This paper introduces a fast image denoising algorithm by estimating noise parameters without prior information about the noise. Under the assumption that additive noise has a Gaussian distribution, the noise parameters were estimated from an observed degraded image, and were used to define the constraints of a noise detection process that was coupled with a Markov random field (MRF). In addition, an adaptive modified weighted Gaussian filter with variable window sizes defined by the constraints on noise detection was used to control the degree of smoothness of the reconstructed image. Experimental results demonstrate the capability of the proposed algorithm.

In this paper, we propose a localization algorithm that uses the time difference of arrival (TDOA) and the angle of arrival (AOA). The problem is formulated in a hybrid linear matrix equation. TDOA and AOA measurements are used for estimating the target's position. Although it is known that the accuracy of TDOA based localization is superior to that of AOA based localization, TDOA based localization has a poor vertical accuracy in deteriorated geometrical conditions. This paper, therefore, proposes a localization algorithm in which the vertical position is estimated by AOA measurements and the horizontal position is estimated by TDOA measurement in order to achieve high location accuracy in three dimensions. In addition, the Lagrange multipliers are obtained efficiently and robustly. The simulation analysis shows that the proposed constrained linear squares (CLS) algorithm is an unbiased estimator, and that it approaches the Cramer-Rao lower bound (CRLB) when the measurement noise and the sensor's location errors are sufficiently small.

This paper analyzes the impact of directional antennas in improving the transmission capacity, defined as the maximum allowable spatial node density of successful transmissions multiplied by their data rate with a given outage constraint, in wireless networks. We consider the case where the gain Gm for the mainlobe of beamwidth can scale at an arbitrarily large rate. Under the beamwidth scaling model, the transmission capacity is analyzed for all path-loss attenuation regimes for the following two network configurations. In dense networks, in which the spatial node density increases with the antenna gain Gm, the transmission capacity scales as Gm4/α, where α denotes the path-loss exponent. On the other hand, in extended networks of fixed node density, the transmission capacity scales logarithmically in Gm. For comparison, we also show an ideal antenna model where there is no sidelobe beam. In addition, computer simulations are performed, which show trends consistent with our analytical behaviors. Our analysis sheds light on a new understanding of the fundamental limit of outage-constrained ad hoc networks operating in the directional mode.

We propose a joint channel, power and rate allocation scheme to minimize the weighted group outage probability of the secondary users (SUs) in a downlink cognitive radio (CR) multicast network coexisting with a primary network, subject to the service outage constraint as well as the interference power constraint and the transmit power constraint. It is validated by simulation results that, compared to the existing schemes, the proposed scheme achieves lower group outage probability.

State-of-the-art background subtraction and foreground detection methods still face a variety of challenges, including illumination changes, camouflage, dynamic backgrounds, shadows, intermittent object motion. Detection of foreground elements via the robust principal component analysis (RPCA) method and its extensions based on low-rank and sparse structures have been conducted to achieve good performance in many scenes of the datasets, such as Changedetection.net (CDnet); however, the conventional RPCA method does not handle shadows well. To address this issue, we propose an approach that considers observed video data as the sum of three parts, namely a row-rank background, sparse moving objects and moving shadows. Next, we cast inequality constraints on the basic RPCA model and use an alternating direction method of multipliers framework combined with Rockafeller multipliers to derive a closed-form solution of the shadow matrix sub-problem. Our experiments have demonstrated that our method works effectively on challenging datasets that contain shadows.

Pseudo-Boolean (PB) problems are Integer Linear Problem restricted to 0-1 variables. This paper discusses on acceleration techniques of PB-solvers that employ SAT-solving of combined CNFs each of which is produced from each PB-constraint via a binary decision diagram (BDD). Specifically, we show (i) an efficient construction of a reduced ordered BDD (ROBDD) from a constraint in band form l ≤ ≤ h, (ii) a CNF coding that produces two clauses for some nodes in an ROBDD obtained by (i), and (iii) an incremental SAT-solving of the binary/alternative search for minimizing values of a given goal function. We implemented the proposed constructions and report on experimental results.

In this paper, an energy harvesting architecture in an Underlay Cooperative Cognitive Network (UCCN) is investigated, in which power constrained Decode-and-Forward relays harvest energy from radio-frequency signals received from a source, and then consume the harvested energy by forwarding the recoded signals to their destination. These recoded signals are launched by a transmitting power which is the harvested energy per a time interval. Based on the energy harvesting architectures that have been studied, two operation protocols are proposed: UCCN with Power Splitting architecture (UCCN-PS), and UCCN with Time Switching architecture (UCCN-TS). The best cooperative relay in both protocols is taken to be the one that satisfies the following conditions: maximum harvested energy, and maximum decoding capacity. As a result of the best relay selection, the signal quality of the selected link from the best relay to the destination is enhanced by the maximum harvested energy. The system performance of the secondary network in the UCCN-PS and UCCN-TS protocols is analyzed and evaluated by the exact closed-form outage probabilities and throughput analyses over Rayleigh fading channels. The Monte Carlo simulation method is performed to verify the theoretical expressions. Evaluations based on outage probability and throughput show that the system performance of the secondary network in the UCCN-PS and UCCN-TS protocols improves when the number of cooperative relays and the interference constraint increase as well as when the primary receiver is farther from the transmitting nodes such as the source and relays of the secondary network. In addition, the throughput performance of the UCCN-PS protocol outperforms that of the UCCN-TS protocol. Finally, the effects of the power splitting ratio, energy harvesting time, energy conversion efficiency, target Signal-to-Noise Ratio (SNR), and location of cooperative relays on the system performance of the secondary network are presented and discussed.

In this paper, optimal control of multi-vehicle systems is studied. In the case where collision avoidance between vehicles and obstacle avoidance are imposed, state discretization is effective as one of the simplified approaches. Furthermore, using state discretization, cooperative actions such as rendezvous can be easily specified by linear temporal logic (LTL) formulas. However, it is not necessary to discretize all states, and partial states (e.g., the position of vehicles) should be discretized. From this viewpoint, a new control method for multi-vehicle systems is proposed in this paper. First, the system in which partial states are discretized is formulated. Next, the optimal control problem with constraints described by LTL formulas is formulated, and its solution method is proposed. Finally, numerical simulations are presented. The proposed method provides us a useful method in control of multi-vehicle systems.

A cloud system is defined as a large scale computer system that contains running high performance computers and responds to a large number of incoming tasks over the Internet. In this paper, we consider the problem to schedule computational jobs efficiently regarding system resource constraint and introduce a cuckoo search (CS) algorithm. Experimental results show that CS outperforms the genetic algorithm in terms of fitness value.

Recently, locality-constrained linear coding (LLC) as a coding strategy has attracted much attention, due to its better reconstruction than sparse coding and vector quantization. However, LLC ignores the weight information of codewords during the coding stage, and assumes that every selected base has same credibility, even if their weights are different. To further improve the discriminative power of LLC code, we propose a weighted LLC algorithm that considers the codeword weight information. Experiments on the KTH and UCF datasets show that the recognition system based on WLLC achieves better performance than that based on the classical LLC and VQ, and outperforms the recent classical systems.

Given an edge-weighted tree with n vertices and a positive integer L, the length-constrained maximum-density path problem is to find a path of length at least L with maximum density in the tree. The density of a path is the sum of the weights of the edges in the path divided by the number of edges in the path. We present an O(n) time algorithm for the problem. The previously known algorithms run in O(nL) or O(n log n) time.

This paper proposes an adaptive band activity ratio control (ABC) with cascaded energy allocation (CEA) scheme to improve end-to-end spectral efficiency for two-hop amplify-and-forward orthogonal frequency division multiplexing relay systems under transmit energy constraint. Subchannel pairing (SP) based spectrum mapping maps spectral components transmitted over high gain subchannels in the source-to-relay link onto high gain subchannels of the relay-to-destination link to improve the spectral efficiency. However, SP suffers from a frame efficiency reduction due to the notification of information of spectral component order. To compensate for the deficiency of SP, the proposed scheme employs dynamic spectrum control with ABC in which spectral components are mapped onto subchannels having high channel gain in each link, while band activity ratio (BAR) is controlled to an optimal value, which is smaller than 1, so that all spectral components are transmitted over relatively high gain subchannels of the two links. To further improve the performance, energy allocation at the source node and the relay node is serially conducted based on convex optimization, and BAR is controlled to improve discrete-input continuous-output memoryless channel capacity at the relay node. In the proposed scheme, since only information of BAR needs to be notified, the notification overhead is drastically reduced compared to that in SP based spectrum mapping. Numerical analysis confirms that the proposed ABC combined with CEA significantly reduces the required notification overhead while achieving almost the same frame error rate performance compared with the SP based scheme.

Most images obtained with imaging sensors contain Bayer patterns and suffer from blurring caused by the lens. In order to convert a blurred Bayer-patterned image into a viewable image, demosaicing and deblurring are needed. These concepts have been major research areas in digital image processing for several decades. Despite their importance, their performance and efficiency are not satisfactory when considered independently. In this paper, we propose a joint deblurring and demosaicing method in which edge direction and edge strength are estimated in the Bayer domain and then edge adaptive deblurring and edge-oriented interpolation are performed simultaneously from the estimated edge information. Experimental results show that the proposed method produces better image quality than conventional algorithms in both objective and subjective terms.

The recent explosive growth in information networks has driven a huge increase in content. For efficient and flexible information retrieval over such large networks, agent technology has received much attention. We previously proposed an agent execution control method for time-constrained information retrieval that finds better results by terminating an agent that has already acquired results of high-enough quality or one that is unlikely to improve the quality of results with continued retrieval. However, this method assumed that all agents have identical time constraints. This leads to a disparity in the obtained score between users who give individual time constraints. In this paper, we propose a fair and efficient scheduling method based on the expected improvement of the highest score (EIS). The proposed method allocates all CPU resources to the agent that has the highest EIS to decrease the difference between users' scores and to increase the mean highest score of requested results.

Most of the recent classification methods require tuning of the hyper-parameters, such as the kernel function parameter and the regularization parameter. Cross-validation or the leave-one-out method is often used for the tuning, however their computational costs are much higher than that of obtaining a classifier. Quadratically constrained maximum a posteriori (QCMAP) classifiers, which are based on the Bayes classification rule, do not have the regularization parameter, and exhibit higher classification accuracy than support vector machine (SVM). In this paper, we propose a multiple kernel learning (MKL) for QCMAP to tune the kernel parameter automatically and improve the classification performance. By introducing MKL, QCMAP has no parameter to be tuned. Experiments show that the proposed classifier has comparable or higher classification performance than conventional MKL classifiers.

Manifold learning based image clustering models are usually employed at local level to deal with images sampled from nonlinear manifold. Multimode patterns in image data matrices can vary from nominal to significant due to images with different expressions, pose, illumination, or occlusion variations. We show that manifold learning based image clustering models are unable to achieve well separated images at local level for image datasets with significant multimode data patterns. Because gray level image features used in these clustering models are not able to capture the local neighborhood structure effectively for multimode image datasets. In this study, we use nearest neighborhood quality (NNQ) measure based criterion to improve local neighborhood structure in terms of correct nearest neighbors of images locally. We found Gist as the optimal image descriptor among HOG, Gist, SUN, SURF, and TED image descriptors based on an overall maximum NNQ measure on 10 benchmark image datasets. We observed significant performance improvement for recently reported clustering models such as Spectral Embedded Clustering (SEC) and Nonnegative Spectral Clustering with Discriminative Regularization (NSDR) using proposed approach. Experimentally, significant overall performance improvement of 10.5% (clustering accuracy) and 9.2% (normalized mutual information) on 13 benchmark image datasets is observed for SEC and NSDR clustering models. Further, overall computational cost of SEC model is reduced to 19% and clustering performance for challenging outdoor natural image databases is significantly improved by using proposed NNQ measure based optimal image representations.

A new artificial fish swarm algorithm (AFSA) for solving the multiple knapsack problem (MKP) is introduced in this paper. In the proposed AFSA, artificial fish (AF) individuals are only allowed to search the region near constraint boundaries of the problem to be solved. For this purpose, several behaviors to be performed by AF individuals, including escaping behavior, randomly moving behavior, preying behavior and following behavior, were specially designed. Exhaustive experiments were implemented in order to investigate the proposed AFSA's performance. The results demonstrated the proposed AFSA has the ability of finding high-quality solutions with very fast speed, as compared with some other versions of AFSA based on different constraint-handling methods. This study is also meaningful for solving other constrained problems.

Hardware Trojan (HT) has emerged as an impending security threat to hardware systems. However, conventional functional tests fail to detect HT since Trojans are triggered by rare events. Most of the existing side-channel based HT detection techniques just simply compare and analyze circuit's parameters and offer no signal calibration or error correction properties, so they suffer from the challenge and interference of large process variations (PV) and noises in modern nanotechnology which can completely mask Trojan's contribution to the circuit. This paper presents a novel HT detection method based on subspace technique which can detect tiny HT characteristics under large PV and noises. First, we formulate the HT detection problem as a weak signal detection problem, and then we model it as a feature extraction model. After that, we propose a novel subspace HT detection technique based on time domain constrained estimator. It is proved that we can distinguish the weak HT from variations and noises through particular subspace projections and reconstructed clean signal analysis. The reconstructed clean signal of the proposed algorithm can also be used for accurate parameter estimation of circuits, e.g. power estimation. The proposed technique is a general method for related HT detection schemes to eliminate noises and PV. Both simulations on benchmarks and hardware implementation validations on FPGA boards show the effectiveness and high sensitivity of the new HT detection technique.

This letter deals with a new cell clustering problem subject to signal-to-interference-plus-noise-ratio (SINR) constraints in uplink network MIMO systems, where multiple base stations (BSs) cooperate for joint processing as forming a cluster. We first prove that the SINRs of users in a certain cluster always increase monotonically as the cluster size increases when the receiver filter that maximizes the SINR is used. Using this result, we propose an efficient clustering algorithm to minimize the maximum number of cooperative BSs in a cluster. Simulation results show that the maximum number of cooperative BSs minimized by the proposed method is close to that minimized by the exhaustive search and the proposed scheme outperforms the conventional one in terms of the outage probability.

In signal restoration problems, we expect to improve the restoration performance with a priori information about unknown target signals. In this paper, the parametric Wiener filter with linear constraints for unknown target signals is discussed. Since the parametric Wiener filter is usually defined as the minimizer of the criterion not for the unknown target signal but for the filter, it is difficult to impose constraints for the unknown target signal in the criterion. To overcome this difficulty, we introduce a criterion for the parametric Wiener filter defined for the unknown target signal whose minimizer is equivalent to the solution obtained by the original formulation. On the basis of the newly obtained criterion, we derive a closed-form solution for the parametric Wiener filter with linear constraints.