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This paper investigates the compress-and-forward scheme for an uplink cloud radio access network (C-RAN) model, where multi-antenna base stations (BSs) are connected to a cloud-computing-based central processor (CP) via capacity-limited fronthaul links. The BSs compress the received signals with Wyner-Ziv coding and send the representation bits to the CP; the CP performs the decoding of all the users’ messages. Under this setup, this paper makes progress toward the optimal structure of the fronthaul compression and CP decoding strategies for the compress-and-forward scheme in the C-RAN. On the CP decoding strategy design, this paper shows that under a sum fronthaul capacity constraint, a generalized successive decoding strategy of the quantization and user message codewords that allows arbitrary interleaved order at the CP achieves the same rate region as the optimal joint decoding. Furthermore, it is shown that a practical strategy of successively decoding the quantization codewords first, then the user messages, achieves the same maximum sum rate as joint decoding under individual fronthaul constraints. On the joint optimization of user transmission and BS quantization strategies, this paper shows that if the input distributions are assumed to be Gaussian, then under joint decoding, the optimal quantization scheme for maximizing the achievable rate region is Gaussian. Moreover, Gaussian input and Gaussian quantization with joint decoding achieve to within a constant gap of the capacity region of the Gaussian multiple-input multiple-output (MIMO) uplink C-RAN model. Finally, this paper addresses the computational aspect of optimizing uplink MIMO C-RAN by showing that under fixed Gaussian input, the sum rate maximization problem over the Gaussian quantization noise covariance matrices can be formulated as convex optimization problems, thereby facilitating its efficient solution. [ABSTRACT FROM PUBLISHER]

This paper investigates the problem of resilient fault-tolerant control for a class of Takagi–Sugeno fuzzy model which is described by fractional order differential equations. In particular, the system input under consideration is subject to actuator saturations, actuator faults, and nonlinearities. The main purpose of this paper is to put forward a generalized nonlinear resilient fault-tolerant controller that can make the considered system robustly asymptotically stable. Utilizing the concept of continuous frequency distribution, a new set of linear matrix inequalities, which are the sufficient conditions for robust asymptotic stability of the closed-loop system, are derived by using the indirect Lyapunov approach. Two numerical examples are included to illustrate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The notion of compressibility of a random measure is a rather general concept which find applications in many contexts from data compression, to signal quantization, and parameter estimation. While compressibility for discrete and continuous measures is generally well understood, the case of discrete-continuous measures is quite subtle. In this paper, we focus on a class of multi-dimensional random measures that have singularities on affine lower-dimensional subsets. We refer to this class of random variables as affinely singular. Affinely singular random vectors naturally arises when considering linear transformation of component-wise independent discrete-continuous random variables. To measure the compressibility of such distributions, we introduce the new notion of dimensional-rate bias (DRB) which is closely related to the entropy and differential entropy in discrete and continuous cases, respectively. Similar to entropy and differential entropy, DRB is useful in evaluating the mutual information between distributions of the aforementioned type. Besides the DRB, we also evaluate the the RID of these distributions. We further provide an upper-bound for the RID of multi-dimensional random measures that are obtained by Lipschitz functions of component-wise independent discrete-continuous random variables (X). The upper-bound is shown to be achievable when the Lipschitz function is $A \mathrm {X}$ , where $A$ satisfies ${\mathrm{ SPARK}}({A_{m\times n}}) = m+1$ (e.g., Vandermonde matrices). When considering discrete-domain moving-average processes with non-Gaussian excitation noise, the above results allow us to evaluate the block-average RID and DRB, as well as to determine a relationship between these parameters and other existing compressibility measures. [ABSTRACT FROM AUTHOR]

The complex voltage variation is an emerging issue caused by the large-scale distributed energy resources (DERs) integration and the forming prosumers. The prosumers with uncertain photovoltaic (PV) generation will serve as autonomous entities for distributed voltage regulation, which have interaction during the voltage regulation process and have not been fully researched. This paper proposed a stochastic game approach for distributed voltage regulation based on autonomous PV prosumers, where the interactive prosumers’ PV uncertainties are formulated as a dynamic process during the voltage regulation. An economic incentive-based voltage regulation model is built for autonomous prosumers, which takes the reactive power as strategies and considers the PV curtailment, reactive power compensations, and PV uncertainties. The Markov decision process is adopted, and the approach of changing discrete PV uncertainties to continuous probability distribution is proposed to solve the stochastic game model, while dealing with the “curse of dimensionality” issue arising from the PV uncertainties. Finally, the case study is conducted on the IEEE 33 and 118 node system with real data, and the simulation results demonstrate the effectiveness of the proposed voltage regulation method in terms of the nodal voltage, prosumers’ utility, PV uncertainties management, and scalability. [ABSTRACT FROM AUTHOR]

The statistical properties of the multivariate gamma–gamma $(\Gamma\Gamma)$ distribution with arbitrary correlation have remained unknown. In this paper, we provide analytical expressions for the joint probability density function (pdf), cumulative distribution function (cdf), and moment generation function (mgf) of the multivariate $\Gamma\Gamma$ distribution with arbitrary correlation. Furthermore, we present novel approximating expressions for the pdf and cdf of the sum of $\Gamma\Gamma$ random variables (RVs) with arbitrary correlation. Based on this statistical analysis, we investigate the performance of radio frequency (RF) and optical wireless communication systems. It is worth noting that the presented expressions include several previous results in the literature as special cases. [ABSTRACT FROM AUTHOR]

Sampling from the lattice Gaussian distribution has emerged as an important problem in coding, decoding, and cryptography. In this paper, the classic Metropolis-Hastings (MH) algorithm in Markov chain Monte Carlo methods is adopted for lattice Gaussian sampling. Two MH-based algorithms are proposed, which overcome the limitation of Klein’s algorithm. The first one, referred to as the independent Metropolis-Hastings-Klein (MHK) algorithm, establishes a Markov chain via an independent proposal distribution. We show that the Markov chain arising from this independent MHK algorithm is uniformly ergodic, namely, it converges to the stationary distribution exponentially fast regardless of the initial state. Moreover, the rate of convergence is analyzed in terms of the theta series, leading to predictable mixing time. A symmetric Metropolis-Klein algorithm is also proposed, which is proven to be geometrically ergodic. [ABSTRACT FROM AUTHOR]

In this paper, we propose opportunistic jammer selection in a wireless security system for increasing the secure degrees of freedom (DoF) between a transmitter and a legitimate receiver (say, Alice and Bob). There is a jammer group consisting of S jammers among which Bob selects K jammers. The selected jammers transmit independent and identically distributed Gaussian signals to hinder the eavesdropper (Eve). Since the channels of Bob and Eve are independent, we can select the jammers whose jamming channels are aligned at Bob, but not at Eve. As a result, Eve cannot obtain any DoF unless it has more than KNj receive antennas, where Nj is the number of each jammer's transmit antenna, and hence KNj can be regarded as defensible dimensions against Eve. For the jamming signal alignment at Bob, we propose two opportunistic jammer selection schemes and find the scaling law of the required number of jammers for target secure DoF by a geometrical interpretation of the received signals. [ABSTRACT FROM PUBLISHER]

The fading AWGN two-user two-hop network is considered where the channel coefficients are independent and identically distributed (i.i.d.) according to a continuous distribution and vary over time. For a broad class of channel distributions, the ergodic sum capacity is characterized to within a constant number of bits/second/hertz, independent of the signal-to-noise ratio. The achievability follows from the analysis of an interference neutralization scheme where the relays are partitioned into M pairs, and interference is neutralized separately by each pair of relays. When M=1, the proposed ergodic interference neutralization characterizes the ergodic sum capacity to within 4 bits/sec/Hz for i.i.d. uniform phase fading and approximately 4.7 bits/sec/Hz for i.i.d. Rayleigh fading. It is further shown that this gap can be tightened to 4\log\pi-4 bits/sec/Hz (approximately 2.6) for i.i.d. uniform phase fading and 4\-4\log (3\pi/8) bits/sec/Hz (approximately 3.1) for i.i.d. Rayleigh fading in the limit of large M. ref refid="fn1"/ id="fn1" asterisk="no"paraThroughout this paper, log(.) denotes the logarithm of base two.para [ABSTRACT FROM AUTHOR]

The set squeezing procedure, a new optimization methodology for solving chance-constrained programming problems under continuous uncertainty distribution, is proposed in this paper. The generally intractable chance constraints and unknown convexity are tackled by a novel analyses of local structure of the feasible set. Based on the newly discovered structure, it is proved that the set squeezing procedure converges and local optimality is guaranteed under mild conditions. Furthermore, efficient algorithms are derived for the set squeezing procedure under the widely used quadratically perturbed constraints. The developed method is applied to the mean squared error (MSE) based probabilistic transceiver design as an application example. Simulation results show that the MSE outage probability can be controlled tightly, which leads to lower transmit power, compared to the existing dominant safe approximation method and the bounded robust optimization method. [ABSTRACT FROM AUTHOR]

Estimating Kullback-Leibler divergence from identical and independently distributed samples is an important problem in various domains. One simple and effective estimator is based on the $k$ nearest neighbor distances between these samples. In this paper, we analyze the convergence rates of the bias and variance of this estimator. Furthermore, we derive a lower bound of the minimax mean square error and show that kNN method is asymptotically rate optimal. [ABSTRACT FROM AUTHOR]

The impact of cooperation on interference management is investigated by studying an elemental wireless network, the so-called symmetric interference relay channel (IRC), from a generalized degrees of freedom (GDoF) perspective. This is motivated by the fact that the deployment of relays is considered as a remedy to overcome the bottleneck of current systems in terms of achievable rates. The focus of this paper is on the regime in which the interference link is weaker than the source-relay link in the IRC. Our approach toward studying the GDoF goes through the capacity analysis of the linear deterministic IRC (LD-IRC). New upper bounds on the sum capacity of the LD-IRC based on genie-aided approaches are established. These upper bounds together with some existing upper bounds are achieved by using four novel transmission schemes. Extending the upper bounds and the transmission schemes to the Gaussian case, the GDoF of the Gaussian IRC is characterized for the aforementioned regime. This completes the GDoF results available in the literature for the symmetric GDoF. It turns out that even if the incoming and outgoing links of the relay are both weaker than the desired channel, involving a relay can increase the GDoF. Interestingly, utilizing the relay in this case can increase the slope of the GDoF from −2 [in the interference channel (IC)] to −1 or 0. This shrinks the regime where ignoring the interference by treating it as noise is optimal. Furthermore, the analysis shows that if the relay ingoing and outgoing links are sufficiently strong, the relay is able to neutralize the interference completely. In this case, the bottleneck of the transmission will be the interference links, and hence, the GDoF increases if the interference link gets stronger. It is shown that in the strong interference regime, in contrast to the IC, the GDoF can be a monotonically decreasing function of the interference level. [ABSTRACT FROM PUBLISHER]

When the information source is a continuous distribution and the rate–distortion function is strictly larger than the Shannon lower bound, the explicit evaluation of the rate–distortion function is not straightforward. We evaluate the rate–distortion function for an independent identically distributed gamma source with respect to the absolute-log distortion measure. The logarithmic transformation reduces this rate–distortion problem to that under the absolute distortion measure. Extending the explicit evaluation of the rate–distortion function for the Gaussian sources, we obtain the parametric form of the rate–distortion function. We show that the optimal distribution of reconstruction consists of a continuous component enclosed by left and right discrete components, and the left discrete component vanishes when the acceptable distortion is small. We further extend the result for a wider class of source distributions. [ABSTRACT FROM AUTHOR]

This paper addresses Bayesian inference of sparse signals by censored measurements. To reduce the sensor's duty-cycle, signals below a threshold are censored, i.e., are set to zero. Sparse signals are random vectors that, or whose elements, are zero with given probabilities. The corresponding probabilistic model induces random measurement and signal vectors of mixed absolute-continuous, discrete, and singular-continuous nature. Therefore, mixed probability densities, the expectation regarding these densities, and a generalized Bayes' rule are constructively derived. For the inference, proper a-posteriori expected loss functions are defined. Their derivative-free minimizations gives Bayesian inferrers similar to traditional minimum-mean-square-error (MMSE), maximum a-posteriori (MAP), and median estimators and detectors. The result provides a unified Bayesian-inference framework. Eventually, this leads to closed-form solutions for the inference problem, numerical results, and the analysis of the probability of censorship. [ABSTRACT FROM PUBLISHER]

Multistate series-parallel system (MSSPS) is a widely used model for representing engineering systems, whose reliability has been extensively analyzed. Universal generating function (UGF) is an efficient method for evaluating the reliability of MSSPS. However, when facing the large-scale MSSPS, where the number of system components and possible states are enormous, calclating the exact system reliability can be rather time-consuming. To evaluate the reliability of large-scale MSSPS more efficiently, this paper proposes an approximation method, named continuization discretization approximation (CDA) method. The CDA approach consists of continuization and discretization processes. The continuization process applies Gaussian approximation method based on the central limit theory and the UGF technique to evaluate parallel subsystems. While the discretization process discretizes the continuous distribution to a discrete one, and proposes an algorithm to evaluate the series subsystems efficiently. The efficiency and accuracy performance of the CDA method can be adjusted by parameters according to the computational resource and the system scale. The newly proposed method is compared to the existing methods in evaluating the large-scale MSSPS. Numerical examples show that the CDA method has evident advantage in computational efficiency with satisfactory accuracy performance. [ABSTRACT FROM AUTHOR]

This paper proposes a new multiobjective two-stage equilibrium optimization method for a supply chain planning problem with uncertain demand. To handle the ambiguity in the distribution of demand, the probability and possibility distributions are integrated to characterize the uncertain demand. As a result, the decision process in our equilibrium optimization problem is divided into two stages. In the first stage, decision variables should be taken before knowing the realizations of uncertain demand; while the second-stage decision variables must be taken after knowing the outcome of subjective uncertainty embedded in demand. On the basis of the proposed dynamic decision scheme, the objectives in the first-stage are constructed via credibilistic optimization methods. The objective and constraints in the second-stage are built via stochastic optimization methods. More specifically, three objectives in the first-stage are constructed based on the expected value operator and conditional value-at-risk of fuzzy variable, and the second-stage optimization model is built as a stochastic expected value model under a probabilistic constraint. When the random parameters follow normal distributions, the proposed equilibrium optimization model is equivalent to a triobjective two-stage credibilistic optimization model. To solve this model, we first employ a sequence of discrete possibility distributions to approximate continuous possibility distributions. Then, we design a new archive-guided multiobjective particle swarm optimization based on decomposition to solve the obtained approximate optimization model. Finally, numerical experiments via a light emitting diode industry problem are conducted to demonstrate the feasibility and effectiveness of the proposed optimization method and new heuristic algorithm. [ABSTRACT FROM AUTHOR]

This paper studies clustering of data sequences using the k-medoids algorithm. All the data sequences are assumed to be generated from unknown continuous distributions, which form clusters with each cluster containing a composite set of closely located distributions (based on a certain distance metric between distributions). The maximum intracluster distance is assumed to be smaller than the minimum intercluster distance, and both values are assumed to be known. The goal is to group the data sequences together if their underlying generative distributions (which are unknown) belong to one cluster. Distribution distance metrics based k-medoids algorithms are proposed for known and unknown number of distribution clusters. Upper bounds on the error probability and convergence results in the large sample regime are also provided. It is shown that the error probability decays exponentially fast as the number of samples in each data sequence goes to infinity. The error exponent has a simple form regardless of the distance metric applied when certain conditions are satisfied. In particular, the error exponent is characterized when either the Kolmogrov–Smirnov distance or the maximum mean discrepancy are used as the distance metric. Simulation results are provided to validate the analysis. [ABSTRACT FROM AUTHOR]

Sampling from a discrete Gaussian distribution is an indispensable part of lattice-based cryptography. Several recent works have shown that the timing leakage from a non-constant-time implementation of the discrete Gaussian sampling algorithm could be exploited to recover the secret. In this paper, we propose a constant-time implementation of the Knuth-Yao random walk algorithm for performing constant-time discrete Gaussian sampling. Since the random walk is dictated by a set of input random bits, we can express the generated sample as a function of the input random bits. Hence, our constant-time implementation expresses the unique mapping of the input random-bits to the output sample-bits as a Boolean expression of the random-bits. We use bit-slicing to generate multiple samples in batches and thus increase the throughput of our constant-time sampling manifold. Our experiments on an Intel i7-Broadwell processor show that our method can be as much as 2.4 times faster than the constant-time implementation of cumulative distribution table based sampling and consumes exponentially less memory than the Knuth-Yao algorithm with shuffling for a similar level of security. [ABSTRACT FROM AUTHOR]

As we know, the classical operational law of uncertain variables initiated by Liu, which gives a major push to the development of uncertainty theory, restricts the uncertain variables being dealt with to those with regular uncertainty distributions. This restriction makes the operational law no longer applicable when some uncertain variables whose distributions are not regular are involved. Therefore, a generalized operational law is proposed in this paper, for uncertain variables with continuous distributions, of which regular distributions can be treated as special cases. By utilizing this extended operational law, the uncertainty distributions of strictly monotonic functions of uncertain variables with continuous (not necessarily regular) distributions can be analytically deduced, similarly to that suggested by the classical one. Furthermore, some new conclusions on the expected values of uncertain variables with continuous distributions are presented as well. Finally, as an important application of the generalized operational law, it is proved that some types of uncertain programming involving uncertain parameters with continuous distributions can be translated into deterministic counterparts, and then be handled by classic optimization techniques without any other additional efforts. [ABSTRACT FROM AUTHOR]

Multivariate generalized Gaussian distributions (MGGDs) have aroused a great interest in the image processing community thanks to their ability to describe accurately various image features, such as image gradient fields. However, so far their applicability has been limited by the lack of a transformation between two of these parametric distributions. In this paper, we propose a novel transformation between MGGDs, consisting of an optimal transportation of the second-order statistics and a stochastic-based shape parameter transformation. We employ the proposed transformation between MGGDs for a color transfer and a gradient transfer between images. We also propose a new simultaneous transfer of color and gradient, which we apply for image color correction. [ABSTRACT FROM AUTHOR]

This paper studies the structure of downlink sum-rate maximizing selective decentralized feedback policies for opportunistic beamforming under finite feedback constraints on the average number of mobile users feeding back. First, it is shown that any sum-rate maximizing selective decentralized feedback policy must be a threshold feedback policy. This result holds for all fading channel models with continuous distribution functions. Second, the resulting optimum threshold selection problem is analyzed in detail. This is a nonconvex optimization problem over finite-dimensional Euclidean spaces. By utilizing the theory of majorization, an underlying Schur-concave structure in the sum-rate function is identified, and the sufficient conditions for the optimality of homogenous threshold feedback policies are obtained. Applications of these results are illustrated for well-known fading channel models such as Rayleigh, Nakagami, and Rician fading channels. Rather surprisingly, it is shown that using the same threshold value at all mobile users is not always a rate-wise optimal feedback strategy, even for a network in which mobile users experience statistically the same channel conditions. For the Rayleigh fading channel model, on the other hand, homogenous threshold feedback policies are proven to be rate-wise optimal if multiple orthonormal data carrying beams are used to communicate with multiple mobile users simultaneously. [ABSTRACT FROM PUBLISHER]

A novel model based on the bivariate inverse Gaussian (IG) distribution is presented for the prediction of the outage performance of a dual site diversity Earth-space system. In the present paper, the exceeded rain attenuation induced on each Earth-space path is assumed that follows the IG distribution. The joint rain attenuation exceedance probability is calculated using the long-term statistical parameters of rain attenuation on each individual slant path and adopting a spatial correlation coefficient of rain attenuation variables as a function of the separation distance of the two Earth-stations. The Earth-Space path Database of Study Group 3 (DBSG3) of International Telecommunication Union (ITU) is employed in order to compare the performance of the new method with the ITU-R P.618-10 outage model. The numerical results from the comparison tests are very encouraging. [ABSTRACT FROM PUBLISHER]

In this paper, a novel analytical method is developed to locate and assess the severity of a true radial displacement (confined to 1 or 2 disk pairs) in an actual continuous-disk transformer winding. An analytical solution becomes possible mainly due to a simple manipulation of the system matrix, that is, to consider sum of the inverse squares of natural frequencies, instead of just the natural frequencies. This leads to an elegant expression that connects displacement location and its severity to changes in natural frequencies. For its implementation, short-circuit natural frequencies and variation of inductance as a function of winding length are the required inputs. Details of the derivation and experimental results are reported. In summary, it emerges that incipient and true radial displacements in an actual continuous-disk transformer winding can be localized with reasonable accuracy and a qualitative assessment of its severity is also possible. [ABSTRACT FROM PUBLISHER]

This paper studies the classification of high-dimensional Gaussian signals from low-dimensional noisy, linear measurements. In particular, it provides upper bounds (sufficient conditions) on the number of measurements required to drive the probability of misclassification to zero in the low-noise regime, both for random measurements and designed ones. Such bounds reveal two important operational regimes that are a function of the characteristics of the source: 1) when the number of classes is less than or equal to the dimension of the space spanned by signals in each class, reliable classification is possible in the low-noise regime by using a one-vs-all measurement design; 2) when the dimension of the spaces spanned by signals in each class is lower than the number of classes, reliable classification is guaranteed in the low-noise regime by using a simple random measurement design. Simulation results both with synthetic and real data show that our analysis is sharp, in the sense that it is able to gauge the number of measurements required to drive the misclassification probability to zero in the low-noise regime. [ABSTRACT FROM PUBLISHER]

We study the problem of lossy joint source-channel coding in a single-user energy harvesting communication system with causal energy arrivals and the energy storage unit may have leakage. In particular, we investigate the achievable distortion in the transmission of a single source via an energy harvesting transmitter over a point-to-point channel. We consider an adaptive joint source-channel coding system, where the length of channel codewords varies based on the available battery charge. We first establish a lower bound on the achievable distortion. Then, as necessary conditions for local optimality, we obtain two coupled equations that determine the mismatch ratio between channel symbols and source symbols as well as the transmission power, both as functions of the battery charge. As examples of continuous and discrete sources, we consider Gaussian and binary sources respectively. For the Gaussian case, we obtain a closed-form expression for the mismatch factor in terms of the $Lambert W$ function, and show that an increasing transmission power policy results in a decreasing mismatch factor policy and vice versa. Finally, we numerically compare the performance of the adaptive mismatch factor scheme to the case of a constant mismatch factor. [ABSTRACT FROM AUTHOR]

Estimation of multiple T2 components within single imaging voxels typically proceeds in one of two ways; a nonparametric grid approximation to a continuous distribution is made and a regularized nonnegative least squares algorithm is employed to perform the parameter estimation, or a parametric multicomponent model is assumed with a maximum likelihood estimator for the component estimation. In this work, we present a Bayesian algorithm based on the principle of progressive correction for the latter choice of a discrete multicomponent model. We demonstrate in application to simulated data and two experimental datasets that our Bayesian approach provides robust and accurate estimates of both the T2 model parameters and nonideal flip angles. The second contribution of the paper is to present a Cramér-Rao analysis of T2 component width estimators. To this end, we introduce a parsimonious parametric and continuous model based on a mixture of inverse-gamma distributions. This analysis supports the notion that T2 spread is difficult, if not infeasible, to estimate from relaxometry data acquired with a typical clinical paradigm. These results justify the use of the discrete distribution model. [ABSTRACT FROM AUTHOR]

The multicast capacity of the Gaussian two-hop relay network with one source, $N$ relays, and L$ destinations is studied. It is shown that a careful modification of the partial decode-forward coding scheme, whereby the relays recover and coherently transmit degraded sets of message parts, achieves the cutset upper bound within (1/2)\log N$ bits regardless of the channel gains and power constraints. This scheme improves upon a previous scheme by Chern and Özgür, which is also based on partial decode-forward yet has an unbounded gap from the cutset bound for L \ge 2$ destinations. When restricted to noncoherent transmission among the relays, the proposed partial decode-forward scheme achieves a slightly larger gap of \log N$ bits from the cutset bound. The computation of this relaxed achievable rate involves evaluating mutual information across possible cuts, providing a very simple linear-complexity algorithm to approximate the single-source multicast capacity of the Gaussian two-hop relay network. [ABSTRACT FROM PUBLISHER]

The secrecy capacity based on the assumption of having continuous distributions for the input signals constitutes one of the fundamental metrics for the existing physical layer security (PHYS) solutions. However, the input signals of real-world communication systems obey discrete distributions. Furthermore, apart from the capacity, another ultimate performance metric of a communication system is its symbol error ratio (SER). In this article, we pursue a radically new approach to PHYS by considering rigorous direct SER optimization exploiting the discrete nature of practical modulated signals. Specifically, we propose a secure precoding technique based on a multi-objective SER criterion, which aims for minimizing the confidential messages’ SER at their legitimate user, while maximizing the SER of the confidential messages leaked to the illegitimate user. The key to this challenging multi-objective optimization problem is to introduce a priority factor that controls the priority of directly minimizing the SER of the legitimate user against directly maximizing the SER of the leaked confidential messages. Furthermore, we define a new metric termed as the security-level, which is related to the conditional symbol error probability of the confidential messages leaked to the illegitimate user. Additionally, we also introduce the secure discrete-input continuous-output memoryless channel (DCMC) capacity referred to as secure-DCMC-capacity, which serves as a classical security metric of the confidential messages, given a specific discrete modulation scheme. The impacts of both the channel’s Rician factor and the correlation factor of antennas on the security-level and the secure-DCMC-capacity are investigated. Our simulation results demonstrate that the proposed priority-aware secure precoding based on the direct SER metric is capable of securing transmissions, even in the challenging scenario, where the eavesdropper has three receive antennas, while the legitimate user only has a single one. [ABSTRACT FROM AUTHOR]

The dynamic analysis of induction motors is supported by well-known theories: the two-axes transformation, and the space vector theory. Yet some inconsistencies with the theory of dynamic systems exist. The machine eigenvalues suggest the existence of two damped oscillators, physically not understandable. The respective eigenfrequencies change with the angular velocity of the reference frame. This contradicts the understanding that eigenfrequencies are inherent system properties. Physically, the dynamics depend on the continuous distribution of magnetic energy and its spatial displacement during transient processes. Information on the system dynamics is lost when dividing the continuum of magnetic energy into discrete portions. Complex state variables associate the dynamics to the propagation in space of distributed magnetic fields. The dynamic analysis reveals the existence single-complex eigenvalues. These define a novel class of system identifiers. They are characterized by having only one imaginary part instead of a conjugate complex pair. The use of complex state variables conveys insight and physical understanding of the dynamic processes within the machine. The approach constitutes an extension to the theory of dynamic systems. [ABSTRACT FROM AUTHOR]

In recent years, recommender systems have become an important part of various applications, supporting both customers and providers in their decision-making processes. However, these systems still must overcome limitations that reduce their performance, like recommendations' overspecialization, cold start, and difficulties when items with unequal probability distribution appear or recommendations for sets of items are asked. A novel approach, addressing the above issues through a case-based recommendation methodology, is presented here. The scope of the presented approach is to generate meaningful recommendations based on items' co-occurring patterns and to provide more insight into customers' buying habits. In contrast to current recommendation techniques that recommend items based on users' ratings or history, and to most case-based item recommenders that evaluate items' similarities, the implemented recommender uses a hierarchical model for the items and searches for similar sets of items, in order to recommend those that are most likely to satisfy a user. [ABSTRACT FROM PUBLISHER]

Dynamics of an ensemble of particles is often studied by tracking a large number of test particles from a chosen initial state to an adequately defined final state. Random distributions of particles over considered parameters are frequently used to describe an initial state of studied ensemble. Randomness of input data insures that there is no overlapping in test particles visualization; however, it hides the dependencies between various input and output parameters. If non-random, uniformly distributed input data is used, relationships between the input and output variables are revealed. Further, mapping of such an input into its output together with an appropriate interpolation scheme contains description of an output corresponding to any desired distribution of input data. A mapping of non-random uniformly distributed input into its output is combined with n-dimensional linear interpolation into a method for studying dynamics of an ensemble of particles. Advantages and efficiency of the method are illustrated using an ion beam interaction with two devices: the four parallel, equally spaced finite wires and the spiral inflector. The method is applicable if interactions between particles within an ensemble are negligible compared to other forces involved. [ABSTRACT FROM PUBLISHER]