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Patient care, research, and decision-making are all aided by real-time medical data analysis in today's rapidly developing healthcare system. The significance of this research comes in the fact that it has the ability to completely change the healthcare system by relocating computing resources closer to the data source, hence facilitating more rapid and accurate analysis of medical data. Latency, privacy concerns, and inability to scale are common in traditional cloud-centric techniques. With their ability to process data close to where it is created, edge and fog computing have the potential to revolutionize medical analysis. The healthcare industry has unique opportunities and problems for the application of edge and fog computing. There must be an emphasis on data security and privacy, workload flexibility, interoperability, resource optimization, and data integration without any interruptions. In this research, it is suggested the Adaptive Heuristic Edge assisted Fog Computing design (AHE-FCD) to solve these issues using a novel architecture meant to improve medical analysis. Together, edge devices and fog nodes may perform distributed data processing and analytics with the help of AHE-FCD. Heuristic algorithms are often employed for optimization issues that establishing an optimum solution using standard approaches is difficult and impossible. Heuristic algorithms utilize search algorithms to explore the search space and identify a result. Improved patient care, medical research, and healthcare process efficiency are all possible to AHE-FCD real-time, low-latency analysis at the edge and fog layers. Improved medical analysis with minimal latency, high reliability, and data privacy are all likely to emerge from the study's findings. As a result, rather from being centralized, operations in a sophisticated distributed system occur at several end points. That helps the situation quicker to detect possible dangers prior to propagate across the network. The AHE-FCD is a promising breakthrough that moves us closer to the realization of advanced medical analysis systems, where prompt and well-informed decision-making is essential to providing excellent healthcare. [ABSTRACT FROM AUTHOR]

In the field of deep learning, the attention mechanism, as a technology that mimics human perception and attention processes, has made remarkable achievements. The current methods combine a channel attention mechanism and a spatial attention mechanism in a parallel or cascaded manner to enhance the model representational competence, but they do not fully consider the interaction between spatial and channel information. This paper proposes a method in which a space embedded channel module and a channel embedded space module are cascaded to enhance the model's representational competence. First, in the space embedded channel module, to enhance the representational competence of the region of interest in different spatial dimensions, the input tensor is split into horizontal and vertical branches according to spatial dimensions to alleviate the loss of position information when performing 2D pooling. To smoothly process the features and highlight the local features, four branches are obtained through global maximum and average pooling, and the features are aggregated by different pooling methods to obtain two feature tensors with different pooling methods. To enable the output horizontal and vertical feature tensors to focus on different pooling features simultaneously, the two feature tensors are segmented and dimensionally transposed according to spatial dimensions, and the features are later aggregated along the spatial direction. Then, in the channel embedded space module, for the problem of no cross-channel connection between groups in grouped convolution and for which the parameters are large, this paper uses adaptive grouped banded matrices. Based on the banded matrices utilizing the mapping relationship that exists between the number of channels and the size of the convolution kernels, the convolution kernel size is adaptively computed to achieve adaptive cross-channel interaction, enhancing the correlation between the channel dimensions while ensuring that the spatial dimensions remain unchanged. Finally, the output horizontal and vertical weights are used as attention weights. In the experiment, the attention mechanism module proposed in this paper is embedded into the MobileNetV2 and ResNet networks at different depths, and extensive experiments are conducted on the CIFAR-10, CIFAR-100 and STL-10 datasets. The results show that the method in this paper captures and utilizes the features of the input data more effectively than the other methods, significantly improving the classification accuracy. Despite the introduction of an additional computational burden (0.5 M), however, the overall performance of the model still achieves the best results when the computational overhead is comprehensively considered. [ABSTRACT FROM AUTHOR]

Standard machine learning and deep learning architectures have been widely used in the field of sentiment analysis, but their performance is unsatisfactory if the input texts are short (e.g., social media posts). Specifically, the accuracy of standard machine learning methods crucially depends on the richness and completeness of the features used to represent the texts, and in the case of short messages, it is often difficult to obtain high-quality features. Conversely, methods based on deep learning can achieve better expressiveness, but these methods are computationally demanding and often suffer from over-fitting. This paper proposes a new adaptive evolutionary computational integrated learning model (AdaECELM) to overcome the problems encountered by traditional machine learning and deep learning models in sentiment analysis for short texts. AdaECELM consists of three phases: feature selection, sub classifier training, and global integration learning. First, a grid search is used for feature extraction and selection of term frequency-inverse document frequency (TF-IDF). Second, cuckoo search (CS) is introduced to optimize the combined hyperparameters in the sub-classifier support vector machine (SVM). Finally, the training set is divided into different feature subsets for sub-classifier training, and then the trained sub-classifiers are integrated and learned using the AdaBoost integrated soft voting method. Extensive experiments were conducted on six real polar sentiment analysis data sets. The results show that the AdaECELM model outperforms the traditional ML comparison methods according to evaluation metrics such as accuracy, precision, recall, and F1-score in all cases, and we report an improvement in accuracy exceeding 4.5%, the second-best competitor. [ABSTRACT FROM AUTHOR]

Compared with general images, objects in remote sensing (RS) images typically exhibit a conspicuous diversity due to their arbitrary orientations. However, many of the prevalent detectors generally apply an inflexible strategy in setting the angles of anchor, ignoring the fact that the number of possible orientations is predictable. Consequently, their processes integrate numerous superfluous angular considerations and hinder their efficiency. To deal with this situation, we propose a dynamic density-driven estimation network (DDE-Net). We design three core modules in DDE-Net: a density-map and mask generation module (DGM), mask routing prediction module (MRM), and spatial-balance calculation module (SCM). DGM is designed for the generation of a density map and mask, which can extract salient features. MRM is for the prediction of object orientation and corresponding weights, which are used to calculate feature maps. SCM is used to affine transform the convolution kernel, which applies an adaptive weighted compute mechanism to enhance the average feature, so as to balance the spatial difference to the rotation feature extraction. A broad array of experimental evaluations have conclusively shown that our methodology outperforms existing state-of-the-art detectors on common aerial object datasets (DOTA and HRSC2016). [ABSTRACT FROM AUTHOR]

This article presents a chromosome-level genome assembly of Ajuga decumbens, a flowering plant used in traditional Chinese medicine. The genome assembly was achieved using PacBio HiFi sequencing and Hi-C sequencing, resulting in a high-quality assembly. The genome annotation revealed a high repeat content and identified protein-coding genes, non-coding RNAs, and unique gene families. Phylogenetic analysis showed the evolutionary relationship of A. decumbens with other angiosperm species. This genome resource will contribute to the exploration of medicinal resources and the cultivation of new varieties in the Ajuga genus. The text discusses the significance of the research on Ajuga decumbens, highlighting its whole genome duplication and its medicinal and economic value. It emphasizes the importance of genomics in understanding genetic diversity, evolutionary relationships, and functional genomics in A. decumbens. The study used various methods to analyze the genomic data and provides access to the data in public repositories. The document is a compilation of references to scientific articles and journals related to plant biotechnology and genomics, providing library patrons with further research options on specific topics. [Extracted from the article]

The exponential function maps the imaginary axis to the unit circle and for many applications this unitarity property is also desirable from its approximations. We show that this property is conserved not only by the |$(k,k)$| -rational interpolant of the exponential on the imaginary axis, but also by |$(k,k)$| -rational barycentric approximants that minimize a linearized approximation error. These results are a consequence of certain properties of singular vectors of Loewner-type matrices associated to linearized approximation errors. Prominent representatives of this class are rational approximants computed by the adaptive Antoulas–Anderson (AAA) method and the AAA–Lawson method. Our results also lead to a modified procedure with improved numerical stability of the unitarity property and reduced computational cost. [ABSTRACT FROM AUTHOR]

Mechanical computing has garnered significant interest as a supplement to traditional electronic computing, which often grapples with issues like high power consumption, security vulnerabilities, and susceptibility to extreme environmental conditions such as intense heat and radiation. Yet, most research in mechanical computing has been limited to the ad hoc design of simple logic gates and has not fully achieved the implementation of simple arithmetic computation within an electricity‐free framework. Additionally, progress in environmentally adaptive computing, crucial for decentralized intelligence, has also been slow. New ground is broken with the development of a mechanical transistor that synergizes a Kirigami thermomechanical sensor and a bistable actuator, enabling in‐memory computing for combinational and sequential logic. The design stands out by employing modular construction, symmetry breaking, and nonlinear materials, crafting logic gates, and memory units that respond to environmental stimuli through thermal delay. These transistors integrate design and material intelligence to establish nonvolatile memories, essential for logic‐in‐memory, and align thermal transport with mechanical deformation for environmental responsiveness. The mechanical transistor heralds a new age in mechanical computing, proving to be as versatile and vital as the electronic transistor has been in the era of modern computing. [ABSTRACT FROM AUTHOR]

This work explored how immersive technologies like virtual reality can be exploited for improved motor learning. While virtual reality is becoming a practical replacement for training that is otherwise expensive, dangerous, or inconvenient to deliver, virtual simulations can also enhance the learning process. Based on the concept of 'attention computing', we developed and tested a novel 'gaze-adaptive' training method within a virtual putting environment augmented with eye and motion tracking. To our knowledge, this work is the first application of attention computing and adaptive virtual reality to sports skill training. Novice golfers were randomly assigned to either standard putting practice in virtual reality (control) or gaze-adaptive training conditions. For gaze-adaptive training, the golf ball was sensitive to the participant's gaze and illuminated when fixated upon, to prompt longer and more stable pre-shot fixations. We recorded the effect of these training conditions on task performance, gaze control, and putting kinematics. Gaze-adaptive training was successful in generating more expert-like gaze control and putting kinematics, although this did not transfer to improved performance outcomes within the abbreviated training paradigm. These findings suggest that gaze-adaptive environments can enhance visuomotor learning and may be a promising method for augmenting virtual training environments. [ABSTRACT FROM AUTHOR]

Reconfigurable devices such as field-programmable gate arrays (FPGAs) offer flexible solutions to workload acceleration with high energy efficiency. Despite such a potential advantage, they often reveal hard to program by application programmers. High-level synthesis languages have been developed to provide higher-level abstractions, allowing the developers to define the FPGA behavior using an imperative programming approach based on C/C++ languages. However, such approaches still leave the developer with the responsibility to harness the low-level optimizations required to develop efficient FPGA programs. Along this line, this paper introduces FSPX, a framework helping programmers to develop FPGA-accelerated data stream processing (DSP) applications. The approach provides a high-level Python API to develop the data-flow graph of operators, which is automatically translated into an efficient Vitis source code targeting Xilinx devices. The execution of the bitstreams implementing two benchmark applications showcases the efficiency of using FPGAs for DSP workloads. In general, FSPX provides, with a reasonable time-to-solution, higher performance compared with state-of-the-art DSP frameworks. [ABSTRACT FROM AUTHOR]

One among several advantages of measure transport methods is that they allow or a unified framework for processing and analysis of data distributed according to a wide class of probability measures. Within this context, we present results from computational studies aimed at assessing the potential of measure transport techniques, specifically, the use of triangular transport maps, as part of a workflow intended to support research in the biological sciences. Scenarios characterized by the availability of limited amount of sample data, which are common in domains such as radiation biology, are of particular interest. We find that when estimating a distribution density function given limited amount of sample data, adaptive transport maps are advantageous. In particular, statistics gathered from computing series of adaptive transport maps, trained on a series of randomly chosen subsets of the set of available data samples, leads to uncovering information hidden in the data. As a result, in the radiation biology application considered here, this approach provides a tool for generating hypotheses about gene relationships and their dynamics under radiation exposure. [ABSTRACT FROM AUTHOR]

As the scale of federated learning expands, solving the Non‐IID data problem of federated learning has become a key challenge of interest. Most existing solutions generally aim to solve the overall performance improvement of all clients; however, the overall performance improvement often sacrifices the performance of certain clients, such as clients with less data. Ignoring fairness may greatly reduce the willingness of some clients to participate in federated learning. In order to solve the above problem, the authors propose Ada‐FFL, an adaptive fairness federated aggregation learning algorithm, which can dynamically adjust the fairness coefficient according to the update of the local models, ensuring the convergence performance of the global model and the fairness between federated learning clients. By integrating coarse‐grained and fine‐grained equity solutions, the authors evaluate the deviation of local models by considering both global equity and individual equity, then the weight ratio will be dynamically allocated for each client based on the evaluated deviation value, which can ensure that the update differences of local models are fully considered in each round of training. Finally, by combining a regularisation term to limit the local model update to be closer to the global model, the sensitivity of the model to input perturbations can be reduced, and the generalisation ability of the global model can be improved. Through numerous experiments on several federal data sets, the authors show that our method has more advantages in convergence effect and fairness than the existing baselines. [ABSTRACT FROM AUTHOR]

Summary: The increasing availability of high‐dimensional data collected from numerous users has led to the need for multi‐dimensional data publishing methods that protect individual privacy. In this paper, we investigate the use of local differential privacy for such purposes. Existing solutions calculate pairwise attribute marginals to construct probabilistic graphical models for generating attribute clusters. These models are then used to derive low‐dimensional marginals of these clusters, allowing for an approximation of the distribution of the original dataset and the generation of synthetic datasets. Existing solutions have limitations in computing the marginals of pairwise attributes and multi‐dimensional distribution on attribute clusters, as well as constructing relational dependency graphs that contain large clusters. To address these problems, we propose LoHDP, a high‐dimensional data publishing method composed of adaptive marginal computing and an effective attribute clustering method. The adaptive local marginal calculates any k‐dimensional marginals required in the algorithm. In particular, methods such as sampling‐based randomized response are used instead of privacy budget splits to perturb user data. The attribute clustering method measures the correlation between pairwise attributes using an effective method, reduces the search space during the construction of the dependency graph using high‐pass filtering technology, and realizes dimensionality reduction by combining sufficient triangulation operation. We demonstrate through extensive experiments on real datasets that our LoHDP method outperforms existing methods in terms of synthetic dataset quality. [ABSTRACT FROM AUTHOR]

It is crucial to speed up the training process of multivariate deep learning models for forecasting time series data in a real-time adaptive computing service with automated feature engineering. Multivariate time series decomposition and recombining (MTS-DR) is proposed for this purpose with better accuracy. A proposed MTS-DR model was built to prove that not only the training time is shortened but also the error loss is slightly reduced. A case study is for demonstrating air quality forecasting in sub-tropical urban cities. Since MTS decomposition reduces complexity and makes the features to be explored easier, the speed of deep learning models as well as their accuracy are improved. The experiments show it is easier to train the trend component, and there is no need to train the seasonal component with zero MSE. All forecast results are visualized to show that the total training time has been shortened greatly and that the forecast is ideal for changing trends. The proposed method is also suitable for other time series MTS with seasonal oscillations since it was applied to the datasets of six different kinds of air pollutants individually. Thus, this proposed method has some commonality and could be applied to other datasets with obvious seasonality. [ABSTRACT FROM AUTHOR]

In the article we consider the problem of achieving high clock frequencies on reconfigurable computing systems with a high degree of utilization of FPGA resources. There are a number of architectural and geometric features of FPGAs, as well as features of the implemented computational structures, which modern CAD systems do not take into account when automatically placing primitives on the FPGA field. This leads to a non-optimal arrangement of functionally dependent primitives on the FPGA field and, as a consequence, an unacceptable length of connections between them. Therefore, the clock frequency of the implemented computing structure turns out to be significantly lower than when arranging critically important primitives in "manual" mode. This article describes the principles that allow you to optimize the placement of computing structure primitives on the FPGA field and minimize the length of traces between primitives. The main architectural and geometric features of the FPGA are illustrated, and the characteristics of computing structures determined by the subject area of the problem being solved are given. Current research into the effectiveness of heuristic placement methods based on the proposed principles allows us to assert that their use in the developed tools for automated placement of computing structures makes it possible to increase the speed of creating topological constraints and the specific performance of reconfigurable computing systems by 20% or more with high utilization of resources compared to standard CAD systems. [ABSTRACT FROM AUTHOR]

Since cloud computing has an abundance of users, the system has to execute a wide range of tasks. Task scheduler methods that are both robust and efficient while delivering the best outcomes are required. The task volume and runtime in the cloud vary rapidly, making task assessment and resource mapping difficult. Security issues, communication delays, and data loss are substantial barriers to scheduling. Furthermore, optimization techniques can be utilized to reduce load and assign tasks so that the user can finish tasks faster. This paper offers a hybrid job scheduling technique for cloud computing using adaptive particle swarm optimization and ant colony optimization particle swarm optimization-ant colony optimization (adaptive PSO-ACO). After rapidly finding the initial solution via particle swarm optimization, the ant colony optimization approach establishes its first pheromone distribution. The suggested hybrid algorithm is compared to standalone PSO and ACO algorithms. Compared to ACO, the percentage decrease is 7.9%. Hybrid method has the lowest total cost, 55% less compared to PSO. Tasks vary when virtual machines (VMs) are constant and VMs vary when tasks are constant. Parameters like final cost, makespan, fitness value, computation time and weighted time are assessed to evaluate the performance of the hybrid task scheduling algorithm. [ABSTRACT FROM AUTHOR]

Atanassov intuitionistic fuzzy set (AIFS)-based C-means algorithms are successful in clustering uncertain or vague real-world datasets. The AIFS-based clustering algorithms are classified into adaptive class and non-adaptive class. An algorithm from the adaptive class computes its feature weight distribution with the help of the given dataset. On the other side, the algorithm belonging to the non-adaptive class mostly computes the feature weight distribution by employing an equally likely approach. The guarantee to reach up to the mark clustering performance is missing within this approach. Simultaneously, the performance gets deteriorated if the datasets showcase noises/irrelevant features. The irrelevant features in the datasets add to the computational cost. So, a feature reduction-equipped clustering algorithm called uni-weighted intuitionistic fuzzy C-means (uW-IFCM) is introduced in the paper. Moreover, the probabilistic weights-based adaptive clustering algorithm, namely bi-weighted probabilistic intuitionistic fuzzy C-means (bW-PIFCM) is proposed under the AIFS environment. The parametric analysis for uW-IFCM is provided to comprehend and compare its performance with bW-PIFCM, PIFCM, IFCM, and FCM algorithms. Here, an intuitionistic data fuzzification technique transforms the real-valued dataset into AIFS dataset, therefore bW-PIFCM and uW-IFCM algorithms cluster the real-valued datasets. The research proposal of Yang and Nataliani in [IEEE Transactions on Fuzzy Systems, 26(2), 817–835] motivates us to introduce a feature reduction-equipped uW-IFCM algorithm. We have considered synthetic datasets and some UCI machine learning datasets for the experimental study of uW-IFCM and bW-PIFCM. The efficacy and the precision of proposed algorithms are tested in terms of some popular benchmark indexes as well. [ABSTRACT FROM AUTHOR]

Affective computing, an interdisciplinary field merging psychology and artificial intelligence, plays a transformative role in adaptive learning. By recognizing and responding to learners' emotions, adaptive systems can personalize learning pathways, enhance engagement and optimize learning outcomes. This paper explores the significance of affective computing in adaptive learning using machine learning, addressing ethical considerations and prospects for creating emotionally intelligent learning environments. [ABSTRACT FROM AUTHOR]

This paper presents ReproRun, a flexible and extensible run-time framework for the reconfiguration of functions in field programmable gate array (FPGA) devices used in popular software-defined radio (SDR) platforms. The FPGA devices embed a hardwired or soft processing system (PS) which communicates with the programmable logic (PL) using a standard embedded bus interface. In order to apply a seamless run-time partial reconfiguration, we made use of all the related building blocks, design guidelines, and tools offered by AMD-Xilinx. In ReproRun, each partial bitstream targeting a reconfigurable region (RR) of the PL area comes with its respective firmware (i.e., software functions) that runs on the PS side. Our work guarantees run-time updates of the firmware without interrupting the functionality of other software processes running in the PS or PL, by employing a specialized controller, denoted as Run-timE firmWare reconfIguration contRollEr (REWIRE). The latter leverages the open asymmetric multiprocessing framework (OpenAMP). The partial bitstreams and respective firmware are fetched from a remote location using the trivial file transfer protocol (TFTP). ReproRun can be applied in different FPGA accelerators residing in disaggregated open radio access network (RAN) equipment, adaptive radio access technologies, and Edge servers hosting virtualized functions. [ABSTRACT FROM AUTHOR]

Control of the generalized chaotic systems' dynamical behaviors is studied here, by which a direct adaptive fuzzy controller is proposed to control a chaotic dynamical system using their Hamilton energy level. A generalized chaotic Lorenz system with hidden attractors is used as the under-control system, and its Hamilton energy is formulated and analyzed. As a practical consideration, the system is requested to be in a specific desired energy level in each explicit situation. A model-based control is computed and an adaptive fuzzy control is proposed based on it to improve the control signal from the magnitude and smoothness points of view. Adaptation law is designed to adjust the fuzzy controller parameters optimally, based on the Lyapunov second law, and guarantee the system stability. Consequent parameters of the fuzzy rules are tuned based on the residual value between the system's Hamilton energy and the desired level. Simulation results show superiority of the proposed controller based on tracking performance and control efficiency indices. [ABSTRACT FROM AUTHOR]

The design of analog computing systems requires significant human resources and domain expertise due to the lack of automation tools to enable these highly energy-efficient, high-performance computing nodes. This work presents the first automated tool flow from a high-level representation to a reconfigurable physical device. This tool begins with a high-level algorithmic description, utilizing either our custom Python framework or the XCOS GUI, to compile and optimize computations for integration into an Integrated Circuit (IC) design or a Field Programmable Analog Array (FPAA). An energy-efficient embedded speech classifier benchmark illustrates the tool demonstration, automatically generating GDSII layout or FPAA switch list targeting. [ABSTRACT FROM AUTHOR]

This article examines the potential of and limits to the use of machine learning for financial regulation. Ideally, if we could fully understand the financial system and agree on long- and short-term regulatory goals, we would be able to write code that carries out the computation that extracts proper representations from the data and makes correct regulatory decisions. We cannot do this yet because of limited sources of data, the bias brought by human beings and algorithmic models, and the difficulty of improving uninterpretable models. Furthermore, since law is a combination of merits and facts, there are difficulties in establishing the ground truth, modeling complex financial systems, and attaining fair outcomes simply based on statistics. Statistics, as a method of inductive learning, can only recognize patterns from existing data. From a methodological perspective, this represents a paradigm shift from observational study (deduction) to data analytics (induction). However, in the financial field, there is a fundamental difference between measurable risks and unknowable uncertainty in the future, which significantly affects the reliability of models to determine regulation based on estimated risks. Therefore, algorithmic models cannot make reliable suggestions about unusual situations, nor deal with complex problems that lack sufficient training data. In cases of algorithmic regulation, despite the predetermined regulatory goals, specific standards should remain adaptive to new data collected from the regulated environment, so as to mitigate bias generated from historical data and the initial model setting. [ABSTRACT FROM AUTHOR]

Learning a directed acyclic graph (DAG) from observational data plays a crucial role in causal inference and machine learning. However, the scarcity of observational data is a common phenomenon in real-world applications, where the current DAG learning methods may cause unsatisfactory performance in the context of small data samples. Data enhancement has been recognized as one of the key techniques for improving the generalization abilities of learning models utilizing small data samples. However, due to the inherent difficulty of sampling small datasets to generate high-quality new data samples, this approach has not been widely used in DAG learning. To alleviate this problem, we propose a data enhancement-based DAG learning (DE-DAG) approach. Specifically, DE-DAG first presents an integrated data sampling strategy for DAG learning and data sampling, then constructs a sample-level adaptive distance computing algorithm for selecting high-quality samples from the sampled datasets, and finally implements a DAG learning method on enhanced datasets consisting of high-quality samples and the original data samples. Experimental results obtained on benchmark datasets demonstrate that our proposed approach outperforms the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

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Active noise control (ANC) is a technique applied to eliminate an unwanted sound by superposing a signal of equal amplitude and opposite phase, sometimes defined as an anti-noise signal, computed through an adaptive algorithm. The study described herein aims to evaluate and compare the performance of some of the most popular algorithms based on the least mean squares (LMS) approach applied to a multichannel active noise control system in a small, enclosed space. The comparison is conducted through an experimental evaluation of the ANC algorithms' performance, carried out on a tractor cabin in a hemi-anechoic chamber, generating the unwanted sound field using a dodecahedron sound source placed outside the enclosure, emitting narrowband and broadband signals. The experimental analysis and the comparison with the results obtained in a free field condition have made it possible to show certain practical limitations when implementing the algorithms. The results show that the feed-forward systems allow for greater stability, avoiding the acoustic feedback from the control loudspeakers to the reference microphone when this is outside the cabin, while the feedback system is the slowest configuration to converge, requiring an internal modeling of the reference signal. With random signals, the feed-forward systems concentrate their performance in the range above 500 Hz, while the feedback system becomes ineffective. [ABSTRACT FROM AUTHOR]

Digital electronics is a technological cornerstone in this modern society that has covered the increasing demand for computing power during the last decades thanks to a periodic doubling of transistor density in integrated circuits. Currently, such scaling law is reaching its fundamental limit, leading to the emergence of a large gamut of applications that cannot be supported by digital electronics, specifically, those that involve real‐time multi‐data processing, e.g., medical diagnostic imaging, robotic control, and autonomous driving, among others. In this scenario, an analog computing approach implemented in a real‐time reconfigurable nonelectronic hardware such as programmable integrated photonics (PIP) can be more efficient than digital electronics to perform these emerging applications. However, actual analog computing models such as quantum and neuromorphic computation were not conceived to extract the unique benefits of PIP (and integrated photonics in general). Here, the foundations of a new computation theory are presented, termed Analog Programmable‐Photonic Computation (APC), explicitly designed to unleash the full potential of PIP technology. Interestingly, APC enables overcoming basic theoretical and technological limitations of existing computational models and can be implemented in other technologies (e.g., in electronics, acoustics or using metamaterials), consequently exhibiting the potential to spark a ground‐breaking impact on the information society. [ABSTRACT FROM AUTHOR]

Nowadays, in contemporary building and energy management systems (BEMSs), the predominant approach involves rule-based methodologies, typically employing supervised or unsupervised learning, to deliver energy-saving recommendations to building occupants. However, these BEMSs often suffer from a critical limitation—they are primarily trained on building energy data alone, disregarding crucial elements such as occupant comfort and preferences. This inherent lack of adaptability to occupants significantly hampers the effectiveness of energy-saving solutions. Moreover, the prevalent cloud-based nature of these systems introduces elevated cybersecurity risks and substantial data transmission overheads. In response to these challenges, this article introduces a cutting-edge edge computing architecture grounded in virtual organizations, federated learning, and deep reinforcement learning algorithms, tailored to optimize energy consumption within buildings/homes and facilitate demand response. By integrating energy efficiency measures within virtual organizations, which dynamically learn from real-time inhabitant data while prioritizing comfort, our approach effectively optimizes inhabitant consumption patterns, ushering in a new era of energy efficiency in the built environment. [ABSTRACT FROM AUTHOR]

Summary: Mobile devices play a vital role in people's day‐to‐day activities and are essential for communication, accessing Internet resources, entertainment, and so forth. In most cases, the performance of mobile applications is restricted due to the inadequate resources of mobile specifications such as controlled battery power, predetermined storage, and limited processing competence. Thus to improve the processing efficiency of resource‐constrained mobile devices, this research proposes a model developed on offloading technique based on the recent literature observations which transfer the resource‐consuming tasks from mobile devices to proximate computing entities. This generic architecture aiming for resource augmentation is formulated with five fundamental components including a mobile device, offloading engine, resource augmentation engine, scheduler, and synchronizer. The offloading engine identifies the resource‐intensive tasks that need to be executed on external entities using soft computing techniques such as neural network training algorithms, fuzzy logic, and neuro‐fuzzy logic processing. The augmentation engine chooses feasible edge computing entities such as Arduino, Raspberry PI controller, and fog devices. The scheduler prioritizes the resource‐consuming tasks for offloading based on their importance. The synchronizer coordinates all the central components by updating the execution status of tasks at regular intervals of time. Further extension of this research work, based on the proposed architecture three resource augmentation frameworks are developed namely (a) an adaptive proximate computing framework (b) a web service‐based IoT framework, and (c) a neuro‐fuzzy hybrid framework. Moreover, these frameworks are realized through the development of resource‐efficient intelligent edge computing IoT mobile apps namely (a) an inventory monitoring app (b) a lecture recording app, and (c) an intelligent transport app. The performance of these three resource augmentation frameworks is analyzed through the simulation results and it illustrates that the neuro‐fuzzy hybrid framework outperforms the other two. The outcome of the proposed research contributions improves the effectiveness of mobile communication by providing seamless services to mobile users regardless of their resource‐limited devices. Besides the devised edge computing architecture supports mobile application developers in the design and deployment of resource‐rich mobile applications in low‐specification mobile devices. Also, there is a strong scope for implementing the proposed generic architecture as an industrial smart product with adequate hardware and software configurations. [ABSTRACT FROM AUTHOR]

Rapid increase in the private and public vehicles fleet causes urban centers heavily populated with limited transport road infrastructure. To overcome this, in real-time scenarios, queue length-based traffic light controllers are being designed utilizing light-weighted S-Edge devices. This system suffers from starvation problems if a road lane at the intersection continuously receives vehicles during peak hours. With this, higher green phase duration can be allocated to the same-lane multiple times despite vehicles on the other lanes' longer waiting time. To tackle this problem, an efficient and smart edge computing (S-Edge)-driven traffic light controller is proposed by accounting the real-time heterogeneous vehicular dynamics at the fog computing node. The fog node executes the proposed fuzzy inference system to generate phase-cycle duration. Further, to allocate the phase duration effectively, a method for estimating the lane pressure is proposed for the edge controller utilizing average queue length and waiting time. S-Edge is a light-weighted actuated traffic light controller that generates traffic light control cycle duration and phase (red/yellow/green) duration. To validate the S-Edge controller, a prototype is developed on an Indian city OpenStreetMap utilizing the low-computing power IoT devices, i.e., Raspberry Pi, and a well-known open-source simulator, i.e., Simulation of Urban MObility. [ABSTRACT FROM AUTHOR]

SM4 national security algorithm has been widely used in data encryption and decryption, However, with the explosive growth of the data volume of the Internet, there are problems such as the slow speed and high CPU utilization of the SM4 algorithm running in pure software mode, while the field programmable gate array or dedicated integrated circuit based on Verilog/VHDL has problems such as poor flexibility and difficulty in upgrading and maintenance. In order to solve the above problems, a design scheme of heterogeneous reconfigurable computing system based on SM4algorithm is proposed. Using high-level synthesis and heterogeneous reconfigurable technology, the customized computing architecture of SM4 algorithm’s electronic cipher book mode and counter mode is designed by optimizing the allocation and transmission of data memory, optimizing the loop, vectorizing the kernel and adding computing units, and the system is deployed on the FPGA heterogeneous platform. The experimental results show that the customized computing architecture of SM4-ECB and SM4-CTR, two mainstream operating modes, on the Intel Stratix 10 GX2800,has a throughput of 109.48Gbps and 63.73Gbps respectively, which is 232.63 times and 141.62 times the throughput of the corresponding mode on the Intel Xeon E5-2650 V2 CPU. The performance of the whole heterogeneous reconfigurable computing system composed of this core module, including data input, encryption and decryption, and output, has also reached 4.90 times and 3.56 times of that of pure software mode. This paper not only realizes customized acceleration for specific modes, but also flexibly supports different computing modes through hardware reconfiguration, taking into account the flexibility and efficiency of the system. [ABSTRACT FROM AUTHOR]

This paper represents the online parameter identification and continuous updating in the controller of Induction Motor Drives (IMD). The online measure of parameters improves the operation of the speed estimator, particularly nearer to the zero speed, and also computes the adaptive slip angular frequency. The parameter of speed estimation is performed by the improved Optimized Constant Rate Reaching Law (OCRRL)-based sliding mode observer (SMO), and electrical parameters of stator resistance, mutual inductance, and rotor time constant estimation is performed by Improved Adaptive SMO (IASMO). To enhance the monitoring reliability driven by parameter mismatches, an Online Parameter Estimation Control (OPEC) is presented. Lyapunov's stability function is used to develop OPEC's motor parameter adaptive rule. The conventional SMO suffers from the problem of higher chattering phenomena at the sliding surface, especially at very low and zero speeds. An improved OCRRL-based SMO dissolves this issue. Further, to enhance the performance by eliminating the time required for manual tuning of gains of observers in IASMO and PI controllers in Indirect Field Oriented Control (IFOC) are obtained by the Grey Wolf Optimization (GWO) technique. The effectiveness and performance of the proposed observers are tested and verified for possible operating conditions. [ABSTRACT FROM AUTHOR]

Fog computing, an emerging paradigm, extends cloud capabilities to the network's edge, enhancing real-time data processing and analysis. This study focuses on optimizing fog computing systems using an adaptive scheduling algorithm. It efficiently categorizes incoming tasks based on their security requirements into private, semi-private, and public groups, distributing them among Mobile Data Centers (MDCs) and Cloud Data Centers (CDCs). By considering various parameters like job quantities, MDCs, CDCs, their capacities, and communication delays, the algorithm ensures tasks are allocated optimally. The performance of three scheduling algorithms (FCFS, SJF, EDF) is analyzed in both homogeneous and heterogeneous fog environments, shedding light on system efficiency, workload distribution, and resource management. This study provides valuable guidance for the design and operation of efficient fog computing infrastructures tailored to the diverse requirements of IoT applications while optimizing resource utilization. [ABSTRACT FROM AUTHOR]

The effectiveness of teaching methods is determined by whether a student is attentive in a lecture or not. In face-to-face classroom teaching, a teacher is able to judge whether students are understanding the subject, based on their facial expressions. However, since the uprise of COVID-19 pandemic, virtual classrooms have found a holding in the field of education and detecting attentiveness of students is a challenge in the same. This paper proposes a student attentiveness model that would detect and monitor a student's eye state to determine their level of attentiveness and provide a real-time feedback mechanism to the teacher. The proposed model employs a histogram of oriented gradient (HOG) method in conjunction with support vector machine (SVM) algorithm for face recognition. It then computes an adaptive eye aspect ratio (AEAR) for each individual student to determine their level of attentiveness. The model is tested on a real-time dataset and validated using classifiers (SVM, decision tree, and random forest). The results of the classifiers verify that the model produces an accuracy of more than 92%. [ABSTRACT FROM AUTHOR]

Wearable electronic devices with next‐generation biocompatible, mechanical, ultraflexible, and portable sensors are a fast‐growing technology. Hardware systems enabling artificial neural networks while consuming low power and processing massive in situ personal data are essential for adaptive wearable neuromorphic edging computing. Herein, the development of an ultraflexible artificial‐synaptic array device with concrete‐mechanical cyclic endurance consisting of a novel heterostructure with an all‐solid‐state 2D MoS2 channel and LiSiOx (lithium silicate) is demonstrated. Enabled by the sequential fabrication process of all layers, by excluding the transfer process, artificial van der Waals devices combined with the 2D‐MoS2 channel and LiSiOx solid electrolyte exhibit excellent neuromorphic synaptic characteristics with a nonlinearity of 0.55 and asymmetry ratio of 0.22. Based on the excellent flexibility of colorless polyimide substrates and thin‐layered structures, the fabricated flexible neuromorphic synaptic devices exhibit superior long‐term potentiation and long‐term depression cyclic endurance performance, even when bent over 700 times or on curved surfaces with a diameter of 10 mm. Thus, a high classification accuracy of 95% is achieved without any noticeable performance degradation in the Modified National Institute of Standards and Technology. These results are promising for the development of personalized wearable artificial neural systems in the future. [ABSTRACT FROM AUTHOR]

Coordination numbers are among the central quantities to describe the local environment of atoms and are thus used in various applications such as structure analysis, fingerprints, and parameters. Yet, there is no consensus regarding a practical algorithm, and many proposed methods are designed for specific systems. In this work, we propose a scale-free and parameter-free algorithm for nearest neighbor identification. This algorithm extends the powerful Solid-Angle based Nearest-Neighbor (SANN) framework to explicitly include local anisotropy. As such, our Anisotropically corrected SANN (ASANN) algorithm provides with a fast, robust, and adaptive method for computing coordination numbers. The ASANN algorithm is applied to flat and corrugated metallic surfaces to demonstrate that the expected coordination numbers are retrieved without the need for any system-specific adjustments. The same applies to the description of the coordination numbers of metal atoms in AuCu nanoparticles, and we show that ASANN based coordination numbers are well adapted for automatically counting neighbors and the establishment of cluster expansions. Analysis of classical molecular dynamics simulations of an electrified graphite electrode reveals a strong link between the coordination number of Cs+ ions and their position within the double layer, a relation that is absent for Na+, which keeps its first solvation shell even close to the electrode. [ABSTRACT FROM AUTHOR]

The von Neumann architecture with separate memory and processing presents a serious challenge in terms of device integration, power consumption, and real-time information processing. Inspired by the human brain that has highly parallel computing and adaptive learning capabilities, memtransistors are proposed to be developed in order to meet the requirement of artificial intelligence, which can continuously sense the objects, store and process the complex signal, and demonstrate an "all-in-one" low power array. The channel materials of memtransistors include a range of materials, such as two-dimensional (2D) materials, graphene, black phosphorus (BP), carbon nanotubes (CNT), and indium gallium zinc oxide (IGZO). Ferroelectric materials such as P(VDF-TrFE), chalcogenide (PZT), HfxZr1−xO2(HZO), In2Se3, and the electrolyte ion are used as the gate dielectric to mediate artificial synapses. In this review, emergent technology using memtransistors with different materials, diverse device fabrications to improve the integrated storage, and the calculation performance are demonstrated. The different neuromorphic behaviors and the corresponding mechanisms in various materials including organic materials and semiconductor materials are analyzed. Finally, the current challenges and future perspectives for the development of memtransistors in neuromorphic system applications are presented. [ABSTRACT FROM AUTHOR]

This paper presents, a novel cactus shaped frequency reconfigurable antenna for sub 10 GHz wireless applications. PIN diode is utilized as an electrical switch to achieve reconfigurability, enabling operation in four different frequency ranges. In the switch ON state mode, the antenna supports 2177-3431 and 6301-8467 MHz ranges. Alternatively, the antenna resonates within 2329-3431 and 4951-6718 MHz while in the OFF state mode. Radiation efficiency values, ranging from 68% to 84%, and gain values, ranging from 1.6 to 4 dB, in the operating frequency bands. the proposed antenna satisfy the practical requirements and expectations. The overall planner dimensions of the proposed antenna model is 40 × 21 mm². Moreover, the measurement results from the prototype support the simulation results. Based on the frequency ranges supported by the antenna, it can be used for multiple wireless standards and services, including Worldwide interoperability and Microwave Access (WiMAX), Wireless Fidelity (Wi-Fi), Bluetooth, Long Term Evolution (LTE) and satellite communications. This increases its applicability for use in mobile terminals. [ABSTRACT FROM AUTHOR]

Field-Programmable Gate Arrays (FPGAs) can be found in an increasing number of application domains, such as the telecom industry, the automotive electronics sector, or automation technology as well as in the area of reconfigurable computing. In recent years, it can be observed that the open-source idea which is known from the software domain for a long time also became popular in the world of hardware and FPGA design. In the era of the Internet of Things, many of today's electronic devices implement some kind of network interface with Ethernet being known as one of the most widely used network standards. Thus, there is consequently a high demand on available Ethernet implementations for FPGA platforms. The goal of this work is to survey available open-source Ethernet MAC IP cores, evaluate existing designs in terms of performance, resource utilization, code quality, or maturity, and to present and summarize the evaluation results herein. Furthermore, advantages of commercial solutions and related publication work are discussed. To the authors' best knowledge, this is the first publication that evaluates and compares existing open-source Ethernet MAC IP cores on a large scale. This work should help designers to select an appropriate open-source Ethernet MAC for an FPGA design and shows possible pitfalls and things to pay attention when using an open-source IP core in general. Finally, the authors would like to show that the open-source community can be also very helpful in the world of hardware in terms of design reuse or time to market. [ABSTRACT FROM AUTHOR]

Software-defined networks (SDN) have gained a lot of attention in recent years as a technique to develop smart systems with a help of the Internet of Things (IoT). Its powerful and centralized architecture makes a balanced contribution to the management of sustainable applications through efficient processes. These networks also systematically keep track of mobile devices and decrease the extra overheads in the communication cost. Many solutions are proposed to cope with data transferring for the critical system, however, mobile devices, on the other hand, require long-distance communication links with minimal retransmissions. Furthermore, the mobile network is highly infected by security attacks and compromised the IoT architecture for both the intermediate layers and end-users. Therefore, this paper presents an adaptive routes migration model for sustainable applications with the collaboration of SDN architecture and limits the disconnectivity time in data transporting along with efficient management of network services. Moreover, its centralized controller fetches the updated information from low-level smart devices and supervised their monitoring efficiently. The proposed model also secures the cloud of things (CoTs) from network threats and protects private data. It provides three levels of security algorithms and supports adaptive computing systems. The proposed model was tested using simulations, and the findings showed that it outperformed other existing studies in terms of packet delivery ratio by 13%, packet loss rate by 15%, transmission error by 22%, computing cost by 17%, and latency by 18%. [ABSTRACT FROM AUTHOR]

Photonic integrated circuits (PICs) have been a research hotspot in recent years. Programmable PICs that have the advantages of versatility and reconfigurability that can realize multiple functions through a common structure have been especially popular. Leveraging on-chip couplers and phase shifters, general-purpose waveguide meshes connected in different topologies can be manipulated at run-time and support a variety of applications. However, current waveguide meshes suffer from relatively a low cell amount and limited bandwidth. Here, we demonstrate a reconfigurable photonic integrated computing chip based on a quadrilateral topology network, where typical analog computing functions, including temporal differentiation, integration, and Hilbert transformation, are implemented with a processing bandwidth of up to 40 GHz. By configuring an optical path and changing the splitting ratio of the optical switches in the network, the functions can be switched and the operation order can be tuned. This approach enables wideband analog computing of large-scale PICs in a cost-effective, ultra-compact architecture. [ABSTRACT FROM AUTHOR]

Neuromorphic computing is expected to bridge cognitive behaviors with computing systems in an efficient, expandable, and biologically inspired way. A pivotal cognitive behavior is the attention mechanism, which is highly important in filtering and regulating spatio‐temporal information. Emerging neuromorphic devices hold prospect in utilizing their internal physical mechanisms for dynamic computing resources. Here, a basic top–down attention computing component consisting of a synaptic transistor and a neuron is proposed, where efficient information processing is realized by combining the inherent device dynamics and the feedback loop. A theoretical model is established in simulation to demonstrate the capabilities of such a computing system in information filtering and control. Notably, new dynamic circuit behaviors, such as conductance oscillation and activate function switching, are discovered from appropriate time parameters. The attention computing component contains rich dynamic behaviors, providing a power and area‐saving method to construct high‐complexity neuromorphic systems for spatio‐temporal signal preprocessing and control. [ABSTRACT FROM AUTHOR]

This paper proposes an architecture to develop machine learning/deep learning models for anomaly network intrusion detection systems on reconfigurable computing platforms. We build two models to validate the framework: Anomaly Detection Autoencoder (ADA) and Artificial Neural Classification (ANC) in the NetFPGA-sume platform. Three published data sets NSL-KDD, UNSW-NB15, and CIC-IDS2017 are used to test the deployed models' throughput, latency, and accuracy. Experimental results with the NetFPGA-SUME show that the ADA model uses 20.97% LUTs, 15.16% FFs, 19.42% BRAM, and 6.81% DSP while the ANC model requires 21.39% LUTs, 15.19% FFS, 14.59% BRAM, and 3.67% DSP. ADA and ANC achieve a bandwidth of up to 28.7 Gbps and 34.74 Gbps, respectively. In terms of throughput, ADA can process at up to 18.7 Gops, while ADA can offer 10 Gops with different datasets. With the NSL-KDD dataset, the ADA model achieves 90.87% accuracy and a false negative rate of 4.86%. The ANC model with UNSW-NB15 and CIC-IDS2017 obtains accuracy of 87.49% and 98.22%, respectively, with the false negative rates achieving 2.0% and 6.2%, respectively. [ABSTRACT FROM AUTHOR]

Specular Reflections often exist in the endoscopic image, which not only hurts many computer vision algorithms but also seriously interferes with the observation and judgment of the surgeon. The information behind the recovery specular reflection areas is a necessary pre-processing step in medical image analysis and application. The existing highlight detection method is usually only suitable for medium-brightness images. The existing highlight removal method is only applicable to images without large specular regions, when dealing with high-resolution medical images with complex texture information, not only does it have a poor recovery effect, but the algorithm operation efficiency is also low. To overcome these limitations, this paper proposes a specular reflection detection and removal method for endoscopic images based on brightness classification. It can effectively detect the specular regions in endoscopic images of different brightness and can improve the operating efficiency of the algorithm while restoring the texture structure information of the high-resolution image. In addition to achieving image brightness classification and enhancing the brightness component of low-brightness images, this method also includes two new steps: In the highlight detection phase, the adaptive threshold function that changes with the brightness of the image is used to detect absolute highlights. During the highlight recovery phase, the priority function of the exemplar-based image inpainting algorithm was modified to ensure reasonable and correct repairs. At the same time, local priority computing and adaptive local search strategies were used to improve algorithm efficiency and reduce error matching. The experimental results show that compared with the other state-of-the-art, our method shows better performance in terms of qualitative and quantitative evaluations, and the algorithm efficiency is greatly improved when processing high-resolution endoscopy images. [ABSTRACT FROM AUTHOR]

AES has been used in many applications to provide the data confidentiality. A new 32-bit reconfigurable and compact architecture for AES encryption and decryption is presented and implemented in non-BRAM FPG in this paper. It can be reconfigured for the options of different key sizes which is very flexible for the users to apply AES for various application environments. The proposed design employs a single-round architecture and subpipeling to minimize the hardware cost. The fully composite field GF((24)2)-based encryption/decryption and keyschedule lead to the lower hardware complexity and efficient subpipelining for 32-bit data path. In addition, a new subpipelined on-the-fly keyschedule over composite field GF((24)2) is proposed for all standard key sizes (128-, 192-, 256-bit) which generates the roundkeys simultaneously and efficiently. This feature is very useful and efficient when the main key has been changed since AES is a symmetric-key cryptography and the session key usually changes frequently. The proposed reconfigurable and compact design has higher throughput and lower hardware cost. It achieves throughputs of 375Mbits/s with 128-bit key, 318Mbits/s with 192-bit key and 275Mbits/s with 256-bit key on VIRTEX XC4VSX25-12, and the total number of slices is 1766. The proposed reconfigurable and compact AES architecture can be efficiently applied in computing-restricted environments such as wireless and embedded devices. [ABSTRACT FROM AUTHOR]

Resource provisioning for cloud computing requires adaptive and accurate prediction of cloud workloads. However, existing studies in workload prediction have faced significant challenges in predicting time-varying cloud workloads of diverse trends and patterns, and the lack of accurate prediction often results in resource waste and violation of Service-Level Agreements (SLAs). We propose a bagging-like ensemble framework for cloud workload prediction with Adaptive Pattern Mining (APM). Within this framework, we first design a two-step method with various models to simultaneously capture the "low frequency" and "high frequency" characteristics of highly variable workloads. For a given workload, we further develop an error-based weights aggregation method to integrate the prediction results from multiple pattern-specific models into a final result to predict a future workload. We conduct experiments to demonstrate the efficacy of APM in workload prediction with various prediction lengths using two real-world workload traces from Google and Alibaba cloud data centers, which are of different types. Extensive experimental results show that APM achieves above 19.62% improvement over several classic and state-of-the-art workload prediction methods for highly variable real-world cloud workloads. [ABSTRACT FROM AUTHOR]

A box culvert is a tunnel under roadways or railways that transports open drains or streams. The geotechnical design of such a structure involves accurate evaluation of the earth pressure on the structure. This short technical note pertains to findings of stability factors in tables and charts as an aid to civil engineers in designing unlined square box culverts. A conventional equation that is similar to Terzaghi's bearing capacity factors (Nc, Ns, and Nγ) is adopted to examine the use of stability factors (Fc, Fs, and Fγ) to estimate the critical support pressures at collapse. Rigorous upper-bound and lower-bound solutions of the stability factors are computed using the adaptive finite-element limit analysis. To validate the present bound solutions, numerical results are compared with those published previously using other methods. Design tables, figures, and examples are presented for practical uses. [ABSTRACT FROM AUTHOR]