Computer Research Articles

A multi-strategy optimizer for energy minimization of multi-UAV-assisted mobile edge computing

Disasters in remote areas often cause damage to communication facilities, which presents significant challenges for rescue efforts. As flexible mobile devices, unmanned aerial vehicles (UAVs) can provide temporary network services to address this issue. This paper studies the use of UAVs as mobile base stations to offer offload computing services for disaster relief devices in affected areas. To ensure reliable communication between disaster relief devices and UAVs, we construct a multi-UAV-assisted mobile edge computing (MEC) system with the objective of minimizing system energy consumption. Inspired by swarm intelligence principles, we propose a multi-strategy optimizer (MSO) that defines various population search functions and employs superior neighborhood methods for population updates. Experimental results demonstrate that MSO achieves superior system energy efficiency and exhibits greater stability compared to several state-of-the-art swarm intelligence algorithms.

Comparative analysis of the effectiveness of mechanical treatment of root canals by various Ni-Ti file systems

INTRODUCTION. One of the main stages of successful endodontic treatment is high-quality mechanical treatment of root canals. А large number of various rotary Ni-Ti files in the dental market poses a difficult choice for the doctor, especially in the context of import substitution. It is necessary to have knowledge about the effectiveness of different system of instruments in a particular clinical case. AIM. With the help of scanning electronic mycoroscopy and computer microtomography, to analyze the quality of mechanical treatment of the root canal with files of domestic and foreign production.MATERIALS AND METHODS. During the study, single-root and multi-root teeth with different root canal curvature were selected, which were distributed according to the Ni-Ti file system used: group 1 – RM files (RusMed), group 2 – ProTaper Universal (Dentsply Mallifier), group 3 – M-two (VDW).RESULTS. The analysis of scanning electron microscopy of tooth showed that the largest number of open dentine tubules is determined when treating the root canal with instruments of the group 1 (55.4±1.37) and group 3 (60.25±1.59). The centering ability with a curvature of the root canal of 20–25° had no significant differences between the tools used. With a curvature of the root canal of 40–45°, the value of this parameter decreased significantly in all groups of instruments, it was greatest in the groups 1 and 3.CONCLUSION. It was revealed that the best quality of mechanical treatment of the root canal was obtained using RM-files and Mtwo in samples with a channel curvature of up to 25°.

Hardware Failure Prediction Modes Powered by Artificial Intelligence

With the increase of the system scale and complexity of modern computer systems and electronic equipment, the physical components (hardware) in computer or electronic equipment are damaged, the performance is reduced or the normal operation of computer and electronic systems is greatly increased due to various reasons. Detecting and predicting hardware faults in time is the key to ensure system stability and reliability. Hardware failure prediction technology based on artificial intelligence (AI) can effectively predict potential hardware failures by analyzing historical data, monitoring equipment operating status in real time, and reducing system downtime and maintenance costs. This paper reviews the current hardware fault prediction methods based on artificial intelligence, discusses the common algorithms, technical challenges, and application scenarios, and looks forward to the future development direction of this field.

Different strokes in randomised strategies: Revisiting Kuhn's theorem under finite-memory assumptions

Two-player (antagonistic) games on (possibly stochastic) graphs are a prevalent model in theoretical computer science, notably as a framework for reactive synthesis.Optimal strategies may require randomisation when dealing with inherently probabilistic goals, balancing multiple objectives, or in contexts of partial information. There is no unique way to define randomised strategies. For instance, one can use so-called mixed strategies or behavioural ones. In the most general setting, these two classes do not share the same expressiveness. A seminal result in game theory — Kuhn's theorem — asserts their equivalence in games of perfect recall.This result crucially relies on the possibility for strategies to use infinite memory, i.e., unlimited knowledge of all past observations. However, computer systems are finite in practice. Hence it is pertinent to restrict our attention to finite-memory strategies, defined as automata with outputs. Randomisation can be implemented in these in different ways: the initialisation, outputs or transitions can be randomised or deterministic respectively. Depending on which aspects are randomised, the expressiveness of the corresponding class of finite-memory strategies differs.In this work, we study two-player concurrent stochastic games and provide a complete taxonomy of the classes of finite-memory strategies obtained by varying which of the three aforementioned components are randomised. Our taxonomy holds in games of perfect and imperfect information with perfect recall, and in games with more than two players. We also provide an adapted taxonomy for games with imperfect recall.

A Near-Infrared Retinomorphic Device with High Dimensionality Reservoir Expression.

Physical reservoir-based reservoir computing (RC) systems for intelligent perception have recently gained attention because they require fewer computing resources. However, the system remains limited in infrared (IR) machine vision, including materials and physical reservoir expression power. Inspired by biological visual perception systems, the study proposes a near-infrared (NIR) retinomorphic device that simultaneously perceives and encodes narrow IR spectral information (at ≈980nm). The proposed device, featuring core-shell upconversion nanoparticle/poly (3-hexylthiophene) (P3HT) nanocomposite channels, enables the absorption and conversion of NIR into high-energy photons to excite more photo carriers in P3HT. The photon-electron-coupled dynamics under the synergy of photovoltaic and photogating effects influence the nonlinearity and high dimensionality of the RC system under narrow-band NIR irradiation. The device also exhibits multilevel data storage capability (≥8 levels), excellent stability (≥2000 s), and durability (≥100 cycles). The system accurately identifies NIR static and dynamic handwritten digit images, achieving recognition accuracies of 91.13% and 90.07%, respectively. Thus, the device tackles intricate computations like solving second-order nonlinear dynamic equations with minimal errors (normalized mean squared errorof 1.06 × 10⁻3 during prediction).

Segmenting hotspots from medical thermal images using Density-based modified FC-Pc FS with spatial information

ABSTRACT Thermal cameras complemented with robust and automated computational intelligence-aided diagnostic systems can be deployed at public gathering points and on smartphones to raise alarms about thriving diseases of their body due to inflammation in a timely manner, thereby strengthening human health systems. In this work, we have proposed two robust Density-based modified Picture Fuzzy Clustering methods with spatial information to segment hotspots from human’s abnormal thermal images. We formulated and solved the objective function by combining P c FS-based clustering method with modified Renyi’s Entropy. We demonstrated the robustness of our methods, DSIFC-Pc FS and DSIMFC-Pc FS, on three publicly available datasets and an artificially created noisy dataset of thermal images. We have obtained a ZC score of 0.933, 0.947 and 0.933 for DB-DMR-IR, DB-FOOT-IR and DB-THY-IR datasets, respectively. Also, our method converged to an optimal partition matrix in minimum iterations. The experimental performance shows that the proposed approaches, DSIFC- P c FS and DSIMFC- P c FS, outperformed other ten related approaches in segmenting the medical thermal images with and without noise, indicating their robustness and efficacy, in terms of various unsupervised evaluation metrics. Also, we statistically analysed their performance in contrast to other methods using the Friedman Test, which advocate their superiority.

ANALISIS SENTIMEN APLIKASI MICROSOFT TEAMS BERDASARKAN ULASAN GOOGLE PLAY STORE MENGGUNAKAN MODEL NEURAL NETWORK DENGAN OPTIMASI ADAPTIVE MOMENT ESTIMATION (ADAM)

The Covid-19 virus pandemic with its rapid spread has made the government enact several policies that can reduce community activities that have the potential to cause crowds including social distancing, work from home (WFH) and online and hybrid learning. One of the platforms that was widely used during the WFH and online learning period was Microsoft Teams. Microsoft Teams is an app that lets people and organizations collaborate and interact online. Microsoft Teams can be downloaded through the Google Play Store and users can leave reviews. Sentiment analysis is used to classify user sentiment towards Microsoft Teams into positive and negative sentiments. Review data obtained from January to November 2022. The classification is carried out using the Neural Network which is a computational system inspired by biological neural networks. Weight optimization is done using Adaptive Moment Estimation which is popular in the field of deep learning because it achieves good results quickly. The classification model in this paper was built with a training test ratio of 80:20. The best hyperparameter combination is node hidden layer 100, learning rate 0.0001, and batch size 32. The accuracy of the ANN classification model was obtained by 88.16%.Keywords: Microsoft Teams, Google Play Store, Sentiment Analysis, Neural Network, Adaptive Moment Estimation

UAV-mounted IRS assisted wireless powered mobile edge computing systems: Joint beamforming design, resource allocation and position optimization

Intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have been recently used in wireless-powered mobile edge computing (MEC) systems to enhance the computation bits and energy harvesting performance. However, in the conventional IRS- and UAV-aided MEC systems, the IRS is installed at fixed locations on a building, which restricts the computation performance. UAV-mounted IRS (UAV-IRS), as a promising technology, combines the advantages of UAV and IRS. Hence, in this work, we study a UAV-IRS wireless-powered MEC system, where multiple UAV-IRSs are considered between Internet of Things (IoT) devices and the base station to improve the computation bits and energy harvesting. The multi-antenna base station first charges the IoT devices via radio frequency signals, and then IoT devices offload their computation tasks to the base station via UAV-IRSs. We formulate a computation bits maximization problem for all IoT devices by jointly determining detection beamforming at IoT devices, active energy beamforming at the base station, power allocation, time slot assignment, CPU frequency, the phase shifts design in the wireless energy transfer (WET) and task offloading, and UAV-IRSs positions. A block coordinate descent (BCD) algorithm by decomposing the introduced problem into four blocks is proposed, while the detection beamforming, active energy beamforming, transmit power, time slot assignment, CPU frequency, and the phase shifts design in the task offloading are derived in closed-form results. Also, the successive convex approximation and semidefinite relaxation (SDR) are adopted to obtain the UAV-IRS positions and the phase shifts in the WET, respectively. The simulation results verify the effectiveness of the presented BCD method compared with the different benchmark schemes.

Pose Estimation of a Cobot Implemented on a Small AI-Powered Computing System and a Stereo Camera for Precision Evaluation.

The precision of robotic manipulators in the industrial or medical field is very important, especially when it comes to repetitive or exhaustive tasks. Geometric deformations are the most common in this field. For this reason, new robotic vision techniques have been proposed, including 3D methods that made it possible to determine the geometric distances between the parts of a robotic manipulator. The aim of this work is to measure the angular position of a robotic arm with six degrees of freedom. For this purpose, a stereo camera and a convolutional neural network algorithm are used to reduce the degradation of precision caused by geometric errors. This method is not intended to replace encoders, but to enhance accuracy by compensating for degradation through an intelligent visual measurement system. The camera is tested and the accuracy is about one millimeter. The implementation of this method leads to better results than traditional and simple neural network methods.

Variational Iteration Method for Solving First Kind Bessel Differential Equation of Order Zero

The aim of this paper is to study the first kind Bessel differential equation of order zero. The variational iteration method (VIM) is used to obtain the approximate solutions of first kind Bessel differential equation (BDE) of order zero. The approximate solution of this equation is calculated in the form of a series which its components are computed, can be calculated rapidlyon computer systems and the convergence of this method is studied. Also two numerical examples are given . The findings, which illustrate the effectiveness and precision of the suggested strategy, are presented in tables and figures.

Concurrent Image Differentiation and Integration Processings Enabled By Polarization‐Multiplexed Metasurface

AbstractOptical computing and image processing performed by sensor front‐end metasurfaces is receiving increasing interest because of advantages such as significant reduction of latency time, energy consumption, and system complexity. Despite the rapid progress, concurrent processing, the most important feature of electronic computing, has not yet been well implemented in optical computing. Here, a metasurface‐based optical image processor that can perform optical differentiation and integration tasks simultaneously is proposed. This optical front‐end processor integrates two coherent transfer functions corresponding to differential and integral convolution kernels into a built‐in metasurface by polarization encoding, allowing concurrent processing of multiple all‐optical computational tasks. The simultaneous differentiation and integration operations on images for edge enhancement and denoising are demonstrated at multiple visible wavelengths. This concurrent processing architecture paves a promising pathway toward multifunctional and higher‐speed image processing for machine vision and biomedical imaging and shows the potential to expedite and potentially supplant certain digital neural network algorithms.

Equipping computational pathology systems with artifact processing pipelines: a showcase for computation and performance trade-offs

BackgroundHistopathology is a gold standard for cancer diagnosis. It involves extracting tissue specimens from suspicious areas to prepare a glass slide for a microscopic examination. However, histological tissue processing procedures result in the introduction of artifacts, which are ultimately transferred to the digitized version of glass slides, known as whole slide images (WSIs). Artifacts are diagnostically irrelevant areas and may result in wrong predictions from deep learning (DL) algorithms. Therefore, detecting and excluding artifacts in the computational pathology (CPATH) system is essential for reliable automated diagnosis.MethodsIn this paper, we propose a mixture of experts (MoE) scheme for detecting five notable artifacts, including damaged tissue, blur, folded tissue, air bubbles, and histologically irrelevant blood from WSIs. First, we train independent binary DL models as experts to capture particular artifact morphology. Then, we ensemble their predictions using a fusion mechanism. We apply probabilistic thresholding over the final probability distribution to improve the sensitivity of the MoE. We developed four DL pipelines to evaluate computational and performance trade-offs. These include two MoEs and two multiclass models of state-of-the-art deep convolutional neural networks (DCNNs) and vision transformers (ViTs). These DL pipelines are quantitatively and qualitatively evaluated on external and out-of-distribution (OoD) data to assess generalizability and robustness for artifact detection application.ResultsWe extensively evaluated the proposed MoE and multiclass models. DCNNs-based MoE and ViTs-based MoE schemes outperformed simpler multiclass models and were tested on datasets from different hospitals and cancer types, where MoE using (MobileNet) DCNNs yielded the best results. The proposed MoE yields 86.15 % F1 and 97.93% sensitivity scores on unseen data, retaining less computational cost for inference than MoE using ViTs. This best performance of MoEs comes with relatively higher computational trade-offs than multiclass models. Furthermore, we apply post-processing to create an artifact segmentation mask, a potential artifact-free RoI map, a quality report, and an artifact-refined WSI for further computational analysis. During the qualitative evaluation, field experts assessed the predictive performance of MoEs over OoD WSIs. They rated artifact detection and artifact-free area preservation, where the highest agreement translated to a Cohen Kappa of 0.82, indicating substantial agreement for the overall diagnostic usability of the DCNN-based MoE scheme.ConclusionsThe proposed artifact detection pipeline will not only ensure reliable CPATH predictions but may also provide quality control. In this work, the best-performing pipeline for artifact detection is MoE with DCNNs. Our detailed experiments show that there is always a trade-off between performance and computational complexity, and no straightforward DL solution equally suits all types of data and applications. The code and HistoArtifacts dataset can be found online at Github and Zenodo, respectively.

Real-time Blood Pressure Prediction on Wearable Devices using Edge Based Deep Neural Networks: A Hardware-software Co-design Approach

This paper presents the hardware realization of a real-time blood pressure (BP) prediction model for wearable devices, utilizing long short-term memory (LSTM) deep neural networks (DNNs). The proposed system uses both electrocardiogram (ECG) and photoplethysmogram (PPG) signal values for BP prediction. It aims to address the limitations of traditional BP measurement methods, providing a low error, minimal computational overhead, more accurate and convenient alternative system for individuals with hypertension or at risk for cardiovascular diseases. The utilization of split matrix approach leads to a reduction in hardware complexity across the entire system. This technique involves breaking down the larger weight matrices used in the computations of DNNs into smaller matrices. This fragmentation results in a decrease in the complexity of the hardware responsible for performing matrix vector multiplications (MVMs) within LSTMs. The resultant architecture of the predictive model gains several advantages, including a lowered level of complexity in terms of the space occupied by individual cells, decreased processing delay, and reduced power consumption. Furthermore, this approach enables the achievement of a notably improved minimum achievable clock period of 2.972 ns. This prediction model can operate locally on wearable devices, reducing the reliance on cloud computing and improving privacy and security. The performance evaluations are carried out using both analytical and implementation results. The results indicate that the proposed model can be practically applied to real-world problems and can potentially enhance the accuracy of various machine-learning tasks.

Comprehensive Malware Classification and Visualization with EfficientNet Based Malware Image Transformations

Malware is a general name given to all malicious software that threatens and prevents the use of information systems. Computers, which have become mandatory in daily life, are constantly under the threat of malware as well as facilitating human life. Therefore, the detection of malware that threatens computer systems is important. This study focuses on the classification of malware. In the study, a deep learning model based on the EfficientNet architecture and the Dynamic Distribution Adaptation Network approach were proposed and these proposed models were tested using the Microsoft Malware Classification Challenge (MMCC) and Dumpware10 datasets. In the study, the process of converting malware into images was discussed and the EfficientNet model was used as the basis for the classification of these images. The EfficientNet backbone-based Dynamic Distribution Adaptation Network achieved 97% accuracy in the MMCC dataset and 96% accuracy in the Dumpware10 dataset. As a result, the EfficientNet architecture proved the effectiveness of deep learning in the classification of malware and cybersecurity.

Very-Large-Scale GPU-Accelerated Nuclear Gradient of Time-Dependent Density Functional Theory with Tamm-Dancoff Approximation and Range-Separated Hybrid Functionals.

Modern graphics processing units (GPUs) provide an unprecedented level of computing power. In this study, we present a high-performance, multi-GPU implementation of the analytical nuclear gradient for Kohn-Sham time-dependent density functional theory (TDDFT), employing the Tamm-Dancoff approximation (TDA) and Gaussian-type atomic orbitals as basis functions. We discuss GPU-efficient algorithms for the derivatives of electron repulsion integrals and exchange-correlation functionals within the range-separated scheme. As an illustrative example, we calculate the TDA-TDDFT gradient of the S1 state of a full-scale green fluorescent protein with explicit water solvent molecules, totaling 4353 atoms, at the ωB97X/def2-SVP level of theory. Our algorithm demonstrates favorable parallel efficiencies on a high-speed distributed system equipped with 256 Nvidia A100 GPUs, achieving >70% with up to 64 GPUs and 31% with 256 GPUs, effectively leveraging the capabilities of modern high-performance computing systems.

The Effects of Omeprazole on the Neuron-like Spiking of the Electrical Potential of Proteinoid Microspheres.

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.

Digital twin technology (DTT) in a developing construction industry: evaluation of the awareness and usage among professionals in Nigeria

Technological innovations play a crucial role in project delivery across all sectors of the economy. This research seeks to assess the awareness of digital twin technology (DTT) among construction industry professionals to promote the sustainable completion of construction projects. A quantitative approach was adopted by using a survey questionnaire with convenience sampling. Questionnaires were self-administered to gather information about the industry professionals’ awareness and utilization of DTT in construction projects. Mean Item Score (MIS), Standard Deviation (SD), Spearman, and Kruskal-Wallis tests were used to analyze the collected data. Professionals within the construction industry have demonstrated a high level of familiarity with various technological advancements with computer systems, Wi-Fi, 2D Graphics, Vertex Digital twin platform, and GPS identified as the most commonly utilized tools in the field. Spearman correlation analysis revealed a significant relationship between DTT awareness and usage levels. The study provided valuable information on the present awareness and DTT utilization within the construction sector. It proposed methods through which professionals can familiarize themselves with empowering tools to enhance productivity and performance within the industry.

Technology Trends and Augmented Reality: A Systematics Literature Review

The real environment is combined with artificial virtual things to create a dynamic, context-based and interactive augmented reality. Therefore, with the latest advances in augmented reality technology and using diverse displays in mobile computing system, augmented reality is affordable for the public. However, its use, as well as the variety of concepts have not yet been fully explored. Therefore, on this page, we describe the variety of industries that have used augmented reality in a variety of uses as well as a variety of displays. Therefore, in order to find relevant data about various industries using augmented reality, a review study involving filtering information from two databases was done. The results of the analysis found a total of 2,929 primary research articles that had been published between 2020 and 2024. While the literature review found that the authors paid attention to the sample size, methodology, devices used, field and results. We conclude that augmented reality design technology is able to thrive by incorporating more fields and capabilities.

Integrated trajectory optimization for UAV-enabled wireless powered MEC system with joint energy consumption and AoI minimization

This paper studies an unmanned aerial vehicle (UAV)-enabled wireless powered mobile edge computing (MEC) system, where a UAV, equipped with RF Chains and MEC servers, can sustainably provide wireless energy for charging Internet of Things (IoT) devices and executing computing tasks from these devices while hovering at designated hover points. Our goal is to minimize the weighted sum of energy consumption and Age of information (AoI) in this system, which depended on the UAV’s hovering time at designated points and its flying time. To achieve this, we jointly optimize the deployment of hover points and the visiting order of these points by the UAV. It is NP hard and mixed-integer non-convex which is difficult to solve by traditional methods. To tackle this problem, we present a trajectory optimization algorithm for joint energy consumption and AoI (TOJEA), which consists of two phases. In the first phase, an Equilibrium Optimizer (EO) algorithm with a variable individual size via its coding and updating strategies, in which each particle (individual) with its concentration (position) represents a target solution i.e. the whole deployment of hover points, is proposed to optimize the number and locations of hover points. Based on the deployment of hover points, a low-complexity greedy algorithm is adopted in the second stage to generate the optimal visiting order for the UAV. Experimental results demonstrate that TOJEA outperforms other algorithms on ten instances with up to 400 IoT devices.

Исследование многоуровневых моделей управления вагонопотоками в адрес припортовых станций с применением теории дифференциальных уравнений

Purpose: To consider the issue of sustainability of multi-level control systems for car flows to port railway stations in the conditions of intellectualization of transportation management processes. To assess the influence of three-level and two-level control systems for the operational work of port roads on the quality of planning the supply of trains to port stations under various modes of operational operation of railways. Determine the conditions for the stability of the car flow control system by constructing and studying the corresponding hard and soft mathematical models described by autonomous systems of ordinary linear differential equations. Methods: In this study, the methods of the theory of stability of solutions of differential equations, as well as the theory of control and dynamical systems are used. A phase space is used in a visual representation, a qualitative and quantitative analysis of the behavior of phase trajectories in it is performed, corresponding to undisturbed and perturbed solutions of systems of differential equations describing the constructed models of control systems. Results: A comparison of the characteristics of multistage car traffic control systems is given. A study of the stability of multilevel models of car traffic management in the context of the intellectualization of management functions is proposed. In the environment of the analytical computing system, the conditions of asymptotic stability in time of a two-level wagon traffic control system are found and studied. Practical importance: The results obtained by computational experiment make it possible to assess the stability of the functioning of port-side transport and technological systems in the context of changes in the organization of production and the intellectualization of transportation management processes. Computer mathematics systems make it possible to implement the heuristic component of research and obtain theoretically sound and visual solutions to problems of optimizing cargo and wagon traffic control modes.