ALBERT

Description: We examine a distributed detection problem in a wireless sensor network, where sensor nodes collaborate to detect a Gaussian signal with an unknown change of power, i.e., a scale parameter. Due to power/bandwidth constraints, we consider the case where each sensor quantizes its observation into a binary digit. The binary data are then transmitted through error-prone wireless links to a fusion center, where a generalized likelihood ratio test (GLRT) detector is employed to perform a global decision. We study the design of a binary quantizer based on an asymptotic analysis of the GLRT. Interestingly, the quantization threshold of the quantizer is independent of the unknown scale parameter. Numerical results are included to illustrate the performance of the proposed quantizer and GLRT in binary symmetric channels (BSCs).

Description: More and more hybrid electric vehicles are driven since they offer such advantages as energy savings and better active safety performance. Hybrid vehicles have two or more power driving systems and frequently switch working condition, so controlling stability is very important. In this work, a two-stage Kalman algorithm method is used to fuse data in hybrid vehicle stability testing. First, the RT3102 navigation system and Dewetron system are introduced. Second, a modeling of data fusion is proposed based on the Kalman filter. Then, this modeling is simulated and tested on a sample vehicle, using Carsim and Simulink software to test the results. The results showed the merits of this modeling.

Description: Histopathological grading of cancer not only offers an insight to the patients’ prognosis but also helps in making individual treatment plans. Mitosis counts in histopathological slides play a crucial role for invasive breast cancer grading using the Nottingham grading system. Pathologists perform this grading by manual examinations of a few thousand images for each patient. Hence, finding the mitotic figures from these images is a tedious job and also prone to observer variability due to variations in the appearances of the mitotic cells. We propose a fast and accurate approach for automatic mitosis detection from histopathological images. We employ area morphological scale space for cell segmentation. The scale space is constructed in a novel manner by restricting the scales with the maximization of relative-entropy between the cells and the background. This results in precise cell segmentation. The segmented cells are classified in mitotic and non-mitotic category using the random forest classifier. Experiments show at least 12% improvement in $F_{1}$ score on more than 450 histopathological images at $40times $ magnification.

Description: This paper proposes a fast multi-band image fusion algorithm, which combines a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image. The well admitted forward model is explored to form the likelihoods of the observations. Maximizing the likelihoods leads to solving a Sylvester equation. By exploiting the properties of the circulant and downsampling matrices associated with the fusion problem, a closed-form solution for the corresponding Sylvester equation is obtained explicitly, getting rid of any iterative update step. Coupled with the alternating direction method of multipliers and the block coordinate descent method, the proposed algorithm can be easily generalized to incorporate prior information for the fusion problem, allowing a Bayesian estimator. Simulation results show that the proposed algorithm achieves the same performance as the existing algorithms with the advantage of significantly decreasing the computational complexity of these algorithms.

Description: In recent years, baggage screening at airports has included the use of dual-energy X-ray computed tomography (DECT), an advanced technology for nondestructive evaluation. The main challenge remains to reliably find and identify threat objects in the bag from DECT data. This task is particularly hard due to the wide variety of objects, the high clutter, and the presence of metal, which causes streaks and shading in the scanner images. Image noise and artifacts are generally much more severe than in medical CT and can lead to splitting of objects and inaccurate object labeling. The conventional approach performs object segmentation and material identification in two decoupled processes. Dual-energy information is typically not used for the segmentation, and object localization is not explicitly used to stabilize the material parameter estimates. We propose a novel learning-based framework for joint segmentation and identification of objects directly from volumetric DECT images, which is robust to streaks, noise and variability due to clutter. We focus on segmenting and identifying a small set of objects of interest with characteristics that are learned from training images, and consider everything else as background. We include data weighting to mitigate metal artifacts and incorporate an object boundary field to reduce object splitting. The overall formulation is posed as a multilabel discrete optimization problem and solved using an efficient graph-cut algorithm. We test the method on real data and show its potential for producing accurate labels of the objects of interest without splits in the presence of metal and clutter.

Description: Feature point matching is a fundamental and challenging problem in many computer vision applications. In this paper, a robust feature point matching algorithm named spatial order constraints bilateral-neighbor vote (SOCBV) is proposed to remove outliers for a set of matches (including outliers) between two images. A directed ${k}$ nearest neighbor ( knn ) graph of match sets is generated, and the problem of feature point matching is formulated as a binary discrimination problem. In the discrimination process, the class labeled matrix is built via the spatial order constraints defined on the edges that connect a point to its knn . Then, the posterior inlier class probability of each match is estimated with the knn density estimation and spatial order constraints. The vote of each match is determined by averaging all posterior class probabilities that originate from its associative inliers set and is used for removing outliers. The algorithm iteratively removes outliers from the directed graph and recomputes the votes until the stopping condition is satisfied. Compared with other popular algorithms, such as RANSAC, RSOC, GTM, SOC and WGTM, experiments under various testing data sets demonstrate strong robustness for the proposed algorithm.

Description: Acoustic localization is an essential technique in speech capturing, speech enhancement, video conferencing, and human–robot interaction. However, in practical situations, localization has to be performed in abominable environments, where the presence of reverberation and noise degrades the performance of available position estimates. Besides, the designed systems should be adaptive to locomotion of targets with low computational complexity. In the context, this paper introduces a robust hierarchical acoustic localization method via time-delay compensation (TDC) and interaural matching filter (IMF). Firstly, interaural time-delay (ITD) and interaural level difference (ILD), which are cues involved in first two layers, respectively, are yielded by TDC all at once. Then, a novel feature named IMF, which can eliminate the difference between binaural signals, is proposed in the third layer. The final decision making is based on a Bayesian rule. The relationships among the three layers are that the former layer provides candidate directions for later ones such that the searching space becomes gradually smaller to reduce matching time. Experiments using both a public database and a real scenario verify that TDC and IMF are robust for acoustic localization, and hierarchical system has less consumption time.

Description: Canonical correlation analysis (CCA) is a widely used data analysis tool that allows to assess the correlation between two distinct sets of signals. It computes optimal linear combinations of the signals in both sets such that the resulting signals are maximally correlated. The weight vectors defining these optimal linear combinations are referred to as “principal CCA directions”. In addition to this particular type of data analysis, CCA is also often used as a blind source separation (BSS) technique, i.e., under certain assumptions, the principal CCA directions have certain demixing properties. In this paper, we propose a distributed CCA (DCCA) algorithm that can operate in wireless sensor networks (WSNs) with a fully connected or a tree topology. The algorithm estimates the $Q$ principal CCA directions from the sensor signal observations collected by the different nodes in the WSN and extracts the corresponding sources. These network-wide principal CCA directions are estimated in a time-recursive fashion without explicitly constructing the corresponding network-wide correlation matrices, i.e., without the need for data centralization. Instead, each node locally computes smaller CCA problems and only transmits compressed sensor signal observations (of dimension $Q$ ), which significantly reduces the bit rate over the wireless links of the WSN. We prove convergence and optimality of the DCCA algorithm, and we demonstrate its performance by means of numerical simulations in a blind source separation scenario.

Description: In this paper, a comprehensive comparison analysis in terms of outage probability and average symbol error ratio (SER) is presented for cooperative cognitive multiple-input and multiple-output (CC-MIMO) multiuser systems with amplify-and-forward (AF) protocol. Specially, we consider two scenarios where the CC-MIMO multiuser systems have the perfect and imperfect channel state information (CSI). The CC-MIMO multiuser systems consist of one multi-antenna source, one single-antenna relay, and multiple multi-antenna destinations. At the secondary source and destinations, the maximal ratio transmission (MRT) and maximal ratio combining (MRC) are employed, respectively. For such CC-MIMO multiuser systems, we first obtain the exact closed-form expressions of outage probability under the two cases where the CC-MIMO multiuser systems have the perfect and imperfect CSI. Then, to reduce the implementation complexity, the tight lower bounds of outage probability and average SER are derived. Finally, to obtain insight, by using the high signal-to-noise ratio (SNR) approximation, the asymptotic estimations of outage probability are achieved. The numerical results show that the derivations are agreed with the simulations, which validate our derivations. At the same time, the results show that, for the systems without perfect CSI, the achievable diversity order reduces to one, regardless of the number of antennas at the cognitive source and destinations as well as the number of the cognitive destinations. Nevertheless, these key parameters affect the coding gain of the CC-MIMO multiuser systems. When the systems have the perfect CSI (or without feedback delay), the achievable diversity gain is determined by the minimum between the number of source’s antennas and the product of the number of destinations and the number of destination’s antennas. For the effect of PU’s parameters, our results indicate that primary systems only affect the coding gain but not the diversity gain.

Description: Currently deep learning has made great breakthroughs in visual and speech processing, mainly because it draws lessons from the hierarchical mode that brain deals with images and speech. In the field of NLP, a topic model is one of the important ways for modeling documents. Topic models are built on a generative model that clearly does not match the way humans write. In this paper, we propose Event Model, which is unsupervised and based on the language processing mechanism of neurolinguistics, to model documents. In Event Model, documents are descriptions of concrete or abstract events seen, heard, or sensed by people and words are objects in the events. Event Model has two stages: word learning and dimensionality reduction. Word learning is to learn semantics of words based on deep learning. Dimensionality reduction is the process that representing a document as a low dimensional vector by a linear mode that is completely different from topic models. Event Model achieves state-of-the-art results on document retrieval tasks.

Description: There has been much research on shrinkage methods for real-valued covariance matrices and their inverses (precision matrices). In spectral analysis of $p$ -vector-valued time series, complex-valued spectral matrices and precision matrices arise, and good shrinkage methods are often required, most notably when the estimated complex-valued spectral matrix is singular. As an improvement on the Ledoit-Wolf (LW) type of spectral matrix estimator we use random matrix theory to derive a Rao-Blackwell estimator for a spectral matrix, its inverse being a Rao–Blackwellized estimator for the spectral precision matrix. A random matrix method has previously been proposed for complex-valued precision matrices. It was implemented by very costly simulations. We formulate a fast, completely analytic approach. Moreover, we derive a way of selecting an important parameter using predictive risk methodology. We show that both the Rao–Blackwell estimator and the random matrix estimator of the precision matrix can substantially outperform the inverse of the LW estimator in a time series setting. Our new methodology is applied to EEG-derived time series data where it is seen to work well and deliver substantial improvements for precision matrix estimation.

Description: In this paper, the state estimation problem for discrete-time linear systems influenced by multiplicative and time-correlated additive measurement noises is considered where the multiplicative noises are zero-mean white noise sequences, and the time-correlated additive noise is described by a linear system model with white noise. An optimal linear estimator for the system under consideration is proposed, which does not require computing the inverse of state transition matrix. The proposed estimator has a recursive structure, and has time-independent computation and storage load. Computer simulations are carried out to demonstrate the performance of the proposed estimator. The simulation results show the superiority of the proposed estimator.

Description: In this paper, we start with the standard support vector machine (SVM) formulation and extend it by considering a general SVM formulation with normalized margin. This results in a unified convex framework that allows many different variations in the formulation with very diverse numerical performance. The proposed unified framework can capture the existing methods, i.e., standard soft-margin SVM, $ell_{1}$ -SVM, and SVMs with standardization, feature selection, scaling, and many more SVMs, as special cases. Furthermore, our proposed framework can not only provide us with more insights on different SVMs from the “energy” and “penalty” point of views, which help us understand the connections and differences between them in a unified way, but also enable us to propose more SVMs that outperform the existing ones under some scenarios.

Description: This paper presents a novel low-complexity motion estimation and mode decision algorithm for encoding multiple quality layers following the H.264/scalable video coding standard, considering both coarse grain scalability (CGS) and medium grain scalability (MGS). The proposed algorithm conducts motion estimation and mode decision only at the base layer (BL) and enforces the higher layers to inherit the motion and mode decisions of the BL. In order for the decision made at the BL to be nearly optimal for all layers, we use the highest layer reconstructed frame as the reference frame for motion estimation and set the Lagrangian multipliers according to the quantization parameter of the current and higher layers. We also propose a simple early skip/direct decision to further boost the encoding speed. Mode decision and motion estimation is conducted at a higher layer only if the layer below it uses the skip/direct mode for a block. Significant complexity reduction can be achieved because the mode and motion estimation is performed at most once for each macroblock. Because the mode and motion information only needs to be transmitted once, we also achieve a slightly better rate-distortion (R–D) performance for typical videos. Experiments have shown more than $2times $ (up to $5times $ ) speedup for a three-layer encoder against the conventional R–D optimized reference software JSVM on both CIF and HD sequences, and for both CGS and MGS, with the tradeoff of the coding efficiency measured by the Bjontegaard delta rate.

Description: We consider multiple-antenna signal detection of primary user transmission signals by a secondary user receiver in cognitive radio networks. The optimal detector is analyzed for the scenario where the number of primary user signals is no less than the number of receive antennas at the secondary user. We first derive exact expressions for the moments of the generalized likelihood ratio test (GLRT) statistic, yielding approximations for the false alarm and detection probabilities. We then show that the normalized GLRT statistic converges in distribution to a Gaussian random variable when the number of antennas and observations grow large at the same rate. Further, using results from large random matrix theory, we derive expressions to compute the detection probability without explicit knowledge of the channel, and then particularize these expressions for two scenarios of practical interest: 1) a single primary user sending spatially multiplexed signals, and 2) multiple spatially distributed primary users. Our analytical results are finally used to obtain simple design rules for the signal detection threshold.

Description: Community detection in a complex network is an important problem of much interest in recent years. In general, a community detection algorithm chooses an objective function and captures the communities of the network by optimizing the objective function, and then, one uses various heuristics to solve the optimization problem to extract the interesting communities for the user. In this article, we demonstrate the procedure to transform a graph into points of a metric space and develop the methods of community detection with the help of a metric defined for a pair of points. We have also studied and analyzed the community structure of the network therein. The results obtained with our approach are very competitive with most of the well-known algorithms in the literature, and this is justified over the large collection of datasets. On the other hand, it can be observed that time taken by our algorithm is quite less compared to other methods and justifies the theoretical findings.

Description: In this paper, we propose a novel unifying framework using a Markov network to learn the relationships among multiple classifiers. In face recognition, we assume that we have several complementary classifiers available, and assign observation nodes to the features of a query image and hidden nodes to those of gallery images. Under the Markov assumption, we connect each hidden node to its corresponding observation node and the hidden nodes of neighboring classifiers. For each observation-hidden node pair, we collect the set of gallery candidates most similar to the observation instance, and capture the relationship between the hidden nodes in terms of a similarity matrix among the retrieved gallery images. Posterior probabilities in the hidden nodes are computed using the belief propagation algorithm, and we use marginal probability as the new similarity value of the classifier. The novelty of our proposed framework lies in the method that considers classifier dependence using the results of each neighboring classifier. We present the extensive evaluation results for two different protocols, known and unknown image variation tests, using four publicly available databases: 1) the Face Recognition Grand Challenge ver. 2.0; 2) XM2VTS; 3) BANCA; and 4) Multi-PIE. The result shows that our framework consistently yields improved recognition rates in various situations.

Description: Ellipse fitting is widely applied in the fields of computer vision and automatic manufacture. However, the introduced edge point errors (especially outliers) from image edge detection will cause severe performance degradation of the subsequent ellipse fitting procedure. To alleviate the influence of outliers, we develop a robust ellipse fitting method in this paper. The main contributions of this paper are as follows. First, to be robust against the outliers, we introduce the maximum correntropy criterion into the constrained least-square (CLS) ellipse fitting method, and apply the half-quadratic optimization algorithm to solve the nonlinear and nonconvex problem in an alternate manner. Second, to ensure that the obtained solution is related to an ellipse, we introduce a special quadratic equality constraint into the aforementioned CLS model, which results in the nonconvex quadratically constrained quadratic programming problem. Finally, we derive the semidefinite relaxation version of the aforementioned problem in terms of the trace operator and thus determine the ellipse parameters using semidefinite programming. Some simulated and experimental examples are presented to illustrate the effectiveness of the proposed ellipse fitting approach.

Description: State-of-the-art web image search frameworks are often based on the bag-of-visual-words (BoVWs) model and the inverted index structure. Despite the simplicity, efficiency, and scalability, they often suffer from low precision and/or recall, due to the limited stability of local features and the considerable information loss on the quantization stage. To refine the quality of retrieved images, various postprocessing methods have been adopted after the initial search process. In this paper, we investigate the online querying process from a graph-based perspective. We introduce a heterogeneous graph model containing both image and feature nodes explicitly, and propose an efficient reranking approach consisting of two successive modules, i.e., incremental query expansion and image-feature voting, to improve the recall and precision, respectively. Compared with the conventional reranking algorithms, our method does not require using geometric information of visual words, therefore enjoys low consumptions of both time and memory. Moreover, our method is independent of the initial search process, and could cooperate with many BoVW-based image search pipelines, or adopted after other postprocessing algorithms. We evaluate our approach on large-scale image search tasks and verify its competitive search performance.

Description: A three-step iterative method with fifth-order convergence as a new modification of Newton’s method was presented. This method is for finding multiple roots of nonlinear equation with unknown multiplicity m whose multiplicity m is the highest multiplicity. Its order of convergence is analyzed and proved. Results for some numerical examples show the efficiency of the new method.

Description: The study of fluid flow through solid matter by computed tomography (CT) imaging has many applications, ranging from petroleum and aquifer engineering to biomedical, manufacturing, and environmental research. To avoid motion artifacts, current experiments are often limited to slow fluid flow dynamics. This severely limits the applicability of the technique. In this paper, a new iterative CT reconstruction algorithm for improved a temporal/spatial resolution in the imaging of fluid flow through solid matter is introduced. The proposed algorithm exploits prior knowledge in two ways. First, the time-varying object is assumed to consist of stationary (the solid matter) and dynamic regions (the fluid flow). Second, the attenuation curve of a particular voxel in the dynamic region is modeled by a piecewise constant function over time, which is in accordance with the actual advancing fluid/air boundary. Quantitative and qualitative results on different simulation experiments and a real neutron tomography data set show that, in comparison with the state-of-the-art algorithms, the proposed algorithm allows reconstruction from substantially fewer projections per rotation without image quality loss. Therefore, the temporal resolution can be substantially increased, and thus fluid flow experiments with faster dynamics can be performed.

Description: Most existing approaches for RGB-D indoor scene labeling employ hand-crafted features for each modality independently and combine them in a heuristic manner. There has been some attempt on directly learning features from raw RGB-D data, but the performance is not satisfactory. In this paper, we propose an unsupervised joint feature learning and encoding (JFLE) framework for RGB-D scene labeling. The main novelty of our learning framework lies in the joint optimization of feature learning and feature encoding in a coherent way, which significantly boosts the performance. By stacking basic learning structure, higher level features are derived and combined with lower level features for better representing RGB-D data. Moreover, to explore the nonlinear intrinsic characteristic of data, we further propose a more general joint deep feature learning and encoding (JDFLE) framework that introduces the nonlinear mapping into JFLE. The experimental results on the benchmark NYU depth dataset show that our approaches achieve competitive performance, compared with the state-of-the-art methods, while our methods do not need complex feature handcrafting and feature combination and can be easily applied to other data sets.

Description: Out-of-focus blur occurs frequently in multispectral imaging systems when the camera is well focused at a specific (reference) imaging channel. As the effective focal lengths of the lens are wavelength dependent, the blurriness levels of the images at individual channels are different. This paper proposes a multispectral image deblurring framework to restore out-of-focus spectral images based on the characteristic of interchannel correlation (ICC). The ICC is investigated based on the fact that a high-dimensional color spectrum can be linearly approximated using rather a few number of intrinsic spectra. In the method, the spectral images are classified into an out-of-focus set and a well-focused set via blurriness computation. For each out-of-focus image, a guiding image is derived from the well-focused spectral images and is used as the image prior in the deblurring framework. The out-of-focus blur is modeled as a Gaussian point spread function, which is further employed as the blur kernel prior. The regularization parameters in the image deblurring framework are determined using generalized cross validation, and thus the proposed method does not need any parameter tuning. The experimental results validate that the method performs well on multispectral image deblurring and outperforms the state of the arts.

Description: This paper presents an optimized low-complexity and high-throughput MIMO signal detector core for detecting spatially multiplexed data streams. The core architecture supports various layer configurations up to 4, while achieving near-optimal performance, and configurable modulation constellations up to 256-QAM on each layer. The core is capable of operating as a soft-input soft-output log-likelihood ratio (LLR) MIMO detector which can be used in the context of iterative detection and decoding. High area-efficiency is achieved via algorithmic and architectural optimizations performed at two levels. First, distance computations and slicing operations for an optimal 2-layer maximum a posteriori MIMO detector are optimized to eliminate use of multipliers and reduce the overhead of slicing in the presence of soft-input LLRs. We show that distances can be easily computed using elementary addition operations, while optimal slicing is done via efficient comparisons with soft decision boundaries, resulting in a simple feed-forward pipelined architecture. Second, to support more layers, an efficient channel decomposition scheme is presented that reduces the detection of multiple layers into multiple 2-layer detection subproblems, which map onto the 2-layer core with a slight modification using a distance accumulation stage and a post-LLR processing stage. Various architectures are accordingly developed to achieve a desired detection throughput and run-time reconfigurability by time-multiplexing of one or more component cores. The proposed core is applied also to design an optimal multiuser MIMO detector for LTE. The core occupies an area of 1.58 MGE and achieves a throughput of 733 Mbps for 256-QAM when synthesized in 90-nm CMOS.

Description: To reduce the energy cost of underwater acoustic sensor networks (UWSNs), the duty cycle (i.e., periodic wake-up and sleep) concept has been used in several medium access control (MAC) protocols. Although these protocols are energy efficient, they sacrifice bandwidth utilization, which leads to lower transmission rate. In order to solve this problem, asynchronous duty cycle with network-coding Asynchronous Duty Cycle with Network-Coding MAC (ADCNC-MAC) is proposed. It contains initialization of the MAC protocol phase and data transmission phase. In the first phase, we use an asynchronous duty cycle to find a rendezvous time for exchanging data. A strategy to select network coder nodes is presented to confirm the number of network coder nodes and distribution in the network coder layer. In the data transmission phase, the network coder nodes transmit using the proposed network-coding-based algorithm and a higher volume of packet will be transmitted to the Sink with the same number of transmissions. Simulation results show that ADCNC-MAC achieves higher power efficiency, improves packet delivery ratio (PDR), and network throughput.

Description: We study a tandem of agents who make decisions about an underlying binary hypothesis, where the distribution of the agent observations under each hypothesis comes from an uncertainty class defined by a 2-alternating capacity. We investigate both decentralized detection rules, where agents collaborate to minimize the error probability of the final agent, and social learning rules, where each agent minimizes its own local minimax error probability. We then extend our results to the infinite tandem network, and derive necessary and sufficient conditions on the uncertainty classes for the minimax error probability to converge to zero when agents know their positions in the tandem. On the other hand, when agents do not know their positions in the network, we study the cases where agents collaborate to minimize the asymptotic minimax error probability, and where agents seek to minimize their worst-case minimax error probability (over all possible positions in the tandem). We show that asymptotic learning of the true hypothesis is no longer possible in these cases, and derive characterizations for the minimax error performance.

Description: Various blind synchronization methods built on the maximum likelihood (ML) principle have been proposed, where the addressed scenarios include additive white Gaussian noise (AWGN), single-path fading, and multipath fading channels. We consider ML blind synchronization over wide-sense stationary uncorrelated scattering (WSSUS) channels. Different from existing studies, we exploit a more complete signal correlation function and find the carrier frequency offset estimate to be the solution of a quartic equation, rather than the phase angle of a complex number. As the truly ML synchronizer (dubbed MLE) is very complicated, we also derive a reduced-complexity alternative (dubbed RCE). It is found that the RCE yields indistinguishable performance from the MLE, at a somewhat lower complexity than an existing rival. We also present an in-depth theoretical analysis and comparison of the performance of various methods. Simulations show that the proposed methods yield rather robust performance in modeling errors of the fading rate and the channel power-delay profile (PDP).

Description: In this paper, the performance of cloud radio access networks (CRANs) where spatially distributed remote radio heads (RRHs) aid the macro base station (MBS) in transmission is analysed. In order to reflect a realistic scenario, the MBS and the RRHs are assumed to be equipped with multiple antennas and distributed according to a Poisson point process. Both, the MBS and the RRHs, are assumed to employ maximal ratio transmission (MRT) or transmit antenna selection (TAS). Considering downlink transmission, the outage performance of three schemes is studied; first is the selection transmission (ST) scheme, in which the MBS or the RRH with the best channel is selected for transmission. In the second scheme, all the RRHs participate (ARP) and transmit the signal to the user, whereas in the third scheme, a minimal number of RRHs, to attain a desired data-rate, participate in transmission (MRP). Exact closed-form expression for the outage probability is derived for the ST scheme. For the ARP and MRP schemes, analytical approximations of the outage probability are derived which are tight at high signal-to-noise ratios. In addition, for the MRP scheme, the minimal number of RRHs required to meet a target data rate is also calculated which can be useful in characterizing the system complexity. Furthermore, the derived expressions are validated through numerical simulation. It is shown that the average diversity gains of these schemes are independent of the intensity/number of RRHs and only depend on the number of antennas on the MBS. Furthermore, the ARP scheme outperforms the ST scheme when the MBS/RRHs transmit with maximum power. However, in case of a sum power constraint and equal power allocation, the ST scheme outperforms the ARP scheme.

Description: This paper proposes a dynamic resource allocation scheme to exploit the mixed timescale channel state information (CSI) knowledge structure in a multi-antenna base station-assisted device-to-device (D2D) network. The short-term multi-antenna beamforming control at each transmit device is adaptive to the local real-time CSI. The long-term routing and flow control is adaptive to the global topology and the long-term global CSI statistics of the D2D network. The design objective is to maximize a network utility function subject to the average transmit power constraint, the flow balance constraints and the instantaneous physical layer capacity constraints. The mixed timescale problem can be decomposed into a short-term beamforming control problem and a long-term flow and routing control problem. Using the stochastic cutting plane, we propose a low complexity, self-learning algorithm, which converges to the global optimal solution without explicit knowledge of the channel statistics. Simulation illustrates performance gains with several baselines.

Description: This paper considers the estimation of multi-scale multi-lag (MSML) channels. The MSML channel model is a good representation for wideband communication channels, such as underwater acoustic communication and radar. This model is characterized by a limited number of paths, each parameterized by a delay, Doppler scale, and attenuation factor. Herein, it is shown that an OFDM signal after passing through the MSML channel exhibits a low rank representation. This feature can be exploited to improve the channel estimation. By characterizing the received signal, it is shown that the MSML channel estimation problem can be adapted to a structured spectral estimation problem. The challenge is that the unknown frequencies are very close to each other due to the small values of Doppler scales. This feature can be employed to show that the data matrix is approximately low-rank. By exploiting structural features of the received signal, the Prony algorithm is modified to estimate the Doppler scales (close frequencies), delays and channel gains. Two strategies using convex and no-convex regularizers to remove noise from the corrupted signal are proposed. These algorithms are iterative based on the alternating direction method of multipliers. A bound on the reconstruction of the noiseless received signal provides guidance on the selection of the relaxation parameter in the optimizations. The performance of the proposed estimation strategies are investigated via numerical simulations, and it is shown that the proposed non-convex method offers up to 7 dB improvement in low SNR and the convex method offers up to 5 dB improvement in high SNR over prior methods for the MSML channel estimation.

Description: We present a novel spatiotemporal saliency detection method to estimate salient regions in videos based on the gradient flow field and energy optimization. The proposed gradient flow field incorporates two distinctive features: 1) intra-frame boundary information and 2) inter-frame motion information together for indicating the salient regions. Based on the effective utilization of both intra-frame and inter-frame information in the gradient flow field, our algorithm is robust enough to estimate the object and background in complex scenes with various motion patterns and appearances. Then, we introduce local as well as global contrast saliency measures using the foreground and background information estimated from the gradient flow field. These enhanced contrast saliency cues uniformly highlight an entire object. We further propose a new energy function to encourage the spatiotemporal consistency of the output saliency maps, which is seldom explored in previous video saliency methods. The experimental results show that the proposed algorithm outperforms state-of-the-art video saliency detection methods.

Description: Hyperspectral unmixing is one of the crucial steps for many hyperspectral applications. The problem of hyperspectral unmixing has proved to be a difficult task in unsupervised work settings where the endmembers and abundances are both unknown. In addition, this task becomes more challenging in the case that the spectral bands are degraded by noise. This paper presents a robust model for unsupervised hyperspectral unmixing. Specifically, our model is developed with the correntropy-based metric where the nonnegative constraints on both endmembers and abundances are imposed to keep physical significance. Besides, a sparsity prior is explicitly formulated to constrain the distribution of the abundances of each endmember. To solve our model, a half-quadratic optimization technique is developed to convert the original complex optimization problem into an iteratively reweighted nonnegative matrix factorization with sparsity constraints. As a result, the optimization of our model can adaptively assign small weights to noisy bands and put more emphasis on noise-free bands. In addition, with sparsity constraints, our model can naturally generate sparse abundances. Experiments on synthetic and real data demonstrate the effectiveness of our model in comparison to the related state-of-the-art unmixing models.

Description: The possibility of having information access anytime and anywhere has caused a huge increase of the popularity of wireless networks. Requirements of users and owners have been ever-increasing. However, concerns about the potential health impact of exposure to radio frequency (RF) sources have arisen and are getting accounted for in wireless network planning. In addition to adequate coverage and reduced human exposure, the installation cost of the wireless network is also an important criterion in the planning process. In this paper, a hybrid algorithm is used to optimize indoor wireless network planning while satisfying three demands: maximum coverage, minimal full installation cost (cabling, cable gutters, drilling holes, labor, etc.), and minimal human exposure. For the first time, wireless indoor networks are being optimized based on these advanced and realistic conditions. The algorithm is investigated for three scenarios and for different configurations. The impact of different exposure requirements and cost scenarios is assessed.

Description: Physical transceiver implementations for wireless communication systems usually suffer from transmit-radio frequency (Tx-RF) and receiver-RF (Rx-RF) impairments. In this paper, we aim to design efficient coordinated beamforming for multicell multiuser multi-antenna systems by fully taking into account the residual transceiver impairments. Our design objectives include both spectral efficiency and energy efficiency. In particular, we first derive the closed-form expression of the mean square error (MSE) which includes the impact of transceiver impairments. Based on that, we propose an alternating optimization algorithm to solve the coordinated multicell beamforming problems with the goal of minimizing the worst user MSE, and the sum MSE. Then, by exploiting the relationship between the minimum mean square error (MMSE) and the achievable rate, we develop a new algorithm to address the sum rate maximization problem. This approach is further generalized to solve the more intractable energy efficiency optimization problem. We prove that all the proposed iterative algorithms guarantee to converge to a stationary point. Numerical results show that our proposed schemes achieve a better performance than conventional coordinated beamforming algorithms that were designed ignoring the transceiver impairments.

Description: Distributed processing over networks relies on in-network processing and cooperation among neighboring agents. Cooperation is beneficial when agents share a common objective. However, in many applications, agents may belong to different clusters that pursue different objectives. Then, indiscriminate cooperation will lead to undesired results. In this paper, we propose an adaptive clustering and learning scheme that allows agents to learn which neighbors they should cooperate with and which other neighbors they should ignore. In doing so, the resulting algorithm enables the agents to identify their clusters and to attain improved learning and estimation accuracy over networks. We carry out a detailed mean-square analysis and assess the error probabilities of Types I and II, i.e., false alarm and misdetection, for the clustering mechanism. Among other results, we establish that these probabilities decay exponentially with the step-sizes so that the probability of correct clustering can be made arbitrarily close to one.

Description: Robust Chinese remainder theorem (CRT) has been recently investigated for both integers and real numbers, where the folding integers are accurately recovered from erroneous remainders. In this paper, we consider the CRT problem for real numbers with noisy remainders that follow wrapped Gaussian distributions. We propose the maximum-likelihood estimation (MLE) based CRT when the remainder noises may not necessarily have the same variances. Furthermore, we present a fast algorithm for the MLE based CRT algorithm that only needs to search for the solution among $L$ elements, where $L$ is the number of remainders. Then, a necessary and sufficient condition on the remainder errors for the MLE CRT to be robust is obtained, which is weaker than the existing result. Finally, we compare the performances of the newly proposed algorithm and the existing algorithm in terms of both theoretical analysis and numerical simulations. The results demonstrate that the proposed algorithm not only has a better performance especially when the remainders have different error levels/variances, but also has a much lower computational complexity.

Description: Sparse signal restoration is usually formulated as the minimization of a quadratic cost function $Vert { mbi { y}} - { mbi { A}} { mbi { x}} Vert_{2}^{2}$ where $ { mbi { A}} $ is a dictionary and $ { mbi { x}} $ is an unknown sparse vector. It is well-known that imposing an $ell _{0}$ constraint leads to an NP-hard minimization problem. The convex relaxation approach has received considerable attention, where the $ell _{0}$ -norm is replaced by the $ell _{1}$ -norm. Among the many effective $ell _{1}$ solvers, the homotopy algorithm minimizes $Vert { mbi { y}} - { mbi { A}} { mbi { x}} Vert_{2}^{2}+lambda Vert { mbi { x}} Vert _{1}$ with respect to $ { mbi { x}} $ for a continuum of $lambda $ ’s. It is inspired by the piecewise regularity of the $ell _{1}$ -regularization path, also referred to as the homotopy path. In this paper, we address the minimization problem $Vert { mbi { y}} - { mbi { A}} { mbi { x}} Vert_{2}^{2}+lambda Vert { mbi { x}} Vert _{0}$ for a continuum of $lambda $ ’s and propose two heuristic search algorithms for $ell _{0}$ -homotopy. Continuation Single Best Replacement is a forward–backward greedy strategy extending the Single Best Replacement algorithm, previously proposed for $ell _{0}$ -minimization at a given $lambda $ . The adaptive search of the $lambda $ -values is inspired by $ell _{1}$ -homotopy. $ell _{0}$ Regularization Path Descent is a more complex algorithm exploiting the structural properties of the $ell _{0}$ -regularization path, which is piecewise constant with respect to $lambda $ . Both algorithms are empirically evaluated for difficult inverse problems involving ill-conditioned dictionaries. Finally, we show that they can be easily coupled with usual methods of model order selection.

Description: In recent work, robust Principal Components Analysis (PCA) has been posed as a problem of recovering a low-rank matrix ${bf L}$ and a sparse matrix ${bf S}$ from their sum, ${bf M}:= {bf L} + {bf S}$ and a provably exact convex optimization solution called PCP has been proposed. This work studies the following problem. Suppose that we have partial knowledge about the column space of the low rank matrix ${bf L}$ . Can we use this information to improve the PCP solution, i.e., allow recovery under weaker assumptions? We propose here a simple but useful modification of the PCP idea, called modified-PCP, that allows us to use this knowledge. We derive its correctness result which shows that, when the available subspace knowledge is accurate, modified-PCP indeed requires significantly weaker incoherence assumptions than PCP. Extensive simulations are also used to illustrate this. Comparisons with PCP and other existing work are shown for a stylized real application as well. Finally, we explain how this problem naturally occurs in many applications involving time series data, i.e., in what is called the online or recursive robust PCA problem. A corollary for this case is also given.

Description: Phased array is widely used in radar systems with its beam steering fixed in one direction for all ranges. Therefore, the range of a target cannot be determined within a single pulse when range ambiguity exists. In this paper, an unambiguous approach for joint range and angle estimation is devised for multiple-input multiple-output (MIMO) radar with frequency diverse array (FDA). Unlike the traditional phased array, FDA is capable of employing a small frequency increment across the array elements. Because of the frequency increment, the transmit steering vector of the FDA-MIMO radar is a function of both range and angle. As a result, the FDA-MIMO radar is able to utilize degrees-of-freedom in the range-angle domains to jointly determine the range and angle parameters of the target. In addition, the Cramér–Rao bounds for range and angle are derived, and the coupling between these two parameters is analyzed. Numerical results are presented to validate the effectiveness of the proposed approach.

Description: In this paper, we propose a novel single-group multicasting relay beamforming scheme. We assume a source that transmits common messages via multiple amplify-and-forward relays to multiple destinations. To increase the number of degrees of freedom in the beamforming design, the relays process two received signals jointly and transmit the Alamouti space-time block code over two different beams. Furthermore, in contrast to the existing relay multicasting scheme of the literature, we take into account the direct links from the source to the destinations. We aim to maximize the lowest received quality-of-service by choosing the proper relay weights and the ideal distribution of the power resources in the network. To solve the corresponding optimization problem, we propose an iterative algorithm which solves sequences of convex approximations of the original non-convex optimization problem. Simulation results demonstrate significant performance improvements of the proposed methods as compared with the existing relay multicasting scheme of the literature and an algorithm based on the popular semidefinite relaxation technique.

Description: In this paper, we propose a new class of iteratively re-weighted least squares (IRLS) for sparse recovery problems. The proposed methods are inspired by constrained maximum-likelihood estimation under a Gaussian scale mixture (GSM) distribution assumption. In the noise-free setting, we provide sufficient conditions ensuring the convergence of the sequences generated by these algorithms to the set of fixed points of the maps that rule their dynamics and derive conditions verifiable a posteriori for the convergence to a sparse solution. We further prove that these algorithms are quadratically fast in a neighborhood of a sparse solution. We show through numerical experiments that the proposed methods outperform classical IRLS for $ell_{tau}$ -minimization with $tauin(0,1]$ in terms of speed and of sparsity-undersampling tradeoff and are robust even in presence of noise. The simplicity and the theoretical guarantees provided in this paper make this class of algorithms an attractive solution for sparse recovery problems.

Description: We consider the problem of approximating optimal in the Minimum Mean Squared Error (MMSE) sense nonlinear filters in a discrete time setting, exploiting properties of stochastically convergent state process approximations. More specifically, we consider a class of nonlinear, partially observable stochastic systems, comprised by a (possibly nonstationary) hidden stochastic process (the state), observed through another conditionally Gaussian stochastic process (the observations). Under general assumptions, we show that, given an approximating process which, for each time step, is stochastically convergent to the state process, an approximate filtering operator can be defined, which converges to the true optimal nonlinear filter of the state in a strong and well defined sense. In particular, the convergence is compact in time and uniform in a completely characterized set of probability measure almost unity. The results presented in this paper can form a common basis for the analysis and characterization of a number of popular but heuristic approaches for approximating optimal nonlinear filters, such as approximate grid based techniques.

Description: A standard assumption for consistent estimation in the errors-in-variables setting is persistency of excitation of the noise-free input signal. We relax this assumption by considering data from multiple experiments. Consistency is obtained asymptotically as the number of experiments tends to infinity. The main theoretical and algorithmic difficulties are related to the growing number of to-be-estimated initial conditions. The method proposed in the paper is based on analytic elimination of the initial conditions and optimization over the remaining parameters. The resulting estimator is consistent; however, achieving asymptotically efficiency is an open problem.

Description: Bayesian filtering aims at estimating sequentially a hidden process from an observed one. In particular, sequential Monte Carlo (SMC) techniques propagate in time weighted trajectories which represent the posterior probability density function (pdf) of the hidden process given the available observations. On the other hand, conditional Monte Carlo (CMC) is a variance reduction technique which replaces the estimator of a moment of interest by its conditional expectation given another variable. In this paper, we show that up to some adaptations, one can make use of the time recursive nature of SMC algorithms in order to propose natural temporal CMC estimators of some point estimates of the hidden process, which outperform the associated crude Monte Carlo (MC) estimator whatever the number of samples. We next show that our Bayesian CMC estimators can be computed exactly, or approximated efficiently, in some hidden Markov chain (HMC) models; in some jump Markov state-space systems (JMSS); as well as in multitarget filtering. Finally our algorithms are validated via simulations.

Description: In this paper, cooperative sensor localization using asynchronous time-of-arrival measurements is investigated. It is well known that localization performance in wireless networks using time-based ranging or pseudo-ranging methods is greatly affected by the accuracy of the timing synchronization between the nodes involved in the estimation. Commonly, the original estimation problem is broken down into two subproblems, the synchronization problem and the localization problem, in what is known as a two-step approach. However, in this paper, the joint synchronization and localization problem is considered and examined for use in cooperative networks. It is discussed that the cooperation between the source nodes eliminates the need for high anchor node densities and improves localization performance significantly. Furthermore, the Cramér-Rao lower bounds (CRLB) and the maximum likelihood (ML) estimator are derived. It is shown that the ML estimator is highly nonlinear and nonconvex and must, therefore, be solved by using computationally complex algorithms. In order to reduce the complexity of the estimation, a novel semidefinite programming (SDP) relaxation method is developed by relaxing the original nonconvex ML problem, in such a way as to reformulate the estimation problem as a convex problem. The performance of the proposed SDP method is shown through computer simulations to nearly equal that of the ML estimator. The approach is also applied to the noncooperative case where it is found to be superior in performance than the previously proposed suboptimal estimators. Finally, complexity analyses are included for the estimators under consideration.

Description: In this paper, we present three improvements to a three-point third order variant of Newton’s method derived from the Simpson rule. The first one is a fifth order method using the same number of functional evaluations as the third order method, the second one is a four-point 10th order method and the last one is a five-point 20th order method. In terms of computational point of view, our methods require four evaluations (one function and three first derivatives) to get fifth order, five evaluations (two functions and three derivatives) to get 10th order and six evaluations (three functions and three derivatives) to get 20th order. Hence, these methods have efficiency indexes of 1.495, 1.585 and 1.648, respectively which are better than the efficiency index of 1.316 of the third order method. We test the methods through some numerical experiments which show that the 20th order method is very efficient.

Description: Robust small target detection of low signal-to-noise ratio (SNR) is very important in infrared search and track applications for self-defense or attacks. Due to the complex background, current algorithms have some unsolved issues with false alarm rate. In order to reduce the false alarm rate, an infrared small target detection algorithm based on saliency detection and support vector machine was proposed. Firstly, we detect salient regions that may contain targets with phase spectrum Fourier transform (PFT) approach. Then, target recognition was performed in the salient regions. Experimental results show the proposed algorithm has ideal robustness and efficiency for real infrared small target detection applications.

Description: In dynamic propagation environments, beamforming algorithms may suffer from strong interference, steering vector mismatches, a low convergence speed and a high computational complexity. Reduced-rank signal processing techniques provide a way to address the problems mentioned above. This paper presents a low-complexity robust data-dependent dimensionality reduction based on an iterative optimization with steering vector perturbation (IOVP) algorithm for reduced-rank beamforming and steering vector estimation. The proposed robust optimization procedure jointly adjusts the parameters of a rank reduction matrix and an adaptive beamformer. The optimized rank reduction matrix projects the received signal vector onto a subspace with lower dimension. The beamformer/steering vector optimization is then performed in a reduced dimension subspace. We devise efficient stochastic gradient and recursive least-squares algorithms for implementing the proposed robust IOVP design. The proposed robust IOVP beamforming algorithms result in a faster convergence speed and an improved performance. Simulation results show that the proposed IOVP algorithms outperform some existing full-rank and reduced-rank algorithms with a comparable complexity.

Description: Recently, wireless sensor networks (WSNs) have drawn great interest due to their outstanding monitoring and management potential in medical, environmental and industrial applications. Most of the applications that employ WSNs demand all of the sensor nodes to run on a common time scale, a requirement that highlights the importance of clock synchronization. The clock synchronization problem in WSNs is inherently related to parameter estimation. The accuracy of clock synchronization algorithms depends essentially on the statistical properties of the parameter estimation algorithms. Recently, studies dedicated to the estimation of synchronization parameters, such as clock offset and skew, have begun to emerge in the literature. The aim of this article is to provide an overview of the state-of-the-art clock synchronization algorithms for WSNs from a statistical signal processing point of view. This article focuses on describing the key features of the class of clock synchronization algorithms that exploit the traditional two-way message (signal) exchange mechanism. Upon introducing the two-way message exchange mechanism, the main clock offset estimation algorithms for pairwise synchronization of sensor nodes are first reviewed, and their performance is compared. The class of fully-distributed clock offset estimation algorithms for network-wide synchronization is then surveyed. The paper concludes with a list of open research problems pertaining to clock synchronization of WSNs.

Description: In this paper, we consider the problem of parameter estimation over sensor networks in the presence of quantized data and directed communication links. We propose a two-stage distributed algorithm aiming at achieving the centralized sample mean estimate in a distributed manner. Different from the existing algorithms, a running average technique is utilized in the proposed algorithm to smear out the randomness caused by the probabilistic quantization scheme. With the running average technique, it is shown that the centralized sample mean estimate can be achieved both in the mean square and almost sure senses, which is not observed in the standard consensus algorithms. In addition, the rates of convergence are given to quantify the mean square and almost sure performances. Finally, simulation results are presented to illustrate the effectiveness of the proposed algorithm and highlight the improvements by using running average technique.

Description: This paper studies a pilot-assisted physical layer data fusion technique known as Distributed Co-Phasing (DCP). In this two-phase scheme, the sensors first estimate the channel to the fusion center (FC) using pilots sent by the latter; and then they simultaneously transmit their common data by pre-rotating them by the estimated channel phase, thereby achieving physical layer data fusion. First, by analyzing the symmetric mutual information of the system, it is shown that the use of higher order constellations (HOC) can improve the throughput of DCP compared to the binary signaling considered heretofore. Using an HOC in the DCP setting requires the estimation of the composite DCP channel at the FC for data decoding. To this end, two blind algorithms are proposed: 1) power method, and 2) modified $K$ -means algorithm. The latter algorithm is shown to be computationally efficient and converges significantly faster than the conventional $K$ -means algorithm. Analytical expressions for the probability of error are derived, and it is found that even at moderate to low SNRs, the modified $K$ -means algorithm achieves a probability of error comparable to that achievable with a perfect channel estimate at the FC, while requiring no pilot symbols to be transmitted from the sensor nodes. Also, the problem of signal corruption due to imperfect DCP is investigated, and constellation shaping to minimize the probability of signal corruption is proposed and analyzed. The analysis is validated, and the promising performance of DCP for energy-efficient physical layer data fusion is illustrated, using Monte Carlo simulations.

Description: In this paper we propose a fast and efficient Jacobi-like approach named JET (Joint Eigenvalue decomposition based on Triangular matrices) for the Joint EigenValue Decomposition (JEVD) of a set of real or complex non-defective matrices based on the LU factorization of the matrix of eigenvectors. Contrarily to classical Jacobi-like JEVD methods, the iterative procedure of the JET approach can be reduced to the search for only one of the two triangular matrices involved in the factorization of the matrix of eigenvectors, hence decreasing the numerical complexity. Two variants of the JET technique, namely JET-U and JET-O, which correspond to the optimization of two different cost functions are described in detail and these are extended to the complex case. Numerical simulations show that in many practical cases the JET approach provides more accurate estimation of the matrix of eigenvectors than its competitors and that the lowest numerical complexity is consistently achieved by the JET-U algorithm. In addition, we illustrate in the ICA context the interest of being able to solve efficiently the (non-orthogonal) JEVD problem. More particularly, based on our JET-U algorithm, we propose a more robust version of an existing ICA method, named MICAR-U. The identifiability of the latter is studied and proved under some conditions. Computer results given in the context of brain interfaces show the better ability of MICAR-U to denoise simulated electrocortical data compared to classical ICA techniques for low signal to noise ratio values.

Description: A new design for successive interference cancellation (SIC) detection for multiple-input multiple-output systems is introduced, and it is developed on the basis of the method of normal equations commonly used to solve the linear least squares problem. On the basis of this design, optimal-ordered and suboptimal-ordered SIC detection algorithms are derived. It is shown that the proposed optimal-ordered SIC detection algorithm offers a complexity reduction ratio of 1.11–1.25 compared to the fastest known optimal-ordered SIC detection algorithm for intermediate and large numbers of antennas and in terms of the average complexity. On the other hand, the proposed suboptimal-ordered SIC detection algorithm requires a lower complexity than the proposed optimal-ordered one and provides a bit-error-rate performance close to that of the optimal-ordered one and better than those of the other suboptimal-ordered SIC detection algorithms.

Description: We consider the problem of signal recovery on graphs. Graphs model data with complex structure as signals on a graph. Graph signal recovery recovers one or multiple smooth graph signals from noisy, corrupted, or incomplete measurements. We formulate graph signal recovery as an optimization problem, for which we provide a general solution through the alternating direction methods of multipliers. We show how signal inpainting, matrix completion, robust principal component analysis, and anomaly detection all relate to graph signal recovery and provide corresponding specific solutions and theoretical analysis. We validate the proposed methods on real-world recovery problems, including online blog classification, bridge condition identification, temperature estimation, recommender system for jokes, and expert opinion combination of online blog classification.

Description: In this paper, we investigate the network power minimization problem for the multicast cloud radio access network (Cloud-RAN) with imperfect channel state information (CSI). The key observation is that network power minimization can be achieved by adaptively selecting active remote radio heads (RRHs) via controlling the group-sparsity structure of the beamforming vector. However, this yields a non-convex combinatorial optimization problem, for which we propose a three-stage robust group sparse beamforming algorithm. In the first stage, a quadratic variational formulation of the weighted mixed $ell_1/ell_2$ -norm is proposed to induce the group-sparsity structure in the aggregated beamforming vector, which indicates those RRHs that can be switched off. A perturbed alternating optimization algorithm is then proposed to solve the resultant non-convex group-sparsity inducing optimization problem by exploiting its convex substructures. In the second stage, we propose a PhaseLift technique based algorithm to solve the feasibility problem with a given active RRH set, which helps determine the active RRHs. Finally, the semidefinite relaxation (SDR) technique is adopted to determine the robust multicast beamformers. Simulation results will demonstrate the convergence of the perturbed alternating optimization algorithm, as well as, the effectiveness of the proposed algorithm to minimize the network power consumption for multicast Cloud-RAN.

Description: Many machine learning frameworks, such as resource-allocating networks, kernel-based methods, Gaussian processes, and radial-basis-function networks, require a sparsification scheme in order to address the online learning paradigm. For this purpose, several online sparsification criteria have been proposed to restrict the model definition on a subset of samples. The most known criterion is the (linear) approximation criterion, which discards any sample that can be well represented by the already contributing samples, an operation with excessive computational complexity. Several computationally efficient sparsification criteria have been introduced in the literature with the distance and the coherence criteria. This paper provides a unified framework that connects these sparsification criteria in terms of approximating samples, by establishing theoretical bounds on the approximation errors. Furthermore, the error of approximating any pattern is investigated, by proposing upper bounds on the approximation error for each of the aforementioned sparsification criteria. Two classes of fundamental patterns are described in detail, the centroid (i.e., empirical mean) and the principal axes in the kernel principal component analysis. Experimental results show the relevance of the theoretical results established in this paper.

Description: Network coding is an emerging technique known to improve the network performance in many aspects. In Vehicular Ad-hoc Networks (VANET), the bandwidth is considered to be one of the most important network resources. In this paper, we propose a network coding technique to improve the bandwidth utilization for non-safety applications in VANET. In a scenario where there are two sources broadcasting the data into the same area at the same time, the relay will use the network coding technique to decrease the number of rebroadcasting events and the consumption of the bandwidth, However, a fundamental problem for the relay when it receives a packet, is whether to wait for a coding opportunity and save the bandwidth or send the packet directly and reduce the delay. In order to address such tradeoff, we introduce two versions of our protocol, namely buffer size control scheme (BSCS) and time control scheme (TCS); by both versions we aim to control the delay that is experienced by the packet at each hop, while achieving better bandwidth utilization.Up to 38 % improvement in the bandwidth utilization has been recorded, and both schemes have shown a considerable amount of control on the imposed delay.

Description: We present a hierarchical grid-based, globally optimal tracking-by-detection approach to track an unknown number of targets in complex and dense scenarios, particularly addressing the challenges of complex interaction and mutual occlusion. Frame-by-frame detection is performed by hierarchical likelihood grids, matching shape templates through a fast oriented distance transform. To allow recovery from misdetections, common heuristics such as nonmaxima suppression within observations is eschewed. Within a discretized state-space, the data association problem is formulated as a grid-based network flow model, resulting in a convex problem casted into an integer linear programming form, giving a global optimal solution. In addition, we show how a behavior cue (body orientation) can be integrated into our association affinity model, providing valuable hints for resolving ambiguities between crossing trajectories. Unlike traditional motion-based approaches, we estimate body orientation by a hybrid methodology, which combines the merits of motion-based and 3D appearance-based orientation estimation, thus being capable of dealing also with still-standing or slowly moving targets. The performance of our method is demonstrated through experiments on a large variety of benchmark video sequences, including both indoor and outdoor scenarios.

Description: Many impulse noise (IN) reduction methods suffer from two obstacles, the improper noise detectors and imperfect filters they used. To address such issue, in this paper, a weighted couple sparse representation model is presented to remove IN. In the proposed model, the complicated relationships between the reconstructed and the noisy images are exploited to make the coding coefficients more appropriate to recover the noise-free image. Moreover, the image pixels are classified into clear, slightly corrupted, and heavily corrupted ones. Different data-fidelity regularizations are then accordingly applied to different pixels to further improve the denoising performance. In our proposed method, the dictionary is directly trained on the noisy raw data by addressing a weighted rank-one minimization problem, which can capture more features of the original data. Experimental results demonstrate that the proposed method is superior to several state-of-the-art denoising methods.

Description: The radix- $2^{k}$ algorithm plays a crucial role in the pipelined implementation of fast Fourier transform (FFT). This paper presents a fixed-point analysis and hardware evaluation of radix- $2^{k}$ FFT under the framework of the single-path delay feedback (SDF) and multi-path delay commutator (MDC) pipelined structure. The investigation is carried out with variable operating word-lengths to ensure the generality. Furthermore, the main streams to fulfill FFT coefficients weighting, namely, the approach using complex multipliers and the one adopting memoryless CORDIC units, are both considered in the analysis. Based on these derivations, a joint optimization of radix- $2^{k}$ algorithm and operating word-length is discussed to achieve a reasonable trade-off between computational accuracy and hardware expenditure. Simulations and experiments indicates that the derived SQNR is reliable to unfold the quantization effects of fixed-point radix- $2^{k}$ FFT. In addition, the proposed joint optimization strategy is capable of providing better solutions to implement the radix- $2^{k}$ FFT processor efficiently.

Description: In this paper, a hierarchical multi-task structural learning algorithm is developed to support large-scale plant species identification, where a visual tree is constructed for organizing large numbers of plant species in a coarse-to-fine fashion and determining the inter-related learning tasks automatically. For a given parent node on the visual tree, it contains a set of sibling coarse-grained categories of plant species or sibling fine-grained plant species, and a multi-task structural learning algorithm is developed to train their inter-related classifiers jointly for enhancing their discrimination power. The inter-level relationship constraint, e.g., a plant image must first be assigned to a parent node (high-level non-leaf node) correctly if it can further be assigned to the most relevant child node (low-level non-leaf node or leaf node) on the visual tree, is formally defined and leveraged to learn more discriminative tree classifiers over the visual tree. Our experimental results have demonstrated the effectiveness of our hierarchical multi-task structural learning algorithm on training more discriminative tree classifiers for large-scale plant species identification.

Description: In this paper, the quickest change detection problem is studied in two-state hidden Markov models (HMM), where the vector parameter $theta$ of the HMM changes from $theta_{0}$ to $theta_{1}$ at some unknown time, and one wants to detect the true change as quickly as possible while controlling the false alarm rate. It turns out that the generalized likelihood ratio (GLR) scheme, while theoretically straightforward, is generally computationally infeasible for the HMM. To develop efficient but computationally simple schemes for the HMM, we first discuss a subtlety in the recursive form of the generalized likelihood ratio (GLR) scheme for the HMM. Then we show that the recursive CUSUM scheme proposed in Fuh (Ann. Statist., 2003) can be regarded as a quasi-GLR scheme for pseudo post-change hypotheses with certain dependence structure between pre- and postchange observations. Next, we extend the quasi-GLR idea to propose recursive score schemes in the scenario when the postchange parameter $theta_{1}$ of the HMM involves a real-valued nuisance parameter. Finally, the Kullback-Leibler (KL) divergence plays an essential role in the quickest change detection problem and many other fields, however it is rather challenging to numerically compute it in HMMs. Here we develop a non-Monte Carlo method that computes the KL divergence of two-state HMMs via the underlying invariant probability measure, which is characterized by the Fredholm integral equation. Numerical study demonstrates an unusual property of the KL divergence for HMM that implies the severe effects of misspecifying the postchange parameter for the HMM.

Description: Long-Term Evolution (LTE) was implemented to fulfill and satisfy users’ needs as well as their demands for an improvised, fast and efficient Quality of service (QoS). A minimal aggregate of waiting time in return would give users a better Quality of experience (QoE). Real-time service packet scheduling is an important process in allocating resources to users. An efficient packet scheduling scheme will be able to cater fairly and efficiently to its users in the LTE network. Hence, studies are performed focusing on real-time traffic which includes video as well as Voice over Internet Protocol (VoIP) transmissions. In this work, the existing exponential rule (EXP rule) is utilized to benchmark our proposed packet scheduling techniques so that we are able to further evaluate the scheduling performance. In response to the increasing likelihood of losing packets in the EXP rule’s algorithm and maximizing the throughput rate, several schemes have been experimented with. The proposed schemes include 1) simplified EXP rule (sEXP Rule), 2) modified EXP rule (mEXP Rule), 3) EXP rule with maximum throughput (MT) (EXP_MT Rule), and 4) enhanced EXP rule with MT (E2M). By adding MT as a weight to the EXP rule, the throughput is maximized, thus providing higher throughput rates for real-time and non-real-time traffic. The simulation results show that the sEXP rule has a better performance in throughput, packet loss rate (PLR), and spectral efficiency for video traffic. Aside from this, our proposed E2M rule performs better than the benchmark EXP rule and outperforms the other proposed schemes, as well. It is observed that the E2M rule has better QoS support for real-time transmission in terms of delay, packet loss, throughput and spectral efficiency, within the LTE network. Hence, our proposed E2M rule is an enhancement of the benchmark EXP rule, which is commonly used in LTE packet scheduling.

Description: Convex optimization is a powerful tool for resource allocation and signal processing in wireless networks. As the network density is expected to drastically increase in order to accommodate the exponentially growing mobile data traffic, performance optimization problems are entering a new era characterized by a high dimension and/or a large number of constraints, which poses significant design and computational challenges. In this paper, we present a novel two-stage approach to solve large-scale convex optimization problems for dense wireless cooperative networks, which can effectively detect infeasibility and enjoy modeling flexibility. In the proposed approach, the original large-scale convex problem is transformed into a standard cone programming form in the first stage via matrix stuffing, which only needs to copy the problem parameters such as channel state information (CSI) and quality-of-service (QoS) requirements to the prestored structure of the standard form. The capability of yielding infeasibility certificates and enabling parallel computing is achieved by solving the homogeneous self-dual embedding of the primal-dual pair of the standard form. In the solving stage, the operator splitting method, namely, the alternating direction method of multipliers (ADMM), is adopted to solve the large-scale homogeneous self-dual embedding. Compared with second-order methods, ADMM can solve large-scale problems in parallel with modest accuracy within a reasonable amount of time. Simulation results will demonstrate the speedup, scalability, and reliability of the proposed framework compared with the state-of-the-art modeling frameworks and solvers.

Description: This paper proposes a two-stage texture synthesis algorithm. At the first stage, a structure tensor map carrying information about the local orientation is synthesized from the exemplar’s data and used at the second stage to constrain the synthesis of the texture. Keeping in mind that the algorithm should be able to reproduce as faithfully as possible the visual aspect, statistics, and morphology of the input sample, the method is tested on various textures and compared objectively with existing methods, highlighting its strength in successfully synthesizing the output texture in many situations where traditional algorithms fail to reproduce the exemplar’s patterns. The promising results pave the way towards the synthesis of accurately large and multi-scale patterns as it is the case for carbon material samples showing laminar structures, for example.

Description: An image search reranking (ISR) technique aims at refining text-based search results by mining images’ visual content. Feature extraction and ranking function design are two key steps in ISR. Inspired by the idea of hypersphere in one-class classification, this paper proposes a feature extraction algorithm named hypersphere-based relevance preserving projection (HRPP) and a ranking function called hypersphere-based rank (H-Rank). Specifically, an HRPP is a spectral embedding algorithm to transform an original high-dimensional feature space into an intrinsically low-dimensional hypersphere space by preserving the manifold structure and a relevance relationship among the images. An H-Rank is a simple but effective ranking algorithm to sort the images by their distances to the hypersphere center. Moreover, to capture the user’s intent with minimum human interaction, a reversed $k$ -nearest neighbor (KNN) algorithm is proposed, which harvests enough pseudorelevant images by requiring that the user gives only one click on the initially searched images. The HRPP method with reversed KNN is named one-click-based HRPP (OC-HRPP). Finally, an OC-HRPP algorithm and the H-Rank algorithm form a new ISR method, H-reranking. Extensive experimental results on three large real-world data sets show that the proposed algorithms are effective. Moreover, the fact that only one relevant image is required to be labeled makes it has a strong practical significance.

Description: The discrete cosine transform (DCT) is known to be asymptotically equivalent to the Karhunen-Loève transform (KLT) of Gaussian first-order auto-regressive (AR(1)) processes. Since being uncorrelated under the Gaussian hypothesis is synonymous with independence, it also yields an independent-component analysis (ICA) of such signals. In this paper, we present a constructive non-Gaussian generalization of this result: the characterization of the optimal orthogonal transform (ICA) for the family of symmetric- $alpha$ -stable AR(1) processes. The degree of sparsity of these processes is controlled by the stability parameter $0 〈 alphaleq2$ with the only non-sparse member of the family being the classical Gaussian AR(1) process with $alpha=2$ . Specifically, we prove that, for $alpha 〈 2$ , a fixed family of operator-like wavelet bases systematically outperforms the DCT in terms of compression and denoising ability. The effect is quantified with the help of two performance criteria (one based on the Kullback-Leibler divergence, and the other on Stein’s formula for the minimum estimation error) that can also be viewed as statistical measures of independence. Finally, we observe that, for the sparser kind of processes with $0 〈 alphaleq 1$ , the operator-like wavelet basis, as dictated by linear system theory, is undistinguishable from the ICA solution obtained through numerical optimization. Our framework offers a unified view that encompasses sinusoidal transforms such as the DCT and a family of orthogonal Haar-like wavelets that is linked analytically to the underlying signal model.

Description: In this paper, we propose a novel method for image fusion with a high-resolution panchromatic image and a low-resolution multispectral (Ms) image at the same geographical location. The fusion is formulated as a convex optimization problem which minimizes a linear combination of a least-squares fitting term and a dynamic gradient sparsity regularizer. The former is to preserve accurate spectral information of the Ms image, while the latter is to keep sharp edges of the high-resolution panchromatic image. We further propose to simultaneously register the two images during the fusing process, which is naturally achieved by virtue of the dynamic gradient sparsity property. An efficient algorithm is then devised to solve the optimization problem, accomplishing a linear computational complexity in the size of the output image in each iteration. We compare our method against six state-of-the-art image fusion methods on Ms image data sets from four satellites. Extensive experimental results demonstrate that the proposed method substantially outperforms the others in terms of both spatial and spectral qualities. We also show that our method can provide high-quality products from coarsely registered real-world IKONOS data sets. Finally, a MATLAB implementation is provided to facilitate future research.

Description: Automatic fluorescent particle tracking is an essential task to study the dynamics of a large number of biological structures at a sub-cellular level. We have developed a probabilistic particle tracking approach based on multi-scale detection and two-step multi-frame association. The multi-scale detection scheme allows coping with particles in close proximity. For finding associations, we have developed a two-step multi-frame algorithm, which is based on a temporally semiglobal formulation as well as spatially local and global optimization. In the first step, reliable associations are determined for each particle individually in local neighborhoods. In the second step, the global spatial information over multiple frames is exploited jointly to determine optimal associations. The multi-scale detection scheme and the multi-frame association finding algorithm have been combined with a probabilistic tracking approach based on the Kalman filter. We have successfully applied our probabilistic tracking approach to synthetic as well as real microscopy image sequences of virus particles and quantified the performance. We found that the proposed approach outperforms previous approaches.

Description: In this paper, we propose a novel model, a discriminatively learned iterative shrinkage (DLIS) model, for color image denoising. The DLIS is a generalization of wavelet shrinkage by iteratively performing shrinkage over patch groups and whole image aggregation. We discriminatively learn the shrinkage functions and basis from the training pairs of noisy/noise-free images, which can adaptively handle different noise characteristics in luminance/chrominance channels, and the unknown structured noise in real-captured color images. Furthermore, to remove the splotchy real color noises, we design a Laplacian pyramid-based denoising framework to progressively recover the clean image from the coarsest scale to the finest scale by the DLIS model learned from the real color noises. Experiments show that our proposed approach can achieve the state-of-the-art denoising results on both synthetic denoising benchmark and real-captured color images.

Description: In cross-view action recognition, what you saw in one view is different from what you recognize in another view, since the data distribution even the feature space can change from one view to another. In this paper, we address the problem of transferring action models learned in one view (source view) to another different view (target view), where action instances from these two views are represented by heterogeneous features. A novel learning method, called heterogeneous transfer discriminant-analysis of canonical correlations (HTDCC), is proposed to discover a discriminative common feature space for linking source view and target view to transfer knowledge between them. Two projection matrices are learned to, respectively, map data from the source view and the target view into a common feature space via simultaneously minimizing the canonical correlations of interclass training data, maximizing the canonical correlations of intraclass training data, and reducing the data distribution mismatch between the source and target views in the common feature space. In our method, the source view and the target view neither share any common features nor have any corresponding action instances. Moreover, our HTDCC method is capable of handling only a few or even no labeled samples available in the target view, and can also be easily extended to the situation of multiple source views. We additionally propose a weighting learning framework for multiple source views adaptation to effectively leverage action knowledge learned from multiple source views for the recognition task in the target view. Under this framework, different source views are assigned different weights according to their different relevances to the target view. Each weight represents how contributive the corresponding source view is to the target view. Extensive experiments on the IXMAS data set demonstrate the effectiveness of HTDCC on learning the common feature space for heterogeneous cross-view action rec- gnition. In addition, the weighting learning framework can achieve promising results on automatically adapting multiple transferred source-view knowledge to the target view.

Description: A complete encoding solution for efficient intra-based depth map compression is proposed in this paper. The algorithm, denominated predictive depth coding (PDC), was specifically developed to efficiently represent the characteristics of depth maps, mostly composed by smooth areas delimited by sharp edges. At its core, PDC involves a directional intra prediction framework and a straightforward residue coding method, combined with an optimized flexible block partitioning scheme. In order to improve the algorithm in the presence of depth edges that cannot be efficiently predicted by the directional modes, a constrained depth modeling mode, based on explicit edge representation, was developed. For residue coding, a simple and low complexity approach was investigated, using constant and linear residue modeling, depending on the prediction mode. The performance of the proposed intra depth map coding approach was evaluated based on the quality of the synthesized views using the encoded depth maps and original texture views. The experimental tests based on all intra configuration demonstrated the superior rate-distortion performance of PDC, with average bitrate savings of 6%, when compared with the current state-of-the-art intra depth map coding solution present in the 3D extension of a high-efficiency video coding (3D-HEVC) standard. By using view synthesis optimization in both PDC and 3D-HEVC encoders, the average bitrate savings increase to 14.3%. This suggests that the proposed method, without using transform-based residue coding, is an efficient alternative to the current 3D-HEVC algorithm for intra depth map coding.

Description: MUSIC is a popular algorithm for estimating the direction of arrival (DOA) in array signal processing applications. In this paper, we analyze the performance of the MUSIC algorithm for a single source system, in the presence of noisy and missing data (when only a random subset of the entries in the data matrix are observed). We prove consistency of the DOA estimate when signal from a single source is impinging on low coherence arrays, and derive an analytic expression for the mean-squared-error (MSE) performance of MUSIC for the case of uniform linear arrays, in the large array and relatively large sample setting. Our analysis is mathematically justified in both the sample rich and deficient regimes. The expression for the MSE is a simple function of array geometry, signal-to-noise ratio (SNR), the fraction of entries observed, and the ratio of the number of sensors to number of snapshots. We derive a phase transition threshold which separates a regime where MUSIC is consistent from a regime where MUSIC is inconsistent. This threshold depends upon the SNR, the probability of observing entries in the data matrix, and number of sensors and snapshots in a simple manner which we make explicit.

Description: Phase retrieval problems involve solving linear equations, but with missing sign (or phase, for complex numbers) information. More than four decades after it was first proposed, the seminal error reduction algorithm of Gerchberg and Saxton and Fienup is still the popular choice for solving many variants of this problem. The algorithm is based on alternating minimization; i.e., it alternates between estimating the missing phase information, and the candidate solution. Despite its wide usage in practice, no global convergence guarantees for this algorithm are known. In this paper, we show that a (resampling) variant of this approach converges geometrically to the solution of one such problem—finding a vector $bf x$ from ${bf y}, {bf A}$ , where ${bf y} = vert {bf A}^T{bf x}vert$ and $vert{bf z}vert$ denotes a vector of element-wise magnitudes of ${bf z}$ —under the assumption that $ {bf A}$ is Gaussian. Empirically, we demonstrate that alternating minimization performs similar to recently proposed convex techniques for this problem (which are based on “lifting” to a convex matrix problem) in sample complexity and robustness to noise. However, it is much more efficient and can scale to large problems. Analytically, for a resampling version of alternating minimization, we show geometric convergence to the solution, and sample complexity that is off by log factors from obvious lower bounds. We also establish close to optimal scaling for the case when the unknown vector is sparse. Our work represents the first theoretical guarantee for al- ernating minimization (albeit with resampling) for any variant of phase retrieval problems in the non-convex setting.

Description: We consider Bayesian blind deconvolution (BD) of an unknown sparse sequence convolved with an unknown pulse. Our goal is to detect the positions where the sparse input sequence is nonzero and to estimate the corresponding amplitudes as well as the pulse shape. For this task, we propose a novel evolution of the single most likely replacement (SMLR) algorithm. Our method uses a modified Bernoulli-Gaussian prior that incorporates a minimum temporal distance constraint. This prior simultaneously induces sparsity and enforces a prescribed minimum distance between the pulse centers. The minimum distance constraint provides an effective way to avoid overfitting (i.e., spurious detected pulses) and improve resolution. The proposed BD method overcomes certain weaknesses of the traditional SMLR-based BD method, which is verified experimentally to result in improved detection/estimation performance and reduced computational complexity. Our simulation results also demonstrate performance and complexity advantages relative to the iterated window maximization (IWM) algorithm and a recently proposed partially collapsed Gibbs sampler method.

Description: In delay tolerant networks (DTNs), the network may not be fully connected at any instant of time, but connections occurring between nodes at different times make the network connected through the entire time continuum. In such a case, traditional routing methods fail to operate because there are no contemporaneous end-to-end paths between sources and destinations. This study examines the routing in DTNs where connections arise in a periodic nature. We analyze various levels of periodicity in order to meet the requirements of different network models. We propose different routing algorithms for different kinds of periodic connections. Our proposed routing methods guarantee the earliest delivery time and minimum hop-count, simultaneously. We evaluate our routing schemes via extensive simulation experiments and compare them to some other popular routing approaches proposed for DTNs. Our evaluations show the feasibility and effectiveness of our schemes as viable routing methods for delay tolerant networks.

Description: We address the problem of super-resolution frequency recovery using prior knowledge of the structure of a spectrally sparse, undersampled signal. In many applications of interest, some structure information about the signal spectrum is often known. The prior information might be simply knowing precisely some signal frequencies or the likelihood of a particular frequency component in the signal. We devise a general semidefinite program to recover these frequencies using theories of positive trigonometric polynomials. Our theoretical analysis shows that, given sufficient prior information, perfect signal reconstruction is possible using signal samples no more than thrice the number of signal frequencies. Numerical experiments demonstrate great performance enhancements using our method. We show that the nominal resolution necessary for the grid-free results can be improved if prior information is suitably employed.

Description: This paper addresses the behavior of a classical multiantenna GLRT test that allows to detect the presence of a known signal corrupted by a multipath propagation channel and by an additive temporally white Gaussian noise with unknown spatial covariance matrix. The paper is focused on the case where the number of sensors $M$ is large, and of the same order of magnitude as the sample size $N$ , a context which is modeled by the large system asymptotic regime $M rightarrow +infty $ , $N rightarrow +infty $ in such a way that $M/N rightarrow c$ for $c in (0,+infty )$ . The purpose of this paper is to study the behaviour of a GLRT statistics in this regime, and to show that the corresponding theoretical analysis allows to accurately predict the performance of the test when $M$ and $N$ are of the same order of magnitude.

Description: This paper derives an asymptotic generalized likelihood ratio test (GLRT) and an asymptotic locally most powerful invariant test (LMPIT) for two hypothesis testing problems: 1) Is a vector-valued random process cyclostationary (CS) or is it wide-sense stationary (WSS)? 2) Is a vector-valued random process CS or is it nonstationary? Our approach uses the relationship between a scalar-valued CS time series and a vector-valued WSS time series for which the knowledge of the cycle period is required. This relationship allows us to formulate the problem as a test for the covariance structure of the observations. The covariance matrix of the observations has a block-Toeplitz structure for CS and WSS processes. By considering the asymptotic case where the covariance matrix becomes block-circulant we are able to derive its maximum likelihood (ML) estimate and thus an asymptotic GLRT. Moreover, using Wijsman’s theorem, we also obtain an asymptotic LMPIT. These detectors may be expressed in terms of the Loève spectrum, the cyclic spectrum, and the power spectral density, establishing how to fuse the information in these spectra for an asymptotic GLRT and LMPIT. This goes beyond the state-of-the-art, where it is common practice to build detectors of cyclostationarity from ad-hoc functions of these spectra.

Description: The impulse response of wireless channels between the $N_t$ transmit and $N_r$ receive antennas of a MIMO-OFDM system are group approximately sparse (ga-sparse), i.e., the $N_tN_r$ channels have a small number of significant paths relative to the channel delay spread and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wireless channels are also group approximately cluster-sparse (gac-sparse), i.e., every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this paper, we cast the problem of estimating the ga-sparse and gac-sparse block-fading and time-varying channels in the sparse Bayesian learning (SBL) framework and propose a bouquet of novel algorithms for pilot-based channel estimation, and joint channel estimation and data detection, in MIMO-OFDM systems. The proposed algorithms are capable of estimating the sparse wireless channels even when the measurement matrix is only partially known. Further, we employ a first-order autoregressive modeling of the temporal variation of the ga-sparse and gac-sparse channels and propose a recursive Kalman filtering and smoothing (KFS) technique for joint channel estimation, tracking, and data detection. We also propose novel, parallel-implementation based, low-complexity techniques for estimating gac-sparse channels. Monte Carlo simulations illustrate the benefit of exploiting the gac-sparse structure in the wireless channel in terms of the mean square error (MSE) and coded bit error rate (BER) performance.

Description: One-dimensional (1-D) and two-dimensional (2-D) frequency estimation for a single complex sinusoid in white Gaussian noise is a classic signal processing problem with numerous applications. It is revisited here through a new unitary principal-singular-vector utilization modal analysis (PUMA) approach, which is realized in terms of real-valued computations. The 2-D unitary PUMA is first formulated as an iteratively weighted least squares optimization problem. Recognizing that only one iteration is sufficient when 2-D unitary PUMA is initialized using least squares, a computationally attractive closed-form solution is then obtained. A variant of 2-D unitary PUMA is also developed for the 1-D case. Due to the real-valued computations and closed-form expression for the frequency estimate, the unitary PUMA is more computationally efficient than a number of state-of-the-art methods. Furthermore, the asymptotic variances of 1-D and 2-D unitary PUMA estimators are theoretically derived, and numerical results are included to demonstrate the effectiveness of the proposed methods.

Description: Distributed estimation over sensor networks has received a lot of attention due to its great promise for broad applicability. In many cases, sensors have constraints on the range of data they can measure. This may cause that the measurements or observations are censored, and hence the value of a measurement or observation could be only partially known. This paper focuses on distributed censored regression over networks and develops a diffusion-based algorithm for the censored regression. The proposed algorithm first adopts an adaptive bias-corrected estimator based on a probit regression model to reduce the adverse effect of censoring on estimation results, and afterwards carries out the least squares procedure to find the estimate of the parameter of interest in a collaborative manner between every node and its neighbors. The theoretical study of convergence in the mean and mean-square sense reveals that the proposed algorithm is asymptotically unbiased and stable under some conditions. Moreover, simulation results show the effectiveness of the proposed algorithm.

Description: Computational load remains a major concern when processing signals by means of sliding transforms. In this paper, we present an efficient algorithm for the fast computation of one-dimensional and two-dimensional sliding discrete Tchebichef moments. To do so, we first establish the relationships that exist between the Tchebichef moments of two neighboring windows taking advantage of Tchebichef polynomials’ properties. We then propose an original way to fast compute the moments of one window by utilizing the moment values of its previous window. We further theoretically establish the complexity of our fast algorithm and illustrate its interest within the framework of digital forensics and more precisely the detection of duplicated regions in an audio signal or an image. Our algorithm is used to extract local features of such a signal tampering. Experimental results show that its complexity is independent of the window size, validating the theory. They also exhibit that our algorithm is suitable to digital forensics and beyond to any applications based on sliding Tchebichef moments.

Description: Due to rapid developments in mobile technology as well as various multimedia features like messaging, browsing, and streaming, user-created mobile contents are increasing, both in terms of quantity and quality, and at the same time are shared in real time. To get into step with such movements, new content-centric networking (CCN) has appeared. However, CCN has not taken the effect of consumer device movements into consideration. So, this paper proposes a partial path extension scheme to provide lower communication overhead, shorter download time, and lower network resource consumption in mobile consumer environments.

Description: In this paper, we describe a model for maximum likelihood estimation (MLE) of the relative abundances of different conformations of a protein in a heterogeneous mixture from small angle X-ray scattering (SAXS) intensities. To consider cases where the solution includes intermediate or unknown conformations, we develop a subset selection method based on k-means clustering and the Cramér-Rao bound on the mixture coefficient estimation error to find a sparse basis set that represents the space spanned by the measured SAXS intensities of the known conformations of a protein. Then, using the selected basis set and the assumptions on the model for the intensity measurements, we show that the MLE model can be expressed as a constrained convex optimization problem. Employing the adenylate kinase (ADK) protein and its known conformations as an example, and using Monte Carlo simulations, we demonstrate the performance of the proposed estimation scheme. Here, although we use 45 crystallographically determined experimental structures and we could generate many more using, for instance, molecular dynamics calculations, the clustering technique indicates that the data cannot support the determination of relative abundances for more than 5 conformations. The estimation of this maximum number of conformations is intrinsic to the methodology we have used here.

Description: Tensor factorization has proven useful in a wide range of applications, from sensor array processing to communications, speech and audio signal processing, and machine learning. With few recent exceptions, all tensor factorization algorithms were originally developed for centralized, in-memory computation on a single machine; and the few that break away from this mold do not easily incorporate practically important constraints, such as non-negativity. A new constrained tensor factorization framework is proposed in this paper, building upon the Alternating Direction Method of Multipliers (ADMoM). It is shown that this simplifies computations, bypassing the need to solve constrained optimization problems in each iteration; and it naturally leads to distributed algorithms suitable for parallel implementation. This opens the door for many emerging big data-enabled applications. The methodology is exemplified using non-negativity as a baseline constraint, but the proposed framework can incorporate many other types of constraints. Numerical experiments are encouraging, indicating that ADMoM-based non-negative tensor factorization (NTF) has high potential as an alternative to state-of-the-art approaches.

Description: A method for authorship attribution based on function word adjacency networks (WANs) is introduced. Function words are parts of speech that express grammatical relationships between other words but do not carry lexical meaning on their own. In the WANs in this paper, nodes are function words and directed edges from a source function word to a target function word stand in for the likelihood of finding the latter in the ordered vicinity of the former. WANs of different authors can be interpreted as transition probabilities of a Markov chain and are therefore compared in terms of their relative entropies. Optimal selection of WAN parameters is studied and attribution accuracy is benchmarked across a diverse pool of authors and varying text lengths. This analysis shows that, since function words are independent of content, their use tends to be specific to an author and that the relational data captured by function WANs is a good summary of stylometric fingerprints. Attribution accuracy is observed to exceed the one achieved by methods that rely on word frequencies alone. Further combining WANs with methods that rely on word frequencies, results in larger attribution accuracy, indicating that both sources of information encode different aspects of authorial styles.

Description: Greed is good. However, the tighter you squeeze, the less you have. In this paper, a less greedy algorithm for sparse signal reconstruction in compressive sensing, named orthogonal matching pursuit with thresholding is studied. Using the global 2-coherence, which provides a “bridge” between the well known mutual coherence and the restricted isometry constant, the performance of orthogonal matching pursuit with thresholding is analyzed and more general results for sparse signal reconstruction are obtained. It is also shown that given the same assumption on the coherence index and the restricted isometry constant as required for orthogonal matching pursuit, the thresholding variation gives exactly the same reconstruction performance with significantly less complexity.

Description: In multiobject inference, the multiobject probability density captures the uncertainty in the number and the states of the objects as well as the statistical dependence between the objects. Exact computation of the multiobject density is generally intractable and tractable implementations usually require statistical independence assumptions between objects. In this paper we propose a tractable multiobject density approximation that can capture statistical dependence between objects. In particular, we derive a tractable Generalized Labeled Multi-Bernoulli (GLMB) density that matches the cardinality distribution and the first moment of the labeled multiobject distribution of interest. It is also shown that the proposed approximation minimizes the Kullback–Leibler divergence over a special tractable class of GLMB densities. Based on the proposed GLMB approximation we further demonstrate a tractable multiobject tracking algorithm for generic measurement models. Simulation results for a multiobject Track-Before-Detect example using radar measurements in low signal-to-noise ratio (SNR) scenarios verify the applicability of the proposed approach.

Description: With bandwidths on the order of a gigahertz in emerging wireless systems, high-resolution analog-to-digital convertors (ADCs) become a power consumption bottleneck. One solution is to employ low resolution one-bit ADCs. In this paper, we analyze the flat fading multiple-input multiple-output (MIMO) channel with one-bit ADCs. Channel state information is assumed to be known at both the transmitter and receiver. For the multiple-input single-output channel, we derive the exact channel capacity. For the single-input multiple-output and MIMO channel, the capacity at infinite signal-to-noise ratio (SNR) is found. We also derive upper bound at finite SNR, which is tight when the channel has full row rank. In addition, we propose an efficient method to design the input symbols to approach the capacity achieving solution. We incorporate millimeter wave channel characteristics and find the bounds on the infinite SNR capacity. The results show how the number of paths and number of receive antennas impact the capacity.

Description: Based on the biorthogonal analysis approach, a multiwindow real-valued discrete Gabor transform (M-RDGT) for periodic sequences is presented to efficiently analyze the dynamic time-frequency content of a signal containing components with multiple and/or time-varying frequencies. The M-RDGT offers a computationally efficient implementation as well as a real-valued formulation of the multiwindow complex-valued discrete Gabor transform (M-CDGT). The completeness condition of the M-RDGT is proved to be equivalent to its biorthogonality constraint between analysis windows and synthesis windows. The M-RDGT can utilize the fast discrete Hartley transform algorithms for fast computation and has a simple relationship with the M-CDGT such that its coefficients can be directly computed from the M-RDGT coefficients. Therefore, the M-RDGT offers an efficient method to compute the M-CDGT. Since the analyzed sequence, analysis and synthesis windows in the existing M-CDGT must have an equal period, if the period of a sequence is very long, solving its windows requires a huge amount of computation and memory and could lead to numerical instability. To overcome this problem, a modified M-RDGT for long-periodic (or even infinite) sequences is presented and its corresponding biorthogonality constraint between analysis windows and synthesis windows is modified, in which the period of the analysis and synthesis windows is independent of the period of a analyzed sequence so that one can apply short windows to process any long-periodic (or even in finite) sequence. Finally, the multirate-based parallel implementation of the M-RDGT is presented, which has shown to be effective and fast for time-frequency analysis.

Description: In this paper, we present a Bayesian approach for spectral unmixing of multispectral Lidar (MSL) data associated with surface reflection from targeted surfaces composed of several known materials. The problem addressed is the estimation of the positions and area distribution of each material. In the Bayesian framework, appropriate prior distributions are assigned to the unknown model parameters and a Markov chain Monte Carlo method is used to sample the resulting posterior distribution. The performance of the proposed algorithm is evaluated using synthetic MSL signals, for which single and multi-layered models are derived. To evaluate the expected estimation performance associated with MSL signal analysis, a Cramer-Rao lower bound associated with model considered is also derived, and compared with the experimental data. Both the theoretical lower bound and the experimental analysis will be of primary assistance in future instrument design.

Description: In this paper we investigate some parallel variants of Broyden’s method and, for the basic variant, we present its convergence properties. The main result is that the behavior of the considered parallel Broyden’s variants is comparable with the classical parallel Newton method, and significantly better than the parallel Cimmino method, both for linear and nonlinear cases. The considered variants are also compared with two more recently proposed parallel Broyden’s method. Some numerical experiments are presented to illustrate the advantages and limits of the proposed algorithms.

Description: Person re-identification aims to match people across non-overlapping camera views, which is an important but challenging task in video surveillance. In order to obtain a robust metric for matching, metric learning has been introduced recently. Most existing works focus on seeking a Mahalanobis distance by employing sparse pairwise constraints, which utilize image pairs with the same person identity as positive samples, and select a small portion of those with different identities as negative samples. However, this training strategy has abandoned a large amount of discriminative information, and ignored the relative similarities. In this paper, we propose a novel relevance metric learning method with listwise constraints (RMLLCs) by adopting listwise similarities, which consist of the similarity list of each image with respect to all remaining images. By virtue of listwise similarities, RMLLC could capture all pairwise similarities, and consequently learn a more discriminative metric by enforcing the metric to conserve predefined similarity lists in a low-dimensional projection subspace. Despite the performance enhancement, RMLLC using predefined similarity lists fails to capture the relative relevance information, which is often unavailable in practice. To address this problem, we further introduce a rectification term to automatically exploit the relative similarities, and develop an efficient alternating iterative algorithm to jointly learn the optimal metric and the rectification term. Extensive experiments on four publicly available benchmarking data sets are carried out and demonstrate that the proposed method is significantly superior to the state-of-the-art approaches. The results also show that the introduction of the rectification term could further boost the performance of RMLLC.

Description: Tomographic iterative reconstruction methods need a very thorough modeling of data. This point becomes critical when the number of available projections is limited. At the core of this issue is the projector design, i.e., the numerical model relating the representation of the object of interest to the projections on the detector. Voxel driven and ray driven projection models are widely used for their short execution time in spite of their coarse approximations. Distance driven model has an improved accuracy but makes strong approximations to project voxel basis functions. Cubic voxel basis functions are anisotropic, accurately modeling their projection is, therefore, computationally expensive. Both smoother and more isotropic basis functions better represent the continuous functions and provide simpler projectors. These considerations have led to the development of spherically symmetric volume elements, called blobs. Set apart their isotropy, blobs are often considered too computationally expensive in practice. In this paper, we consider using separable B-splines as basis functions to represent the object, and we propose to approximate the projection of these basis functions by a 2D separable model. When the degree of the B-splines increases, their isotropy improves and projections can be computed regardless of their orientation. The degree and the sampling of the B-splines can be chosen according to a tradeoff between approximation quality and computational complexity. We quantitatively measure the good accuracy of our model and compare it with other projectors, such as the distance-driven and the model proposed by Long et al. From the numerical experiments, we demonstrate that our projector with an improved accuracy better preserves the quality of the reconstruction as the number of projections decreases. Our projector with cubic B-splines requires about twice as many operations as a model based on voxel basis functions. Higher accuracy projectors can be used to - mprove the resolution of the existing systems, or to reduce the number of projections required to reach a given resolution, potentially reducing the dose absorbed by the patient.

Description: Despite important recent advances, the vulnerability of biometric systems to spoofing attacks is still an open problem. Spoof attacks occur when impostor users present synthetic biometric samples of a valid user to the biometric system seeking to deceive it. Considering the case of face biometrics, a spoofing attack consists in presenting a fake sample (e.g., photograph, digital video, or even a 3D mask) to the acquisition sensor with the facial information of a valid user. In this paper, we introduce a low cost and software-based method for detecting spoofing attempts in face recognition systems. Our hypothesis is that during acquisition, there will be inevitable artifacts left behind in the recaptured biometric samples allowing us to create a discriminative signature of the video generated by the biometric sensor. To characterize these artifacts, we extract time-spectral feature descriptors from the video, which can be understood as a low-level feature descriptor that gathers temporal and spectral information across the biometric sample and use the visual codebook concept to find mid-level feature descriptors computed from the low-level ones. Such descriptors are more robust for detecting several kinds of attacks than the low-level ones. The experimental results show the effectiveness of the proposed method for detecting different types of attacks in a variety of scenarios and data sets, including photos, videos, and 3D masks.

Description: Target representation is a necessary component for a robust tracker. However, during tracking, many complicated factors may make the accumulated errors in the representation significantly large, leading to tracking drift. This paper aims to improve the robustness of target representation to avoid the influence of the accumulated errors, such that the tracker only acquires the information that facilitates tracking and ignores the distractions. We observe that the locally mutual relations between the feature observations of temporally obtained targets are beneficial to the subspace representation in visual tracking. Thus, we propose a novel subspace learning algorithm for visual tracking, which imposes joint row-wise sparsity structure on the target subspace to adaptively exclude distractive information. The sparsity is induced by exploiting the locally mutual relations between the feature observations during learning. To this end, we formulate tracking as a subspace sparsity inducing problem. A large number of experiments on various challenging video sequences demonstrate that our tracker outperforms many other state-of-the-art trackers.

Description: Color-to-gray (C2G) image conversion is the process of transforming a color image into a grayscale one. Despite its wide usage in real-world applications, little work has been dedicated to compare the performance of C2G conversion algorithms. Subjective evaluation is reliable but is also inconvenient and time consuming. Here, we make one of the first attempts to develop an objective quality model that automatically predicts the perceived quality of C2G converted images. Inspired by the philosophy of the structural similarity index, we propose a C2G structural similarity (C2G-SSIM) index, which evaluates the luminance, contrast, and structure similarities between the reference color image and the C2G converted image. The three components are then combined depending on image type to yield an overall quality measure. Experimental results show that the proposed C2G-SSIM index has close agreement with subjective rankings and significantly outperforms existing objective quality metrics for C2G conversion. To explore the potentials of C2G-SSIM, we further demonstrate its use in two applications: 1) automatic parameter tuning for C2G conversion algorithms and 2) adaptive fusion of C2G converted images.

Description: In this paper, we propose a skin classification method exploiting faces and bodies automatically detected in the image, to adaptively initialize individual ad hoc skin classifiers. Each classifier is initialized by a face and body couple or by a single face, if no reliable body is detected. Thus, the proposed method builds an ad hoc skin classifier for each person in the image, resulting in a classifier less dependent from changes in skin color due to tan levels, races, genders, and illumination conditions. The experimental results on a heterogeneous data set of labeled images show that our proposal outperforms the state-of-the-art methods, and that this improvement is statistically significant.

Description: Local binary descriptors are attracting increasingly attention due to their great advantages in computational speed, which are able to achieve real-time performance in numerous image/vision applications. Various methods have been proposed to learn data-dependent binary descriptors. However, most existing binary descriptors aim overly at computational simplicity at the expense of significant information loss which causes ambiguity in similarity measure using Hamming distance. In this paper, by considering multiple features might share complementary information, we present a novel local binary descriptor, referred as ring-based multi-grouped descriptor (RMGD), to successfully bridge the performance gap between current binary and floated-point descriptors. Our contributions are twofold. First, we introduce a new pooling configuration based on spatial ring-region sampling, allowing for involving binary tests on the full set of pairwise regions with different shapes, scales, and distances. This leads to a more meaningful description than the existing methods which normally apply a limited set of pooling configurations. Then, an extended Adaboost is proposed for an efficient bit selection by emphasizing high variance and low correlation, achieving a highly compact representation. Second, the RMGD is computed from multiple image properties where binary strings are extracted. We cast multi-grouped features integration as rankSVM or sparse support vector machine learning problem, so that different features can compensate strongly for each other, which is the key to discriminativeness and robustness. The performance of the RMGD was evaluated on a number of publicly available benchmarks, where the RMGD outperforms the state-of-the-art binary descriptors significantly.