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The cells in an organism emit different amounts of proteins according to their clinical state (healthy/pathological, for instance). The resulting proteomic profile can be used for early detection, diagnosis, and therapy planning. In this paper, we study the classification of a proteomic sample from the point of view of an inverse problem with a joint Bayesian solution, called inversion-classification. We propose a hierarchical physical forward model and present encouraging results from both simulation and clinical data.

The takeoff point in this paper is a random variable X for which large positive values are desired. When its probability distribution is highly skewed, the possibility of a long fat-tailed distribution can lead to an expected value, \mu =\mathbb E[ X] , which is unduly optimistic. For the reverse case when small values of X are desired, the ideas in this paper are applied to - X . This issue of over-optimism in the expected value is particularly important when a mission-critical random variable is involved. For example, when considering earthquake intensity or flood levels, reporting an understated expected value to a technically unsophisticated general public would be considered by many as highly undesirable. The Conservative Expected Value (CEV) of X , denoted by \mathbb CEV ( X ) is a new definition provided in this paper. It is a metric which we argue is particularly useful when risk aversion must be highly emphasized. At the same time, while being conservative, the CEV is also aimed at avoiding reflection of excessive pessimism. When, then \mathbb CEV ( X ), while being conservative, is defined in such a way so as not to be unduly pessimistic. In classical analysis, enhancement of a calculation often includes the variance \sigma ^ 2 \,\, = var( X ). However, when large X -values are desired, this may further distort one’s overview of the risk at hand; e.g., if X is profit, values above the mean should not be penalized. With these one-sidedness considerations in mind, we work with a new reward-risk pair, the CEV and the so-called Conservative Semi-Variance (CSV) of X , denoted by \mathbb CSV ( X ). Whereas the CEV definition is entirely new, the CSV definition is motivated by ideas in the area of finance. This paper also illustrates calculation of the CEV and CSV pair for a number of classical probability distributions and includes description of a number of properties of these metrics which suggest that this new theory is mathematically rich. Finally, we demonstrate the potential for application of the theory via two numerical examples. [ABSTRACT FROM AUTHOR]

In recent years, camera identification methods have attracted attention in the field of digital forensics. The existing camera identification methods use features, such as the Exif header data and image noise, that indicate the characteristics of the camera. Of them, photo-response non-uniformity (PRNU) noise contains the unique features of an image sensor and is different for each individual camera. A camera identification method using the PRNU noise should have high identification ability, and a camera identification method using the pairwise magnitude relations of the clustered PRNU noise was previously proposed. In general, identification accuracy is estimated from test data sets, such as the Dresden image database. However, identification accuracy can be evaluated only with respect to the range of images within a database in the conventional evaluation method. A more detailed accuracy evaluation method is required for practical use. Furthermore, studies have not yet reported a false acceptance rate (FAR) evaluation method for the clustered PRNU pair-based camera identification capable of guaranteeing a low FAR (e.g., \rm FAR=10^-9 ). In this paper, we proposed a new pixel clustering method that guarantees An FAR for camera identification using pairs of clustered PRNU noise, and evaluate its FAR based on a probability calculation of a mathematical model. In addition, we investigate the appropriate cluster size by using the Shapiro–Wilk test for an FAR evaluation. We show that it is possible to reliably calculate the FAR of a clustered PRNU noise pair-based camera identification method by using the proposed evaluation method. To demonstrate the validity of our calculations, we compare the actual identification result with the result of the proposed calculation. In this case, we used 16 958 query images from the Dresden image database, which is a benchmark data set. The results of our evaluation indicate that this identification method maintains a false rejection rate of less than 5% (10%) for 5 (8) of the 10 tested cameras even for FAR = 10^-9 . [ABSTRACT FROM PUBLISHER]

Mobile crowd sensing systems have been widely used in various domains but are currently facing new challenges. On one hand, the increasingly complex services need a large number of participants to satisfy their demand for sensory data with multidimensional high quality-of-information (QoI) requirements. On the other hand, the willingness of their participation is not always at a high level due to the energy consumption and its impacts on their regular activities. In this paper, we introduce a new metric, called “QoI satisfaction ratio,” to quantify how much collected sensory data can satisfy a multidimensional task's QoI requirements in terms of data granularity and quantity. Furthermore, we propose a participant sampling behavior model to quantify the relationship between the initial energy and the participation of participants. Finally, we present a QoI-aware energy-efficient participant selection approach to provide a suboptimal solution to the defined optimization problem. Finally, we have compared our proposed scheme with existing methods via extensive simulations based on the real movement traces of ordinary citizens in Beijing. Extensive simulation results well justify the effectiveness and robustness of our approach. [ABSTRACT FROM PUBLISHER]

This paper focuses on assessing the electric field exposure in the whole substation by the fault tree analysis. The component-level exposure situation along the path is studied by the reliability analysis, where the field distribution and the moving people are integrated. The system-level exposure situation is then analyzed by generating the exposure fault tree based on the component-level data. The exposure failure probability of the whole substation is ultimately evaluated by analyzing the fault tree's series-parallel system. The influences of the layers and the bounded parameters on the size of the fault tree are also studied in this paper. The results of the case study show that, with the help of the fault tree analysis, the complex exposure situation in a large substation can be clearly evaluated by a probability value. [ABSTRACT FROM PUBLISHER]

Alarm trippoints are critical parameters of industrial alarm systems in process industries. This paper proposes a method to design optimal alarm trippoints for univariate analog process variables based on so-called alarm probability plots. An alarm probability plot involves four statistics, namely, the transition probability and the averaged transition step toward an alarm trippoint, the transition probability and the averaged transition step away from an alarm trippoint. Theoretical expressions of the four statistics are developed based on the Markov chain theory. The statistics are estimated with a proper determination of quantification intervals transferring analog process variables to discrete state variables. A discriminant function composed of the four statistics is used to determine an optimal choice of alarm trippoints. The effectiveness of the proposed method is illustrated via numerical and industrial examples. [ABSTRACT FROM AUTHOR]

This paper outlines a novel method for calculating the probability that an individual exposed to an electrical hazard will experience ventricular fibrillation (v.f.) as a result. This method was developed to deal with high-impact low-likelihood (HILL) events where a minute percentage of the population is exposed to, or is susceptible to, the hazards created by the event. The consequences of a HILL event can be extreme, for example, death, but effective management of HILL events is a quandary for society since the likelihood of the extreme outcome is low. The framework presented here was developed to analyze and manage electrical hazards associated with ground potential rise during power system faults. It can, however, be applied to other events involving infrequent exposure of small percentages of the population to electrical hazards. Where electrical hazards occur as HILL events, the range of sensitivity in the population becomes critical so it is necessary to ensure that the more extreme individuals are explicitly considered. Most commonly used methods for estimating complex probabilities (for example, Monte–Carlo) are poorly suited to this application as they focus on the behavior of the “bulk” of probability distributions rather than characterizing the tails. The method outlined facilitates deliberate consideration of the complete range of sensitivities to electricity demonstrated by the human population. It also has the additional benefit of significantly reduced computational requirements while remaining intuitive in application. The validity of the method is confirmed by reproducing previous work investigating the probability of v.f. of step voltages using the safety criteria outlined in IEEE standard 80. [ABSTRACT FROM AUTHOR]

We study the coverage performance of multiantenna [multiple-input multiple-output (MIMO)] communications in heterogeneous networks (HetNets). Our main focus is on open-loop and multistream MIMO zero-forcing beamforming at the receiver. Network coverage is evaluated adopting tools from stochastic geometry. Besides fixed-rate transmission (FRT), we also consider adaptive-rate transmission (ART) while its coverage performance, despite its high relevance, has so far been overlooked. On the other hand, while the focus of the existing literature has solely been on the evaluation of coverage probability per stream, we target coverage probability per communication link&#x2014.comprising multiple streams—which is shown to be a more conclusive performance metric in multistream MIMO systems. This, however, renders various analytical complexities rooted in statistical dependence among streams in each link. Using a rigorous analysis, we provide closed-form bounds on the coverage performance for FRT and ART. These bounds explicitly capture impacts of various system parameters including densities of BSs, SIR thresholds, and multiplexing gains. Our analytical results are further shown to cover popular closed-loop MIMO systems, such as eigen-beamforming and space-division multiple access. The accuracy of our analysis is confirmed by extensive simulations. The findings in this paper shed light on several important aspects of dense MIMO HetNets: first, increasing the multiplexing gains yields lower coverage performance; second, densifying network by installing an excessive number of low-power femto BSs allows the growth of the multiplexing gain of high-power, low-density macro-BSs without compromising the coverage performance; and third, for dense HetNets, the coverage probability does not increase with the increase of deployment densities. [ABSTRACT FROM AUTHOR]

In spite of the significant advancements in wireless connectivity, the static form of the network infrastructure cannot guarantee an uninterrupted operation of the ever-growing wireless consumer electronics in emergency situations such as natural disasters. In such occasions, employing flexible aerial nodes can tackle this issue by recovering the communication rapidly, when the need for connectivity is of utmost importance. In this paper, we study the use of aerial nodes for communication recovery after a communication breakdown. We provide an analytical model of the recovery probability that demonstrates the capabilities of such networks. In the performance evaluation, we show the effects of the altitude and the distance between the aerial nodes on the recovery probability and verify them with simulations. Moreover, we introduce our testbed and preliminary experimental work that shows promising results for aerial networks. Finally, we discuss useful insights for the network design and present some open issues that exist in this field. [ABSTRACT FROM AUTHOR]

This paper presents a framework for computing successful decoding probability of random network coding (RNC) in wireless networks. As cooperation emerges due to the naturally occurring broadcasting in wireless links, the application of RNC in wireless networks enables random network coded cooperation (RNCC). The theoretical successful decoding probability of RNCC systems is derived by obtaining the ratio of the full rank and the rank deficient matrices. The full rank condition of the global encoding matrix indicates the successful decoding of source symbols. The results of a single relay along with a relay selection scheme are also investigated. The validity of the presented theoretical expressions is demonstrated through identical simulation results. An implementation scenario is also presented to demonstrate the practical usage effectiveness of RNC in real-time applications, by using software-defined radio nodes. [ABSTRACT FROM PUBLISHER]

We corroborate that a synergy effect may emerge when the dually superposed orbital angular momentum (OAM) states with two adjacent OAM numbers pass through a weak fluctuation regime of the atmospheric turbulence. We show that this novel effect closely depends on the transition probabilities between this two OAM eigenstates for superposition. While such probabilities become larger, the synergy effect becomes more obvious. Our examples show that the synergy effect enables OAM states to better resist phase fluctuation caused by the atmospheric turbulence. [ABSTRACT FROM PUBLISHER]

Acoustic impulse response shaping is a prefiltering technique to reduce the perceptible effects of room reverberation. The room impulse responses to be shaped must first be measured, and these measurements can contain errors. Furthermore, room responses vary with changes in temperature and humidity and also with changes in measurement position. This letter presents a method for enhancing the robustness of the shaping procedure so that the effects of these errors and changes are minimized. The method uses a stochastic model of the channel variations to explicitly limit the probability of large deviations from the desired performance. The method is evaluated on realistic channel perturbations and the resulting shaped responses are shown to comply with the robustness specification. [ABSTRACT FROM AUTHOR]

With the development in machine learning area, future networks are better at predicting user demands. By proactively disseminating content into target users’ caches through device-to-device communication, wireless traffic can be greatly offloaded. However, practical users are selfish and incentive needs are to be provided to encourage them to help spread the content. In this letter, we consider the incentive design in content dissemination process with awareness of link failure. A dynamic incentive mechanism is proposed based on Markov decision process and the optimal incentives are determined for different network states. Simulation results show that our dynamic scheme achieves a higher network utility than the optimal fixed incentive scheme with an average gain of 7.23% under information asymmetry and 48.23% with partial information. [ABSTRACT FROM PUBLISHER]

We analyze the outage probability for the orthogonal space-time block code based multiple-input multiple-output (MIMO) relay networks, composed of one source, one relay, and a single destination. The relay forwards the decoded and interleaved information sequence even though the information part may contain error(s), according to the lossy forward strategy. In spatially independent MIMO channels, we find that the diversity order of the relay network can be interpreted and formulated by the well-known max-flow min-cut theorem. Moreover, we extend the analysis to the case of spatially correlated MIMO channels. Approximated explicit expressions for the outage probabilities are obtained in high signal-to-noise ratio regime. [ABSTRACT FROM PUBLISHER]

We investigate the problem of data synchronization in which a sender has a set of packets to be distributed to all the receivers via a broadcast channel. Initially, each receiver has some fraction of the packets. At each time slot, the sender might broadcast a packet to all the receivers. The goal is to find a broadcast scheme that minimizes the number of time slots until all the receivers successfully obtain all the packets. We propose two probabilistic models on how the initial fractions of packets at receivers are distributed. These models arise naturally in many large-scale systems, such as peer-to-peer networks, data centers, and distributed storage systems. Based on these models, we establish probabilistic bounds and asymptotic results on the minimum number of time slots to successfully transmit all the packets to all the receivers. Next, we propose and analyze a number of random network coding algorithms for finding the approximately optimal solution. Theoretical analysis and simulations are provided to verify the probabilistic bounds and the proposed algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the throughput of wireless systems in the presence of random data arrivals and quality-of-service (QoS) requirements, which are statistically characterized by the queueing-bound violation probability. By combining the concepts of effective capacity and effective bandwidth, we propose a unified analytical framework to investigate the achievable throughput. Employing the proposed unified framework, we further acquire the explicit expressions of the throughput of QoS guaranteed wireless systems with the channel state information (CSI) known/unknown at the transmitter. Specifically, the acquired throughput is characterized by the first- and second-order statistics of the random data arrivals, and it is shown that the QoS requirements affect the throughput by the second-order statistics of the random data arrivals and the random data transmissions. In addition, our theoretical analysis demonstrates that the throughput under QoS constraints is tighter than the conventional stable throughput, and that the first-order statistic of the random data arrivals is sufficient to characterize the throughput when the system can tolerate an arbitrarily long queueing delay. In particular, we prove that the queueing-bound violation probability decays exponentially with the queueing bound. Finally, simulation results corroborate the theoretical analysis. [ABSTRACT FROM PUBLISHER]

In this paper, the achievable rate of buffer-aided diamond relay systems under delay constraints is studied. The effective capacity, characterizing the maximum constant arrival rate to the source while satisfying the statistical end-to-end delay constraints, is obtained for the conventional relay selection protocol, fixed decode-and-forward relaying schemes, and selective forwarding protocols. A selection forwarding policy that maximizes the delay exponent at the source is proposed, which is shown to be independent of delay constraints and select the relay with better channel conditions to forward the messages. Through numerical results, it is demonstrated that the selection forwarding strategy achieves superior performance than the conventional relay selection and fixed scheduling schemes under a variety of delay constraints. [ABSTRACT FROM PUBLISHER]

An iterative method is presented to determine the most probable voltage stability limit by identifying the closest saddle node bifurcation in a probability-loading space starting at the expected initial system state. The iterative method is applied to the probability space to identify the closest saddle node bifurcation by means of the Mahalanobis distance. Further the method provides an ellipsoidal load subspace corresponding to the most probable bifurcation. This ellipsoid is a lower bound for the probability of bifurcation. To verify the proposed methodology examples are given and the minimum Mahalanobis distance, for dependent and independent load patterns, is compared with the minimum Euclidian distance. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive two-way relay cooperation scheme is studied for multiple-relay cognitive radio networks to improve the performance of secondary transmissions. The power allocation and relay selection schemes are derived to minimize the secondary outage probability where only statistical channel information is needed. Exact closed-form expressions for secondary outage probability are derived under a constraint on the quality of service of primary transmissions in terms of the required primary outage probability. To better understand the impact of primary user interference on secondary transmissions, we further investigate the asymptotic behaviors of the secondary relay network, including power allocation and outage probability, when the primary signal-to-noise ratio goes to infinity. Simulation results are provided to illustrate the performance of the proposed schemes. [ABSTRACT FROM PUBLISHER]

This letter presents a wavelength-resolution synthetic aperture radar incoherent change detection method based on image stack, i.e., there are more than one reference or/and surveillance image. Considering image stack in statistical hypothesis test for change detection is expected to result into a simple mathematical expression for implementation and provide better change detection results. As presented in this letter, a statistical hypothesis test is developed on bivariate Gaussian distribution for an image stack of two reference images and one surveillance image. The requirement for the image stack is three images associated with three measurements with no change between two of them. A detection method with simple processing scheme is proposed. The method is experimented with 24 CARABAS data sets. The results indicate that high average detection probability, e.g., 96%, with very low false alarm rate, e.g., only 0.19/km2, is obtained with the proposal. [ABSTRACT FROM PUBLISHER]

In this letter, we revisit single-user multiple-input single-output beamforming with imperfect channel state information (CSI) at the transmitter. We present a CSI estimation model that is suitable for finite-scatterer directional channel models with uncertainty in the relative phase shifts of the scattering components. We show that in this case the distribution of the effective beamformed channel does not follow one of the well-known channel fading distributions and leads to complicated outage calculations. We show that the presented model generalizes popular existing models and we relate our investigations for the special cases of one and two specular components to existing results. We furthermore consider signal outage optimal beamforming for the case of two specular components. [ABSTRACT FROM AUTHOR]

In this paper, the exact Fisher information matrix (EFIM) is derived for target localization with range measurements under imperfect detection. We treat the probability of detection (PD) as a target state-dependent parameter, and take a partial derivative of PD with respect to the target state during the calculation of the EFIM. By introducing an additional information impact parameter (AIIP), we define a parameter to reveal the divergence between EFIM and the original Fisher information matrix. We analytically prove that the AIIP is bandwidth invariant, and subsequently we know that the divergence parameter is inversely linear with the square of the signal bandwidth. Moreover, we also analyze the connection of the divergence parameter with the signal-to-noise ratio (SNR) and the false alarm rate by simulation. The results suggest that approximating PD as a state-free parameter is feasible for most of the real radar applications with low false alarm rate, large bandwidth, and moderate SNR, except for some specific continuous wave radar systems, which operate with a small bandwidth and high false alarm rate. [ABSTRACT FROM PUBLISHER]

More and more attention has been paid to the heterogeneous semiconductor device based on the wafer-bonding method due to its high-quality wafer-bonded active layer. In this paper, we first theoretically study the dependence of the single-photon properties, including the single-photon detection efficiency (SPDE), dark count rate (DCR), and afterpulsing probability (AP), of the wafer-bonded Ge/Si single-photon avalanche photodiode (SPAD) on the interface state at the Ge/Si (hydrophilic reaction) and Si-Si (hydrophobic reaction or surface-activated method) wafer-bonded interface based on the Poisson statistics. It is found that the interface state density and the energy level position of the interface state significantly affect the linear-mode electric field distribution (avalanche probability), 3-dB bandwidth (dark carriers), gain (quantum efficiency), and effective transit time (trapped carriers), which in turn affect the DCR and the SPDE. Furthermore, the dependence of the AP on the avalanche charge and the interface recombination rate is clarified as well. It is expected that this paper may give guidance for the fabrication of high-performance SPAD. [ABSTRACT FROM PUBLISHER]

Based on a 12-cell transmission line code model, the effects of cell-driving jitters on the output voltage (both the rise time and the flat-top waveform) of magnetically insulated induction voltage adders (MIVAs) were analyzed in detail. Simulation results show that the rise time (0.1–0.9) of the MIVA output voltage is a function of $\sigma $ (the standard deviation of the cell-driving jitters) when N (the number of cells connected in series for an MIVA) is small. When cell-driving jitters follow a normal probability distribution, the rise time of the output voltages also accord well with normal distributions. Parameters of these distributions are analytically deduced and presented. For any experimental shot, there is a fiducial probability that the rise time falls into the analytic solution range (within a certain error range). With the same error range, the fiducial probability decreases as $\sigma $ increases, while probabilities that the rise time are higher or lower than this range increase synchronously. Simulation results also indicate that cell-driving jitters have fewer effects on the flat-top peak of the MIVA output voltage. However, a series of ripple voltages is added on the flat-top waveform, and the average peak of these ripple voltages increases as $\sigma $ increases. [ABSTRACT FROM PUBLISHER]

We consider the statistical problem of binary classification, which means attaching a random observation X from a separable metric space E to one of the two classes, 0 or 1. We prove that the consistent estimation of conditional probability p(X)= \mathsf P(Y=1 \mid X) , where Y is the true class of $X$ , is equivalent to the consistency of a class of empirical classifiers. We then investigate for what classes P$ there exist an estimate \hat p$ that is consistent uniformly in p\in P$ . We show that this holds if and only if P , where $\mu $ is the distribution of $X$ . In the case, where $E$ is countable, we give a complete characterization of classes $\Pi $ , allowing consistent estimation of $p$ , uniform in $(\mu ,p)\in \Pi $ . [ABSTRACT FROM AUTHOR]

In this paper, we consider an important practical industrial process identification problem where the time delay can change at every sampling instant. We model the time-varying discrete time-delay mechanism by a Markov chain model and estimate the Markov chain parameters along with the time-delay sequence simultaneously. Besides time-varying delay, processes with both time-invariant and time-variant model parameters are also considered. The former is solved by an expectation-maximization (EM) algorithm, while the latter is solved by a recursive version of the EM algorithm. The advantages of the proposed identification methods are demonstrated by numerical simulation examples and an evaluation on pilot-scale experiments. [ABSTRACT FROM AUTHOR]

The nonredundant voltage-source inverter, which exploits the possibility of minimum additional components for reconfiguration and operation, is a promising solution to manage faults in one inverter leg. However, control schemes at prefault and postfault conditions are always designed, separately making it hard for control scheme adaptation from normal to fault tolerant. In order to achieve smooth transitions from prefault to postfault, an embeddable fault-tolerant method based on predictive torque control (PTC) is proposed. A generalized voltage vectors, switching command, and cost function accounting for both healthy and faulty conditions are introduced to carry out a general control scheme. A novel fault-diagnosis method based on probability density analysis of the sampling currents is presented for open-circuit fault isolation under PTC scheme. Seamless likely and rapid transient processes from fault occurrence to postfault operation are achieved. Experimental results have demonstrated that the proposed scheme can make power switch fault easily managed by the controller itself. [ABSTRACT FROM AUTHOR]

This paper addresses the detection of a suddenly arriving dynamic profile of a finite duration often called a transient change. In contrast to the traditional abrupt change detection, where the post-change period is assumed to be infinitely long, the detection of a suddenly arriving transient change should be done before it disappears. The detection of transient changes after their disappearance is considered as missed. Hence, the traditional quickest change detection criterion, minimizing the average detection delays provided a prescribed false alarm rate, is compromised. The proposed optimality criterion minimizes the worst case probability of missed detection provided that the worst case probability of false alarm during a certain period is upper bounded. A suboptimal CUSUM-type transient change detection algorithm, based on a subclass of truncated Sequential Probability Ratio Tests, is proposed. The optimization of the proposed algorithm in this subclass leads to a specially designed Finite Moving Average Test. The proposed method is analyzed theoretically and by simulation. A special attention is paid to the case of Gaussian observations with a dynamic profile. [ABSTRACT FROM AUTHOR]

We consider a multichannel Cognitive Radio Network (CRN), where secondary users sequentially sense channels for opportunistic spectrum access. In this scenario, the Channel Selection Algorithm (CSA) allows secondary users to find a vacant channel with the minimal number of channel switches. Most of the existing CSA literature assumes exponential ON-OFF time distribution for primary user's (PU) channel occupancy pattern. This exponential assumption might be helpful to get performance bounds; but not useful to evaluate the performance of CSA under realistic conditions. An in-depth analysis of independent spectrum measurement traces reveals that wireless channels have typically heavy-tailed PU OFF times. In this paper, we propose an extension to the Predictive CSA framework and its generalization for heavy tailed PU OFF time distribution, which represents realistic scenarios. In particular, we calculate the probability of channel being idle for hyper-exponential OFF times to use in CSA. We implement our proposed CSA framework in a wireless test-bed and comprehensively evaluate its performance by recreating the realistic PU channel occupancy patterns. The proposed CSA shows significant reduction in channel switches and energy consumption as compared to Predictive CSA which always assumes exponential PU ON-OFF times. Through our work, we show the impact of the PU channel occupancy pattern on the performance of CSA in multichannel CRN. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification is a challenging task when only a small number of labeled samples are available due to the difficult, expensive, and time-consuming ground campaigns required to collect the ground-truth information. It is also known that the classification performance is highly dependent on the size of the labeled data. In this letter, a semisupervised learning-based hyperspectral image classification framework is proposed as a solution to these problems. One of the contributions of this letter is the selection of the initial labeled training samples with a subtractive clustering-based approach, which provides the most informative samples for graph-based self-training. Another contribution is the decision-level combination of results obtained by support vector machines and kernel sparse representation classifiers. Additionally, a combination of the spatial and spectral information by creating a window structure is also proposed via integrating contextual information from the neighboring pixels. The explanatory experiments confirm that the proposed framework offers better and more promising results, even using a small number of initial labeled samples. [ABSTRACT FROM PUBLISHER]

Resilient monitoring systems (RMSs) are sensor networks that degrade gracefully under cyber-attacks on their sensors. The recently developed RMSs, while being effective in the attacked sensors identification and isolation, exhibited a drawback in their operation—an exponentially increasing assessment time as a function of the number of sensors in the network. To combat this curse of dimensionality, a decomposition approach has been proposed, which led to a dramatic reduction of the assessment time, irrespective of the sensor network dimensionality. However, information losses and, thus, reductions in the level of resiliency due to the decomposition, have not been investigated. This paper is intended to carry out such an investigation. Specifically, it derives conditions for lossless decomposition in terms of the Renyi-2 entropy. The development is based on the analysis of matrices, which characterize coupling of process variables and on a monotonicity property of the Dempster-Shafer combination rule on a class of functions, which arise within the RMS operation. [ABSTRACT FROM PUBLISHER]

Humans who operate in high altitudes for prolonged durations often suffer from hypoxia. The commencement of physiological and cognitive changes due to the onset of hypoxia may not be immediately apparent to the exposed individual. These changes can go unrecognized for minutes and even hours and may lead to serious performance degradation or complete incapacitation. A dynamic system capable of monitoring and detecting decreased physiologic states due to the onset of hypoxia has the potential to prevent adverse outcomes. In this study, we develop a real-time hypoxia monitoring system based on a parallel $M$ -ary decision fusion architecture. Blood oxygen saturation levels and altitude readings are the inputs and estimates of the level of hypoxia are the outputs. We develop new temporal evolution models for blood oxygen saturation and functional impairment with respect to varying altitude. The proposed models enable accurate tracking of various hypoxia levels based on the duration of stay of the subject at an altitude. Using a Bayesian decision-making formulation, the system generates global estimates of the degree of hypoxia. The detection system is tested against synthetic and real datasets to demonstrate applicability and accuracy. [ABSTRACT FROM AUTHOR]

Approximate computing can decrease the design complexity with an increase in performance and power efficiency for error resilient applications. This brief deals with a new design approach for approximation of multipliers. The partial products of the multiplier are altered to introduce varying probability terms. Logic complexity of approximation is varied for the accumulation of altered partial products based on their probability. The proposed approximation is utilized in two variants of 16-bit multipliers. Synthesis results reveal that two proposed multipliers achieve power savings of 72% and 38%, respectively, compared to an exact multiplier. They have better precision when compared to existing approximate multipliers. Mean relative error figures are as low as 7.6% and 0.02% for the proposed approximate multipliers, which are better than the previous works. Performance of the proposed multipliers is evaluated with an image processing application, where one of the proposed models achieves the highest peak signal to noise ratio. [ABSTRACT FROM PUBLISHER]

Breaking the fronthaul capacity limitations is vital to make cloud radio access network scalable and practical. One promising way is aggregating several remote radio units (RRUs) as a cluster to share a fronthaul link, so as to enjoy the statistical multiplexing gain brought by the spatial randomness of the traffic. In this letter, a tractable model is proposed to analyze the fronthaul statistical multiplexing gain. We first derive the user blocking probability caused by the limited fronthaul capacity, including its upper and lower bounds. We then obtain the limits of fronthaul statistical multiplexing gain when the cluster size approaches infinity. Analytical results reveal that the user blocking probability decreases exponentially with the average fronthaul capacity per RRU, and the exponent is proportional to the cluster size. Numerical results further show considerable fronthaul statistical multiplexing gain even at a small to medium cluster size. [ABSTRACT FROM PUBLISHER]

The computation of measurement (or missed detection) to target-association probabilities is an essential part of many tracking algorithms. This paper generalizes previous work to show the relationship between measurement assignment and the matrix permanent given the possibility of missed detections and clutter. A simple expression is given for the target-measurement association probabilities in terms of the matrix permanent and efficient algorithms for computing the matrix permanent are summarized. [ABSTRACT FROM PUBLISHER]

In this paper, we address the problem of the universal coding of natural numbers. A new numeration system is introduced, which is based on variable- $r$ meta-Fibonacci sequences and it is a generalization of the Zeckendorf numeration system. This new numeration system is used to construct binary, prefix-free, uniquely decodable universal codes called meta-Fibonacci codes. The main advantage of these codes is that they are parametrized by a sequence of numbers, the sequence o. By controlling the growth of the values of this sequence, we can control the length of the code word. This means that we can provide a general framework for building efficient universal coders for natural numbers. Such framework is applied to the upper bounds of the code word length defined by Leung-Yan-Cheong and Cover (1978), Levenshtein (1968), and Ahlswede (1997). There is no other code meeting these bounds. In each case, we build meta-Fibonacci codes and demonstrate that the upper bound of their code word length is satisfied up to an additive constant, thereby solving these open problems. The framework may be applied to other upper bounds that satisfy Kraft inequality. [ABSTRACT FROM PUBLISHER]

This paper investigates the impact of the channel state information (CSI) and antenna correlation at the multi-antenna relay on the performance of wireless powered dual-hop amplify-and-forward relaying systems. Depending on the available CSI at the relay, two different scenarios are considered, namely, instantaneous CSI and statistical CSI where the relay has access only to the antenna correlation matrix. Adopting the power-splitting architecture, we present a detailed performance study for both cases. Closed-form analytical expressions are derived for the outage probability and ergodic capacity. In addition, simple high signal-to-noise ratio (SNR) outage approximations are obtained. Our results show that, antenna correlation itself does not affect the achievable diversity order, the availability of CSI at the relay determines the achievable diversity order. Full diversity order can be achieved with instantaneous CSI, while only a diversity order of one can be achieved with statistical CSI. In addition, the transmit antenna correlation and receive antenna correlation exhibit different impacts on the ergodic capacity. Moreover, the impact of antenna correlation on the ergodic capacity also depends heavily on the available CSI and operating SNR. [ABSTRACT FROM PUBLISHER]

The cylindrical vector beams (CVBs) compose two circularly polarized vortex beams carrying orbital angular momentum (OAM) states with the opposite topological charge. We derived the probabilities for OAM of high-order CVB propagation through turbulent atmosphere. The calculated probabilities of OAM are based on the polarization insensitive element detection and phase perturbation coherence function assumption. The numerical calculation results show that the probabilities of OAM state spreading to neighbor states of CVBs through turbulent atmosphere increase with the propagation distance increase. Under the same conditions, the spreading of OAM for CVBs are weaker than Laguerre–Gaussian beam propagation through turbulent atmosphere. The results show that the OAM state of CVBs could improve the performance of optical communications systems in atmospheric turbulence. [ABSTRACT FROM PUBLISHER]

In this letter, we propose a novel statistical active contours method for using an arbitrary number of level set functions to segment an image into regions of the corresponding amount. First, a new updating of level set functions for every region is derived from the probabilistic models of image data. Second, a novel geometric prior that deduced from the level-set-based curve evolution is introduced to obtain the probabilistic label. Therefore, updating of the level set functions and estimation of the distribution parameter are run alternately in a fast manner. Finally, in order to further enhance the efficiency, we initialize the level set function by the statistical approach to draw near object boundary. We experimentally evaluate our proposed approach on complicated real-world images and demonstrate its good performance in practice. [ABSTRACT FROM PUBLISHER]

The fundamental trade-off in simultaneous wireless information and power transfer (SWIPT) is investigated from the point of view of power-splitting the signals of randomly deployed cellular base stations employing directional antennas. Leveraging tools from stochastic geometry and using realistic antenna gain profiles, we derive novel closed form expressions enabling an in-depth understanding of this trade-off and how a system-level optimization can be achieved. [ABSTRACT FROM PUBLISHER]

Random refraction occurs in turbulence and through a wavy water-air interface. It creates distortion that changes in space, time and with viewpoint. Localizing objects in three dimensions (3D) despite this random distortion is important to some predators and also to submariners avoiding the salient use of periscopes. We take a multiview approach to this task. Refracted distortion statistics induce a probabilistic relation between any pixel location and a line of sight in space. Measurements of an object's random projection from multiple views and times lead to a likelihood function of the object's 3D location. The likelihood leads to estimates of the 3D location and its uncertainty. Furthermore, multiview images acquired simultaneously in a wide stereo baseline have uncorrelated distortions. This helps reduce the acquisition time needed for localization. The method is demonstrated in stereoscopic video sequences, both in a lab and a swimming pool. [ABSTRACT FROM PUBLISHER]

There is a strong impetus to commercialize emerging technology, tempered by safety expectations and regulatory compliance requirements [1]. Such is the case for medical implant devices, where successful operation of devices can be life-saving, but while consequences of failure are severe. Recent advances in this technology aimed at enabling remote access to a device facilitate remote and more accurate monitoring of patient health. In doing so, original equipment manufacturers (OEMs) both satisfy market pressures and potentially introduce new avenues of risk that increase end-user vulnerability [2]. Vulnerability contributed by technological systems is known to researchers [3]-[5] as an important consideration in individual vulnerability. This study aims to quantify the contribution of technological configuration to end-user vulnerability, specifically the additional risk of Internet-enabled medical implant devices. [ABSTRACT FROM PUBLISHER]

As the base of quality-of-service (QoS) guarantee and system optimizing, performance analysis is very important for interference-limited networks. The performance of a user is closely related to its service rate. However, in interference-limited networks, the transmissions of users interfere with each other, and the power control and unsaturated traffic make the interference relationship among users more complicated. Hence, the service rates of users are correlated with each other and are difficult to derive. According to the relation between the queue length and the service rate, we propose an $N$- dimensional Markov chain, which models queue states of users, to study the performance of an interference-limited system. By calculating the conditional service rate, which is only related to the channel quality given the power policy, the transition matrix is derived. By using the transition matrix, we solve the Markov chain and obtain the stationary probability of the queue length of each user, which enables the calculations of other performance metrics, i.e., delay and packet overflow probability, and so on. Furthermore, to reduce the computational complexity, we decompose the proposed $N$- dimensional Markov chain. Then, based on the decomposition, we propose an iterative algorithm. The comparisons between simulation results and analytical results under different scenarios verify that our method can provide good estimations of each user's performance. The results derived by our method can determine the condition under which the power control algorithm for the saturated system is suitable in unsaturated networks and in which the new power policy is needed. [ABSTRACT FROM PUBLISHER]

In practical scenarios with random arrival and departure of primary users (PUs), existing simultaneous sensing and transmission schemes allocated the same weight to each sample, and did not consider low signal-to-noise ratio (SNR) situations. This paper proposes an adaptive weighted sensing scheme with simultaneous transmission for dynamic PU traffic. It uses a power function based on the corresponding sampling sequence and could reveal the actual PU state in near real time. The power exponent is further adjusted to the sensing situations to achieve lowest false alarm probability under a certain detection probability constraint. Then, an analytical model considering all possible PU state transitions is developed to evaluate achievable interference, throughput, and energy efficiency. Furthermore, the optimal frame duration yielding both optimal false alarm probability and throughput is computed. After that, a fast search algorithm is proposed to track the optimal duration at an exponential convergence rate. Simulation results are provided to validate the analytical model and demonstrate the improvement in low SNR. The results indicate that the proposed scheme can achieve lower false alarm probability and higher energy efficiency over a wide SNR range than that of the existing weighting schemes, which are based on probability, geometric sequence, and equal weighting. [ABSTRACT FROM AUTHOR]

We report on new fluctuation dynamics of the high resistance state of Hafnia-based RRAM devices after RESET. We observe that large amplitude fluctuations occur more frequently immediately after programming and their frequency of occurrence decays in the tens of microseconds. The fluctuation amplitude, on the other hand, does not decay noticeably over the entire millisecond read time. While post-programming instability and post-programming resistance dispersion have both been reported in the literature, the relaxation in the frequency of occurrence without a commensurate amplitude decay is new. Since picosecond pulses were used for our RESET operation, post-programming thermalization occurs on the nanosecond time scale. This clearly eliminates a thermally driven mechanism as the cause of the observed fluctuation behavior. Furthermore, reducing the READ voltage by tenfold does not have any effect and also eliminates read disturb as possible cause. [ABSTRACT FROM PUBLISHER]

In an IaaS cloud, virtual machines (VMs), also called instances, may be classified as reserved instances and on-demand instances. The reserved instances having long-term commitments and one-time payment are appropriate for the steady or predictable workloads, while for short-term, spiky or unpredictable workloads, the on-demand instances having flexible hourly payment and no long-term commitments may be more suitable for reducing the cost. In this paper, we consider the economical provisioning of reserved and/or on-demand instances for meeting time-varying computing workload of compute-intensive applications. In order to achieve this, we conceive a strategy for determining the amount of the purchased instances dynamically in order to minimize the total computing cost while keeping quality-of-service (QoS). By mapping QoS as the overload probability, we propose a dynamic instance provisioning strategy based on the large deviation principle, which is capable of calculating the minimum number of instances for the upcoming demands subject to the overload probability below a desired threshold. In addition, a reserved instance provisioning strategy for further reducing the total cost is also proposed by applying the autoregressive (AR) model to calculate the number of reserved instances for the average computation requirements. Finally, the simulations are performed based on real workload traces to show the attainable performance of the proposed instance provisioning strategy for the computing service in an IaaS cloud. [ABSTRACT FROM PUBLISHER]

Caching popular content files at small-cell base stations (SBSs) has emerged as a promising technique to meet the overwhelming growth in mobile data demand. Despite the plethora of work in this field, a specific aspect has been overlooked. It is assumed that all users remain stationary during data transfer and therefore a complete copy of the requested file can always be downloaded by the associated SBSs. In this work, we revisit the caching problem in realistic environments where moving users intermittently connect to multiple SBSs encountered at different times. Due to connection duration limits, users may download only parts of the requested files. Requests for files that failed to be delivered on time by the SBSs are redirected to the coexisting macro-cell. We introduce an optimization framework that models user movements via random walks on a Markov chain aimed at minimizing the load of the macro-cell. As the main contribution, we put forward a distributed caching paradigm that leverages user mobility predictions and innovative information-mixing methods based on the principle of network coding. Systematic experiments based on measured traces of human mobility patterns demonstrate that our approach can offload 65 \textpercent more macro-cell traffic than existing caching schemes in realistic settings. [ABSTRACT FROM AUTHOR]

A new spectrum sensing scheme is proposed for detecting a primary signal corrupted by generalized Gaussian noise (GGN). It is assumed that the power of the primary signal is much smaller than noise power and that the secondary users (SUs) take advantage of multiple receiver antennas. The proposed scheme, which is referred to as soft-limited polarity coincidence array (SL-PCA), is characterized by a slope parameter that controls the operation of its limiter. It is shown that by optimizing the slope parameter, the proposed scheme outperforms its predecessor (i.e., PCA), as well as the well-known energy detection (ED) scheme in the presence of heavy-tailed GGN. It is also observed that the SL-PCA scheme can achieve superior performance with a single receiver antenna, whereas the PCA scheme cannot be used when the SUs are equipped with only one receiver antenna. Finally, the sensitivity of the proposed scheme to the slope parameter is investigated for several scenarios. It is observed that even when the slope parameter is not optimum, SL-PCA can still outperform the PCA and ED schemes, provided that the slope parameter is appropriately chosen. [ABSTRACT FROM PUBLISHER]

The motion of cathode spots plays a crucial important role in removing oxide layer on a metal surface by vacuum arc. In this paper, the characteristics of the motion of a single cathode spot on metal surface with oxide layer are investigated experimentally. Experiments are conducted in a detachable vacuum chamber. A hollow copper anode with a hole of 10~\text mm \times 10 mm is used in order to observe the 2-D motion of the cathode spot. The motion of the cathode spot during the descaling process is photographed by a high-speed digital camera with an exposure time of 2~\mu \texts . Experimental results indicate that there are slow motion and fast motion of the cathode spot during descaling process, and the slow motion is the basic characteristic in most of the descaling time. The probability distribution of the cathode spot’s displacement and resident time, the average velocity, and the movement parameter S^2/t are analyzed quantitatively. The results indicate that with the increase of gap distance or the decrease of oxide layer thickness, the cathode spot becomes more active. [ABSTRACT FROM PUBLISHER]

In this paper, a new cooperation structure for spectrum sensing in cognitive radio networks is proposed, which outperforms the existing commonly used ones in terms of energy efficiency. The efficiency is achieved in the proposed design by introducing random interruptions in the cooperation process between the sensing nodes and the fusion center, along with a compensation process at the fusion center. Regarding the hypothesis testing problem concerned, first, the proposed system behavior is thoroughly analyzed and its associated likelihood-ratio test is provided. Next, based on a general linear fusion rule, the statistics of the global test summary are derived and the sensing quality is characterized in terms of the probability of false alarm and probability of detection. Then, the optimization of the overall detection performance is formulated according to the Neyman–Pearson criterion (NPC) and it is discussed that the optimization required is indeed a decision-making process with uncertainty which incurs prohibitive computational complexity. The NPC is then modified to achieve a good affordable solution by using semidefinite programming (SDP) techniques and it is shown that this new solution is nearly optimal according to the deflection criterion. Finally, the effectiveness of the proposed architecture and its associated SDP is demonstrated by simulation results. [ABSTRACT FROM PUBLISHER]