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Attacks on IoT devices are increasing day by day. Since IoT devices nowadays have become an integral part of our daily lives, the data gathered from IoT devices benefits intruders in many ways. Financial and Healthcare institutions also allow their customers to use their services by using handheld IoT devices. Smart homes and autonomous vehicles use many IoT devices to gather data through the built-in sensors and send it to the Edge server for further processing. The computation result on the Edge server determines the decision to fulfill the user-specific needs. As these data are vital in the future cycle of execution of an intelligent algorithm of IoT device software program, hence the data are not just of temporary use, but it is transferred to a Cloud server for permanent storage. The data flows from IoT sensors to the Edge server, then from the Edge server to the Cloud server. Here the riskiest part for data to stay is on the Edge server. To counter such a security risk, we proposed and implemented the Adaptive Epsilon Greedy Reinforcement Learning (AEGRL) method which is the extension of the traditional Epsilon (ℇ) greedy reinforcement learning method. The proposed method works efficiently for both static and dynamic environments. Experimental results show that our proposed security method outperforms the recent similar security approaches in terms of scalability, robustness, and accuracy. Article Highlights: The research work proposed in this paper is to prevent malicious attacks on the IoT edge server since the edge server continuously gathers data from the surrounding IoT devices. The following are the main highlights of the paper, which bring novelty to our research work. We have considered the dynamic nature of traffic data concerning volume and the pattern of malicious data. The metadata of the malicious packets keeps changing by the smart hackers who also use intelligent tools to cross the common security barriers. Considering the dynamic environment over the Edge server we extended the traditional reinforcement learning approach to include Adaptive ability on the dynamic constraints. We experimented with both simulation-based and real-time environments. [ABSTRACT FROM AUTHOR]

This study examines a new approach to hybrid neuromorphic devices by studying the impact of omeprazole–proteinoid complexes on Izhikevich neuron models. We investigate the influence of these metabolic structures on five specific patterns of neuronal firing: accommodation, chattering, triggered spiking, phasic spiking, and tonic spiking. By combining omeprazole, a proton pump inhibitor, with proteinoids, we create a unique substrate that interfaces with neuromorphic models. The Izhikevich neuron model is used because it is computationally efficient and can accurately simulate the various behaviours of cortical neurons. The results of our simulations show that omeprazole–proteinoid complexes have the ability to affect neuronal dynamics in different ways. This suggests that they could be used as adjustable components in bio-inspired computer systems. We noticed a notable alteration in the frequency of spikes, patterns of bursts, and rates of adaptation, especially in chattering and triggered spiking behaviours. The findings indicate that omeprazole–proteinoid complexes have the potential to serve as adaptable elements in neuromorphic systems, presenting novel opportunities for information processing and computation that have origins in neurobiological principles. This study makes a valuable contribution to the expanding field of biochemical neuromorphic devices and establishes a basis for the development of hybrid bio-synthetic computational systems. [ABSTRACT FROM AUTHOR]

Unlike other natural disasters, drought is one of the most severe threats to all living beings globally. Due to global climate change, the frequency and duration of droughts have increased in many parts of the world. Therefore, accurate prediction and forecasting of droughts are essential for effective mitigation policies and sustainable research. In recent research, the use of ensemble global climate models (GCMs) for simulating precipitation data is common. The objective of this research is to enhance the multi-model ensemble (MME) for improving future drought characterizations. In this research, we propose the use of relative importance metric (RIM) to address collinearity effects and point-wise discrepancy weights (PWDW) in GCMs. Consequently, this paper introduces a new statistical framework for weighted ensembles called the discrepancy-enhanced beta weighting ensemble (DEBWE). DEBWE enhances the weighted ensemble data of precipitation simulated by multiple GCMs. In DEBWE, we addressed uncertainties in GCMs arising from collinearity and outliers. To evaluate the effectiveness of the proposed weighting framework, we compared its performance with the simple average multi-model ensemble (SAMME), Taylor skill score ensemble (TSSE), and mutual information ensemble (MIE). Based on the Kling–Gupta efficiency (KGE) metric, DEBWE outperforms all competitors across all evaluation criteria. These inferences are based on the analysis of historical simulated data from 22 GCMs in the CMIP6 project. The quantitative performance indicators strongly support the superiority of DEBWE. The median and mean KGE values for DEBWE are 0.2650 and 0.2429, compared to SAMME (0.1000, 0.0991), TSSE (0.2600, 0.2397), and MIE (0.1550, 0.1511). For drought assessment, we computed the adaptive standardized precipitation index (SPI) for three future scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. The steady-state probabilities suggest that normal drought (ND) is the most frequent condition, with extreme events (dry or wet) being less probable. [ABSTRACT FROM AUTHOR]

Background: Novel on‐board CBCT allows for improved image quality and Hounsfield unit accuracy. When coupled with online adaptive tools, this may have potential to allow for simulation and treatment to be completed in a single on‐table session. Purpose: To study the feasibility of a high‐efficiency radiotherapy treatment workflow without the use of a separate session for simulation imaging. The dosimetric accuracy, overall efficiency, and technical feasibility were used to evaluate the clinical potential of CT simulation‐free adaptive radiotherapy. Methods: Varian's Ethos adaptive radiotherapy treatment platform was upgraded with a novel CBCT system, HyperSight which reports image quality and Hounsfield unit accuracy specifications comparable to standard fan‐beam CT. Using in‐house developed MATLAB software, CBCT images were imported into the system and used for planning. Two test cases were completed on anthropomorphic phantoms equipped with small volume ion chambers (cross‐calibrated to an ADCL traceable dose standard) to evaluate the feasibility and accuracy of the workflows. A simulated palliative spine treatment was planned with 8 Gy in one fraction, and an intact prostate treatment was planned with 60 Gy in 20 fractions. The CBCTs were acquired using HyperSight with default thorax and pelvis imaging protocols and reconstructed using an iterative algorithm with scatter removal, iCBCT Acuros. CBCTs were used for contouring and planning, and treatment was delivered via an online adaptive workflow. In addition, an external dosimetry audit was completed using only on‐board CBCT imaging in an end‐to‐end head and neck phantom irradiation. Results: An extended‐field CBCT acquisition can be acquired in 12 s, in addition to the time for longitudinal table shifts, and reconstructed in approximately 1 min. The superior‐inferior extent for the CBCT planning images was 38.2 cm, which captured the full extent of relevant anatomy. The contouring and treatment planning for the spine and prostate were completed in 30 and 18 min, respectively. The dosimetric agreement between ion chamber measurements and the treatment plan was within a range of −1.4 to 1.6%, and a mean and standard deviation of 0.41 ± 1.16%. All metrics used in the external audit met the passing criteria, and the dosimetric comparison between fan‐beam and CBCT techniques had a gamma passing rate of 99.0% with a criteria of 2%/2 mm. Conclusion: Using an in‐house workflow, CT simulation‐free radiation therapy was shown to be feasible with acceptable workflow efficiency and dosimetric accuracy. This approach may be particularly applicable for urgent palliative treatments. With the availability of software to enable this workflow, and the continued advancement of on‐treatment adaptation, single‐visit radiation therapy may replace current practice for some clinical indications. [ABSTRACT FROM AUTHOR]

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]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]

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]

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]

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]

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]

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]

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]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]

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]

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]

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]

Metal‐semiconductor heterostructures providing geometrically reproducible and abrupt Schottky nanojunctions are highly anticipated for the realization of emerging electronic technologies. This specifically holds for reconfigurable field‐effect transistors, capable of dynamically altering the operation mode between n‐ or p‐type even during run‐time. Targeting the enhancement of fabrication reproducibility and electrical balancing between operation modes, here a nanoscale Al‐Si‐Al nanowire heterostructure with single elementary, monocrystalline Al leads and sharp Schottky junctions is implemented. Utilizing a three top‐gate architecture, reconfiguration on transistor level is enabled. Having devised symmetric on‐currents as well as threshold voltages for n‐ and p‐type operation as a necessary requirement to exploit complementary reconfigurable circuits, selected implementations of logic gates such as inverters and combinational wired‐AND gates are reported. In this respect, exploiting the advantages of the proposed multi‐gate transistor architecture and offering additional logical inputs, the device functionality can be expanded by transforming a single transistor into a logic gate. Importantly, the demonstrated Al‐Si material system and thereof shown logic gates show high compatibility with state‐of‐the‐art complementary metal‐oxide semiconductor technology. Additionally, exploiting reconfiguration at the device level, this platform may pave the way for future adaptive computing systems with low‐power consumption and reduced footprint, enabling novel circuit paradigms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel algorithm for energy-efficient low-latency dynamic mobile edge computing (MEC), in the context of beyond 5G networks endowed with reconfigurable intelligent surfaces (RISs). We consider a scenario where new computing requests are continuously generated by a set of devices and are handled through a dynamic queueing system. Building on stochastic optimization tools, we devise a dynamic learning algorithm that jointly optimizes the allocation of radio resources (i.e., power, transmission rates, sleep mode and duty cycle), computation resources (i.e., CPU cycles), and RIS reflectivity parameters (i.e., phase shifts), while guaranteeing a target performance in terms of average end-to-end delay. The proposed strategy enables dynamic control of the system, performing a low-complexity optimization on a per-slot basis while dealing with time-varying radio channels and task arrivals, whose statistics are unknown. The presence and optimization of RISs helps boosting the performance of dynamic MEC, thanks to the capability to shape and adapt the wireless propagation environment. Numerical results assess the performance in terms of service delay, learning, and adaptation capabilities of the proposed strategy for RIS-empowered MEC. [ABSTRACT FROM AUTHOR]

The problem that ABC (Artificial Bee Colony) algorithm is good at exploration but poor at exploitation for the numerical optimization is investigated in this paper. PA-ABC (Parameter Adaptive ABC) algorithm is proposed, which adopts different search equations with different search abilities for the employed bee and the onlooker bee. Firstly, the best-so-far solution is introduced into each search equation to enhance exploitation; secondly, the employed bee uses two random solutions to search, so as to keep high ability of exploration; thirdly, the onlooker bee searches around a random solution to keep population diversity; most importantly, adaptive parameter computed by fitness function is introduced in the search equation of the onlooker bee, which makes the search step adjust according to the search process. So the search equation of the employed bee has balanced abilities of exploration and exploitation, while the search equation of the onlooker bee can make the search focus transfer from exploration to exploitation adaptively. The experiment results on benchmark functions show that the search performance of PA-ABC is higher than or at least comparable to basic ABC and typical improved ABCs. In addition, compared to the performance of the state-of-the-art ABC variants under their original parameter configuration, PA-ABC is verified to have similar performance to them. [ABSTRACT FROM AUTHOR]

Dynamic models can help adapt to climate change since they inform on the impacts of decisions and future events on sustainability. They make it possible to follow the evolution of variables over time, to model exogenous events and adaptive policies and to compute sustainability indicators. Various model types based on different worldviews exist, and they give rise to different indicators. Modellers generally choose only one type of model, limiting the variety of indicators. However, decision-makers, who have to be creative to face global change, need a wider diversity of indicators. The objective of this paper is to show the diversity of insights one can get by using alternative system indicators and their decision implications. We test our "very diverse indicators" approach and illustrate its results for a population at risk of flooding and a water-basin manager who can help the population implement protection measures. We test many variations, including e.g. viability theory and agent-based modelling, and different indicators of viability, resilience, efficiency and equity, based on comparable data sets. We show possible synergies of the obtained diversity of insights: for example, one indicator says that it is urgent to act and another which is the best policy to use. We discuss the difficulties of implementation and the benefits of our approach for the decision-maker. • Different and multidisciplinary modelling choices to address the challenge of climate change. • Various indicators produced by different modelling choices to provide a more complete and a less biased view of the problem. • Building indicators to inform decision-makers in terms of viability, resilience, efficiency and equity. • Indicators dealing differently with uncertain future events and dynamics to provide more robust information and decision. [ABSTRACT FROM AUTHOR]

Deep neural networks have been deployed in various hardware accelerators, such as graph process units (GPUs), field-program gate arrays (FPGAs), and application specific integrated circuit (ASIC) chips. Normally, a huge amount of computation is required in the inference process, creating significant logic resource overheads. In addition, frequent data accessions between off-chip memory and hardware accelerators create bottlenecks, leading to decline in hardware efficiency. Many solutions have been proposed to reduce hardware overhead and data movements. For example, specific lookup-table (LUT)-based hardware architecture can be used to mitigate computing operation demands. However, typical LUT-based accelerators are affected by computational precision limitation and poor scalability issues. In this paper, we propose a search-based computing scheme based on an LUT solution, which improves computation efficiency by replacing traditional multiplication with a search operation. In addition, the proposed scheme supports different precision multiple-bit widths to meet the needs of different DNN-based applications. We design a reconfigurable computing strategy, which can efficiently adapt to the convolution of different kernel sizes to improve hardware scalability. We implement a search-based architecture, namely SCA, which adopts an on-chip storage mechanism, thus greatly reducing interactions with off-chip memory and alleviating bandwidth pressure. Based on experimental evaluation, the proposed SCA architecture can achieve 92%, 96% and 98% computational utilization for computational precision of 4 bit, 8 bit and 16 bit, respectively. Compared with state-of-the-art LUT-based architecture, the efficiency can be improved four-fold. [ABSTRACT FROM AUTHOR]

Current edge computing systems are deployed in highly complex application scenarios with dynamically changing requirements. In order to provide the expected performance and energy efficiency values in these situations, the use of heterogeneous hardware/software platforms at the edge has become widespread. However, these computing platforms still suffer from the lack of unified software-driven programming models to efficiently deploy multi-purpose hardware-accelerated solutions. In parallel, edge computing systems also face another huge challenge: operating under multiple conditions that were not taken into account during any of the design stages. Moreover, these conditions may change over time, forcing self-adaptation mechanisms to become a must. This paper presents an integrated architecture to exploit hardware-accelerated data-parallel models and transparent hardware/software multithreading. In particular, the proposed architecture leverages the ARTICo3 framework and ReconOS to allow developers to select the most suitable programming model to deploy their edge computing applications onto run-time reconfigurable hardware devices. An evolvable hardware system is used as an additional architectural component during validation, providing support for continuous lifelong learning in smart edge computing scenarios. In particular, the proposed setup exhibits online learning capabilities that include learning by imitation from software-based reference algorithms. Experimental results show the benefits of the proposed approach, exposing different run-time tradeoffs (e.g., computing performance versus functional correctness of the evolved solutions), and highlighting the benefits of using scalable data-parallel models to perform circuit evolution under dynamically changing application scenarios. [ABSTRACT FROM AUTHOR]

Efficient lattice-based cryptosystems operate with polynomial rings with the Number Theoretic Transform (NTT) to reduce the computational complexity of polynomial multiplication. NTT has therefore become a major arithmetic component (thus computational bottleneck) in various cryptographic constructions like hash functions, key-encapsulation mechanisms, digital signatures, and homomorphic encryption. Although there exist several hardware designs in prior work for NTT, they all are isolated design instances fixed for specific NTT parameters or parallelization level. This article provides an extensive study of flexible design methods for NTT implementation. To that end, we evaluate three cases: (1) parametric hardware design, (2) high-level synthesis (HLS) design approach, and (3) design for software implementation compiled on soft-core processors, where all are targeted on reconfigurable hardware devices. We evaluate the designs that implement multiple NTT parameters and/or processing elements, demonstrate the design details for each case, and provide a fair comparison with each other and prior work. On a Xilinx Virtex-7 FPGA, compared to HLS and processor-based methods, the results show that the parametric hardware design is on average $4.4\times$ 4. 4 × and $73.9\times$ 73. 9 × smaller and $22.5\times$ 22. 5 × and $19.3\times$ 19. 3 × faster, respectively. Surprisingly, HLS tools can yield less efficient solutions than processor-based approaches in some cases. [ABSTRACT FROM AUTHOR]

Dynamic resources management in reconfigurable processors often manifests as a hard online decision-making task, which should yield premier solutions that must meet Quality-of-Service (QoS) requirements while maximizing the system’s efficiency. Most prior works rely on a hard-to-train predictor to model the complicated relationships between processor configurations and performance. To decide the proper resource allocation, the predictor needs to tentatively evaluate a group of possible configurations, and then decide the best configuration for the workload. This tedious process has an expensive runtime overhead for resource configuration in processors. Besides, prior works focus on improving the prediction accuracy, however, higher performance prediction cannot guarantee a good system outcome. Inspired by recent advances in adversarial learning, we present a generative adversarial network (GAN)-based framework, Amphis, which can directly generate the on-demand processor configuration for any scheduled-in application. By evaluating Amphis on a reconfigurable processor with 18 different workloads, our results demonstrate that the GAN-based method provides tremendous overhead reduction (up to 90%) compared to the SOTA prediction-based method WNNM while providing higher resource utilization. [ABSTRACT FROM AUTHOR]

There has been a lot of research on edge-computing task offloading in deep reinforcement learning (DRL). Deep reinforcement learning is one of the important algorithms in the current AI field, but there is still room for improvement in the time cost and adaptive correction ability of the algorithm. This paper studies the application of DRL algorithms in edge-computing task offloading, and its key innovation is to propose an MADRLCO algorithm, which is based on the design idea of the Actor–Critic framework, uses the DNN model to act as an Actor, and can more accurately locate the initial decision through iterative training, and use the LSTM model to optimize the Critic, which can be more accurate. The optimal decision can be located in a short period of time. The main work of this paper is divided into three parts: (1) The AC algorithm of the Actor–Critic framework in DRL is proposed to be applied to edge-computing task offloading. (2) To address the weak generalization ability of the basic version of the Actor–Critic algorithm in multi-objective optimization, the sequential quantitative correction and adaptive correction parameter K method are used to optimize the Critic frame, thereby improving the generalization ability of the model in multi-objective decision-making and improving the rationality of decision-making results. (3) Aiming at the problem of large time cost in the critical framework of the model, a search algorithm for resource allocation-related parameters based on the time-series prediction method is proposed (time-series forecasting is a research branch of pattern recognition), which reduces the time overhead of the algorithm and improves the adaptive correction capability of the model. The algorithm in this paper can adapt to not only the time-varying network channel state, but also the time-varying number of device connections. Finally, it is proved by experiments that compared with the DRL calculation offloading algorithm based on DNN plus binary search, the MADRLCO algorithm reduces the model training time by 66.27%, and in the environment of the time-varying number of devices in the metasystem, the average model average. The standard calculation rate is 0.0403 higher than that of the current optimal algorithm. [ABSTRACT FROM AUTHOR]

Reconfigurable computing can significantly improve the performance and energy efficiency of many applications. However, FPGA-based chips are evolving rapidly, increasing the difficulty of evaluating the impact of new capabilities such as HBM and high-speed links. In this paper, a real-world application was implemented on different FPGAs in order to better understand the new capabilities of modern FPGAs and how new FPGA technology improves performance and scalability. The aforementioned application was the preconditioned conjugate gradient (PCG) method that is utilized in underground analysis. The implementation was done on four different FPGAs, including an MPSoC, taking into account each platform's characteristics. The results show that today's FPGA-based chips offer eight times better performance on a memory-bound problem than 5-year-old FPGAs, as they incorporate HBM and can operate at higher clock frequencies. [ABSTRACT FROM AUTHOR]

Target tracking is crucial to many applications in wireless sensor networks (WSNs). Existing tracking schemes used in WSNs can basically be classified two categories, clustering and predicting. Considering network clustering consumes much energy for limited-energy WSNs, a predicting target tracking scheme is proposed called MC-MPMC (measurement compensation-based mixture population Monte Carlo) which tracks the target based on predicted locations in this work. Adaptive mixture PMC model for generating proposals varying from each iteration is proposed to guarantee sampling diversity. And also, extra measurements or observations generating method is introduced to compensate missed prediction locations or false estimations, avoiding tracking behavior degradation. Firstly, samples drawn from the proposals of next iteration can be generated by a mixture method to avoid sample degeneracy. Secondly, sample weights are jointly computed based on adaptive fusion of compensation measurement and true measurements. Thirdly, HTC method is combined to MC-MPMC scheme to decrease energy consumption in WSNs. Then, the proposed method is verified through comprehensive experiments about tracking error, delay and consumption predictions. Moreover, performance comparisons of MC-MPMC with other tracking schemes are also proposed. [ABSTRACT FROM AUTHOR]

This work introduced a new BIST scheme under a functional test approach. It solves the problem of determining in-field the specifications of lowpass filters embedded in PSoC1 devices with zero hardware overhead. The user can implement the BIST we propose here because it just requires the information and resources freely offered by the manufacturer, the nominal specifications of the filter, and its location within the resources array of the chip. It consists of a software routine that reconfigures the resources available in the device to synthesize all the circuitry needed for the test: stimuli generation, filter response measurement and analysis, data processing, and a communication interface to output the test results. The proposal is based on the transient response analysis method (TRAM), a test strategy for analog filters used to determine the functional parameters of the circuits under test. The BIST performance was evaluated by comparing it against frequency response and TRAM measurements. The laboratory results showed low errors and good repeatability, validating the proposal. [ABSTRACT FROM AUTHOR]

The rapid development of Cloud Computing (CC) has led to the release of many services in the cloud environment. Service composition awareness of Quality of Service (QoS) is a significant challenge in CC. A single service in the cloud environment cannot respond to the complex requests and diverse requirements of the real world. In some cases, one service cannot fulfill the user's needs, so it is necessary to combine different services to meet these requirements. Many available services provide an enormous QoS and selecting or composing those combined services is called an Np-hard optimization problem. One of the significant challenges in CC is integrating existing services to meet the intricate necessities of different types of users. Due to NP-hard complexity of service composition, many metaheuristic algorithms have been used so far. This article presents the Artificial Bee Colony and Genetic Algorithm (ABCGA) as a metaheuristic algorithm to achieve the desired goals. If the fitness function of the services selected by the Genetic Algorithm (GA) is suitable, a set of services is further introduced for the Artificial Bee Colony (ABC) algorithm to choose the appropriate service from, according to each user's needs. The proposed solution is evaluated through experiments using Cloud SIM simulation, and the numerical results prove the efficiency of the proposed method with respect to reliability, availability, and cost. [ABSTRACT FROM AUTHOR]

The digital signal processing approaches were investigated as a preliminary indicator for discriminating between the protein coding and non-coding regions of DNA. This is because a three-base periodicity (TBP) has already been proven to exist in protein-coding regions arising from the length of codons (three nucleic acids). This demonstrates that there is a prominent peak in the energy spectrum of a DNA coding sequence at frequency 1 3 rad/sample. However, because DNA sequences are symbolic sequences, these should be mapped into one or more signals such that the hidden information is highlighted. We propose, therefore, two new algorithms for computing adaptive mappings and, by using them, finding periodicities. Both such algorithms are based on the spectral envelope approach. This adaptive approach is essentially important since a single mapping for any DNA sequence may ignore its intrinsic properties. Finally, the improved performance of the new methods is verified by using them with synthetic and real DNA sequences as compared to the classical methods, especially the minimum entropy mapping (MEM) spectrum, which is also an adaptive method. We demonstrated that our method is both more accurate and more responsive than all its counterparts. This is especially important in this application since it reduces the risks of a coding sequence being missed. [ABSTRACT FROM AUTHOR]

This paper presents two improved and adaptive models based on functional neural network and Autoregression (FNNAR) analysis. These models have been developed for estimating the fundamental component of nonlinear and varying load current and computing the exact compensation required in a power distribution system. The proposed FNNAR analysis involves two steps: The first step is designed to estimate the fundamental current in terms of polynomial or trigonometric functional expansion terms; while, the second step involves computations based on the weighted sum of the delayed output terms. An activation function is additionally incorporated to account for the nonlinearity and sudden variations of load current. Both the FNNAR models are developed and their parameters computed in an adaptive manner from the input–output data. The simulation results on a single-phase 110 V, 50 Hz system power distribution system are validated by a scaled down experimental model showing hardware results depicting load compensation. Adequate comparison of the two developed models is also discussed in the paper with two advanced variants of conventional algorithms viz. Least means square algorithm and second order generalized integrator based filtering technique. [ABSTRACT FROM AUTHOR]