ALBERT

Description: Clustered microcalcifications (MCs) in mammograms are an important early sign of breast cancer in women. Their accurate detection is important in computer-aided detection (CADe). In this paper, we integrated the possibilistic fuzzy c-means (PFCM) clustering algorithm and weighted support vector machine (WSVM) for the detection of MC clusters in full-field digital mammograms (FFDM). For each image, suspicious MC regions are extracted with region growing and active contour segmentation. Then geometry and texture features are extracted for each suspicious MC, a mutual information-based supervised criterion is used to select important features, and PFCM is applied to cluster the samples into two clusters. Weights of the samples are calculated based on possibilities and typicality values from the PFCM, and the ground truth labels. A weighted nonlinear SVM is trained. During the test process, when an unknown image is presented, suspicious regions are located with the segmentation step, selected features are extracted, and the suspicious MC regions are classified as containing MC or not by the trained weighted nonlinear SVM. Finally, the MC regions are analyzed with spatial information to locate MC clusters. The proposed method is evaluated using a database of 410 clinical mammograms and compared with a standard unweighted support vector machine (SVM) classifier. The detection performance is evaluated using response receiver operating (ROC) curves and free-response receiver operating characteristic (FROC) curves. The proposed method obtained an area under the ROC curve of 0.8676, while the standard SVM obtained an area of 0.8268 for MC detection. For MC cluster detection, the proposed method obtained a high sensitivity of 92 % with a false-positive rate of 2.3 clusters/image, and it is also better than standard SVM with 4.7 false-positive clusters/image at the same sensitivity.

Description: We investigate the detection and localization properties of harmonic tags working at microwave frequencies. A two-tone interrogation signal and a dedicated signal processing scheme at the receiver are proposed to eliminate phase ambiguities caused by the short signal wavelength and to provide accurate distance/position estimation even in the presence of clutter and multipath. The theoretical limits on tag detection and localization accuracy are investigated starting from a concise characterization of harmonic backscattered signals. Numerical results show that accuracies in the order of centimeters are feasible within an operational range of a few meters in the RFID UHF band.

Description: This paper proposes a general model of superdirectivity to provide analytical and closed-form solutions for arbitrary sensor arrays. Based on the equivalence between the maximum directivity factor and the maximum array gain in the isotropic noise field, Gram-Schmidt orthogonalization is introduced and recursively transformed into a matrix form to conduct pre-whitening and matching operations that result in superdirectivity solutions. A Gram-Schmidt mode-beam decomposition and synthesis method is then presented to formally implement these solutions. Illustrative examples for different arrays are provided to demonstrate the feasibility of this method, and a reduced rank technique is used to deal with the practical array design for robust beamforming and acceptable high-order superdirectivity. Experimental results that are provided for a linear array consisting of nine hydrophones show the good performance of the technique. A superdirective beampattern with a beamwidth of 48.05° in the endfire direction is typically achieved when the inter-sensor spacing is only 0.09λ (λ is the wavelength), and the directivity index is up to 12 dB, which outperforms that of the conventional delay-and-sum counterpart by 6 dB.

Description: In this paper, we introduce a new biometric verification and template protection system which we call THRIVE. The system includes novel enrollment and authentication protocols based on threshold homomorphic encryption where a private key is shared between a user and a verifier. In the THRIVE system, only encrypted binary biometric templates are stored in a database and verification is performed via homomorphically randomized templates, thus, original templates are never revealed during authentication. Due to the underlying threshold homomorphic encryption scheme, a malicious database owner cannot perform full decryption on encrypted templates of the users in the database. In addition, security of the THRIVE system is enhanced using a two-factor authentication scheme involving user’s private key and biometric data. Using simulation-based techniques, the proposed system is proven secure in the malicious model. The proposed system is suitable for applications where the user does not want to reveal her biometrics to the verifier in plain form, but needs to prove her identity by using biometrics. The system can be used with any biometric modality where a feature extraction method yields a fixed size binary template and a query template is verified when its Hamming distance to the database template is less than a threshold. The overall connection time for the proposed THRIVE system is estimated to be 336 ms on average for 256-bit biometric templates on a desktop PC running with quad core 3.2 GHz CPUs at 10 Mbit/s up/down link connection speed. Consequently, the proposed system can be efficiently used in real-life applications.

Description: This paper is concerned with spatial properties of linear arrays of antennas spaced less than half wavelength. Possible applications are in multiple-input multiple-output (MIMO) wireless links for the purpose of increasing the spatial multiplexing gain in a scattering environment, as well as in other areas such as sonar and radar. With reference to a receiving array, we show that knowledge of the received field can be extrapolated beyond the actual array size by exploiting the finiteness of the interval of real directions from which the field components impinge on the array. This property permits to increase the performance of the array in terms of angular resolution. A simple signal processing technique is proposed allowing formation of a set of beams capable to cover uniformly the entire horizon with an angular resolution better than that achievable by a classical uniform-weighing half-wavelength-spaced linear array. Results are also applicable to active arrays. As the above approach leads to arrays operating in super-directive regime, we discuss all related critical aspects, such as sensitivity to external and internal noises and to array imperfections, and bandwidth, so as to identify the basic design criteria ensuring the array feasibility.

Description: No description available

Description: Cooperative spectrum sensing exploits the spatial diversity to improve the detection of occupied channels in cognitive radio networks (CRNs). Cooperative compressive spectrum sensing (CCSS) utilizing the sparsity of channel occupancy further improves the efficiency by reducing the number of reports without degrading detection performance. In this paper, we firstly and mainly propose the referred multi-candidate orthogonal matrix matching pursuit (MOMMP) algorithms to efficiently and effectively detect occupied channels at fusion center (FC), where multi-candidate identification and orthogonal projection are utilized to respectively reduce the number of required iterations and improve the probability of exact identification. Secondly, two common but different approaches based on threshold and Gaussian distribution are introduced to realize the multi-candidate identification. Moreover, to improve the detection accuracy and energy efficiency, we propose the matrix construction based on shrinkage and gradient descent (MCSGD) algorithm to provide a deterministic filter coefficient matrix of low t-average coherence. Finally, several numerical simulations validate that our proposals provide satisfactory performance with higher probability of detection, lower probability of false alarm and less detection time.

Description: This article provides a general Bayesian approach to the tasks of linear and nonlinear acoustic echo cancellation (AEC). We introduce a state-space model with latent state vector modeling all relevant information of the unknown system. Based on three cases for defining the state vector (to model a linear or nonlinear echo path) and its mathematical relation to the observation, it is shown that the normalized least mean square algorithm (with fixed and adaptive stepsize), the Hammerstein group model, and a numerical sampling scheme for nonlinear AEC can be derived by applying fundamental techniques for probabilistic graphical models. As a consequence, the major contribution of this Bayesian approach is a unifying graphical-model perspective which may serve as a powerful framework for future work in linear and nonlinear AEC.

Description: A new reweighted proportionate affine projection algorithm (RPAPA) with memory and row action projection (MRAP) is proposed in this paper. The reweighted PAPA is derived from a family of sparseness measures, which demonstrate performance similar to mu-law and the l 0 norm PAPA but with lower computational complexity. The sparseness of the channel is taken into account to improve the performance for dispersive system identification. Meanwhile, the memory of the filter’s coefficients is combined with row action projections (RAP) to significantly reduce computational complexity. Simulation results demonstrate that the proposed RPAPA MRAP algorithm outperforms both the affine projection algorithm (APA) and PAPA, and has performance similar to l 0 PAPA and mu-law PAPA, in terms of convergence speed and tracking ability. Meanwhile, the proposed RPAPA MRAP has much lower computational complexity than PAPA, mu-law PAPA, and l 0 PAPA, etc., which makes it very appealing for real-time implementation.

Description: Electrocardiogram (ECG) is conducted to monitor the electrical activity of the heart by presenting small amplitude and duration signals; as a result, hidden information present in ECG data is difficult to determine. However, this concealed information can be used to detect abnormalities. In our study, a fast feature-fusion method of ECG heartbeat classification based on multi-linear subspace learning is proposed. The method consists of four stages. First, baseline and high frequencies are removed to segment heartbeat. Second, as an extension of wavelets, wavelet-packet decomposition is conducted to extract features. With wavelet-packet decomposition, good time and frequency resolutions can be provided simultaneously. Third, decomposed confidences are arranged as a two-way tensor, in which feature fusion is directly implemented with generalized N dimensional ICA (GND-ICA). In this method, co-relationship among different data information is considered, and disadvantages of dimensionality are prevented; this method can also be used to reduce computing compared with linear subspace-learning methods (PCA). Finally, support vector machine (SVM) is considered as a classifier in heartbeat classification. In this study, ECG records are obtained from the MIT-BIT arrhythmia database. Four main heartbeat classes are used to examine the proposed algorithm. Based on the results of five measurements, sensitivity, positive predictivity, accuracy, average accuracy, and t-test, our conclusion is that a GND-ICA-based strategy can be used to provide enhanced ECG heartbeat classification. Furthermore, large redundant features are eliminated, and classification time is reduced.

Description: This paper presents a distributed approach to optimal power flow (OPF) in an electrical network, suitable for application in a future smart grid scenario where access to resource and control is decentralized. The non-convex OPF problem is solved by an augmented Lagrangian method, similar to the widely known ADMM algorithm, with the key distinction that penalty parameters are constantly increased. A (weak) assumption on local solver reliability is required to always ensure convergence. A certificate of convergence to a local optimum is available in the case of bounded penalty parameters. For moderate sized networks (up to 300 nodes, and even in the presence of a severe partition of the network), the approach guarantees a performance very close to the optimum, with an appreciably fast convergence speed. The generality of the approach makes it applicable to any (convex or non-convex) distributed optimization problem in networked form. In the comparison with the literature, mostly focused on convex SDP approximations, the chosen approach guarantees adherence to the reference problem, and it also requires a smaller local computational complexity effort.

Description: This paper presents a new complex adaptive notch filter to estimate and track the frequency of a complex sinusoidal signal. The gradient-adaptive lattice structure instead of the traditional gradient one is ad...

Description: Developing compressed sensing (CS) theory reveals that optimal reconstruction of an unknown signal can be achieved from very limited observations by utilizing signal sparsity. For inverse synthetic aperture ra...

Description: Recently, sparse representation has been successfully used in single image super-resolution reconstruction. Unlike the traditional single image super-resolution methods such as image interpolation, the super-r...

Description: In this paper, we study inter-operator spectrum sharing and intra-operator resource allocation in shared spectrum access communication systems and propose efficient dynamic solutions to address both inter-oper...

Description: We explore joint training strategies of DNNs for simultaneous dereverberation and acoustic modeling to improve the performance of distant speech recognition. There are two key contributions. First, a new DNN s...

Description: The standard median filter based on a symmetric moving window has only one tuning parameter: the window width. Despite this limitation, this filter has proven extremely useful and has motivated a number of ext...

Description: The multichannel Wiener filter (MWF) is a well-established noise reduction technique for speech processing. Most commonly, the speech component in a selected reference microphone is estimated. The choice of th...

Description: This paper presents an optimal watermark embedding method combining spread spectrum and quantization. In the method, the host signal vector is quantized to embed a multiple-bit watermark, and meanwhile, the qu...

Description: Tracking algorithms have important applications in detection of humans and vehicles for border security and other areas. For large-scale deployment of such algorithms, it is critical to provide methods for the...

Description: This paper presents an incremental learning algorithm for the hybrid RBF-BP (ILRBF-BP) network classifier. A potential function is introduced to the training sample space in space mapping stage, and an increme...

Description: In this paper, we propose and investigate two novel techniques to perform multiple relay selection in large multi-hop decode-and-forward relay networks. The two proposed techniques exploit sparse signal recove...

Description: In this work, we show that by using a recursive random forest together with an alpha beta filter classifier, it is possible to classify radar tracks from the tracks’ kinematic data. The kinematic data is from ...

Description: In this work, a novel family of state space adaptive algorithms is introduced. The proposed family of algorithms is derived based on stochastic gradient approach with a generalized least mean cost function J[k]=E[∥ε[k]∥2L ] for any integer L. Since this generalized cost function is having power `2L’, it includes the whole family of the power of two-based algorithms by having different values of L. The novelty of the work resides in the fact that such a cost function has never been used in the framework of state space model. It is a well-known fact that the knowledge of state space model improves the estimation of state parameters of that system. Hence, by employing the state space model with a generalized cost function, we provide an efficient way to estimate the state parameters. The proposed family of algorithms inherit simplicity in its structure due to the use of stochastic gradient approach in contrast to the other model-based algorithms such as Kalman filter and its variants. This fact is supported by providing a comparison of the computational complexities of these algorithms. More specifically, the proposed family of algorithms has computational complexity far lesser than that of the Kalman filter. The stability of the proposed family of algorithms is analysed by providing the convergence analysis. Extensive simulations are presented to provide concrete justification and to compare the performances of the proposed family of algorithms with that of the Kalman filter.

Description: Image annotation has been a challenging problem due to the well-known semantic gap between two heterogeneous information modalities, i.e., the visual modality referring to low-level visual features and the semantic modality referring to high-level human concepts. To bridge the semantic gap, we present an extension of latent Dirichlet allocation (LDA), denoted as class-specific Gaussian-multinomial latent Dirichlet allocation (csGM-LDA), in an effort to simulate the human’s visual perception system. An analysis of previous supervised LDA models shows that the topics discovered by generative LDA models are driven by general image regularities rather than the semantic regularities for image annotation. To address this, csGM-LDA is introduced by using class supervision at the level of visual features for multimodal topic modeling. The csGM-LDA model combines the labeling strength of topic supervision with the flexibility of topic discovery, and the modeling problem can be effectively solved by a variational expectation-maximization (EM) algorithm. Moreover, as natural images usually generate an enormous size of high-dimensional data in annotation applications, an efficient descriptor based on Laplacian regularized uncorrelated tensor representation is proposed for explicitly exploiting the manifold structures in the high-order image space. Experimental results on two standard annotation datasets have shown the effectiveness of the proposed method by comparing with several state-of-the-art annotation methods.

Description: Femtocell is a novel technology that is used for escalating indoor coverage as well as the capacity of traditional cellular networks. However, interference is the limiting factor for performance improvement due to co-channel deployment between macrocells and femtocells. The traditional network planning is not feasible because of the random deployment of femtocells. Therefore, self-organization approaches are the key to having successful deployment of femtocells. This study presents the joint resource block (RB) and power allocation task for the two-tier femtocell network in a self-organizing manner, with the concern to minimizing the impact of interference and maximizing the energy efficiency. In this study, we analyze the performance of the system in terms of the energy efficiency, which is composed of both the transmission and circuit power. Most of the previous studies investigate the performance regarding the throughput requirement of the two-tier femtocell network while the energy efficiency aspect is largely ignored. Here, the joint allocation task is modeled as a non-cooperative game which is demonstrated to exhibit pure and unique Nash equilibrium. In order to reduce the complexity of the proposed non-cooperative game, the joint RB and power allocation task is divided into two subproblems: an RB allocation and a particle swarm optimization-based power allocation. The analysis of the proposed game is carried out in terms of not only energy efficiency but also throughput. With practical 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE) parameters, the simulation results illustrate the superior performance of the proposed game as compared to the traditional methods. Also, the comparison is carried out with the joint allocation scheme which only considers the throughput as the objective function. The results illustrate that significant performance improvement is achieved in terms of energy efficiency with slight loss in the throughput. The analysis in regard to energy efficiency and throughput of the two-tier femtocell network is carried out in terms of the performance metrics, which include convergence, impact of varying RBs, impact of femtocell density, and the fairness index.

Description: Noisy low resolution (LR) images are always obtained in real applications, but many existing image magnification algorithms can not get good result from a noisy LR image. We propose a two-step image magnification algorithm to solve this problem. The proposed algorithm takes the advantages of both regularization-based method and learning-based method. The first step is based on total variation (TV) regularization and the second step is based on sparse representation. In the first step, we add a constraint on the TV regularization model to magnify the LR image and at the same time to suppress the noise in it. In the second step, we propose an order-changed dictionary training algorithm to train the dictionaries which is dominated by texture details. Experimental results demonstrate that the proposed algorithm performs better than many other algorithms when the noise is not serious. The proposed algorithm can also provide better visual quality on natural LR images.

Description: Bayesian filter is an efficient approach for multi-target tracking in the presence of clutter. Recently, considerable attention has been focused on probability hypothesis density (PHD) filter, which is an intensity approximation of the multi-target Bayesian filter. However, PHD filter is inapplicable to cases in which target detection probability is low. The use of this filter may result in a delay in data processing because it handles received measurements periodically, once every sampling period. To track multiple targets in the case of low detection probability and to handle received measurements in real time, we propose a sequential measurement-driven Bayesian filter. The proposed filter jointly propagates the marginal distributions and existence probabilities of each target in the filter recursion. We also present an implementation of the proposed filter for linear Gaussian models. Simulation results demonstrate that the proposed filter can more accurately track multiple targets than the Gaussian mixture PHD filter or cardinalized PHD filter.

Description: Electric grids are interconnected complex systems consisting of generation, transmission, distribution, and active loads, recently called prosumers as they produce and consume electric energy. Additionally, these encompass a vast array of equipment such as machines, power transformers, capacitor banks, power electronic devices, motors, etc. that are continuously evolving in their demand characteristics. Given these conditions, signal processing is becoming an essential assessment tool to enable the engineer and researcher to understand, plan, design, and operate the complex and smart electronic grid of the future. This paper focuses on recent developments associated with signal processing applied to power system analysis in terms of characterization and diagnostics. The following techniques are reviewed and their characteristics and applications discussed: active power system monitoring, sparse representation of power system signal, real-time resampling, and time-frequency (i.e., wavelets) applied to power fluctuations.

Description: In this work, we present a novel method for approximating a normal distribution with a weighted sum of normal distributions. The approximation is used for splitting normally distributed components in a Gaussian mixture filter, such that components have smaller covariances and cause smaller linearization errors when nonlinear measurements are used for the state update. Our splitting method uses weights from the binomial distribution as component weights. The method preserves the mean and covariance of the original normal distribution, and in addition, the resulting probability density and cumulative distribution functions converge to the original normal distribution when the number of components is increased. Furthermore, an algorithm is presented to do the splitting such as to keep the linearization error below a given threshold with a minimum number of components. The accuracy of the estimate provided by the proposed method is evaluated in four simulated single-update cases and one time series tracking case. In these tests, it is found that the proposed method is more accurate than other Gaussian mixture filters found in the literature when the same number of components is used and that the proposed method is faster and more accurate than particle filters.

Description: Convolutional autoencoders (CAEs) are unsupervised feature extractors for high-resolution images. In the pre-processing step, whitening transformation has widely been adopted to remove redundancy by making adjacent pixels less correlated. Pooling is a biologically inspired operation to reduce the resolution of feature maps and achieve spatial invariance in convolutional neural networks. Conventionally, pooling methods are mainly determined empirically in most previous work. Therefore, our main purpose is to study the relationship between whitening processing and pooling operations in convolutional autoencoders for image classification. We propose an adaptive pooling approach based on the concepts of information entropy to test the effect of whitening on pooling in different conditions. Experimental results on benchmark datasets indicate that the performance of pooling strategies is associated with the distribution of feature activations, which can be affected by whitening processing. This provides guidance for the selection of pooling methods in convolutional autoencoders and other convolutional neural networks.

Description: This paper introduces a modified random sample consensus (M-RANSAC) and short-time fractional Fourier transform (STFRFT)-based algorithm for feature extraction of synthetic aperture radar (SAR) scattering cent...

Description: In this paper, we study real-time scalable video broadcast over wireless networks using instantly decodable network coding (IDNC). Such real-time scalable videos have hard deadline and impose a decoding order on the video layers. We first derive the upper bound on the probability that the individual completion times of all receivers meet the deadline. Using this probability, we design two prioritized IDNC algorithms, namely the expanding window IDNC (EW-IDNC) algorithm and the non-overlapping window IDNC (NOW-IDNC) algorithm. These algorithms provide a high level of protection to the most important video layer, namely the base layer, before considering additional video layers, namely the enhancement layers, in coding decisions. Moreover, in these algorithms, we select an appropriate packet combination over a given number of video layers so that these video layers are decoded by the maximum number of receivers before the deadline. We formulate this packet selection problem as a two-stage maximal clique selection problem over an IDNC graph. Simulation results over a real scalable video sequence show that our proposed EW-IDNC and NOW-IDNC algorithms improve the received video quality compared to the existing IDNC algorithms.

Description: The application of device-to-device (D2D) communication in cellular networks can significantly improve the efficiency of spectrum utilization, which benefits local area cooperative services. On the other hand, network coding can realize more efficient cooperation among cellular users. Thus, it is natural to combine network coding with D2D communication in order to further increase the system sum rate. In this paper, a coalitional graph game framework is proposed to jointly accomplish resource allocation and relay selection, two challenging problems in network coding-aided D2D communication networks. It is shown that this framework can model D2D communication combined with various network coding schemes. Based on the coalitional graph game framework, a distributed algorithm with low computational complexity is proposed to solve the formulated problem for physical layer network coding-aided D2D communication. Finally, the performance of the proposed transmission scheme is evaluated through extensive simulations.

Description: This paper studies the delay reduction problem for instantly decodable network coding (IDNC)-based device-to-device (D2D) communication-enabled networks. Unlike conventional point-to-multipoint (PMP) systems in which the wireless base station has the sufficient computation abilities, D2D networks rely on battery-powered operations of the devices. Therefore, a particular emphasis on the computation complexity needs to be addressed in the design of delay reduction algorithms for D2D networks. While most of the existing literature on IDNC directly extend the delay reduction PMP schemes, known to be NP-hard, to the D2D setting, this paper proposes to investigate and minimize the complexity of such algorithms for battery-powered devices. With delay minimization problems in IDNC-based systems being equivalent to a maximum weight clique problems in the IDNC graph, the presented algorithms, in this paper, can be applied to different delay aspects. This paper introduces and focuses on the reduction of the maximum value of the decoding delay as it represents the most general solution. The complexity of the solution is reduced by first proposing efficient methods for the construction, the update, and the dimension reduction of the IDNC graph. The paper, further, shows that, under particular scenarios, the problem boils down to a maximum clique problem. Due to the complexity of discovering such maximum clique, the paper presents a fast selection algorithm. Simulation results illustrate the performance of the proposed schemes and suggest that the proposed fast selection algorithm provides appreciable complexity gain as compared to the optimal selection one, with a negligible degradation in performance. In addition, they indicate that the running time of the proposed solution is close to the random selection algorithm.

Description: An important characteristic of a cognitive radar is the capability to adjust its transmitted waveform to adapt to the radar environment. The adaptation of the transmit waveform requires an effective framework ...

Description: Several information recovery systems use functions to determine similarity among objects in a collection. Such functions require a similarity threshold, from which it becomes possible to decide on the similari...

Description: Adaptive filtering algorithms promise an improvement of the active noise control (ANC) problem encountered in many scenarios. Just to name a few, the Filtered-X Least Mean Square (FXLMS) algorithm, the Leaky F...

Description: To mitigate inter-carrier interference due to large carrier frequency offset (CFO) in an uplink single-carrier frequency division multiple access (SC-FDMA) system, a three-tap adaptive frequency-domain decisio...

Description: Subspace-based direction-of-arrival (DoA) estimation commonly relies on the Principal-Component Analysis (PCA) of the sensor-array recorded snapshots. Therefore, it naturally inherits the sensitivity of PCA ag...

Description: K-singular value decomposition (K-SVD) is a frequently used dictionary learning (DL) algorithm that iteratively works between sparse coding and dictionary updating. The sparse coding process generates sparse c...

Description: This paper reports the investigation on the micromotion characteristics of chaff dipoles and a chaff cloud. Micromotion models of chaff dipoles and steady-state chaff clouds are discussed. Fourier transform me...

Description: In this paper, online non-negative discriminative dictionary learning for tracking is proposed, which combines the advantages of the global dictionary learning model and the class-specific dictionary learning ...

Description: A robust object tracking algorithm is proposed in this paper based on an online discriminative appearance modeling mechanism. In contrast with traditional trackers whose computations cover the whole target reg...

Description: A two-dimensional (2D) sparse signal model is developed for pulse Doppler MIMO radars. Using this model, we develop the 2D sparse learning via iterative minimization (2D SLIM) algorithm. Simulation results show that the 2D SLIM compared to the 1D SLIM drastically reduces the computational burden while both of them have the same performance. Also, for estimation of range-angle-Doppler parameters, the 2D SLIM outperforms the matched filter (MF), smoothed L0-norm (SL0), iterative adaptive approach (IAA), and spectral projected gradient for l 1 -norm minimization (SPGL1) algorithms.

Description: Two improvements of power system electromechanical oscillation estimation via subspace identification are proposed in this paper. Singular value-based criteria are introduced not only to improve the computation efficiency for determining the model order of the estimated system but also to improve the accuracy of the estimates. A mode matching method based on the characteristics of accurate mode shape estimates is proposed to ensure the correctness of the estimates. Numerical results from three different scale power systems illustrate the feasibility of the proposed methods.

Description: The weighted sum-rate maximization (WSRMax) problem is important for the radio resource allocation in wireless networks. In this paper, we focus on solving the WSRMax optimization problem in a Home eNodeB (HeNB) wireless network for the multiple input single output (MISO) downlink transmission. Based on an ameliorated max-min multi-cell uplink-downlink throughput duality, we design a distributed algorithm which contains Loop1 and Loop2 associating with the max-min procedure, respectively. Especially, in Loop2 procedure, we propose a new distributed iteration scheme with the complete proof. During the implementation of the proposed algorithm, randomly deployed HeNBs only need to share information with their neighboring nodes. Simulation results show that the two processes can converge to a stable state, and the network capacity is dramatically improved with the coordination among HeNBs.

Description: In this work, we propose a sparsity-exploiting dynamic rainfall monitoring methodology using rain-induced attenuation measurements from microwave links. To estimate rainfall field intensity dynamically from a ...

Description: We propose a line-of-sight (LOS)/non-line-of-sight (NLOS) mixture source localization algorithm that utilizes the weighted least squares (WLS) method in LOS/NLOS mixture environments, where the weight matrix i...

Description: Maximum hands-off control is a control mechanism that maximizes the length of the time duration on which the control is exactly zero. Such a control is important for energy-aware control applications, since it...

Description: The paper addresses the fault detection (FD) problem for a class of discrete-time Markov jump linear systems (MJLSs) with deficient transition rates, which simultaneously considers the totally known, partly un...

Description: Inspired by biological systems, molecular communication has been proposed as a new communication paradigm that uses biochemical signals to transfer information from one nano device to another over a short distance. The biochemical nature of the information transfer process implies that for molecular communication purposes, the development of molecular channel models should take into consideration diffusion phenomenon as well as the physical/biochemical kinetic possibilities of the process. The physical and biochemical kinetics arise at the interfaces between the diffusion channel and the transmitter/receiver units. These interfaces are herein termed molecular antennas. In this paper, we present the deterministic propagation model of the molecular communication between an immobilized nanotransmitter and nanoreceiver, where the emission and reception kinetics are taken into consideration. Specifically, we derived closed-form system-theoretic models and expressions for configurations that represent different communication systems based on the type of molecular antennas used. The antennas considered are the nanopores at the transmitter and the surface receptor proteins/enzymes at the receiver. The developed models are simulated to show the influence of parameters such as the receiver radius, surface receptor protein/enzyme concentration, and various reaction rate constants. Results show that the effective receiver surface area and the rate constants are important to the system’s output performance. Assuming high rate of catalysis, the analysis of the frequency behavior of the developed propagation channels in the form of transfer functions shows significant difference introduce by the inclusion of the molecular antennas into the diffusion-only model. It is also shown that for t 〉 〉 0 and with the information molecules’ concentration greater than the Michaelis-Menten kinetic constant of the systems, the inclusion of surface receptors proteins and enzymes in the models makes the system act like a band-stop filter over an infinite frequency range.

Description: No description available

Description: In this paper, we propose an adaptive waveform polarization method for the estimation of target scattering matrix in the presence of clutter. The proposed sequential algorithm, based on the concept of sequential minimum mean square error (MSE) estimation, to determine the coefficients of the scattering matrix, guarantees the convergence and the resulting computational complexity is linear with the number of iterations. The effectiveness of the proposed method is validated through numerical results, underlining the performance improvement given by joint transmission and reception (Tx/Rx) polarization optimization for the scalar system. Also, the results show that the vector system with transmission polarization optimization provides a comparative performance as the scalar measurement system employing joint Tx/Rx polarization optimization. Less computation burden highlights the advantage of the former mode.

Description: A novel feature set for low-dimensional signal representation, designed for classification or clustering of non-stationary signals with complex variation in time and frequency, is presented. The feature repres...

Description: There is no doubt that big data are now rapidly expanding in all science and engineering domains. While the potential of these massive data is undoubtedly significant, fully making sense of them requires new w...

Description: Traditional contrast enhancement techniques were developed to enhance the dynamic range of images with narrow histograms. However, it is not unusual that an image with a broad histogram still suffers from low ...

Description: Auto-focusing task, which automatically obtains the best image focus, plays an important role to improve the image definition for the industrial image measurement application. Image-based auto-focusing is one ...

Description: The problem of 1-bit compressive sampling is addressed in this paper. We introduce an optimization model for reconstruction of sparse signals from 1-bit measurements. The model targets a solution that has the ...

Description: In this article, we consider a spectrum sharing cognitive radio network with a transmit antenna selection scheme, subject to the peak interference constraint of primary user (PU) and the maximum transmit power...

Description: An array signal recovery algorithm based on sparse signal reconstruction theory is proposed for a single-RF-channel digital beamforming (DBF) array. A single-RF-channel antenna array is a low-cost antenna arra...

Description: We consider an iterative design of independently operating local quantizers at nodes that should cooperate without interaction to achieve application objectives for distributed estimation systems. We suggest a...

Description: Simultaneous localization and mapping (SLAM) is widely used in many robotic applications and autonomous navigation. This paper presents a study of FastSLAM2.0 computational complexity based on a monocular visi...

Description: Nyquist folding receiver (NYFR) is a novel ultra-wideband receiver architecture which can realize wideband receiving with a small amount of equipment. Linear frequency modulated/binary phase shift keying (LFM/...

Description: Big data, characterized by huge volumes of continuously varying streams of information, present formidable challenges in terms of acquisition, processing, and transmission, especially when one considers novel ...

Description: This paper examines the performance of two position-velocity-measured (PVM) α- β- γ tracking filters. The first estimates the target acceleration using the measured velocity, and the second, which is proposed for the first time in this paper, estimates acceleration using the measured position. To quantify the performance of these PVM α- β- γ filters, we analytically derive steady-state errors that assume that the target is moving with constant acceleration or jerk. With these performance indices, the optimal gains of the PVM α- β- γ filters are determined using a minimum-variance filter criterion. The performance of each filter under these optimal gains is then analyzed and compared. Numerical analyses clarify the performance of the PVM α- β- γ filters and verify that their accuracy is better than that of the general position-only-measured α- β- γ filter, even when the variance in velocity measurement noise is comparatively large. We identify the conditions under which the proposed PVM α- β- γ filter outperforms the general α- β- γ filter for different ratios of noise variance in the velocity and position measurements. Finally, numerical simulations verify the effectiveness of the PVM α- β- γ filters for a realistic maneuvering target.

Description: Different from conventional phased array, which provides only angle-dependent beampattern, frequency diverse array (FDA) employs a small frequency increment across the antenna elements and thus results in a range angle-dependent beampattern. However, due to imperfect electronic devices, it is difficult to ensure accurate frequency increments, and consequently, the array performance will be degraded by unavoidable frequency increment errors. In this paper, we investigate the impacts of frequency increment errors on FDA beampattern. We derive the beampattern errors caused by deterministic frequency increment errors. For stochastic frequency increment errors, the corresponding upper and lower bounds of FDA beampattern error are derived. They are verified by numerical results. Furthermore, the statistical characteristics of FDA beampattern with random frequency increment errors, which obey Gaussian distribution and uniform distribution, are also investigated.

Description: Traditional remote sensing images classification methods focused on using a large amount of labeled target data to train an efficient classification model. However, these approaches were generally based on the target data without considering a host of auxiliary data or the additional information of auxiliary data. If the valuable information from auxiliary data could be successfully transferred to the target data, the performance of the classification model would be improved. In addition, from the perspective of practical application, these valuable information from auxiliary data should be fully used. Therefore, in this paper, based on the transfer learning idea, we proposed a novel information transferring approach to improve the remote sensing images classification performance. The main rationale of this approach is that first, the information of the same areas associated with each pixel is modeled as the intra-class set, and the information of different areas associated with each pixel is modeled as the inter-class set, and then the obtained texture feature information of each area from auxiliary is transferred to the target data set such that the inter-class set is separated and intra-class set is gathered as far as possible. Experiments show that the proposed approach is effective and feasible.

Description: Speech signals in enclosed environments are often distorted by reverberation and noise. In speech communication systems with several randomly distributed microphones, involving a dynamic speaker and unknown source location, it is of great interest to monitor the perceived quality at each microphone and select the signal with the best quality. Most of existing approaches for quality estimation require prior information or a clean reference signal, which is unfortunately seldom available. In this paper, a practical non-intrusive method for quality assessment of reverberated speech signals is proposed. Using a statistical model of the reverberation process, we examine the energies as measured by unidirectional elements in a microphone array. By measuring the power ratio, we obtain a measure for the amount of reverberation in the received acoustic signals. This measure is then utilized to derive a blind estimation of the direct-to-reverberation energy ratio in the room. The proposed approach attains a simple, reliable, and robust quality measure, shown here through persuasive simulation results.

Description: Demand-side load management is able to significantly improve the energy efficiency of smart grids. Since the electricity production cost depends on the aggregate energy usage of multiple consumers, an important incentive problem emerges: self-interested consumers want to increase their own utilities by consuming more than the socially optimal amount of energy during peak hours since the increased cost is shared among the entire set of consumers. To incentivize self-interested consumers to take the socially optimal scheduling actions, we design a new class of protocols based on review strategies. These strategies work as follows: first, a review stage takes place in which a statistical test is performed based on the daily prices of the previous billing cycle to determine whether or not the other consumers schedule their electricity loads in a socially optimal way. If the test fails, the consumers trigger a punishment phase in which, for a certain time, they adjust their energy scheduling in such a way that everybody in the consumer set is punished due to an increased price. Using a carefully designed protocol based on such review strategies, consumers then have incentives to take the socially optimal load scheduling to avoid entering this punishment phase. We rigorously characterize the impact of deploying protocols based on review strategies on the system’s as well as the users’ performance and determine the optimal design (optimal billing cycle, punishment length, etc.) for various smart grid deployment scenarios. Even though this paper considers a simplified smart grid model, our analysis provides important and useful insights for designing incentive-compatible demand-side management schemes based on aggregate energy usage information in a variety of practical scenarios.

Description: The REVERB challenge provides a common framework for the evaluation of feature extraction techniques in the presence of both reverberation and additive background noise. State-of-the-art speech recognition systems perform well in controlled environments, but their performance degrades in realistic acoustical conditions, especially in real as well as simulated reverberant environments. In this contribution, we utilize multiple feature extractors including the conventional mel-filterbank, multi-taper spectrum estimation-based mel-filterbank, robust mel and compressive gammachirp filterbank, iterative deconvolution-based dereverberated mel-filterbank, and maximum likelihood inverse filtering-based dereverberated mel-frequency cepstral coefficient features for speech recognition with multi-condition training data. In order to improve speech recognition performance, we combine their results using ROVER (Recognizer Output Voting Error Reduction). For two- and eight-channel tasks, to get benefited from the multi-channel data, we also use ROVER, instead of the multi-microphone signal processing method, to reduce word error rate by selecting the best scoring word at each channel. As in a previous work, we also apply i-vector-based speaker adaptation which was found effective. In speech recognition task, speaker adaptation tries to reduce mismatch between the training and test speakers. Speech recognition experiments are conducted on the REVERB challenge 2014 corpora using the Kaldi recognizer. In our experiments, we use both utterance-based batch processing and full batch processing. In the single-channel task, full batch processing reduced word error rate (WER) from 10.0 to 9.3 % on SimData as compared to utterance-based batch processing. Using full batch processing, we obtained an average WER of 9.0 and 23.4 % on the SimData and RealData, respectively, for the two-channel task, whereas for the eight-channel task on the SimData and RealData, the average WERs found were 8.9 and 21.7 %, respectively.

Description: No description available

Description: A new method to smooth the target hybrid state with Gaussian mixture measurement likelihood-integrated track splitting (GMM-ITS) in the presence of clutter for a high pulse repetition frequency (HPRF) radar is proposed. This method smooths the target state at fixed lag N and considers all feasible multi-scan target existence sequences in the temporal window of scans in order to smooth the target hybrid state. The smoothing window can be of any length N. The proposed method to smooth the target hybrid state at fixed lag is also applied to the enhanced multiple model (EMM) tracking algorithm. Simulation results indicate that the performance of fixed lag smoothing GMM-ITS significantly improves false track discrimination and root mean square errors (RMSEs).

Description: Currently, communication between nanomachines is an important topic for the development of novel devices. To implement a nanocommunication system, diffusion-based molecular communication is considered as a promising bio-inspired approach. Various technical issues about molecular communications, including channel capacity, noise and interference, and modulation and coding, have been studied in the literature, while the resource allocation problem among multiple nanomachines has not been well investigated, which is a very important issue since all the nanomachines share the same propagation medium. Considering the limited computation capability of nanomachines and the expensive information exchange cost among them, in this paper, we propose a game-theoretic framework for distributed resource allocation in nanoscale molecular communication systems. We first analyze the inter-symbol and inter-user interference, as well as bit error rate performance, in the molecular communication system. Based on the interference analysis, we formulate the resource allocation problem as a non-cooperative molecule emission control game, where the Nash equilibrium is found and proved to be unique. In order to improve the system efficiency while guaranteeing fairness, we further model the resource allocation problem using a cooperative game based on the Nash bargaining solution, which is proved to be proportionally fair. Simulation results show that the Nash bargaining solution can effectively ensure fairness among multiple nanomachines while achieving comparable social welfare performance with the centralized scheme.

Description: For super-resolution (4K ? 2K) displays, super-resolution technologies, which can upsample videos to higher resolution and achieve better visual quality, become more and more important currently. In this paper, an iterative enhanced super-resolution (IESR) system which is based on two-pass edge-dominated interpolation, adaptive enhancement, and adaptive dithering techniques is proposed. The two-pass edge-dominated interpolation with a simple and regular kernel can sharpen visual quality while the adaptive enhancement can provide high-frequency perfection and the adaptive dithering conveys naturalization enhancement such that the proposed IESR system achieves better peak signal-to-noise ratio (PSNR) and exhibits better visual quality. Experimental results indicate that the proposed IESR system, which improves PSNR up to 28.748 dB and promotes structural similarity index measurement (SSIM) up to 0.917611 in averages, is better than the other existing methods. Simulations also exhibit that the proposed IESR system acquires lower computational complexity than the methods which achieve similar visual quality.

Description: Secure sharing of digital images is becoming an important issue. Consequently, many schemes for ensuring image sharing security have been proposed. However, existing approaches focus on the sharing of a single image, rather than multiple images. We propose three kinds of sharing methods that progressively reveal n given secret images according to the sensitivity level of each image. Method 1 divides each secret image into n parts and then combines and hides the parts of the images to get n steganographic (stego) JPEG codes of equal importance. Method 2 is similar; however, it allocates different stego JPEG codes of different `weights? to indicate their strength. Method 3 first applies traditional threshold-sharing to the n secret images, then progressively shares k keys, and finally combines the two sharing results to get n stego JPEG codes. In the recovery phase, various parameters are compared to a pre-specified low/middle/high (L/M/H) threshold and, according to the respective method, determine whether or not secret images are reconstructed and the quality of the images reconstructed. The results of experiments conducted verify the efficacy of our methods.

Description: In a perfect world, we would have `brick wall? filters, no-distortion amplifiers and mixers, and well-coordinated spectrum operations. The real world, however, is prone to various types of unintentional and intentional interference of technogenic (man-made) origin that can disrupt critical communication systems. In this paper, we introduce a methodology for mitigating technogenic interference in communication channels by analog nonlinear filters, with an emphasis on the mitigation of out-of-band and adjacent-channel interference.Interference induced in a communications receiver by external transmitters can be viewed as wide-band non-Gaussian noise affecting a narrower-band signal of interest. This noise may contain a strong component within the receiver passband, which may dominate over the thermal noise. While the total wide-band interference seen by the receiver may or may not be impulsive, we demonstrate that the interfering component due to power emitted by the transmitter into the receiver channel is likely to appear impulsive under a wide range of conditions. We give an example of mechanisms of impulsive interference in digital communication systems resulting from the nonsmooth nature of any physically realizable modulation scheme for transmission of a digital (discontinuous) message.We show that impulsive interference can be effectively mitigated by a nonlinear differential limiters (NDLs). An NDL can be configured to behave linearly when the input signal does not contain outliers. When outliers are encountered, the nonlinear response of the NDL limits the magnitude of the respective outliers in the output signal. The signal quality is improved in excess of that achievable by the respective linear filter, increasing the capacity of a communications channel. The behavior of an NDL, and its degree of nonlinearity, is controlled by a single parameter in a manner that enables significantly better overall suppression of the noise-containing impulsive components compared to the respective linear filter. Adaptive configurations of NDLs are similarly controlled by a single parameter and are suitable for improving quality of nonstationary signals under time-varying noise conditions. NDLs are designed to be fully compatible with existing linear devices and systems and to be used as an enhancement, or as a low-cost alternative, to the state-of-art interference mitigation methods.

Description: We present a new patch-based image restoration algorithm using an adaptive Wiener filter (AWF) with a novel spatial-domain multi-patch correlation model. The new filter structure is referred to as a collaborative adaptive Wiener filter (CAWF). The CAWF employs a finite size moving window. At each position, the current observation window represents the reference patch. We identify the most similar patches in the image within a given search window about the reference patch. A single-stage weighted sum of all of the pixels in the similar patches is used to estimate the center pixel in the reference patch. The weights are based on a new multi-patch correlation model that takes into account each pixel?s spatial distance to the center of its corresponding patch, as well as the intensity vector distances among the similar patches. One key advantage of the CAWF approach, compared with many other patch-based algorithms, is that it can jointly handle blur and noise. Furthermore, it can also readily treat spatially varying signal and noise statistics. To the best of our knowledge, this is the first multi-patch algorithm to use a single spatial-domain weighted sum of all pixels within multiple similar patches to form its estimate and the first to use a spatial-domain multi-patch correlation model to determine the weights. The experimental results presented show that the proposed method delivers high performance in image restoration in a variety of scenarios.

Description: No description available

Description: A new two-dimensional direction-of-arrival estimation algorithm called 2D- l 1-singular value decomposition (SVD) and its improved version called enhanced-2D- l 1-SVD are proposed in this paper. They are designed for rectangular arrays and can also be extended to rectangular arrays with faulty or missing elements. The key idea is to represent direction-of-arrival with two decoupled angles and then successively estimate them. Therefore, two-dimensional direction finding can be achieved by applying several times of one-dimensional sparse reconstruction-based direction finding methods instead of directly extending them to two-dimensional situation. Performance analysis and simulation results reveal that the proposed method has a much lower computational complexity and a similar statistical performance compared with the well-known l 1-SVD algorithm, which has several advantages over conventional direction finding techniques due to the application of sparse signal reconstruction. Moreover, 2D- l 1-SVD has better robustness to the assumed number of sources over l 1-SVD.

Description: In this paper, we propose a novel scheme of orthogonal frequency division multiplexing (OFDM) for intensity modulation direct detection (IM/DD) optical systems. By using this novel scheme of an OFDM optical system, not only odd subcarriers but also even subcarriers can be modulated to transmit a clipping optical signal. A conventional asymmetrically clipping optical (ACO)-OFDM is applied to modulate odd subcarriers while even subcarriers are modulated by a novel technique called a symmetrically clipping optical (SCO)-OFDM. Although both the asymmetrically clipping noise caused by ACO-OFDM and the symmetrically clipping noise caused by SCO-OFDM fall onto the even subcarriers, the former interference can be estimated and removed at the receiver. Thus, SCO-OFDM symbols carried on the even subcarriers can be recovered by subtracting the estimated ACO-OFDM clipping noise from the received signal. Then the SCO-OFDM clipping noise can be removed by subtraction due to its special transmission format. Note that no DC bias added on all subcarriers makes this novel scheme achieve better performance in terms of both power efficiency and symbol error rate (SER).

Description: At the contention-based synchronization, e.g., initial ranging process, it is crucial to identify multiple users through ranging codes and estimate the corresponding parameters such as timing offset and frequency offset. This paper presents an improved parameter estimation and pairing algorithm for initial ranging process in orthogonal frequency-division multiple access (OFDMA) uplink systems by exploiting multidimensional harmonic retrieval (HR) technique. Unlike most existing techniques that estimate each parameters independently and associate the ranging codes with the estimated parameters manually, the proposed method estimates multiple user's ranging code, timing and frequency offsets simultaneously, and pair up all the multiple parameters automatically. The simulation results confirm that the proposed technique not only improves the ranging code detection capability and adjusts the acquisition range of the estimators but also increases the maximum number of resolvable users for given samples.

Description: This paper considers a family of pure analog coding schemes constructed from dynamic systems which are governed by chaotic functions—baker’s map function and its variants. Various decoding methods, including maximum likelihood (ML), minimum mean square error (MMSE), and mixed ML-MMSE decoding algorithms, have been developed for these novel encoding schemes. The proposed mirrored baker’s and single-input baker’s analog codes perform a balanced protection against the fold error (large distortion) and weak distortion and outperform the classical chaotic analog coding and analog joint source-channel coding schemes in literature. Compared to the conventional digital communication system, where quantization and digital error correction codes are used, the proposed analog coding system has graceful performance evolution, low decoding latency, and no quantization noise. Numerical results show that under the same bandwidth expansion, the proposed analog system outperforms the digital ones over a wide signal-to-noise (SNR) range.

Description: This paper presents a new blind and robust image watermarking scheme based on discrete wavelet transform (DWT) and discrete cosine transform (DCT). Two DCT-transformed sub-vectors are used to embed the bits of the watermark sequence in a differential manner. The original sub-vectors are obtained by the sub-sampling of the approximation coefficients of the DWT transform of the host image. During the extraction stage, the simple difference between the corresponding sub-vectors of the watermarked image, gives directly the embedded watermark sequence. Experimental results demonstrate that the proposed technique successfully fulfills the requirement of imperceptibility and provides high robustness against a number of image-processing attacks, such as JPEG compression, noise adding, low-pass filtering, sharpening, and bit-plane removal. Our scheme exhibits also an acceptable to good performance against some geometrical attacks such as resizing and cropping.

Description: This work aims at reducing the embedding distortion of prediction-error expansion (PE)-based reversible watermarking. In the classical PE embedding method proposed by Thodi and Rodriguez, the predicted value is rounded to integer number for integer prediction-error expansion (IPE) embedding. The rounding operation makes a constraint on a predictor’s performance. In this paper, we propose a non-integer PE (NIPE) embedding approach, which can proceed non-integer prediction errors for embedding data into an audio or image file by only expanding integer element of a prediction error while keeping its fractional element unchanged. The advantage of the NIPE embedding technique is that the NIPE technique can really bring a predictor into full play by estimating a sample/pixel in a noncausal way in a single pass since there is no rounding operation. A new noncausal image prediction method to estimate a pixel with four immediate pixels in a single pass is included in the proposed scheme. The proposed noncausal image predictor can provide better performance than Sachnev et al.’s noncausal double-set prediction method (where data prediction in two passes brings a distortion problem due to the fact that half of the pixels were predicted with the watermarked pixels). In comparison with existing several state-of-the-art works, experimental results have shown that the NIPE technique with the new noncausal prediction strategy can reduce the embedding distortion for the same embedding payload.

Description: In through wall radar sensing, the wall parameters estimation (WPE) problem has been a topic that attracts a lot of attention since the wall parameters, i.e., the permittivity and the thickness, are of crucial importance to locate the targets and to produce a well-focused image, but they are usually unknown in practice. To solve this problem, in this paper, the support vector regression (SVR), a powerful tool for regression analysis, is introduced, and its performance on WPE, provided it is used it in the regular way, is investigated. Unfortunately, it is shown that the regular use of SVR cannot afford satisfactory estimation results since the sample data used in SVR, namely the received echoes from the walls, are seriously interfered with the echoes from the targets which are located near the walls. In view of this limitation, a novel SVR-based WPE approach that consists of three stages is proposed by this paper. In the first stage, three regression functions are trained by SVR, one of which will output the estimate of the permittivity in the second stage, and the others are designed to output two instrumental variables for estimating the thickness. In the third stage, the estimate of thickness will be achieved by minimizing a predefined cost function wherein the estimated permittivity and the outputted instrumental variables are involved. The better robustness and higher estimation accuracy of the proposed approach compared to the regular use of SVR are validated by the numerical experimental results using finite-difference time-domain simulations.

Description: The recently released REverberant Voice Enhancement and Recognition Benchmark (REVERB) challenge includes a reverberant automatic speech recognition (ASR) task. This paper describes our proposed system based on multi-channel speech enhancement preprocessing and state-of-the-art ASR techniques. For preprocessing, we propose a single-channel dereverberation method with reverberation time estimation, which is combined with multichannel beamforming that enhances direct sound compared with the reflected sound. In addition, this paper also focuses on state-of-the-art ASR techniques such as discriminative training of acoustic models including the Gaussian mixture model, subspace Gaussian mixture model, and deep neural networks, as well as various feature transformation techniques. Although, for the REVERB challenge, it is necessary to handle various acoustic environments, a single ASR system tends to be overly tuned for a specific environment, which degrades the performance in the mismatch environments. To overcome this mismatch problem with a single ASR system, we use a system combination approach using multiple ASR systems with different features and different model types because a combination of various systems that have different error patterns is beneficial. In particular, we use our discriminative training technique for system combination that achieves better generalization by making systems complementary with the modified discriminative criteria. Experiments show the effectiveness of these approaches, reaching 6.76 and 18.60 % word error rates on the REVERB simulated and real test sets. These are 68.8 and 61.5 % relative improvements over the baseline.

Description: Video multicast is becoming more and more popular in wireless multimedia applications, in which one major challenge is to offer heterogeneous users with a graceful degradation against varying packet loss ratios and channel noise. In this paper, we propose a multi-scale compressed sensing-based wireless video multicast scheme, abbreviated as MCS-cast. The encoder of MCS-cast decomposes each video frame through a discrete wavelet transform (DWT) and explores an optimized compressed sensing (CS) rate to sample/measure each DWT level. The CS measurements are then packed in such a way that all packets are made as equally important as possible, while each packet includes different percentages of different DWT levels. Finally, the packets are transmitted via an analog-like modulator with mapping of the measurements into a very dense constellation. We demonstrate that because of larger percentages of more important DWT levels in each packet, packet loss leads to a much reduced influence on the reconstruction quality. Experimental results show that our MCS-cast preserves the property of graceful degradation for heterogeneous users and can outperform the state-of-the-art SoftCast by up to 3 dB in PSNR at high packet loss ratios (over the same noisy channel).

Description: Unbiased estimation is an efficient alternative to optimal estimation when the noise statistics are not fully known and/or the model undergoes temporary uncertainties. In this paper, we investigate the effect of embedded unbiasedness (EU) on optimal finite impulse response (OFIR) filtering estimates of linear discrete time-invariant state-space models. A new OFIR-EU filter is derived by minimizing the mean square error (MSE) subject to the unbiasedness constraint. We show that the OFIR-UE filter is equivalent to the minimum variance unbiased FIR (UFIR) filter. Unlike the OFIR filter, the OFIR-EU filter does not require the initial conditions. In terms of accuracy, the OFIR-EU filter occupies an intermediate place between the UFIR and OFIR filters. Contrary to the UFIR filter which MSE is minimized by the optimal horizon of N opt points, the MSEs in the OFIR-EU and OFIR filters diminish with N and these filters are thus full-horizon. Based upon several examples, we show that the OFIR-UE filter has higher immunity against errors in the noise statistics and better robustness against temporary model uncertainties than the OFIR and Kalman filters.

Description: Radio interferometric positioning systems are developed for localization in wireless sensor networks (WSNs), and they have the potential to yield highly accurate location information at low computational cost and implementation complexity. In the radio interferometric positioning system (RIPS), two transmitters transmit sinusoidal signals at slightly different frequencies, and two receivers pass the received signals through square-law devices to produce low-frequency differential signals. However, a squaring operation increases the noise power, leading to performance loss. To avoid this problem, a receiver for the RIPS using undersampling techniques (RIPS-u) has been proposed. In this paper, we investigate the performance of the RIPS with a square-law device (RIPS-sq) and the performance of the RIPS-u through theoretical and experimental analyses. Specifically, we compute Cramér-Rao lower bounds (CRLBs) of the range and location estimates in both systems and show that the RIPS-u has lower CRLBs than the RIPS-sq. Furthermore, we have carried out experimental tests by implementing both systems on National Instruments (NI) Universal Software Radio Peripherals (USRPs). From both the theoretical and experimental results, the effectiveness of the RIPS-u over the RIPS-sq is confirmed.

Description: We introduce an ensemble learning method for temporal data that uses a mixture of hidden Markov models (HMM). We hypothesize that the data are generated by K models, each of which reflects a particular trend in the data. The proposed approach, called ensemble HMM (eHMM), is based on clustering within the log-likelihood space and has two main steps. First, one HMM is fit to each of the N individual training sequences. For each fitted model, we evaluate the log-likelihood of each sequence. This results in an N-by-N log-likelihood distance matrix that will be partitioned into K groups using a relational clustering algorithm. In the second step, we learn the parameters of one HMM per cluster. We propose using and optimizing various training approaches for the different K groups depending on their size and homogeneity. In particular, we investigate the maximum likelihood (ML), the minimum classification error (MCE), and the variational Bayesian (VB) training approaches. Finally, to test a new sequence, its likelihood is computed in all the models and a final confidence value is assigned by combining the models’ outputs using an artificial neural network. We propose both discrete and continuous versions of the eHMM.Our approach was evaluated on a real-world application for landmine detection using ground-penetrating radar (GPR). Results show that both the continuous and discrete eHMM can identify meaningful and coherent HMM mixture components that describe different properties of the data. Each HMM mixture component models a group of data that share common attributes. These attributes are reflected in the mixture model’s parameters. The results indicate that the proposed method outperforms the baseline HMM that uses one model for each class in the data.

Description: The quality-of-service (QoS) criteria (measured in terms of the minimum capacity requirement in this paper) are very important to practical indoor power line communication (PLC) applications as they greatly affect the user experience. With a two-way multicarrier relay configuration, in this paper we investigate the joint terminals and relay power optimization for the indoor broadband PLC environment, where the relay node works in the amplify-and-forward (AF) mode. As the QoS-constrained power allocation problem is highly non-convex, the globally optimal solution is computationally intractable to obtain. To overcome this challenge, we propose an alternating optimization (AO) method to decompose this problem into three convex/quasi-convex sub-problems. Simulation results demonstrate the fast convergence of the proposed algorithm under practical PLC channel conditions. Compared with the conventional bidirectional direct transmission (BDT) system, the relay-assisted two-way information exchange (R2WX) scheme can meet the same QoS requirement with less total power consumption.

Description: We consider the estimation of the speech short-time spectral amplitude (STSA) using a parametric Bayesian cost function and speech prior distribution. First, new schemes are proposed for the estimation of the cost function parameters, using an initial estimate of the speech STSA along with the noise masking feature of the human auditory system. This information is further employed to derive a new technique for the gain flooring of the STSA estimator. Next, to achieve better compliance with the noisy speech in the estimator’s gain function, we take advantage of the generalized Gamma distribution in order to model the STSA prior and propose an SNR-based scheme for the estimation of its corresponding parameters. It is shown that in Bayesian STSA estimators, the exploitation of a rough STSA estimate in the parameter selection for the cost function and the speech prior leads to more efficient control on the gain function values. Performance evaluation in different noisy scenarios demonstrates the superiority of the proposed methods over the existing parametric STSA estimators in terms of the achieved noise reduction and introduced speech distortion.

Description: This paper presents adaptive link selection algorithms for distributed estimation and considers their application to wireless sensor networks and smart grids. In particular, exhaustive search-based least mean squares (LMS) / recursive least squares (RLS) link selection algorithms and sparsity-inspired LMS / RLS link selection algorithms that can exploit the topology of networks with poor-quality links are considered. The proposed link selection algorithms are then analyzed in terms of their stability, steady-state, and tracking performance and computational complexity. In comparison with the existing centralized or distributed estimation strategies, the key features of the proposed algorithms are as follows: (1) more accurate estimates and faster convergence speed can be obtained and (2) the network is equipped with the ability of link selection that can circumvent link failures and improve the estimation performance. The performance of the proposed algorithms for distributed estimation is illustrated via simulations in applications of wireless sensor networks and smart grids.

Description: Erasure-correcting codes, which support local repair of codeword symbols, have attracted substantial attention recently for their application in distributed storage systems. This paper investigates a generalization of the usual locally repairable codes. In particular, this paper studies a class of codes with the following property: any small set of codeword symbols can be reconstructed (repaired) from a small number of other symbols. This is referred to as cooperative local repair. The main contribution of this paper is bounds on the trade-off of the minimum distance and the dimension of such codes, as well as explicit constructions of families of codes that enable cooperative local repair. Some other results regarding cooperative local repair are also presented, including an analysis for the well-known Hadamard/Simplex codes.AMS subject classification: 94B05, 94B25, 94B65, 94B60.

Description: Chunked codes are efficient random linear network coding (RLNC) schemes with low computational cost, where the input packets are encoded into small chunks (i.e., subsets of the coded packets). During the network transmission, RLNC is performed within each chunk. In this paper, we first introduce a simple transfer matrix model to characterize the transmission of chunks and derive some basic properties of the model to facilitate the performance analysis. We then focus on the design of overlapped chunked codes, a class of chunked codes whose chunks are non-disjoint subsets of input packets, which are of special interest since they can be encoded with negligible computational cost and in a causal fashion. We propose expander chunked (EC) codes, the first class of overlapped chunked codes that have an analyzable performance, where the construction of the chunks makes use of regular graphs. Numerical and simulation results show that in some practical settings, EC codes can achieve rates within 91 to 97 % of the optimum and outperform the state-of-the-art overlapped chunked codes significantly.