Understanding Smart Sensors - Nomads.usp

164 Understanding Smart Sensors7.7 Other Control AreasComputationally intense models such as hidden Markov modeling (HMM)(which is used in automatic speech recognition) will be used more frequently asavailable computing power increases [23]. Higher performance for real-timecontrol is achieved in DSPs and multiprocessor systems. For example, automaticspeech recognition (ASR) has been implemented in ROM on a DSPchip. The HMM technique treats speech as a stochastic process for matchingthe input to a word command. In those systems, sensor input from a microphonedirectly affects the signal-to-noise ratio of the input. Limited bandwidthand high-noise systems, such as telephone lines, still cause problems to someexisting speech recognition systems. However, continuing improvements inalgorithms and the computational engine are improving the process.Multiprocessor computing is used today for controlling industrial robotics.Those powerful processors support the implementation of machine vision,speech recognition, and optical character recognition. Techniques used in multiprocessinginclude common Fourier transform, convolution, linear prediction,dynamic time warping, template matching, and spectral processing [24].The discrete Fourier transform is used to determine the frequency componentsof a time series. Multiplying a matrix of cosine and sine functions withthe time-series vector yields the frequency-series vector. By computing a 64-element time series vector concurrently on 64 processors (one component perprocessor), processing time was reduced to 1 64 of the uniprocessor’s time.Convolution is used for filtering a time series. It requires calculating theinner products of time-series sections with a coefficient vector that defines thefilter characteristics. By using a 16-element convolution kernel or softwarefunction and distributing the elements in a 128 processor with only one broadcastof the time-series vector, the computation was performed 128 times faster.Linear prediction is used to obtain smoothed, compact spectral representationsof a signal. It requires calculating the autocorrelation function for thetime signal. That is identical to convolution calculation with the convolutionkernel replaced by a section of the time series for autocorrelation. By processingone coefficient per processor, the autocorrelation function is calculated N timesfaster, where N is the number of autocorrelation coefficients computed. Linearpredictive coding is frequently used for speech recognition.Dynamic time warping calculates the similarity between two signals thathave differing nonlinear time-alignment patterns. The computation determinesthe optimum path through a matrix of distances formed from the squaredEuclidean distances between discrete sample sections of the signals. Multiplepaths are explored based on the selected algorithm, and the minimum total distancedefines the similarity and best alignment path. Template matching in