Computer Operations Research Articles

WIMS : A Modern Web-Based MIPS Simulator for Improved Learning in Computer Architecture and Operating Systems

The MIPS processor is well-regarded in teaching Computer Architecture and Operating Systems due to its simpler architecture and efficient design. Simulators are a highly effective method for learning MIPS. Current simulators, like MARS, face issues such as lack of data path visualization, outdated interfaces, and installation requirements, which impede student learning. This work aims to develop an accessible online MIPS simulator featuring a modern code editor, data path visualization, step-by-step execution, and memory visualization. The simulator runs in a web browser, eliminating installation barriers and enhancing usability. This tool aims to improve the educational experience and deepen students’ understanding of MIPS architecture and assembly programming.

Page Replacement Algorithms: A Survey of Performance and Case Studies

This system uses paging as part of memory management in a computer's operating system. Page replacement algorithms are responsible for deciding which pages should be replaced when new allocations are required. Paging happens when there is a page fault, and there are insufficient or no free pages available for allocation. Keywords— Operating System, Page Replacement Algorithms, Virtual Memory Management, Page Allocation

UAV Mission Computer Operation Mode Optimization Focusing on Computational Energy Efficiency and System Responsiveness

The rising popularity of UAVs and other autonomous control systems coupled with real-time operating systems has increased the complexity of developing systems with the proper robustness, performance, and reactivity. The growing demand for more sophisticated computational tasks, proportionally larger payloads, battery limitations, and smaller take-off mass requires higher energy efficiency for all avionics and mission computers. This paper aims to develop a technique for experimentally studying the indicators of reactivity and energy consumption in a computing platform for unmanned aerial vehicles (UAVs). The paper provides an experimental assessment of the ‘Boryviter 0.1’ computing platform, which is implemented on the ATSAMV71 microprocessor and operates under the open-source FreeRTOS operating system. The results are the basis for developing algorithms and energy-efficient design strategies for the mission computer to solve the optimization problem. This paper provides experimental results of measurements of the energy consumed by the microcontroller and estimates of the reduction in system energy consumption due to additional time costs for suspending and resuming the computer’s operation. The results show that the ‘Boryviter 0.1’ computing platform can be used as a UAV mission computer for typical flight control tasks requiring real-time computing under the influence of external factors. As a further work direction, we plan to investigate the proposed energy-saving algorithms within the planned NASA F’Prime software flight framework. Such an investigation, which should use the mission computer’s actual flight computation load, will help to qualify the obtained energy-saving methods and their implementation results.

2D Memory Enabled by Electrical Stimulation‐Induced Defect Engineering for Complicated Neuromorphic Computing

AbstractDefect engineering is extensively utilized in 2D memory devices due to its effectiveness in enhancing charge‐trapping ability. However, conventional defect modulation techniques usually introduce only single types of carrier traps and cannot reconfigure trap types and densities after device fabrication. Here, for the first time, electrical stimulation‐driven long‐range migration of Cu ions within CuInP2S6 (CIPS) films is demonstrated to simultaneously introduce both electron and hole traps and enable reconfigurable modulation of interfacial defect trapping. This process is referred to as “electrical stimulation‐induced defect engineering”. By integrating these defect traps and the dual‐gate coupling effect, the memory window‐to‐scan range (MW/S.R) ratio, which reflects the device's charge trapping ability, doubled and peaked at 78.1% at Vbg = ± 80 V. Additionally, the dual‐gate memory device based on the graphene/CIPS/h‐BN/WSe2 heterostructure exhibits a maximum on/off ratio reaching 107 for multi‐level storage states, integrating neuromorphic computing and logic operations within a single platform. With 81 storage states and paired‐pulse facilitation (PPF), it achieves ≈90% accuracy in reservoir computing (RC) simulations. These results highlight the potential of electrical stimulation‐induced defect engineering for next‐generation electronics.

Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions

With the rapid development of space exploration technology, the detection of extraterrestrial bodies has become increasingly important. Among these, path planning and target recognition and positioning technologies are particularly critical for applications in intelligent agents with low computational power operating in complex environments. This paper presents a novel approach to path planning on low-resolution lunar surface maps by introducing an improved A* algorithm with an adaptive heuristic function. This innovation enhances robustness in environments with limited map accuracy and enables paths that maintain maximum distance from obstacles. Additionally, we innovatively propose the Dynamic Environment Target Identification and Localization (DETIL) algorithm, which identifies unknown obstacles and employs spatiotemporal clustering to locate points of interest. Our main contributions provide valuable references for the aerospace industry, particularly in lunar exploration missions. The simulation results demonstrate that the improved A* algorithm reduces the maximum elevation difference by 55% and the maximum cumulative elevation difference by 68% compared to the traditional A* algorithm. Furthermore, the DETIL algorithm’s obstacle identification component successfully recognizes all the obstacles along the path, and its spatiotemporal clustering improves the average number of target discoveries by 152% over the conventional DBSCAN clustering approach.

Hand Gesture Enabled Operation for Computer Using Deep Learning

This project aims to develop a system that detects hand gestures and prints corresponding instructions for computer operators. The system utilizes Deep learning algorithms CNN and mediapipe to recognize hand gestures, and then prints instructions for the operator to perform specific computer operations.Operations like Notepad Open,Shutdown,Volume Up,Volume Down etc.The system will consist of a webcam, a Deep learning model, and a printer. The webcam will capture images of hand gestures, which will be processed using the Deep learning model to identify the specific gesture. The system will then print the corresponding instruction on the printer. The system will be designed to be user-friendly and adaptable to different environments and hardware configurations.The system aims to improve efficiency, reduce errors, and enhance accessibility for computer operators. The proposed system consists of a hand gesture detection module, instruction printing module, and computer operation module. The system is trained using a dataset of hand gestures and corresponding computer operations. Experimental results show that the system achieves high accuracy in gesture recognition and instruction printing. The system has potential applications in various fields, including gaming, education, and healthcare. Key Words: Hand gesture detection, Deep learning, instruction printing, computer operation, accessibility, Mediapipe, CNN.

Quantum Entanglement and Qubit Interactions: The Key to Quantum Supremacy

Abstract. Quantum computing operates in a fundamentally different way from classical computing by harnessing the principles of quantum mechanics to process information. Quantum supremacy is achieved when a quantum computer can solve problems that are beyond the capabilities of classical systems, including the human brain, showcasing its superior processing power. To attain quantum supremacy, quantum entanglement and qubit interactions play a pivotal role. Quantum entanglement occurs when qubits are interconnected in a manner where the state of one qubit directly influences the state of others, enabling the quantum computer to perform multiple operations simultaneously. Moreover, effective interaction between qubits is essential for the performance of complex calculations in quantum systems, highlighting the significance of coherence and error correction. Understanding the importance of coherence in preventing and rectifying errors in quantum computations is crucial. This paper aims to explore the critical aspects of quantum entanglement and qubit interactions, which are foundational to the operation of quantum computers. By delving into these key concepts, the paper aims to elucidate their significant roles in achieving quantum supremacy. The discussion will center on how quantum entanglement, which allows enhanced computational parallelism through qubit interconnection, and efficient qubit interactions vital for complex computations, contribute to surpassing the capabilities of classical computers. Comprehending these principles is crucial for advancing quantum computing technology and overcoming the challenges to unleash its full potential.

Deep Autoencoder for Real-Time Single-Channel EEG Cleaning and Its Smartphone Implementation Using TensorFlow Lite With Hardware/Software Acceleration.

To remove signal contamination in electroencephalogram (EEG) traces coming from ocular, motion, and muscular artifacts which degrade signal quality. To do this in real-time, with low computational overhead, on a mobile platform in a channel count independent manner to enable portable Brain-Computer Interface (BCI) applications. We propose a Deep AutoEncoder (DAE) neural network for single-channel EEG artifact removal, and implement it on a smartphone via TensorFlow Lite. Delegate based acceleration is employed to allow real-time, low computational resource operation. Artifact removal performance is quantified by comparing corrupted and ground-truth clean EEG data from public datasets for a range of artifact types. The on-phone computational resources required are also measured when processing pre-saved data. DAE cleaned EEG shows high correlations with ground-truth clean EEG, with average Correlation Coefficients of 0.96, 0.85, 0.70 and 0.79 for clean EEG reconstruction, and EOG, motion, and EMG artifact removal respectively. On-smartphone tests show the model processes a 4 s EEG window within 5 ms, substantially outperforming a comparison FastICA artifact removal algorithm. The proposed DAE model shows effectiveness in single-channel EEG artifact removal. This is the first demonstration of a low-computational resource deep learning model for mobile EEG in smartphones with hardware/software acceleration. This work enables portable BCIs which require low latency real-time artifact removal, and potentially operation with a small number of EEG channels for wearability. It makes use of the artificial intelligence accelerators found in modern smartphones to improve computational performance compared to previous artifact removal approaches.

Application of Contextual Approach in Improving Computational Operation Skills in Students with Learning Delays

<table border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="369"><p>This study aims to analyze and determine the effect of applying the contextual approach in improving the ability of calculation operations in students with learning delays. This study uses a quantitative approach with a single-subject experiment method, often called Single Subject Research (SSR), with an A-B-A design. The subject of this study is grade VIII students at SMP Islam Mandiri. The data collection technique in this study was carried out using observation and a test of computational operation ability. The results of the baseline-1 phase showed a stable score and increased when the treatment was carried out in the intervention phase; after the treatment was carried out, the research subjects were then given a test in the baseline-2 phase, and the results obtained increased with a score of 100 in each session. The percentage overlap of the three phases is at the level of 0%. Thus, the results show that applying the contextual approach positively improves the ability to calculate operations in students with learning delays.</p></td></tr></tbody></table>

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.

Multi-Layer QCA Reversible Full Adder-Subtractor Using Reversible Gates for Reliable Information Transfer and Minimal Power Dissipation on Universal Quantum Computer

The effects of quantum mechanics dominate nanoscale devices, where Moore’s law no longer holds true. Additionally, with the recent rapid development of quantum computers, the development of reversible gates to overcome the problems of energy and information loss and the nano-level quantum-dot cellular automata (QCA) technology to efficiently implement them are in the spotlight. In this study, a full adder-subtractor, a core operation of the arithmetic and logic unit (ALU), the most important hardware device in computer operations, is implemented as a circuit capable of reversible operation using QCA-based reversible gates. The proposed circuit consists of one reversible QCA gate and two Feynman gates and is designed as a multi-layer structure for efficient use of area and minimization of delay. The proposed circuit is tested on QCADesigner 2.0.3 and QCADesigner-E 2.2 and shows the best performance and lowest energy dissipation. In particular, it shows tremendous improvement rates of 180% and 562% in two representative standard design cost indicators compared to the best existing studies, and also shows the highest circuit average output polarization.

A Decision Tree Application for the Development of a Novel Approach to the Traveling Salesman Issue

The Traveling Salesman Problem (TSP) is a combinatorial problem related to computer science and operations research. There are many methods proposed in the literature to solve TSP with profit and losses. In this classical problem, the objective is to visit a finite number of locations exactly once, one by one, while minimizing the total distance traveled by the connecting links (arcs) needed to reach these sites (nodes). This study presents a new procedure that applies TSP and makes use of the decision tree notion. Anywhere in the TSP network, where n is the total number of places (nodes) in the network, this system ends after n-1 iterations. The method's step-by-step computational criteria make it efficient and readily applicable. To demonstrate the process's validity and efficiency, numerical examples are provided.

Efficient branch-and-bound algorithms for finding triangle-constrained 2-clubs

In the Vertex Triangle 2-Club problem, we are given an undirected graph G and aim to find a maximum-vertex subgraph of G that has diameter at most 2 and in which every vertex is contained in at least ℓ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell $$\\end{document} triangles in the subgraph. So far, the only algorithm for solving Vertex Triangle 2-Club relies on an ILP formulation (Almeida and Brás in Comput Oper Res 111:258–270, 2019). In this work, we develop a combinatorial branch-and-bound algorithm that, coupled with a set of data reduction rules, outperforms the existing implementation and is able to find optimal solutions on sparse real-world graphs with more than 100,000 vertices in a few minutes. We also extend our algorithm to the Edge Triangle 2-Club problem where the triangle constraint is imposed on all edges of the subgraph.

Peningkatan Kinerja Pegawai Pada Bagian Pelaporan Administrasi Keuangan Pada Dinas Pekerjaan Umum Dan Tata Ruang Kabupaten Agam

This research aims to improve employee performance in the Financial Administration Reporting Section (Public Works and Spatial Planning Department of Agam Regency). The employee placement system at the Agam Regency Public Works and Spatial Planning Service is based on competency, education, training, and skills. The research was conducted using qualitative methods with a sample of informants consisting of revenue treasurers, computer operators, financial treasurers, and financial staff at the public works and spatial planning departments of Agam Regency. This research uses interview analysis techniques with stages, namely data reduction, data presentation, and conclusion drawing. The research results show that the third indicator used is education and training. Competency and skills have not become a priority. Then, related to the process of achieving employee work targets at the Public Works and Spatial Planning Department of Agam Regency, it is influenced by three main factors, namely employee ability, work motivation, and rewards. The most basic issue is the ability of employees. Whereas there are many programs carried out by the Public Works and Spatial Planning Department of Agam Regency in order to stimulate employees and encourage their performance, employee limitations are the main obstacle. Work motivation activities range from leadership roles to providing rewards for employees who have achieved significant contributions.

Recent Progress in Memrsitor Array Structures and Solutions for Sneak Path Current Reduction

AbstractMemristors have diverse potential for improving data storage through linear memory control and synaptic operation in AI and neuromorphic computing. Prior research on optimizing memristors in next‐generation devices has generally indicated that emerging arrays and vertical structures can improve memory density, although special fabrication steps are required to realize improved operation. Until now, many obstructions, such as the sneak path current and forming processes from the initial device in array structure operation at the device level, have limited the development of array‐based memristor devices for further progressing circuits and integrated design. In this paper, memristor array studies are examined that have suggested solutions for sneak path current and forming operation problems at the device level. Ultimately, representative solutions are proposed to progress memristors into array structures by introducing the latest research on one diode‐one RRAM (1D1R), one selector‐one RRAM (1S1R), overshoot suppressed RRAM (OSRRAM), self‐rectifying cell (SRC), charge trap memory (CTM) and their applications. Additionally, essential details demonstrating the practical implementation of these devices in crossbar array memory are investigated. Finally, the advantages and perspectives of these array‐based memristor solutions are summarized.

Design of a cybersecurity defense system based on big data and artificial intelligence

With the development of big data and artificial intelligence technologies, hacker attack techniques and capabilities have continuously improved, and the methods of network attacks have diversified. Without cybersecurity, there is no national security. Therefore, cybersecurity has become a focal point of attention. To comprehensively enhance the network security defense capabilities of computer operating systems, aligning with the rapid development trends of big data and artificial intelligence technologies, this study focuses on constructing an efficient, stable, and practical cybersecurity defense system. This system deeply integrates advanced technologies of big data analysis and artificial intelligence, thoroughly analyzing the current status of network information security in computer operating systems and closely aligning with the practical needs of design and production. The aim is to provide highly valuable reference solutions for the field of cybersecurity defense.

DIGITALISASI BERBASIS MASYARAKAT DI DESA TEBING DAN DESA TUIK SERTA WARGA BELAJAR PKBM BINA BANGSA KABUPATEN BANGKA BARAT, BANGKA BELITUNG

Knowledge and training on digitalization by providing to residents in Tebing Village, Tuik Village, and PKBM Bina Bangsa Learning Residents, it is hoped that they will be able to overcome obstacles and problems in the village development process. In its implementation, it is carried out in several stages, namely preparation, implementation and evaluation. The results of the implementation of this activity show that before the implementation of the activity, residents in Tebing Village, Tuik Village and PKBM Bina Bangsa Learning Community did not understand much about digitalization, but after the implementation of the activity carried out by residents or the community in Tebing Village, Tuik Village and PKBM Bina Bangsa showed that there was increasing knowledge and competence in computer operation, especially as proven through post-test results

Facilitating practical fault-tolerant quantum computing based on color codes

Color codes are a promising topological code for fault-tolerant quantum computing. Insufficient research on color codes has delayed their practical application. In this work, we address several key issues to facilitate practical fault-tolerant quantum computing based on color codes. First, by introducing decoding graphs with error-rate-related weights, we obtain the threshold of 0.47% of the 6.6.6 triangular color code under the standard circuit-level noise model, narrowing the gap to that of the surface code. Second, our work first investigates the circuit-level decoding of color code lattice surgery, then gives an efficient decoding algorithm, which is crucial to perform logical operations in a quantum computer with two-dimensional architectures. Last, a state injection protocol of the triangular color code is proposed, reducing the output magic state error rate in one round of 15 to 1 distillation by two orders of magnitude compared to a previous rough protocol. We also prove that our protocol offers the lowest logical error rates for state injection among all possible codes. Published by the American Physical Society 2024

Two-stage data-driven optimal energy management and dynamic real-time operation in networked microgrid based on a deep reinforcement learning approach

Given the significant challenges posed by the vast and diverse data in energy management, this study introduces a two-stage approach: optimal energy management system (OEMS) and dynamic real-time operation (DRTOP). These stages employ a multi-agent policy-oriented deep reinforcement learning (DRL) approach, aiming to minimize operating and energy exchange costs through interactions in the networked microgrid (NMG) energy market. The primary objectives include minimizing the distribution system operator (DSO) cost and optimizing the exchanged power between DSO and NMG, and the power transmission losses and the secondary include minimizing MG’s operating cost, optimal use of renewable energy resources (RER) and energy storage systems (ESS), minimizing the exchanged power cost with the main grid and, risk analysis. The OEMS&DRTOP model is developed based on the Stackelberg game theory and the DRL structure. The DRL model is developed in two offline learning and online distributed operation phases to minimize the computational burden, time, and DRL operation process. This study’s results show the high efficiency of the presented approach to minimizing the operating cost, the exchanged power based on the price uncertainty, power transmission losses, and, RER and ESSs optimal participation. In addition, regarding computational load, the proposed concept demonstrates a 12.9% reduction compared to the dueling deep Q-network method and a 17% reduction compared to the deep Q-network method. Also regarding computational time, the proposed concept demonstrates a 17.13% reduction compared to the dueling deep Q-network method and a 25.6% reduction compared to the deep Q-network method.

Thinking on Improving the Teaching Quality of Chemical Engineering Specialty Experiment Course in Xinjiang Universities in the New Era

This paper focuses on the practical teaching of the traditional engineering major-chemical engineering and technology major.And taking the construction of the new engineering discipline as an opportunity, with the goal of cultivating applied talents, combined with the characteristics of chemical engineering and technology majors and students in Xinjiang universities, this paper put forward some thoughts on the improvement of experimental teaching quality of chemical engineering specialty.By introducing Ideal character teaching in All Courses, strictly controlling experimental teaching and assessment processes, and appropriately combining computer operations in the three aspects of teaching content, teaching process and assessment process, we can further consolidate teaching achievements, improve teaching quality, and cultivate applied talents with more Xinjiang characteristics.