Face super-resolution (FSR) is a challenging task, especially when the low-resolution (LR) image does not have sufficient information to build a high-resolution (HR) image. In this paper, we propose a quality-assisted domain transfer framework that uses a two-stage pipeline for efficient face SR. Instead of treating all LR images uniformly, we use a quality evaluator to classify images in different LR domains with distinct quality levels and determine the starting points for domain transfer adaptively. We further design a new residual domain learning strategy that integrates the LR domain with learned residual information. This approach allows us to avoid the correction formulation in diffusion models and the momentum correction in the originally proposed domain transfer approach and enables the model to learn the transformation from the LR domain to the HR domain gradually while preserving content information. The experimental results demonstrate competitive performance with the best inference speed among all approaches.

Joint learning on diverse ultrasonic source scenarios presents a challenge in preserving stable gen-eralization due to the combination of heterogeneity of different sources and the inconsistency of joint learning features. Previous joint learning studies, which are not source-resilient frameworks, may not preserve stable generalization when trained on diverse source scenarios. Furthermore, the limited variations insingle-source data and the interference from ultrasound imaging, which are common in ultrasonic source scenarios, further decrease generalization. To address these problems, we pro posed a source-resilient joint learning framework consisting of three stages: 1) Source transforming, where our 1-to-N transformation unifies diverse source scenarios for source-resiliency. 2) Our feature enhancement modules model the source-resilient joint learning network, including a manifold-constraint normalization module (MCNM) for addressing heterogeneity by minimizing manifold-based loss, a task-consistent attention module (TCAM) shares the multi-scale features with self-attention to address inconsistency, and an adaptive feature-shifting module (AFSM) for feature-level augmentation to overcome single-source data.3) Our ultrasound-hybrid linear mapping (USmapping) cascades speckle randomization and mask-guiding Monge-Kantorovitch linear mapping to achieve ultrasonic style randomization for addressing the interference of ultrasonic data. Our framework was evaluated on eight ultrasound datasets from various scanners at multiple center sand surpassed previous comparable studies in both segmentation (DSCWAvgof 75.7%) and classification (AUROCWAvgof 68.8%) tasks. Our framework has the potential to serve as a general framework for enhancing the performance of joint learning under diverse ultrasonic source scenarios.

This paper proposes a low-complexity sparse representation algorithm for direction-of-arrival (DOA) estimation of coherent signals under mutual coupling for uniform linear arrays (ULAs). At first, the problem is formulated as a block sparse signal recovery problem from array measurements based on the Toeplitz structure of the mutual coupling matrix. Then, it is addressed by the expectation-maximization (EM)-Gaussian scale mixture (GSM)-damping generalized approximate message passing (DGAMP) algorithm at each snapshot. Finally, the DOA estimates are obtained by applying a block norm operation to the signals from all snapshots and identifying the peaks of the resulting vector. By directly utilizing the array measurements, the algorithm successfully avoids the rank deficiency in the array covariance matrix resulting from coherent signals. The proposed algorithm has significantly lower complexity than existing sparse representation algorithms, and enables hardware concurrency for real-time DOA estimation since each snapshot is processed independently. Simulation results demonstrate that the performance of the proposed method is superior to state-of-the-art methods.

Recent advances in medical image segmentation models combine convolution with the attention mechanism which provides an effective approach to formulate long-term dependencies. However, many works either replaced the convolutional layers with attention layers or embedded attention layers into convolutional neural network (CNN)-based models. To explore the potential of hybrid architecture, we propose a simple cascade module that builds up multiple receptive fields using convolutional kernels with different sizes and learns global context via self-attention layers. Benefiting from the powerful representation ability of the proposed module, multilayer perceptrons (MLPs) with shift operation are adopted to bridge the encoder and decoder to reduce the model size without losing accuracy. Experiments show that our model consistently outperforms the latest 2D and 3D models by large margins on three public tasks and is more resilient to shape, size, and boundary variations. The code is available at https://github.com/cicailalala/AERFNet.

Classical source number estimators are usually derived under the assumption of uniform white noise; hence, they are ineffective with unknown nonuniform noise. Current advanced estimators designed for nonuniform noise are computationally expensive and often unable to yield satisfactory performance in unfavorable conditions, such as low signal-to-noise ratio, small number of snapshots, and sources with different transmit power. To address this, this paper proposes a new method to estimate the number of sources under nonuniform noise. The proposed method first constructs a likelihood ratio statistic as a function of the maximum likelihood estimation of the array covariance matrix, which is estimated by a subspace estimation algorithm. Then, based on the asymptotic theory of the likelihood ratio, the number of sources is estimated via a sequence of hypothesis tests. Theoretical analysis demonstrates that the proposed estimator is consistent in the general asymptotic regime. Simulation results show that the proposed estimator achieves a higher correct detection probability in unfavorable conditions and is more robust against nonuniformity of noise than state-of-the-art estimators.

The instrumental variable pseudolinear Kalman filter (IV-PLKF) algorithm, used for 3D angle-of-arrival (AOA) target tracking, has been proven to be more robust to initialization errors, with superior estimation performance and lower computational complexity compared to other state-of-the-art methods. However, the IV-PLKF algorithm requires prior knowledge of the state and angle measurement noise information, which is not available in practice. Improper selection of these values or mismatches due to time-varying changes can significantly impact the stability and estimation performance of the algorithm. To address this issue, this paper proposes a new adaptive fading (AF-) IV-PLKF algorithm that adaptively mitigates the possible scale mismatches in the state and measurement noise covariance matrices and the IV parameters. Simulation results demonstrate that the proposed algorithm outperforms the conventional IV-PLKF under mismatched state and measurement noise covariance scenarios. Moreover, the proposed method can even achieve comparable estimation performance to that of IV-PLKF with perfect knowledge of the noise information.

Dynamic state estimation (DSE) of synchronous machines is essential to real-time monitoring, protection, and control of power systems. DSE can be significantly affected by bad data due to outliers, cyber attack and model uncertainties. This paper proposes a new robust adaptive fading (AF) unscented Kalman filter (UKF) for DSE, which utilizes the AF-UKF to minimize possible scale mismatches in the state and measurement noise covariance matrices of the KF to mitigate these uncertainties. A robust extension of the AF-UKF based on robust statistics is also developed to effectively detect and suppress bad data at each KF update. The proposed method was evaluated and compared with conventional algorithms on the Northeastern Power Coordinating Council 48-machine 140-bus system. Results showed that the proposed decentralized DSE algorithm yields more accurate and reliable performance than conventional methods under bad-data and noise covariance mismatches.

Breast tumor segmentation of ultrasound images provides valuable information of tumors for early detection and diagnosis. Accurate segmentation is challenging due to low image contrast between areas of interest; speckle noises, and large inter-subject variations in tumor shape and size. This paper proposes a novel Multi-scale Dynamic Fusion Network (MDF-Net) for breast ultrasound tumor segmentation. It employs a two-stage end-to-end architecture with a trunk sub-network for multiscale feature selection and a structurally optimized refinement sub-network for mitigating impairments such as noise and inter-subject variation via better feature exploration and fusion. The trunk network is extended from UNet++ with a simplified skip pathway structure to connect the features between adjacent scales. Moreover, deep supervision at all scales, instead of at the finest scale in UNet++, is proposed to extract more discriminative features and mitigate errors from speckle noise via a hybrid loss function. Unlike previous works, the first stage is linked to a loss function of the second stage so that both the preliminary segmentations and refinement subnetworks can be refined together at training. The refinement sub-network utilizes a structurally optimized MDF mechanism to integrate preliminary segmentation information (capturing general tumor shape and size) at coarse scales and explores inter-subject variation information at finer scales. Experimental results from two public datasets show that the proposed method achieves better Dice and other scores over state-of-the-art methods. Qualitative analysis also indicates that our proposed network is more robust to tumor size/shapes, speckle noise and heavy posterior shadows along tumor boundaries. An optional post-processing step is also proposed to facilitate users in mitigating segmentation artifacts. The efficiency of the proposed network is also illustrated on the "Electron Microscopy neural structures segmentation dataset". It outperforms a state-of-the-art algorithm based on UNet-2022 with simpler settings. This indicates the advantages of our MDF-Nets in other challenging image segmentation tasks with small to medium data sizes.

This paper studies the diffusion (Diff) control for multichannel ANC systems, where a group of controllers and error microphones are physically distributed at different locations within a large area. In this case, the conventional consensus agreement for controllers cannot be reached. To solve this problem, a new Diff filtered-x least mean squares (Diff-FxLMS) algorithm that incorporates the knowledge of spatial smoothness is proposed. Compared to conventional Diff control criteria that have a fixed spatial smoothing strategy, the proposed Diff-FxLMS adjusts a so called spatial regularization (SR) coefficient adaptively such that the neighboring controllers keep their decision variables close to one another to minimize the global cost function while giving preference to possibly distinct local signals. A detailed performance analysis is carried out and verified by simulation. Based on the analysis, a variable SR formula is derived. The performance of the proposed algorithm is also compared with conventional methods.

This paper develops a framework for the design of diffusion adaptive algorithms, where a network of nodes aim to estimate system parameters from the collected distinct local data stream. We explore the time and spatial knowledge of system responses and model their evolution in both time and spatial domain. A weighted maximum a posteriori probability (MAP) is used to derive an adaptive estimator, where recent data has more influence on statistics via weighting factors. The resulting recursive least squares (RLS) local estimate can be implemented by the QR decomposition (QRD). To mediate the distinct spatial information incorporation within neighboring estimates, a variable spatial regularization (VSR) parameter is introduced. The estimation bias and variance of the proposed algorithm are analyzed. A new diffusion VSR QRRLS (Diff-VSR-QRRLS) algorithm is derived that balances the bias and variance terms. Simulations are carried out to illustrate the effectiveness of the theoretical analysis and evaluate the performance of the proposed algorithm.

Ensuring the positive definiteness and avoiding ill conditioning of the Hessian update in the stochastic Broyden-Fletcher-Goldfarb-Shanno (BFGS) method are significant in solving nonconvex problems. This article proposes a novel stochastic version of a damped and regularized BFGS method for addressing the above problems. While the proposed regularized strategy helps to prevent the BFGS matrix from being close to singularity, the new damped parameter further ensures the positivity of the product of correction pairs. To alleviate the computational cost of the stochastic limited memory BFGS (LBFGS) updates and to improve its robustness, the curvature information is updated using the averaged iterate at spaced intervals. The effectiveness of the proposed method is evaluated through the logistic regression and Bayesian logistic regression problems in machine learning. Numerical experiments are conducted by using both synthetic data set and several real data sets. The results show that the proposed method generally outperforms the stochastic damped LBFGS (SdLBFGS) method. In particular, for problems with small sample sizes, our method has shown superior performance and is capable of mitigating ill-conditioned problems. Furthermore, our method is more robust to the variations of the batch size and memory size than the SdLBFGS method.

This paper presents an efficient multiplier-less inference (MLI) approach of deep autoencoders (DAE) for wearable healthcare systems. It employs a novel grouped multiplier block (GMB) module to reduce computational/hardwired complexity of DAE during inference process. First, the fixed weights of DAE are transformed into sum-of-powers-of-two (SOPOT) representations so that multiplications in DAE can be realized as limited adds and shifts only. Further, a GMB is designed to reuse the partial sums in generating the products from the same inputs, which can greatly reduce the adds required. Experimental results show that our proposed MLI method is effective and efficient for wearable healthcare systems to reduce computational/hardwired complexity as well as to offer a faster software implementation.

This paper proposes a novel multi-Laplacian prior (MLP) and augmented Lagrangian method (ALM) approach for gene interactions and putative transcription factors (TFs) identification from time-course gene microarray data. It employs a non-linear time-varying auto-regressive (N-TVAR) model and the Maximum-A-Posteriori-Probability method for incorporating the multi-Laplacian prior and the continuity constraint. The MLP allows connections to/from a gene to be better preserved for putative TF identification in non-stationarity gene regulatory network as compared with conventional$L_1$-based penalties. Moreover, the ALM allows the resultant non-smooth$L_1$-based penalties to be decoupled from the remaining smooth terms, so that the former and latter can be efficiently solved using a low-complexity proximity operator and smooth optimization technique, respectively. Synthetic and real time-course gene microarray datasets are tested to evaluate the performance of the proposed method. Experimental results show that the proposed method gives better accuracy and higher computational speed than our previous work using smoothed approximation. Moreover, its performance, without the use of ChIP-chip data, is found to be highly comparable with other state-of-the-art methods integrating both ChIP-chip and gene microarray data. It suggests that the proposed method may serve as a useful exploratory tool for putative TF identification with reduced experimental cost.

This paper proposes a novel probabilistic representation of color image pixels (PRCI) and investigates its applications to similarity construction in motion estimation and image segmentation problems. The PRCI explores the mixture representation of the input image(s) as prior information and describes a given color pixel in terms of its membership in the mixture. Such representation greatly simplifies the estimation of the probability density function from limited observations and allows us to derive a new probabilistic pixel-wise similarity measure based on the continuous domain Bhattacharyya coefficient. This yields a convenient expression of the similarity measure in terms of the pixel memberships. Furthermore, this pixel-wise similarity is extended to measure the similarity between two image regions. The usefulness of the proposed pixel/region-wise similarities is demonstrated by incorporating them respectively in a dense image descriptor-based multi- layered motion estimation problem and an unsupervised image segmentation problem. Experimental results show that i) the integration of the proposed pixel-wise similarity in dense image-descriptor construction yields improved peak signal to noise ratio performance and higher tracking accuracy in the multi-layered motion estimation problem, and ii) the proposed similarity measures give the best performance in terms of all quantitative measurements in the unsupervised superpixel- based image segmentation of the MSRC and BSD300 datasets.

This paper proposes a new algorithm for automatic estimation of muscle fiber orientation (MFO) in musculoskeletal ultrasound images, which is commonly used for both diagnosis and rehabilitation assessment of patients. The algorithm is based on a novel adaptive fading Bayesian Kalman filter (AF-BKF) and an automatic region of interest (ROI) extraction method. The ROI is first enhanced by the Gabor filter (GF) and extracted automatically using the revoting constrained Radon transform (RCRT) approach. The dominant MFO in the ROI is then detected by the RT and tracked by the proposed AF-BKF, which employs simplified Gaussian mixtures to approximate the non-Gaussian state densities and a new adaptive fading method to update the mixture parameters. An AF-BK smoother (AF-BKS) is also proposed by extending the AF-BKF using the concept of Rauch-Tung-Striebel smoother for further smoothing the fascicle orientations. The experimental results and comparisons show that: 1) the maximum segmentation error of the proposed RCRT is below nine pixels, which is sufficiently small for MFO tracking; 2) the accuracy of MFO gauged by RT in the ROI enhanced by the GF is comparable to that of using multiscale vessel enhancement filter-based method and better than those of local RT and revoting Hough transform approaches; and 3) the proposed AF-BKS algorithm outperforms the other tested approaches and achieves a performance close to those obtained by experienced operators (the overall covariance obtained by the AF-BKS is 3.19, which is rather close to that of the operators, 2.86). It, thus, serves as a valuable tool for automatic estimation of fascicle orientations and possibly for other applications in musculoskeletal ultrasound images.

This paper proposed a robust variable forgetting factor (VFF) QS-decomposition algorithm for subspace tracking in impulse noise environment. The QS-decomposition algorithm was originally proposed to estimate recursively the principal orthonormal subspace of covariance matrix of a vector time series. Motivated by the close relationship between the projection approximation subspace tracking (PAST) algorithm and the recursive least squares (RLS) with multiple outputs, the local optimal forgetting factor (LOFF) algorithm recently proposed is extended to multiple outputs and is incorporated into the QS algorithm to improve its tracking and steady state mean squares error performances in nonstationary and stationary environment respectively. Furthermore, the M-estimation function is employed to improve the robustness of the proposed VFF QS-tracker against possible impulses or outliers which may be encountered in practice. Experimental results show that the proposed robust VFF-QS algorithm is able to achieve better performance in stationary and nonstationary environments than the conventional QS algorithm, especially in the presence of impulsive noise.

This paper proposes a novel consensus gene selection criteria for partial least squares-based gene microarray analysis. By quantifying the extent of consistency and distinctiveness of the differential gene expressions across different double cross validations (CV) or randomizations in terms of occurrence and randomization p-values, the proposed criteria are able to identify a more comprehensive genes associated with the underlying disease. A Distributed GPU implementation has been proposed to accelerate the gene selection problem and about 8-11 times speed up has been achieved based on the microarray datasets considered. Simulation results using various cancer gene microarray datasets show that the proposed approach is able to achieve highly comparable classification accuracy in comparing with many conventional approaches. Furthermore, enrichment analysis on the selected genes for Diffused Large B Cell Lymphoma (DLBCL) and Prostate Cancer datasets and show that only the proposed approach is able to identify gene lists enriched in different pathways with significant p-values. In contrast, sufficient statistical significance cannot be found for conventional SVM-RFE and the t-test. The reliability in identifying and establishing statistical significance of the gene findings makes the proposed approach an attractive alternative for cancer related researches based on gene expression profiling or other similar data.

This paper proposes a new multi-view human body tracking and model-based rendering system with a textured deformable human body model and explores its practical application to view transfer in image-based rendering (IBR). The proposed approach first reconstructs an initial 3-D model of the human subject offline using Kinect depth cameras or from a general 3-D model if such information is unavailable in the original data. The human pose of the subject is then tracked with multi-view videos using an annealed particle filter (APF)-based tracker with a new color-based likelihood function and a Bayesian-Kalman filter smoother. The previous captured model can then be deformed to the new position for model-based rendering. An immediate application of the proposed approach is to support fly-over effects for view transfer in IBR systems with limited cameras. It avoids the reconstruction of the complete dynamic 3-D model where a large number of cameras may be required. Moreover, for static background, the background can be rendered using IBR with precaptured depth maps to further enhance the user's experience. To reduce the artifacts during fly-over caused by tracking errors and model deformation, a novel morphing technique utilizing a new free-form deformation-based artifacts suppression (FFD-AS) method and other user interface design techniques are also proposed. It allows smooth transition between the original view and the model-rendered views. The performance of the proposed algorithm is evaluated using the publicly available HumanEva dataset and our captured RGB-D multi-view dataset. Experimental results show that the proposed APF-based tracker offered improved tracking performance compared with the conventional bidirectional silhouette likelihood criterion. The proposed morphing approach is also shown to be effective in mitigating the rendering artifacts during view transfer.

This paper presents a distributed optimal power flow approach based on Augmented Lagrangian (AL) and Sequential Quadratic Programming (SQP). It is able to separate the OPF into smaller sub-problems, which could be iteratively solved individually using the SQP. This utilizes the SQP for largescale problems with non-linear objective functions and constraints. Simulation and comparison using the IEEE 30 and 118 buses examples show that the proposed distributed approach is able to achieve comparable performance with other benchmark centralized solvers provided by the FMINCON in MATPOWER. This suggests the proposed approach may serve an attractive alternative to other OPF algorithms.

Missing data imputation is often required in wireless sensor networks (WSNs) to fill up missing measurements due to transmission loss, hardware failure and other factors. In this paper, we propose new variable forgetting factor (VFF) and regularization extensions to the recursive dynamic factor analysis (RDFA) algorithm for imputation of missing data in WSN data. It takes advantage of the correlated structure of the redundancy among WSN measurements by decomposing WSN measurements into orthogonal factor loadings and de-correlated factors. A new local polynomial model (LPM) based variable forgetting factor is proposed for the RDFA algorithm and it enables us to better adapt to the time-varying environment. Finally, ℓ2 regularization is further incorporated to RDFA for improving the numerical conditioning. Experimental results using a real WSN dataset show that the proposed algorithm is able to achieve better accuracy than other conventional approaches.

This paper presents preliminary results and findings of a dynamic gene regulatory network analysis obtained from Saccharomyces cerevisiae (budding yeast) time-course DNA microarray data using a new Alternative Direction Methods of Multipliers (ADMM) based maximum a posteriori probability and time-varying autoregression model (MAP-TVAR) approach. It employs the Li-regularization based sparsity and continuity constraints, which facilitate the identification of sparse GRNs and reduce the estimation variance respectively. Simulation results using synthetic dataset show that the proposed ADMM-based extension not only performs better than our previous work in terms of identification accuracy but also is able to achieve considerable speedup. This enables us to process the whole genome of the budding yeast containing 10,715 genes and 15 timepoints more efficiently. We are able to identify gene interactions aligning well with some natural phenomena and reported in yeast cell cycle related literature. These suggest that the MAP-TVAR approach may serve as a useful tool for large-scale time-varying GRNs analysis using gene microarray data and other related datasets.

Ultrasonography is an important diagnostic imaging technique for visualization of tendons, which provides useful health diagnostic and fundamental information in neuromuscular studies of human motion systems. Conventional ultrasonic-based tendon studies, however, are highly dependent on subjective experience of operators due to various impairments of ultrasound images. Dynamic changes of muscle and tendon deformation in a sequence can hardly be manually processed. Consequently, there is an urgent need for automatic analysis of tendon behavior. This paper proposes an automatic ultrasonic tendon tracking algorithm to extract the shape deformation of central tendon of rectus femoris (CT-RF) from ultrasonic image sequences. The tracking problem is complicated by the highly deformable tendon, time-varying brightness, and the inconspicuousness of the target. To address this difficult tracking problem, we proposed a new intensity-compensated free-form deformation (IC-FFD)-based tracking algorithm with local shape refinement (LSR). Experimental results and comparison show that the proposed IC-FFD-LSR algorithm outperforms IC-FFD and conventional methods such as MI-FFD in CT-RF tracking.

This paper proposes a variable forgetting factor QRD-based recursive least squares algorithm with bias compensation (VFF-QR-RLS-BC) for system identification with input noise. The new algorithm is based on the least square estimation with bias compensation framework and it employs a variable forgetting factor to improve the tracking speed and a QRD-based implementation for recursively solving the LS problem with bias compensation. Simulation results show that the proposed method can obtain improved convergence rate in sudden system change environment and satisfactory performance under stationary environment.

Studies of time-varying or dynamic brain connectivity (BC) using functional magnetic resonance imaging (fMRI) are crucial to understand the relationship between different brain regions. This paper presents a novel method for estimating dynamic BC using a time-varying multivariate autoregressive (AR) model with spatial sparsity and temporal continuity constraints. The problem is formulated as a maximum a posterior probability (MAP) estimation problem and solved as a least square problem with Li-regularization for imposing the constraints. The Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method is employed to estimate the model parameters for making inference of dynamic BC. The proposed method was evaluated using synthetic data and visual checkerboard task experiment fMRI data. The results show that the method can effectively capture transient information transfer among visual-related brain regions whereas controlled areas not related to the process remain inactive. These verify the effectiveness and reduced variance of the proposed method for investigating dynamic task-related BC from fMRI data.

When considering the problem of direction-of-arrival (DOA) estimation, uniform noise is often assumed and hence, the corresponding noise covariance matrix is diagonal and has identical diagonal entries. However, this does not always hold true since the noise is nonuniform in certain applications and a model of arbitrary diagonal noise covariance matrix should be adopted. To this end, a simple approach to handling the unknown nonuniform noise problem is proposed. In particular, an iterative procedure is developed to determine the signal subspace and noise covariance matrix. As a consequence, existing subspace-based DOA estimators such as MUSIC can be applied. Furthermore, the proposed method converges within very few iterations, in each of which closed-form estimates of the signal subspace and noise covariance matrix can be achieved. Hence, it is much more computationally attractive than conventional methods which rely on multi-dimensional search. It is shown that the proposed method enjoys good performance, simplicity and low computational cost, which are desirable in practical applications.

In this study, an automatic muscle fiber orientation tracking approach based on Bayesian Kalman Filter (BKF) is proposed. The BKF employs a Gaussian mixture (GM) representation of the state and noise densities and a novel direct density simplifying algorithm for avoiding the exponential complexity growth of conventional Kalman filters (KFs) using GM. In this paper, the ultrasound image is firstly enhanced by a bank of Gabor Filters (GFs) based on the GM of the state density in BKF. Then, a bank of localized radon transforms (LRTs) are used to extract muscle fiber orientations and the dominant orientation is obtained by minimizing an energy function. Finally, the dominant orientation is fed back to the BKF as an observation. The performance of the proposed approach is compared with existing methods on five subjects over 1000+ clinical ultrasound images. Experimental results show that the proposed method can achieve accurate and robust measurements of fascicle orientation and outperforms all the existing methods.

This paper proposes a multi-view articulated human motion tracking approach with textured deformable mesh model. Firstly, a subject-specific mesh model is initialized by using linear blend skinning method. The model is then textured according to the multi-view image observations. We introduce a segmentation-based method to refine the appearance of the subject. With the textured mesh model, a color-based likelihood (CbL) is also proposed for human body tracking with Annealed Particle Filter (APF). Experiments in the paper show that the performance can be considerately improved by using CbL as the measurement for pose tracking.

This paper presents a depth map restoration scheme for both the raw and projected depth map from Kinect v2 sensor. Based on IR-depth consistency, erroneous depth readings around foreground objects are removed by an edge aware consistency correction method. Moreover, a joint adaptive kernel regression algorithm is designed to upsample the sparse depth map after the projection from Kinect v2 sensor's depth camera to its full HD video camera. The structural information in the high resolution color image is implicitly utilized to guide the upsampling of depth map. The effectiveness of the proposed upsampling algorithm is illustrated by experimental results and comparisons on both real Kinect v2 data and Middlebury dataset.

Appearance modelling and tracking strategy are two fundamental problems in visual object tracking. In this paper, the appearance of the object is modeled by a spatial context based bag of multiple models (BMM). The BMM keeps multiple hypotheses and utilizes spatial information to perform tracking. Furthermore, a novel Bayesian Kalman filter is used as the tracking strategy to handle fast movement and acceleration of the tracked object. Experimental results show that our method can successfully handle complex scenarios with complicated background, long-term occlusion and fast movement.

This paper proposes a dynamic nonlinear autoregressive model based algorithm for gene regulatory networks (GRNs) identification with biological stage change detection using the L1-regularization. This allows subtle variations in the same state to be penalized and prominent changes across adjacent states to be captured. Furthermore, by assuming local-stationarity within each detected biological state, the number of network parameters can be significantly reduced. Simulation results using a dynamic synthetic dataset and a real time course Drosophila Melanogaster DNA microarray dataset shows that the proposed method is able to achieve better identification accuracy in comparing with other conventional approaches. Moreover, it is able to identify the biological state change point precisely and identify the GRNs with effectiveness. These suggest that the proposed approach may provide an attractive alternative in GRNs identification problem.

This paper proposes a new class of local polynomial modeling (LPM)-based variable forgetting factor (VFF) recursive least squares (RLS) algorithms called the LPM-based VFF RLS (LVFF-RLS) algorithms. It models the time-varying channel coefficients as local polynomials so as to obtain the expressions of the bias and variance terms in the mean square error (MSE) of the RLS algorithm. A new locally optimal VFF (LOVFF) is then derived by minimizing the resulting MSE and the theoretical analysis is found to be in good agreement with experimental results. Methods for estimating the parameters involved in this LOVFF are also developed, resulting in an improved RLS algorithm with VFF. The algorithm is further extended to include variable regularization and a QR decomposition (QRD) version which is numerically more stable and amenable to multiplier-less implementation using coordinate rotation digital computer (CORDIC) algorithm. Applications of these algorithms to frequency estimation and adaptive beamforming in time-varying speech and audio signals are also presented to illustrate the effectiveness of the proposed algorithms. Simulations show that the convergence and tracking performance of the proposed algorithms compare favorably with conventional algorithms.

Unlike most conventional techniques with static model assumption, this paper aims to estimate the time-varying model parameters and identify significant genes involved at different timepoints from time course gene microarray data. We first formulate the parameter identification problem as a new maximum a posteriori probability estimation problem so that prior information can be incorporated as regularization terms to reduce the large estimation variance of the high dimensional estimation problem. Under this framework, sparsity and temporal consistency of the model parameters are imposed using L1-regularization and novel continuity constraints, respectively. The resulting problem is solved using the L-BFGS method with the initial guess obtained from the partial least squares method. A novel forward validation measure is also proposed for the selection of regularization parameters, based on both forward and current prediction errors. The proposed method is evaluated using a synthetic benchmark testing data and a publicly available yeast Saccharomyces cerevisiae cell cycle microarray data. For the latter particularly, a number of significant genes identified at different timepoints are found to be biological significant according to previous findings in biological experiments. These suggest that the proposed approach may serve as a valuable tool for inferring time-varying gene regulatory networks in biological studies.

This paper presents a new superpixel-based hand gesture recognition system based on a novel superpixel earth mover's distance metric, together with Kinect depth camera. The depth and skeleton information from Kinect are effectively utilized to produce markerless hand extraction. The hand shapes, corresponding textures and depths are represented in the form of superpixels, which effectively retain the overall shapes and color of the gestures to be recognized. Based on this representation, a novel distance metric, superpixel earth mover's distance (SP-EMD), is proposed to measure the dissimilarity between the hand gestures. This measurement is not only robust to distortion and articulation, but also invariant to scaling, translation and rotation with proper preprocessing. The effectiveness of the proposed distance metric and recognition algorithm are illustrated by extensive experiments with our own gesture dataset as well as two other public datasets. Simulation results show that the proposed system is able to achieve high mean accuracy and fast recognition speed. Its superiority is further demonstrated by comparisons with other conventional techniques and two real-life applications.

The study is aimed to investigate in vivo behaviors of the rectus femoris muscle during isometric contraction by integrating simultaneously recorded electromyography (EMG), mechanomyography (MMG), and ultrasonography (US). We developed an experimental platform for simultaneous acquisition of EMG, MMG, US, as well as the torque, during isometric muscle contraction. Features from multimodal signals and images were then automatically extracted and calibrated to present time-varying characteristics of muscle behaviors. We further applied local polynomial regression (LPR) to reveal nonlinear and transient relationships between multimodal muscle features and torque. The results suggested that the proposed multimodal signal acquisition and integration are capable of providing novel and complete information about in vivo muscle contraction. The proposed experimental platform is a potentially useful tool for muscle assessment in various clinical and practical applications.

In this paper, we address the problem of estimating the two-dimensional (2-D) directions of arrival (DOA) of multiple signals, by means of a sparse L-shaped array. The array consists of one uniform linear array (ULA) and one sparse linear array (SLA). The shift-invariance property of the ULA is used to estimate the elevation angles with low computational burden. The source waveforms are then obtained by the estimated elevational angles, which together with each sensor of the SLA, considered as a linear regression model, will be used to estimate the azimuth angle by the modified total least squares (MTLS) technique. The new algorithm yields correct parameter pairs without requiring the computationally expensive pairing operation, and therefore, it has at least two advantages over the previous L-shaped array based algorithms: less computational load and better performance due to using the SLA. Simulation results show that our method provides accurate and consistent 2-D DOA estimation results which could not be achieved by other methods with comparable computational complexity.

An AVS- and object- based coding approach to support scalable coding of plenoptic videos (PVs) is proposed. PVs are simplified dynamic light fields in which the videos are taken at regularly spaced locations along line segments, which can provide a continuum of view point using view synthesis. In previous works, we have explored the object-based coding of PVs using the MPEG-4 object coding framework so as to facilitate its rendering and processing. This paper further studies the object-based coding of PVs based on the more efficient Audio Video Standard of China (AVS) and incorporate scalability for supporting users with different bandwidths. AVS is China's national standard and it adopts an MPEG-like hybrid compression framework. The proposed AVS-based object coding framework extend AVS by employing temporal and spatial prediction to support multi-view coding for better coding efficiency. Reduced inter-coding dependency is also utilized to facilitate the issue of random access and selective transmission. Moreover, object-based temporal scalability based on a hierarchical-P structure is also illustrated.

This paper proposes a new visual object tracking algorithm using a novel Bayesian Kalman filter (BKF) with simplified Gaussian mixture (BKF-SGM). The new BKF-SGM employs a GM representation of the state and noise densities and a novel direct density simplifying algorithm for avoiding the exponential complexity growth of conventional KFs using GM. Together with an improved mean shift (MS) algorithm, a new BKF-SGM with improved MS (BKF-SGM-IMS) algorithm with more robust tracking performance is also proposed. Experimental results show that our method can successfully handle complex scenarios with good performance and low arithmetic complexity.

This paper proposed a new convex-optimization-based two-pass rate control method for object coding of China's audio video coding standard (AVS). The algorithm adopts a two-pass methodology to overcome the important interdependency problem between rate control and rate distortion optimization. An exponential model is used to describe the rate-distortion behavior of the codec so as to perform frame-level and object-level rate control under the two-pass framework. Convex programming is utilized to solve for the resultant optimal bit allocation problem. Moreover, the region-of-interest (ROI) functionality is also realized at the object-level. The good performance and effectiveness of this method are illustrated using experimental results.

This paper introduces a new method for adaptively estimating the time-varying autocorrelation (TV-AC) of nonstationary signals and studies its application to time-frequency analysis. The proposed method employs local estimation with a sliding window having a certain bandwidth to estimate the TV-AC locally. The window bandwidths are selected adaptively by a local plug-in rule to address the bias and variance tradeoff problem. Further, based on the proposed adaptive TV-AC estimation, a new time-frequency analysis method called adaptive windowed minimum variance spectral estimation (AWMVSE) is developed. Simulation results show that the proposed adaptive TV-AC estimation method and AWMVSE method have improved performances over conventional estimators with a fixed window.

This paper proposes a new articulated human motion tracking and pose estimation algorithm using an improved silhouette extraction method with view adaptive fusion. It is developed around the baseline algorithm in HumanEva, which uses the Annealed Particle Filter (APF). Shadow detection and removal and a level-set method are employed to achieve better silhouette extraction. An adaptive view fusion approach is also proposed to improve the matching between the human 3D model and the observations. Experimental results show that the proposed approach has considerably better performance than the baseline algorithm in the HumanEva dataset, due to better shadow handling and data fusion of multiple views.

This paper presents a new bandwidth adaptive nonlocal kernel regression (BA-NLKR) algorithm for image and video restoration. NLKR is a recent approach for improving the performance of conventional steering kernel regression (SKR) and local polynomial regression (LPR) in image/video processing. Its bandwidth, which controls the amount of smoothing, however is chosen empirically. The proposed algorithm incorporates the intersecting confidence intervals (ICI) bandwidth selection method into the framework of NLKR to facilitate automatic bandwidth selection so as to achieve better performance. A parallel implementation of the proposed algorithm is also introduced to reduce significantly its computation time. The effectiveness of the proposed algorithm is illustrated by experimental results on both single image and videos super resolution and denoising.

This paper proposes a new regularized transform domain normalized LMS (R-TDNLMS) algorithm and studies its mean and mean square convergence performances. The proposed algorithm extends the conventional TDNLMS algorithm by imposing a regularization term on the filter coefficients to reduce the variance of estimators due to the lacking of excitation in a certain frequency band or in the presence of modeling errors. Difference equations describing the mean and mean square convergence behaviors of this algorithm are derived so as to characterize its convergence condition and steady-state excess mean square error (MSE). It shows that regularization can help to reduce the MSE by trading slight bias for variance. Based on this analysis, a new formula to select the regularization parameter for white Gaussian inputs is proposed, which leads to a new variable regularized TDNLMS (VR-TDNLMS) algorithm. Computer simulations are conducted to examine the improved convergence performance, steady-state MSE and robustness to power-varying inputs of the proposed algorithm and verify the effectiveness of the theoretical analysis. Furthermore, the application of the proposed VR-TDNLMS algorithm to the design and implementation of acoustic system identification and active noise control (ANC) systems show that they considerably outperforms traditional TDNLMS algorithms at low excitation or in the presence of modeling errors. Moreover, the theoretical analysis provides simple design formulas for achieving a given excess MSE (EMSE) and step-size bound for stable operation.

This paper proposes a new variable regularized QR decompPosition (QRD)-based recursive least M-estimate (VR-QRRLM) adaptive filter and studies its convergence performance and acoustic applications. Firstly, variableL2regularization is introduced to an efficient QRD-based implementation of the conventional RLM algorithm to reduce its variance and improve the numerical stability. Difference equations describing the convergence behavior of this algorithm in Gaussian inputs and additive contaminated Gaussian noises are derived, from which new expressions for the steady-state excess mean square error (EMSE) are obtained. They suggest that regularization can help to reduce the variance, especially when the input covariance matrix is ill-conditioned due to lacking of excitation, with slightly increased bias. Moreover, the advantage of the M-estimation algorithm over its least squares counterpart is analytically quantified. For white Gaussian inputs, a new formula for selecting the regularization parameter is derived from the MSE analysis, which leads to the proposed VR-QRRLM algorithm. Its application to acoustic path identification and active noise control (ANC) problems is then studied where a new filtered-x (FX) VR-QRRLM ANC algorithm is derived. Moreover, the performance of this new ANC algorithm under impulsive noises and regularization can be characterized by the proposed theoretical analysis. Simulation results show that the VR-QRRLM-based algorithms considerably outperform the traditional algorithms when the input signal level is low or in the presence of impulsive noises and the theoretical predictions are in good agreement with simulation results.

This paper proposes a new state-regularized (SR) and QR decomposition based recursive least squares (QRRLS) algorithm with variable forgetting factor (VFF) for recursive coefficient estimation of time-varying autoregressive (AR) models. It employs the estimated coefficients as prior information to minimize the exponentially weighted observation error, which leads to reduced variance and bias over traditional regularized RLS algorithm. It also increases the tracking speed by introducing a new measure of convergence status to control the FF. Simulations using synthetic and real speech signals show that the proposed method has improved tracking performance and reduced estimation error variance than conventional TVAR modeling methods during rapid changing of AR coefficients.

This paper addresses the problem of beamforming for antenna arrays in the presence of mismatches between the true and nominal steering vectors. A new method for robust beamforming is proposed by minimizing the array output power while controlling the array mainlobe response. Due to the presence of the non-convex response constraints, a new approach based on iteratively linearizing the non-convex constraints is proposed to reformulate the non-convex problem to a series of second-order cone programming (SOCP) subproblems, each of which can be optimally solved by well-established convex optimization techniques. Simulation results show that the proposed method offers better performance than conventional methods tested.

The Logistic Principal Component Regression (LPCR) has found many applications in classification of high-dimensional data, such as tumor classification using microarray data. However, when the measurements are contaminated and/or the observations are mislabeled, the performance of the LPCR will be significantly degraded. In this paper, we propose a new robust LPCR based on M-estimation, which constitutes a versatile framework to reduce the sensitivity of the estimators to outliers. In particular, robust detection rules are used to first remove the contaminated measurements and then a modified Huber function is used to further remove the contributions of the mislabeled observations. Experimental results show that the proposed method generally outperforms the conventional LPCR under the presence of outliers, while maintaining a performance comparable to that obtained under normal condition.

The filtered-x LMS (FxLMS) algorithm has been widely used in active noise control (ANC) systems, where the secondary path is usually estimated online by injecting auxiliary noises. In such an ANC system, the ANC controller and the secondary-path estimator are coupled with each other, which make it difficult to analyze the performance of the entire system. Therefore, a comprehensive performance analysis of broadband ANC systems is not available currently to our best knowledge. In this paper, the convergence behavior of the FxLMS algorithm in broadband ANC systems with online secondary-path modeling is studied. Difference equations which describe the mean and mean square convergence behaviors of the adaptive algorithms are derived. Using these difference equations, the stability of the system is analyzed. Finally, the coupled equations at the steady state are solved to obtain the steady-state excess mean square errors (EMSEs) for the ANC controller and the secondary-path estimator. Computer simulations are conducted to verify the agreement between the simulated and theoretically predicted results. Moreover, using the proposed theoretical analysis, a systematic and simple design procedure for ANC systems is proposed. The usefulness of the theoretical results and design procedure is demonstrated by means of a design example.

This paper proposes a movable image-based rendering (M-IBR) system for improving the viewing freedom and environmental modeling capability of conventional static IBR systems. The system supports object-based rendering and 3-D reconstruction capability and consists of three main components.An improved video stabilization method to reduce the shaky motion frequently encountered in movable IBR systems. It employs local polynomial regression (LPR) to automatically select an appropriate bandwidth for smoothing the estimated motion.

This paper proposes an image-based approach for the capturing, rendering and display of ancient Chinese artifacts for cultural heritage preservation. A multiple-camera circular array is proposed to record images of the artifacts, which forms a simplified circular light field (SCLF). A systematic image-based approach and associate algorithms such as segmentation, depth estimation and shape morphing are developed for rendering new views of the Chinese artifacts. An object-based compression scheme is also proposed to reduce the data size for storage and transmission of the texture, depth maps and alpha maps associated with the object-based circular light field. Spatial redundancies among the various images are exploited to improve the coding performance, while avoiding excessive complexity in selective decoding of the light field to support fast rendering speed. To allow the Chinese artifacts to be viewed over the internet, scalable prioritized transmission and rendering schemes of the SCLF with low latency were also developed. The multiple views so synthesized enable the ancient artifacts to be displayed in 3-D/multi-view displays. Several collections from the University Museum and Art Gallery at The University of Hong Kong were captured and excellent rendering results are obtained.

This paper proposes a new method for preliminary identification of gene regulatory networks (GRNs) from gene microarray cancer databased on ridge partial least squares (RPLS) with recursive feature elimination (RFE) and novel Brier and occurrence probability measures. It facilitates the preliminary identification of meaningful pathways and genes for a specific disease, rather than focusing on selecting a small set of genes for classification purposes as in conventional studies. First, RFE and a novel Brier error measure are incorporated in RPLS to reduce the estimation variance using a two-nested cross validation (CV) approach. Second, novel Brier and occurrence probability-based measures are employed in ranking genes across different CV subsamples. It helps to detect different GRNs from correlated genes which consistently appear in the ranking lists. Therefore, unlike most conventional approaches that emphasize the best classification using a small gene set, the proposed approach is able to simultaneously offer good classification accuracy and identify a more comprehensive set of genes and their associated GRNs. Experimental results on the analysis of three publicly available cancer data sets, namely leukemia, colon, and prostate, show that very stable gene sets from different but relevant GRNs can be identified, and most of them are found to be of biological significance according to previous findings in biological experiments. These suggest that the proposed approach may serve as a useful tool for preliminary identification of genes and their associated GRNs of a particular disease for further biological studies using microarray or similar data.

The transform domain normalized least mean squares (TDNLMS) algorithm is an efficient adaptive algorithm, which offers fast convergence speed with a reasonably low arithmetic complexity. However, its convergence speed is usually limited by the fixed step-size so as to achieve a low desired misadjustment. In this paper a new switch-mode noise-constrained TDNLMS (SNC-TDNLMS) algorithm is proposed. It employs a maximum step-size mode in initial convergence and a noise-constrained mode afterwards to improve the convergence speed and steady state performance. The mean and mean square convergence behaviors of the proposed algorithm are studied to characterize its convergence condition and steady-state excess mean square error (EMSE). Based on the theoretical results, an automatic threshold selection scheme for mode switching is developed. Computer simulations are conducted to show the effectiveness of the proposed algorithm and verify the theoretical results.

This paper proposes two subspace-based methods for direction-of-arrival (DOA) estimation of coherent signals for uniform linear arrays (ULAs) with unknown mutual coupling. Based on the banded symmetric Toeplitz structure of the mutual coupling matrix (MCM), the conventional spatial smoothing method can be exploited for DOA estimation of coherent signals. We firstly study a new scheme with spatial smoothing on the middle subarray, which can be considered as an ideal array without uncertainties based on the special property of mutual coupling of ULAs. Next, an alternative method with larger effective antenna aperture is proposed. Unlike the first method, the latter one smoothes on the whole array instead of the middle subarray and a better performance is achieved. Numerical examples are given to illustrate the performance of these two algorithms for coherent DOA estimation in the presence of mutual coupling.

This paper proposes a system for photorealistic interactive rendering of ancient Chinese artifacts for cultural heritage preservation using multiview images captured by a circular multiple-camera array. It employs 3D reconstruction and precomputed shadow field techniques to enable real-time relighting and object interaction. Moreover, Gabor features are employed to improve the robustness of line matching along epipolar lines and robust radial basis function modeling is employed to suppress possible outliers arising from false matching. Using the 3D model reconstructed, the precomputed shadow field is employed to provide real-time rendering/relighting and object movement, after acceleration on a graphic processing unit (GPU). Excellent rendering results are obtained and the ancient Chinese artifacts can be displayed in modern multi-view displays and conventional stereo systems.

This paper develops a subspace-based method for direction-of-arrival (DOA) estimation of uniform linear array (ULA) in the presence of mutual coupling. As the mutual coupling coefficient between two sensor elements is inversely related to their separation and is negligible when they are separated by a few wavelengths, the mutual coupling matrix (MCM) of a ULA can be well approximated as a banded symmetric Toeplitz matrix, which greatly reduces the number of unknown parameters to be estimated. Using the subspace principle, we propose a new method for joint estimation of the DOAs of incoming signals and banded symmetric Toeplitz MCM by reconstructing the steering vector to a specific matrix form. The proposed method achieves a better performance especially for weak signals than the method in, since the whole array, instead of the middle subarray in, is used for DOA estimation. Simulation results illustrate that both DOAs and mutual coupling coefficients can be estimated efficiently with the proposed method.

This paper proposes a new method for identification of time-varying autoregressive (TVAR) models based on local polynomial modeling (LPM) and applies it to investigate the dynamic spectral information of event-related electroencephalogram (EEG). The proposed method models the TVAR coefficients locally by polynomials and estimates those using least-squares estimation with a kernel having a certain bandwidth. A data-driven variable bandwidth selection method is developed to obtain the optimal bandwidth, which minimizes the mean squared error (MSE). Simulation results show that the LPM-based TVAR identification method outperforms conventional methods for different scenarios. The advantages of the LPM method make it a useful high-resolution time-frequency analysis (TFA) technique for nonstationary biomedical signals like EEG. Experimental results show that the LPM method can reveal more meaningful time-frequency characteristics than wavelet transform.

Image-based rendering (IBR) is an emerging and promising technology for photo-realistic rendering of scenes and objects from a collection of densely sampled images and videos. This paper proposes an image-based approach to the rendering and multi-view display of ancient Chinese artifacts for cultural heritage preservation. A multiple-camera circular array was constructed to record images of the artifacts. Novel techniques for segmenting and rendering new views of the artifacts from the sampled images are developed. The multiple views so synthesized enable the ancient artifacts to be displayed in modern multi-view displays and conventional stereo systems. Several collections from the University Museum and Art Gallery at the University of Hong Kong are captured and excellent rendering results are obtained.

A new object-based coding system for a class of dynamic image-based representations called plenoptic videos (PVs) is proposed. PVs are simplified dynamic light fields, where the videos are taken at regularly spaced locations along line segments instead of a 2-D plane. In the proposed object-based approach, objects at different depth values are segmented to improve the rendering quality. By encoding PVs at the object level, desirable functionalities such as scalability of contents, error resilience, and interactivity with an individual image-based rendering (IBR) object can be achieved. Besides supporting the coding of texture and binary shape maps for IBR objects with arbitrary shapes, the proposed system also supports the coding of grayscale alpha maps as well as depth maps (geometry information) to respectively facilitate the matting and rendering of the IBR objects. Both temporal and spatial redundancies among the streams in the PV are exploited to improve the coding performance, while avoiding excessive complexity in selective decoding of PVs to support fast rendering speed. Advanced spatial/temporal prediction methods such as global disparity-compensated prediction, as well as direct prediction and its extensions are developed. The bit allocation and rate control scheme employing a new convex optimization-based approach are also introduced. Experimental results show that considerable improvements in coding performance are obtained for both synthetic and real scenes, while supporting the stated object-based functionalities.

This paper studies the convergence behaviors of the noise-constrained normalized least mean squares (NCNLMS) algorithm recently proposed in the work of Chan et al. (2008). Like its LMS counterpart, the NCNLMS algorithm employs the prior knowledge of the additive noise to adjust its step-size. Following (Wei et al., 2001), the convergence behaviors of the NCLMS under the noise mismatch cases are firstly derived. Using a novel transformation approach and the small step-size properties of the NCNLMS algorithm at convergence, the mean and mean squares behaviors of this algorithm are derived. The validity of the proposed analysis is verified well by computer simulations and the relative merits of the NCLMS and NCNLMS algorithms are also compared.

This paper studies the convergence behaviors of the fast least mean M-estimate/Newton adaptive filtering algorithm proposed in (Y. Zhou et al.,2004), which is based on the fast LMS/Newton principle and the minimization of an M-estimate function using robust statistics for robust filtering in impulsive noise. By using the Price's theorem and its extension for contaminated Gaussian (CG) noise case, the convergence behaviors of the fast LMM/ Newton algorithm with Gaussian inputs and both Gaussian and CG noises are analyzed. Difference equations describing the mean and mean square behaviors of this algorithm and step size bound for ensuring stability are derived. These analytical results reveal the advantages of the fast LMM/Newton algorithm in combating impulsive noise, and they are in good agreement with computer simulation results.

This paper proposes a new two-stage method for restoring image corrupted by additive impulsive and Gaussian noise based on local polynomial regression (LPR) and edge preserving regularization. In LPR, the observations are modeled locally by a polynomial using least-squares criterion with a kernel controlled by a certain bandwidth matrix. A refined intersection confidence intervals (RICI) adaptive scale selector for symmetric kernel is applied in LPR to achieve a better bias-variance tradeoff. The method is further extended to steering kernel with local orientation to adapt better to local characteristics of images. The resulting steering-kernel-based LPR with RICI method (SK-LPR-RICI) is applied to smooth images contaminated with Gaussian noise. Furthermore, to remove the impulsive noise in images, an edge-preserving regularization method is employed prior to SK-LPR-RICI and it gives rise to a two-stage method for suppressing both additive impulsive and Gaussian noises. Simulation results show that the proposed method performs satisfactorily and the SK-LPR-RICI method significantly improves the performance after edge-preservation regularization in suppressing the impulsive noise.

This paper studies an M-estimation-based method for linear estimation with weighted L1 regularization and its recursive implementation. Motivated by the sensitivity of conventional least-squares-based L1-regularized linear estimation (Lasso) in impulsive noise environment, an M-estimator-based Lasso (M-Lasso) method is introduced to restrain the outliers and an iterative re-weighted least-squares (IRLS) algorithm is proposed to solve this M-estimation problem. Moreover, instead of using the matrix inversion formula, QR decomposition (QRD) is employed in the M-Lasso for recursive implementation with a lower arithmetic complexity. Simulation results show that the M-estimation-based Lasso performs considerably better than the traditional LS-based Lasso in suppressing the impulsive noise, and its recursive QRD algorithm has a good performance in online processing.

This paper proposes an object-based approach to a class of dynamic image-based representations called ldquoplenoptic videos,rdquo where the plenoptic video sequences are segmented into image-based rendering (IBR) objects each with its image sequence, depth map, and other relevant information such as shape and alpha information. This allows desirable functionalities such as scalability of contents, error resilience, and interactivity with individual IBR objects to be supported. Moreover, the rendering quality in scenes with large depth variations can also be improved considerably. A portable capturing system consisting of two linear camera arrays was developed to verify the proposed approach. An important step in the object-based approach is to segment the objects in video streams into layers or IBR objects. To reduce the time for segmenting plenoptic videos under the semiautomatic technique, a new object tracking method based on the level-set method is proposed. Due to possible segmentation errors around object boundaries, natural matting with Bayesian approach is also incorporated into our system. Furthermore, extensions of conventional image processing algorithms to these IBR objects are studied and illustrated with examples. Experimental results are given to illustrate the efficiency of the tracking, matting, rendering, and processing algorithms under the proposed object-based framework.

A novel algorithm for the mobile station (MS) location estimation with none line of sight (NLOS) error in wireless network is proposed in this paper. In the proposed algorithm, the MS location estimation problem is formulated as a simple linear approximation problem in vector space with the data corrupted by the NLOS errors. The M-estimator is employed to eliminate the NLOS errors, and a recursive algorithm is developed to solve the M-estimation normal equations. Compared to the conventional algorithms, the proposed algorithm does not rely on the prior knowledge of the statistical model of the measurement noise. Another advantage is that the proposed algorithm can track the slow moment of MS due to the recursive nature, which is hardly to achieve in other algorithms. The algorithm also possesses low arithmetic complexity. Effectiveness of the proposed algorithm is verified by the numerical simulations.

Image-based rendering (IBR) is an emerging technology for photo-realistic rendering of scenes from a collection of densely sampled images and videos. Recently, an object-based approach for a class of dynamic image-based representations called plenoptic videos was proposed in order to improve the rendering quality in large environment. Since images and videos are special cases of the plenoptic function, many conventional image processing algorithms such as coding, segmentation, etc have similar analogy in IBR. This paper is devoted to the extension of some commonly used image processing algorithms to IBR using the object-based approach and their possible applications. Experimental results using plenoptic videos as an example are also given to illustrate the basic concept.

This paper introduces two new time-frequency analysis methods originated from the minimum variance spectral estimation (MVSE) for nonstationary time-series. First, a windowed MVSE (WMVSE) extends the conventional MVSE by windowing the observation data to obtain a time-frequency distribution for the time-series. Moreover, the window lengths are selected adaptively by the intersection of confidence intervals (ICI) rule to improve the time-frequency resolution. Secondly, a new recursive MVSE (RMVSE) is developed to process the input samples recursively at a lower arithmetic complexity for online time-frequency analysis. Simulation results show that the proposed WMVSE with adaptive windows offers better frequency resolutions than the Fourier-transformed-based time-frequency distributions, and the RMVSE has a good performance when tracking sinusoidal signals

This paper proposes a new recursive adaptive beamforming algorithms for uniform concentric spherical array (UCSA) having nearly frequency invariant (FI) characteristics. New recursive adaptive beamforming algorithms based on the least mean square (LMS) algorithm and the generalized sidelobe canceller (GSC) structure are proposed. Simulation results show that the proposed adaptive FI-UCSA beamformer requires much fewer variable taps than the conventional UCSA for the same steady-state performance, while offering much faster convergence speed. Simulation results also show that the proposed FI-UCSA has uniform resolutions around both the elevation and azimuth angles.

This paper studies the time-frequency analysis of trunk response based on surface electromyography (EMG) under an experimental protocol of sudden load release. Due to difficulties in feature extraction of surface EMG in short-time muscular reflex using conventional time-frequency analysis algorithms, a novel method called adaptive Lomb periodogram (ALP) is employed to improve the time and frequency resolutions by adaptively selecting the window sizes. Experimental results demonstrate the improved time-frequency resolution of the ALP in analyzing the EMG signals compared with conventional methods using scalogram. The findings of this study would provide important information in devising objective and quantitative protocol for neuromuscular function assessment, which are applicable to rehabilitation of low back pain patients, motor control and training, elderly fall prevention and ergonomic studies.

This paper proposes a new minimum variance spectral estimation (MVSE)-based time-frequency analysis (TFA) for click-evoked otoacoustic emissions (CEOAEs). The conventional MVSE is extended to TFA by windowing the observation data to obtain a time-frequency distribution for the time-series. Based on the characteristics of CEOAEs, the window size is given a small value at high frequencies and a large value at low frequencies. The adaptive window size yields the proposed frequency-dependent WMVSE (FDWMVSE). The FDWMVSE integrates the advantages of adaptive window selection of wavelet transform and good resolution of MVSE. Experimental results show that the FDWMVSE can achieve better frequency resolution than other TFA methods when applied to synthesized and real CEOAEs

This paper proposes a new digital beamformer for uniform concentric spherical array (UCSA) having nearly frequency invariant (FI) characteristics. The basic principle is to transform the received signals to the phase mode and remove the frequency dependency of the individual phase mode through the use of a digital beamforming network. It is shown that the far field pattern of the array is determined by a set of weights and it is approximately invariant over a wide range of frequencies. FI UCSAs are electronic steerable in both the azimuth angle and elevation angle, unlike their concentric circular array counterpart. A design example is given to demonstrate the design and performance of the proposed FI UCSA

In this paper, we propose a new method for spatial-temporal error concealment in video coding using joint motion-image inpainting. The proposed method combines motion inpainting and adaptive Markov random field (MRF) based diffusion as robust motion inpainting. Image inpainting is employed to refine the result. With robust motion inpainting, effective compromise between temporal and spatial method is achieved for each point in the missing marcroblock (MB), which provides satisfactory visual quality of the restored frame with complex motion in video.

This paper presents a convex optimization-based frame-level rate control algorithm for motion compensated hybrid DCT/DPCM video coding. By modifying the existing rate-distortion models, an improved empirical rate-distortion model with more flexibility is proposed to explain the experimental observations. The convexity and monotonicity of the proposed model are exploited to formulate the frame-level bit allocation problem as a convex programming problem. Thus the bit allocation among the frames with different picture types can be solved using convex programming methods such as the interior-point methods, if the optimal solution exists. Different importance weights for various picture types (I-, P-, etc) can also be incorporated into the scheme in order to account for the relative importance of different picture types due to their inter-dependency. The relevant model parameters are determined using previously encoded frames by means of linear regression. Simulation results show that the proposed algorithm achieves a considerably better picture quality in terms of PSNR than the conventional approaches for the tested video sequences. Therefore, it is a good alternative to these conventional approaches.

This paper proposes a recursive algorithm for estimating the adaptive function coefficients autoregressive (AFAR) models. Due to its recursive nature, its arithmetic complexity is relatively lower than conventional methods. Furthermore, a new M-estimation-based AFAR parameter estimation algorithm is developed to suppress the effect of impulsive outliers. Simulation results show that the M-estimation-based algorithm offers improved performance than the conventional LS-based algorithm

This paper proposes two new methods for optimizing hardware resources in finite wordlength implementation of multiple-output (MO) linear time invariant systems. The hardware complexity is measured by the exact internal wordlength used for each intermediate data. The first method relaxes the wordlength from integer to real-value and formulates the design problem as a geometric programming, from which an optimal solution of the relaxed problem can be determined. The second method is based on a discrete optimization method called the marginal analysis method, and it yields the desired wordlengths in integer values. By combining these two methods, a hybrid method is also proposed, which is found to be very effective for large scale MO systems. Design example shows that the proposed algorithms offer better results and a lower design complexity than conventional methods

In this paper, a joint transmitter and receiver beamformers design algorithm for downlink multiple input multiple output multicarrier code-division multiple access (MIMO MC-CDMA) system is proposed. The algorithm is iterative in nature where the transmitter beamformers and the receiver beamformers are determined alternately. The transmitter beamforming problem with a given receiver beamformer is formulated as a convex programming problem, which can be solved optimally using second order cone programming (SOCP), while the receiver beamforming problem is formulated as a constrained optimization problem with an analytical solution. The convergence of the algorithm is analyzed and the performance of the proposed algorithm is evaluated by computing simulation

This paper analyzes the performance of the GP-NLMS algorithm, revealing the nature of its fast convergence as well as its deficiency of inducing bigger steady state error. Based on the analysis, a class of improved generalized-proportionate stepsize LMS (GPS-LMS) algorithms are proposed. With an efficient switching mechanism, the new algorithms can dynamically switch between the GP-NLMS and conventional LMS-type algorithms to achieve fast initial convergence and tracking speed and low steady state error. Computer simulations verified the superior performance of the proposed algorithms

This paper presents a new Kalman filter-based subspace tracking algorithm and its application to directions of arrival (DOA) estimation. An autoregressive (AR) process is used to describe the dynamics of the subspace and a new adaptive Kalman filter with variable measurements (KFVM) algorithm is developed to estimate the time-varying subspace recursively from the state-space model and the given observations. For stationary subspace, the proposed algorithm will switch to the conventional PAST to lower the computational complexity. Simulation results show that the adaptive subspace tracking method has a better performance than conventional algorithms in DOA estimation for a wide variety of experimental condition

In this paper, we present a robust multiuser detector for wireless multicarrier code-division multiple access (MC-CDMA) systems under time-varying narrowband interference (NBI). The conventional least-squares (LS) channel estimators and multiuser detectors will perform poorly when narrowband interfering signals contaminate the multicarrier systems. A new weighted least M-estimate (WLM) multiuser detector is proposed to jointly suppress multiple access interference (MAI) and time-varying NBI. The WLM multiuser detector resorts to M-estimate and weighted least-squares (WLS) techniques. A weighted recursive least M-estimate (WRLM) channel estimator is exploited to estimate the time-varying frequency-selective fading channels in the presence of NBI. Numerical results show that the proposed WLM multiuser detector significantly outperforms over the conventional linear decorrelator, the robust decorrelating detector with M-estimate and the WLS detector under NBI

This paper proposes a new QR-decomposition-based recursive frequency estimation algorithm for multiple sinusoids based on the linear prediction (LP) approach. It extends the batch processing algorithm of So et al. in order to process the input samples recursively at a much lower arithmetic complexity for supporting on-line applications. Furthermore, a weighted least M-estimate (WLM) algorithm is developed to improve robustness to impulsive noise. Simulation results show that the robust recursive frequency estimator has a better performance than the conventional LS estimation in impulsive noise environment

This paper proposes a new design method for a class of two-channel 2D non-separable perfect reconstruction (PR) filter banks (FBs) using the multiplet FBs. 1D multiplet FBs are PR FBs that can be obtained by frequency transformation of a prototype PR FB in the conventional lifting structure so that a better frequency characteristics can be obtained and varied online to process different signals. By employing the 1D to 2D transformation of Phoong et al., new 2D PR multiplet FBs with quincunx, hourglass, and parallelogram spectral support are obtained. These nonseparable multiplet FBs can be cascaded to realize new PR directional FB for image processing and motion analysis. The design procedure is very general and it can be applied to both linear-phase and low-delay 2D FBs. Design examples are given to demonstrate the usefulness of the proposed method

This paper studies the theory and design of a class of recombination nonuniform FBs (RNFB) with low-delay (LD) FIR and IIR filters. The conditions for suppressing the spurious response and achieving a good frequency characteristic for these LD FIR/IIR RNFBs are developed. The proposed LD FIR RNFBs have a lower system delay than their linear-phase counterparts, at the expense of slight increase in phase distortion of the analysis filters and arithmetic complexity. By model reducing the LD FIR uniform FBs by the modified model reduction method, an IIR RNFB with a similar characteristic can be readily obtained. A design example is given illustrate the effectiveness of the proposed method

This paper proposes a new Kalman filter-based algorithm for multichannel autoregressive (AR) spectrum estimation and adaptive coherence analysis with variable number of measurements. A stochastically perturbed k -order difference equation constraint model is used to describe the dynamics of the AR coefficients and the intersection of confidence intervals (ICI) rule is employed to determine the number of measurements adaptively to improve the time-frequency resolution of the AR spectrum and coherence function. Simulation results show that the proposed algorithm achieves a better time-frequency resolution than conventional algorithms for non-stationary signals

This paper presents a new Kalman filter-based power spectral density estimation (PSD) algorithm for nonstationary pressure signals. The pressure signal is assumed to be an autoregressive (AR) process, and a stochastically perturbed difference equation constraint model is used to describe the dynamics of the AR coefficients. The proposed Kalman filter frame uses variable number of measurements to estimate the time-varying AR coefficients and yield the PSD estimation with better time-frequency resolution. Simulation results show that the proposed algorithm achieves a better time-frequency resolution than conventional algorithms for nonstationary pressure signals

This paper studies the optimal least squares (LS) and minimax design of hybrid filter bank analog to digital converters (HFB ADCs) using second order cone programming (SOCP). The resulting problem is convex and it allows linear and quadratic constraints such as prescribed signal reconstruction magnitude flatness and aliasing canceling error to be incorporated. Two new robust HFB ADC design algorithms based on stochastic uncertainty and worst-case uncertainty models are also proposed. Design results show that the SOCP approach offers more flexibility than conventional methods and the robust design algorithms are more robust to parameter uncertainties than the SOCP design when the uncertainties are not taken into account

Image-based rendering (IBR) is an emerging technology for photo-realistic rendering of scenes from a collection of densely sampled images and videos. Recently, an object-based approach for a class of dynamic image-based representations called plenoptic videos was proposed. This paper proposes an automatic object tracking approach using the level-set method. Our tracking method, which utilizes both local and global features of the image sequences instead of global features exploited in previous approach, can achieve better tracking results for objects, especially with non-uniform energy distribution. Due to possible segmentation errors around object boundaries, natural matting with Bayesian approach is also incorporated into our system. Furthermore, a MPEG-4 like object-based algorithm is developed for compressing the plenoptic videos, which consist of the alpha maps, depth maps and textures of the segmented image-based objects from different video plenoptic streams. Experimental results show that satisfactory renderings can be obtained by the proposed approaches.

This paper proposes a new digital beamformer for a uniform concentric circular array (UCCA) having nearly frequency invariant (FI) characteristics. The basic principle is to transform the received signals to the phase mode and remove the frequency dependency of the individual phase mode through the use of a digital beamforming network. The far field pattern of the array is determined by a set of weights and it is approximately invariant over a wide range of frequencies. Compared with the FI uniform circular array (UCA), FI UCCAs are able to achieve a wider bandwidth. Design examples are given to demonstrate the principle of the proposed UCCA-FIB and its application to broadband DOA estimation of coherent sources.

This article introduces a robust adaptive Lomb periodogram (RALP) for time-frequency (TF) analysis of a time series with sinusoidal and transient components, which are possibly non-uniformly sampled. It extends the conventional Lomb spectrum by windowing the observation data and adaptively selects the window lengths by the intersection of confidence intervals (ICI) rule. The influence of transient components on the conventional time-frequency representation can be moderated using M-estimation of robust statistics. Instead of treating the transient components as impulsive noise and removing them, the proposed RALP TF distribution yields separately a time domain representation of the transient components and a conventional TF representation of the sinusoidal components, which greatly improves the visualization and detection of these components. Simulation results show that the proposed RALP differentiates the two kinds of components well, and offers better time and frequency resolutions than the conventional Lomb periodogram.

An object-based compression scheme for a class of dynamic image-based representations called "plenoptic videos" (PVs) is studied in this paper. PVs are simplified dynamic light fields in which the videos are taken at regularly spaced locations along a line segment instead of a 2-D plane. To improve the rendering quality in scenes with large depth variations and support the functionalities at the object level for rendering, an object-based compression scheme is employed for the coding of PVs. Besides texture and shape information, the compression of geometry information in the form of depth maps is also supported. The proposed compression scheme exploits both the temporal and spatial redundancy among video object streams in the PV to achieve higher compression efficiency. Experimental results show that considerable improvements in coding performance are obtained for both synthetic and real scenes. Moreover, object-based functionalities such as rendering individual image-based objects are also illustrated.

This paper presents a system for capturing and rendering a dynamic image-based representation called the plenoptic video. It is a simplified light field for dynamic environments, where user viewpoints are constrained to the camera plane of a linear array of video cameras. Important issues such as multiple camera calibration, real-time compression, decompression and rendering are addressed. The system consists of a camera array of eight Sony CCX-Z11 CCD cameras and eight Pentium 4 1.8-GHz computers connected together through a 100 Base-T local area network. It is possible to perform software-assisted real-time MPEG-2 compression at a resolution of (720/spl times/480). Using selective transmission, we are able to stream continuously plenoptic video with (256/spl times/256) resolution at a rate of 15 f/s over the network. For rendering from raw data on the hard disk, real-time rendering can be achieved with a resolution of (720/spl times/480) and a rate of 15 f/s. A new compression algorithm using both temporal and spatial predictions is also proposed for the efficient compression of the plenoptic videos. Experimental results demonstrate the usefulness of the proposed parallel processing based system in capturing and rendering high-quality dynamic image-based representations using off-the-shelf equipment, and its potential applications in visualization and immersive television systems.

This paper proposes a new image-based rendering (IBR) technique called "concentric mosaic" for virtual reality applications. IBR using the plenoptic function is an efficient technique for rendering new views of a scene from a collection of sample images previously captured. It provides much better image quality and lower computational requirement for rendering than conventional three-dimensional (3-D) model-building approaches. The concentric mosaic is a 3-D plenoptic function with viewpoints constrained on a plane. Compared with other more sophisticated four-dimensional plenoptic functions such as the light field and the lumigraph, the file size of a concentric mosaic is much smaller. In contrast to a panorama, the concentric mosaic allows users to move freely in a circular region and observe significant parallax and lighting changes without recovering the geometric and photometric scene models. The rendering of concentric mosaics is very efficient, and involves the reordering and interpolating of previously captured slit images in the concentric mosaic. It typically consists of hundreds of high-resolution images which consume a significant amount of storage and bandwidth for transmission. An MPEG-like compression algorithm is therefore proposed in this paper taking into account the access patterns and redundancy of the mosaic images. The compression algorithms of two equivalent representations of the concentric mosaic, namely the multiperspective panoramas and the normal setup sequence, are investigated. A multiresolution representation of concentric mosaics using a nonlinear filter bank is also proposed.

Space-time coding has received considerable interest recently as a simple transmit diversity technique for improving the capacity and data rate of a channel without bandwidth expansion. Most research in space-time coding, however, assumes that the symbol timing at the receiver is perfectly known. In practice, this has to be estimated with high accuracy. In this paper, a new symbol-timing estimator for space-time coding systems is proposed. It improves the conventional algorithm of Naguib et al. such that accurate timing estimates can be obtained even if the oversampling ratio is small. Analytical mean-square error (MSE) expressions are derived for the proposed estimator. Simulation and analytical results show that for a modest oversampling ratio (such as Q equal to four), the MSE of the proposed estimator is significantly smaller than that of the conventional algorithm. The effects of the number of transmit and receive antennas, the oversampling ratio, and the length of training sequence on the MSE are also examined.

The paper proposes a new method for performing multi-resolution analysis (MRA) of non-uniform data with jump discontinuities and impulsive noise using robust M-estimator-based local polynomial regression (LPR). The basic idea is to interpolate the smoothed estimate, after performing the robust LPR, on a uniform grid in order to perform the MRA using the ordinary wavelet transform. Simulation results show that the new approach performs better than traditional LS-based LPR in preserving jump discontinuities and suppressing isolated impulses when intersection confident intervals (ICI) bandwidth selection is employed.

This paper studies the rendering and post-processing of a dynamic image-based representation called the simplified dynamic light fields (SDLF) (or plenoptic videos) with depth information. The user viewpoints are limited to a camera line to simplify the capturing and compression processes. By associating each image pixel with its depth value, methods for improving the rendering quality and detecting occlusions are proposed. Due to the limited sampling at depth discontinuities, adaptive lowpass filtering is applied to the detected occluded regions near object boundaries in order to suppress the aliasing artifacts. Rendering results using computer-generated images show that considerable improvement in rendering quality even for dynamic scenes with large depth variations.

A new symbol-timing estimator for space-time coding systems is proposed. It improves the conventional algorithm of Naguib et al. such that accurate timing estimates can be obtained even if the oversampling ratio is small (such as oversampling ratio Q=4). The increase in implementation complexity with respect to that of the conventional algorithm is very small. The requirements and the design procedures for the training sequences are discussed. Analytical and simulation results show that the estimation mean square error of the proposed estimator is significantly smaller than that of the conventional algorithm.

This letter proposes a new method for designing finite-impulse response (FIR) filters with variable characteristics. The impulse response of the variable digital filter (VDF) is parameterized as a linear combination of functions in the spectral or tuning parameters. Using the least square objective function, the optimal solution is obtained by solving a system of linear equations. Design results show that this method is simple and effective in designing FIR VDF with good frequency characteristics. Furthermore, by using a piecewise polynomial, instead of an ordinary polynomial, more complicated frequency characteristics, or a larger tuning range can be approximated.

This paper proposes a new method for designing a class of two-channel perfect reconstruction (PR) linear-phase FIR filterbanks (FBs) and wavelets previously proposed by Phoong et al. By expressing the given K-regularity constraints as a set of linear equality constraints in the design variables, the design problem using the minimax error criterion can be solved using semidefinite programming (SDP). Design examples show that the proposed method is very effective and it yields equiripple stopband response while satisfying the given K-regularity condition.

Variable digital filters (VDFs) are useful to the implementation of digital receivers because its frequency characteristics such as fractional delays and cutoff frequencies can be varied online. It is shown that the optimal minimax design of VDFs with passband linear-phase can be formulated and solved as a semi-definite programming (SDP) problem, which is a powerful convex optimization method. In addition, other objective functions, such as least squares, and linear and convex quadratic inequality constraints can readily be incorporated. Design examples using a variable fractional delay (VFD) and a variable cutoff frequency (VCF) FIR filters are given to demonstrate the effectiveness of the proposed approach.

This paper studies the compression of a dynamic image-based rendering (IBR) representation called simplified dynamic light fields (SDLF). It is obtained by constraining the viewpoints in a dynamic environment along a line instead of a 2D plane. The SDLFs have a dimensionality of four, which considerably simplifies their capture and data compression. A new coding algorithm for SDLFs using a modified MPEG-2 algorithm is proposed. It employs both temporal and spatial predictions from the reference video streams to explore better the redundancy among the light field images. Experimental results, using a synthetic SDLF, show that the proposed compression scheme offers a 2 dB improvement in PSNR over a similar coding scheme using only temporal prediction.

This paper proposes a new method for designing digital all-pass filters with a minimax design criterion using semidefinite programming (SDP). The frequency specification is first formulated as a set of linear matrix inequalities (LMI), which is a bilinear function of the filter coefficients and the ripple to be minimized. Unlike other all-pass filter design methods, additional linear constraints can be readily incorporated. The overall design problem turns out to be a quasi-convex constrained optimization problem (solved using the SDP) and it can be solved through a series of convex optimization sub-problems and the bisection search algorithm. The convergence of the algorithm is guaranteed. Nonlinear constraints such as the pole radius constraint of the filters can also be formulated as LMI using the Rouche theorem. It was found that the pole radius constraint allows an additional tradeoff between the approximation error and the stability margin in finite wordlength implementation. The effectiveness of the proposed method is demonstrated by several design examples.

This paper proposes a new family of QR-based block LMS (QR-BLMS) algorithms for adaptive parameter estimation. It extends the QR-based LMS (QR-LMS) algorithm by handling a block of data vectors at a time instead of one single input vector. Moreover, it is shown that when there is only one new input vector in the block and the others are obtained from previous time instants, this QR-BLMS algorithm yields a new QR-based implementation of the well-known affine projection algorithm (APA). Simulation results for an acoustic echo canceller showed that the QR-BLMS and QR-APA algorithms perform better than the QR-LMS algorithm.

Space-time coding has received considerable interest recently as a simple transmit diversity technique for improving the capacity and data rate of a channel without bandwidth expansion. Most research work in space-time coding, however, assumed that the symbol timing at the receiver is perfectly known. In practice, this has to be estimated with high accuracy. In this paper, two symbol timing recovery algorithms for space-time coding systems are proposed. The first one is based on orthogonal training sequences and approximated log likelihood function. It is an improvement of a previous symbol timing synchronization algorithm in that high estimation accuracy can be achieved even when the over sampling factor is small. The second one employs the squaring algorithm. It offers good performance and does not require training sequences.

This article proposes an efficient implementation of the Farrow (1988) structure using sum-of-powers-of-two (SOPOT) coefficients and multiplier-block (MB). In particular, a novel algorithm for designing the Farrow coefficients in SOPOT form is detailed. Using the SOPOT coefficient representation, coefficient multiplication can be implemented with limited number of shifts and additions. Using MB, the redundancy between multipliers can be fully exploited through the reuse of the intermediate results generated. Design examples show that the proposed method can greatly reduce the complexity of the Farrow structure while providing comparable phase and amplitude responses.

We survey the techniques for image-based rendering (IBR) and for compressing image-based representations. Unlike traditional three-dimensional (3-D) computer graphics, in which 3-D geometry of the scene is known, IBR techniques render novel views directly from input images. IBR techniques can be classified into three categories according to how much geometric information is used: rendering without geometry, rendering with implicit geometry (i.e., correspondence), and rendering with explicit geometry (either with approximate or accurate geometry). We discuss the characteristics of these categories and their representative techniques. IBR techniques demonstrate a surprising diverse range in their extent of use of images and geometry in representing 3-D scenes. We explore the issues in trading off the use of images and geometry by revisiting plenoptic-sampling analysis and the notions of view dependency and geometric proxies. Finally, we highlight compression techniques specifically designed for image-based representations. Such compression techniques are important in making IBR techniques practical.

This paper proposes a new architecture for the implementation of multiplier-less FIR digital filters with programmable sum-of-powers-of-two (SOPOT) or canonical signed digit (CSD) coefficient representations. The multiplier-less FIR filter is implemented as the direct form structure with the filter coefficients represented as SOPOT representation, which can be realized as limited number of shifts and additions. Traditional VLSI implementations of multiplier-less FIR filters are usually hardwired and the filter coefficients cannot be programmed online. The proposed architecture is very modular in the structure and it can be connected to implement the multiplier-less FIR filter with arbitrary filter order and SOPOT terms using programmable SOPOT coefficients. The structure is also pipelined to achieve a high data throughput rate at low hardware cost. The proposed architecture was implemented and tested using the Altera FLEX 10K Field Programmable Gate Arrays (FPGA). The finite wordlength effect such as signal roundoff and overflow errors are also taken into account. A design example is given to demonstrate the feasibility of the proposed architecture.

This paper studies the application of perfect reconstruction nonlinear filter banks (NFB) to the efficient rendering and progressive transmission of light fields. The reference pictures in the conventional disparity-compensated prediction encoder are decomposed using the NFB to reduce the amount of main memory needed to support fast rendering. The NFB has very low arithmetic complexity for reconstruction and small filter support which considerably simplifies the random access operations. It can also be applied to the predicted light field images to support progressive transmission. Different prediction and reconstruction strategies are also investigated to achieve different tradeoffs between memory requirement and decoding speed.

This letter proposes a new multiplierless approximation of the discrete Fourier transform (DFT) called the multiplierless fast Fourier transform-like (ML-FFT) transformation. It makes use of a novel factorization to parameterize the twiddle factors in the conventional radix-2/sup n/ or split-radix FFT algorithms as certain rotation-like matrices and approximates the associated parameters using the sum-of-powers-of-two (SOPOT) or canonical signed digits (CSD) representations. The ML-FFT converges to the DFT when the number of SOPOT terms used increases and has an arithmetic complexity of O(N log/sub 2/ N) additions, where N = 2/sup m/ is the transform length. Design results show that the NM-FFT offers flexible tradeoff between arithmetic complexity and numerical accuracy in approximating the DFT.

This paper proposes new integer approximations of the lapped transforms, called the integer lapped transforms (ILT), and studies their applications to image coding. The ILT are derived from a set of orthogonal sinusoidal transforms having short integer coefficients, which can be implemented with simple integer arithmetic. By employing the same scaling constants in these integer sinusoidal transforms, integer versions of the lapped orthogonal transform (LOT), the lapped biorthogonal transform (LBT), and the hierarchical lapped biorthogonal transform (HLBT) are developed. The ILTs with 5-b integer coefficients are found to have similar coding gain (within 0.06 dB) and image coding performances as their real-valued counterparts. Furthermore, by representing these integer coefficients as sum of powers-of-two coefficients (SOPOT), multiplier-less lapped transforms with very low implementation complexity are obtained. In particular, the implementation of the eight-channel multiplier-less integer LOT (ILOT), LBT (ILBT), and HLBT (IHLBT) require 90 additions and 44 shifts, 98 additions and 59 shifts, and 70 additions and 38 shifts, respectively.

This paper proposes a new adaptive filtering algorithm called the Huber Prior Error-Feedback Least Squares Lattice (H-PEF-LSL) algorithm for robust adaptive filtering in an impulse noise environment. It minimizes a modified Huber M-estimator-based cost function, instead of the least squares cost function. In addition, the simple modified Huber M-estimate cost function also allows us to perform the time and order recursive updates in the conventional PEF-LSL algorithm so that the complexity can be significantly reduced to O(M), where M is the length of the adaptive filter. The new algorithm can also be viewed as an efficient implementation of the recursive least M-estimate (RLM) algorithm (Zou et al., 2000), which has a complexity of O(M/sup 2/). Simulation results show that the proposed H-PEF-LSL algorithm is more robust than the conventional PEFLSL algorithm in suppressing the adverse influence of the impulses at the input and desired signals with small additional computational cost.

Subspace tracking is an efficient method to reduce the complexity of signal subspace estimation. Recursive least square-based (RLS) subspace tracking algorithms such as the PAST algorithm is attractive because they estimate the signal subspace adaptively and continuously and the computational complexity is relatively low. Unfortunately, the RLS algorithm is well known to be very sensitive to impulse noise and it's performance can degraded substantially. In this paper, a robust PAST algorithm, based on the concept of robust statistics, is proposed. The robustness is achieved by making the underlying RLS iteration more robust to impulse interference. This new method is also applicable to other RLS-based algorithms. In particular, a robust statistic based impulsive noise detector is incorporated into the subspace tracking algorithm. The impulses in the input data vector are detected, and they are prevented from corrupting the estimated subspace for further tracking. We also propose a new restoring mechanism to handle long burst of consecutive impulses, which is a very difficult problem to handle in practice. Simulation results show the proposed algorithm offers satisfactory robustness against individual and consecutive impulses, while the PAST algorithm degrades dramatically in similar impulse noise environment. For nominal Gaussian noise, the proposed robust subspace tracking algorithm offers similar performance as the PAST algorithm.

This paper proposes a new design and implementation method for a class of multiplierless 2-channel 1D and 2D nonseparable perfect reconstruction (PR) filterbanks (FB). It is based on the structure proposed by S.M. Phoong et al. (see IEEE Trans. Sig. Proc., vol.43, p.649-64, 1995) and the use of multiplier blocks (MB). The latter technique allows one to further reduce the number of adders in implementing these multiplier-less FB by almost 50%, compared to the conventional method using sum of powers of two coefficients (SOPOT) alone. Furthermore, by generalizing the 1D to 2D transformation of Phoong et al., new 2D PR FBs with quincunx, hourglass, and parallelogram spectral support are obtained. These nonseparable FBs can be cascaded to realize new multiplierless PR directional FB for image processing and motion analysis. Design examples are given to demonstrate the usefulness of the proposed method.

This paper studies the scalable coding and progressive transmission of concentric mosaic to support interactive applications over a LAN or the Internet. Concentric mosaic is an effective 3D image based representation of a static scene. A typical concentric mosaic might consist of thousands of images, which poses significant problem in digital storage and transmission. A new multiresolution decomposition for supporting progressive transmission of a concentric mosaic is proposed. Instead of using the popular 9/7 wavelet filterbank, a nonlinear perfect reconstruction filter bank with lower arithmetic complexity is employed. It also considerably simplifies the random access operation of the slit images during rendering. By encoding the subband signals into different layers, a scalable compressed bit stream of the concentric mosaic is obtained. Therefore, progressive transmission of concentric mosaic, using a combination of these layers, to support devices with different capabilities become possible.

This paper proposes efficient data compression and transmission techniques for panoramic video. Panoramic videos have been used as a means for representing dynamic scenes or paths along a static environment. They allow the user to change viewpoints interactively at a point in time or space. High-resolution panoramic videos, while desirable, consume a significant amount of storage and bandwidth for transmission, and make real-time decoding very compute-intensive. A high performance MPEG-like compression algorithm, which takes into account the random access requirements and the redundancies of the panoramic video, is presented. The transmission aspects of panoramic video over cable network, LAN and Internet are also briefly discussed.

This paper proposes an efficient class of perfect reconstruction (PR) modulated filter banks (MFB) using sum-of-powers-of-two (SOPOT) coefficients. This is based on a modified factorization of the DCT-IV matrix and the lossless lattice structure of the prototype filter, which allows the coefficients to be represented in SOPOT form without affecting the PR condition. A genetic algorithm (GA) is then used to search for these SOPOT coefficients. Design examples show that SOPOT MFB with a good frequency characteristic can be designed with very low implementation complexity. The usefulness of the approach is demonstrated with a 16 channel design example.

In this paper, an efficient method for designing perfect reconstruction (PR) two-channel finite impulse response (FIR) filter banks with low system delay is proposed. It is based on the use of nonlinear-phase FIR function in a structure previously proposed by Phoong et al. (see IEEE Trans. Signal Processing, vol.43, p.649-64, Mar. 1995). The design problem is formulated as a complex polynomial approximation problem and is solved effectively using the Remez exchange algorithm with very low design complexity. Design examples show that filter banks with flexible stopband attenuation and system delay can be readily obtained by the proposed algorithm.

In this paper, the theory and design of a class of PR cosine-modulated nonuniform filter bank is proposed. It is based on a structure previously proposed by Cox (1986), where the outputs of a uniform filter bank are combined or merged by means of the synthesis section of another filter bank with smaller channel number. Simplifications are imposed on this structure so that the design procedure can be considerably simplified. Due to the use of cosine modulated filter banks as the original and recombination filter banks, excellent filter quality and low design and implementation complexities can be achieved. Problems with these merging techniques such as spectrum inversion, equivalent filter representations and protrusion cancellation are also addressed. As the merging is performed after the decimation, the arithmetic complexity is lower than other conventional approaches. Design examples show that PR nonuniform filter banks with high stopband attenuation and low design and implementation complexities can be obtained by the proposed method.

A new family of two-channel low-delay filter banks and wavelet bases using the PR structure of Phoong, Kim and Vaidyanathan (1995) with sum of powers-of-two (SOPOT) coefficients are proposed. In particular, the functions alpha(z) and beta(z) in the structure are chosen as nonlinear-phase FIR and IIR filters, and the design of such multiplier-less filter banks is performed using the genetic algorithm. The proposed design method is very simple to use, and is sufficiently general to construct low-delay filter banks with flexible lengths, delays, and regularity. Several design examples are given to demonstrate the usefulness of the proposed method.

Image based rendering using the plenoptic function is an efficient technique for re-rendering at different viewpoints. We study the sampling and reconstruction problem of plenoptic function as a multidimensional sampling problem. The spectral support of plenoptic function is found to be an important quantity in the efficient sampling and reconstruction of such a function. A spectral analysis for the light field, a 4D plenoptic function, is performed. Its spectrum, as a function of the depth function of the scene, is then derived. This result enables us to estimate the spectral support of the light field given some prior estimate of the depth function. Results using a piecewise constant depth model show significant improvement in rendering of the light field images. The design of the reconstruction filter is also discussed.

This paper proposes a new image based rendering technique called concentric mosaic for virtual reality applications. It is constructed by capturing vertical slit images when a camera is moving around a set of concentric circles. Concentric mosaic allows the user to move freely in a circular region and observe significant parallax and lighting changes without recovering the geometric and photometric scene model. The rendering of concentric mosaic is very efficient, which amounts to reordering and interpolating of previously captured slit images in the concentric mosaic. Concentric mosaic typically consists of hundreds of high-resolution images, which consumes significant amount of storage and bandwidth for transmission. An MPEG-like compression algorithm is therefore proposed taking advantages of the access patterns and redundancies of the mosaic images. Experimental results show that real-time reconstruction of novel views with good image quality can be achieved in a Pentium II 300 MHz PC.

In this paper, a new family of multiplier-less modulated filter banks, called the SOPOT MFB, is presented. The coefficients of the proposed filter banks consist of sum of powers-of-two coefficients (SOPOT), which require only simple shifts and additions for implementation. The modulation matrix and the prototype filter are derived from the fast DCT-IV algorithm of Wang (1984) and a lattice structure. The design of the SOPOT MFB is performed using the genetic algorithm (GA). An 16-channel SOPOT MFB with 34 dB stopband attenuation is given as an example, and its average number of terms per SOPOT coefficient is only 2.6.

In this paper, a new approach for designing two-channel PR FIR filter banks with low system delay is proposed. It is based on the generalization of the structure previously proposed by Phoong et al. (1995). Such structurally PR filter banks are parameterized by two functions /spl alpha/(z) and /spl beta/(z) which can be chosen as linear-phase FIR or allpass functions to construct FIR/IIR filter banks with good frequency characteristics. In this paper, the more general case of using different nonlinear-phase FIR functions for /spl beta/(z) and /spl alpha/(z) is studied. As the linear-phase requirement is relaxed, higher stopband attenuation can still be achieved at low system delay. The design of the proposed low-delay filter banks is formulated as a complex polynomial approximation problem, which can be solved by the Remez exchange algorithm or analytic formula with very low complexity. The usefulness of the proposed algorithm is demonstrated by several design examples.

In this paper, a new robust adaptive lattice-ladder filter for impulsive noise suppression is proposed. The filter is obtained by applying the non-linear filtering technique reported by Kim and Efron (1995) and the robust statistic approach to the gradient adaptive lattice filter. A systematic method is also developed to determine the corresponding threshold parameters for impulse suppression. Simulation results showed that the performance of the proposed algorithm is better than the conventional RLS, N-RLS, the gradient adaptive lattice normalised-LMS (GAL-NLMS), RMN and ATNA algorithms when the input and desired signals are corrupted by individual and consecutive impulses. The initial convergence, steady-state error, computational complexity and tracking capability of the proposed algorithm are also comparable to the conventional GAL-NLMS algorithm.

This paper studies the problem of plenoptic sampling in image-based rendering (IBR). From a spectral analysis of light field signals and using the sampling theorem, we mathematically derive the analytical functions to determine the minimum sampling rate for light field rendering. The spectral support of a light field signal is bounded by the minimum and maximum depths only, no matter how complicated the spectral support might be because of depth variations in the scene. The minimum sampling rate for light field rendering is obtained by compacting the replicas of the spectral support of the sampled light field within the smallest interval. Given the minimum and maximum depths, a reconstruction filter with an optimal and constant depth can be designed to achieve anti-aliased light field rendering. Plenoptic sampling goes beyond the minimum number of images needed for anti-aliased light field rendering. More significantly, it utilizes the scene depth information to determine the minimum sampling curve in the joint image and geometry space. The minimum sampling curve quantitatively describes the relationship among three key elements in IBR systems: scene complexity (geometrical and textural information), the number of image samples, and the output resolution. Therefore, plenoptic sampling bridges the gap between image-based rendering and traditional geometry-based rendering. Experimental results demonstrate the effectiveness of our approach.

In this paper, a new method for designing perfect reconstruction (PR) two-channel causal stable IIR filter banks is introduced. It is based on a structure previously proposed by Phoong et al. (1995). By using a combination of allpass and linear-phase FIR functions, the bumping problem found in the conventional structural PR filter bank is significantly suppressed. The design problem is formulated as a polynomial approximation problem and is solved effectively using the Remez exchange algorithm. Filter banks with flexible stopband attenuation and system delay can readily be obtained using the proposed algorithm.

This letter proposes a method for designing a class of M-channel, causal, stable, perfect reconstruction (PR) IIR cosine-modulated filter banks (CMFB). The proposed CMFB has the same denominator for all its polyphase components in the prototype filter. Therefore, the PR condition is considerably simplified, and it is relatively simple to satisfy the PR and the causal-stable requirements of the IIR CMFB. Design examples show that the proposed IIR CMFB has sharper cutoff, higher stopband attenuation, and passband flatness than its FIR counterparts, especially when the system delay is small.

This paper proposes a recursive least M-estimate (RLM) algorithm for robust adaptive filtering in impulse noise. It employs an M-estimate cost function, which is able to suppress the effect of impulses on the filter weights. Simulation results showed that the RLM algorithm performs better than the conventional RLS, NRLS, and the OSFKF algorithms when the desired and input signals are corrupted by impulses. Its initial convergence, steady-state error, computational complexity, and robustness to sudden system change are comparable to the conventional RLS algorithm in the presence of Gaussian noise alone.

In this paper, a robust M-estimate adaptive filter for impulse noise suppression is proposed. The objective function used is based on a robust M-estimate. It has the ability to ignore or down weight large signal error when certain thresholds are exceeded. A systematic method for estimating such thresholds is also proposed. An advantage of the proposed method is that its solution is governed by a system of linear equations. Therefore, fast adaptation algorithms for traditional linear adaptive filters can be applied. In particular, a M-estimate recursive least square (M-RLS) adaptive algorithm is studied in detail. Simulation results show that it is more robust against individual and consecutive impulse noise than the MN-LMS and the N-RLS algorithms. It also has fast convergence speed and a low steady state error similar to its RLS counterpart.

JPEG is an international standard for still image compression. The JPEG baseline algorithm allows users to supply the custom quantization table and Huffman table to control the compression ratio and the quality of the encoded image. Methods for determining the quantization matrix are usually based on (i) rate-distortion theory and (ii) spatial masking effects of the human visual system. Wu and Gersho (1993) proposed a recursive algorithm for generating picture-adaptive quantization tables based on rate-distortion approach but the complexity of the encoding algorithm is rather high. In this paper, we propose improvements to the Wu-Gersho's algorithm and a new bit allocation algorithm. Simulation results show that our new algorithm is superior to the Wu-Gersho's algorithm in terms of speed and peak signal to noise ratio (PSNR). Moreover, by incorporating the human visual system (HVS), our proposed coder can encode images with better visual quality.

Due to the low design and implementation complexity of one-dimensional modulated filter banks (MFB), it is very desirable to study their nonseparable generalization to two dimensions (2D). In this paper, we introduce a new two-dimensional (2D) nonseparable generalization of the MFB. Each analysis filter is obtained by combining two shifted copies of the prototype filter. By considering a new cosine-modulation, it is found that the filterbank can achieve perfect reconstruction and has identical structure to the 1-D modulated filter banks. Bi-orthogonal and structural PR linear-phase cascade structures can also be obtained.

Motion compensation is an effective method for reducing temporal redundancy found in video sequence compression. However, the complexity of the full-search block matching algorithm (BMA) is extremely high and a number of fast algorithms have been proposed to reduce the computational complexity of the BMA. We propose three techniques for reducing the arithmetic complexity of the BMA. They are namely: (1) domain decimation, (2) error function subsampling, and (3) multiple candidates search. The domain decimation and error function decimation try to reduce the arithmetic complexity by reducing the number of operations in each comparison, the search range and number of locations searched. The multiple candidates search is a technique used to increase the robust of such algorithms. Combination of these techniques can generate algorithms with different tradeoff between arithmetic complexity and quality of prediction.

In this paper we present a fast algorithm to reduce the computational complexity of block motion estimation. The reduction is obtained from the use of a new hexagonal subsampling pattern and the domain decimation method introduced by Cheng and Chan (see Proc. IEEE ICASSP, vol.4, p.2313, 1996). The multiple candidates search method is also introduced to improve the robustness of the algorithm. Computer simulation shows that the performance is very close to that of the full search.

In this paper, a new buffer control algorithm for motion-compensated hybrid DPCM/DCT coding (like H.261 and MPEG-1 I pictures) is presented. The algorithm uses the bit allocation algorithm to determine the quantization scale factor of each macroblock under a given target bit rate. An important advantage of the algorithm is that it has precise control of the buffer and avoids buffer overflow events which is a severe problem in low bit rate video coders. Furthermore, the coder is able to allocate bits to the picture as a whole, resulting in better rate-distortion trade-off. Simulation results show that the H.261 coder, using the proposed algorithm, can achieve a higher PSNR and better visual quality than a codec using a conventional buffer control algorithm.

This paper proposes a new family of perfect reconstruction (PR) linear phase filter banks called the generalized lapped transform (GLT). The GLT differs from the traditional lapped orthogonal transform (LOT) in that it is nonorthogonal and hence offers more freedom to avoid blocking effects and improve the coding gain. Since the GLT can also be viewed as a generalization of the traditional discrete cosine transform (DCT), fast algorithms for their implementation are also available.

It is well known that efficient perfect reconstruction (PR) FIR filter banks can be obtained by modulating a prototype filter. We present a class of two-dimensional nonseparable rectangular sampling PR modulated filter banks. The modulation is constrained to be separable while the prototype filter is allowed to be nonseparable. This lead to considerably simplification in the PR condition and design procedure. The resulting PR condition on the prototype is equivalent to the 4 channels PR property of a set of polyphase components. A design example of a 4-channel length (8/spl times/8) orthogonal nonseparable filter is also given to illustrate the proposed method.>

This paper proposes a new family of perfect reconstruction (PR) filter banks called quadrature modulated filter banks (QMFB) by imposing structural constraints in the polyphase matrix. The single stage orthonormal QMFB is a complete modulated version of the two channel conjugate quadrature filters (CQF) because it is identical to the CQF when M=2. More general cascade linear phase orthogonal and nonorthogonal PR systems are also derived. In particular, we are able to obtain more general orthogonal and nonorthogonal versions of the Lapped Orthogonal Transform (LOT).>

Real-time image processing usually requires enormous throughput rate and huge amount of operations. Parallel processing in form of specialized hardware or multiprocessing are therefore indispensable. This paper describes a flexible programmable image processing system using Field Programmable Gate Array (FPGA). The logic cell nature of current available FPGA is most suitable for performing real-time bit-level image processing operations using bit-level systolic concept. Here, we proposed a flexible architecture, PIPS, for the integration of these programmable hardware and digital signal processors (DSP) to handle bit-level as well as arithmetic operations found in many image processing applications. The versatility of the system is demonstrated for the implementation of a 1D median filter.>