Zhengning Wang

Low-Complexity Iterative Equalization for Symbol-Reconstruction-Based OFDM Receivers Over Doubly Selective Channels

Time selectivity of multipath channels introduces significant inter-carrier interference (ICI) in OFDM systems demanding high levels of mobility and capacity. In this paper, a piecewise channel approximation is presented for improving the structure of the frequency-domain channel gain matrix, leading to a low-complexity iterative equalization scheme for OFDM receivers. The equalization is a sequential data detection based on a parallel-filtering architecture, avoiding matrix inversion adopted by classical equalizers with ICI cancellation. Meanwhile, information redundancy in the traditional cyclically prefixed OFDM is investigated for time-invariant channels, then a method of symbol reconstruction (SR) is proposed to enhance OFDM system performance, including compensation for SNR loss and improvements on the accuracy of parameter estimation. On the basis of the appropriate channel modeling in this work, the SR processing is proved to be effective for OFDM receivers over doubly selective channels. The iterative equalization, combined with the SR processing, is applied to the DVB-H receiver design. Numerical simulation indicates that the proposed equalizer significantly improves the immunity of OFDM systems to time selectivity with very little complexity increased, in contrast to the conventional one-tap equalizer without ICI cancellation.

A unified framework for spatiotemporal salient region detection

This article presents a new bottom-up framework for spatiotemporal salient region detection. The generated saliency map can uniformly highlight the salient regions. In the proposed framework, the spatial visual saliency and the temporal visual saliency are first computed, respectively, then they are fused with a dynamic scheme to generate the final spatiotemporal saliency map. In the spatial attention model, the approach of joint embedding of spatial and color cues is adopted to compute the spatial saliency map. In the temporal attention model, we propose a novel histogram of average optical flow to measure the motion contrast of the different pixels. The method can suppress the motion noise efficiently because the statistical distribution of optical flow in a patch is comparatively stable. Furthermore, we combine the spatial and the temporal saliency maps through an adaptive fusion method, in which a novel motion entropy is proposed to evaluate the motion contrast of the input video. Extensive experiments demonstrate that our method can obtain higher quality saliency map compared with state-of-the-art methods.

Adaptive Interpolation for Pilot-Aided Channel Estimator in OFDM System

In OFDM system, channel estimator with fixed-coefficient interpolation gives rise to significant estimation errors when the estimator mismatches the experienced time-varying channel. This paper presents a pilot-aided channel estimator adopting a double one-dimensional (1-D) polyphase subfilter structure, in which a few interpolating coefficients are applicable to all subcarriers of an OFDM symbol. By analyzing the channel interpolation for OFDM receiver, we propose a two-dimensional (2-D) adaptation scheme of the interpolating coefficient, based on a block least-mean-square algorithm, which requires none of knowledge on channel statistics. The 2-D adaptation scheme is simplified as two types of 1-D schemes respectively for the time and frequency dimensions, based on the known frequency- and time-direction interpolating coefficients. By simultaneously exploiting channel correlation in the time and frequency dimensions, the adaptive interpolators are trained to match channel properties. Furthermore, the proposed schemes of adaptive interpolation demand lower implementation cost, compared with the existing methods. Simulation results show that the adaptive interpolators converge at a high rate, and that the simplified schemes outperform the conventional methods in the high-speed mobile channel and the single-frequency-networks channel with long-delay echoes, while preserving a low implementation complexity.

Saliency-based superpixels

Superpixels provide an over-segmentation representation of a natural image. However, they lack information of the entire object. In this paper, we propose a method to obtain superpixels through a merging strategy based on the bottom-up saliency values of superpixels. The proposed method aims to obtain meaningful superpixels, i.e., make the objects as complete as possible. The proposed method creates an over-segmented representation of an image. The saliency value of each superpixel is calculated through a biologically plausible saliency model in a way of statistical theory. Two adjacent superpixels are merged if the merged superpixel is more salient than the unmerged ones. The merging process is performed in an iterative way. Experimental evaluation on test images shows that the obtained saliency-based superpixels can extract the salient objects more effectively than the existing methods.

Adaptive complex interpolator for channel estimation in pilot-aided OFDM system

In an orthogonal frequency division multiplexing system, conventional interpolation techniques cannot correctly balance performance and overhead when estimating dynamic long-delay channels in single frequency networks (SFNs). In this study, classical filter analysis and design methods are employed to derive a complex interpolator for maximizing the resistible echo delay in a channel estimator on the basis of the correlation between frequency domain interpolating and time domain windowing. The coefficient computation of the complex interpolator requires a key parameter, i.e., channel length, which is obtained in the frequency domain with a tentative estimation scheme having low implementation complexity. The proposed complex adaptive interpolator is verified in a simulated digital video broadcasting for terrestrial/handheld receiver. The simulation results indicate that the designed channel estimator can not only handle SFN echoes with more than 200 μs delay but also achieve a bit-error rate performance close to the optimum minimum mean square error method, which significantly outperforms conventional channel estimation methods, while preserving a low implementation cost in a short-delay channel.

Saliency detection using a central stimuli sensitivity based model

In this paper, a novel method is proposed to predict attention in image scenes by using a central stimuli sensitivity based saliency model. The proposed method is based on the general “center-surround” visual attention mechanism and the spatial frequency response of the human visual system (HVS). Following three biologically inspired principles, the saliency value is computed by two “scatter matrices” which are used to measure the similarity and distinctness within and between two classes, i.e., the center and surrounding regions, respectively. In order to detect salient objects with different size, the saliency of a pixel is estimated via the saliency support region of the pixel, which is the most salient region centered at the pixel with respect to the surrounding region. The proposed method which is compliant with human perceptual characteristics enables the prediction of human fixations. Experimental results on three eye tracking datasets verify the effectiveness of the method and show that the proposed method outperforms the state-of-the-art methods on the visual saliency detection task.

Saliency detection from joint embedding of spatial and color cues

Visual saliency detection provides an important methodology for many computer vision applications. In this paper, we propose a novel method to detect salient regions from an image. To detect pixel-level saliency, this method uses joint embedding of spatial and color cues, i.e., spatial constraint based saliency, color double-opponent saliency, and similarity distribution based saliency. Finally, a multi-layer structure is adopted to merge the three terms into a saliency map. In order to make the saliency map consistent, we perform a region-based saliency detection by incorporating a multi-scale segmentation technique. The proposed method was evaluated on the MSRA benchmark images. Experimental results show that our method outperforms the state-of-the-art methods on visual saliency detection by achieving both higher precision and better recall.

Simplex particle swarm optimization for block matching algorithm

In this paper, a novel block matching algorithm SPSO based on hybrid simplex method (SM) and particle swarm optimization (PSO) is proposed, which combines the high accurate local search ability of SM and the powerful global search ability of PSO. To reduce computational complexity and improve the performance of block matching algorithm, the selection of initial habitats based on fixed points and random points, the early termination and the SM searching after finishing each iterating of PSO are used. Experimental results verify that the SPSO algorithm can achieve better PSNR value than conventional block matching algorithms TSS and DS with little sacrifice of time consumption, especially for violent motion.

Simple equalization of OFDM signal over doubly selective channels

In this paper, a piece-wise approximation for doubly selective channel is presented for improving the structure of the frequency-domain channel gain matrix of OFDM. The proposed model is exploited to simplify the zero-forcing (ZF) equalizer, of which the linearized case is studied and applied to the DVB-H receiver design. The simulation indicates that the developed equalizer enforces considerably the immunity of the DVB-H receiver to time selectivity with very little complexity increased.

Complex Coefficient Interpolation Based Channel Estimation for OFDM in Single-Frequency Networks

In orthogonal frequency division multiplexing (OFDM) system operating in single-frequency broadcasting networks (SFN), classical real-coefficient-based channel interpolation schemes are challenged by long-delay echoes. Based on the equivalence between frequency interpolating and time-domain windowing, conventional filter design methods are employed to develop a complex low pass interpolator enhancing the robustness of channel estimator against SFN long-delay echoes. The high-performance interpolator is verified in a simulated digital video broadcasting for terrestrial/handheld (DVB-T/H) receiver. The simulation results indicate that the designed channel estimator can handle the SFN echoes with more than 200 μs delay, which significantly outperforms conventional CE methods.