The authors outline a single (two-) microphone design of an ANC (adaptive noise cancellation) system that avoids prediction of colored noise, thus achieving the optimal prediction residual that is theoretically achiev...
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The authors outline a single (two-) microphone design of an ANC (adaptive noise cancellation) system that avoids prediction of colored noise, thus achieving the optimal prediction residual that is theoretically achievable. The design forces the colored acoustic noise to its inaccessible white generating sequence. The system, which is less sensitive to parameter estimation errors than previous approaches, is easily extended to an array of cancellation speakers. computer simulation results using actually recorded machine noise are presented to illustrate (simulated) system performance. Parameter sensitive analysis shows that the present design is far less sensitive to identification than are deterministic designs.< >
An algorithm for detecting neural processes in serial optical sections for use in an automated three-dimensional neural reconstruction system is presented. This parsimonious, nonlinear, psychophysically motivated algo...
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An algorithm for detecting neural processes in serial optical sections for use in an automated three-dimensional neural reconstruction system is presented. This parsimonious, nonlinear, psychophysically motivated algorithm addresses the problems specific to neural element detection and localization, viz., images with minimal resolution, operators with small spatial supports, highly curved, filamentous features, large variation in feature intensity profile, poor signal-to-noise ratio, and determination of depth without stereo. One first finds the magnitude and orientation of the maximum intensity second directional derivative. A family of curves is locally fitted to these data, and the projections of the data on the curve family are found. If a pixel lies on a curve with sufficient total projection, it is labeled with the magnitude, orientation, curvature, spatial extent, and element displacement. Depth is interpolated from the spatial extent data for corresponding neighborhoods in three adjacent (in depth) images by using an approximation to the depth-dependent optical point spread function. Experimental results using photomicrographs of cat visual cortex are presented.< >
An adaptive VLSI neuroprocessor based on vector quantization algorithm has been developed for real-time high-ratio image compression applications. This VLSI neural-network-based vector quantization (NNVQ) module combi...
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An adaptive VLSI neuroprocessor based on vector quantization algorithm has been developed for real-time high-ratio image compression applications. This VLSI neural-network-based vector quantization (NNVQ) module combines a fully parallel vector quantizer with a pipelined codebook generator for a broad area of data compression applications. The NNVQ module is capable of producing good-quality reconstructed data at high compression ratios more than 20. The vector quantizer chip has been designed, fabricated, and tested. It contains 64 inner-product neural units and a high-speed extendable winner-take-all block. This mixed-signal chip occupies a compact silicon area of 4.6*6.8 mm/sup 2/ in a 2.0- mu m scalable CMOS technology. The throughput rate of the 2- mu m NNVQ module is 2 million vectors per second and its equivalent computation power is 3.33 billion connections per second.< >
The frequency-sensitive competitive learning (FSCL) algorithm and its associated VLSI neuroprocessor have been developed for adaptive vector quantisation (AVQ). Simulation results show that the FSCL algorithm is capab...
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The frequency-sensitive competitive learning (FSCL) algorithm and its associated VLSI neuroprocessor have been developed for adaptive vector quantisation (AVQ). Simulation results show that the FSCL algorithm is capable of producing a good-quality codebook for AVQ at high compression ratios of more than 20 in real time. This VLSI neural-network-based vector quantization design includes a fully parallel vector quantizer and a pipelined codebook generator to provide an effective data compression scheme. It provides a computing capability as high as 3.33 billion connections per second. Its performance can achieve a speedup of 750 compared with SUN-3/60 and a compression ratio of 33 at a signal-to-noise ratio of 23.81 dB.< >
A trainable VLSI neuroprocessor for adaptive vector quantization based upon the frequency-sensitive competitive learning algorithm has been developed for high-speed high-ratio image compression applications. Simulatio...
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A trainable VLSI neuroprocessor for adaptive vector quantization based upon the frequency-sensitive competitive learning algorithm has been developed for high-speed high-ratio image compression applications. Simulation results show that such an algorithm is capable of producing good-quality reconstructed image at compression ratios of more than 20. This design includes a fully parallel vector quantizer and a pipelined codebook generator which obtains a time complexity O(1) for each quantization vector. A 5*5-dimensional vector quantizer prototype chip has been designed, fabricated and tested. It contains 64 inner-product neural units and an extendable winner-take-all block. This mixed-signal chip occupies a compact Si area of 4.6*6.8 mm/sup 2/ in 2.0- mu m scalable CMOS technology.< >
Several algorithms for segmenting multifrequency synthetic aperture radar (SAR) complex data into regions of similar and homogeneous backscattering characteristics are presented. The image model is composed of two mod...
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Several algorithms for segmenting multifrequency synthetic aperture radar (SAR) complex data into regions of similar and homogeneous backscattering characteristics are presented. The image model is composed of two models, one for the multifrequency complex amplitudes (i.e., speckle), and the other for the region labels. The speckle model is derived from SAR physics. The corresponding analysis illustrates the importance of having a good knowledge of the characteristics of the SAR imaging and processing systems to correctly model the high order statistics of speckle. The region model, on the other hand, uses a multilevel Ising model (a Markov random field) to represent the grouping of pixels into regions. The two models are combined using Bayes' rule to define an optimal region labeling of the scene given the multifrequency complex amplitudes. Two alternatives are presented that can be implemented on an optimization network. The performance of the segmentation technique is illustrated.< >
The authors develop a path metric for sequential search based on the linear model. The metric forms the heart of an edge-linking algorithm that combines edge elements enhanced by an optimal filter. From a starting nod...
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The authors develop a path metric for sequential search based on the linear model. The metric forms the heart of an edge-linking algorithm that combines edge elements enhanced by an optimal filter. From a starting node, transitions are made to the goal nodes by a maximum likelihood metric. This metric requires only local calculations on the search space and its use in edge linking provides more accurate results than other linking techniques.< >
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