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This paper is an in-depth review on silicon implementations of threshold logic gates that covers several decades. In this paper, we will mention early MOS threshold logic solutions and detail numerous very-large-scale integration (VLSI) implementations including capacitive (switched capacitor and floating gate with their variations), conductance/current (pseudo-nMOS and output-wired-inverters, including a plethora of solutions evolved from them), as well as many differential solutions. At the end, we will briefly mention other implementations, e.g., based on negative resistance devices and on single electron technologies.

The iterative Born method is an inverse technique that has been used successfully in ultrasound imaging. However, the calculation cost of the standard iterative Born method is high, and parallel computation is limited to the forward problem. In this work, two methods are introduced to increase the rate of convergence of the iterative Born algorithm. These methods are tested on three different objects. The results are promising, with both algorithms giving accurate results at lower computational cost. The first method, referred to as the coarse resolution initial value (CRIV) method, uses the iterative Born algorithm for a coarse grid to quickly estimate the initial value of the object to be reconstructed. From this initial value, the final image is obtained for a finer grid with additional iterations. The cost of this method is 40% less than that of the iterative Born technique. The second method, the quadriphase source (QS) method, simultaneously uses four single sources, and object reconstruction for each is performed in parallel; the reconstruction results for all four sources then are averaged to obtain the final image. The cost of this method is 20% less than that of the standard iterative Born method. When the object to be reconstructed is of low contrast and/or has a small phase shift, the QS method is very promising because parallel computation can be used to solve both the forward and inverse problems. However, the QS method fails for high contrast objects.

This paper investigates various classification techniques, applied to subband coding of images, as a way of exploiting the nonstationary nature of image subbands. The advantages of subband classification are characterized in a rate-distortion framework in terms of "classification gain" and overall "subband classification gain." Two algorithms, maximum classification gain and equal mean-normalized standard deviation classification, which allow unequal number of blocks in each class, are presented. The dependence between the classification maps from different subbands is exploited either directly while encoding the classification maps or indirectly by constraining the classification maps. The trade-off between the classification gain and the amount of side information is explored. Coding results for a subband image coder based on classification are presented. The simulation results demonstrate the value of classification in subband coding.

A lattice-based vector quantizer (VQ) and noiseless code are proposed for transform and subband image coding. The quantization is simple to implement, and no vector codebooks need to be stored. The noiseless code enumerates lattice codevectors based on their (weighted) l(1) norm. A software implementation is able to handle lattice codebooks of size 2(256). The image coding performance is shown to be comparable or superior to the best encoding methods reported in the literature.

A size-limited filter bank (SLFB) is a maximally decimated filter bank operating on a finite length (duration) input signal resulting in subband components whose total number of independent samples is equal to the number of input samples. A theoretical framework for the design, analysis, and implementation of such filter banks resulting from applying FIR separable filter banks to finite length signals is presented. The concept of maximizing theoretical coding gain (TCG) using optimal bit allocation is generalized for this special case. Using TCG, the relative merits of various different SLFBs are addressed.

This paper concerns the use of a priori information on the symmetry of cross differentials available for problems that seek to approximate the gradient of a differentiable function. We derive the appropriate network constraints to incorporate the symmetry information, show that the constraints do not reduce the universal approximation capabilities of feedforward networks, and demonstrate how the constraints can improve generalization.

A multirate (MR) filter bank is called size-limited if the total number of output samples equals the number of input samples. A method called symmetric extension improved performance in subband image compression systems compared to the earlier method of circular convolution. However, the symmetric extension method was developed only for two-band uniform filter banks, and required even-length linear phase analysis filters. The authors generalize the symmetric extension method to the M-band, possibly nonuniform filter banks, where M=/>2. The length restriction on the analysis filters is relaxed.

A description is given of CMOS electronic circuits which emulate natural neurons at a more detailed level than that typically used by artificial neural network models. A pulse-firing circuit which realizes general short-term neuron dynamics is discussed. Both fixed and programmable synapse circuits for realizing long-term dynamics are also described. Together, these establish the basic structures required for the implementation of programmable impulse neural networks.