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Purpose: To compare occlusal schemes with condylar inclination and anterior guidance in dentate individuals. Materials and Methods: A total of 26 dentate patients of Indian origin between the ages of 18 and 30 with canine-guided occlusion and 26 patients with group function occlusion were included in the study. The School of Articulator Munich (SAM) Axioquick system was used to assess the condylar guidance and eccentric tracings of the patients. For analyses, Student t test was used. For quantitative data, the means and SDs were calculated. For statistical analyses, the probability of type-I error of .05 was considered statistically significant. Results: The mean condylar guidance for canine-guided and group function occlusion was 38.4 ± 12.7 degrees and 30.5 ± 12.5 degrees on the right side, respectively. On the left side it was 36.5 ± 13.0 degrees and 27.5 ± 12.0 degrees for canine-guided and group function occlusion, respectively, with a statistically significant difference (P = .01). The condylar guidance, incisal guidance, Bennett angle, protrusion, left lateral and right lateral movement, and left and right lateral angles were analyzed statistically between the two types of occlusions. The results showed a statistically significant difference between the two groups for all parameters except right condylar guidance. Furthermore, all parameters were higher in the canine-guided occlusion group over group function occlusion. Conclusions: Within the study limitations, it was concluded that the condylar guidance was steeper in canine-guided occlusion than in group function occlusion. The eccentric parameters were steeper in canine-guided occlusion than in group function occlusion. This study showcases that the condylar guidance might not play a major role in determining the occlusal scheme but that the incisal guidance determines the occlusal scheme predominantly in Class 1 occlusion patients. [ABSTRACT FROM AUTHOR]

Building Information Modelling (BIM) has emerged as a transformative tool in the Architecture, Engineering, and Construction (AEC) industries, offering significant potential to improve project efficiency and outcomes. This study explores the impact of BIM implementation on project time and cost by analyzing critical factors such as design errors, unbudgeted changes, Requests for Information (RFIs), labour dynamics, and scheduling across multiple case studies. Quantitative results reveal that BIM adoption reduces project timelines by an average of 20% and costs by 15%, while also decreasing design errors by 30% and RFIs by 25%. Furthermore, BIM enhances collaboration among stakeholders, improves design visualization, and facilitates better risk assessment, leading to more informed decision-making throughout the project lifecycle. These findings are synthesized into a decision-making framework that estimates the probability of errors, evaluates their potential time and cost implications, and ensures alignment with project budgets. The framework serves as a strategic guide for project teams to assess the suitability of BIM for specific projects, thereby optimizing decision-making processes and improving overall project performance. Additionally, the study examines the role of BIM in sustainability by reducing material waste and improving resource allocation. This study addresses a critical gap in the field by systematically evaluating the interrelationships among BIM's impacts on key project parameters, which have often been treated in isolation in prior research. The importance of this work lies in its provision of a structured methodology to harness BIM's capabilities, demonstrating its value in delivering significant time and cost efficiencies while enhancing project quality. By integrating empirical analysis with practical applications, this research contributes to the growing body of knowledge on BIM adoption and provides actionable insights for AEC professionals seeking to optimize project outcomes. [ABSTRACT FROM AUTHOR]

To tackle the difficulties of the point prediction in quantifying the reliability of landslide displacement prediction, a data-driven combination-interval prediction method (CIPM) based on copula and variational-mode-decomposition associated with kernel-based-extreme-learning-machine optimized by the whale optimization algorithm (VMD-WOA-KELM) is proposed in this paper. Firstly, the displacement is decomposed by VMD to three IMF components and a residual component of different fluctuation characteristics. The key impact factors of each IMF component are selected according to Copula model, and the corresponding WOA-KELM is established to conduct point prediction. Subsequently, the parametric method (PM) and non-parametric method (NPM) are used to estimate the prediction error probability density distribution (PDF) of each component, whose prediction interval (PI) under the 95% confidence level is also obtained. By means of the differential evolution algorithm (DE), a weighted combination model based on the PIs is built to construct the combination-interval (CI). Finally, the CIs of each component are added to generate the total PI. A comparative case study shows that the CIPM performs better in constructing landslide displacement PI with high performance. [ABSTRACT FROM AUTHOR]

Numerous traffic crashes occur at intersections in urban areas. Understanding the mechanism of driving errors at intersections and providing effective solutions will significantly reduce traffic injuries. The objective of this study is to examine the age and hazard level's effects on driver left-turn errors at urban intersections. In order to achieve this goal, this study customized the cognitive reliability and error analysis method (CREAM) for left-turn driving task at intersections, and driver error profiles were investigated using a driving simulation experiment as a case study. The results showed that age did not significantly affect drivers' left turn error probability, while the error probability of both groups of drivers increased with the hazard level. Drivers were most likely to have inadequate monitoring and misjudgment in left-turn maneuvers, more so in the older group. Young drivers were more likely to make planning, decision-making, and adjustment errors compared to older drivers. In addition, observation errors were the most likely cognitive (74.5%). This study achieves the common and characteristic performance of human errors under the three hazardous intersections for the two age groups, and provides a basis for the design of effective training modules and driver assistance systems. [ABSTRACT FROM AUTHOR]

Point-of-care testing devices to measure haemoglobin (Hgb) frequently inform transfusion decision-making in surgery. This study aimed to examine their accuracy in surgery, focusing on Hgb concentrations of 60–100 g L−1, a range with higher potential for transfusion. This was a prospective diagnostic cohort study focused on method comparison, conducted at two academic hospitals. Consecutive patients undergoing noncardiac surgery and requiring point-of-care Hgb measurements were eligible. Hgb concentrations from arterial and central venous blood samples were measured concurrently using three devices and compared with laboratory Hgb. The primary outcome was individual pairwise comparisons between point-of-care and laboratory Hgb values; agreement was determined based on a threshold of within 4 g L−1. The primary analysis consisted of computing limits of agreement. A total of 1735 intraoperative blood samples were collected (1139 participants); 680 samples had a laboratory Hgb <100 g L−1. The limits of agreement among those with Hgb <100 g L−1 were –9.5 to 8.0 g L−1 for HemoCue®, –16.2 to 11.5 g L−1 for i-STAT®, and –14.7 to 40.5 g L−1 for Rad-67®. HemoCue was associated with a 5.8% incidence of potentially clinically significant transfusion error, whereas i-STAT and Rad-67 were associated with 25.3% and 28.2%, respectively. HemoCue yielded Hgb measurements within 10 g L−1 in 98% of intraoperative blood samples. No point-of-care Hgb device demonstrated limits of agreement that were smaller than the agreement difference of 4 g L−1. Despite this, HemoCue can be safely used to inform transfusion decisions in surgery, given its error probability of <4% in transfusion scenarios. [ABSTRACT FROM AUTHOR]

Generalized pairwise comparisons is a statistical method that allows comparing two groups of subjects based on a set of hierarchically ordered outcomes. It provides a general measure of treatment effect called the net treatment benefit. The method offers a "natural" way of handling missing data: whenever a comparison of two subjects is not possible for a higher priority outcome due to missingness, the comparison is made using the next outcome in the hierarchy. We have investigated the impact of this naïve way of dealing with missing data on the type-I error probability and power of the test. It appears that the naïve net treatment benefit estimator is biased even under missingness completely at random and fails to guarantee the type-I error probability control at the nominal level. Applying inverse probability weighting reduces the bias but does not provide adequate type-I error probability control. Multiple imputation removes the bias and establishes the control of the type-I error probability. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hybrid decode-and-forward and soft information relaying (HDFSIR) strategy to mitigate error propagation in coded cooperative communications. In the HDFSIR approach, the relay operates in decode-and-forward (DF) mode when it successfully decodes the received message; otherwise, it switches to soft information relaying (SIR) mode. The benefits of the DF and SIR forwarding strategies are combined to achieve better performance than deploying the DF or SIR strategy alone. Closed-form expressions for the outage probability and symbol error rate (SER) are derived for coded cooperative communication with HDFSIR and energy-harvesting relays. Additionally, we introduce a novel normalized log-likelihood-ratio based soft estimation symbol (NL-SES) mapping technique, which enhances soft symbol accuracy for higher-order modulation, and propose a model characterizing the relationship between the estimated complex soft symbol and the actual high-order modulated symbol. Furthermore, the hybrid DF-SIR strategy is extended to a distributed Alamouti space-time-coded cooperative network. To evaluate the~performance of the proposed HDFSIR strategy, we implement extensive Monte Carlo simulations under varying channel conditions. Results demonstrate significant improvements with the hybrid technique outperforming individual DF and SIR strategies in both conventional and distributed Alamouti space-time coded cooperative networks. Moreover, at a SER of 10 − 3 , the proposed NL-SES mapping demonstrated a 3.5 dB performance gain over the conventional averaging one, highlighting its superior accuracy in estimating soft symbols for quadrature phase-shift keying modulation. [ABSTRACT FROM AUTHOR]

Summary: Perfect detection of unused/underutilized frequency band of the primary user (PU) in the cognitive radio communication systems is an essential component for its efficient utilization by the secondary/cognitive user (CU) without any interference. In the cooperative spectrum sensing scenario, the presence or absence of PU is determined by analyzing the decisions of each CU engaged in the collaboration. In this paper, we have emphasized on the significance of spectrum sensing duration in the optimal threshold selection approach in the cooperative spectrum sensing scenarios for a range of the received PU signal signal‐to‐noise ratio (SNR) from low to high values. The sensing duration parameter is taken as ts = 2.5 ms in the higher SNR environment, whereas in the lower SNR environment when the optimal threshold condition fails, ts is kept dynamic to satisfy minimum target values of sensing performance parameters. We have computed the spectrum sensing performance parameters and throughput using the same approach and compared the results with those obtained from the constant false‐alarm rate (CFAR), constant detection rate (CDR), and minimized error probability (MEP) threshold selection approaches. The spectrum sensing performance computed by the proposed approach is significantly improved as compared with the other reported approaches. At SNR = −24 dB, global probability of false alarm Qf = 0.002 and global probability of detection Qd = 0.999 are achieved. However, the throughput is relatively less for low SNR values. [ABSTRACT FROM AUTHOR]

The main obstacle to large scale quantum computing are the errors present in every physical qubit realization. Correcting these errors requires a large number of additional qubits. Two main avenues to reduce this overhead are (i) low-density parity check (LDPC) codes requiring very few additional qubits to correct errors (ii) cat qubits where bit-flip errors are exponentially suppressed by design. In this work, we combine both approaches to obtain an extremely low overhead architecture. Assuming a physical phase-flip error probability ϵ ≈ 0.1% per qubit and operation, one hundred logical qubits can be implemented on a 758 cat qubit chip, with a total logical error probability per cycle and per logical qubit ϵL ≤ 10−8. Our architecture also features two major advantages. First, the hardware implementation of the code can be realised with short-range qubit interactions in 2D and low-weight stabilizers, under constraints similar to those of the popular surface code architecture. Second, we demonstrate how to implement a fault-tolerant universal set of logical gates with an additional layer of routing cat qubits stacked on top of the LDPC layer, while maintaining the local connectivity. Furthermore, our architecture benefits from a high capacity of parallelization for these logical gates. Implementing full quantum error correction incurs a significant hardware overhead. Here, the authors propose a quantum computing architecture combining superconducting cat qubits with 2D local LDPC codes, featuring low overhead and ease of implementation. [ABSTRACT FROM AUTHOR]

We introduce a general framework for regression in the errors-in-variables regime, allowing for full flexibility about the dimensionality of the data, observational error probability density types, the (nonlinear) model type and the avoidance of ad-hoc definitions of loss functions. In this framework, we introduce model fitting for partially unpaired data, i.e., for given data groups the pairing information of input and output is lost (semi-supervised). This is achieved by constructing mixture model densities, which directly model the loss of pairing information allowing inference. In a numerical simulation study linear and nonlinear model fits are illustrated as well as a real data study is presented based on life expectancy data from the world bank utilizing a multiple linear regression model. These results show that high quality model fitting is possible with partially unpaired data, which opens the possibility for new applications with unfortunate or deliberate loss of pairing information in data. [ABSTRACT FROM AUTHOR]

The explosion of Internet-of-Thing enables several interconnected devices but also gives rise chance for unauthorized parties to compromise sensitive information through wireless communication systems. Covert communication therefore has emerged as a potential candidate for ensuring data privacy in conjunction with physical layer transmission to render two lines of defense. In this paper, we aim to enhance the individual transmission of nearby users in non-orthogonal multiple access (NOMA) systems under scenarios of an eavesdropper who monitors covert transmission before decoding covert information. For this problem, we first provide a comprehensive analysis of the NOMA system in terms of outage probability (OP), secrecy outage probability (SOP), and detection error probability (DEP), where all of them are quantified in exact and asymptotic closed-form expressions. Besides, we have also derived closed-form formulas for users' covert and public rates. Under the system requirements of the maximal OP and SOP and the minimal DEP, we formulate the optimization of resource power allocation to: 1) minimize the OP of covert communication and 2) maximize the covert rate. Thanks to the developed analytical expressions, we obtain closed-form expressions for the sub-optimal power allocation coefficient for each problem. Simulation results validate the efficacy of the analytical mathematical frameworks and reveal that the proposed approaches of power allocation can provide attractive performance improvement compared to fixed power allocations only. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multiple-input multiple-output receiver with deep learning. To obtain the high performance of the maximum likelihood receiver while maintaining the low computational complexity of the linear receiver, we apply a signal-to-noise ratio-based receiver-type decision method. Specifically, the criterion and threshold, which are based on the ratio between the symbol error probabilities of both receivers, to decide the receiver type are presented. For this, we analyze the signal-to-noise ratio gain of the zero-forcing receiver and exploit it as an input feature of the DNN. The simulation results demonstrate that the proposed receiver achieves a symbol error rate performance nearly identical to that of the maximum likelihood receiver with low computational complexity. [ABSTRACT FROM AUTHOR]

The spectrum sensing is a major significant task in cognitive radio networks (CRNs) to avoid the unacceptable interference to primary users (PUs). Here, the threshold value determines the effectiveness of spectrum sensing and regarded as a sensing system. The fixed threshold used by the current energy detection‐based spectrum sensing (SS) techniques does not provide sufficient safety for the main users. The threshold is determined by lowering the complete probability of decision error in addition to these guidelines. Therefore, an energy detection using nonparametric amplitude quantization optimized with arithmetic optimization algorithm for enhanced spectrum sensing in CRNs (ED‐NAQ‐AOA‐SS CRN) is proposed in this paper to acquire the ideal threshold for decreasing the total error probability. The proposed method achieves greater probability of detection of 99.67%, 98.38%, 92.34%, and 97.45%, lower settling time of 98.33%, 89.34%, 83.12%, and 88.96%, and lower error rate of 93.15%, 91.25%, 79.90%, and 92.88% compared with existing techniques, like intelligent spectrum sharing and sensing in CRN with adaptive rider optimization algorithm (AROA), a novel technique for spectrum sensing in CRN utilizing fractional gray wolf optimization with the cuckoo search optimization (GWOCS), and adaptive neuro‐fuzzy inference scheme depending on cooperative spectrum sensing optimization in CRNs (ANFIS). [ABSTRACT FROM AUTHOR]

Accurate estimation of three-dimensional (3D) information from captured images is essential in numerous computer vision applications. Although binocular stereo vision has been extensively investigated for this task, its reliability is conditioned by the baseline between cameras. A larger baseline improves the resolution of disparity estimation but increases the probability of matching errors. This research presents a reliable method for disparity estimation through progressive baseline increases in multiocular vision. First, a robust rectification method for multiocular images is introduced, satisfying epipolar constraints and minimizing induced distortion. This method can improve rectification error by 25% for binocular images and 80% for multiocular images compared to well-known existing methods. Next, a dense disparity map is estimated by stereo matching from the rectified images with the shortest baseline. Afterwards, the disparity map for the subsequent images with an extended baseline is estimated within a short optimized interval, minimizing the probability of matching errors and further error propagation. This process is iterated until the disparity map for the images with the longest baseline is obtained. The proposed method increases disparity estimation accuracy by 20% for multiocular images compared to a similar existing method. The proposed approach enables accurate scene characterization and spatial point computation from disparity maps with improved resolution. The effectiveness of the proposed method is verified through exhaustive evaluations using well-known multiocular image datasets and physical scenes, achieving superior performance over similar existing methods in terms of objective measures. [ABSTRACT FROM AUTHOR]

In the present occupied world, time is an indispensable issue which cannot be overseen by seeing every single wonder with our tight schedule. So, now daily's automatic projects are being favored over manual systematic works to make life easier and simpler in all angles. To make it a great achievement Internet of Things is the most recent web innovation created. IoT has major role in creating many advancements in today's world. In many of the educational institute or business service center, it is essential to maintain a proper attendance records. This research focuses on replacing the manual attendance recording system into an automated one and also maintaining the attendance records, i.e., percentage of attendance with higher accuracy. The proposed methodology in this undertaking is the improvement of internet of things-based RFID attendance system. Currently, RFID technology has been widely used in various fields such as agriculture, transportation, sales and in many other sectors. Apart from these sectors, this technology can also be utilized for automating attendance taking system in educational sectors too. The normal method of taking attendance manually is usually a time consuming one and also there is a probability of error. This manual method may also lead to misconducting and also there is wastage of valuable time. In order to overcome this problem, the present manual attendance taking system can be replaced by a smart attendance system by implementing RFID technology. This research pretends much useful for the staff members and making the attendance taking process in an easiest and efficient way. [ABSTRACT FROM AUTHOR]

Multimedia applications, such as image processing including image and video transfer, heavily rely on reduction. The traditional device methods to picture reduction use more space, energy, and processing time. The majority of current efforts use the Golomb‐Rice encoding, due to its larger memory requirement and higher computing difficulty. So, this research concentrated on hardware design‐oriented probability run length (PRL) coding technique based on lossless colour image compression. The block truncation coding (BTC) features of the compression process are used by the suggested PRL method. The proposed image compression hardware consists of various modules such as a Parameter calculator, fuzzy table, bitmap generator, BTC parameters training, prediction, and error control, and PRL‐based finite state machine (PRL‐FSM). The proposed image compressor utilizes the parameter calculator block, which estimates the block type based on the image pixel intensities for each sub‐block. Thus, each block of the image is compressed by using a new block type and generates a variable block size. The proposed method utilizes the PRL‐BTC encoding method, which also calculates the probability of error between the compressed image to the test image. The process is iterated until the performance trade‐off between hardware cost and compression ratio (CR) is achieved. Hence, both smooth regions and non‐smooth regions of images are perfectly compressed by the probability‐based block selection. The simulation results show that the proposed method resulted in a better area, power, delay metrics, peak signal‐to‐noise ratio (PSNR), and CR compared to the state of art approaches. [ABSTRACT FROM AUTHOR]

We consider the problem of approximating a function in a general nonlinear subset of L 2 , when only a weighted Monte Carlo estimate of the L 2 -norm is accessible. The concept of sample complexity, i.e. the number of sample points necessary to achieve a prescribed error with high probability, is of particular interest in this setting. Reasonable worst-case bounds for this quantity exist only for particular model classes, like linear spaces or sets of sparse vectors. However, the existing bounds are very pessimistic for more general sets, like tensor networks or neural networks. Restricting the model class to a neighborhood of the best approximation allows us to derive improved worst-case bounds for the sample complexity. When the considered neighborhood is a manifold with positive local reach, its sample complexity can be estimated through the sample complexities of the tangent and normal spaces and the manifold's curvature. [ABSTRACT FROM AUTHOR]

Blockchain's decentralized, transparent, and immutable nature has revolutionized digital transactions by removing the need for central authorities. Ethereum stands out among blockchain platforms for facilitating secure peer-to-peer transactions via smart contracts. Despite its transformative potential, blockchain faces challenges, particularly with the PoW consensus algorithm, which demands high energy consumption and raises centralization concerns. This affects the scalability of Blockchain by reducing the throughput. This paper explores machine learning (ML) integration to address these challenges, specifically focusing on optimizing miner selection in the Ethereum blockchain based on predicted transaction times. The study compares the performance of various machine learning models, including ElasticNet, Lasso Regression, Multilayer Perceptron (MLP) Regression in optimizing miner selection for reduced transaction times on the Ethereum blockchain. This study advances the ongoing research on integrating machine learning with blockchain to address the shortcomings of traditional Proof of Work (PoW) systems. It emphasizes the potential of machine learning to propel future innovations in blockchain technology. [ABSTRACT FROM AUTHOR]

Relay protection devices must operate continuously throughout the year without anomalies. With the integration of advanced technology and process chips in secondary equipment, new risks need to be addressed to ensure the reliability of these relay protection devices. One such risk is the impact of α-particles inducing single event effects (SEEs) on the secondary equipment. To date, there has been limited assessment of the effects of α-particles on relay protection devices from a system perspective. This study evaluates the impact of SEE on relay protection devices through a Monte Carlo simulation, which is verified by α-particle radiation, fault injection, and fault tree analysis. It discusses the influence of SEEs with and without hardening measures in place. Additionally, this study examines the soft error probability when the target processor runs both general workloads and specific application workloads. The current research proposes a low-cost and effective reliability assessment method for secondary equipment considering single event effects. The findings provide new insights for the enhancement of future electric power grid systems. [ABSTRACT FROM AUTHOR]

In this paper we carry out an in-depth study on the average decoding error probability of the random parity-check matrix ensemble over the erasure channel under three decoding principles, namely unambiguous decoding, maximum likelihood decoding and list decoding. We obtain explicit formulas for the average decoding error probabilities of the random parity-check matrix ensemble under these three decoding principles and compute the error exponents. Moreover, for unambiguous decoding, we compute the variance of the decoding error probability of the random parity-check matrix ensemble and the error exponent of the variance, which implies a strong concentration result, that is, roughly speaking, the ratio of the decoding error probability of a random linear code in the ensemble and the average decoding error probability of the ensemble converges to 1 with high probability when the code length goes to infinity. [ABSTRACT FROM AUTHOR]

Objective: This study aims to investigate the impact of stimulus environments (Virtual Theatre vs Monitor) and error probabilities (20 % vs 50 %) on attentional states, Error Potentials (ErrP), and machine learning classification performance. Approach: EEG signals were recorded using different protocols, and features were extracted for subsequent analysis from single-trial response and attention level was computed from the second preceding error processing stimulation. Results: The results indicate significant differences across conditions: the Monitor environment consistently elicited higher and faster ErrP responses and elevated attentional states compared to Virtual Theatre. Additionally, classification performance in the Monitor environment outperformed Virtual Theatre consistently. Further analysis revealed that the 20 % error probability protocol yielded increased ErrP responses, heightened attentional states, and superior classification performance compared to the 50 % protocol. Classification performance under the 20 % error probability condition consistently exceeded 75 % validation and test sets. Moreover, a significant correlation between attention-related features and ErrP characteristics was observed, highlighting the intricate relationship between error processing and attentional engagement. Relevance: These findings underscore the importance of considering stimulus environments and error probabilities in cognitive neuroscience research and machine learning applications. Understanding these factors can inform experimental design and model development, ultimately advancing our comprehension of cognitive processes and enhancing real-world applications of machine learning algorithms. [ABSTRACT FROM AUTHOR]

A major focus of the simulation literature is the study of optimal budget allocation. The goal is to divide a simulation budget between alternatives with unknown values in a manner that leads to efficient identification of the best alternative. Existing analytical techniques, based on large deviations theory, are limited to finite sets of alternatives, each of which is assigned a certain proportion of the budget. In "A New Rate-Optimal Sampling Allocation for Linear Belief Models," Zhou and Ryzhov develop the first provably optimal budget allocation for a continuous problem where linear regression is used to model the value of a choice. The allocation is expressible in closed form and is simpler and easier to implement than analogous solutions for the discrete setting. This work bridges the emerging literature on contextual (regression-based) learning and the well-known statistical problem of optimal experimental design. We derive a new optimal sampling budget allocation for belief models based on linear regression with continuous covariates, where the expected response is interpreted as the value of the covariate vector, and an "error" occurs if a lower-valued vector is falsely identified as being better than a higher-valued one. Our allocation optimizes the rate at which the probability of error converges to zero using a large deviations theoretic characterization. This is the first large deviations-based optimal allocation for continuous decision spaces, and it turns out to be considerably simpler and easier to implement than allocations that use discretization. We give a practicable sequential implementation and illustrate its empirical potential. Funding: This work was supported by the National Science Foundation [Grant CMMI-2112828]. [ABSTRACT FROM AUTHOR]

In order to mitigate DFE error propagation to reduce bit error rate (BER), a 25Gb/s Non-Return-To-Zero (NRZ) transceiver including a five-tap decision feedback equalizer (DFE) combined with a dedicated feed forward equalizer (FFE) was first constructed and was found to be helpful in improving signal integrity and reduce errors in high-speed data transmission, which can lead to more reliable and efficient communication. In this transceiver, the effect of multi-tap FFE on error probability with different burst error run lengths was evaluated through a cumulative-probability distribution model. Then, forward error correction (FEC) with different correction capabilities can be also employed to correct burst errors for a better BER performance. Probability-based BER with single/double burst error correcting FEC was derived and a joint optimization of FFE and FEC can be further explored to achieve the target BER. Results show that compared with two-tap FFE, FFE with a post-cursor tap can obtain a BER improvement of 1.5dB at the BER of 10−7, and also enlarge the horizontal opening degree of DFE, which is 0.74 unit interval (UI). Double burst error correcting FEC obtaining 2.2dB coding gain at the BER of 10−7 outperforms single one. When the FEC error correcting capability is 16, the combination of double burst error correcting FEC and three-tap FFE can reduce BER from 10−9 to approximately 10−12. FEC with a larger error correction capability is more likely to achieve the target BER and reduce the complexity of the combination equalizer while ensuring a better equalization performance. The proposed NRZ transceiver, as a part of the physical interface, can be applied to high-speed interconnection scenarios, such as Ethernet, PCIe, and Chiplet. [ABSTRACT FROM AUTHOR]

We consider the problem of signal detection in Gaussian white noise. Test statistics are linear combinations of squares of Fourier coefficient estimators or 핃2-norms of kernel estimators. We point out necessary and sufficient conditions when nonparametric sets of alternatives have a given rate of exponential decay for type II error probabilities. [ABSTRACT FROM AUTHOR]

Humans navigate diverse social relationships and concurrently interact across multiple social contexts. An individual's behavior in one context can influence behavior in other contexts. Different payoffs associated with interactions in the different domains have motivated recent studies of the evolution of cooperation through the analysis of multichannel games where each individual is simultaneously engaged in multiple repeated games. However, previous investigations have ignored the potential role of network structure in each domain and the effect of playing against distinct interacting partners in different domains. Multiplex networks provide a useful framework to represent social interactions between the same set of agents across different social contexts. We investigate the role of multiplex network structure and strategy linking in multichannel games on the spread of cooperative behavior in all layers of the multiplex. We find that multiplex structure along with strategy linking enhances the cooperation rate in all layers of the multiplex compared to a well-mixed population in Prisoners' Dilemma games, provided the network structure is identical across layers. The effectiveness of strategy linking in enhancing cooperation depends on the degree of similarity of the network structure across the layers and perception errors due to imperfect memory. Higher cooperation rates are achieved when the degree of structural overlap of the different layers is sufficiently large, and the probability of perception error is relatively low. Our work reveals how the social network structure in different layers of a multiplex can affect the spread of cooperation by limiting the ability of individuals to link strategies across different social domains. Author summary: People hold simultaneous membership in different social networks where they are engaged in interactions with neighbors, some of whom may be common across all networks while others may be unique to a specific social network. The distinct nature of interactions in different social networks can be modeled as games with distinct benefits. How does the nature of the interactions in different social networks as well as the underlying structure of each network affect cooperation levels in the population? Can linking behaviors across different social networks aid in enhancing cooperation levels across all such networks? We use the framework of repeated games on multiplex network-structured populations to address these questions. We find that linking behaviors can be favorable for increasing cooperation levels in all social networks if there is significant structural overlap between the different networks and perception errors in recalling past actions of interacting neighbors, are low. [ABSTRACT FROM AUTHOR]

This paper explores selection diversity in the context of MIMO 3-user interference channels with interference alignment (IA), focusing on enhancing reliability through diversity order (DO) analysis. While degrees of freedom (DoF) have traditionally been emphasized for throughput optimization in IA, limited research has addressed diversity order to improve error performance. In this work, we define a conditional DO and propose a beamforming vector selection method that achieves a conditional DO of M 2 / 2 , where M is the number of antennas per transceiver. The proposed scheme employs a two-stage decoding approach, combining zero-forcing for interference cancellation with maximum likelihood (ML) decoding for desired signal recovery, which is augmented by orthogonalization techniques. Simulations demonstrate the superiority of the proposed scheme in both error probability and conditional DO values compared to conventional IA methods, particularly in scenarios with higher antenna counts. These results provide insights into optimizing IA for enhanced reliability and form the foundation for future exploration of advanced decoding and beamforming strategies. [ABSTRACT FROM AUTHOR]

This work studies rollout design problems with a focus of suitable choices of rollout rate under the standard Type I and Type II error probabilities control framework. The main challenge of rollout design is that data is often observed in a lump-sum manner from a spatio-temporal point of view: (1) temporally, only the sum of data in a given sliding time window can be observed; (2) spatially, there are two subgroups for the data at each time step: control and treatment, but one can only observe the total values instead of individual values from each subgroup. We develop rollout tests of lump-sum data under both fixed-sample-size and sequential settings, subject to the constraints on Type I and Type II error probabilities. Numerical studies are conducted to validate our theoretical results. [ABSTRACT FROM AUTHOR]

This paper addressed the trade‐off between timeliness and reliability in joint communication and over‐the‐air computation offloading (JCACO) system under short‐packet communications (SPCs). The inevitable decoding errors introduced by SPC lead to errors in the data aggregation process of over‐the‐air computation (AirComp). Due to limited resources, pursuing high reliability may prevent the JCACO system from meeting delay requirements, resulting in a trade‐off between reliability and timeliness. To address this issue, this paper investigates the timeliness and reliability of the JCACO system. Specifically, the moment generating function method is used to derive the delay outage probability (DOP) of the JCACO system, and the outage probability of AirComp is calculated based on the errors that occur during its data aggregation process. The paper established an asymptotic relationship between blocklength, DOP, and AirComp outage probability (AOP). To balance timeliness and reliability, an AOP minimization problem is formulated under constraints of delay, queue stability, and limited resources based on computation offloading strategies and beamformer design. To overcome the issues of slow convergence and susceptibility to local optima in traditional algorithms, this paper proposed a stochastic successive mean‐field game (SS‐MFG) algorithm. This algorithm utilizes stochastic continuous convex approximation methods to leverage Nash equilibria among different users, achieving faster convergence to the global optimal solution. Numerical results indicate that SS‐MFG reduces AOP by up to 60%, offering up to a 20% improvement in optimization performance compared to other algorithms while also converging faster. [ABSTRACT FROM AUTHOR]

Nutritional status is a measure of the condition of a person's body, which can be seen from the food consumed and the use of substances in the body. The purpose of this study is to determine the relationship between nutritional status and intellectual intelligence in elementary school children in grades 4-5 at SDN 219 Inpres Pannambungan. In this study, the research design was observational, with a cross-sectional approach using total sampling. This study uses an intellectual intelligence test tool, namely the CFIT (Culture Fair Intelligence Test. Data analysis was carried out using the SPSS application by conducting univariate analysis related to respondent characteristics, independent variables, and dependent variables, then continued by conducting bivariate analysis, namely the Kolmogorov-Smirnov normality test and the Spearman correlation test. The results of this study showed that the correlation value (-2.72) with the probability value or error level (p=0.035) was smaller than the significant standard value (a=0.05). This study concludes that there is a significant relationship between nutritional status and the intellectual intelligence of children of SDN 219 Pannambungan Presidential Instruction Maros Regency. [ABSTRACT FROM AUTHOR]

The isotropic Cauchy distribution is a member of the central α -stable family that plays a role in the set of heavy-tailed distributions similar to that of the Gaussian density among finite second-moment laws. Given a sequence of n observations, we are interested in characterizing the performance of Likelihood Ratio Tests, where two hypotheses are plausible for the observed quantities: either isotropic Cauchy or isotropic Gaussian. Under various setups, we show that the probability of error of such detectors is not always exponentially decaying with n, with the leading term in the exponent shown to be logarithmic instead, and we determine the constants in that leading term. Perhaps surprisingly, the optimal Bayesian probabilities of error are found to exhibit different asymptotic behaviors. [ABSTRACT FROM AUTHOR]

We consider a two‐dimensional time‐inhomogeneous birth‐death process to model the time‐evolution of fake news in a population. The two components of the process represent, respectively: (i) the number of individuals (say spreaders) who know the rumor and intend to spread it and (ii) the number of individuals (say inactives) who have forgotten the rumor previously received. We employ the probability generating function‐based approach to obtain the moments and the covariance of the two‐dimensional process. We also analyze a new adimensional index to study the correlation between the two components. Some special cases are considered in which both the expected numbers of spreaders and inactives are equal to suitable sigmoidal curves, which are often adopted in modeling growth phenomena. Finally, we provide an application based on real data related to the diffusion of fake news, in which the optimal choice of the sigmoidal curve that fit the datasets is based on the minimization of the mean square error and of the relative absolute error. [ABSTRACT FROM AUTHOR]

A novel structural reliability analysis method that combines the improved beluga whale optimization (IBWO) and the arctangent function‐based maximum entropy method (AMEM) is proposed in this paper. It aims to augment the accuracy of failure probability prediction in structural reliability analysis based on the traditional maximum entropy method (MEM). First, the arctangent function is introduced to avoid the effects of truncation error and numerical overflow in the traditional MEM. The arctangent function can nonlinearly transform the structural performance function defined on the infinite interval into a transformed performance function defined on the bounded interval. Subsequently, the undetermined Lagrange multipliers in the maximum entropy probability density function (MEPDF) of the transformed performance function are obtained using IBWO at a swifter convergence speed with heightened convergence accuracy. Finally, the MEPDF of the transformed performance function can be obtained by combining the IBWO and AMEM, and the structural failure probability can be predicted. The analysis of the metro bogie frame as an engineering example reveals that compared with the traditional MEM using the genetic algorithm to solve the Lagrange multipliers, the proposed method diminishes the relative error in failure probability prediction from 20.51% to only 0.09%. This method significantly enhances the prediction accuracy of failure probability. [ABSTRACT FROM AUTHOR]

This commentary seeks to improve the design and analysis of trials undertaken to obtain approval of drugs for treatment of rare diseases. Methodological analysis reveals that use of hypothesis testing in the Food and Drug Administration drug approval process is harmful. Conventional asymmetric error probabilities bias the approval process against approval of new drugs. Hypothesis testing is inattentive to the relative magnitudes of losses to patient welfare when types 1 and 2 errors occur. Requiring the sample size to be large enough to guarantee the specified statistical power particularly inhibits the development of new drugs for treating rare diseases. Rarity of a disease makes it difficult to enroll the number of trial subjects needed to meet the statistical power standards for drug approval. Use of statistical decision theory in drug approval would overcome these serious deficiencies of hypothesis testing. Sample size would remain relevant to drug approval, but the criterion used to evaluate sample size would change. Rather than judging sample size by statistical power, the Food and Drug Administration could require a sample to be large enough to provide a specified nearness to optimality of the approval decision. Using nearness to optimality to set sample size and making approval decisions to minimize distance from optimality would particularly benefit the evaluation of drugs for treatment of rare diseases. It would enable a dramatic reduction in sample size relative to current norms, without compromising the clinical informativeness of trials. • The use of hypothesis testing in the Food and Drug Administration drug approval process is harmful. Conventional asymmetric error probabilities bias the approval process against approval of new drugs. Requiring the sample size to be large enough to guarantee the specified statistical power particularly inhibits the development of new drugs for treating rare diseases. • Use of statistical decision theory in drug approval would overcome these serious deficiencies of hypothesis testing. Sample size would remain relevant to drug approval, but the criterion used to evaluate sample size would change. Rather than judge sample size by statistical power, the Food and Drug Administration could require a sample to be large enough to provide a specified nearness to optimality of the approval decision. [ABSTRACT FROM AUTHOR]

Summary: This article proposes a novel angle representation method for floating‐point number, which eliminates the need for DSP (Digital Signal Processor) resources and reduces the resource usage when performing floating‐point multiplication. Compared with the implementation of floating‐point multiplier using IP cores, the implementation of approximate multiplication based on lookup tables can achieve an average reduction of 58.2% in LUTs (look‐up tables) and an average increase of 20.4% in frequency for mantissa widths ranging from 3 to 12 bits. Additionally, it can also save an average of 23.2% in registers. Analysis of PDP (power‐delay product)/LUT to MRED (mean relative error distance)/PRED (probability of relative error distance) among other approximate multipliers shows that the proposed design extends the Pareto front. At last, simulation of a three‐level inverter is implemented to verify the effectiveness of the multiplier. [ABSTRACT FROM AUTHOR]

Some of the required characteristics for a true machine theory of mind (MToM) include the ability to (1) reproduce the full diversity of human thought and behavior, (2) develop a personalized model of an individual with very limited data, and (3) provide an explanation for behavioral predictions grounded in the cognitive processes of the individual. We propose that a certain class of cognitive models provide an approach that is well suited to meeting those requirements. Being grounded in a mechanistic framework like a cognitive architecture such as ACT‐R naturally fulfills the third requirement by mapping behavior to cognitive mechanisms. Exploiting a modeling paradigm such as instance‐based learning accounts for the first requirement by reflecting variations in individual experience into a diversity of behavior. Mechanisms such as knowledge tracing and model tracing allow a specific run of the cognitive model to be aligned with a given individual behavior trace, fulfilling the second requirement. We illustrate these principles with a cognitive model of decision‐making in a search and rescue task in the Minecraft simulation environment. We demonstrate that cognitive models personalized to individual human players can provide the MToM capability to optimize artificial intelligence agents by diagnosing the underlying causes of observed human behavior, projecting the future effects of potential interventions, and managing the adaptive process of shaping human behavior. Examples of the inputs provided by such analytic cognitive agents include predictions of cognitive load, probability of error, estimates of player self‐efficacy, and trust calibration. Finally, we discuss implications for future research and applications to collective human–machine intelligence. [ABSTRACT FROM AUTHOR]

As maximum power point tracking (MPPT) algorithms have developed towards multi-task intelligent computing, processors in photovoltaic power generation control systems must be capable of achieving a higher performance. However, the challenges posed by the complex environment of photovoltaic fields with regard to processor reliability cannot be overlooked. To address these issues, we proposed a novel approach. Our approach uses error rate and performance as switching metrics and performs joint statistics to achieve efficient adaptive switching. Based on this, our work designed a redundancy-mode switchable three-core processor system to balance performance and reliability. Additionally, by analyzing the relationship between performance and reliability, we proposed optimization methods to improve reliability while ensuring a high performance was maintained. Finally, we designed an error injection method and verified the system's reliability by analyzing the error rate probability model in different scenarios. The results of the analysis show that compared with the traditional MPPT controller, the redundancy mode switchable multi-core processor system proposed in this paper exhibits a reliability approximately 5.58 times that of a non-fault-tolerant system. Furthermore, leveraging the feature of module switching, the system's performance has been enhanced by 26% compared to a highly reliable triple modular redundancy systems, significantly improving the system's reliability while ensuring a good performance is maintained. [ABSTRACT FROM AUTHOR]

This work considers the low‐rank approximation of a matrix A(t)$$ A(t) $$ depending on a parameter t$$ t $$ in a compact set D⊂ℝd$$ D\subset {\mathbb{R}}^d $$. Application areas that give rise to such problems include computational statistics and dynamical systems. Randomized algorithms are an increasingly popular approach for performing low‐rank approximation and they usually proceed by multiplying the matrix with random dimension reduction matrices (DRMs). Applying such algorithms directly to A(t)$$ A(t) $$ would involve different, independent DRMs for every t$$ t $$, which is not only expensive but also leads to inherently non‐smooth approximations. In this work, we propose to use constant DRMs, that is, A(t)$$ A(t) $$ is multiplied with the same DRM for every t$$ t $$. The resulting parameter‐dependent extensions of two popular randomized algorithms, the randomized singular value decomposition and the generalized Nyström method, are computationally attractive, especially when A(t)$$ A(t) $$ admits an affine linear decomposition with respect to t$$ t $$. We perform a probabilistic analysis for both algorithms, deriving bounds on the expected value as well as failure probabilities for the approximation error when using Gaussian random DRMs. Both, the theoretical results and numerical experiments, show that the use of constant DRMs does not impair their effectiveness; our methods reliably return quasi‐best low‐rank approximations. [ABSTRACT FROM AUTHOR]

We aim to improve both the energy harvesting efficiency and communication reliability of overlay networks powered by harvested energy. To this end, multiple antennas are considered to collect energy efficiently and perform reliable decoding by selecting the transmitting antenna and applying maximum‐ratio combining. To further improve communication reliability, nonorthogonal multiple access (NOMA)‐relied decoding is applied to the secondary receiver. For performance evaluation, exact formulas for the secondary/primary outage probability are derived in a closed form. The evaluation results show that the proposed method substantially outperforms a baseline without the NOMA‐relied decoding in all the system settings. The performance of the proposed method is determined by multiple specifications and optimized by allocating the times for energy harvesting and information processing. [ABSTRACT FROM AUTHOR]

Solid-state drives (SSDs) have become the preferred storage solution for performance-critical applications due to their high speed, durability, and energy efficiency. However, the inherent characteristics of NAND flash memory, such as block-level erasure and data fragmentation, necessitate frequent garbage collection (GC) operations to reclaim storage space. These operations, while essential, introduce significant performance overhead, particularly in modern SSD controllers (SSDCs) that utilize network-on-chip (NoC) architectures. In such architectures, GC requires substantial data transfer over interconnects for error correction, leading to increased latency and reduced throughput. This paper presents a novel GC scheme designed to minimize latency in NoC-based SSDCs. Unlike conventional methods that unconditionally transfer data for error correction, the proposed approach selectively determines the data transfer path based on the presence of errors. By leveraging the low error probability of NAND flash memory, this scheme avoids unnecessary data traversal across the interconnect, significantly reducing GC overhead. A hardware implementation using task queues ensures efficient parallelism without disrupting other operations. The experimental results demonstrate that the proposed scheme improves SSD performance across various real-world workloads, achieving up to a 26.9% reduction in average latency and a 50.0% reduction in peak latency compared to traditional GC methods. These findings highlight the potential of optimizing data traversal paths in NoC architectures, providing a scalable solution for enhancing SSD performance for diverse applications. [ABSTRACT FROM AUTHOR]

For any pair of probability measures defined on a common space, their relative information spectra——specifically, the distribution functions of the loglikelihood ratio under either probability measure——fully encapsulate all that is relevant for distinguishing them. This paper explores the properties of the relative information spectra and their connections to various measures of discrepancy including total variation distance, relative entropy, Rényi divergence, and general f -divergences. A simple definition of sufficient statistics, termed I -sufficiency, is introduced and shown to coincide with longstanding notions under the assumptions that the data model is dominated and the observation space is standard. Additionally, a new measure of discrepancy between probability measures, the NP-divergence, is proposed and shown to determine the area of the error probability pairs achieved by the Neyman-Pearson binary hypothesis tests. For independent identically distributed data models, that area is shown to approach 1 at a rate governed by the Bhattacharyya distance. [ABSTRACT FROM AUTHOR]

In this study, the focus is on the reduction of channel attenuation through diversity techniques. The effectiveness of the diversity system was evaluated based on its ability to lower the carrier noise ratio (CNR) and carrier-to-interference ratio (CIR) at a specific probability of error (Pe). The analysis concentrated on statistically reducing the fading dynamic range of the average CNR and CIR. The aim was to examine two branches (N=2) of diversity systems that employed pre-detection combining, a highly advantageous and effective method in radio mobile communications. The initial phase of the research involved investigating the statistical properties of CNR and CIR without diversity using a Rayleigh fading model. Diversity was then introduced into the experiment. Maximal ratio (MRC) combining, Equal gain (EGC) combining and Selection (SC) combining were simulated and compared. The results showed that the use of diversity techniques significantly reduced the fading dynamic range of the instantaneous CNR γ compared to the instantaneous CIR λ of about 10-3dB Furthermore, maximal-ratio combining (MRC) demonstrated the highest level of performance improvement compared to other methods. [ABSTRACT FROM AUTHOR]

We conduct a non-asymptotic study of the Cross-Validation (CV) estimate of the generalization risk for learning algorithms dedicated to extreme regions of the covariates space. In this context which has recently been analysed from an Extreme Value Analysis perspective, the risk function measures the algorithm's error given that the norm of the input exceeds a high quantile. The main challenge within this framework is the negligible size of the extreme training sample with respect to the full sample size and the necessity to re-scale the risk function by a probability tending to zero. We open the road to a finite sample understanding of CV for extreme values by establishing two new results: an exponential probability bound on the K-fold CV error and a polynomial probability bound on the leave-p-out CV. Our bounds are sharp in the sense that they match state-of-the-art guarantees for standard CV estimates while extending them to encompass a conditioning event of small probability. We illustrate the significance of our results regarding high dimensional classification in extreme regions via a Lasso-type logistic regression algorithm. The tightness of our bounds is investigated in numerical experiments. [ABSTRACT FROM AUTHOR]

The spinning reserve demand in high renewable penetration power systems is increasing significantly due to the stochastic and unpredictable nature of renewable power. This paper defines the expected load not supplied ratio (ELNSR) and models uncertainty factors such as unit forced outage rates, load and wind power output prediction errors based on probability density functions. It derives a quantitative relationship between system operating reserves and the ELNSR and uses this quantitative relationship as a constraint for power generation scheduling. Based on this, a coordinated generation and reserve scheduling model for power systems with large-scale wind power is established. Case study results demonstrate that the proposed model can balance both economy and reliability, coordinate the output allocation between wind power and thermal power, and provide an optimized allocation scheme for operating reserves among thermal power units to meet corresponding reliability requirements. [ABSTRACT FROM AUTHOR]

The HQC post-quantum cryptosystem enables two parties to share noisy versions of a common secret binary string, and an error-correcting code is required to deal with the mismatch between both versions. This code is required to deal with binary symmetric channels with as large a transition parameter as possible, while guaranteeing, for cryptographic reasons, a decoding error probability of provably not more than 2-128. This requirement is non-standard for digital communications, and modern coding techniques are not amenable to this setting. This paper explains how this issue is addressed in the last version of HQC: precisely, we introduce a coding scheme that consists of concatenating a Reed–Solomon code with the tensor product of a Reed–Muller code and a repetition code. We analyze its behavior in detail and show that it significantly improves upon the previous proposition for HQC, which consisted of tensoring a BCH and a repetition code. As additional results, we also provide a better approximation of the weight distribution for HQC error vectors, and we remark that the size of the exchanged secret in HQC can be reduced to match the protocol security which also significantly improves performance. [ABSTRACT FROM AUTHOR]

In low-latency and high-reliability communication systems, a channel coding operates within finite blocklengths, making the data rate a function of both decoding error probability and blocklength. This paper proposes a deep learning based universal beamforming design for a multi-user downlink systems, considering finite blocklength transmission. By leveraging blocklength as a side information in deep neural network (DNN), we develop an efficient beamforming design that is universally applicable to the varying blocklengths, aiming to maximize the sum rate under finite blocklength and decoding error conditions. Through numerical results, we demonstrate the superiority of the proposed scheme over existing beamforming design methods. [ABSTRACT FROM AUTHOR]

Despite the achievements in the field of instrumental methods of pollutant analysis, human error (HE) is still a significant issue affecting the quality of data obtained during environmental analysis and should be taken into account for quality risk management in the laboratory and field. Numerous scenarios that depend on the performance shaping factor (PSF) can lead to HE in the chemical analysis of environmental pollutants. Considering this, we applied, for the first time, the Success likelihood index method (SLIM) for the identification and quantification of HE in the analysis of polluting substances. As a case study, a spectrophotometric determination of ammonia concentration in water was examined. By applying SLIM, the impact of PSFs, such as procedure, experience, training, time, communication, and teamwork, on the occurrence of HE for specific activities was assessed by experts. It is estimated that "taking an unrepresentative sample" is the error with the highest probability of occurrence. The obtained results indicate that experience and training, followed by procedures and time, are PSFs that contribute to the greatest extent to the reduction of errors during the analysis of polluting substances. Considering the above-mentioned, the appropriate corrective measures that would lead to a reduction of HE in the analysis of pollutants are proposed. [ABSTRACT FROM AUTHOR]

Suppose that an informant (test, expert, device, perceptual system, etc.) is unlikely to err when pronouncing on a particular subject matter. When this is so, it might be tempting to defer to that informant when forming beliefs about that subject matter. How is such an inferential process expected to fare in terms of truth (leading to true beliefs) and evidential fit (leading to beliefs that fit one's total evidence)? Using a medical diagnostic test as an example, we set out a formal framework to investigate this question. We establish seven results and make one conjecture. The first four results show that when the test's error probabilities are low, the process of deferring to the test can score well in terms of (i) both truth and evidential fit, (ii) truth but not evidential fit, (iii) evidential fit but not truth, or (iv) neither truth nor evidential fit. Anything is possible. The remaining results and conjecture generalize these results in certain ways. These results are interesting in themselves—especially given that the diagnostic test is not sensitive to the target disease's base rate—but also have broader implications for the more general process of deferring to an informant. Additionally, our framework and diagnostic example can be used to create test cases for various reliabilist theories of inferential justification. We show, for example, that they can be used to motivate evidentialist process reliabilism over process reliabilism. [ABSTRACT FROM AUTHOR]

ABSTRACT Cognitive radio is a technology that allows the unlicensed users to access the licensed spectrum based on sensing information perceived by the users. Cognitive radio user generally attempts to access the unutilized band of licensed user. However, the emergence/reappearance of primary users during ongoing cognitive user's transmission creates the interference at the primary receiver and data loss at cognitive receiver. In this context, the spectrum monitoring is a vital process of the cognitive radio network during data transmission to continuously monitor the primary user's presence or absence on a licensed channel. In this manuscript, we have utilized the spectrum monitoring technique on earlier existing MAC protocol for CR network to detect the emergence/reappearance of primary users throughout the data transmission period of cognitive users. The spectrum monitoring is employed to reduce the interference at primary receiver, which is measured in terms of interference power (IP) and data loss at CU receiver. The closed form expressions of data loss and IP for the monitoring and without monitoring scenarios are provided. In addition, the probability of monitoring error's (which causes misinterpretation of channel's status by the cognitive users) impact on these performance parameters is also provided and simulated. Further, earlier MAC protocol has significant amount of bandwidth wastage in case primary user disappears or reappears during cognitive user's data transmission, which is shown mathematically and simulated as well. However, incorporation of the monitoring phenomenon even with significant amount of monitoring error has reduced bandwidth wastage, which is measured in bps. In addition, the throughput calculation of the proposed system and the effect of monitoring error on the throughput of the proposed system are also seen. As per the results, the maximum data loss and IP occurred at licensed channel's busy probability value of 0.5, which is 23.22 bps and 3.487 W, respectively, at maximum monitoring error value. However, minimum data loss and IP are achieved at licensed busy channel's probability values of zero and one. In addition, the resource wastage at licensed channel's busy probability value of 0.5 is varying from 2.322 to 23.22 bps for varying the monitoring error value from 0.1 to 1, respectively, giving highest resource wastage at maximum monitoring error value. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a non‐orthogonal multiple access (NOMA)‐based precoded quadrature spatial modulation (PQSM) technique (NOMA‐PQSM) has been proposed for the downlink scenario. In NOMA‐PQSM, two intended receiving antennas are activated at any time instant. One antenna is activated for the in‐phase component of the transmitted signal, and another one is activated for the quadrature phase component, on the basis of data bits. NOMA‐PQSM provides benefits like improved spatial diversity and spectral efficiency in comparison with spatial modulation. This work uses zero forcing (ZF) precoding over downlink flat fading Rayleigh multiple input multiple output (MIMO) channels, to limit the channel's deteriorating effect on transmitted signal, assuming perfect channel state information (CSI) at the transmitter. A low complexity receiver based on the successive interference cancellation is used. An expression for the upper bound of average bit error probability is derived. Moreover, the expressions for the sum mutual information of users and its lower bound are also derived. The proposed scheme is compared with the preprocessing aided spatial modulation (PSM)‐based counterpart. Monte Carlo simulations reveal that the NOMA‐PQSM scheme outperforms its orthogonal counterpart and the PSM scheme. [ABSTRACT FROM AUTHOR]