Visual Multiple Object Tracking (MOT) typically utilizes appearance and motion clues for associations. However, these features may be limited under certain challenging scenarios, such as appearance ambiguity and frequent occlusions. In this paper, we introduce a novel deep RF-affinity neural network (DRFAN) that enhances visual tracking with the aid of a passive wireless positioning device, Radio Frequency Identification (RFID). DRFAN aims to solve object tracking by introducing a new concept of a "candidate trajectory" to indicate target movement. This approach fundamentally deviates from existing fusion methods that rely on known visual tracks. Instead, DRFAN exclusively uses detection bounding boxes and RFID signals. The proposed method overcomes the limitations of visual tracking by swiftly resuming correct tracking whenever a failure occurs. This is the first time using signals from low-cost passive RFID tags to achieve image-level localization, and a discriminative neural network is designed specifically for RFID-assisted visual association. Our experimental results validate the robustness and applicability of the proposed approach.

Clipping distortion is a common problem when the amplitude of input signals exceeds the desired region of an analog-to-digital converter. Unlimited Sensing Framework (USF) alleviates the clipping distortion by folding out-of-range signals into a within-range via modulo operations. The USF signal recovery assumes an infinitesimal residual step time in the modulo operation which is generally practically infeasible. Its recovery error is inevitable due to the remainder sampling error during the residual step transition time. Instead of the infinitesimal assumption, a residual-remainder coupled USF is proposed to eliminate the remainder sampling error by coupling the residual sampling component. It is shown that the proposed coupling framework does not only relax the oversampling rate in the original USF to approaching the Nyquist sampling rate, but also provides a more accurate signal recovery capability as the remainder sampling error is eliminated by the coupling of the residual components.

Radio frequency identification (RFID) is a form of wireless communication that has received much attention in recent years due to low costs of passive RFID tags and availability of commercial-off-the-shelf (COTS) RFID devices. Existing indoor localization and tracking methods based on RFID do not perform well in dynamic environments with severe multi-path interference. In this paper, we propose a robust Angle of Arrival (AoA) estimation method for mobile RFID tags in a rich multi-path environment with a large feasible area. The proposed method Tri-AoA consists of three essential modules, phase likelihood estimation, Received Signal Strength Indicator (RSSI) likelihood estimation and a deep learning algorithm. The phase likelihood estimation module exploits the concept of an antenna array to provide a basic estimation of an AoA, but with an ambiguity. The RSSI likelihood estimation module helps alleviate the ambiguity. To achieve a more robust estimation of AoA for mobile RFID tags, we construct a 2-dimensional feature image that contains AoA estimation from the phase and RSSI modules. We then develop a deep learning algorithm to analyze this image to improve the AoA tracking accuracy as well as the robustness by suppressing the multi-path interference. The experimental results show that our system outperforms existing approaches by achieving a median error of$2.36^{\mathrm{o}}$in a$3m\times 4m$area using four COTS RFID antennas. We also show that our system can realize real-time performance on a personal computer.

A machine learning based defect detection system generally requires a new training procedure upon a new product category. In industry applications where there are variant product categories, a re-training upon category changing could be time expensive and unacceptable. In this work, a two-layer neural networks are proposed for cross-category defect detection without re-training. Different from traditional neural networks, the proposed method learns differences from image-pairs containing certain structural similarity rather than from a single image. With the assumption that different categorical objects could share certain structural similarity indicated by these learned image pairwise differences, a pairwise Siamese neural network is used in the proposed neural networks for defect detection. The cross-category capability of the proposed method is evidenced via experiments based on real-world factory datasets.

Nonnegative matrix factorization (NMF) has attracted increasing attention in data mining and machine learning . However, existing NMF methods have some limitations. For example, some NMF methods seriously suffer from noisy data contaminated by outliers, or fail to preserve the geometric information of the data and guarantee the sparse parts-based representation. To overcome these issues, in this paper, a robust and sparse NMF method, called correntropy based dual graph regularized nonnegative matrix factorization with local coordinate constraint (LCDNMF) is proposed. Specifically, LCDNMF incorporates the geometrical information of both the data manifold and the feature manifold, and the local coordinate constraint into the correntropy based objective function. The half-quadratic optimization technique is utilized to solve the nonconvex optimization problem of LCDNMF, and the multiplicative update rules are obtained. Furthermore, some properties of LCDNMF including the convergence, relation with gradient descent method , robustness, and computational complexity are analyzed. Experiments of clustering demonstrate the effectiveness and robustness of the proposed LCDNMF method in comparison to several state-of-the-art methods on six real world image datasets.

In this paper, we first investigate into six popular entropies extracted from a set of intrinsic mode functions (IMFs) as a feature pattern for radar-based mini-size unmanned aerial vehicles (mini-UAV) classification. The six entropies include Shannon entropy, spectral entropy, log energy entropy, approximate entropy, fuzzy entropy and permutation entropy. Via an empirical comparison among the six entropies on real measurement radar data, the first three are selected as the representative due to their high efficiency and accuracy. To enhance the classification accuracy, the three selected entropies are then extracted from eight different sets of IMFs obtained by signal downsampling, and then fused at feature level. The nonlinear support vector machine classifier is adopted to predict the class label of unseen test radar signals. Our empirical results on a set of real-world continuous wave radar data show that the proposed method outperforms the state-of-the-art method in terms of the mini-UAV classification accuracy.

In this paper, a novel quaternion adaptive filtering algorithm is proposed for a unified processing of 3D and 4D data, called quaternion least mean kurtosis (QLMK) algorithm. Multi-dimensional signals exhibit a complex nonlinear relationship and couple among different components. Considering that quaternion has huge advantage in terms of the representation of 3D and 4D signal, quaternion algebra is employed to derive the quaternion least mean square (QLMS) algorithm for hypercomplex signal processes. However, QLMS originates from the least mean square (LMS) algorithm, which may result in performance degradation when the signal is non-Gaussian. Due to the desirable performance of the least mean kurtosis (LMK) algorithm in non-Gaussian situation, in the present work we extend the original LMK algorithm to quaternion domain to manage the 3D and 4D signal processes. The analysis shows that QLMK provides a solution that is responsive to dynamically changing environments. Simulations on prediction of 4D Saito's chaotic circuit and 3D Lorenz attractor confirm the desirable performance of the proposed method.

In this paper, we investigate the convergence performance of a sparsified kernel least mean square (KLMS) algorithm in which the input is added into the dictionary only when the prediction error in amplitude is larger than a preset threshold. Under certain conditions, we derive an approximate value of the steady-state excess mean square error (EMSE). Simulation results confirm the theoretical predictions and provide some interesting findings, showing that the sparsification can not only be used to constrain the network size (hence reduce the computational burden) but also be used to improve the steady-state performance in some cases.

Document image binarization is a crucial step towards optical character recognition and analysis. One common way to achieve image binarization is thresholding. Thresholding methods can be divided into global and local ones in terms of the regional information used in obtaining the threshold values. Both methods have their respective drawbacks. Global methods can not adapt to background variations while local methods have the problem of local widow size determination. Hybrid methods that combines both local and global thresholds and can alleviating these drawbacks. In this paper, the hybrid threshold method is utilized and trade-off between the local and global contents is determined using variational optimization. The proposed algorithm is tested on (H-)DIBCO benchmarks and has shown superior performance to one state-of-the-art document image binarization method.

This paper proposes a method based on the bag-of-words (BoW) and the softmax regression for microscopic image classification. Essentially, the locality-constrained linear coding (LLC) is adopted for local feature encoding. Compared with the traditionally adopted vector quantization (VQ) in the BoW framework, the LLC encodes local structures of microscopic images with lower quantization errors and generates a sparse image representation. This enables the use of linear classifiers with low computational complexity. A softmax regression classifier is then adopted to address the multi-categorical classification task where the confidence of categorical prediction is quantified by posterior probabilities. Compared with other linear classifiers (such as the linear SVM) which only assign labels to images, such probabilistic outputs provide extra quantitative information to analyze misclassified images. Our experiments on the 2D-Hela and the PAP smear data sets show significant performance improvement of the proposed method comparing with competing methods using different features and classifiers under the BoW framework.

Constrained adaptive filtering algorithms have been extensively studied in many applications. Most existing constrained adaptive filtering algorithms are developed under the mean square error (MSE) criterion, which is an ideal optimality criterion under Gaussian noises. This assumption however fails to model the behavior of non-Gaussian noises found in practice. Motivated by the robustness and simplicity of maximum correntropy criterion (MCC) for non-Gaussian impulsive noises, this paper proposes a new adaptive filtering algorithm called constrained maximum correntropy criterion (CMCC). Specifically, CMCC incorporates a linear constraint into a MCC filter to solve a constrained optimization problem explicitly. The proposed adaptive filtering algorithm is easy to implement, has low computational complexity, and can significantly outperform those MSE based constrained adaptive algorithms in heavy-tailed impulsive noises. Additionally, the mean square convergence behaviors are studied under energy conservation relation, and a sufficient condition to ensure the mean square convergence and the steady-state mean square deviation (MSD) of the CMCC algorithm are obtained. Simulation results confirm the theoretical predictions under both Gaussian and non-Gaussian noises, and demonstrate the excellent performance of the novel algorithm by comparing it with other conventional methods.

Based on the knowledge that input data distribution is important for learning, a data density-dependent quantization scheme (DQS) is proposed for sparse input data representation. The usefulness of the representation scheme is demonstrated by using it as a data preprocessing unit attached to the well-known least squares support vector machine (LS-SVM) for application on big data sets. Essentially, the proposed DQS adopts a single shrinkage threshold to obtain a simple quantization scheme, which adapts its outputs to input data density. With this quantization scheme, a large data set is quantized to a small subset where considerable sample size reduction is generally obtained. In particular, the sample size reduction can save significant computational cost when using the quantized subset for feature approximation via the Nyström method. Based on the quantized subset, the approximated features are incorporated into LS-SVM to develop a data density-dependent quantized LS-SVM (DQLS-SVM), where an analytic solution is obtained in the primal solution space. The developed DQLS-SVM is evaluated on synthetic and benchmark data with particular emphasis on large data sets. Extensive experimental results show that the learning machine incorporating DQS attains not only high computational efficiency but also good generalization performance.

Independent component analysis (ICA) by an information measure has seen wide applications in engineering. Different from traditional probability density function based information measures, a probability survival distribution based Cauchy-Schwartz information measure for multiple variables is proposed in this paper. Empirical estimation of survival distribution is parameter-free which is inherited by the estimation of the new information measure. This measure is proved to be a valid statistical independence measure and is adopted as an objective function to develop an ICA algorithm which is validated by an experiment. This work shows promising potential regarding the use of survival distribution based information measure for ICA.

Survival information potential (SIP) is defined by the survival distribution function instead of the probability density function (PDF) of a random variable. SIP can be used as a risk function equipped with learning error compensation ability while this SIP based risk function does not involve the estimation of PDF. This is desirable for a robust learning application in view of the error compensation ability. The learning error compensation scheme provided by SIP requires rank information of learning errors. The accuracy of error compensation desires a large number of input data but is computationally expensive. It is shown that the error compensation can be approximated by an error-related distribution. Based on this approximation, a dictionary based error compensation scheme is proposed to obtain a fixed-budget recursive online learning method. This proposed method is compared with several well-known online learning methods including least-mean-square method, least absolute deviation method, affine projection algorithm, recursive least-mean-square method, and sliding window based SIP method. Simulation results validate the outstanding smooth and consistent convergence performance of the proposed method particularly in α-stable-noise environments.

Kernel least mean square is a simple and effective adaptive algorithm, but dragged by its unlimited growing network size. Many schemes have been proposed to reduce the network size, but few takes the distribution of the input data into account. Input data distribution is generally important in view of both model sparsification and generalization performance promotion. In this paper, we introduce an online density-dependent vector quantization scheme, which adopts a shrinkage threshold to adapt its output to the input data distribution. This scheme is then incorporated into the quantized kernel least mean square (QKLMS) to develop a density-dependent QKLMS (DQKLMS). Experiments on static function estimation and short-term chaotic time series prediction are presented to demonstrate the desirable performance of DQKLMS.

A weighted least squares scheme based on an empirical survival error potential function is proposed in this paper. The empirical survival error potential function provides an error compensation scheme for noise distributions far from being Gaussian. This error compensation procedure is efficiently implemented via a weighted least squares formulation where an analytical solution form is obtained. The performance of the developed scheme is extensively tested on 16 benchmark data sets where the results show promising potential of the proposed empirical survival error distribution compensation scheme for binary pattern classification.