Fast Decoding Research Articles

Enhanced Multiple-Bit Differential Detection of DOQPSK

In this paper, we consider multiple-bit differential detection (MBDD) of differential offset quadrature phase-shift keying (DOQPSK) with conventional bit-wise differential encoding (DE). We propose two novel MBDD schemes that outperform Simon's MBDD and approach the performance of coherent detection (CD) as the observation window size N increases. The first scheme uses the full available detection window spanning N-1 bit intervals, and is referred to as F-MBDD. A careful bit-error rate (BER) analysis shows that a significant performance improvement can be realized by reducing the detection window size to N-3 bit intervals. The resulting improved MBDD (I-MBDD) of DOQPSK with bit-wise DE achieves a similar performance as Phoel's MBDD of DOQPSK with more complex symbol-wise DE. For both F-MBDD and I-MBDD, tight upper bounds and accurate BER approximations are derived. In addition, since the complexity per bit decision of brute-force decoding of MBDD of DOQPSK grows exponentially with N, we develop optimum and suboptimum fast decoding algorithms with complexities per bit decision of orders log(N+1) and N, respectively.

A Fast Fractal Image Compression Method Based Entropy

Fractal image compression gives some desirable properties like resolution independence, fast decoding, and very competitive rate-distortion curves. But still suffers from a (sometimes very) high encoding time, depending on the approach being used. This paper presents a method to reduce the encoding time of this technique by reducing the size of the domain pool based on the Entropy value of each domain block. Experimental results on standard images show that the proposed method yields superior performance over conventional fractal encoding.

Communicating over nonstationary nonflat wireless channels

We develop the concept of joint time-frequency estimation of wireless channels. The motivation is to optimize channel usage by increasing the signal-to-noise ratio (SNR) after demodulation while keeping training overhead at a moderate level. This issue is important for single-input single-output (SISO) and multiple-input multiple-output (MIMO) systems but particularly so for the latter. Linear operators offer a general mathematical framework for symbol modulation in channels that vary both temporally and spectrally within the duration and bandwidth of one symbol. In particular, we present a channel model that assumes first-order temporal and spectral fluctuations within one symbol or symbol block. Discrete prolate spheroidal sequences (Slepian sequences) are used as pulse-shaping functions. The channel operator in the Slepian basis is almost tridiagonal, and the simple intersymbol interference pattern can be exploited for efficient and fast decoding using Viterbi's algorithm. To prove the concept, we use the acoustic channel as a meaningful physical analogy to the radio channel. In acoustic 2 /spl times/ 2 MIMO experiments, our method produced estimation results that are superior to first-order time-only, frequency-only, and zeroth-order models by 7.0, 9.4, and 11.6 db. In computer simulations of cellular wireless channels with realistic temporal and spectral fluctuations, time-frequency estimation gains us 12 to 18 db over constant-only estimation in terms of received SNR when signal-to-receiver-noise is 10 to 20 db. The bit error rate (BER) decreases by a factor of two for a binary constellation.

Generation of fast interpreters for Huffman compressed bytecode

Embedded systems often have severe memory constraints requiring careful encoding of programs. For example, smart cards have on the order of 1K of RAM, 16K of non-volatile memory, and 24K of ROM. A virtual machine can be an effective approach to obtain compact programs but instructions are commonly encoded using one byte for the opcode and multiple bytes for the operands, which can be wasteful and thus limit the size of programs runnable on embedded systems. Our approach uses canonical Huffman codes to generate compact opcodes with custom-sized operand fields and with a virtual machine that directly executes this compact code. We present techniques to automatically generate the new instruction formats and the decoder. In effect, this automatically creates both an instruction set for a customized virtual machine and an implementation of that machine. We demonstrate that, without prior decompression, fast decoding of these virtual compressed instructions is feasible. Through experiments on Scheme and Java, we demonstrate the speed of these decoders. Java benchmarks show an average execution slowdown of 9%. The reductions in size highly depend on the original bytecode and the training samples, but typically vary from 40% to 60%.

Inverted Index Compression Using Word-Aligned Binary Codes

We examine index representation techniques for document-based inverted files, and present a mechanism for compressing them using word-aligned binary codes. The new approach allows extremely fast decoding of inverted lists during query processing, while providing compression rates better than other high-throughput representations. Results are given for several large text collections in support of these claims, both for compression effectiveness and query efficiency.

Stochastic Analysis of Turbo Decoding

This paper proposes a stochastic framework for dynamic modeling and analysis of turbo decoding. By modeling the input and output signals of a turbo decoder as random processes, we prove that these signals become ergodic when the block size of the code becomes very large. This basic result allows us to easily model and compute the statistics of the signals in a turbo decoder. Using the ergodicity result and the fact that a sum of lognormal distributions is well approximated using a lognormal distribution, we show that the input-output signals in a turbo decoder, when expressed using log-likelihood ratios (LLRs), are well approximated using Gaussian distributions. Combining the two results above, we can model a turbo decoder using two input parameters and two output parameters (corresponding to the means and variances of the input and output signals). Using this model, we are able to reveal the whole dynamics of a decoding process. We have discovered that a typical decoding process is much more intricate than previously known, involving two regions of attraction, several fixed points, and a stable equilibrium manifold at which all decoding trajectories converge. Some applications of the stochastic framework are also discussed, including a fast decoding scheme

Signal constellations for quasi-orthogonal space-time block codes with full diversity

Space-time block codes (STBCs) from orthogonal designs proposed by Alamouti, and Tarokh-Jafarkhani-Calderbank have attracted considerable attention lately due to their fast maximum-likelihood (ML) decoding and full diversity. However, the maximum symbol transmission rate of an STBC from complex orthogonal designs for complex signals is only 3/4 for three and four transmit antennas, and it is difficult to construct complex orthogonal designs with rate higher than 1/2 for more than four transmit antennas. Recently, Jafarkhani, Tirkkonen-Boariu-Hottinen, and Papadias-Foschini proposed STBCs from quasi-orthogonal designs, where the orthogonality is relaxed to provide higher symbol transmission rates. With the quasi-orthogonal structure, the quasi-orthogonal STBCs still have a fast ML decoding, but do not have the full diversity. The performance of these codes is better than that of the codes from orthogonal designs at low signal-to-noise ratio (SNR), but worse at high SNR. This is due to the fact that the slope of the performance curve depends on the diversity. It is desired to have the quasi-orthogonal STBCs with full diversity to ensure good performance at high SNR. In this paper, we achieve this goal by properly choosing the signal constellations. Specifically, we propose that half of the symbols in a quasi-orthogonal design are chosen from a signal constellation set A and the other half of them are chosen from a rotated constellation e/sup j/spl phi// A. The resulting STBCs can guarantee both full diversity and fast ML decoding. Moreover, we obtain the optimum selections of the rotation angles /spl phi/ for some commonly used signal constellations. Simulation results show that the proposed codes outperform the codes from orthogonal designs at both low and high SNRs.

Low-cost and fast partial decoding of compressed videos

We propose a fast partial decoding algorithm for content access to MPEG compressed videos, where full decompression is not necessarily required, such as compressed video browsing, content analysis, and specific pattern search. The proposed decoding bypasses the inverse DCT via an approximation process to extract average pixels directly from compressed DCT coefficients. Although such extracted pixels may incur differences compared with their fully decompressed counterparts, extensive experiments show that such partially decoded video frames preserve their content very well and achieve reasonable perceptual quality in terms of visual inspections.

Using Data Compression in Automatic Test Equipment for System-on-Chip Testing

Compression has been used in automatic test equipment (ATE) to reduce storage and application time for high volume data by exploiting the repetitive nature of test vectors. The application of a binary compression method to an ATE environment for manufacturing is studied using a technique, referred to as reuse. In reuse, compression is achieved by partitioning the vector set and removing repeating segments. This process has O(n/sup 2/) time complexity for compression (where n is the number of vectors) with a simple hardware decoding circuitry. It is shown that for industrial system-on-chip (SoC) designs, the efficiency of the reuse compression technique is comparable to sophisticated software techniques with the advantage of easy and fast decoding. Two shift register-based decompression schemes are presented; they can be either incorporated into internal scan chains or built in the tester's head. The proposed compression method has been applied to industrial test and data and an average compression rate of 84% has been achieved.

An orthogonal space-time coded CPM system with fast decoding for two transmit antennas

Trellis-coded space-time (TC-ST) coding for continuous-phase modulation (TC-ST-CPM) was recently proposed by Zhang and Fitz. In this paper, we propose an orthogonal space-time coding for CPM (OST-CPM) systems and two transmit antennas. In the proposed OST-CPM, signals from two transmit antennas at any time t are orthogonal while both of them have continuous phases. Similar to Alamouti's OST coding for phase-shift keying (PSK) and quadrature amplitude modulation (QAM) systems, the newly proposed OST-CPM has a fast decoding algorithm.

Class-based Gaussian selection for efficient decoding in PTM HMMs

A new Gaussian selection (GS) method is presented for fast decoding in phonetic tied-mixture (PTM) hidden Markov models (HMMs). For efficient likelihood computation, a constraint is imposed on the context-dependent weights as well as the number of Gaussians. Experimental results demonstrate the superiority of the proposed method over conventional GS methods.

Condensed Table of Huffman Coding, a New Approach to Efficient Decoding

This letter introduces a new technique for designing and decoding Huffman codes. The key idea is to define a condensed Huffman table (CHT) that is smaller than the ordinary Huffman table and which leads to fast decoding. For example, the new approach has been shown to reduce the memory consumption by a factor of eight, compared with the single-side grown Huffman table.

Fast and optimal decoding for machine translation

A good decoding algorithm is critical to the success of any statistical machine translation system. The decoder's job is to find the translation that is most likely according to a set of previously learned parameters (and a formula for combining them). Since the space of possible translations is extremely large, typical decoding algorithms are only able to examine a portion of it, thus risking to miss good solutions. Unfortunately, examining more of the space leads to unacceptably slow decodings. In this paper, we compare the speed and output quality of a traditional stack-based decoding algorithm with two new decoders: a fast but non-optimal greedy decoder and a slow but optimal decoder that treats decoding as an integer-programming optimization problem.

A novel product coding and recurrent alternate decoding scheme for image transmission over noisy channels

In this letter, we present a novel product channel coding and decoding scheme for image transmission over noisy channels. Two convolutional codes with at least one recursive systematic convolutional code are employed to construct the product code. Received data are decoded alternately in two directions. A constrained Viterbi algorithm is proposed to exploit the detection results of cyclic redundancy check codes so that both reduction in error patterns and fast decoding speed are achieved. Experiments with image data coded by the algorithm of set partitioning in hierarchical trees exhibit results better than those currently reported in the literature.

Hybrid pixel-based data hiding and block-based watermarking for error-diffused halftone images

A low computational complexity noise-balanced error diffusion (NBEDF) technique is proposed for embedding watermarks into error-diffused images. The visual decoding pattern can be perceived when two or more similar NBEDF images are overlaid, even in a high activity region. Also, with the modified improved version of NBEDF, two halftone images can be made from two totally different gray-tone images, and still provide a clear and sharp visual decoding pattern. With self-decoding techniques, we can also decode the pattern with only one NBEDF image. However, the NBEDF method is not so robust to damage due to printing or other distortions. Thus, a kernels-alternated error diffusion (KAEDF) technique is proposed. By using them alternately in the halftone process, we find that two well-known kernels (Jarvis, J.F. et al., 1976; Stucki, P., 1981) are compatible. In the decoder, because the spectral distributions of Jarvis and Stucki kernels are different in the 2D fast Fourier transform domain, we use the cumulative squared Euclidean distance criterion to determine each cell in a watermarked halftone image belonging to either Jarvis or Stucki, and then decode the watermark. Furthermore, because the detailed textures of Jarvis and Stucki patterns are somewhat different in the spatial domain, the lookup table (LUT) technique is also used for fast decoding. From simulation results, the correct decoding rates for both techniques are high and extremely robust, even after printing and scanning processes. Finally, we extend the hybrid NBEDF and KAEDF algorithms to two color EDF halftone images, where 8 independent KAEDF watermarks and 16 NBEDF watermarks can be inserted and still achieve a high-quality result.

An optical error-correction code with spectrum domain decoding

We propose an optically (though not all-optically) decodable error-correction code. The structure of the proposed code is defined in the Fourier spectrum domain. The Fourier spectrum of the codewords has zeros. Thus, we can view any signal appearing in these spectrum zeros as the syndrome, since the codewords have no energy in these spectrum zeros. The optical technique of the Fourier transform is used for fast decoding speed. The proposed code is nonlinear and of length eight. This code has a total of eight codewords (so the code rate is (log/sub 2/8)/8=3/8) and can correct 1.75 consecutive bit errors on average.

A fast multispeed comma-free reed-solomon decoder for W-CDMA applications using foldable systolic array architecture

This brief proposes a fast multispeed comma-free Reed-Solomon (CFRS) decoder for the frame synchronization and code-group identification in the cell search of the Third Generation Partnership Project wide-band code-division multiple access/frequency division duplexing (W-CDMA/FDD) system. A foldable systolic array is proposed to achieve fast decoding and provide flexible tradeoffs between power consumption, chip size, and decoding latency. Multispeed decoding, an idea that is useful for cell search in different application scenarios, can also be achieved with the same array architecture. The proposed CFRS decoder is implemented in a 3.3-V 0.35-μm CMOS technology with 2.2 × 2.2 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> core area and power dissipation of 13.3 and 1.23 mW in high- and low-speed decoding modes, respectively.

Codes in the Vandermonde F -Metric and Their Application

F-metrics are metrics based on projective sets. In this paper, construction of optimal codes for a special F-metric associated with a generalized Vandermonde matrix is given. Encoding and fast decoding algorithms are described. A public-key cryptosystem is considered as an example of a possible application of codes constructed.

Microarray image compression: SLOCO and the effect of information loss

Microarray image technology is a powerful tool for monitoring the expression of thousands of genes simultaneously. Each microarray experiment produces immense amounts of image data, and efficient storage and transmission require compression that takes advantage of microarray image structure. In this paper we develop a compression scheme for microarray images which can be either lossless or lossy with successive refinements. Existing measures of distortion such as mean squared pixel-wise error and visual fidelity are not appropriate for microarray images. We introduce a new measure of distortion for lossy compression: the sensitivity of microarray information extraction to compression loss. Furthermore, our scheme has a coded data structure that allows fast decoding and reprocessing of image sub-blocks, and includes summary statistics and image segmentation information. The average lossless compression ratio is 1.83:1 for our cDNA test images and 2.43:1 for our inkjet test images, comparable or better than state-of-the-art lossless schemas, yet with additional structure and information. At an average lossy compression ratio of 8:1 for cDNA microarrays, we find that our scheme minimizes the effects of compression loss compared to other algorithms. We show that the variability in differential gene expression levels extracted from lossily vs. losslessly compressed microarray images is less than both the variability between different arrays and the variability between different extraction algorithms. In fact, lossy compression can improve the estimation of gene expression levels for cDNA images.

A novel turbo decoder architecture for hand-held communication devices

Mobile communication systems, such as IMT-2000, adopt turbo codes because they provide reliable, bandwidth-efficient communication over AWGN channels. The BER of turbo codes is close to the Shannon limits. As a mobile communication device can use only a limited power supply, use of low power design techniques is essential for successful device implementation. This paper proposes an architecture for the turbo decoder which adopts a fast decoding structure by initial threshold setting such that the number of decoding iterations can be reduced without BER performance degradation. The proposed decoding structure can be easily modified for other MAP decoders by employing a simple decision module. The initial iteration threshold has been decided via simulations by varying decoder parameters (mean/normalized-variance of the log-likelihood ratio; BER of the decoder outputs). Performance validations for the proposed decoder have been performed under the IMT-2000 high-speed data transmission testbed with the channel characteristic being set to BER=10/sup -6/. Simulation results show 55%-90% reductions in the number of decoding iterations when compared with conventional turbo decoders with a fixed number of iterations.