Showing papers by "Guangming Shi published in 2008"

UWB Echo Signal Detection With Ultra-Low Rate Sampling Based on Compressed Sensing

Abstract: A major challenge in ultra-wide-band (UWB) signal processing is the requirement for very high sampling rate. The recently emerging compressed sensing (CS) theory makes processing UWB signal at a low sampling rate possible if the signal has a sparse representation in a certain space. Based on the CS theory, a system for sampling UWB echo signal at a rate much lower than Nyquist rate and performing signal detection is proposed in this paper. First, an approach of constructing basis functions according to matching rules is proposed to achieve sparse signal representation because the sparse representation of signal is the most important precondition for the use of CS theory. Second, based on the matching basis functions and using analog-to-information converter, a UWB signal detection system is designed in the framework of the CS theory. With this system, a UWB signal, such as a linear frequency-modulated signal in radar system, can be sampled at about 10% of Nyquist rate, but still can be reconstructed and detected with overwhelming probability. The simulation results show that the proposed method is effective for sampling and detecting UWB signal directly even without a very high-frequency analog-to-digital converter.

Adaptive Nonseparable Interpolation for Image Compression With Directional Wavelet Transform

Abstract: The adaptive directional lifting-based wavelet transform (ADL) locally adapts the filtering directions to the local properties of the image. In this letter, instead of using the conventional interpolation filter for the directional prediction with fractional-pel accuracy, a new two-dimensional nonseparable adaptive interpolation filter is proposed. The adaptive filter is calculated for every fractional-pel direction so as to minimize the energy of the prediction error. The tradeoff between reducing the prediction error and the overhead to code the interpolation filter is discussed. This enables coding gains of up to 0.98 dB, compared to ADL coder, and up to 2.4 dB, compared to the JPEG 2000 for typical test images.

Lossy to lossless image compression based on reversible integer DCT

Abstract: A progressive image compression scheme is investigated using reversible integer discrete cosine transform (RDCT) which is derived from the matrix factorization theory. Previous techniques based on DCT suffer from bad performance in lossy image compression compared with wavelet image codec. And lossless compression methods such as IntDCT, I2I-DCT and so on could not compare with JPEG-LS or integer discrete wavelet transform (DWT) based codec. In this paper, lossy to lossless image compression can be implemented by our proposed scheme which consists of RDCT, coefficients reorganization, bit plane encoding, and reversible integer pre- and post-filters. Simulation results show that our method is competitive against JPEG-LS and JPEG2000 in lossless compression. Moreover, our method outperforms JPEG2000 (reversible 5/3 filter) for lossy compression, and the performance is even comparable with JPEG2000 which adopted irreversible 9/7 floating-point filter (9/7F filter).

Signal-adapted directional lifting scheme for image compression

Abstract: In this paper, we propose a new adaptive lifting scheme that not only locally adapts the filtering directions to the orientations of image features, but also adapts the lifting filters to the statistic properties of image signal. The proposed approach refines previous adaptive directional lifting-based wavelet transform (ADL) by combining directional lifting and adaptive lifting filters to form a unified framework. The image signal is first segmented into regions of textures and edges with close directional features. The lifting filters are then effectively designed. The prediction step is designed to minimize the prediction error of the image signal, and the update step is designed to minimize the reconstruction error. Significant improvements on objective and subjective quality over conventional 2-D wavelet transform and previous ADL transform are achieved.

A compressive sensing approach of multiple descriptions for network multimedia communication

Abstract: A new multiple description coding (MDC) approach is proposed based on the theory of compressive sensing (CS). The CS theory allows a signal to be reconstructed from a small number of its random measurements if the signal is sparse in some space. An attractive property of CS for MDC applications is that the reconstruction error only depends on the number but not on which of the transmitted measurements that are received. By treating each CS measurement as a description, we have a balanced MDC scheme with fine description granularity and low encoding complexity. Another advantage of the new MDC approach is that all signals can be coded the same but decoded in different spaces for better sparse reconstruction.

Image denoising based on improved adaptive directional lifting wavelet transform

Abstract: In this paper we propose a new denoising technique based on improved adaptive directional lifting wavelet transform (ADL). Using this method can separate noise from image signal distinctly by the extraordinary ability of ADL to represent the edges and textures. However, in the smooth regions of a noisy image, ADL is expensive and inaccurate. Therefore, we construct the ADL in an anti-noise way based on pixel pattern classification. Compared with the traditional ADL for image compression, there is no restriction of side-information, so the optimal strategies of direction determination and transformation can be selected by the judgment of different pixel patterns. Experimental results show that the proposed technique outperforms traditional wavelet and lifting scheme in both PSNR and visual quality, especially for the images with rich texture features such as remote sensing images.

A parallel sampling scheme for ultra-wideband signal based on the random projection

Abstract: High-rate analog-to-digital converters (ADC's) are ubiquitous and critical components in advance signal processing, such as software radio and ultra wideband (UWB) radar system. In this paper, we propose an alternative parallel sampling scheme, which is based on random projection, for an ultra- wideband analog to digital conversion. The sampling rate by using proposed scheme can be reduced to 1/m Nyquist sampling rate where m is the number of parallel channel. Compared with conventional ADCs based on pulse code modulation (PCM), the proposed method makes the sub-Nyquist sampling possible. And compared with other parallel sampling scheme methods, it can be easy implemented without any apriori knowledge about the signal. And it is very suitable for sampling UWB signals with short time support. Additionally, the detail implementation structure, performance analysis and simulation results of proposed method are given.

Method for signal decomposition and denoising based on nonuniform cosine-modulated filter banks

Abstract: In this paper, a novel method for signal decomposition and denoising is proposed based on a nonuniform filter bank (NUFB), which is derived from a uniform filter bank. With this method, the signal is firstly decomposed into M subbands using a uniform filter bank. Then according to their energy distribution, the corresponding consecutive filters are merged to compose the nonuniform filters. With the resulting NUFB, the signal can be readily matched and flexibly decomposed according to its power spectrum distribution. As another advantage, this method can be used to detect and remove the narrow-band noise from the corrupted signal. To verify the proposed method, a simulation of extracting the main information of an audio signal and removing its glitch is given.

Analysis of realizable rational decimated nonuniform filter banks with direct structure

Abstract: In the design of nonuniform filter banks (NUFBs) with direct structure, the location of each analysis filter, corresponding to the sampling factor satisfying maximal decimation condition, should be set properly to avoid large aliasing. In this paper, a necessary and sufficient condition for the setting of the location of each analysis filter is derived. The NUFBs, we focus on, have rational decimation factors. Based on the derived condition, the frequency support of each analysis filter for the realizable NUFBs can be determined directly in such a way that the analysis filters can extract the corresponding bands of the input signal. This provides a guideline for the design of NUFBs with direct structure in choosing proper locations of analysis filters.

The design of M-channel linear phase wavelet-like filter banks

Abstract: This paper investigates M-channel linear-phase (LP) wavelet-like filter banks The term ldquowavelet-likerdquo is justified because the filter bank is of near-perfect-reconstruction (NPR) We firstly derive some conditions for designing M-channel LP NPR filter banks and then propose a design method of wavelet-like filter banks The proposed wavelet-like filter bank is obtained by imposing regularity constraints on the LP NPR filter bank By using some simple algebraic transformations, we convert the original filter into the wavelet-like filter without nonlinear optimization With this method, we not only preserve the desired LP property, but also obtain good NPR property as the original NPR filter bank, such as high stopband attenuation Finally, examples show that LP wavelet-like filter banks with even and odd numbers of channels are capable of achieving different degrees of regularity

Direct design of linear-phase nonuniform filter banks with arbitrary integer decimation factors

Abstract: In this paper, we study the direct design of M-channel nonuniform filter banks (NUFBs) and propose a novel method for the NUFBs with linear-phase (LP) property. The filter banks we are concerned with have integer decimation factors and satisfy the maximal decimation condition. We derive a design criterion for the LP NUFBs. In addition, we propose certain modification on the direct structure, achieving LP NUFBs with arbitrary integer decimation factors. A detailed analysis on the new structure is presented to show that the significant aliasing can also be cancelled. Design examples are given to show that the proposed method is capable of achieving near-perfect-reconstruction NUFBs with LP property.

A study of specific neural substrate for face processing

Abstract: Recently, there were debates about the neural substrate of face processing, namely, whether the lateral middle fusiform was involved in face processing of visual expertise and categorization at individual level or specialized only for face processing. In the present study, Chinese characters were taken as the ideal comparison stimuli to reveal the neural substrate for face processing, due to their high similarity to faces on a variety of dimensions. The results demonstrated there was very strong correlation between the activation pattern elicited by faces and characters in the fusiform, whereas the greater response was observed for faces than for characters in the right middle lateral fusiform gyrus, suggesting that FFA may be a special neural substrate for face processing.