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Phishing scams are increasing as the technical skills and costs of phishing attacks diminish, emphasizing the need for rapid, precise, and low-cost prevention measures. Based on a character-level convolutional neural network (CNN), we present CNN-Fusion, an effective and lightweight phishing URL detection method. Our basic idea is to deploy multiple variants of one-layer CNN with various-sized kernels in parallel to extract multi-level features. Observing that differences between phishing and benign URLs might exhibit a strong spatial correlation, we choose SpatialDropout1D, making the model more robust and preventing it from memorizing the training data. To further reduce the probability of errors that may cause by irrelevant or noisy features, we apply a max-over time pooling technique over the feature map to pick only the most important feature. Finally, the model is evaluated using five publicly available datasets containing 1.85 million phishing and benign URLs. Other than that, we assess the model against AI adversarial attacks, known as "Offensive AI." Compared to existing methods, experiments demonstrate that our approach enjoys advantages in 5 times less training time and much more in memory consumption, achieving an average accuracy above 99% on five different datasets as well as on AI-generated malicious attacks. [ABSTRACT FROM AUTHOR]

Neural decoders were introduced as a generalization of the classic Belief Propagation (BP) decoding algorithms. In this work, we propose several neural decoders with different permutation invariant structures for BCH codes and punctured RM codes. Firstly, we propose the cyclically equivariant neural decoder which makes use of the cyclically invariant structure of these two codes. Next, we propose an affine equivariant neural decoder utilizing the affine invariant structure of those two codes. Both these two decoders outperform previous neural decoders when decoding cyclic codes. The affine decoder achieves a smaller decoding error probability than the cyclic decoder, but it usually requires a longer running time. Similar to using the property of the affine invariant property of extended BCH codes and RM codes, we propose the list decoding version of the cyclic decoder that can significantly reduce the frame error rate(FER) for these two codes. For certain high-rate codes, the gap between the list decoder and the Maximum Likelihood decoder is less than 0.1 dB when measured by FER. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent reflecting surface (IRS), which consists of L reflecting elements (REs), is utilized to improve the coverage and reliability of half-duplex relaying (HDR) and full-duplex relaying (FDR) wireless communication systems. We provide a rigorous analysis on the system performance, which is represented by the exact form expressions of outage probability (OP), symbol error probability (SEP), and ergodic capacity (EC), of the HDR-IRS and FDR-IRS systems over Rayleigh fading channels. All derived expressions are validated by Monte-Carlo simulations. It is shown that IRS can significantly improve the performance of HDR and especially FDR networks, in which IRS can suppress the impact of the loopback interference at the relay. [ABSTRACT FROM AUTHOR]

In this study, we investigate the performance of aerial full-duplex relay (AFDR) systems. In particular, we examine two practical scenarios: one where there is no direct link between the transmitter and user, and another where such a link exists. For both scenarios, we develop mathematical models to calculate the closed-form expressions of symbol error probabilities (SEPs) for AFDR systems, using a realistic channel aligned with the fifth generation (5G) and beyond yardsticks. To take care of residual self-interference (RSI) in AFDR, we propose an optimal power allocation strategy. Our numerical findings indicate that the performance in the case with a direct link is considerably higher than that in the case without this link. Additionally, we analyze thoroughly the effects of key parameters such as transmit power, RSI levels, carrier frequencies, and AFDR positions. The effect of RSI is significantly strong, and our proposed optimal power allocation method substantially improves system performance, especially in high transmit power scenarios where error floors may occur. Importantly, the optimal power level is dramatically lower than the conventional value where the optimal power cannot be found. Thus, besides reducing the SEPs, optimal power also helps to prolong the time of operation of AFDR. Monte-Carlo simulations are performed to confirm the accuracy of our derived expressions and demonstrate the efficacy of the proposed approaches in practical scenarios. [ABSTRACT FROM AUTHOR]

The article discusses the importance of laboratory investigations in patient management, particularly in acute care setups where a high volume and frequency of sampling for laboratory analyses are observed. The research emphasizes the need for well-planned protocols to eliminate errors in investigation reports, focusing on the pre-analytical phase of laboratory investigations. The aim is to improve the quality of lab reporting processes and eliminate pre-analytic laboratory errors in order to enhance patient care. [Extracted from the article]

The article discusses the prevalence of chromosomal mosaicism in blastocyst-stage human embryos, which can lead to aneuploidies and pregnancy loss. Preimplantation genetic testing for aneuploidy (PGT-A) aims to identify chromosomally normal embryos for transfer, but the rates of aneuploid, euploid, and mosaic embryos, as well as the errors in meiosis and mitosis that produce them, are largely unknown. Using approximate Bayesian computation (ABC), researchers estimated a 39-43% probability of meiotic error per meiosis and a 1.0-3.0% probability of mitotic error per mitosis, highlighting the challenges in accurately assessing embryo chromosomal status. [Extracted from the article]

The article discusses the use of agentic artificial intelligence (AI) platforms, such as Loon Lens 1.0, for streamlining title and abstract screening in systematic literature reviews (SLRs). The study evaluates the calibration of Loon Lens 1.09's confidence ratings to prioritize citations for human review, showing that targeted human review significantly improves precision while maintaining high sensitivity and specificity. The findings suggest that a calibrated model can optimize human-AI collaboration in screening processes, addressing the challenge of lower precision in automated approaches. Further research is needed to assess the generalizability of these findings across diverse review contexts. [Extracted from the article]

Reconfigurable intelligent surfaces (RIS) are considered for Terahertz (THz) communication system in this paper. The THz channel is assumed to follow κ − μ − shadowed fading model and performance analysis for the system under study are performed in terms of outage probability, ergodic capacity and average bit error rate (ABER). As well, asymptotic analysis at high but pragmatic signal to noise ratio (SNR) values are obtained for these metrics. In addition, the impact of the channel parameters, κ , μ and the shadowing m s , along with the number of reflecting elements and misalignment error (i.e., pointing error impairments) in RIS is investigated and thoroughly discussed. It is revealed that increasing the number of reflecting surfaces and the channel parameter μ enhances the overall performance; whereas higher m s , κ , operating frequency and misalignment error is shown to severely degrades the performance. It is also reported that the system diversity gain is related to the number of reflecting surfaces, μ , and the pointing error impairment. [ABSTRACT FROM AUTHOR]

A novel space shift keying (SSK) multiple–input multiple–output (MIMO) technique based on Steiner triple system is proposed and analyzed in this article. SSK attracted considerable research interest in the past few years driven by the several promised inherent advantages including low error probability, low computational complexity and a very simple hardware implementation with very low cost and power consumption. Yet, the spectral efficiency of SSK increases with a base two logarithm of the number of transmit antennas and high data rates are only viable with a massive and impractical number of transmit antennas. Alternatively, generalized SSK (GSSK) scheme is considered, where a combination of antennas is activated at each time instant. GSSK promises the use of arbitrary number of transmit antennas not necessarily a power of two integer. Also, GSSK can attain high data rate with low number of transmit antennas at the cost of substantial degradation in the error performance. In this study, a Steiner triple system is utilized to propose a tailored SSK scheme with substantial reduction in the required number of transmit antennas, without compromising the error probability. It is shown that the proposed Steiner–SSK (S–SSK) MIMO system achieves almost identical error performance to a conventional SSK system but with nearly 90% reduction in the number of transmit antennas. As well, the average bit error rate (ABER) of S–SSK is shown to outperform GSSK by at least 3dB. It is also reported that a S–SSK system accomplishes significant reduction in hardware cost, power consumption, and computational complexity as compared to conventional SSK scheme. Yet, GSSK is shown to marginally outperforms S–SSK in these metrics as it requires smaller number of transmit antennas per a target spectral efficiency. [ABSTRACT FROM AUTHOR]

The author explores the algorithm of language and its use as a tool for communication. She tells about attempts of artificial intelligence to make predictions of what she will say and how this made her feel that her free will had been usurped. She discusses ethics and moral thinking as caring and having responsibility towards words and expressions spoken in ordinary language. She explains language and speech as statistical systems of causality and the error propagation in computer operation.

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A recent study conducted by McMaster University and Hamilton Health Sciences in Canada proposes a pragmatic approach for monitoring emerging efficacy data in phase III randomized controlled trials. The study emphasizes the importance of protecting study integrity and participant safety through effective monitoring. The research suggests a new monitoring approach, the "Population Health Research Institute boundary," which controls error probability and outperforms other group sequential boundaries. This study provides valuable insights for the early termination of clinical trials and contributes to the field of clinical research. [Extracted from the article]

A recent study from the University of Bristol in the United Kingdom explores new data on the CDC and FDA, focusing on algorithms for tropical group testing in the context of PCR testing for COVID-19. The research introduces tropical versions of classical non-adaptive algorithms and compares their performance with traditional algorithms, highlighting the additional information gained from learning the outcomes of tests. The study suggests that in certain scenarios, the tropical DD algorithm can recover more information with fewer tests and may be optimal in specific situations. This peer-reviewed research provides valuable insights into improving group testing strategies. [Extracted from the article]

A recent study conducted by the All-Russian Dairy Research Institute focused on developing a method to predict the bioavailability of enteral nutrition products (ENPs) through a comprehensive analysis of their components and indicators. By utilizing in silico methods, the research aimed to streamline and reduce the costs of clinical studies typically required to determine bioavailability. The study identified statistically significant components and indicators that define the intended purpose of ENPs, with a focus on products for diabetes mellitus and thermal injury. The research concluded with the development of a principle for predicting bioavailability, which can be applied to the design of various types of ENPs, including meat, dairy, and plant-based products. [Extracted from the article]

Researchers from the University of Southern Florida have developed a hybrid deep learning system for multi-class liver cancer classification and segmentation. The system accurately classifies liver tumors into three categories with an average accuracy of 99.19%. The innovative framework can help radiologists and doctors identify liver tumors more accurately, outperforming other state-of-the-art techniques. The research has been peer-reviewed and provides valuable insights into liver cancer diagnosis and classification. [Extracted from the article]

This document provides information about a clinical trial, NCT06600230, that is currently recruiting participants in Turkey. The study aims to evaluate the effectiveness of patient education and exercise interventions in treating dysmenorrhea, or menstrual pain, compared to self-methods like resting or heat application. The trial will involve young women between the ages of 18 and 35 who have been diagnosed with primary dysmenorrhea. The study is in the interventional phase and is expected to collect data until September 30, 2024. [Extracted from the article]

AI errors pose a significant challenge, hindering real-world applications. This work introduces a novel approach to cope with AI errors using weakly supervised error correctors that guarantee a specific level of error reduction. Our correctors have low computational cost and can be used to decide whether to abstain from making an unsafe classification. We provide new upper and lower bounds on the probability of errors in the corrected system. In contrast to existing works, these bounds are distribution agnostic, non-asymptotic, and can be efficiently computed just using the corrector training data. They also can be used in settings with concept drifts when the observed frequencies of separate classes vary. The correctors can easily be updated, removed, or replaced in response to changes in distributions within each class without retraining the underlying classifier. The application of the approach is illustrated with two relevant challenging tasks: (i) an image classification problem with scarce training data, and (ii) moderating responses of large language models without retraining or otherwise fine-tuning. • In this work, we introduce a novel approach to address the challenge of random AI errors. • It enables correcting errors with provable guarantees regardless of data distribution and with small training sets. • The theory is illustrated with examples highlighting the application of the method to challenging machine learning problems. [ABSTRACT FROM AUTHOR]

Specialized hardware accelerators beyond von-Neumann, that offer processing capability in where the data resides without moving it, become inevitable in data-centric computing. Emerging non-volatile memories, like Ferroelectric Field-Effect Transistor (FeFET), are able to build compact Logic-in-Memory (LiM). In this work, we investigate the probability of error (Perror) in FeFET-based XNOR LiM, demonstrating the new trade-off between the speed and reliability. Using our reliability model, we present how Binarized Neural Networks (BNNs) can be proactively trained in the presence of XNOR-induced errors towards obtaining robust BNNs at the design time. Furthermore, leveraging the trade-off between Perror and speed, we present a run-time adaptation technique, that selectively trades-off Perror and XNOR speed for every BNN layer. Our results demonstrate that when a small loss (e.g., 1%) in inference accuracy could be accepted, our design-time and run-time techniques provide error-resilient BNNs that exhibit 75% and 50% (FashionMNIST) and 38% and 24% (CIFAR10) XNOR speedups, respectively. [ABSTRACT FROM AUTHOR]

Two active hypothesis testing problems are formulated. In these problems, the agent can perform a fixed number of experiments and then decide on one of the hypotheses or declare its experiments inconclusive. The first problem is an asymmetric formulation in which the objective is to minimize the probability of incorrectly declaring a particular hypothesis to be true while ensuring that the probability of correctly declaring that hypothesis is moderately high. This formulation is a generalization of the formulation in the Chernoff–Stein lemma to an active setting. The second problem is a symmetric formulation in which the objective is to minimize the misclassification probability while ensuring that the true hypothesis is declared conclusively with moderately high probability. For these problems, lower and upper bounds on the optimal misclassification probabilities are derived and these bounds are shown to be asymptotically tight. Novel experiment selection strategies are provided. It is shown that these strategies are asymptotically optimal and using numerical experiments, it is demonstrated that these strategies have a significantly better performance in the nonasymptotic regime. [ABSTRACT FROM AUTHOR]

The probabilistic-constrained tracking control issue is investigated for a class of time-varying nonlinear stochastic systems with sensor saturation, deception attacks and limited bandwidth in an unified framework. The saturation of sensors is quantified by a sector-bound-based function satisfying certain conditions, and the random deception attacks are considered and modeled by a random indicator variable. To gain more efficient utilization of communication channels, a Round-Robin (RR) protocol is utilized to orchestrate the transmission order of measurements. The main purposes of this study aim to plan an observer-based tracking controller to achieve the following goals: (1) the related performance indicators of the estimation error is less than given bound at each time step; and (2) the violation probability of the tracking error confined in a predefined scope is supposed to be higher than a prescribed scalar and the area is minimized at each instant. In order to reach these requirements, a group of recursive linear matrix inequalities (RLMIs) are developed to estimate the state and design the tracking controller at the same time. Finally, two simulation examples are exploited to illustrate the availability and flexibility of the proposed scheme. [ABSTRACT FROM AUTHOR]

The product of two Gaussian Q -functions is actively used in computing the error probabilities of various digital modulation schemes. Evaluating symbol error probability (SEP) integral involving the product of Gaussian Q -functions over fading distributions is complicated. The closed-form solutions are not available always, therefore to derive these solutions approximations are used. In this paper, an approximation to the product of two Gaussian- Q functions as a sum of exponentials is presented. Further it is used to evaluate the error probabilities of modulation techniques over fading distributions in wide range of scenarios. The knowledge of moment generating function (MGF) of a fading model is sufficient enough to derive the closed-form solution to SEP integrals. Numerical results demonstrate superior accuracy of proposed approximation over other existing competing approximations. Furthermore the proposed solutions are fairly simple as the MGF of fading channels involve fundamental mathematical functions. [ABSTRACT FROM AUTHOR]

The paper presents a comprehensive closed-form performance analysis framework for multihop communications over Weibull fading channels. This framework may be of interest in different applications in the contexts of beyond-5G (B5G) and Internet of Things (IoT) use cases. The analyzed scheme consists generally of multiple regenerative relays, and we also consider generalized high-order quadrature amplitude modulation (M -QAM) transmissions. To take into consideration the channel fading, we adopt the Weibull model for its largely flexible ability to cover different channel conditions in different application contexts. The end-to-end performance is evaluated in terms of outage probability, bit error probability (BER), symbol error probability (SER), block error rate (BLER), ergodic capacity, and energy efficiency (EE). For all the metrics, we present exact closed-form expressions—along with their asymptotic behavior—capitalizing on the powerful generalized hypergeometric functions. To illustrate the utility of the obtained analytical results, we derive two BER- and EE-optimal transmit power allocation strategies, and we discuss the resulting performance gains. The exactness of our analysis is illustrated by numerical examples, and assessed via Monte-Carlo simulations for different system and channel parameters. Finally, as a secondary contribution, and noting the increasing popularity of single and bivariate Fox's H -function, we provide generalized Matlab codes for computing these functions which are of practical utility in many fields. [ABSTRACT FROM AUTHOR]

Handwritten signature is one of the most important behavioral biometrics and plays an important role in the field of identity verification. It is regarded as a legal means to verify personal identity by administrative and financial institutions. Traditional manual signature verification requires large labor costs and the probability of verification error is relatively high. Nowadays, tablets are often used for signature capturing, which motivates us to explore the feasibility of using tablets for signature verification. In this paper, we propose an online handwriting s ignature v erification system based on s ound and v ibration (SVSV) generated during the signing process. We develop an application to collect signature-related vibration and sound data. We first extract the time domain features of the sound signal and use Fast Fourier Transform to extract the frequency domain features of the sound data. For vibration data, we use Discrete Cosine Transform for dimensionality reduction and feature extraction. Then we fuse the sound and vibration features. Finally, we design an efficient one-class classifier based on the Convolutional Neural Network to perform signature verification. Through extensive experiments with 12 volunteers, the results show that SVSV is a robust and efficient system with an AUC of 0.984 and an EER of 0.05. [ABSTRACT FROM AUTHOR]

This paper studies Gaussian random fields with Matérn covariance functions with smooth parameter ν > 2. Two cases of parameter spaces, the Euclidean space and N -dimensional sphere, are considered. For such smooth Gaussian fields, we have derived the explicit formulae for the expected Euler characteristic of the excursion set, the expected number and height distribution of critical points. The results are valuable for approximating the excursion probability in family-wise error control and for computing p-values in peak inference. [ABSTRACT FROM AUTHOR]

Testing for uniformity for any given data set on the circle is an important first step before any further inference. One important class of tests are those based on spacings, which assume that the data are measured on a continuous scale. In practice however, the observed data may come grouped, or the recorded observations may be rounded values. Ignoring this fact can result in incorrect Type I error probabilities and inference, especially if the degree of rounding is severe or if the sample size is large. In this article, we propose a simple modification to such rounded data, which then allows us to continue to use the Rao's spacing test and its exact critical values, without affecting the probability of Type I error. We provide theoretical justification for the suggested modification, as well as simulation studies that demonstrate its strong and robust performance. [ABSTRACT FROM AUTHOR]

A recent train accident in West Bengal, India, has highlighted the absence of the Kavach safety system, which is designed to prevent accidents when two trains are on the same track. Kavach is a cab signaling train control system developed by the Research Design and Standards Organisation (RDSO) and has been adopted as the National Automatic Train Protection (ATP) system. The Indian Railways plans to deploy the safety system on the Delhi-Guwahati and Delhi-Howrah routes, aiming to cover over 6,000 km of tracks by next year. Currently, Kavach is in place on over 1,500 km of tracks, and the government aims to cover around 34,000 km of rail network with the safety system. The accident is still under investigation, but it is suspected that the loco pilot and his assistant on the goods train overshot a red signal and collided with the Kanchanjungha Express. Fortunately, there were no unreserved coaches at the rear of the Kanchanjungha Express, and all the passengers killed and injured were in the guard-cum-luggage van. [Extracted from the article]

• An adaptive clustering learning approach based on an event-triggered scheme over time-varying multitask networks is proposed. • Mean and mean-square performance analyses of the proposed algorithm are provided. • The error probability of false alarm and false detection of the clustering mechanism is analyzed. • An extensive simulation is given to validate the analytical performance of the distributed learning strategy over time-varying multitasking. With increasing research on distributed processing in networks, adaptive learning strategies have gradually attracted researchers' attention. The traditional adaptive learning strategy mainly aims at a single task unchanged over time, while real networks often entail multitasks scenarios with tasks that change over time. Furthermore, although cooperation among agents is beneficial for single task time-invariant networks, agents' indiscriminate cooperation in time-varying multitask networks may cause undesirable effects. In this paper, an adaptive clustering learning approach based on an event-triggered scheme over time-varying multitask networks is proposed. With this process, agents are enabled to distinguish their clusters to determine cooperative neighbors and improve the accuracy of estimation over networks. The mean-square of the proposed algorithm is analyzed in detail, and the error probability of false alarms and false detections of the clustering mechanism is evaluated. An extensive simulation is given to validate the analytical performance of the distributed learning strategy over time-varying multitasks. [ABSTRACT FROM AUTHOR]

In this study, the probabilistic-constrained outlier-resistant tracking control problem is investigated for a class of stochastic nonlinear systems subject to measurement outliers, randomly occurring faults and stochastic nonlinearities. The measurement outliers (e.g. the abnormal values of sensor measurements), which are mainly caused by sensor outages, faults or some unpredictable sudden but sharp disturbances, would inevitably bring certain negative effects on control performance if not tackled appropriately. Different from the commonly used saturation-function-based method, new type of event-trigger-based outlier-resistant communication scheme is proposed in this paper. Based on the proposed scheme, not only those "unnecessary" measurements are not permitted for transmission, but also the measurement outliers are discarded. The main purpose of the current study is to design a novel tracking control scheme such that both the H ∞ control performance and specific probabilistic constraint (i.e., the probability of the tracking error falling in a prescribed set is larger than a preset value) are satisfied simultaneously. Furthermore, a finite-horizon probabilistic-constrained H ∞ tracking control algorithm is developed to co-design the controller gain matrices and parameters of the event-triggered communication scheme. Finally, one example is utilized to illustrate the efficiency of the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, we combine practically verified results from circuit theory with communication-theoretic laws. As a result, we obtain closed-form theoretical expressions linking fundamental system design and environment parameters with the power consumption of analog front ends (AFEs) for communication receivers. This collection of scaling laws and bounds is meant to serve as a theoretical reference for practical low power AFE design. We show how AFE power consumption scales with bandwidth, SNDR , and SIR. We build our analysis based on two well established power consumption studies and show that although they have different design approaches, they lead to the same scaling laws. The obtained scaling laws are subsequently used to derive relations between AFE power consumption and several other important communication system parameters, namely, digital modulation constellation size, symbol error probability, error control coding gain, and coding rate. Such relations, in turn, can be used when deciding which system design strategies to adopt for low-power applications. For instance, we show how AFE power scales with environment parameters if the performance is kept constant and we use these results to illustrate that adapting to fading fluctuations can theoretically reduce AFE power consumption by at least 20x. [ABSTRACT FROM AUTHOR]

Deep generative models have demonstrated their effectiveness in learning latent representation and modeling complex dependencies of time series. In this article, we present a smoothness-inducing sequential variational auto-encoder (VAE) (SISVAE) model for the robust estimation and anomaly detection of multidimensional time series. Our model is based on VAE, and its backbone is fulfilled by a recurrent neural network to capture latent temporal structures of time series for both the generative model and the inference model. Specifically, our model parameterizes mean and variance for each time-stamp with flexible neural networks, resulting in a nonstationary model that can work without the assumption of constant noise as commonly made by existing Markov models. However, such flexibility may cause the model fragile to anomalies. To achieve robust density estimation which can also benefit detection tasks, we propose a smoothness-inducing prior over possible estimations. The proposed prior works as a regularizer that places penalty at nonsmooth reconstructions. Our model is learned efficiently with a novel stochastic gradient variational Bayes estimator. In particular, we study two decision criteria for anomaly detection: reconstruction probability and reconstruction error. We show the effectiveness of our model on both synthetic data sets and public real-world benchmarks. [ABSTRACT FROM AUTHOR]

This paper investigates the probabilistic-constrained distributed filtering problem for a class of nonlinear stochastic systems with state constraints and cyber attacks. The considered cyber attacks are periodic denial-of-service (DoS) attacks which are modeled by a kind of periodic pulse-width-modulated (PWM) jamming signals. Different from some existing works, by using the proposed filter design method, the probability of the filtering error exceeding a threshold can be guaranteed below a certain level quantitatively. Furthermore, in order to economize the limited bandwidth resources, an event-triggered communication scheme (ETS) is designed for the data transmission. The aim of the problem addressed is to design a time-varying distributed filters such that: 1) the probability of the filtering error restricted to a given ellipsoid is larger than a specified value; and 2) the obtained ellipsoid threshold is minimized in the sense of matrix norm at each time point. To achieve this purpose, a recursive linear matrix inequality method is utilized and sufficient conditions are derived. Moreover, the filter parameters are explicitly determined in terms of the solution to certain matrix inequalities. Finally, the reliability and applicability of the proposed distributed filtering strategy are demonstrated by an illustrative example. [ABSTRACT FROM AUTHOR]

In this paper, we consider a wireless multiple-input multiple-output (MIMO) full-duplex relaying (FDR) network, where a source node communicates with a destination node via the help of a decode-and-forward (DF) relay node. This relay node has the capability of harvesting the energy from radio frequency (RF) signals, in particular, from the source node via time-switching energy harvesting protocol. To enhance the quality of transmission, diversity techniques are applied at both transmitter and receiver. Specifically, transmit antenna selection (TAS) and maximal ratio transmission (MRT) schemes are employed at the transmitter, while both selection combining (SC) and maximal ratio combining (MRC) techniques are considered at the destination node. The main contribution of this paper is an in-depth performance and optimal analysis of the time-switching energy harvesting (EH) protocol by deriving the closed-form expressions of outage probability (OP), system throughput, and symbol error probability (SEP) of the considered system over Rayleigh fading channel and then solving the optimization problem of EH duration, where the instantaneous throughput is used as the objective function. It is also shown in this paper that although a portion of signal is used for power transfer, the obtained diversity gain of this system is still equal to the minimum number of the antennas configured at the source or at the destination. Finally, numerical results are presented to demonstrate the validity of our analysis and to show the advantages of MIMO-FDR system with RF EH. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of adaptive neural tracking control for a class of nonstrict-feedback stochastic nonlinear time-delayed systems with unknown input saturation and unknown disturbance input. A smooth function is selected to approximate the non-smooth saturation function. By using the command filter method scheme, the well-known problem of the explosion of complexity, which appears in the classical backstepping methods, is avoided. To approximate the unknown nonlinear functions, the radial basis function neural networks (NNs) are deployed. In addition, considering the minimal learning parameter method makes the updating law independent of the number of neural nodes and the order of the system. By using the mean-value theorem, the adaptive NN tracking control law is obtained systematically regardless of pre-known knowledge of input saturation bounds. The proposed control scheme guarantees that the state trajectories of the closed-loop system are semi-global uniformly ultimately bounded in probability and the tracking error finally stays in a small neighborhood around the origin. Eventually, numerical and practical examples show the performance of the proposed controller design. [ABSTRACT FROM AUTHOR]

In 2007, contrary to other approximations, Karagiannidis et al. proposed a more accurate approximation of the Gaussian Q function and its integer powers, for all the positive arguments. However, when it is used to compute the symbol error probability (SEP) of various coherent digital modulation techniques over parametric fading distributions like the κ − μ shadowed fading, it loses its analytical tractability. In this paper, using Taylor series approximation of the exponential function, we comprehend the approximation of interest (with accuracy intact) which facilitates in the simplification of the key integrals used in the SEP computation of digital modulation techniques over κ − μ shadowed fading distribution. Apart from various applications, the significance of the κ − μ shadowed fading statistics lies in the fact that it unifies most of the popular fading models like one sided Gaussian, Rayleigh, Nakagami- m , Nakagami- q , Rician, Rician-shadowed, η − μ and κ − μ , under one umbrella. In order to show the utility of the proposed work, the SEP of hexagonal-QAM is calculated over Rayleigh fading channel which is one of the widely used cases of the κ − μ shadowed fading distribution. Monte Carlo simulations have also been carried out to justify the accuracy of the analysis. [ABSTRACT FROM AUTHOR]

In this article, we consider the Kalman filter (KF)-based state estimation in the multisensor networked control systems. The sensors communicate plant state observations to a remote state estimator over a multiantenna random access network, resulting in random fading in measurements and rank-deficient observation updates in KF. In contrast to the conventional literature where average state estimation mean-square-error (MSE) is adopted as the estimation performance measure, we analyze the state estimation MSE tail distribution which guarantees fine-grained characterizations of the MSE sample path. We developed various sufficient conditions for the well behaved tail distribution of the MSE. Specifically, we propose the observable and unobservable cone decomposition of the transformed coordinate system induced by the singular value decomposition of the measurement matrix, which enables us to overcome the limitation of MSE analysis with rank deficient measurement matrix. In the noiseless plant case, we establish the sufficient condition such that the state estimation MSE is a supermartingale in high SNR regime. Via martingale convergence theorems, we show that the state estimation MSE is almost surely stable, which results in outlier-free MSE sample path. In the noisy plant case, we show that the state estimation MSE is uniformly upper bounded by a Markov chain in high SNR regime. We establish a sufficient condition for the existence of the steady-state distribution of the Markov chain. Via analyzing the stochastic fixed point equation associated with the stationary distribution of the Markov chain, we provide the closed-form expression for the MSE tail probability. We further established a power law that provides closed-form characterizations of the tradeoff between the communication resource and the MSE tail decay rate, where more communication resource consumption leads to faster exponential decaying MSE tail. We also recover the results in the existing literature when applying our developed theory to the scalar systems. [ABSTRACT FROM AUTHOR]

We investigate a remote estimation problem in which a transmitter observes a Markov source and chooses the power level to transmit it over a time-varying packet-drop channel. The channel is modeled as a channel with Markovian state where the packet drop probability depends on the channel state and the transmit power. A receiver observes the channel output and the channel state and estimates the source realization. The receiver also feeds back the channel state and an acknowledgment for successful reception to the transmitter. We consider two models for the source—finite state Markov chains and first-order autoregressive processes. For the first model, using ideas from team theory, we establish the structure of optimal transmission and estimation strategies and identify a dynamic program to determine optimal strategies with that structure. For the second model, we assume that the noise process has unimodal and symmetric distribution. Using ideas from majorization theory, we show that the optimal transmission strategy is symmetric and monotonic and the optimal estimation strategy is like Kalman filter. Consequently, when there are a finite number of power levels, the optimal transmission strategy may be described using thresholds that depend on the channel state. Finally, we propose a simulation-based approach (renewal Monte Carlo) to compute the optimal thresholds and optimal performance and elucidate the algorithm with an example. [ABSTRACT FROM AUTHOR]

As a core part of the electronic warfare (EW) system, de-interleaving is used to separate interleaved radar signals. The de-interleaving algorithm based on the fuzzy adaptive resonance theory (fuzzy ART) is plagued by the problems of premature saturation and performance improving dilemma. This study proposes a dual fuzzy vigilance ART (DFV-ART) algorithm to address these problems and make the following improvements. Firstly, a correction method is introduced to prevent the network from prematurely saturating; then, the fuzzy vigilance models (FVM) are constructed to replace the conventional vigilance parameter, reducing the error probability in the overlapping region; finally, a dual vigilance mechanism is introduced to solve the performance improving dilemma. Simulation results show that the proposed algorithm could improve the clustering accuracy (quantization error dropped 60%) and the de-interleaving performance (clustering quality increased by 10%) while suppressing the excessive proliferation of categories. [ABSTRACT FROM AUTHOR]

A recent study from the University of Mannheim in Germany explores the use of Bayesian t tests in psychological research. Unlike classical test procedures, Bayesian tests can measure statistical evidence in favor of the null hypothesis and allow for optional stopping. However, the study highlights that the probabilities of statistical decision errors are not explicitly controlled in Bayesian tests. To address this issue, the researchers propose a sequential test procedure called the Waldian t test, which combines Bayesian t tests with Abraham Wald's sequential probability ratio test. The Waldian t test satisfies both frequentist and Bayesian desiderata and provides a user-friendly web application for interested researchers. [Extracted from the article]

This paper studies the causes and distribution of reconstruction errors in subtractive dither structure and provides solution to minimize these errors. We present the generation of reconstruction errors in two types of quantization, namely mid-riser and mid-tread, with their corresponding distributions. We then construct a 12-bit pipeline analog to digital converters (ADC) simulation model and use a 16-bit digital to analog converter (DAC) to convert the dither sequence into analog signals. We perform statistical analysis on the resulting data, verify the accuracy of the distribution, and find that reconstruction error only occurs when the dither signal accuracy surpasses that of the ADC. Additionally, we find that mid-tread quantization has the lowest probability of reconstruction error. Finally, we analyze the impact of reconstruction error on the signal to noise ratio (SNR) and spurious free dynamic range (SFDR) at various input frequencies. Our findings show that the reconstruction errors' effect on the SFDR and SNR of the input signal is less than 1 dB. [ABSTRACT FROM AUTHOR]

Researchers from Federal University Sergipe have developed a new framework to study human-AI interaction in the context of the Prisoner's Dilemma game. Using agent-based simulations and genetic algorithms, they found that the best strategies in the game depend on the context and the probability of encountering the same agent again. They identified memory-one strategies that outperformed other well-known strategies in all conditions, highlighting their potential for programming algorithms that interact with humans in trusted contexts. This research provides valuable insights into the evolving field of human-AI interaction. [Extracted from the article]

Contemporary Internet-of-Things (IoT) devices in general and Internet-of-Vehicles (IoV) networks in specific are taking the benefits from using full-duplex (FD) communication with the help of relay nodes to facilitate data transmission. However, self-interference between transmit and receive antennas at the relay node may cause big trouble if self-interference cancellation (SIC) is not applied. In this paper, a rigorous analysis on the performance of FD two-way relay networks (FDTWRNs) for IoV applications under imperfect conditions is provided. Different from earlier studies on the performance of FDTWRNs where one or two imperfect factors were considered, this work simultaneously takes into account many practical issues in the implementation of the IoV networks at the physical layer, including errors in channel state information at all channel links, imperfect SIC and transceiver hardware impairment at all nodes. For relaying protocol, we consider amplify-and-forward with both fixed-gain (FG) and variable-gain (VG) protocols. The exact closed-form expressions (in terms of the system parameters) of key performance factors such as signal-to-interference-plus-noise-distortion-and-error ratio (SINER), outage probability (OP), achievable data rate (ADR), and average symbol error probabilities (ASEP) of the considered system over Rayleigh fading channels and under the combined effect of imperfect CSI, SIC, and transceiver hardware are derived mathematically using probabilistic model analysis. The correctness of the analysis is then verified by Monte Carlo simulations. Drawing upon both analytical and numerical findings, the efficacy of the FDTWRNs under consideration is meticulously and comprehensively assessed in comparison to its performance in ideal conditions characterized by one or multiple critical factors. The disparity between perfect and imperfect scenarios serves as a valuable metric for appraising the practical viability of the considered FDTWRNs when deployed in practical IoV networks. [ABSTRACT FROM AUTHOR]

• The traditional amplitude quantization (TAQ) and the traditional phase quantization (TPQ) are reviewed, and the error probabilities of the two algorithms are deduced theoretically. • The regular vector partition quantization (RVPQ) based on TAQ and TPQ is proposed, and the error probability of RVPQ is deduced. Moreover, we propose the optimal RVPQ. • An irregular VPQ algorithm utilizing K-means clustering is proposed, which is named basic K-means quantization (BKQ). BKQ can not only improve the consistency of quantization results, but also ensure the security of key generation. • Lossy K-means quantization (LKQ) and compensation K-means quantization (CKQ) algorithm are proposed, which are two improved algorithms based on BKQ. • We propose the balance mechanism (BM) for three K-means quantization. The mechanism can make samples fall into each quantization region with equal probability, so as to improve the randomness of generated keys. Most existing key generation schemes cannot achieve the optimal performance because the quantization algorithm cannot make full use of channel information, some schemes use amplitude quantization and others use phase quantization. This paper proposes the concept of vector partitioning quantization (VPQ), and several new quantization algorithms are obtained further, which can utilize amplitude and phase at the same time. First, the traditional amplitude quantization (TAQ) and the traditional phase quantization (TPQ) are reviewed in detail. Second, the regular vector partitioning quantization (RVPQ) is proposed based on TAQ and TPQ, and the error probabilities of RVPQ is deduced, moreover, the concept of optimal RVPQ is presented. Then, three irregular vector partitioning quantization (IVPQ) algorithms are proposed which utilizes K-means clustering, including basic K-means quantization (BKQ), lossy K-means quantization (LKQ) and compensation K-means quantization (CKQ). The three K-means quantization algorithms can not only reduce the quantization error rate greatly, but also ensure the confidentiality of key generation. To overcome the problem of weak uniformity caused by K-means, we put forward the balance mechanism (BM) and apply it to three K-means quantization algorithms. Finally, the simulation results show the VPQ algorithms proposed are superior to previous methods. [ABSTRACT FROM AUTHOR]

The generalized detector (GD) can be implemented at the low signal-to-noise ratio (SNR) in cognitive radio (CR) systems to improve the spectrum sensing performance under correlated antenna array elements. The weighted GD (WGD) and the generalized likelihood ratio test for GD (GLRT-GD) are proposed to be used for coarse spectrum sensing when the noise power is known and unknown, respectively. The GD optimal detection threshold is defined based on the minimum probability of error criterion for various fading channels, namely, the additive white Gaussian noise (AWGN), Nakagami- m , and Rayleigh fading channels. The performance of the proposed algorithms are compared with the spectrum sensing performance of the energy detector (ED), weighted ED (WED), maximum-minimum eigenvalue (MME) detector, generalized likelihood ratio test for ED (GLRT-ED), matched filter (MF), arithmetic to geometric mean (AGM) detector, scaled largest eigenvalue (SLE) detector, moment based detector (MBD), covariance based detector (CBD), and others. The simulation results demonstrate superiority in the spectrum sensing performance of the proposed algorithms in comparison with the above-mentioned detectors. For example, the GLRT-GD achieves the SNR gain equal to 1.2 dB, 4.0 dB, and 4.5 dB in comparison with GLRT-ED, MME, and GM detectors, respectively, at the probability of false alarm P FA = 0.1. The WGD and GLRT-GD implementation allows us to achieve a considerable spectrum sensing performance improvement at small number of samples under the low SNR and the correlated antenna array elements. [ABSTRACT FROM AUTHOR]

Abstract In this paper, the conditional triplet-wise error probability is proposed to improve Gallager's first bounds based on the general framework of parameterized Gallager's first bounding technique (GFBT) of binary linear block codes over additive white Gaussian noise (AWGN) channels, which can alleviate the repeated accumulations caused by the use of the pair-wise error probability. Within the recently proposed bounding framework based on nested Gallager regions, three well-known upper bounds, namely, the sphere bound (SB) of Herzberg and Poltyrev, the tangential bound (TB) of Berlekamp, and the tangential-sphere bound (TSB) of Poltyrev, are visited. Within the proposed bounding framework based on conditional triplet-wise error probability, the three well-known bounds can be improved by exploring more detailed geometrical structure of the code when upper bounding the error probabilities. Numerical results show that the proposed bounding framework is useful since the proposed bounds can even improve the TSB, which is considered as one of the tightest upper bounds. [ABSTRACT FROM AUTHOR]

The accurate quantification of human error probability (HEP) has long been a main pursuit for most human reliability analysis (HRA) methods. This paper proposes a strategy to analyze and predict human error on the basis of a further modification of the existing cognitive reliability and error analysis method (CREAM) in HRA. Through providing a broader definition for the key parameters used in the quantification method, this paper produces a relatively flexible strategy to determine the nominal HEP (HEP0). Basing on this strategy, the actual quantification of HEP in CREAM is able to be applied to more verified conditions. To prove the validity of the method proposed, a case study of spacecraft launch is also introduced, in which the prediction derived from the method is proved consistent with real field data. [ABSTRACT FROM AUTHOR]

This paper presents a low-complexity chase (LCC) decoder for Reed–Solomon (RS) codes, which uses a novel method for the selection of test vectors that is based on the analysis of the symbol error probabilities derived from simulations. Our results show that the same performance as the classical LCC is achieved with a lower number of test vectors. For example, the amount of test vectors is reduced by half and by 1/16 for the RS(255,239) and RS(255,129) codes, respectively. We provide an evidence that the proposed method is suitable for RS codes with different rates and Galois fields. In order to demonstrate that the proposed method results in a reduction of the complexity of the decoder, we also present a hardware architecture for an RS(255,239) decoder that uses 16 test vectors. This decoder achieves a coding gain of 0.56 dB at the frame error rate that is equal to 10−6 compared with hard-decision decoding, which is higher than that of an $\eta =5$ LCC. The implementation results in ASIC show that a throughput of 3.6 Gb/s can be reached in a 90-nm process and 29.1XORs are required. The implementation results in Virtex-7 FPGA devices show that the decoder reaches 2.5 Gb/s and requires 5085 LUTs. [ABSTRACT FROM AUTHOR]

We study the problem of securely estimating the states of an unstable dynamical system subject to nonstochastic disturbances. The estimator obtains all its information through an uncertain channel, which is subject to nonstochastic disturbances as well, and an eavesdropper obtains a disturbed version of the channel inputs through a second uncertain channel. An encoder observes and block encodes the states in such a way that, upon sending the generated codeword, the estimator's error is bounded and a security criterion is satisfied, thereby ensuring that the eavesdropper obtains as little state information as possible. Two security criteria are considered and discussed with the help of a numerical example. A sufficient condition on the uncertain wiretap channel, i.e., the pair formed by the uncertain channel from the encoder to the estimator and the uncertain channel from the encoder to the eavesdropper is derived, which ensures that a bounded estimation error and security are achieved. This condition is also shown to be necessary for a subclass of uncertain wiretap channels. To formulate the condition, the zero-error secrecy capacity of uncertain wiretap channels is introduced, i.e., the maximal rate at which data can be transmitted from the encoder to the estimator in such a way that the eavesdropper is unable to reconstruct the transmitted data. Finally, the zero-error secrecy capacity of uncertain wiretap channels is studied. [ABSTRACT FROM AUTHOR]

In this paper, we develop an orthogonal time frequency space (OTFS) modulation based integrated (radar) sensing and communication (ISAC) technique, namely OTFS-ISAC, for the high mobility multiuser system. In particular, at the transmitter, it can alternate the modes between sensing and communication using time division multiplexing so that it takes advantage of radar assistance to design the OTFS frame structure for reducing the overhead of guard symbols. At the receiver, based on the embedded pilot-aided channel estimation, it employs zero-forcing (ZF) equalization. For performance analysis, we derive the error probability of channel estimation based on radar sensing, and find the outage probability of downlink communication. Then, we introduce an adaptive power allocation scheme at the transmitter to optimize the outage probability of the communication link. Our simulations show that, by properly selecting N , M , and the pilot symbol strength, the proposed OTFS-ISAC scheme can reduce the overhead of guard symbols while achieving promising reliability performance of the downlink transmission with relative low channel estimation error. [ABSTRACT FROM AUTHOR]

A recent research study conducted by the New Jersey Institute of Technology explores the decision-making processes of cells in response to signals. The study focuses on a signaling system with two outputs, specifically the tumor necrosis factor (TNF) and two transcription factors, the nuclear factor kB (NFkB) and the activating transcription factor-2 (ATF-2). By analyzing the concentration levels of these molecules, the researchers were able to compute decision thresholds and error probabilities, shedding light on cellular decision-making and its potential role in pathological conditions. This study provides valuable insights into the complex biology of cells and their responses to signals. [Extracted from the article]

Researchers from National Taiwan University have developed an algorithm called YOLOv8 that can detect fractures in pediatric wrist trauma X-ray images. The algorithm uses data augmentation to improve its performance and has achieved a state-of-the-art mean average precision (mAP 50) of 0.638, which is higher than previous versions of the algorithm. The researchers have also designed an application called "Fracture Detection Using YOLOv8 App" to assist surgeons in diagnosing fractures and reducing the probability of error analysis. This research provides a valuable tool for surgeons in the field of pediatric wrist trauma. [Extracted from the article]