A low-power read-out circuit design for a heterogeneously integrated chemoresistive micro-/nano-sensor arrays is presented. The circuit is based on a sigma delta modulation architecture, to reduce power consumption and to allow for a wider range of resistance measurements. The design and layout of the circuit, in 180nm CMOS is presented, along with measurement results from the prototype IC. The chip successfully reads out resistances ranging from 20kOmega to 600 kOmega with a measured power consumption of 3 mW. The die area occupied by the readout circuit is 180 um times 300 um.

Embedded block coding (EBCOT Tier-1) is the most computationally intensive part of the JPEG2000 image coding standard. Past research on fast EBCOT Tier-1 hardware implementations has concentrated on cycle-efficient context formation, and achieve improved throughput at the cost of reduced coding efficiency. In this paper, a new fast EBCOT Tier-1 design called the Split Arithmetic Encoder (SAE) process is presented. The proposed process exploits concurrency to obtain improved throughput with a lower penalty in coding efficiency. The design is for a complete EBCOT Tier-1 process, and considers system-level issues such as the need for buffering. A hardware architecture for the proposed process was developed, realized in VHDL, and synthesized for implementation on an Altera field programmable gate array. Results show, on average, a 55% improvement in processing time over a serial architecture for a set of sixteen test images.

Embedded block coding, EBCOT tier-1, is the most computationally intensive part of the JPEG2000 image coding standard. Past research on fast EBCOT tier-1 hardware implementations has concentrated on cycle efficient context formation. These pass-parallel architectures require that JPEG2000's three mode switches be turned on; thus, coding efficiency is sacrificed for improved throughput. In this paper a new fast EBCOT tier-1 design is presented: it is called the split arithmetic encoder (SAE) process. The proposed process exploits concurrency to obtain improved throughput while preserving coding efficiency. The SAE process is evaluated using two methods: clock cycle estimation, and FPGA hardware implementation. Both methods achieve throughput improvement; the hardware implementation exhibits the largest speedup.

Design techniques for high-performance, fixed-point, multiplierless filter banks are presented. Image compression using the biorthogonal 9/7 discrete wavelet transform provides a motivating example. Image compression and hardware performance of two commonly used filter structures, direct and cascade, and two known filter bank structures, nonpolyphase and polyphase, are compared. A technique is shown for designing a fixed-point polyphase filter structure, which is highly efficient from a hardware standpoint, such that image-compression quality is not significantly deteriorated by the use of fixed-point mathematics. The result is a polyphase structure with about twice the throughput rate of nonpolyphase structures, and peak signal-to-noise ratio values for lossy compression within 0.2 decibels of those achieved using floating-point filters.

An architecture for the lifting-based two-dimensional discrete wavelet transform is presented. The architecture is easily scaled to accommodate different numbers of lifting steps. The architecture has regular data flow and low control complexity, and achieves 100% hardware utilization. Symmetric extension of the image to be transformed is handled in a way that does not require additional computations or clock cycles. The architecture is investigated in terms of hardware parameters such as memory size and number of ports, number of memory accesses, latency, and throughput. The proposed architecture achieves higher throughput and uses less embedded memory than architectures based on convolutional filter banks.

The lifting structure has been shown to be computationally efficient for implementing filter banks. The hardware implementation of a filter bank requires that the lifting coefficients be quantized. The quantization method determines compression performance, hardware size, hardware speed and energy. We investigate the implementation of two lifting coefficient sets, rational and irrational, for the biorthogonal 9/7 wavelet. Six different approaches are used to find optimal quantized lifting coefficients from these sets. We find that the best hardware and PSNR performance is obtained using the rational coefficient set quantized with gain compensation and lumped scaling.

Design techniques for high performance, fixed-point, multiplierless filter banks are presented. A technique is developed for designing a fixed-point polyphase filter structure such that the hardware is highly efficient and the image compression quality is minimally impacted by the use of fixed-point mathematics. Image compression using the biorthogonal 9/7 discrete wavelet transform provides a motivating example. The results show that the implemented structure has a significant impact on the resulting hardware cost, throughput, and compression quality. We achieve a polyphase structure with twice the throughput rate of non-polyphase structures and image compression fidelity within 0.2 dB of the unquantized, floating-point filters.

The JPEG2000 standard includes a perceptual weighting method based on the contrast sensitivity function (CSF). Recent literature shows that perceptual methods based on subband standard deviation are also effective in image compression. We present two new perceptual weighting methods that combine information from both the human contrast sensitivity function as well as the standard deviation within a subband or code-block. We compare these two new sets of perceptual weights to the JPEG2000 CSF weights. The results indicate that our new weights performed better than the JPEG2000 CSF weights for high frequency images. Weights based solely on subband standard deviation are shown to perform worse than JPEG2000 CSF weights for all images at all compression ratios.

Wavelet modulation sends multiple copies of data at multiple rates through an unknown channel. We present new methods that result in dramatic bit error rate performance improvements when multiple rates are available and utilized for demodulation. Our results indicate significantly better performance than binary phase-shift keying in fading channels. We also introduce and illustrate new algorithms that identify the channel type and mitigate the effects of intersymbol interference in frequency selective fading channels.

Recently developed balanced multiwavelets have proven effective for image compression; however, their performance falls slightly short of the scalar wavelets. A previous comparison of some scalar wavelet and multi wavelet properties illustrated their importance for grayscale image compression. In this paper we present another property: the perfect reconstruction (PR) property; it too appears to explain the performance differences. This paper compares the compression performance of 3 scalar wavelets and 5 balanced multiwavelets on 7 color images. We observe that SA4depicts the best performance amongst the multi wavelets in terms of both subjective quality and PSNR; this matches our expectation based on our results that indicate that it is the only BMW which satisfies the PR conditions. Although SA4rivals the biorthogonal scalar wavelets in terms of many desirable properties, it suffers from a lower balancing/vanishing order. This difference explains the 0.01–0.47 dB performance gap between the best scalar wavelet and the best balanced multiwavelet.

This paper is concerned with the use of a novel motorized lens to perform segmentation of image sequences. The lens has the effect of introducing small, repeating movements of the camera center, so that objects appear to translate in the image by an amount that depends on distance from the plane of focus. For a stationary scene, optical flow magnitudes and direction are therefore directly related to three-dimensional object distance from the observer. We describe a segmentation-procedure that exploits these controlled observer movements, and we present experimental results that demonstrate the successful extraction of objects at different depths. Potential applications of this approach include video compression, compositing, and passive range sensing.

Advances in wavelet transforms and quantization methods have produced algorithms capable of surpassing the existing image compression standards like the Joint Photographic Experts Group (JPEG) algorithm. For best performance in image compression, wavelet transforms require filters that combine a number of desirable properties, such as orthogonality and symmetry. However, the design possibilities for wavelets are limited because they cannot simultaneously possess all of the desirable properties. The relatively new field of multiwavelets shows promise in obviating some of the limitations of wavelets. Multiwavelets offer more design options and are able to combine several desirable transform features. The few previously published results of multiwavelet-based image compression have mostly fallen short of the performance enjoyed by the current wavelet algorithms. This paper presents new multiwavelet transform and quantization methods and introduces multiwavelet packets. Extensive experimental results demonstrate that our techniques exhibit performance equal to, or in several cases superior to, the current wavelet filters.

The phase retrieval problem arises when a signal must be reconstructed from only the magnitude of its Fourier transform; if the phase information were also available, the signal could simply be synthesized using the inverse Fourier transform. In continuous phase retrieval, most previous solutions rely on discretizing the problem and then employing an iterative algorithm. We avoid this approximation by using wavelet expansions to transform this uncountably infinite problem into a linear system of equations. The wavelet bases permit a solution by incorporating a priori signal information and they provide a structured system of equations which results in a fast algorithm. Our solutions obviate the stagnation problems associated with iterative algorithms, they are computationally simpler and more stable than previous non-iterative algorithms, and they can accommodate noisy Fourier magnitude information. This paper develops our 1-D continuous, non-minimum phase retrieval algorithm and illustrates its effectiveness with numerical examples.

The 1-D phase retrieval problem is to reconstruct a signal given the modulus of its Fourier transform. Conventional solution techniques approximate the solution by discretizing this continuous problem. Our solution obviates this approximation by employing basis functions to better represent a continuous signal. In particular, we use wavelet bases because they provide a structure which permits the use of a fast algorithm and because they incorporate a priori knowledge of the signal. This paper develops our algorithm and illustrates its effectiveness with numerical examples.

The wavelet transform is applied to integral equations with Toeplitz kernels. Such integral equations arise in inverse scattering and linear least-squares estimation. The result is a system of equations with block-slanted-Toeplitz structure. In previous approaches, this linear system was sparsified by neglecting all entries below some threshold. However, in inverse scattering, the Toeplitz kernel may not be a rapidly decreasing function due to reflections from great depths. In this case, neglecting entries below a threshold will not work since the system matrix is ill-conditioned. We use the different approach of exploiting the block-slanted-Toeplitz structure to obtain fast algorithms similar to the multichannel Levinson and Schur algorithms. Since it is exact to within the wavelet-basis approximation, this different approach should prove to be a valuable alternative to the approximate approach of sparsification in cases when the latter does not work.