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Capturing image with defocused background by using a large aperture is a widely used technique in digital single-lens reflex (DSLR) camera photography. It is also desired to provide this function to smart phones. In this paper, a new algorithm is proposed to synthesize such an effect for a single portrait image. The foreground portrait is detected using a face prior based salient object detection algorithm. Then with an improved gradient domain guided image filter, the details in the foreground are enhanced while the background pixels are blurred. In this way, the background objects are defocused and thus the foreground objects are emphasized. The resultant image looks similar to image captured using a camera with a large aperture. The proposed algorithm can be adopted in smart phones, especially for the front cameras of smart phones. [ABSTRACT FROM AUTHOR]

The low-rank matrix completion problem is fundamental to a number of tasks in data mining, machine learning, and signal processing. This paper considers the problem of adaptive matrix completion in time-varying scenarios. Given a sequence of incomplete and noise-corrupted matrices, the goal is to recover and track the underlying low rank matrices. Motivated from the classical least-mean square (LMS) algorithms for adaptive filtering, three LMS-like algorithms are proposed for estimating and tracking low-rank matrices. Performance of the proposed algorithms is provided in form of nonasymptotic bounds on the tracking mean-square error. Tracking performance of the algorithms is also studied via detailed simulations over real-world datasets. [ABSTRACT FROM PUBLISHER]

In this paper, we aim to obtain the optimal tradeoff between the average delay and the average power consumption in a communication system. In our system, the arrivals occur at each timeslot according to a Bernoulli arrival process, and are buffered at the transmitter waiting to be scheduled. We consider a finite buffer and allow the scheduling decision to depend on the buffer occupancy. In order to capture the realism in communication systems, the transmission power is assumed to be an increasing and convex function of the number of packets transmitted in each timeslot. This problem is modeled as a constrained Markov decision process (CMDP). We first prove that the optimal policy of the Lagrangian relaxation of the CMDP is deterministic and threshold-based. We then show that the optimal delay-power tradeoff curve is convex and piecewise linear, and the optimal policies of the original problem are also threshold-based. Based on the results, we propose an algorithm to obtain the optimal policy and the optimal tradeoff curve. We also show that the proposed algorithm is much more efficient than using general methods. The theoretical results and the algorithm are validated by linear programming and simulations. [ABSTRACT FROM PUBLISHER]

By employing the finite-time control method, the consensus control algorithm for higher-order multiagent systems is designed in this paper. Under a neighbor-based rule, a higher-order finite-time consensus algorithm is explicitly constructed, which only uses local information. The finite-time consensus control algorithm can guarantee that the state consensus is achieved in a finite time. In addition, for multiagent systems having a leader-following structure, the consensus algorithm is also designed. Finally, two examples are presented to show the effectiveness. [ABSTRACT FROM AUTHOR]

In intelligent vehicular communication networks, a hybrid communication architecture is used which combines both centralized and ad hoc transmission schemes. In order to maximize the end-to-end delivery ratio while reducing the network overhead, one important problem is to efficiently design the data forwarding algorithm to guarantee the quality of data transmission. In this paper, by considering the traveling information and vehicular space-crossing community structure, two metrics, “space–time approachability” and “social approachability,” are defined to provide the absolute and relative geographical information of the forthcoming contacts, respectively. Then, a novel data-forwarding algorithm, called approachability-based algorithm, is proposed, which utilizes two metrics together for better routing quality. We evaluate the proposed approachability-based algorithm utilizing San Francisco Cabspotting and Shanghai Taxi Movement datasets. Simulation results show that the approachability-based data forwarding algorithm can achieve better performance than the popular data forwarding algorithms ZOOM and BUBBLE RAP in all the interested scenarios. [ABSTRACT FROM PUBLISHER]

This paper introduces a mathematical model that incorporates the pertinent optical and physiological properties of skin reflections with the objective to increase our understanding of the algorithmic principles behind remote photoplethysmography (rPPG). The model is used to explain the different choices that were made in existing rPPG methods for pulse extraction. The understanding that comes from the model can be used to design robust or application-specific rPPG solutions. We illustrate this by designing an alternative rPPG method, where a projection plane orthogonal to the skin tone is used for pulse extraction. A large benchmark on the various discussed rPPG methods shows that their relative merits can indeed be understood from the proposed model. [ABSTRACT FROM AUTHOR]

Autoencoders are deep learning architectures that learn feature representation by minimizing the reconstruction error. Using an autoencoder as baseline, this paper presents a novel formulation for a class sparsity based supervised encoder, termed as CSSE. We postulate that features from the same class will have a common sparsity pattern/support in the latent space. Therefore, in the formulation of the autoencoder, a supervision penalty is introduced as a joint-sparsity promoting l2,1 -norm. The formulation of CSSE is derived for a single hidden layer and it is applied for multiple hidden layers using a greedy layer-by-layer learning approach. The proposed CSSE approach is applied for learning face representation and verification experiments are performed on the LFW and PaSC face databases. The experiments show that the proposed approach yields improved results compared to autoencoders and comparable results with state-of-the-art face recognition algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a novel global search algorithm, implementing principal component optimization (PCO) in combination with mesh adaptive direct search (MADS) is proposed. The PCO is based on principal component analysis, a widely used multivariate technique in statistics, while MADS is a highly effective local search algorithm, implemented to find local optima. Their integration reduces computation time and improves convergence reliability to a global optimum. The proposed algorithm is verified via application of a test function and comparison to conventional hybrid algorithms such as particle swarm optimization with MADS. Finally, the algorithm is applied to the optimal design of an interior permanent magnet synchronous machine for minimization of total harmonic distortion and torque ripple via finite element analysis. [ABSTRACT FROM AUTHOR]

Tree comparison metrics are an important tool for the study of phylogenetic trees. Path-difference distances measure the dissimilarity between two phylogenetic trees (on the same set of taxa) by comparing their path-length vectors. Various norms can be applied to this distance. Three important examples are the $l_{1}\text{-},\;l_{2}\text{-}$l1-,l2-, and $l_{{\infty }}$l∞-norms. The previous best algorithms for computing path-difference distances all have $O(n^{2})$O(n2) running time. In this paper, we show how to compute the $l_{1}$l1-norm path-difference distance in $O(n\;{\log}^{2}\;n)$O(nlog2n) time and how to compute the $l_{2}$l2- and $l_{{\infty }}$l∞-norm path-difference distances in $O(n\;{\log}\;n)$O(nlogn) time. By extending the presented algorithms, we also show that the $l_{p}$lp-norm path-difference distance can be computed in $O(pn\;{\log}^{2}\;n)$O(pnlog2n) time for any positive integer $p$p. In addition, when the integer $p$p is even, we show that the distance can be computed in $O(p^{2}n\;{\log}\;n)$O(p2nlogn) time as well. [ABSTRACT FROM AUTHOR]

The emerging of depth-camera technology is paving the way for a variety of new applications and it is believed that plane detection is one of them. In fact, planes are common in man-made living structures, thus their accurate detection can benefit many visual-based applications. The use of depth information allows detecting planes characterized by complex pattern and texture, where the texture-based plane detection algorithms usually fail. In this paper, we propose a robust depth-driven plane detection (DPD) algorithm which consists of two parts: the growing-based plane detection and a two-stage refinement. The proposed approach starts from the seed patch with the highest planarity and uses the estimated equation of the growing plane and a dynamic threshold function to steer the growing process. Aided with this mechanism, each seed patch can grow to its maximum extent, and then the next seed patch starts to grow. This process is iteratively repeated so as to detect all the planes. Moreover, the refinement is proposed to tackle two common problems suffered by growing-based approaches, the over-growing problem, and the under-growing problem. Validated by extensive experiments, the proposed DPD algorithm is able to accurately detect planes and robust to various testing conditions. In terms of applications, it can be used as the pre-processing step for a variety of applications, such as, planar object recognition, super-resolution of the time-of-flight depth images with intrinsically low resolution. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of estimating the head pose and body orientation of a person from a low-resolution image. Under this setting, it is difficult to reliably extract facial features or detect body parts. We propose a convolutional random projection forest (CRPforest) algorithm for these tasks. A convolutional random projection network (CRPnet) is used at each node of the forest. It maps an input image to a high-dimensional feature space using a rich filter bank. The filter bank is designed to generate sparse responses so that they can be efficiently computed by compressive sensing. A sparse random projection matrix can capture most essential information contained in the filter bank without using all the filters in it. Therefore, the CRPnet is fast, e.g., it requires $0.04\;\mathrm{ms}$ to process an image of $50\times 50$ pixels, due to the small number of convolutions (e.g., 0.01 percent of a layer of a neural network) at the expense of less than 2 percent accuracy. The overall forest estimates head and body pose well on benchmark datasets, e.g., over 98 percent on the HIIT dataset, while requiring $3.8\;\mathrm{ms}$ without using a GPU. Extensive experiments on challenging datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in low-resolution images with noise, occlusion, and motion blur. [ABSTRACT FROM AUTHOR]

Secure masking schemes have been proven in theory to be secure countermeasures against side-channel attacks. The security framework proposed by Ishai, Sahai, and Wagner, known as the Ishai–Sahai–Wagner scheme, is one of the most acceptable secure models of the existing $d$ th-order masking schemes, where $d$ represents the masking order and plays the role of a security parameter. However, a gap may exist between scheme and design. Several analyses have determined that the glitch has been regarded as the main challenge of masking in hardware designs. A practical method of locating the precise position of leakage points (LPs) in the original hardware design is very rare. Existing research on this glitch mainly focuses on the first-order leakages; however, higher-order analysis can combine several shares to recover the secret key. In this paper, we propose a practical method, sensitive glitch location (SGL) method to locate the less order leakage in hardware design. Specifically, the SGL method can locate any-order of LP in the hardware implementation of $d$ th-order masking schemes. We conducted experiments and verified that the time complexity of SGL on the $d$ th-order masking schemes is ${O(nm)}$ , where $m$ is the number of signals and $n$ is the number of shares in masking scheme. It can therefore be regarded as an efficient tool for the masking designs. In addition, we analyzed the $d$ th-order masking scheme proposed by Rivain and Prouff (2010) along with the SecMult algorithm from the Rivain–Prouff countermeasure, which has been analyzed by our SGL. The experimental results verified that a higher-order leakage may exist in certain hardware designs, even the masking scheme has been proven as a secure countermeasure. To the best of our knowledge, SGL is the first tool that can be used to locate any-order of power/electromagnetic LP in hardware designs. It thus shows the weakness in the original design file of hardware implementations. This property can help designers directly improve the real security of the designs. Moreover, SGL returns the path of the leakages, which can elucidate the original cause and propagation of the weakness. [ABSTRACT FROM AUTHOR]

In this paper, we describe topology-efficient discovery (TED), the first designated topology-efficient discovery algorithm for underwater acoustic networks. Topology information is essential for network operations. By knowing the network topology, information sources can determine destination nodes and routing possibilities for their packets and schedule transmissions accordingly. It is therefore of interest to use a schedule for topology discovery to be used at the early stages of network operation. Such schedule must efficiently perform topology discovery and also guarantee a convergence time at the end of which topology discovery is complete and the network switches to its steady-state scheduling protocol. Considering this need, we offer a TED algorithm aimed to discover acoustic links and to assess their reliability. Designed to reduce the time overhead posed by the topology discovery phase, TED allows nodes to share time slots while controlling the number of possible collisions such that the delay of the topology discovery process is minimized. TED also discovers scheduling conflicts by discovering node pairs whose transmissions block one another, often referred to as near–far node pairs (NFNPs). Information about NFNPs can assist for power control or to increase channel utilization if interference cancelation techniques are employed. TED is applicable for underwater networks consisting of modems whose transmission range is on the order of a few kilometer and above, and when the maximal distance between the nodes is known to be large or assumed equal to the modem's transmission range. Numerical results show that under these conditions, TED accurately detects the network topology in a much shorter time compared to benchmark methods. The results also show that, using TED, more packets are received, and thus the accuracy in determining the reliability of communication links increases. We also report results from a sea experiment demonstrating the applicability of TED and its benefits in a real-world scenario. [ABSTRACT FROM AUTHOR]

Homography has wide applications in aerial photographic surveys, camera calibration, traffic scene analysis, and sports science, such as player and team performance evaluation. Unlike the mainstream homography that utilizes points as matching features, homography estimation for sports and traffic video can achieve higher accuracy and speed by utilizing straight lines in the scenes, which convey more information than points. Owing to the more stringent requirement of accuracy and computational speed for advanced video analysis, this paper presents a novel homography computational algorithm. Three major novelties are proposed and validated, which are multiple points Hough transform for straight line extraction, correspondence initialization by angle to estimate a set of quasi-optimal solutions, and the feature correspondences optimization to achieve a minimized error using genetic algorithm. With these contributions, the experiments have shown that the proposed algorithm can improve the homography computational accuracy by up to 130% and reduce the processing time by up to 96% over the state-of-the-art algorithms for the same purposes. [ABSTRACT FROM AUTHOR]

Recovering low-rank matrices from incomplete observations is a fundamental problem with many applications, especially in recommender systems. In theory, under certain conditions, this problem can be solved by convex or nonconvex relaxation. However, most existing provable algorithms suffer from superlinear per-iteration cost, which severely limits their applicability to large-scale problems. In this paper, we propose a novel fuzzy double trace norm minimization (DTNM) method for recommender systems. We first present a tractable DTNM model, in which we can integrate both the user social relationship and the user reputation information using a fuzzy weighting way and coupling fuzzy matrix factorization. In essence, our model is a Schatten- ${1/2}$ quasi-norm minimization problem. Moreover, we develop two efficient augmented Lagrangian algorithms to solve the proposed problems, and prove the convergence of our algorithms. Finally, we investigate the empirical recoverability properties of our model and its advantage over classical trace norm. Extensive experimental results on both synthetic and real-world data sets verified both the efficiency and effectiveness of our method compared with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

As a promising way for analyzing data, sparse modeling has achieved great success throughout science and engineering. It is well known that the sparsity/low-rank of a vector/matrix can be rationally measured by nonzero-entries-number ( $l_0$ norm)/nonzero- singular-values-number (rank), respectively. However, data from real applications are often generated by the interaction of multiple factors, which obviously cannot be sufficiently represented by a vector/matrix, while a high order tensor is expected to provide more faithful representation to deliver the intrinsic structure underlying such data ensembles. Unlike the vector/matrix case, constructing a rational high order sparsity measure for tensor is a relatively harder task. To this aim, in this paper we propose a measure for tensor sparsity, called Kronecker-basis-representation based tensor sparsity measure (KBR briefly), which encodes both sparsity insights delivered by Tucker and CANDECOMP/PARAFAC (CP) low-rank decompositions for a general tensor. Then we study the KBR regularization minimization (KBRM) problem, and design an effective ADMM algorithm for solving it, where each involved parameter can be updated with closed-form equations. Such an efficient solver makes it possible to extend KBR to various tasks like tensor completion and tensor robust principal component analysis. A series of experiments, including multispectral image (MSI) denoising, MSI completion and background subtraction, substantiate the superiority of the proposed methods beyond state-of-the-arts. [ABSTRACT FROM AUTHOR]

We consider nonlinear zero-forcing (ZF) precoding design to improve the downstream G.fast peak-rates when only a few users in the cable binder are active. In order to compute the optimal nonlinear ZF precoder under per-line power constraints (PLPCs), we present a novel low-complexity dual decomposition algorithm, in which the key is the use of Lagrange multiplier based virtual precoders to transform the PLPCs into an easier virtual sum-power constraint (SPC), such that the SPC-optimality of the QR decomposition-based precoder may be exploited. We show a reduced computational complexity of this algorithm over the state-of-the-art SVD-block-diagonalization-based dual decomposition algorithm. We present simulations of a 10-line cable binder that demonstrate substantial peak-rate gains over standard QR decomposition-based ZF precoding in DSL, due to the increasingly stronger crosstalk channels in the G.fast frequency range (up to 212 MHz). Furthermore, we show that the proposed algorithm naturally extends to the scenario with multiple lines terminating at the customer premise equipments. [ABSTRACT FROM AUTHOR]

In this paper, fast capacitor assignment algorithms capable of finding a decoupling solution scheme with a minimum number of components that meets a predefined target impedance with a semi-arbitrary shape within a few seconds to an hour for a given power distribution network (PDN) are proposed. The proposed algorithms also provide identification of shadowed decap ports in a PDN based on an inductance matrix extraction from the given PDN model. Elimination of those shadowed decap ports with the proposed algorithms enables more effective layouts and cost savings on PCB designs. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of registering multiple point sets. Solutions to this problem are often approximated by repeatedly solving for pairwise registration, which results in an uneven treatment of the sets forming a pair: a model set and a data set. The main drawback of this strategy is that the model set may contain noise and outliers, which negatively affects the estimation of the registration parameters. In contrast, the proposed formulation treats all the point sets on an equal footing. Indeed, all the points are drawn from a central Gaussian mixture, hence the registration is cast into a clustering problem. We formally derive batch and incremental EM algorithms that robustly estimate both the GMM parameters and the rotations and translations that optimally align the sets. Moreover, the mixture's means play the role of the registered set of points while the variances provide rich information about the contribution of each component to the alignment. We thoroughly test the proposed algorithms on simulated data and on challenging real data collected with range sensors. We compare them with several state-of-the-art algorithms, and we show their potential for surface reconstruction from depth data. [ABSTRACT FROM PUBLISHER]

In this paper, a novel rate-control algorithm based on the region adaptive R- $\lambda $ model is proposed for depth maps coding. First, in order to obtain an accurate rate control for depth maps coding, a modified frame level bit allocation method based on coding bits statistical distribution of depth maps is proposed. Second, considering that different areas in a depth map have an imparity effect on virtual view rendering, the blocks of the depth map are divided into two types, namely, interested blocks for virtual view rending (IBV) and noninterested blocks for virtual view rending (NIBV). Then, two different R- $\lambda $ models are derived for IBV and NIBV, respectively. The optimal bitrates for IBV and NIBV are determined by solving an optimization problem. After that, based on the regional R- $\lambda $ models, the optimal Lagrange multipliers are calculated for both IBV and NIBV. Finally, the largest coding unit (LCU) level rate control is performed by adaptively adjusting the Lagrange multiplier to avoid blocking artifacts and smooth the quality of coding. Experimental results demonstrate that the proposed method can achieve considerable BD-PSNR gains compared with the unified rate-quantization model and conventional R- $\lambda $ model-based algorithms in terms of rendered virtual views quality. [ABSTRACT FROM AUTHOR]

A new ramped convergence particle swarm optimization (RCPSO) algorithm to design radio frequency identification (RFID) reader antenna for gain optimization is presented in this paper. The algorithm breaks down the optimization problem by considering only a subset of dimensions at a time, hence overcoming curse of dimensionality. It uses a multi-start approach that alleviates premature convergence. It applies heterogeneous boundary conditions to errant particles. In this paper, the effectiveness of using RCPSO for RFID reader antenna design will be investigated by a number of iterations required to achieve optimum solution along with the quality of solution. RCPSO is built specifically for antenna design. For the microstrip antenna, a significant bandwidth improvement is achieved and the overall antenna dimensions are kept practically the same. The circular gain improves by a significant 2 dBiC over super high frequency (SHF). [ABSTRACT FROM PUBLISHER]

This paper compares the application of two deterministic and three nondeterministic optimization algorithms to global transformer design optimization (TDO). Two deterministic optimization algorithms (mixed-integer nonlinear programming and heuristic algorithm) are compared to three nondeterministic approaches (harmony search, differential evolution, and genetic algorithm). All these algorithms are integrated in design optimization software applied and verified in the manufacturing industry. The comparison yields significant conclusions on the efficiency of the algorithms and the selection of the most suitable one for the TDO problem. [ABSTRACT FROM AUTHOR]

This paper studies the optimal control problem for large-scale systems with unknown parameters and dynamics. By using robust adaptive dynamic programming (RADP) method, a decentralized optimal control design is given for large-scale systems with unmatched uncertainties. The convergence of the proposed RADP algorithm and the asymptotic stability of the closed-loop large-scale system are studied rigorously. Finally, a numerical example of a large-scale power system is adopted to illustrate the effectiveness of the obtained algorithm. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the problem of distributed channel selection for interference mitigation in a canonical communication network. The channel is assumed time-varying, and the active user set is considered dynamically variable due to the specific service requirement. This problem is formulated as an exact potential game, and the optimality property of the solution to this problem is first analyzed. Then, we design a low-complexity fully distributed no-regret learning algorithm for channel adaptation in a dynamic environment, where each active player can independently and automatically update its action with no information exchange. The proposed algorithm is proven to converge to a set of correlated equilibria with a probability of 1. Finally, we conduct simulations to demonstrate that the proposed algorithm achieves near-optimal performance for interference mitigation in dynamic environments. [ABSTRACT FROM AUTHOR]

In this paper, we design novel distributed scheduling algorithms for multiuser multiple-input multiple-output systems and evaluate the resulting system capacity analytically. In particular, we consider algorithms which do not require sending channel state information to a central processing unit, nor do they require communication between the users themselves, yet, the resulting capacity closely approximates that of a centrally controlled system, which is able to schedule the strongest user in each time-slot. In other words, multiuser diversity is achieved in a distributed fashion. Our analysis is based on a novel application of the point-process approximation. This technique, besides tackling previously suggested models successfully, allows an analytical examination of new models, such as nonhomogeneous cases (nonidentically distributed users) or various quality of service considerations. This results in asymptotically exact expressions for the capacity of the system under these schemes, solving analytically problems which to date had been open. Possible applications include, but are not limited to, modern 4G networks, such as 3GPP LTE, or random access protocols. [ABSTRACT FROM PUBLISHER]

A low-cost very large scale integration (VLSI) implementation of real-time correction of barrel distortion for video-endoscopic images is presented in this paper. The correcting mathematical model is based on least-squares estimation. To decrease the computing complexity, we use an odd-order polynomial to approximate the back-mapping expansion polynomial. By algebraic transformation, the approximated polynomial becomes a monomial form which can be solved by Hornor's algorithm. With the iterative characteristic of Hornor's algorithm, the hardware cost and memory requirement can be conserved by time multiplexed design. In addition, a simplified architecture of the linear interpolation is used to reduce more computing resource and silicon area. The VLSI architecture of this paper contains 13.9-K gates by using a 0.18 \mum CMOS process. Compared with some existing distortion correction techniques, this paper reduces at least 69% hardware cost and 75% memory requirement. [ABSTRACT FROM AUTHOR]

This paper proposes a low complexity n-dimensional (nD) FastICA algorithm and architecture by introducing the concept of coordinate rotation where n\geq 2. The proposed algorithm can merge the two key steps of conventional FastICA—preprocessing and update and is therefore capable of reducing the hardware complexity of the conventional FastICA significantly as demonstrated in this paper. Hardware implementation can further be simplified due to the recursive nature of the proposed algorithm where the same 2D hardware module can be used as the fundamental core to implement nD architecture. Together with the algorithm formulation, its functionality is also validated and hardware complexity is analyzed and compared with the conventional nD FastICA. [ABSTRACT FROM PUBLISHER]

To efficiently trade off system sum-rate and link fairness, this paper is dedicated to maximizing the sum of \alpha-fair utility in spectrum-sharing networks, where multiple interfering links share one channel. In the literature, three special cases, including \alpha=\mbox{0} (sum-rate maximization), \alpha=\mbox{1} (proportional fairness), and \alpha=\infty (max-min fairness), have been investigated; the complexity for cases \mbox{1} < \alpha < \infty and \mbox{0} < \alpha < \mbox{1} is still unknown. In this paper, we prove that the problem is convex when \mbox1 < \alpha < \infty and is NP-hard when \mbox0 < \alpha < \mbox1. To deal with the latter case, we transform the objective function and represent it by the difference of two concave functions (D.C.). Then, a power allocation algorithm is proposed with fast convergence to a local optimal point. Simulation results show that the proposed algorithm can obtain global optimality in two-link cases when \mbox0 < \alpha < \mbox1. In addition, we can get a flexible tradeoff between sum-rate and fairness in terms of Jain's index by adjusting $\alpha$. [ABSTRACT FROM AUTHOR]

Triple patterning lithography (TPL) is regarded as a promising technique to handle the manufacturing challenges in the 14nm technology node and beyond. It is necessary to consider TPL in early design stages to make the layout more TPL friendly and reduce the manufacturing cost. In this paper, we propose a flow to co-optimize cell layout decomposition and detailed placement. Our cell decomposition approach can enumerate all coloring solutions with the minimum number of stitches. The experimental results show that our approach can outperform the existing work in all aspects of stitch number, half-perimeter wirelength (HPWL), and running time. We further extend our placer to consider the displacement of cells as a constraint and as an objective, respectively, which can help to preserve the quality of the input placement. Effectiveness of the extensions is verified by the experiments. [ABSTRACT FROM AUTHOR]

Metaheuristics have recently become the forefront of the current research as a powerful way to deal with many electromagnetic optimization problems. Based on the simulation of the herding behavior of krill individuals, a krill herd (KH) algorithm was recently proposed to solve optimization problems. In order to extend the classical mono-objective KH algorithm approach, this paper proposes a new multiobjective KH (MOKH) algorithm and a modified MOKH approach using the beta distribution in the inertia weight tuning. Numerical results on a multiobjective constrained brushless direct current motor design problem show that the evaluated MOKH algorithms present a promising performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel memetic algorithm, which is explorative particle swarm optimization (ePSO), combined with mesh adaptive direct search and apply it to the design of a permanent magnet synchronous machine (PMSM). The ePSO, which is modified from the PSO, drastically improves search time and iteration number at an exploration search stage. Unlike the existing methods, the proposed rule of start point selection takes an advantage of minimizing the search time. By applying the proposed algorithm to PMSM, we clarify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The design of an electromagnetic machine is a nonlinear, multivariable, and multimodal optimization problem that incurs a great deal of computation time when calculating electromagnetic fields. To overcome these problems effectively, this paper proposes a new evolutionary multimodal optimization algorithm based on the Big Bang–Big Crunch method and aided by a surrogate model using the theory of compressed sensing. Its efficiency is demonstrated by assessing the optimization results for test functions. Moreover, to evaluate the feasibility of its application to an electromagnetic problem, an interior permanent magnet motor is designed using the proposed algorithm. The obtained results confirm that the proposed method has the ability to search for multiple optimal solutions with a good computational efficiency by reducing the number of fitness function evaluations during the optimization process. [ABSTRACT FROM AUTHOR]

This paper presents a study of different fuzzy neural network (FNN) learning control methods for brushless dc (BLDC) motor drives. The FNN combines fuzzy logic (FL) with the learning capabilities of an artificial neural network. The study designs an FNN structure and defines four different training algorithms for the FNN, namely, backpropagation (BP), extended Kalman filter (EKF), genetic (GEN), and particle swarm optimization (PSO). These algorithms are examined in the simple application of pattern matching an input set to an output set and determine the strengths and weaknesses of each algorithm. Tests of each learning algorithm by a pattern matching benchmark are achieved via dSPACE DSP MATLAB/Simulink environment and allows for the capability for adaptive self-tuning of the weights and memberships of the input parameters. Thus, this adds a self-learning capability to the initial fuzzy design for operational adaptively and implements the solution on real hardware using a BLDC motor drive system. The success of the adaptive FNN-controlled BLDC motor drive system is verified by experimental results. Testing results show that the EKF method is the superior method of the four for this specific application. The BP method was also somewhat successful, nearly matching the pattern but not to the accuracy of the EKF. The GEN and PSO methods did not demonstrate success. Demonstrating the proposed self-learning FNN control on real hardware realizes the solution. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has received an increasing attention in recent years. Embedded intelligent terminal (EIT), an indispensable part of IoT, works not only as a sensor but also as a primary processor. Due to the limited power resource of EIT, it is important to study how to improve the efficiency of its power use. To tackle this problem, we propose an energy-saving algorithm, Bayesian idle time prediction (BIP). The basic idea of BIP is to explore historical information and obtain a better estimation of idle time. In this paper, we provide a theoretical analysis of BIP and compare our method with three existing algorithms [weighted idle-time-prediction (IP) algorithm, IP algorithm, and running time fixed threshold in IP algorithm] with respect to energy-saving potential, as well as system delay under a random number of tasks. Both simulation and field experiment results demonstrate the advantages of our algorithm in energy saving. [ABSTRACT FROM AUTHOR]

In Part I of the study of the tensor deflation for CANDECOMP/PARAFAC, we have shown that the rank-1 tensor deflation is applicable under some conditions. Part II of the study presents several initialization algorithms suitable for the algorithm proposed in Part I. In addition, Part II contains an algorithm for the case when one or more factor matrices in the estimated model is constrained to be orthogonal. Finally, Part II provides an error analysis of the tensor deflation algorithm, which shows that there is a marginal loss of accuracy of the deflation algorithm compared to the ordinary CP decomposition. [ABSTRACT FROM PUBLISHER]

Microaggregation is a technique for disclosure limitation aimed at protecting the privacy of data subjects in microdata releases. It has been used as an alternative to generalization and suppression to generate $k$ -anonymous data sets, where the identity of each subject is hidden within a group of $k$ subjects. Unlike generalization, microaggregation perturbs the data and this additional masking freedom allows improving data utility in several ways, such as increasing data granularity, reducing the impact of outliers, and avoiding discretization of numerical data. $k$ -Anonymity, on the other side, does not protect against attribute disclosure, which occurs if the variability of the confidential values in a group of $k$ subjects is too small. To address this issue, several refinements of $k$ -anonymity have been proposed, among which $t$ -closeness stands out as providing one of the strictest privacy guarantees. Existing algorithms to generate $t$ -close data sets are based on generalization and suppression (they are extensions of $k$ -anonymization algorithms based on the same principles). This paper proposes and shows how to use microaggregation to generate $k$ -anonymous $t$ -close data sets. The advantages of microaggregation are analyzed, and then several microaggregation algorithms for $k$ -anonymous $t$ -closeness are presented and empirically evaluated. [ABSTRACT FROM PUBLISHER]

To decrease potentially harmful effects to the environment and reduce costs of cellular network operators, the exploration of energy saving is significant. In this paper, we shed light on the following energy consumption minimization (ECM) problem: “how to select the smallest set of active base stations (BSs) that can preserve the quality of service (the minimum data rate) required by the users.” Since the ECM problem belongs to the field of integer programming and is NP-hard, we propose two low-complexity approximation algorithms that yield performance guarantees: the iterative minimal set cover algorithm and the iterative maximal coverage (IMC) algorithm. Moreover, by leveraging the submodularity theory, we establish the performance bound of the IMC algorithm. Finally, the computational complexity of our proposed algorithms is analyzed, and a simulation platform is developed to evaluate the number of active BSs of proposed approaches. The numerical results demonstrate that our proposed algorithms outperform the conventional counterpart. [ABSTRACT FROM AUTHOR]

This paper reports on the development of dynamic characteristic analysis algorithm for irreversible demagnetization in brushless dc motors. In general, in static characteristic analysis of irreversible demagnetization, only the $d$ -axis magnetic flux components are considered. As a result, this type of analysis cannot be used to accurately analyze the characteristics of irreversible demagnetization while the motor is actually being driven. Thus, we developed a dynamic characteristic analysis algorithm that we utilize to compare the static and the dynamic characteristic analyses of irreversible demagnetization. Consequently, we propose a maximum current limit for motor designs that reduces the impact of irreversible demagnetization. [ABSTRACT FROM AUTHOR]

In space communication environments, most studies in delay tolerant networks (DTNs) adopt experimental approaches and they have shown that the utilization efficiency of link bandwidth can be affected by changing packet sizes at the bundle layer and the convergence layer. However, the lack of theoretical work makes it difficult to find the optimal packet sizes in general scenarios. In this paper, we focus on this important issue by performing a theoretical analysis to solve the packet size optimization problems in both one-hop and multihop DTNs. As our analysis is based on the widely used DTN architecture with Bundle Protocol (BP) and Licklider Transmission Protocol (LTP), the theoretical results can be applied to any DTN scenario using this architecture. We formulate packet size optimization problems in DTNs as nonconvex optimization problems. Furthermore, a goodput enhancement algorithm (GEA) is also proposed to find the optimal packet sizes in order to maximize goodput in DTNs. We conduct numerical analysis as well as simulations to validate our theoretical analysis and study the factors that determine the optimal packet sizes for BP and LTP. Numerical results show that GEA achieves significant performance improvement compared with schemes where packet size optimization on BP and LTP is not jointly considered. [ABSTRACT FROM PUBLISHER]

Polar codes, as the first provable capacity-achieving error-correcting codes, have received much attention in recent years. However, the decoding performance of polar codes with traditional successive-cancellation (SC) algorithm cannot match that of the low-density parity-check or Turbo codes. Because SC list (SCL) decoding algorithm can significantly improve the error-correcting performance of polar codes, design of SCL decoders is important for polar codes to be deployed in practical applications. However, because the prior latency reduction approaches for SC decoders are not applicable for SCL decoders, these list decoders suffer from the long-latency bottleneck. In this paper, we propose a multibit-decision approach that can significantly reduce latency of SCL decoders. First, we present a reformulated SCL algorithm that can perform intermediate decoding of 2 b together. The proposed approach, referred as 2-bit reformulated SCL (2b-rSCL) algorithm, can reduce the latency of SCL decoder from ( 3n-2 ) to ( 2n-2 ) clock cycles without any performance loss. Then, we extend the idea of 2-b-decision to general case, and propose a general decoding scheme that can perform intermediate decoding of any 2^K bits simultaneously. This general approach, referred as \textit 2^K -bit reformulated SCL ( 2^K b-rSCL) algorithm, can reduce the overall decoding latency to as short as n/2^K-2-2 cycles. Furthermore, on the basis of the proposed algorithms, very large-scale integration architectures for 2b-rSCL and 4b-rSCL decoders are synthesized. Compared with a prior SCL decoder, the proposed (1024, 512) 2b-rSCL and 4b-rSCL decoders can achieve 21% and 60% reduction in latency, 1.66 and 2.77 times increase in coded throughput with list size 2, and 2.11 and 3.23 times increase in coded throughput with list size 4, respectively. [ABSTRACT FROM PUBLISHER]

In blind hyperspectral unmixing (HU), the pure-pixel assumption is well known to be powerful in enabling simple and effective blind HU solutions. However, the pure-pixel assumption is not always satisfied in an exact sense, especially for scenarios where pixels are heavily mixed. In the no-pure-pixel case, a good blind HU approach to consider is the minimum volume enclosing simplex (MVES). Empirical experience has suggested that MVES algorithms can perform well without pure pixels, although it was not totally clear why this is true from a theoretical viewpoint. This paper aims to address the latter issue. We develop an analysis framework wherein the perfect endmember identifiability of MVES is studied under the noiseless case. We prove that MVES is indeed robust against lack of pure pixels, as long as the pixels do not get too heavily mixed and too asymmetrically spread. The theoretical results are supported by numerical simulation results. [ABSTRACT FROM PUBLISHER]

Consider two remote nodes having binary sequences $X$ and Y$ , respectively. Y$ is an edited version of , where the editing involves random deletions, insertions, and substitutions, possibly in bursts. The goal is for the node with Y$ to reconstruct X$ with minimal exchange of information over a noiseless link. The communication is measured in terms of both the total number of bits exchanged and the number of interactive rounds of communication. This paper focuses on the setting where the number of edits is , where $n$ is the length of $X$ . We first consider the case where the edits are a mixture of insertions and deletions (indels), and propose an interactive synchronization algorithm with near-optimal communication rate and average computational complexity of $O(n)$ arithmetic operations. The algorithm uses interaction to efficiently split the source sequence into substrings containing exactly one deletion or insertion. Each of these substrings is then synchronized using an optimal one-way algorithm based on the single-deletion correcting channel codes of Varshamov and Tenengolts. We then build on this synchronization algorithm in three different ways. First, it is modified to work with a single round of interaction. The reduction in the number of rounds comes at the expense of higher communication, which is quantified. Next, we present an extension to the practically important case where the insertions and deletions may occur in (potentially large) bursts. Finally, we show how to synchronize the sources to within a target Hamming distance. This feature can be used to differentiate between substitution and indel edits. In addition to theoretical performance bounds, we provide several validating simulation results for the proposed algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a cellular/infostation integrated network that supports on-demand data service delivery over a wireless link to users in a high-speed train. For the requests with different lifetimes and prices that they are willing to pay, we develop the optimal packet schedule that aims at earning the maximum revenue. To this end, we formulate the problem as an integer linear program (ILP). The problem cannot be solved efficiently by existing ILP solvers with guaranteed polynomial-time complexity in the worst case. By exploring the special structure of the formulated ILP, we propose a novel Checker algorithm and prove that this algorithm is guaranteed to find the offline optimal schedule in polynomial time. Based on the relevant insights, we further develop a class of online Checker algorithms that require only causal knowledge of service demands and wireless channel capacities. It is established that these online algorithms have a worst-case competitive ratio of 1/2, i.e., total revenue earned by them is at least half as much as the revenue earned by an offline optimal schedule that knows the complete a priori knowledge of future requests and channel conditions. Simulation results are provided to demonstrate the merit of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

This paper considers bit-loading algorithms to maximize throughput under total power and spectral mask constraints in interference-free OFDM systems. The contribution is twofold. First, we propose a simple criterion to switch between two well-known algorithms from the literature: the conventional Greedy and Greedy-based bit-removing (with maximum allowable bit loading initialization) algorithms. Second, we present a new low-complexity loading algorithm that exploits the bit vector obtained by rounding the water-filling algorithm solution to the associated continuous-input rate maximization problem as an efficient initial bit vector of the Greedy algorithm. We theoretically prove that this bit vector has two interesting properties. The first one states that it is an efficient bit vector, i.e., there is no movement of a bit from one subcarrier to another that reduces the total used power. The second one states that the optimized throughput, starting from this initial bit vector, is achieved by adding or removing bits on each subcarrier at most once. Simulation results show the efficiency of the proposed algorithm, i.e., the achievable throughput is maximized with significant reduction of computation cost as compared to many algorithms in the literature. [ABSTRACT FROM PUBLISHER]

We study delivering delay-sensitive data to a group of receivers with minimum latency. This latency consists of the time that the data spends in overlay links as well as the delay incurred at each overlay node, which has to send out a piece of data several times over a finite-capacity network connection. The latter part is a significant portion of the total delay as we show in the paper, yet it is often ignored or only partially addressed by previous multicast algorithms. We analyze the actual delay in multicast trees and consider building trees with minimum-average and minimum-maximum delay. We show the NP-hardness of these problems and prove that they cannot be approximated in polynomial time to within any reasonable approximation ratio. We then present a set of algorithms to build minimum-delay multicast trees that cover a wide range of application requirements—min-average and min-max delay, for different scales, real-time requirements, and session characteristics. We conduct comprehensive experiments on different real-world datasets, using various overlay network models. The results confirm that our algorithms can achieve much lower delays (up to 60% less) and up to orders-of-magnitude faster running times (i.e., supporting larger scales) than previous related approaches. [ABSTRACT FROM PUBLISHER]

The heat exchanging process is a strong nonlinear and cascade industrial unit, which is effected by the large random disturbances in the steam pressure, the outdoor temperature, and the water discharged by users. To solve this problem, in this paper, a PI controller with unmodeled dynamic compensation is combined with a dual-rate control, and a dynamic model of the supply water temperature in the outer loop with linear model and unknown higher order nonlinear is established by introducing the dynamics of the closed-loop control system for the steam flow-rate with a lifting method. This is then used in designing a one-step optimal PI control algorithm with unmodeled dynamics compensation. Second, a data-driven dual-rate control method is proposed for the control of water temperature and steam flow-rate. The stability and convergence of the proposed algorithm is analyzed. Finally, both, a contrast simulated experiment with the interval intelligent cascade control algorithm and an industrial application are included to demonstrate that the proposed method can control water temperature and steam flow-rate with large random disturbances within the target range of values for process requirements. [ABSTRACT FROM AUTHOR]