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We present our computationally efficient approach to modeling tunable graphene-based active metadevices, where the integral multivariate surface conductivity is reformulated in the time and frequency domains with physically interpretable and fast-to-compute integration-free terms. The derivation is built on an expansion to power series of $ {z}= -\text {exp}(-\mu /k_{B}T)$ that converges very fast for non-zero chemical potential values. The model reveals interesting decomposition of graphene response into a universal constant term plus a damped oscillator (digamma functions in the frequency domain) plus non-oscillating correction terms for near-zero potentials. The number of terms in that series is analyzed theoretically for a given accuracy. In practice, only a few series terms are required, making our approach very efficient for simulation of active metasurfaces compared with direct integration of Kubo’s formulas. A simple performance test comparing run times with our code versus the numerical integration of the original Kubo’s formulas demonstrates a speedup exceeding 103. The proposed models can be critical for the initial ellipsometric characterization of graphene and advanced global optimization of graphene-controlled metadevices. [ABSTRACT FROM AUTHOR]

We analyze the electrostatic interaction between two equal-sized charged conducting spheres. We obtain exact closed-form expressions for the capacitance coefficients and the electrostatic force in terms of the special q -digamma function. Additionally, we provide simpler-to-use approximate expressions for the capacitance coefficients and the force between the spheres, and then compare the approximations with the exact results. [ABSTRACT FROM PUBLISHER]

Conventional continuous emotion prediction systems are typically trained to predict the ‘average’ of affect ratings obtained from multiple human annotators. These systems, however, ignore the ambiguity inherent in the perceived emotions, which is not captured by the ‘average rating’. This paper presents a novel ambiguity-aware continuous emotion prediction system that predicts the time-varying emotion state as a series of beta distributions. Our recent work has shown beta distributions to be an effective parametric model of a collection of affect ratings. This work develops an appropriate cost function that enables neural networks to be trained to predict beta distributions. It also investigates the choice of parameterization of the beta distribution, the choice of activation functions of the output layer, and the tractability of gradient definitions in combination with the loss function. The proposed framework is implemented using a Bag-of-Audio-Words front-end and an LSTM-based back-end and evaluated on the RECOLA dataset. In addition to comparison with baseline systems that only predict the ‘average rating’, the effectiveness with which the predictions represent ambiguity in perceived emotions is also evaluated. Experimental results reveal that the proposed approach outperforms other ambiguity-aware systems, especially when predicting valence. [ABSTRACT FROM AUTHOR]

In real-world scenarios, the information quality provided by RGB and thermal (RGB-T) sensors often varies across samples. This variation will negatively impact the performance of semantic segmentation models in utilizing complementary information from RGB-T modalities, resulting in a decrease in accuracy and fusion credibility. Dynamically estimating the uncertainty of each modality for different samples could help the model perceive such information quality variation and then provide guidance for a reliable fusion. With this in mind, we propose a novel uncertainty-guided trustworthy fusion network (UTFNet) for RGB-T semantic segmentation. Specifically, we design an uncertainty estimation and evidential fusion (UEEF) module to quantify the uncertainty of each modality and then utilize the uncertainty to guide the information fusion. In the UEEF module, we introduce the Dirichlet distribution to model the distribution of the predicted probabilities, parameterized with evidence from each modality and then integrate them with the Dempster–Shafer theory (DST). Moreover, illumination evidence gathering (IEG) and multiscale evidence gathering (MEG) modules by considering illumination and target multiscale information, respectively, are designed to gather more reliable evidence. In the IEG module, we calculate the illumination probability and model it as the illumination evidence. The MEG module can collect evidence for each modality across multiple scales. Both qualitative and quantitative results demonstrate the effectiveness of our proposed model in accuracy, robustness, and trustworthiness. The code will be accessible at https://github.com/KustTeamWQW/UTFNet. [ABSTRACT FROM AUTHOR]

The complex Wishart distribution is a widely used statistical model for multilook polarimetric synthetic aperture radar (PolSAR) image data, of which the equivalent number of looks (ENL) is a critical parameter. Over the past decades, various estimators have been developed to estimate the ENL of complex Wishart distribution, of which the maximum likelihood (ML) estimator is important since it is asymptotically unbiased and has a small variance. However, this estimator is very time-consuming since it has no analytical solution and is usually solved numerically. To address this problem, this letter proposes an efficient ML estimator of ENL by deriving an approximate closed-form solution. Moreover, to estimate the ENL map of a PolSAR image, we also develop an efficient way to compute the local sample statistics parallelly. The experimental results on two PolSAR images show that our method yields highly approximate ENL values as the traditional ML estimator while is much more efficient. It costs less than 0.8 s on a general laptop to estimate the ENL map of a PolSAR image with 900 $\times $ 1024 pixels. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) image understanding is crucial in remote sensing applications, but it is hindered by its intrinsic noise contamination, called speckle. Sophisticated statistical models, such as the $\mathcal {G}^{0}$ family of distributions, have been employed to SAR data and many of the current advancements in processing this imagery have been accomplished by extracting information from these models. In this letter, we propose improvements to parameter estimation in $\mathcal {G}^{0}$ distributions using the Method of Log-Cumulants (LCum). First, using Bayesian modeling, we construct the regularly produced reliable heterogeneity estimates under both $\mathcal {G}^{0}_{A}$ and $\mathcal {G}^{0}_{I}$ models. Second, we make use of an approximation of the Trigamma function to compute the estimated heterogeneity in constant time, making it considerably faster than the existing method for this task. Finally, we show how we can use this method to achieve fast and reliable SAR image understanding based on heterogeneity information. [ABSTRACT FROM AUTHOR]

Aging bridge infrastructure may jeopardize safety, and its economic impact has raised concerns worldwide. Reinforced concrete is a major component of bridge infrastructure, whose condition is directly related to reliability and safety. However, evaluating and maintaining reinforced concrete remains a complex issue, as it is often exposed to a dynamic and stochastic environment. In this article, we provide an integrated approach to capturing the characteristics of reinforced concrete deterioration by synthesizing knowledge from the physical model and a stochastic model. Then, a semi-Markov decision process is constructed for optimizing the maintenance of reinforced concrete. To improve the computation efficiency of the approach, we design the policy iteration algorithm with finite evaluations to reduce computation cost. The designed approach can further calibrate the temporal and complex dynamic information of the reinforced concrete deterioration and has a better performance than that of the Markov decision process. Finally, we explore the sensitivity of the maintenance policy under the changing of cost rates and durations of the maintenance activities. [ABSTRACT FROM AUTHOR]

Bayesian optimization (BO) is popular for a analog circuit sizing problem recently. However, BO can only work well in small-scale circuit. Scaling BO to common circuit optimization (typically dimension > 10) is difficult due to the curse of dimensionality. In this article, we proposed a new BO algorithm (cBO), which can scale BO to more larger scale circuit and improve the optimization result. First, according to the fact that a specification of the analog integrated circuit is mainly determined by a few transistors, dropout strategy is adopted in our algorithm. At each iteration, only a subset of selected variables to be optimized. A variables selection strategy based on mutual information analysis and a new fill-in strategy are proposed. Second, the $g_{m}/I_{D}$ methodology-based optimization strategy is proposed. Optimization variables are not width and the length of transistors, but $g_{m}/I_{D}$ , $I_{D}$ and length. $g_{m}$ , $I_{D}$ and $g_{m}/I_{D}$ have a direct relationship to circuit performance. This is equivalent to search in circuit feature space, which could achieve better performance and handle constraints more easily. Four commonly used circuits, including low dropout regulator, two stage amplifiers, Bandgap voltage reference (VR), and VR are used for validation. Compared with other high-dimensional BO, common BO, and commercial tools (Cadence ADE_GXL), the proposed algorithm is found to produce the best optimization result and best stability. And, the ablation study shows the effectiveness and efficiency of two ingredients of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are foreseen to constitute promising airborne communication devices as a benefit of their superior channel quality. But UAV-to-ground (U2G) communications are vulnerable to eavesdropping. Hence, we conceive a sophisticated physical layer security solution for improving the secrecy rate of multi-antenna aided U2G systems. Explicitly, the secrecy rate of the U2G MIMO wiretap channels is derived by using random matrix theory. The resultant explicit expression is then applied in the joint optimization of the MIMO transceiver and the UAV location relying on an alternating optimization technique. Our numerical results show that the joint transceiver and location optimization conceived facilitates secure communications even in the challenging scenario, where the legitimate channel of confidential information is inferior to the eavesdropping channel. [ABSTRACT FROM AUTHOR]

In this article, a novel normal-skew mixture (NSM) distribution is presented to model the normal and/or heavy-tailed and/or skew nonstationary distributed noises. The NSM distribution can be formulated as a hierarchically Gaussian presentation by leveraging a Bernoulli distributed random variable. Based on this, a novel robust Kalman filtering framework can be developed utilizing the variational Bayesian method, where the one-step prediction and measurement-likelihood densities are modeled as NSM distributions. For implementation, several exemplary robust Kalman filters (KFs) are derived based on some specific cases of NSM distribution. The relationships between some existing robust KFs and the presented framework are also revealed. The superiority of the proposed robust Kalman filtering framework is validated by a target tracking simulation example. [ABSTRACT FROM AUTHOR]

Impairments baseband model in-phase and quadrature-phase Imbalance (IQI) and Residual hardware impairments (RHI) are two key factors degrading the performance of wireless communication systems (WCSs), particularly when high-frequency bands are employed, as in 5G systems and beyond. The impact of either IQI or RHI on the performance of various WCSs has been investigated exclusively in a separate way. To fill this gap, in this paper, the joint effect of both IQI and RHI on the performance of a WCS subject to Hyper Fox’s H-fading (HFHF) channel, is investigated. Such a fading model generalizes most, if not all, of well-known fading and turbulence models. To this end, closed-form expressions for the outage probability (OP), Average channel capacity (ACC) under constant power with optimum rate adaptation (ORA) policy, and average symbol error probability (ASEP) for both coherent and non-coherent modulation schemes are derived. Specifically, all the analytical expressions are derived for three different scenarios: (i) ideal Tx and Rx impaired, (ii) Tx impaired and ideal Rx, and (iii) both Tx and Rx are impaired. Further, asymptotic expressions for OP, ACC under ORA policy, and ASEP are obtained, based on which, insightful discussions on the IQI and RHI impacts are made. $\alpha -\mu $ and Málaga $\mathcal {M}$ turbulence with pointing error distribution models have been considered as particular cases of the HFHF distribution. The analytical derivations, endorsed by simulation results, demonstrate that the RF impairments’ effects should be seriously taken into account in the design of next-generation wireless technologies. [ABSTRACT FROM AUTHOR]

Detecting latent changes in the spatiotemporal pattern of bike sharing is critical to recognizing the impacts of various exogenous factors and significant events. Although extensive studies have investigated the spatiotemporal pattern dynamics in transportation systems, research that explicitly models the changing regularity and captures its latent changes is very scarce. To fill this research gap, we develop a change-point topic model that incorporates multinomial logit models into multi-dimensional latent Dirichlet allocation to decompose the spatiotemporal pattern into a mixture of activity patterns; meanwhile, our model captures the changing regularity of activity prevalence and its latent changes by integrating Dirichlet multinomial regression and hidden Markov models. We estimate the parameters of our model based on collapsed Gibbs sampling. We conduct numerical experiments using publicly available bike sharing trip records collected in New York. Results show that our model successfully distinguishes several meaningful activity categories, such as commuting. Furthermore, the detected pattern changes offer several insights into the impacts of associated events. Our model also improves the goodness of fit and predictive performance for the spatiotemporal attributes of trips. [ABSTRACT FROM AUTHOR]

In this paper, the extended $\eta$ - ${\mathcal F}$ composite fading distribution is proposed. This new distribution is simple, characterized in terms of physical parameters, able to model environments where the multipath fading coexists with shadowing and takes into account the clustering imbalance between the in-phase and quadrature components. For the aforementioned distribution, new expressions are derived for the probability density function (PDF); cumulative distribution function, higher-order moments and moment generating function of the instantaneous signal-to-noise ratio; joint envelope-phase PDF and amount of fading. Subsequently, expressions are deduced for the outage probability, average symbol error probability and average channel capacity. An asymptotic analysis is also provided. Several curves are presented and the theoretical results are corroborated by Monte-Carlo simulations. Furthermore, an empirical validation is demonstrated in an underwater acoustic scenario, in which a better fit between the theoretical PDF and the empirical data is perceived for the extended $\eta$ - ${\mathcal F}$ model, when compared to the extended $\eta$ - $\mu$ and $\kappa$ - $\mu$ shadowed distributions. [ABSTRACT FROM AUTHOR]

The reconfigurable intelligent surface (RIS) is a promising technology that is anticipated to enable high spectrum and energy efficiencies in future wireless communication networks. This paper investigates optimum location-based RIS selection policies in RIS-aided wireless networks to maximize the end-to-end signal-to-noise ratio for product-scaling and sum-scaling path-loss models where the received power scales with the product and sum of the transmitter-to-RIS and RIS-to-receiver distances, respectively. These scaling laws cover the important cases of end-to-end path-loss models in RIS-aided wireless systems. The random locations of all available RISs are modeled as a Poisson point process. To quantify the network performance, the outage probabilities and average rates attained by the proposed RIS selection policies are evaluated by deriving the distance distribution of the chosen RIS node as per the selection policies for both product-scaling and sum-scaling path-loss models. We also propose a limited-feedback RIS selection framework to achieve distributed network operation. The outage probabilities and average rates obtained by the limited-feedback RIS selection policies are derived for both path-loss models as well. The numerical results show notable performance gains obtained by the proposed RIS selection policies. [ABSTRACT FROM AUTHOR]

This paper comprehensively studies the physical layer security in frequency division duplex multi-antenna cellular systems, where the multi-antenna base stations (BSs), legitimate users (LUs), and eavesdroppers are all randomly located. Each BS employs artificial-noise (AN)-aided multi-user linear beamforming with limited channel state information feedback. Based on the stochastic geometry theory, we first derive an analytical expression of a lower bound on the ergodic secrecy rate (ESR) of the typical LU without assuming asymptotes for any system parameter. We then develop a tight closed-form approximation on the optimal number of feedback bits to maximize a lower bound on the per-user net ESR, which takes into consideration the cost of uplink spectral efficiency for limited feedback. Moreover, the power allocation coefficient between message-bearing signals and AN can be optimized by using a bisection search method. Our main finding is that, the optimum number of feedback bits scales linearly with the path-loss exponent and the number of antennas, and scales logarithmically with the channel coherence time. The derived analytical results can also provide system-level insights into the ESR performance of the multi-antenna random cellular networks with limited feedback and the optimal system design. Numerical results are also presented to verify the obtained results. [ABSTRACT FROM AUTHOR]

Random noise attenuation (NA) is a key step in seismic field data processing. With the rise of artificial intelligence, deep learning (DL) algorithms are gradually introduced into seismic random noise suppression. The most commonly used DL paradigm takes mean squared error (MSE) as loss function, the default assumption is that its error term obeys independently identically distribution (IID) Gaussian, and its noise level involved preset-hyperparameters in the local area of data cannot be adjusted adaptively in the training phase. This leads to the poor generalization of deep denoiser on Non-IID noises. In this study, we propose a DL framework based on Non-IID pixel-wise Gaussian noise modeling, which integrates NA and noise level estimation into a unique Bayesian framework. The framework can adaptively characterize the noise and data distribution in the local area of noisy data through the variational inference (VI) technique, which allows the network to see more noises of varying degrees and learn effective information from them. Thus, our proposed framework called VI-Non-IID inclines to have better noise characterization and generalization capabilities, which brings better performance on seismic field NA. Furthermore, we conduct a series of experiments on seismic synthetic and field data to test the performance of two implementations of VI-Non-IID: VI-Non-IID (Unet) and VI-Non-IID denoising convolution network (DnCNN). A lot of results validate the superiority of our proposed VI-Non-IID framework. Specifically, VI-Non-IID can explicitly predict the denoised data and its corresponding noise level map simultaneously, and succeed in attenuating unknown field noises while preserving the useful seismic signals. [ABSTRACT FROM AUTHOR]

In this paper, an approximate closed-form probability density function expression for the sum of lognormal-Rician turbulence channels with Rayleigh pointing errors is developed. The results of Kolmogorov-Smirnov goodness-of-fit statistical tests show that the proposed approximation is highly accurate across a wide range of channel conditions. Also, the analysis of approximation error is presented in detail, and it indicates that a more efficient approximation can be achieved for larger coherence parameter $r$ and smaller variance $\sigma _{z}^{2}$. To reveal the importance of proposed approximation, the closed-form expressions for the ergodic capacity, outage probability, and bit-error rate are derived in terms of Meijer’s G-function. The performance of multiple-input multiple-output (MIMO) free-space optical (FSO) systems with equal gain combining (EGC) diversity technique are analyzed in detail under different scenarios, including the number of transmit and receive apertures, turbulence channels, and presence of pointing errors. It is observed that MIMO technology can offer a significant improvement in FSO performance when compared with the single-input single-output (SISO) systems. The ergodic capacity and BER performance at high signal-to-noise ratio are also obtained to provide further insights. Numerical results demonstrate the accuracy of the proposed approach. [ABSTRACT FROM AUTHOR]

We present a data-driven framework for learning fair universal representations (FUR) that guarantee statistical fairness for any learning task that may not be known a priori. Our framework leverages recent advances in adversarial learning to allow a data holder to learn representations in which a set of sensitive attributes are decoupled from the rest of the dataset. We formulate this as a constrained minimax game between an encoder and an adversary where the constraint ensures a measure of usefulness (utility) of the representation. The resulting problem is that of censoring, i.e., finding a representation that is least informative about the sensitive attributes given a utility constraint. For appropriately chosen adversarial loss functions, our censoring framework precisely clarifies the optimal adversarial strategy against strong information-theoretic adversaries; it also achieves the fairness measure of demographic parity for the resulting constrained representations. We evaluate the performance of our proposed framework on both synthetic and publicly available datasets. For these datasets, we use two tradeoff measures: censoring vs. representation fidelity and fairness vs. utility for downstream tasks, to amply demonstrate that multiple sensitive features can be effectively censored even as the resulting fair representations ensure accuracy for multiple downstream tasks. [ABSTRACT FROM AUTHOR]

In this paper, an approximate closed-form probability density function expression for the Lognormal-Rician atmospheric turbulence with generalized pointing errors model is developed by using a series representation. The Kolmogorov-Smirnov (KS) goodness-of-fit statistical tool is employed to verify the accuracy of the proposed approximation. We demonstrate that a more efficient approximation can be achieved for the smaller jitter variances and nonzero boresight displacements. To reveal the importance of the proposed approximation, the approximate analytic expressions for the ergodic capacity and outage performance of single-input single-output free-space optical (FSO) systems are derived. It is observed that the pointing errors significantly degrade the performance of FSO links. By using the obtained asymptotic expressions, the impact of pointing errors on the ergodic capacity is considerably mitigated by selecting the optimum beam width at the transmitter side. We find that the optimum beam width is independent of atmospheric turbulence and the Pearson correlation coefficient in high signal-to-noise ratio conditions. All of the derived analytical results are confirmed by Monte Carlo simulation results. [ABSTRACT FROM AUTHOR]

Fault impulse extraction under strong background noise and/or multiple interferences is a challenging task for bearing fault diagnosis. Sparse representation has been widely applied to extract fault impulses and can achieve state-of-the-art performance. However, most of the current methods rely on carefully tuning several hyperparameters and suffer from possible algorithmic degradation due to the approximate regularization and/or heuristic sparsity model. To overcome these drawbacks, in this article, we present a sparse Bayesian learning (SBL) framework for bearing fault diagnosis, and then propose two group-sparsity learning algorithms to extract fault impulses, where the first one exploits the group-sparsity of fault impulses only, whereas the second one utilizes additional periodicity behavior of fault impulses. Due to the inherent learning capability of the SBL framework, the proposed algorithms can tune hyperparameters automatically and do not require any prior knowledge. Another advantage is that our solutions are maximum a $posteriori$ estimators in the sense of Bayesian optimality, which can yield higher accuracy. Results on both simulated and real datasets demonstrate the superiority of the developed algorithms. [ABSTRACT FROM AUTHOR]

The underwater turbulence channel is modelled and a unified statistical distribution is applied for characterizing orbital angular momentum (OAM) propagation in underwater wireless optical communication (UWOC) systems. Based on Monte-Carlo simulations, the effects of turbulences are characterized by the multiple phase screens model considering both the coherence width and scintillation index. The phase screen samples are processed by the randomized spectral sampling discrete Fourier transform (DFT) technique. To validate the propagated field distribution, both the phase structure function and optical transfer function are derived and evaluated with the aid of ensemble-averaged results. The Generalized Gamma Distribution (GGD) enriched by an additional independent parameter is applied for modelling the probability density function (PDF) of both the reference-channels, fluctuating irradiance as well as the intermodal crosstalk irradiance between different OAM modes. Furthermore, based on the PDF, the performance metrics of both single input multiple output (SISO) and multiple input multiple output (MIMO) systems are analyzed, based on the average capacity, the bit-error rate and the outage probability. [ABSTRACT FROM AUTHOR]

Railway point machines (RPMs) is one of the most vital devices closely related to the efficiency and safety of train operation. Considering the advantages of contactlessness and easy-to-collect of sound signals, a novel sound-based fault diagnosis method for RPMs is proposed. First, fractional calculus is introduced to wavelet packet decomposition energy entropy (WPDE). Fractional WPDE (FWPDE) is then proposed, which is verified to be a more effective tool for fault feature representation. Second, coarse-grain process is firstly introduced to FWPDE. Novel feature named multi-scale FWPDE is developed, which can significantly improve fault diagnosis accuracy. Third, to select optimal feature set and optimize the hyperparameters of support vector machine (SVM) at the same time, a synchronous optimization strategy based on binary particle swarm optimization (BPSO) is presented, which can further improve the diagnosis accuracy. The superiority and effectiveness of the proposed method are verified by comparing to some existing fault diagnosis methods. The diagnosis accuracies of reverse-normal and normal-reverse switching processes reach 99.33% and 99.67%, respectively. Especially, the proposed method is suitable for diagnosis of similar faults, which can also provide reference for similar research fields. [ABSTRACT FROM AUTHOR]

This article develops a robust global strategy for identifying the linear parameter varying (LPV) errors-in-variables (EIVs) systems subjected to randomly missing observations and outliers. The parameter interpolated LPV autoregressive exogenous model with an uncertain/noisy input is investigated and a nonlinear state-space model is considered for the input generation model (IGM). The parameters estimation of the LPV EIV systems with nonideal observations is realized using the expectation–maximization algorithm which is particular effective for the incomplete data issue. To ensure the robustness in the identification, the Student’s t-distribution which is characterized by its adjustable degree of freedom, is used to handle the measurement non-normality. Since the posterior distributions of the latent states in the IGM are also involved in the identification process and they are difficult to calculate directly, the particle filter is introduced to recursively approximate them instead. Finally, the verification examples are given to demonstrate the effectiveness of the developed strategy. [ABSTRACT FROM AUTHOR]

A new approach to $L_{2}$ -consistent estimation of a general density functional using $k$ -nearest neighbor distances is proposed, where the functional under consideration is in the form of the expectation of some function $f$ of the densities at each point. The estimator is designed to be asymptotically unbiased, using the convergence of the normalized volume of a $k$ -nearest neighbor ball to a Gamma distribution in the large-sample limit, and naturally involves the inverse Laplace transform of a scaled version of the function $f$. Some instantiations of the proposed estimator recover existing $k$ -nearest neighbor based estimators of Shannon and Rényi entropies and Kullback–Leibler and Rényi divergences, and discover new consistent estimators for many other functionals such as logarithmic entropies and divergences. The $L_{2}$ -consistency of the proposed estimator is established for a broad class of densities for general functionals, and the convergence rate in mean squared error is established as a function of the sample size for smooth, bounded densities. [ABSTRACT FROM AUTHOR]

New analytical expressions and numerical results for the mode coefficients, the directivity, and the quality factor and computationally convenient expressions for the input admittance of a symmetrical biconical antenna of arbitrary length $L$ and cone angle $\theta _{0}$ are presented. The quality factor for a wide-angle biconical antenna is evaluated using three alternative formulations: 1) the evanescent energy stored outside the circumscribing sphere; 2) the total evanescent energy stored in all space; and 3) by equivalent circuit model, and these are all compared with Chu’s lower limit for an ideal antenna. Numerical calculations based on the analytical formula for antenna admittance confirm the conjecture that Foster’s reactance theorem remains invalid even for perfectly conducting antennas. Furthermore, the variation of directivity of a wide-angle biconical antenna is a slowly varying function of its electrical length and is shown to depart significantly from that of a thin cylindrical dipole. Finally, the ratio of directivity to $Q$ of an electrically small biconical antenna is shown to approach 78% of the value of an ideal omnidirectional antenna. [ABSTRACT FROM AUTHOR]

In this article, we propose an adaptation of the algorithmic reduction theory of lattices to binary codes. This includes the celebrated LLL algorithm (Lenstra, Lenstra, Lovasz, 1982), as well as adaptations of associated algorithms such as the Nearest Plane Algorithm of Babai (1986). Interestingly, the adaptation of LLL to binary codes can be interpreted as an algorithmic version of the bound of Griesmer (1960) on the minimal distance of a code. Using these algorithms, we demonstrate—both with a heuristic analysis and in practice—a small polynomial speed-up over the Information-Set Decoding algorithm of Lee and Brickell (1988) for random binary codes. This appears to be the first such speed-up that is not based on a time-memory trade-off. The above speed-up should be read as a very preliminary example of the potential of a reduction theory for codes, for example in cryptanalysis. [ABSTRACT FROM AUTHOR]

Approximation, extension, and merging of probability distributions support inductive reasoning. They serve to modeling, knowledge, and preference elicitation as well as to a soft cooperation within various decision-making (DM) scenarios. The theory dubbed as the fully probabilistic design of DM strategies unifies the design of these operations on distributions. The unification decreases the danger of their improper choice and use. Still there is an uncertainty how the gained tools should be wielded. This article diminishes it by spelling out conditions ruling their exploitation. This article serves as an updated description of these tools, provides examples of their use, and guides their tailoring to diverse scenarios. [ABSTRACT FROM AUTHOR]

This paper considers an uplink massive machine-type communication scenario with a massive number of antennas, where a large number of user devices are connected to a base station (BS) and the user traffic is sporadic. We propose a novel hybrid message passing (HMP) algorithm to achieve joint active detection and channel estimation (JADCE) by exploiting the channel characteristics in both the angular domain and user domain, which is expected to enhance the performance of JADCE compared with the conventional approaches without the knowledge of such a consideration. The user grouping is performed simultaneously with JADCE, which provides the prerequisites of the joint spatial division and multiplexing to achieve significant savings both in the downlink training and feedback of channel state information at the transmitter. Based on the acquired knowledge of user grouping, we further propose a per-group processing based JADCE (PGP-JADCE) approach. It significantly reduces the computational overhead for JADCE and the BS is capable to process all the user groups in parallel. Further, the analysis of detection error probabilities and channel estimation error of PGP-JADCE is provided. [ABSTRACT FROM AUTHOR]

A general class of Bayesian lower bounds when the underlying loss function is a Bregman divergence is demonstrated. This class can be considered as an extension of the Weinstein–Weiss family of bounds for the mean squared error and relies on finding a variational characterization of Bayesian risk. This approach allows for the derivation of a version of the Cramér–Rao bound that is specific to a given Bregman divergence. This new generalization of the Cramér–Rao bound reduces to the classical one when the loss function is taken to be the Euclidean norm. In order to evaluate the effectiveness of the new lower bounds, the paper also develops upper bounds on Bayesian risk, which are based on optimal linear estimators. The effectiveness of the new bound is evaluated in the Poisson noise setting. [ABSTRACT FROM AUTHOR]

Prognoses of Traumatic Brain Injury (TBI) outcomes are neither easily nor accurately determined from clinical indicators. This is due in part to the heterogeneity of damage inflicted to the brain, ultimately resulting in diverse and complex outcomes. Using a data-driven approach on many distinct data elements may be necessary to describe this large set of outcomes and thereby robustly depict the nuanced differences among TBI patients’ recovery. In this work, we develop a method for modeling large heterogeneous data types relevant to TBI. Our approach is geared toward the probabilistic representation of mixed continuous and discrete variables with missing values. The model is trained on a dataset encompassing a variety of data types, including demographics, blood-based biomarkers, and imaging findings. In addition, it includes a set of clinical outcome assessments at 3, 6, and 12 months post-injury. The model is used to stratify patients into distinct groups in an unsupervised learning setting. We use the model to infer outcomes using input data, and show that the collection of input data reduces uncertainty of outcomes over a baseline approach. In addition, we quantify the performance of a likelihood scoring technique that can be used to self-evaluate the extrapolation risk of prognosis on unseen patients. [ABSTRACT FROM AUTHOR]

Demand response (DR) programs engage distributed demand-side resources, e.g., controllable residential and commercial loads, in providing ancillary services for electric power systems. Ensembles of these resources can help reducing system load peaks and meeting operational limits by adjusting their electric power consumption. To equip utilities or load aggregators with adequate decision-support tools for ensemble dispatch, we develop a Markov Decision Process (MDP) approach to optimally control load ensembles in a privacy-preserving manner. To this end, the concept of differential privacy is internalized into the MDP routine to protect transition probabilities and, thus, privacy of DR participants. The proposed approach also provides a trade-off between solution optimality and privacy guarantees, and is analyzed using real-world data from DR events in the New York University microgrid in New York, NY. [ABSTRACT FROM AUTHOR]

In this work, we study the outage probability (OP) at the destination of an intelligent reflecting surface (IRS) assisted communication system in a $\kappa -\mu $ fading environment. A practical system model that takes into account the presence of phase error due to quantization at the IRS when a) source-destination (SD) link is present and b) SD link is absent is considered. First, an exact expression is derived, and then we derive three simple approximations for the OP using the following approaches: (i) uni-variate dimension reduction, (ii) moment matching and, (iii) Kullback–Leibler (KL) divergence minimization. The resulting expressions for OP are simple to evaluate and quite tight even in the tail region. The validity of these approximations is demonstrated using extensive Monte Carlo simulations. We also study the impact of the number of bits available for quantization, the position of IRS with respect to the source and destination and the number of IRS elements on the OP for systems with and without an SD link. We also demonstrate how the method of moment matching and KL divergence minimization can be used to analyze systems experiencing spatial correlation between the IRS elements. [ABSTRACT FROM AUTHOR]

This study presents PRISM, a probabilistic simplex component analysis approach to identifying the vertices of a data-circumscribing simplex from data. The problem has a rich variety of applications, the most notable being hyperspectral unmixing in remote sensing and non-negative matrix factorization in machine learning. PRISM uses a simple probabilistic model, namely, uniform simplex data distribution and additive Gaussian noise, and it carries out inference by maximum likelihood. The inference model is sound in the sense that the vertices are provably identifiable under some assumptions, and it suggests that PRISM can be effective in combating noise when the number of data points is large. PRISM has strong, but hidden, relationships with simplex volume minimization, a powerful geometric approach for the same problem. We study these fundamental aspects, and we also consider algorithmic schemes based on importance sampling and variational inference. In particular, the variational inference scheme is shown to resemble a matrix factorization problem with a special regularizer, which draws an interesting connection to the matrix factorization approach. Numerical results are provided to demonstrate the potential of PRISM. [ABSTRACT FROM AUTHOR]

Objective: While understanding the interaction patterns among simultaneous recordings of spike trains from multiple neuronal units is a key topic in neuroscience, existing methods either do not consider the inherent point-process nature of spike trains or are based on parametric assumptions. This work presents an information-theoretic framework for the model-free, continuous-time estimation of both undirected (symmetric) and directed (Granger-causal) interactions between spike trains. Methods: The framework computes the mutual information rate (MIR) and the transfer entropy rate (TER) for two point processes $X$ and $Y$ , showing that the MIR between $X$ and $Y$ can be decomposed as the sum of the TER along the directions $X \rightarrow Y$ and $Y \rightarrow X$. We present theoretical expressions and introduce strategies to estimate efficiently the two measures through nearest neighbor statistics. Results: Using simulations of independent and coupled point processes, we show the accuracy of MIR and TER to assess interactions even for weakly coupled and short realizations, and demonstrate the superiority of continuous-time estimation over the standard discrete-time approach. We also apply the MIR and TER to real-world data, specifically, recordings from in-vitro preparations of spontaneously-growing cultures of cortical neurons. Using this dataset, we demonstrate the ability of MIR and TER to describe how the functional networks between recording units emerge over the course of the maturation of the neuronal cultures. Conclusion and Significance: the proposed framework provides principled measures to assess undirected and directed spike train interactions with more efficiency and flexibility than previous discrete-time or parametric approaches, opening new perspectives for the analysis of point-process data in neuroscience and many other fields. [ABSTRACT FROM AUTHOR]

With the increased number of synthetic aperture radar (SAR) systems that have been launched in recent years, many high-quality images have been produced for precise earth observation. The number of available images requires new feature extraction techniques to capture enough discriminant information from high-resolution SAR images. To effectively characterize different land covers, a multiscale local Fisher pattern (MLFP) is proposed to extract both statistical and spatial properties in this work. First, the Fisher vector (FV) of each pixel is calculated after the statistical distribution of high-resolution SAR intensities is modeled via a Gamma mixture model. Then, the generative and discriminant information implied in the FV is exploited after the extraction of the geometrical information between neighboring pixels within different scales. Two experiments are conducted to demonstrate the performance of the proposed MLFP, which are high-resolution SAR image land-cover classification and SAR scene classification. Compared with some existing approaches, the proposed MLFP shows superior performance after the statistical and spatial structure information from different land covers is exploited. [ABSTRACT FROM AUTHOR]

We derive improved and easily computable upper bounds on the capacity of the discrete-time Poisson channel under an average-power constraint and an arbitrary constant dark current term. This is accomplished by combining a general convex duality framework with a modified version of the digamma distribution considered in previous work of the authors. [ABSTRACT FROM AUTHOR]

In this article we study weighted sums of $n$ i.i.d. Gamma($\alpha $) random variables with nonnegative weights. We show that for $n \geq 1/\alpha $ the sum with equal coefficients maximizes differential entropy when variance is fixed. As a consequence, we prove that among nonnegative quadratic forms in $n$ independent standard Gaussian random variables, a diagonal form with equal coefficients maximizes differential entropy, under a fixed variance. This provides a sharp lower bound for the relative entropy between a nonnegative quadratic form and a Gaussian random variable. Bounds on capacities of transmission channels subjects to $n$ independent additive gamma noises are also derived. [ABSTRACT FROM AUTHOR]

Considering the advantages of contactless fault diagnosis, a novel sound based fault diagnosis method for railway point machines (RPMs) is proposed. Firstly, a denoising method based on empirical mode decomposition (EMD) is proposed. The useful intrinsic mode functions (IMFs) are selected using kurtosis and energy criteria to reconstruct the denoised signal. Then, multi-scale fractional permutation entropy (MFPE) is proposed inspired by fractional calculus, which is more powerful than the classical multi-scale permutation entropy (MPE). And a two-scale algorithm is developed to avoid neglecting the information contained at the end of the signals in the coarse-graining process. Finally, the feasibility and superiority of the proposed method (D-FMPE-T) based on denoising method and two-scale algorithm are verified using support vector machine optimized by particle swarm optimization (PSO-SVM) through comparing with some commonly used feature extraction and classification methods. Besides, CWRU is also utilized for verification of the superiority of the proposed method. Experiment results show that the proposed method performs best. The identification accuracies on normal-reverse and reverse-normal switching processes, and CWRU data sets reach 99.3%, 99%, and 99.17% respectively, demonstrating the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the average achievable rate and error probability of a reconfigurable intelligent surface (RIS) aided systems is investigated for the finite blocklength (FBL) regime. The performance loss due to the presence of phase errors arising from limited quantization levels as well as hardware impairments at the RIS elements is also discussed. First, the composite channel containing the direct path plus the product of reflected channels through the RIS is characterized. Then, the distribution of the received signal-to-noise ratio (SNR) is matched to a Gamma random variable whose parameters depend on the total number of RIS elements, phase errors and the channels’ path loss. Next, by considering the FBL regime, the achievable rate expression and error probability are identified and the corresponding average rate and average error probability are elaborated based on the proposed SNR distribution. Furthermore, the impact of the presence of phase error due to either limited quantization levels or hardware impairments on the average rate and error probability is discussed. The numerical results show that Monte Carlo simulations conform to matched Gamma distribution to received SNR for sufficiently large number of RIS elements. In addition, the system reliability indicated by the tightness of the SNR distribution increases when RIS is leveraged particularly when only the reflected channel exists. This highlights the advantages of RIS-aided communications for ultra-reliable and low-latency systems. The difference between Shannon capacity and achievable rate in FBL regime is also discussed. Additionally, the required number of RIS elements to achieve a desired error probability in the FBL regime will be significantly reduced when the phase shifts are performed without error. [ABSTRACT FROM AUTHOR]

According to the theory and interpretation method of information geometry, this work presents a novel curvature-based saliency method for ship detection in synthetic aperture radar (SAR) images. First, the nonlinear anisotropic diffusive process has been adopted to eliminate clutter while preserving the local ship target structure in SAR images. Then, a novel curvature-based saliency method for super-pixels in the filtered image is presented, which is used to exploit the microstructure feature of statistical manifold. Finally, a statistical classification method is used to realize the location of targets. The experimental results on real SAR images show that the proposed method can achieve good performance. [ABSTRACT FROM AUTHOR]

Accurately and promptly detecting downhole abnormalities is of great importance to ensure safe and efficient operation of geological drilling systems. In view of the lack of abnormal data and the presence of multiple operating states, this article proposes a new abnormality detection method for geological drilling processes based on the Jensen–Shannon divergence and adaptive alarm limits. The major contributions are twofold: 1) a novel framework is proposed to detect downhole abnormalities by identifying normal operating conditions first and then establishing the normal operating zones; 2) an adaptive alarm limit design method is proposed to determine the normal operating zones by modeling the relation between the distribution feature and the variational trend feature based on the boundary points. The effectiveness and practicability of the proposed method are demonstrated by an industrial case study. The results demonstrate that the proposed method has superior performance to other methods in downhole abnormality detection. [ABSTRACT FROM AUTHOR]

Due to the complex static, dynamic, and large-scale characteristics for modern industrial processes, in this article, we propose a double-layer distributed monitoring approach based on multiblock slow feature analysis and multiblock independent component analysis. To this end, the processed dataset is divided into the static and dynamic blocks on the basis of the sequential information of each variable in the first layer. Considering the correlations between the variables in the large-scale processes, the sequential correlation matrices in two blocks are calculated, which serves as the second-layer block division rule. Then, the static and dynamic blocks are further divided into several static and dynamic subblocks in which the variables in each subblock are strongly correlated and in the same state. The slow feature analysis and independent component analysis monitoring models are, respectively, generated for the dynamic and static subblocks. Finally, the monitoring results in each subblock are integrated by Bayesian inference to get the final statistics. The average fault detection rate of the proposed method for the Tennessee Eastman process is 0.842, while those of the other traditional methods are lower than 0.75, which shows the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Normalized maximum likelihood (NML) distribution of probabilistic model gives the optimal code length function in the sense of minimax regret. Despite its optimal property, the calculation of NML distribution is not easy, and existing efficient methods have been focusing on its asymptotic behavior, or on specific models. This paper gives an efficient way to calculate NML by integral on parameter domain, not on data domain, showing that NML distribution is a Bayesian predictive distribution with a complex prior, based on our novel Fourier expansion approach. Our results provide an integrated way to calculate NML for exponential family and also include a non-asymptotic version of previous work on asymptotic behavior for general cases. The applications of our methodology are not limited to but also include normal distribution, Gamma distribution, Weibull distribution, and von Mises distribution. [ABSTRACT FROM AUTHOR]

A robust multiple model strategy for linear parameter varying (LPV) systems identification relied on Student’s t-distribution is studied in this article. Since most industrial processes operate over several working points to meet multiple production objectives, a single model is generally insufficient to describe the process behavior over the whole operating range. To cope with this issue, the multiple model strategy is employed and at the same time, the outliers and missing measurements problems are both considered in this article. The finite impulse response (FIR) model is chosen to describe the local behavior of the LPV system. The statistical scheme based on the t-distribution is utilized to model the system noise so as to deal with the outliers. The problem of parameters estimation with incomplete outputs is addressed by using the expectation-maximization (EM) algorithm. The feasibility and effectiveness of the developed approach are proved via a mechanical unit. [ABSTRACT FROM AUTHOR]

Data-driven inferential sensors have been increasingly applied to estimating important yet difficult-to-measure quality-relevant variables in industrial processes. However, complicated data characteristics (such as nonlinearities, non-Gaussianities, uncertainties, outlying data, etc.) existing in industrial datasets impose significant difficulties in developing inferential sensors with high accuracy. In particular, modeling process dynamics with sequential data is practically very important but quite challenging. In order to deal with such issues, this article proposes a dynamic variational Bayesian Student's t mixture regression (D-VBSMR) with hidden variables propagation. In D-VBSMR, the probabilistic mixture model structure can deal with nonlinearities, non-Gaussianities, and uncertainties; meanwhile, the Student's t-distribution with heavy tails enables D-VBSMR to be highly robust against outliers. Most importantly, the first-order Markov chain is used to connect hidden state variables, such that the process dynamic characteristic can be effectively modeled. In addition, parameter learning for D-VBSMR is also developed by adopting the variational inference framework and forward–backward algorithm. Experimental results on both a numerical example and a real industrial process validate the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]