.au/pdfs/training/sensorSystems/05%20Active%20Ranging%20Sensors.pdf[3] Article, Available:http://www.substructure.com/education/what_is_sonar_and_what_exactly_does_a_sonar_system_do.html[4] Article, Available: http://gralston1.home.mindspring.com/Sidescan.html[5] Joint Interim Report Bahamas Marine Mammal Stranding Event of 15-16March 2000, December 2001, U.S. Article, Available:http://www.nmfs.noaa.gov/pr/pdfs/health/stranding_bahamas2000.pdf[6] Article, available: http://www.worksmartinc.net/ andhttp://www.worksmartinc.net/how-ground-penetrating-radar-works.php[7] Fawzy Abujarad, Galal Nadimy, and Abbas Omar, “Clutter reduction anddetection of landmine objects in ground penetrating radar data using singularvalue decomposition (SVD),” Proc. 3rd International Workshop on AdvancedGround Penetrating Radar, pp.37-41, 2005.[8] Fawzy Abujarad, Jaostigmeier, and A. S. Omar, “Clutter removal forlandmine using different signal processing techniques,” Proceedings of theth10 International conference on Ground Penetrating Radar, pp. 697-700,2004.[9] Fawzy Abujarad, Galal Nadimy, and Abbas Omar, “Wavelet packets forGPR detection of non-metallic anti-personnel land mines based on higherorder-statistic,”Proc. 3rd International Workshop on Advanced GroundPenetrating Radar, pp.21-24, 2005.[10] Aapo Hyvärinen and Erkki Oja, “Independent component analysis:algorithms and applications,” Neural Networks, vol. 13, pp. 411-430, 2000.[11] Fawzy Abujarad and A.S. Omar, “GPR data processing using thecomponent separation methods PCA and ICA,” IEEE International Workshopon Imaging Systems and Techniques, April 2006.TechTalk@KPITCummins, Volume 5, Issue 3, 201239

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A Voyage in ConcertCars and SensorsAbout the AuthorChaitanya S. RajguruCREST,KPIT Cummins Infosystems Ltd.,Pune, India.Areas of InterestComputing Hardware and Algorithms,VLSI Technology, and Automotive SystemsTechTalk@KPITCummins, Volume 5, Issue 3, 201241

I. IntroductionPliny the Elder, a Roman scholar from the firstcenturyAD, had wisely remarked, “The onlycertainty is that nothing is certain.” Everythingaround us is constantly changing. That is why weneed sensors. As our environment is complex, weneed to know a lot about it, and sensors offer usthat knowledge. Sensors can overcome worldlyuncertainties to tell us what exactly is going onaround us. Humans of course have many sensorsbuilt into our bodies; we depend fully upon oursenses of touch, sight, etc. for even the simplesttasks such as walking and putting food in ourmouths. Now think of a car as an 'active shell' thatwe enter whenever we need to move quickly fromone place to another. This is similar to our using awrench or controlling a robot remotely, in that themachines extend our physical capabilities. In thatsense, cars, too, are physical extensions of people,and it is no surprise that we are building more andmore sensory intelligence into them.In this article, we will take a few glimpses at thehistory, the present, and the future of automobiles.We are particularly interested in the role played bysensors and signal processing in the developmentof vehicles, and in creating new vehicle features. Inthe end, we will discuss how to bring somefuturistic ideas to reality.II. Back to the FutureAutomobiles have evolved greatly over the pastcentury and half. They started with steam power,then experimented with electricity, and finallysettled on petroleum as a fuel. A constant passionfor better vehicles and better features has led tocontinuous innovation in their design andmanufacturing. We feel it may be interesting topeek briefly into the past to see what people'svision for the future was back then, and to seewhether sensors played a role.The vision of future vehicles in the 1940's and the1950's, particularly in the United States, washeavily influenced by the contemporary spaceprogram. Long, sleek, rocket-like car designs werefavored, and they became reality through the nexttwo decades. Figure 1 shows a car design from thatera. Sensors did not play a major role here; thefocus was on the exterior shape and aesthetics.Fig.1. 1955 Gilda Streamline X Coupé by Ghia(Source: http://www.yatzer.com/Dream-cars-of-thefuture-since-1950)During the 1960's and 1970's, interest shifted fromaesthetics towards safety, emissions, and economy.Airbags were first reported in 1951, but they wereimperfect – they would not deploy quickly enoughin a crash. Allen K. Breed solved that seriousproblem in 1967 by developing a newelectromechanical sensor. This sensor allowedairbags to inflate within just 30 milliseconds of thecrash. That made the airbag a true life-saving device.Also during the 1960's, the oxygen sensor wasdeveloped at Robert Bosch GmbH. This sensorallowed engineers to measure the air-fuel ratio atthe input to the engine. The air-fuel ratio has a directimpact on harmful tailpipe emissions. Thus, preciseemission control was now made possible. Thedevelopment of electronic fuel injection andcatalytic converters followed, which furtherimproved fuel economy and reduced emissions.Additional sensors were also developed to measuremore vehicle parameters such as oil quality and fuelpressure, and this innovation cycle has resulted insignificant improvements in automobile fueleconomy and pollution control.III. Catching Up with the PresentThe cars of today are highly advanced as comparedto their early twentieth-century predecessors.They can travel three times faster, go 2.5 timesfarther, are 50% more fuel-efficient, and cost aboutthe same as compared to their 'great-grandfather,'the venerable Ford Model T of 1908 [1][2]. They doall this while keeping their occupants about twotimes more safe. How do they do that?Firstly, today's vehicles are designed better. Engine,transmission, and chassis technologies havebecome much more sophisticated. Theseimprovements were possible, thanks to superiortesting and simulation capabilities. It was sensorsthat made all the difference – whether in measuring42 TechTalk@KPITCummins, Volume 5, Issue 3, 2012

engine efficiency to find the peak, or in wind tunneltesting for improving aerodynamics, or in crashtesting for making cars safer for occupants.Dynamometers are a good example of early use ofsensors for improving designs. Dynamometersthwere invented in the 18 century to measure thetorque (rotational force) developed by engines.Figure 2 depicts a rope brake dynamometer likeone invented and patented by Lord Kelvin in 1858,which incorporated a spring balance as the sensor(visible as an circular dial at the bottom of thefigure). These instruments have allowed engineersto measure important engine characteristics asseen in figure 3. It is immediately obvious from thefigure that the 'sweet spot' for peak torqueoperation is around 3,600 rpm (rotations perminute) for this engine. The automobile designercan then design the transmission gear ratiosaccordingly to keep the engine operating at thesweet spot. We can similarly measure engine fuelefficiency vs. engine speed using other sensors.Such quantified measurements have been vital tothe progress of automobile engineering, andindeed, of all engineering and scientific disciplines.Sensors have thus enabled path-breakinginventions.Secondly, vehicles heavily rely on sensors duringoperation to improve functionality and efficiency. Ahundred or more sensors are embedded in variousparts of a modern vehicle. They continuouslymonitor and report over a hundred differentparameters – from the fuel level, to the occurrenceof a side-impact crash. We estimate that there areabout one million bits of data generatedcontinuously every second by a modern vehicle'ssensors, assuming that there are 200 sensorssending an average of four bits of data one thousandtimes per second.Analyzing all this data quickly to make real-timedecisions requires significant processing power.Modern vehicles contain from 30 to 70 onboardcomputers for monitoring all the sensors andcontrolling everything from the engine to the lightsto the climate control unit. Great leaps insemiconductor technology have made today'sembedded computing units (ECU's) up to 100 timesmore powerful than the venerable IBM 7090 from50 years ago, which was used by NASA duringseveral early space flights. That is considerablecomputing power being put to use for making ourvehicles safer and smarter!Fig. 2. Rope Brake Dynamometer (Source: http://en.wikisource.org/wiki/911_Encyclop%C3%A6dia_Britannica/Dynamometer)Fig. 3. Typical Engine Performance Curves(Source: http://www.mpoweruk.com/piston_engines.htm )Fig.4. Noisy and Filtered Signals(Source: http://code.ucsd.edu/~pcosman/fluoroscopy/index.html)TechTalk@KPITCummins, Volume 5, Issue 3, 201243

Sensors interface with the real world, and aresubject to all its uncertainties and fluctuations.Sensor outputs are notoriously temperamentaland noisy. These output signals must be carefullyfiltered and processed before use. That is quitesimilar to how you are focusing your attention onthis article right now, and are filtering out the restof the visible 'clutter' in your field of view. Signalfilters and conditioning circuits likewise removeunwanted noise and retain the useful information,as shown in figure 4. The cleaned-up signal is thensent to the ECU that interprets the signal anddetermines, say, whether that shock to the vehicleis the result of a crash, or whether it is just due to abump in the road.Such advanced sensors, hardware, and softwarehave created wonderful new possibilities. Onesuch feature is electronic stability control,which offers a safe and comfortable ride bycompensating for disturbances in the road surface.For example, parts of the road may be slippery dueto loose soil, water, or ice. The driver may noticethis too late or perhaps not at all. Sensors,however, are always on the lookout. Multiplesensors continuously monitor the vehicle speed,wheel speeds (which are unequal when somewheels slip on a slick surface), steering direction,and even road surface quality, among otherparameters.These sensor outputs are cleaned up and sent tothe ECU responsible for stability control. TheECU's software interprets the sensors' datacollectively and determines whether the situationis unsafe, requiring intervention. For example,when a wheel is over ice, it loses traction andspeeds up. That is a trigger for corrective action.On the other hand, wheel speeds are also unequalduring safe, controlled turns, and the computerneed not interfere then. The software can initiateactions when needed to increase stability, such asapplying a precise degree of braking on slippingwheels, or even steering the vehicle to regaincontrol. Sensors thus enable an 'invisible helpinghand' that protects the vehicle and its occupants.IV. Dreaming about TomorrowLet us now switch gears and think about what thefuture holds. The vehicles of tomorrow promise tobe highly energy-efficient, lightweight, and easy todrive. One such new feature is drive-by-wire.Today, we steer our cars using the familiar steeringwheel, which has a direct, mechanical link to thefront wheels. That mechanical link makes thevehicle heavier, consumes precious space, andmakes the vehicle difficult to steer, especially inlarge vehicles. Sensors (and a lot of goodengineering) could eliminate the mechanical link,creating an electronic link instead. What will theresult be? Significant space savings, radical newdriver interfaces such as joysticks, and tunablevehicle handling characteristics. Fly-by-wiretechnology is already in use in airplanes, though itshigh cost is currently a barrier to adoption inautomobiles.Biometric sensors are common in medicalequipment today, and will soon show up in your car.They hold the promise of hassle-free security (byallowing access only to the owner of the car),personalization (by adapting the seatingenvironment to the driver, for example), andhealth-on-the-go (by monitoring stress levels andhelping drivers relax).If there is a 'killer app' for sensors in future vehicles,that is driverless cars, or autonomous vehicles.The primary difference between autonomous carsand others is their ability to 'understand' theirenvironment and take appropriate actions. Thisability is implemented using sensors and signalprocessing.Autonomous cars are popular enough in books andmovies already, though they are not quite ready forthe market yet. They have been successfullydemonstrated many times since the 1980's. Twosemi-autonomous robotic cars by Daimler-Benzand UniBwM covered 1000 km on a three-laneParis highway in heavy traffic in 1994. Four electriccars completed the first driverless intercontinentalroad trip in 2010, traveling 13,000 km from Italy toChina, during the VisLab IntercontinentalAutonomous Challenge. Google has made newswith its driverless cars, which have collectivelytraveled an impressive 225,000 km, though withoccasional human intervention. One Google car iseven licensed for use on public roads in the state ofNevada, USA as of May 2012, as shown in figure 5.44 TechTalk@KPITCummins, Volume 5, Issue 3, 2012

Fig. 5. Google's Driverless Car has anOfficial Nevada State License(Source: http://techland.time.com/2012/05/08/googles-driverless-cars-nowofficially-licensed-in-nevada/)We can expect more driverless cars on the roadssoon. All major carmakers are already developingautonomous vehicles, and are planning to launchthem during this decade. Some are already offeringautomatic parking. Others will initially provideautonomous driving in slow traffic, while yetothers are working on 'trains of cars' movingcooperatively and efficiently on highways.A big challenge in bringing driverless vehicles to thepublic has been the high cost of the advancedsensors and other necessary hardware. Thesecond requirement is that the sensors andsoftware are designed to be road-ready for anyconceivable situation, because lives will be at stake.Finally, the autonomous capability must offer asignificant advantage over manual driving in termsof safety, comfort, and productivity. These aremajor challenges for developers of sensors andautonomous technologies, who are working hardto meet them. The technology shall catch up tohuman drivers' capability and then surpass it overthe next ten to twenty years, and the innovationjourney promises to be exciting!V. Exciting Possibilities for UsLet us put our immediate concerns aside for aminute, stare out of the window, and imagine thefuture … what do we see? What other sensorbasedtechnologies and features can we dream upfor the future of automobiles?Sensors in FabricsSensors are becoming small enough and easyenough to use in fabrics in embedded form. Inautomobiles, they could be embedded in the seatand headrest to literally 'stay in touch' with thedriver and passengers. They could monitor driveralertness, stress levels, and health indicators. Theycould also simply make people comfortable byautomatically tuning the climate controls to, say,quickly cool down a hot and sweating person bycirculating air through the seat. Sensors embeddedin the steering wheel could detect the presence ofalcohol in the driver's blood stream and preventaccidents.Haptics and Voice InterfacesHaptics is a clever technology that can simulatetouch and mimic different surface textureselectronically. One could use it to, say, instantlymake the smooth surface of the gearshift lever feelrough to suggest downshifting to a driver. Hapticinterfaces take advantage of the sophisticated touchsensors in our hands and fingers, and directlystimulate them to convey an intuitive message. Thiscan allow for faster response as compared to relyingon sight and sound: human response to touch hasbeen reported to be 28% and 34% faster thanauditory and visual stimuli respectively [3]. Userinterfaces for infotainment systems could usehaptics and actually add or remove physical buttons– for example, removing irrelevant radio 'preset'buttons when playing back recorded music. Thatway, drivers can keep their eyes on the road andoperate the system using touch feedback.Passengers would use a less confusing interface …to avoid that helpless feeling you get when you facea 37-button, dual-screen, multi-menu, all-in-oneinfotainment gizmo for the first time! Voiceinterfaces will offer another convenient choice forusers, but their accuracy first needs to be improvedfor noisy car environments and for workingaccurately with people using different languages andaccents.Personal SpaceCars have long been personalized transportationunits. The varieties of models and makes, featureoptions, as well as a vibrant after-market in customseating, electronics, and decorations, all aretestimony to that. In the future, cars will come to betreated as a 'home away from home,' withTechTalk@KPITCummins, Volume 5, Issue 3, 201245

important conveniences of connectivity andcomfort taken to the next level. This is similar tohow the mobile phone is fast becoming a 'personalstatement,' with many connectivity options and'look-and-feel' personalization choices. Likewise,the car will reflect the tastes and choices ofthe owner: some may incorporate 'green'technologies, while others could choose to createa cool audio-video experience on wheels for theirpassengers, and still others may show off radicalnew exterior shapes and colors that adapt to theenvironment. We may wish to have a vehicle withselectable 'ride' settings, depending upon whetherwe are with our parents or with our friends.Sensors will play an important role in bringing theabove features to us – some determining the bestway to soothe or energize the occupants, othersmonitoring vehicle parameters and helpingcustomize the ride to 'gentle' or 'sporty,' and stillothers keeping a close eye on the environment andresponding to it. We can create whatever we canimagine – but only by using sensors!VI. ConclusionThe ability to sense the environment and torespond to changes seems to be a definingproperty of living beings. Engineers are adoptingthe sensory abilities of living beings for machines,which are becoming more and more versatile.Sensors are now irreplaceably essential toindustry; to paraphrase Einstein (with dueapologies), “Sensors without control are lame;control without sensors is blind.” We believe thatimproved sensors and their creative use will yieldtremendous new capabilities. Let us explore thosepossibilities and help create a wonderful newfuture!References[1] Article - “Ford Model T,” available:http://en.wikipedia.org/wiki/Ford_Model_T[2] Specifications - “Ford Fusion,” available:http://www.ford.com/cars/fusion/specifications/[3] “Finger Response Times to Visual, Auditoryand Tactile Modality Stimuli,” Annie W.Y. Ngand Alan H.S. Chan, Proceedings of theInternational MultiConference of Engineersand Computer Scientists (IMECS) 2012,volume II, Mar 2012.The Third EyeHubble Space Telescope, named afterastronomer E. P. Hubble, is the first orbitaloptical observatory of a very large size. Ittook more than 10 years to build thetelescope. It was launched in the space in1990 with the help of shuttle 'Atlantis'. Thetelescope observes 24 hours a day andalways has perfect seeing. Astronauts ofspace shuttle 'Endeavour' repaired it in1993. They replaced some of the criticalcomponents of the telescope. The telescopeis at the altitude of 550 km. Its weight isequal to 11110 kg and its length is 13.3 m.Hubble Space TelescopeThe Milky Way46 TechTalk@KPITCummins, Volume 5, Issue 3, 2012

Research PublicationsNight Vision Enhancement Using Wigner DistributionAuthors : Vinay G. Vaidya, Chandrashekhar N. PadoleConference : IEEE International Symposium on Communications,Control and Signal Processing (ISCCSP) 08, Malta, EuropeAbstract :Night vision has been a subject of interest of various sectors. Border patrol, automotive industry, defense arejust a few examples. Accidents at night could potentially prove fatal. Thus, lately there is a significant interestform automotive industry in conducting research in night vision.In this paper, we propose a new method for enhancing images taken with poor light conditions at night. Thismethod is a modified version of the Wigner Distribution. Wigner distribution is one of the time–frequencyrepresentations. It has good capability of dealing with noisy images.In this paper, we have shown how the modified Wigner distribution can be effectively used for supplementingdriver's vision during night and during snow conditions. The new method is evaluated with Mean Squared Error(MSE) criterion. Quantitative results and visual inspection show the robustness and effectiveness of thisalgorithm.Research PublicationsLossless Image Compression based on Data FoldingAuthors : Krishnan Kutty, Smita Nair, Suresh YervaConference : International Conference on Recent trend InInformation Technology (ICRTIT) 2011, Anna University, Chennai, IndiaAbstract:The paper presents an approach for lossless image compression in spatial domain for continuous-tone imagesusing a novel concept of image folding. The proposed method uses the property of adjacent neighborredundancy for prediction. In this method, column folding followed by row folding is applied iteratively on theimage until the image size reduces to a smaller pre-defined value. For column folding, elements of even columnsare subtracted from elements of odd columns. Thereafter, row folding is applied on odd columns in a similarfashion. In row folding, even rows are subtracted from odd rows and the resultant odd rows are used for nextiteration. The difference data, thus obtained, is stored in a tile format; which along with the reduced image isencoded and transmitted. The proposed method is compared with the existing standard lossless imagecompression algorithms and the results show comparative performance. Data folding technique is a simpleapproach for compression that provides good compression efficiency and has lower computational complexityas compared to the standard SPIHT technique for lossless compression.TechTalk@KPITCummins, Volume 5, Issue 3, 201247

Research PublicationsA Real Time Video Stabilization AlgorithmAuthors : Tarun Kancharla, Sanjyot GindiConference : International Conference on Advances inComputing and Communications (ACC-2011), Cochin, IndiaAbstract :Jitter or unintentional motion during image capture, poses a critical problem for any image processingapplication. Video stabilization is a technique used to correct images against unintentional camera motion. Wepropose a simple and fast video stabilization algorithm that can be used for real time pre-processing of images,which is especially useful in automotive vision applications. Corner and edge based features have been used forthe proposed stabilization method. An affine model is used to estimate the motion parameters using thesefeatures. A scheme to validate the features and a variant of iterative least squares algorithm to eliminate theoutliers is also proposed. The motion parameters obtained are smoothed using a moving average filter, whicheliminates the higher frequency jitters obtained due to unintentional motion. The algorithm can be used tocorrect translational and rotational distortions arising in the video due to jitter.MS ThesisIntegrated Active and Passive Safety Systemfor Better Safety in Passenger CarsAuthors : Austin L. A, Thanga IlangoMS Thesis with the Industry Supervisor from KPIT CumminsOccupant safety is one of the most researched areas in design of an automobile as it is not only one of the keyselling feature but also because of the ever increasing safety standards mandated by various national bodies. Thishas led to the development of new technologies like adaptive restrain systems and integrated active and passivesafety systems. This project investigates the possibilities of integrating the active and passive safety system andreduction of restraint forces felt by the occupants for the better safety of the occupants.The methodology adopted for the integration of active and passive safety system in this project is by developingnew concepts which utilise the signals from active safety systems such as ESP, radar etc, for better performance ofthe passive safety systems. Three such concepts have been developed and studied to determine the best of them.The concept model was evaluated and the design specifications were arrived at analytically. A frontal impactmodel available in Madymo 6.1 has been used to for evaluating the effectiveness of the concept in reducing theinjuries due to various forces acting on the occupant during a crash.From the analytical study of the concept it has been shown that such a system can be designed without causingless harm to the occupant. From the simulation of the concept it has been see that there is a reduction of forcesexperienced by tibia and femur by 38%. Though there is not much difference in the neck forces and HIC values,the additional chest travel distance available with the implementation of the concept will be helpful in optimizingthe safety systems such as adaptive belt retraction or force limited seat belt system. This also gives additional timewith which the airbags can be deployed more effectively.48 TechTalk@KPITCummins, Volume 5, Issue 3, 2012

About KPIT Cummins Infosystems LimitedKPIT Cummins partners with global automotive and industrial corporations inbringing products faster to their target markets. We help customers globalizetheir process and systems efficiently through a unique blend of domain-intensivetechnology and process expertise. As leaders in our space, we are singularlyfocused on co-creating technology products and solutions to help our customersbecome efficient, integrated, and innovative manufacturing enterprises. We havefiled for 38 patents in the areas of Automotive Technology, Hybrid Vehicles, HighPerformance Computing, Driver Safety Systems, Battery Management System,and Semiconductors.About CRESTCenter for Research in Engineering Sciences and Technology (CREST) is focusedon innovation, technology, research, and development in emerging technologies.Our vision is to build KPIT Cummins as the global leader in selected technologiesof interest, to enable free exchange of ideas, and to create an atmosphere ofinnovation throughout the company. CREST is now recognized and approvedR & D Center by the Department of Scientific and Industrial Research, India. Thisjournal is an endeavor to bring you the latest in scientific research and technology.Invitation to Write ArticlesOur forthcoming issue to be released in January 2013 will be based on“Breaking the Barriers” We invite you to share your knowledge bycontributing to this journal.Format of the ArticlesYour original articles should be based on the central theme of “ Breaking theBarriers” The length of the articles should be between 1200 to 1500 words.Appropriate references should be included at the end of the articles. All thepictures should be from public domain and of high resolution. Please include abrief write-up and a photograph of yourself along with the article. The last datefor submission of articles for the next issue is September 30, 2012.To send in your contributions, please write to crest@kpitcummins.com .To know more about us, log on to www.kpitcummins.com .SMKPIT CumminsInfosystems LimitedInnovation for customersYou can make a differenceinitiative

yTechTalk@KPITCummins July - Sept 2012Dr. Dennis GaborNobel Laureate(1900-1979)“The future cannot be predicted, but futures can be invented.It was man's ability to invent which has madehuman society what it is.”35 & 36, Rajiv Gandhi Infotech Park,Phase - 1, MIDC, Hinjawadi, Pune - 411 057, India.