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This paper is concerned with the topological structure, reachability, and stabilization for Boolean control networks (BCNs) with state constraints via event-triggered control (ETC) scheme. In the first part, the topological structure of BCNs with state constraints is studied. Under the framework of state constrain, the definitions of fixed point and cycle are first defined. A novel phenomenon is that there may exist two kinds constrained fixed points, which are, respectively, named as the livelock and deadlock ones. It is different with the traditional fixed point. Accordingly, a formula is presented to calculate the number of constrained fixed points and constrained cycles. In the second part, the constrained reachability and stabilization problem of the event-triggered controlled BCNs are investigated. Two necessary and sufficient criteria are, respectively, obtained. Furthermore, an algorithm is developed to design all feasible controllers. Finally, a reduced model of the lac operon in the Escherichia coli is shown to illustrate the efficiency of the obtained results. [ABSTRACT FROM AUTHOR]

In this paper, a computationally efficient algorithm for Bayesian joint change point detection (CPD) in multiple time series is presented. The data generation model includes a number of change configurations (CC), each affecting a unique subset of the time series, which introduces correlation between the positions of change points (CPs) in the monitored time series. The inference objective is to identify joint changes and the associated CC. The algorithm consists of two stages: First, a univariate CPD algorithm is applied separately to each of the involved time series. The outcomes of this step are maximum a posteriori (MAP) detected CPs and posterior distributions of CPs conditioned on the MAP CPs. These outcomes are used in combination to approximate the posterior for the CCs. In the second algorithm stage, dynamic programming is used to find the maxima of this approximate CC posterior. The algorithm is applied to synthetic data, and it is shown to be both significantly faster and more accurate compared to a previously proposed algorithm designed to solve similar problems. Also, the initial algorithm is extended with steps from the maximization–maximization algorithm, which allows the hyperparameters of the data generation model to be estimated jointly with the CCs, and we show that these estimates coincide with estimates obtained from a Markov chain Monte Carlo algorithm. [ABSTRACT FROM AUTHOR]

Motivated by the work of Dahrouj and Yu in applying the Han-Kobayashi transmission strategy for mitigating the intercell interference in a multi-cell multi-user multiple-input single-output interference network (MISO IN), this paper considers splitting messages into private and common parts in a multi-cell multi-user MIMO IN. Specifically, the covariances of the private messages and common messages are designed to optimize either the sum rate or the minimal rate. The common messages and private messages are decoded in sequence using successive decoding. This paper shows how these difficult optimization problems can be adequately solved by means of d.c. (difference of concave functions) optimization over a simple convex set. Numerical and simulation results also reveal the great advantage of our proposed solutions for various types of INs. In particular, the proposed solutions are shown to outperform the algorithm developed by Dahrouj and Yu for the simpler case of the MISO IN. [ABSTRACT FROM PUBLISHER]

This paper proposes a kinematics model with four noncoplanar points’ Cartesian coordinates for a spatial parallel manipulator, which is called the tetrahedron coordinate method. The sufficient and necessary criteria of utilizing the Cartesian coordinates of the four noncoplanar points are proved. Because the constraint equations are either quadratic or linear, and the coordinates are complete Cartesian, the derivative matrix of the constraint equations only consists of linear or constant elements that are the advantages of the general natural coordinate method as well. However, the number of variables of the general natural coordinate method will increase with the increasing number of investigated points. The tetrahedron coordinate approach proposed in this paper does not need to induce any new variables when more points on the manipulator are considered. As a result, it has a prevailing advantage over the general natural coordinate method. This advantage is especially explicit when establishing the kinematics models for complex spatial parallel manipulators with three to six degrees of freedom, the virtues of which are demonstrated by a case study. [ABSTRACT FROM PUBLISHER]

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]

This paper considers the statistical performance of the MUSIC method under the condition that two closely spaced sources impinging on an array of sensors are effectively resolved, i.e., the spectrum exhibits two peaks in the neighborhood of the true directions-of-arrival (DOA). The MUSIC algorithm is known to have an infinite resolution power in theory. However, in the presence of modeling errors, sources can not be resolved with certainty, even if the array correlation matrix is perfectly known. The focus of this paper is to predict the bias and variance of the DOA estimates taking into account the possible resolution failure of MUSIC. This performance prediction, based on our recent mathematical investigation, is new to the best of our knowledge. A general mathematical framework to derive closed form expressions of the bias and variance versus the model mismatch, conditioned on a general statistical resolution test is proposed. In order to illustrate our mathematical approach, statistical tests with one and two conditions, respectively, are investigated. The accuracy of the performance prediction is illustrated in a simulation study. It is found that the proposed approach outperforms the "classical" technique, which ignores the possible resolution failure of the MUSIC algorithm. Therefore, our results provide better tools for determining the necessary antenna calibration accuracy to achieve some targeted specifications on the estimator performance. [ABSTRACT FROM AUTHOR]

Hardware/software (HW/SW) partitioning is one of the key challenges in HW/SW codesign. This paper presents efficient algorithms for the HW/SW partitioning problem, which has been proved to be NP-hard. We reduce the HW/SW partitioning problem to a variation of knapsack problem that is approximately solved by searching 1D solution space, instead of searching 2D solution space in the latest work cited in this paper, to reduce time complexity. Three heuristic algorithms are proposed to determine suitable partitions to satisfy HW/SW partitioning constraints. We have shown that the time complexity for partitioning a graph with n nodes and m edges is significantly reduced from O(dx ⋅ dy ⋅ n³) to O(n log n + d ⋅ (n + m)), where d and dx ⋅ dy are the number of the fragments of the searched 1D solution space and the searched 2D solution space, respectively. The lower bound on the solution quality is also proposed based on the new computing model to show that it is comparable to that reported in the literature. Moreover, empirical results show that the proposed algorithms produce comparable and often better solutions when compared to the latest algorithm while reducing the time complexity significantly. [ABSTRACT FROM AUTHOR]

This paper considers the noncooperative maximization of mutual information in the vector Gaussian interference channel in a fully distributed fashion via game theory. This problem has been widely studied in a number of works during the past decade for frequency-selective channels, and recently for the more general multiple-input multiple-output (MIMO) case, for which the state-of-the art results are valid only for nonsingular square channel matrices. Surprisingly, these results do not hold true when the channel matrices are rectangular and/or rank deficient matrices. The goal of this paper is to provide a complete characterization of the MIMO game for arbitrary channel matrices, in terms of conditions guaranteeing both the uniqueness of the Nash equilibrium and the convergence of asynchronous distributed iterative waterfilling algorithms. Our analysis hinges on new technical intermediate results, such as a new expression for the MIMO waterfilling projection valid (also) for singular matrices, a mean-value theorem for complex matrix-valued functions, and a general contraction theorem for the multiuser MIMO watefilling mapping valid for arbitrary channel matrices. The quite surprising result is that uniqueness/convergence conditions in the case of tall (possibly singular) channel matrices are more restrictive than those required in the case of (full rank) fat channel matrices. We also propose a modified game and algorithm with milder conditions for the uniqueness of the equilibrium and con- vergence, and virtually the same performance (in terms of Nash equilibria) of the original game. [ABSTRACT FROM AUTHOR]

This paper describes a method of developing wide-band multi-port system equivalents for use with real-time digital power system simulators. The proposed equivalent combines a frequency dependent network equivalent (FDNE) for the high frequency electromagnetic transients and a transient stability analysis (TSA) type simulation block for the electromechanical transients. The frequency dependent characteristic for FDNE is obtained by curve-fitting frequency domain admittance characteristics using the vector fitting method. The paper also introduces an approach for approximating the frequency dependent characteristic of large power networks from readily available typical power-flow data. The paper shows how the TSA algorithm can be adapted to a real-time platform. The validity of this method is confirmed with examples, including the study of a multi infeed HVDC system based network. [ABSTRACT FROM AUTHOR]

This paper describes the application of the continuous Newton's method to the power flow problem. This method basically consists in formulating the power flow problem as a set of autonomous ordinary differential equations. Based on this formal analogy, we propose an entire family of numerically efficient algorithms for solving ill-conditioned or badly-initialized power flow cases. The paper also shows that the classical Newton-Raphson's method and most robust power flow techniques proposed in the literature are particular cases of the proposed formulation. An example based on a 1254-bus model of the UCTE system is presented and discussed. [ABSTRACT FROM AUTHOR]

Traditional online security assessment determines whether the system is secure or not, but how secure or insecure is not explicitly indicated. This paper develops probabilistic indices, risk, to assess real-time power system security level. Risk captures not only event likelihood, but also consequence. System security level associated with low voltage and overload can be optimally controlled, using the NSGA multiobjective optimization method. A security diagram is used to visualize operating conditions in a way that enables both risk-based and traditional deterministic views. An index for cascading overloads is used to evaluate the Pareto optimal solutions. This paper shows that the multiobjective approach results in less risky and less costly operating conditions, and it provides a practical algorithm for implementation. The IEEE 24-bus RTS-1996 system is analyzed to show that risk-based system security control results in lower risk, lower cost, and less exposure to cascading outages. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of blind identification of a convolutive multiple-input-multiple-output (MIMO) system with No outputs and Ni inputs. While many methods have been proposed to blindly identify convolutive MIMO systems with No ≥ Ni (overdetermined), very scarce results exist for the case of No < Ni (underdetermined), all of which refer to systems that either have some special structure or special No and Ni values. In this paper, we show that, as long as min(No, Ni) ≥ 2, independent of whether the system is overdetermined or underdetermined, we can always find the appropriate order of statistics that guarantees identifiability of the system response within trivial ambiguities. We also propose an algorithm to reach the solution, that consists of parallel factorization (PARAFAC) of a K-way tensor containing K th-order statistics of the system outputs, followed by an iterative scheme. For a certain order of statistics K, we provide the description of the class of identifiable MIMO systems. We also show that this class can be expanded by applying PARAFAC decomposition to a pair of tensors instead of one tensor. The proposed approach constitutes a novel scheme for estimation of underdetermined systems, and improves over existing approaches for overdetermined systems. [ABSTRACT FROM AUTHOR]

Recently, Gallager's bounding techniques have been used to derive tight performance bounds for coded systems in fading channels. Most works in this field have thus far dealt with coherent decoding. This paper develops Gallager bounds for noncoherent systems in fading channels. Unlike coherent decoding, the exact error probability of a noncoherent decoder/detector conditioned on the fading coefficients does not admit a closed-form expression. This difficulty is overcome in this paper by employing the Chernoff technique. Although it weakens the bounds to some extent, the Chernoff technique enables the derivations of the limit-before-average (LBA) bound and Gallager bounds in closed form for noncoherent fading channels. Numerical examples show that the proposed bounds are convergent and are tighter than the conventional union bound. [ABSTRACT FROM AUTHOR]

In medical diagnosis, doctors must often determine what medical tests (e.g., X-ray and blood tests) should be ordered for a patient to minimize the total cost of medical tests and misdiagnosis. In this paper, we design cost-sensitive machine learning algorithms to model this learning and diagnosis process. Medical tests are like attributes in machine learning whose values may be obtained at a cost (attribute cost), and misdiagnoses are like misclassifications which may also incur a cost (misclassification cost). We first propose a lazy decision tree learning algorithm that minimizes the sum of attribute costs and misclassification costs. Then, we design several novel "test strategies" that can request to obtain values of unknown attributes at a cost (similar to doctors' ordering of medical tests at a cost) in order to minimize the total cost for test examples (new patients). These test strategies correspond to different situations in real-world diagnoses. We empirically evaluate these test strategies, and show that they are effective and outperform previous methods. Our results can be readily applied to real-world diagnosis tasks. A case study on heart disease is given throughout the paper. [ABSTRACT FROM AUTHOR]

The active appearance model (AAM) is a powerful tool for modeling images of deformable objects and has been successfully used in a variety of alignment, tracking, and recognition applications. AAM uses subspace-based deformable models to represent the images of a certain object class. In general, fitting such complicated models to previously unseen images using standard optimization techniques is a computationally complex task because the gradient matrix has to be numerically computed at every iteration. The critical feature of AAM is a fast convergence scheme which assumes that the gradient matrix is fixed Around the optimal coefficients for all images. Our work in this paper starts with the observation that such a fixed gradient matrix inevitably specializes to a certain region in the texture space, and the fixed gradient matrix is not a good estimate of the actual gradient as the target texture moves away from this region. Hence, we propose an adaptive AAM algorithm that linearly adapts the gradient matrix according to the composition of the target image's texture to obtain a better estimate for the actual gradient. We show that the adaptive AAM significantly outperforms the basic AAM, especially in images that are particularly challenging for the basic algorithm. In terms of speed and accuracy, the idea of a linearly adaptive gradient matrix presented in this paper provides an interesting compromise between a standard optimization technique that recomputes the gradient at every iteration and the fixed gradient matrix approach of the basic AAM. [ABSTRACT FROM AUTHOR]

In the evolving submicron technology, making it particularly attractive to use decentralized designs. A common form of decentralization adopted in processors is to partition the execution core into multiple clusters. Each cluster has a small instruction window, and a set of functional units. A number of algorithms have been proposed for distributing instructions among the clusters. The first part of this paper analyzes (qualitatively as well as quantitatively) the effect of various hardware parameters such as the type of cluster interconnect, the fetch size, the cluster issue width, the cluster window size, and the number of clusters on the performance of different instruction distribution algorithms. The study shows that the relative performance of the algorithms is very sensitive to these hardware parameters and that the algorithms that perform relatively better with four or fewer clusters are generally not the best ones for a larger number of clusters. This is important, given that with an imminent increase in the transistor budget, more clusters are expected to be integrated on a single chip. The second part of the paper investigates alternate interconnects that provide scalable performance as the number of clusters is increased. In particular, it investigates two hierarchical interconnects—a single ring of crossbars and multiple rings of crossbars—as well as instruction distribution algorithms to take advantage of these interconnects. Our study shows that these new interconnects with the appropriate distribution techniques achieve an IPC (instructions per cycle) that is 15-20 percent better than the most scalable existing configuration, and is within 2 percent of that achieved by a hypothetical ideal processor having a 1-cycle latency crossbar interconnect. These results confirm the utility and applicability of hierarchical interconnects and hierarchical distribution algorithms in clustered processors. [ABSTRACT FROM AUTHOR]

Conventional divisible load scheduling algorithms attempt to achieve optimal partitioning of massive loads to be distributed among processors in a distributed computing system in the presence of communication delays in the network. However, these algorithms depend strongly upon the assumption of prior knowledge of network parameters and cannot handle variations or lack of information about these parameters. In this paper, we present an adaptive strategy that estimates network parameter values using a probing technique and uses them to obtain optimal load partitioning. Three algorithms, based on the same strategy, are presented in the paper, incorporating the ability to cope with unknown network parameters. Several illustrative numerical examples are given. Finally, we implement the adaptive algorithms on an actual network of processor nodes using MPI implementation and demonstrate the feasibility of the adaptive approach. [ABSTRACT FROM AUTHOR]

General failure event data from various sources are often used to estimate the failure probability for the system of interest, especially when s-dependence exists among component failures, where common cause failure plays an important role. Failure event data from different sources must be reasonably explained, and correctly applied, so that the information about load environment, and component/system property can be used correctly. In estimating the probability for s-dependent system failure, both the load distribution, and component strength distribution are much more important than component failure probability index. Based on the relationship among different multiple failures, this paper presents a data mapping approach to estimating dependent system failure probability through multiple failure event data of other systems with different sizes. The underlying assumption on data mapping is that failures of different multiples (including single) are correlated with each other for a group of components if they are subjected to the same or correlated random load (loads). Taking the situation of a group of s-independent components operating under the same random load as an example, the likelihood of a component failure at a trial depends not only on the strength of the individual component but also on the realization of the random load. The likelihood of a specific multiple failure at a trial is also determined by both the component strengths, and the realization of the random toad. Further- more, if a larger load sample appears, the likelihoods for failure are higher. Conversely, if a smaller load sample appears, the likelihoods of failure are lower. We emphasized in this paper that system failure event data should be interpreted & applied under the principle that various multiple failures are distinguished by their respective failure multiplicity and/or system size, and are inherently interrelated through correlated load environments. The approach starts with determining the load parameter, and component strength parameter according to multiple (including single) failure event data available. Then, these parameters are used to calculate the probability of multiple failures for systems of different sizes. This approach is applicable to predict high multiple failure probability based on low multiple failure event data. Examples of estimating multiple failure probabilities of EDG (emergency diesel generators) with mapped data illustrate that the proposed approach is desirable. [ABSTRACT FROM AUTHOR]

The article informs that the no free lunch theorem says that there is no single evolutionary algorithm (EA) which can solve all possible optimization problems efficiently. Over the entire problem space, the performance of all EAs is equivalent. For a given optimization problem, people need to focus on designing problem-oriented algorithms in order to improve the algorithm's performance. For any NP-hard problem, people only have limited heuristic knowledge to use when designing EAs that solve it. This paper, aims to discover the role of heuristic knowledge in EAs. Through a comparative study of three different EAs for solving the node covering problem, the paper investigates the role of heuristic knowledge in EAs finding a feasible solution and the optimal solution.

This paper presents a new approach to economic dispatch (ED) problems with nonsmooth cost functions using a particle swarm optimization (PSO) technique. The practical ED problems have nonsmooth cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult using any mathematical approaches. In this paper, a modified PSO (MPSO) mechanism is suggested to deal with the equality and inequality constraints in the ED problems. A constraint treatment mechanism is devised in such a way that the dynamic process inherent in the conventional PSO is preserved. Moreover, a dynamic search-space reduction strategy is devised to accelerate the optimization process. To show its efficiency and effectiveness, the proposed MPSO is applied to test ED problems, one with smooth cost functions and others with nonsmooth cost functions considering valve -point effects and multi-fuel problems. The results of the MPSO are compared with the results of conventional numerical methods, Tabu search method, evolutionary programming approaches, genetic algorithm, and modified Hop- field neural network approaches. [ABSTRACT FROM AUTHOR]

Tight frames, also known as general Welch-bound-equality sequences, generalize orthonormal systems. Numerous applications-including communications, coding, and sparse approximation-require finite-dimensional tight frames that possess additional structural properties. This paper proposes an alternating projection method that is versatile enough to solve a huge class of inverse eigenvalue problems (IEPs), which includes the frame design problem. To apply this method, one needs only to solve a matrix nearness problem that arises naturally from the design specifications. Therefore, it is the fast and easy to develop versions of the algorithm that target new design problems. Alternating projection will often succeed even if algebraic constructions are unavailable. To demonstrate that alternating projection is an effective tool for frame design, the paper studies some important structural properties in detail. First, it addresses the most basic design problem: constructing tight frames with prescribed vector norms. Then, it discusses equiangular tight frames, which are natural dictionaries for sparse approximation. Finally, it examines tight frames whose individual vectors have low peak-to-average-power ratio (PAR), which is a valuable property for code-division multiple-access (CDMA) applications. Numerical experiments show that the proposed algorithm succeeds in each of these three cases. The appendices investigate the convergence properties of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we investigate how to topologically and geometrically characterize the intersection relations between a movable convex polygon A and a set [I] of possibly Overlapping polygons fixed in the plane. More specifically, a subset Φ C [I] is called an intersection relation if there exists a placement of A that intersects, and only intersects, Φ. The objective of this paper is to design an efficient algorithm that finds a finite and discrete representation of all of the intersection relations between A and [I]. Past related research only focuses on the complexity of the free space of the configuration space between A and [I] and how to move or place an object in this free space. However, there are many applications that require the knowledge of not only the free space, but also the intersection relations. Examples are presented to demonstrate the rich applications of the formulated problem on intersection relations. [ABSTRACT FROM AUTHOR]

There exist a significant number of benchmarks for evaluating the performance of boundary detection algorithms, most of them relying on some sort of comparison of the automatically-generated boundaries with human-labeled ones. Such benchmarks are composed of a representative image data set, as well as a comparison measure on the universe of boundary images. Despite many such data sets and measures have been proposed, there is no clear way of knowing which combinations of them are the most suitable for the task. In this paper, we introduce four criteria that allow for a sensible evaluation of the performance of a comparison measure on a given data set. The criteria mimic the way in which humans understand boundary images, as well as their ability to recognize the underlying scenes. These criteria can, as a final goal, quantify the ability of the boundary detection benchmarks to evaluate the performance of boundary detection methods, either edge-based or segmentation-based. [ABSTRACT FROM AUTHOR]

Waveform design is a pivotal component of the fully adaptive radar construct. In this paper, we consider waveform design for radar space time adaptive processing (STAP), accounting for the waveform dependence of the clutter correlation matrix. Due to this dependence, in general, the joint problem of receiver filter optimization and radar waveform design becomes an intractable, nonconvex optimization problem, Nevertheless, it is, however, shown to be individually convex either in the filter or in the waveform variables. We derive constrained versions of a) the alternating minimization algorithm, b) proximal alternating minimization, and c) the constant modulus alternating minimization, which, at each step, iteratively optimizes either the STAP filter or the waveform independently. A fast and slow time model permits waveform design in radar STAP, but the primary bottleneck is the computational complexity of the algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, the problem of outer beamformer design based only on channel statistic information is considered for two-stage beamforming for multi-user massive MIMO downlink, and the problem is approached based on signal-to-leakage-plus-noise ratio (SLNR). To eliminate the dependence on the instantaneous channel state information, a lower bound on the average SLNR is derived by assuming zero-forcing (ZF) inner beamforming, and an outer beamformer design method that maximizes the lower bound on the average SLNR is proposed. It is shown that the proposed SLNR-based outer beamformer design problem reduces to a trace quotient problem (TQP), which is often encountered in the field of machine learning. An iterative algorithm is presented to obtain an optimal solution to the proposed TQP. The proposed method has the capability of optimally controlling the weighting factor between the signal power to the desired user and the interference leakage power to undesired users according to different channel statistics. Numerical results show that the proposed outer beamformer design method yields significant performance gain over existing methods. [ABSTRACT FROM PUBLISHER]

We comment on a paper which describes a quaternion-based method for the strap-down inertial navigation systems/synthetic aperture radar (SINS/SAR) integrated navigation system. Although the paper proposes an interesting strategy, there exists a flaw in the algorithm regarding the quaternion averaging involved in the unscented particle filtering (UPF) algorithm. This letter suggests how to fix the problem. [ABSTRACT FROM AUTHOR]

Inertial navigation applications are usually referenced to a rotating frame. Consideration of the navigation reference frame rotation in the inertial navigation algorithm design is an important but so far less seriously treated issue, especially for super high-speed flying vehicles or the future ultraprecision navigation system of several meters per hour. A rigorous approach is proposed to tackle the issue of navigation frame rotation in velocity/position computation by use of the newly-devised velocity/position integration formulae in the Part I companion paper. The two integration formulae set a well-founded cornerstone for the velocity/position algorithms' design that makes the comprehension of the inertial navigation computation principle more accessible to practitioners, and different approximations to the integrals involved give birth to various velocity/position update algorithms. Two-sample velocity and position algorithms are derived to exemplify the design process. In the context of level-flight airplane examples, the derived algorithm is analytically and numerically compared with the typical algorithms that exist in the literature. The results throw light on the problems in existing algorithms and the potential benefits of the derived algorithm. [ABSTRACT FROM AUTHOR]

A novel framework of the motion-compensated 3-D wavelet transform (MC3DWT) for video denoising is presented in this paper. The motion-compensated temporal wavelet transform is first performed on a sliding window of video frames consisting of previously denoised frames and the current noisy frame. The 2-D spatial wavelet transform is then performed on the temporal subband frames, thus realizing a 3-D wavelet transform. Any of established wavelet-based still image denoising algorithms can then be applied to the high-pass 3-D subbands. The operation of the inverse 2-D spatial wavelet transform followed by the inverse temporal wavelet transform reconstructs the video frames in the buffer. The denoised current frame may be used as an output for real-time processing; meanwhile, the past frames can be updated, one of which may be used as a delayed output for post-processing or for real-time processing that allows some amount of delay. The proposed MC3DWT framework integrates both the spatial filtering and recursive temporal filtering into the 3-D wavelet domain and effectively exploits both the spatial and temporal redundancies. Experimental results have demonstrated a superior visual and quantitative performance of the proposed scheme for various levels of noise and motion. [ABSTRACT FROM AUTHOR]

In this paper, we present a new method for multiplying polynomials in Chebyshev form. Our approach has two steps. First, the well-known Karatsuba's algorithm is applied to polynomials constructed by using Chebyshev coefficients. Then, from the obtained result, extra arithmetic operations are used to write the final result in Chebyshev form. The proposed algorithm has a quadratic computational complexity. We also compare our method to other approaches. [ABSTRACT FROM AUTHOR]

In this paper, a new processing scheme for compensating the liftoff effect is proposed to increase the signal-to-noise ratio of measurements carried out when a duralumin plate with artificially manufactured defects was scanned with eddy-current probes. The mathematical algorithm is based on the spatial frequency behavior of the output signals with the liftoff distance (the distance between the probe used for inspection and the sample) and its equivalent form (deconvolution) in the spatial domain. Results are presented for measurements performed at three different distances for the same defect of the duralumin plate [ABSTRACT FROM AUTHOR]

Compressed sensing (CS) decoding algorithms can efficiently recover an N-dimensional real-valued vector x to within a factor of its best k-term approximation by taking m = O(k log N/k) measurements y = Φx. If the sparsity or approximate sparsity level of x were known, then this theoretical guarantee would imply quality assurance of the resulting estimate. However, because the underlying sparsity of the signal x is unknown, the quality of a CS estimate ... using m measurements is not assured. It is nevertheless shown in this paper that sharp bounds on the error ||x - ...||𝓁N2 can be achieved with no effort. More precisely, suppose that a maximum number measurements m is preimposed. One can reserve 10 log p of these m measurements and compute a sequence of possible estimates (...j)pj=1 to x from the m - 10 log p remaining measurements; errors ||x - ...j||𝓁N2 for j = 1,..., p can then be bounded high probability. As a consequence, numerical upper and lower bounds on the error between x and the best k-term approximation to x can be estimated for p values of k with almost no cost. observation has applications outside CS as well. [ABSTRACT FROM AUTHOR]

An important problem in the realization of floating-point subtraction is the identification of the position of the first nonzero digit in a radix-represented number, since the significand usually is to be represented left-normalized in the part of the word(s) allocated for representing its value. There are well known log-time algorithms for determining this position for numbers in nonredundant representations, which may also be applied to suitably (linear-time) transformed redundant representations. However, due to the redundancy in the latter case, the position thus determined may need a correction by one. When determination of the shift amount is to be performed in parallel with conversion to nonredundant representation (the subtraction), it must be performed on the redundant representation. This is also the case when the significand is to be retained in a redundant representation until the final rounding. This paper presents an improved algorithm for determining the need for a correction of the normalization shift amount, which can be run in parallel with the algorithm finding the "approximate" position. [ABSTRACT FROM AUTHOR]

Fingerprint analysis is typically based on the location and pattern of detected singular points in the images. These singular points (cores and deltas) not only represent the characteristics of local ridge patterns but also determine the topological structure (i.e., fingerprint type) and largely influence the orientation field. In this paper, we propose a novel algorithm for singular points detection. After an initial detection using the conventional Poincar6 Index method, a so-called DORIC feature is used to remove spurious singular points. Then, the optimal combination of singular points is selected to minimize the difference between the original orientation field and the model-based orientation field reconstructed using the singular points. A core-delta relation is used as a global constraint for the final selection of singular points. Experimental results show that our algorithm is accurate and robust, giving better results than competing approaches. The proposed detection algorithm can also be used for more general 2D oriented patterns, such as fluid flow motion, and so forth. [ABSTRACT FROM AUTHOR]

Transformation invariance is an important property in pattern recognition, where different observations of the same object typically receive the same label. This paper focuses on a transformation-invariant distance measure that represents the minimum distance between the transformation manifolds spanned by patterns of interest. Since these manifolds are typically nonlinear, the computation of the manifold distance (MD) becomes a nonconvex optimization problem. We propose representing a pattern of interest as a linear combination of a few geometric functions extracted from a structured and redundant basis. Transforming the pattern results in the transformation of its constituent parts. We show that, when the transformation is restricted to a synthesis of translations, rotations, and isotropic scalings, such a pattern representation results in a closed-form expression of the manifold equation with respect to the transformation parameters. The MD computation can then be formulated as a minimization problem whose objective function is expressed as the difference of convex functions (DC). This interesting property permits optimally solving the optimization problem with DC programming solvers that are globally convergent. We present experimental evidence which shows that our method is able to find the globally optimal solution, outperforming existing methods that yield suboptimal solutions. [ABSTRACT FROM AUTHOR]

Quadtrees and linear quadtrees are well-known hierarchical data structures to represent square images of size 2r x 2r. Finding the neighbors of a specific leaf node is a fundamental operation for many algorithms that manipulate quadtree data structures. In quadtrees, finding neighbors takes O(r) computational time for the worst case, where r is the resolution (or height) of a given quadtree. Schrack [1] proposed a constant-time algorithm for finding equal-sized neighbors in linear quadtrees. His algorithm calculates the location codes of equal-sized neighbors; it says nothing, however, about their existence. To ensure their existence, additional checking of the location codes is needed, which usually takes 0(r) computational time. In this paper, a new algorithm to find the neighbors of a given leaf node in a quadtree is proposed which requires just 0(1) (i.e., constant) computational time for the worst case. Moreover, the algorithm takes no notice of the existence or nonexistence of neighbors. Thus, no additional checking is needed. The new algorithm will greatly reduce the computational complexities of almost all algorithms based on quadtrees. [ABSTRACT FROM AUTHOR]

This paper presents a new and simple decoding algorithm for layered space time block codes such as the two independent Alamouti's codes which are also called the double space-time transmit diversity (DSTTD) system. By using group interference suppression and successive interference cancellation, we can treat DSTTD as two independent space-time block codes (STBC). We can then decode both of these STBC's through a simple maximum likelihood (ML) detector with null space-based interference cancellation. We also compare the proposed interference cancellation (IC) scheme with the conventional MMSE IC scheme. The performance of the proposed IC scheme is comparable to that of the MMSE IC scheme while the complexity reduction factor of the proposed scheme can be up to S compared to the MMSE IC scheme. [ABSTRACT FROM AUTHOR]

Optimum soft decoding of sources compressed with variable length codes and quasi-arithmetic codes, transmitted over noisy channels, can be performed on a bit/symbol trellis. However, the number of states of the trellis is a quadratic function of the sequence length leading to a decoding complexity which is not tractable for practical applications. The decoding complexity can be significantly reduced by using an aggregated state model, while still achieving close to optimum performance in terms of bit error rate and frame error rate. However, symbol a posteriori probabilities can not be directly derived on these models and the symbol error rate (SER) may not be minimized. This paper describes a two-step decoding algorithm that achieves close to optimal decoding performance in terms of SER on aggregated state models. A performance and complexity analysis of the proposed algorithm is given. [ABSTRACT FROM AUTHOR]

In power distribution networks the load varies within any given time frame. It may, therefore, seem that a good approach to reduce losses would be the solving of a network reconfiguration problem to suit each of the significant load variations. However, frequent changes in configuration can trigger outages or cause transient problems; they are best avoided. A recent formulation of this problem explicitly considers load variations and proposes to restrain frequent reconfigurations by assuming that network topologies will remain unchanged for a given planning period. This formulation leads to a much larger optimization problem than that traditionally used for network reconfiguration; moreover, it requires a new approach to optimization which is capable of dealing with energy flows instead of only instantaneous power flows. Such an approach is proposed in this paper, which discusses the design of an adaptive hybrid genetic algorithm that fulfills these new requirements. Key concepts in evolutionary computation and analysis of distribution systems are explored to develop this new algorithm. Application to real case studies certifies its benefits. [ABSTRACT FROM AUTHOR]