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Signal Processing Letters, IEEE

Early Access Articles

Early Access articles are new content made available in advance of the final electronic or print versions and result from IEEE's Preprint or Rapid Post processes. Preprint articles are peer-reviewed but not fully edited. Rapid Post articles are peer-reviewed and edited but not paginated. Both these types of Early Access articles are fully citable from the moment they appear in IEEE Xplore.

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Displaying Results 1 - 25 of 28
  • An efficient Jacobi-like deflationary ICA algorithm: application to EEG denoising

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    In this paper, we propose a Jacobi-like Deflationary ICA algorithm, named JDICA. More particularly, while a projection-based deflation scheme inspired by Delfosse and Loubaton’s ICA technique (DelLR) is used, a Jacobi-like optimization strategy is proposed in order to maximize a fourth order cumulant-based contrast built from whitened observations. Experimental results obtained from simulated epileptic EEG data mixed with a real muscular activity and from the comparison in terms of performance and numerical complexity with the FastICA, RobustICA and DelLR algorithms, show that the proposed algorithm offers the best trade-off between performance and numerical complexity when a low number ( 12) of electrodes is available. View full abstract»

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  • Compressed Sensing Performance of Random Bernoulli Matrices with High Compression Ratio

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    This paper studies the sensing performance of random Bernoulli matrices with column size n much larger than row size m. It is observed that as the compression ratio n=m increases, this kind of matrices tends to present a performance floor regarding the guaranteed signal sparsity. The performance floor is effectively estimated with the formula 12 (pm=2+1). To the best of our knowledge, it is the first time in compressed sensing, a theoretical estimation is successfully proposed to reflect the real performance. View full abstract»

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  • A Volumetric SRP with Refinement Step for Sound Source Localization

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    This paper proposes an efficient method based on the steered-response power (SRP) technique for sound source localization using microphone arrays: the refined volumetric SRP (RV-SRP). By deploying a sparser volumetric grid, the RV-SRP achieves a significant reduction of the computational complexity without sacrificing the accuracy of location estimates. In addition, a refinement step improves on the compromise between complexity and accuracy. Experiments conducted in both simulated- and real-data scenarios show that the RV-SRP outperforms state-of-the-art methods in accuracy with lower computational cost. View full abstract»

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  • Efficient Soft-Input Soft-Output MIMO Chase Detectors for Arbitrary Number of Streams

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    We present novel soft-input soft-output (SISO) multiple-input multiple-output (MIMO) detectors based on the Chase detection principle [1] in the context of iterative detection and decoding (IDD). The proposed detector complexity is linear in the signal modulation constellation size and the number of spatial streams. Two variants of the SISO detector are developed, referred to as SISO block decision-feedback Chase detector (BChase) and SISO linear Chase detector (L-Chase). An efficient method is presented that uses the decoder output to modulate the signal constellation decision boundaries inside the detector leading to the SISO detector architecture. The performance of these detectors significantly improves with just a few number of IDD iterations. The effect of transmit and receive antenna correlation is simulated. For the high-correlation case, the superiority of SISO B-Chase over the SISO L-Chase is demonstrated. View full abstract»

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  • Automatic Feeding Control for Dense Aquaculture Fish Tanks

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    This paper introduces an efficient visual signal processing system to continuously control the feeding process of fish in aquaculture tanks. The aim is to improve the production profit in fish farms by controlling the amount of feed at an optimal rate. The automatic feeding control includes two components: 1) a continuous decision on whether the fish are actively consuming feed, and 2) automatic detection of the number of excess feed populated on the water surface of the tank using a two-stage approach. The amount of feed is initially detected using the correlation filer applied to an optimum local region within the video frame, and then followed by a SVM-based refinement classifier to suppress the falsely detected feed. Having both measures allows us to accurately control the feeding process in an automated manner. Experimental results show that our system can accurately and efficiently estimate both measures. View full abstract»

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  • A Fine Resolution Frequency Estimator Based on Double Sub-segment Phase Difference

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    A DFT-based frequency estimator with low computational complexity is proposed, which is derived by extracting the phase difference from the peak DFT bins of two sub-segments from input samples. Some of its statistical properties, such as asymptotical unbiasedness and error variance, are derived. Furthermore, an iterative procedure of frequency estimate is also presented. Simulation results show that the proposed estimator’s accuracy is close to those of AM estimator and CO estimator. View full abstract»

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  • Training Design and Channel Estimation in Uplink Cloud Radio Access Networks

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    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (563 KB)  

    To decrease the training overhead and improve the channel estimation accuracy in uplink cloud radio access networks (C-RANs), a superimposed-segment training design is proposed. The core idea of the proposal is that each mobile station superimposes a periodic training sequence on the data signal, and each remote radio heads prepends a separate pilot to the received signal before forwarding it to the centralized base band unit pool. Moreover, a complex-exponential basis-expansion-model based channel estimation algorithm to maximize a posteriori probability is developed, where the basis-expansion-model coefficients of access links (ALs) and the channel fading of wireless backhaul links are first obtained, after which the time-domain channel samples of ALs are restored in terms of maximizing the average effective signal-to-noise ratio (AESNR). Simulation results show that the proposed channel estimation algorithm can effectively decrease the estimation mean square error and increase the AESNR in C-RANs, thus significantly outperforming the existing solutions. View full abstract»

    Open Access
  • A Statistical Method for Improved 3D Surface Detection

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    In this letter we present a new 3D statistical method for surface detection which provides improvements over competitive methods both in terms of noise suppression and detection of complete surfaces. The methods are applied to both synthetically created image volumes, and MRI data. Accuracy against a ground truth is assessed using the quantitative figure of merit performance measure, with the statistical methods outperforming both a 3D implementation of the gradient Canny operator and a 3D optimal steerable filter method. The results also confirm how 3D surface detection methods avoid missing surface information by successfully locating complete boundaries irrespective of the object orientation and plane of image capture. We conclude that the statistical 3D methods are capable of producing more accurate surface maps in textured images, that reflect the 3D boundary information, improving on current 2D and 3D standards. View full abstract»

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  • Expected Power Bound for Two-Dimensional Digital Filters in the Fornasini-Marchesini Local State-Space Model

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    This paper examines the expected power bound (EPB) for two-dimensional (2-D) digital filters in the Fornasini- Marchesini local state-space (FMLSS) model with Wiener process noise. The goal of this paper is to establish a new criterion whereby 2-D digital filters in the FMLSS form have a 2-D EPB. The criterion also ensures asymptotic stability without Wiener process noise. A numerical example demonstrates the usefulness of the proposed result. The criterion in the paper and that in [1] provide a systematic framework for EPB of 2-D digital filters. View full abstract»

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  • Locality-constrained Sparse Auto-Encoder for Image Classification

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (317 KB)  

    We propose a locality-constrained sparse auto-encoder (LSAE) for image classification in this paper. Previous work has shown that the locality is more essential than sparsity for classification task. We here introduce the concept of locality into auto-encoder, which enables the auto-encoder to encode similar inputs using similar features. The proposed LSAE can be trained by the existing backprop algorithm; no complicated optimization is involved. Experiments on the CIFAR-10, STL-10 and Caltech-101 datasets validate the effectiveness of LSAE for classification task. View full abstract»

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  • Real-Time Impulse Noise Suppression from Images Using an Efficient Weighted-Average Filtering

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    In this paper, we propose a method for real-time high density impulse noise suppression from images. In our method, we first apply an impulse detector to identify the corrupted pixels and then employ an innovative weighted-average filter to restore them. The filter takes the nearest neighboring interpolated image as the initial image and computes the weights according to the relative positions of the corrupted and uncorrupted pixels. Experimental results show that the proposed method outperforms the best existing methods in both PSNR measure and visual quality and is quite suitable for real-time applications. View full abstract»

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  • A Step Size Control Method for Deficient Length FBLMS Algorithm

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    In many practical situations, where the length of the system impulse response is extremely long, the adaptive filter usually works in an under-modeling situation. In our previous work, we found that for deficient-length frequencydomain block least-mean-square (FBLMS) algorithm, the steady-state solution depends on the step size selected. The deficient-length FBLMS algorithm will converge to the Wiener solution only if the same step size is selected for each frequency bin. Numerous variable step-size methods for FBLMS algorithm have been proposed. However, almost all of them cannot converge to the Wiener solution in the under-modeling situation. In this letter, a step size control method for deficient-length FBLMS algorithm is proposed. Effectiveness of the proposed algorithm is demonstrated through computer simulations. View full abstract»

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  • Sparse-Distinctive Saliency Detection

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4407 KB)  

    In this letter, we propose a novel saliency model for saliency detection, named sparse-distinctive (SD) saliency model. Different from the existing models that only consider sparsity or distinctness of image, the proposed model computes saliency based on sparsity and distinctness. The basic idea is that sparsity and distinctness contribute to saliency simultaneously and play different roles under different scenes. This sparse-distinctive saliency model is based on some key ideas introduced in this letter and supported by psychological evidence. Experimental results on public benchmark eye-tracking datasets show that considering the sparsity and distinctness for saliency can improve the accuracy of predicting human fixations, and the proposed model outperforms the mainstream models on predicting human fixations. View full abstract»

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  • Separation of Dependent Autoregressive Sources Using Joint Matrix Diagonalization

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    This letter proposes a novel technique for the blind separation of autoregressive (AR) sources. The latter relies on the joint diagonalization (JD) of appropriate AR matrix coefficients of the observed signals and can be applied to the separation of statistically dependent sources. The developed algorithm is referred to as ’DARSS-JD’ (for Dependent AR Source Separation using JD). Through the simulation experiments, DARSS-JD is shown to overcome existing second order separation methods with a relatively moderate computational cost. View full abstract»

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  • Near-Duplicate Image Retrieval Based on Contextual Descriptor

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    The state of the art of technology for near-duplicate image retrieval is mostly based on the Bagof- Visual-Words model. However, visual words are easy to result in mismatches because of quantization errors of the local features the words represent. In order to improve the precision of visual words matching, contextual descriptors are designed to strengthen their discriminative power and measure the contextual similarity of visual words. This paper presents a new contextual descriptor that measures the contextual similarity of visual words to immediately discard the mismatches and reduce the count of candidate images. The new contextual descriptor encodes the relationships of dominant orientation and spatial position between the referential visual words and their context. Experimental results on benchmark Copydays dataset demonstrate its efficiency and effectiveness for near-duplicate image retrieval. View full abstract»

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  • Component Extraction for Non-stationary Multi-component Signal Using Parameterized De-chirping and Band-pass Filter

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    In most applications, component extraction is important when components of non-stationary multi-component signal are key features to be monitored and analyzed. Existing methods are either sensitive to noise or forced to select a proper time-frequency representation for the considered signal. In this paper, we present a novel component extraction method for non-stationary multi-component signal. The proposed method combines parameterized de-chirping and band-pass filter to obtain components of multi-component signal, which avoids dealing with time-frequency representation of the signal and works well under heavy noise. In addition, it is able to analyze the multi-component signal whose components have intersected instantaneous frequency trajectories. Simulation results show that the proposed method is promising in analyzing complicated multi-component signals. Moreover, it works effective in a high noise environment in terms of improving the output signal-to-noise rate for the interested component. View full abstract»

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  • Salient Object Detection with Higher Order Potentials and Learning Affinity

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    In this paper, we propose a novel graph-based salient object detection algorithm which exploits higher order potential to capture the cross-scale grouping cues instead of using multi-scale graph model or naive multi-scale fusion (i.e. individually compute a saliency result for each scale and then combine them). And, we investigate the importance of graph affinities in graph labeling. We take both local (spatial distribution) and nonlocal (feature distribution) priors into account and learn the pairwise similarity values in a semisupervised manner, thereby obtaining a faithful graph affinity model. With the guidance of foreground and background seeds, salient object detection is formulated as a labeling inference problem. Extensive experiments on two large benchmark datasets demonstrate the proposed method performs well when against the state-of-the-art methods in terms of accuracy. View full abstract»

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  • MR Image Reconstruction with Convolutional Characteristic Constraint (CoCCo)

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    The problem of recovering an image from limited or sparsely sampled Fourier measurements occurs in the application of magnetic resonance imaging. To address this problem, we propose a novel MR image reconstruction method with convolutional characteristic constraints. We first estimate the convolutional characteristics using standard compressed sensing method in a parallel fashion. Then we use the recovered image characteristics to constrain the target image function. The image characteristics should either be sparser or of higher SNR than the original image to enable superior performance. In this work, we studied using thirteen kernels and experiments based on a brain data set were conducted. It is demonstrated that the proposed method outperforms the existing methods in terms of high quality imaging due to multiple characteristic constraints and the robustness to measurement noise. View full abstract»

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  • Low Cost Pre-coder Design for MIMO AF Two-way Relay Channel

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    In this letter, we revisit the pre-coder design problem for the amplify-and-forward two-way relay channel with multiple antennas equipped at each node. Based on generalized singular value decomposition (GSVD), we propose a novel pre-coding technique named Hermitian Relay Pre-coding (HRP) with a relatively low design complexity. Compared with the existing GSVD pre-coding method, HRP can achieve a higher rate by avoiding zero-forcing operation but keeping the same design complexity. View full abstract»

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  • Semi-Local Structure Patterns for Robust Face Detection

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    In many image processing and computer vision problems, including face detection, local structure patterns such as local binary patterns (LBP) and modified census transform (MCT) have been adopted in widespread applications due to their robustness against illumination changes. However, being reliant on the local differences between neighboring pixels, they are inevitably sensitive to noise. To overcome the problem of noise-vulnerability of the conventional local structure patterns, we propose semi-local structure patterns (SLSP), a novel feature extraction method based on local region-based differences. The SLSP is robust to illumination variations, distortion, and sparse noise because it encodes the relative sizes of the central region with locally neighboring regions into a binary code. The principle of SLSP leads noise-robust expansions of LBP and MCT feature extraction frameworks. In a statistical analysis, we find that the proposed methods transform a substantial amount of random noise patterns in face images into more meaningful uniform patterns. The empirical results on the MIT+CMU dataset and FDDB (face detection dataset and benchmark) show that the proposed semi-local patterns applied to LBP and MCT feature extraction frameworks outperform the conventional LBP and MCT features in AdaBoost-based face detectors, with much higher detection rates. View full abstract»

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  • Person Re-identification based on Spatiogram Descriptor and Collaborative Representation

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    Feature and metric designing are two vital aspects in person re-identification. In this letter, we firstly propose a novel spatiogram based person descriptor. Such spatiograms of different image regions from several color channels are calculated and accumulated to create a histogram vector and two distinctive spatial statistical vectors. Secondly, through further investigating the multi-shot set-based metric based on the recent collaborative representation model, we propose an effective and efficient multi-shot metric, which fuses the residual and coding coefficients after collaboratively coding samples on all person classes. Finally, we evaluate the proposed descriptor and metric with other published methods on benchmark datasets. Our methods not only achieve state-of-the-art results but also are novel, straightforward and computationally efficient, which will facilitate the real-time surveillance applications such as pedestrian tracking. View full abstract»

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  • A Generic Construction of Z-Periodic Complementary Sequence Sets with Flexible Flock Size and Zero Correlation Zone Length

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    Without limitation on the number of mates, Z-periodic complementary sequence (ZPCS) sets, which have potential applications in multi-carriers CDMA communication systems and MIMO channel estimation, can support more users than conventional complementary sequence sets. In this paper, a generic construction of ZPCS sets is proposed based on perfect sequences and orthogonal matrices. It generalizes the earlier constructions by Li et al., and produces new ZPCS sets which cannot generated by earlier ones. The parameters of our ZPCS sets are flexible and are thus suitable for different application scenarios. View full abstract»

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  • Visual tracking via temporally smooth sparse coding

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    Sparse representation has been popular in visual tracking recently for its robustness and accuracy. However, for most conventional sparse coding based trackers, the target candidates are considered independently between consecutive frames. This paper shows that the temporal correlation of these frames can be exploited to improve the performance of tracking and makes the tracker more robust to noise. Furthermore, to improve the tracking speed, we revisit a more efficient method for `1 norm problem, marginal regression, which can solve the sparse coding problem more efficiently. Consequently we can realize real-time tracking based on the temporal smooth sparse representation. Extensive experiments have been done to demonstrate the effectiveness and efficiency of our method. View full abstract»

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  • Adaptive Integral Operators for Signal Separation

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    The operator-based signal separation approach uses an adaptive operator to separate a signal into a set of additive subcomponents. In this paper, we show that differential operators and their initial and boundary values can be exploited to derive corresponding integral operators. Although the differential operators and the integral operators have the same null space, the latter are more robust to noisy signals. Moreover, after expanding the kernels of Frequency Modulated (FM) signals via eigen-decomposition, the operator-based approach with the integral operator can be regarded as the matched filter approach that uses eigen-functions as the matched filters. We then incorporate the integral operator into the Null Space Pursuit (NSP) algorithm to estimate the kernel and extract the subcomponent of a signal. To demonstrate the robustness and efficacy of the proposed algorithm, we compare it with several state-ofthe- art approaches in separating multiple-component synthesized signals and real-life signals. View full abstract»

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  • Subcategory-aware Object Detection

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    In this paper, we introduce a subcategory-aware object detection framework to detect generic object classes with high intra-class variace. Motivated by the observation that the object appearance demonstrates some clustering property, we split the training data into subcategories and train a detector for each subcategory. Since the proposed ensemble of detectors relies heavily on subcategory clustering, we propose an effective subcategories generation method that is tuned for the detection task. More specifically, we first initialize subcategories by constrained spectral clustering based on mid-level image features used in object recognition. Then we jointly learn the ensemble detectors and the latent subcategories in an alternative manner. Our performance on the PASCAL VOC 2007 detection challenges and INRIA Person dataset is comparable with state-of-the-art, even with much less computational cost. View full abstract»

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The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing.

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Editor-in-Chief
Peter Willett
University of Connecticut
Storrs, CT 06269
peter.willett@uconn.edu