In real-time digital-signalprocessing systems, data often enter or leave the computationally intensive parts at small integer multiples of the clocking interval. In these cases, traditional microprocessor-based archi...
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Withthe commercial availability of high speed digital signal processors, it is now possible to implement all the linear predictive coding (LPC) tasks (excluding D-A/A-D conversion) on a single chip. In this paper, a ...
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Withthe commercial availability of high speed digital signal processors, it is now possible to implement all the linear predictive coding (LPC) tasks (excluding D-A/A-D conversion) on a single chip. In this paper, a very small, high quality, full-duplex, 10th order 2.4 kbps LPC vocoder is described. A single Texas Instruments TMS-320 microprocessor performs LPC analysis, pitch detection, synthesis, and data I/O. At the time of writing this paper, a total of 20 off-the-shelf integrated circuits were used occupying two thirds of a 14cm × 18cm wirewrap board (excluding power supply). the total power dissipation is less than 2 watts. the chip count may be reduced by a factor of two by combining the random logic on a semi-custom integrated circuit. When produced commercially, the cost of this vocoder should be considerably less than existing LPC units.
Image reconstruction from projections has been extensively studied in radioastronomy and medical imaging. the same techniques can be applied to multiple target detection tasks such as radar or sonar signalprocessing....
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Image reconstruction from projections has been extensively studied in radioastronomy and medical imaging. the same techniques can be applied to multiple target detection tasks such as radar or sonar signalprocessing. However, in medical imaging and radioastronomy, the images to be reconstructed are generally "compact" and the ratio between the required image size and resolution is small compared to that of the target detection problem where the images are sparse and fine resolution is required. therefore a larger number of basis functions will be necessary to discretize the general image. thus medical image reconstruction techniques applied directly to target detection can result in excessive memory requirements, computational time and required number of measurements. With modifications that consider the sparseness and positiveness of the images, reconstruction techniques have a potentially valuable application in the multiple target detection problem. In this paper we propose two modifications of the medical image reconstruction technique for the multi-target detection problem. One consists of pre-processingthe data in order to reduce the total image to a smaller set of regions likely to contain targets. the second consists of dividing the image into pixels much larger than the expected size of a target and estimating the total target intensity for each pixel.
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