Computer Hardware Research Articles

Constructing renewable energy sphere for efficient search

In the field of renewable energy (RE), traditional methods for understanding, development, and education have heavily relied on 2D table formats for data retrieval and presentation. However, these formats suffer from inefficiency and an inability to fully capture the relationships between variables. In response to this challenge, Li et al. (2022) demonstrated that their therapeutic 3D sphere system significantly outperformed the therapeutic 2D table in query processing time. Furthermore, Shih et al. (2020) utilized a T-test to underscore the superiority of the TED Talks sphere over the TED Talks 2D table format website in terms of convenience, query processing efficiency, usefulness, and intuitiveness. Building upon this foundation, this study referenced and enhanced the work of Li et al. by introducing a three-dimensional spherical visualization model driven by cluster-based binary integer programming (BIP). This novel approach resulted in a 58% reduction in information retrieval time and achieved an impressive 86.4% user satisfaction rating. It is worth noting that compared to the previous Systematic Review and Synthesis (SRS), user satisfaction experienced a significant 19% improvement. This improvement enhances the search efficiency for RE information, thereby fostering public awareness and development of RE. Taking energy supply-demand balance sheets as an example, this study demonstrates that the spherical retrieval system (SRS) constructed by the visualization 3D spherical model resolves the issue of inefficient retrieval in 2D table formats, and the newly added functionality of highlighting objects on the spherical surface intuitively illustrates the one-to-many relationships between individual RE supply and demand. To optimize hardware and software performance, data mining techniques are employed to cluster supply data, reducing complexity, and improving optimization efficiency. The experiment is that testers search for the same targets and compared the proposed 3D SRS with the Taiwan Power Company (Taipower) 2D website in terms of retrieval speed and accuracy. The statistical results of the T-test indicate that the proposed 3D SRS is more effective and accurate than the Taipower 2D website. The research findings indicate: 1. The new 3D SRS reduces query time by 58%. 2. Adopting the k-means algorithm for clustering and batch optimization can transform from an initially infeasible solution to finding the optimal solution within 12 s. This method also reduces computer software and hardware costs, providing effective solutions for relevant applications. These technologies can be used for the education, promotion, publicity, and dissemination of RE.

Advances on MXene-Based Memristors for Neuromorphic Computing: A Review on Synthesis, Mechanisms, and Future Directions.

Neuromorphic computing seeks to replicate the capabilities of parallel processing, progressive learning, and inference while retaining low power consumption by drawing inspiration from the human brain. By further overcoming the constraints imposed by the traditional von Neumann architecture, this innovative approach has the potential to revolutionize modern computing systems. Memristors have emerged as a solution to implement neuromorphic computing in hardware, with research based on developing functional materials for resistive switching performance enhancement. Recently, two-dimensional MXenes, a family of transition metal carbides, nitrides, and carbonitrides, have begun to be integrated into these devices to achieve synaptic emulation. MXene-based memristors have already demonstrated diverse neuromorphic characteristics while enhancing the stability and reducing power consumption. The possibility of changing the physicochemical properties through modifications of the surface terminations, bandgap, interlayer spacing, and oxidation for each existing MXene makes them very promising. Here, recent advancements in MXene synthesis, device fabrication, and characterization of MXene-based neuromorphic artificial synapses are discussed. Then, we focus on understanding the resistive switching mechanisms and how they connect with theoretical and experimental data, along with the innovations made during the fabrication process. Additionally, we provide an in-depth review of the neuromorphic performance, making a connection with the resistive switching mechanism, along with a compendium of each relevant performance factor for nonvolatile and volatile applications. Finally, we state the remaining challenges in MXene-based devices for artificial synapses and the next steps that could be taken for future development.

The carbon footprint of French hospital ophthalmology consultations.

France carbon footprint, with 604 million tons of CO2 equivalent (CO2eq) annual emissions, far exceeds the worldwide scientific specifications to not exceed 2 °C of warming in 2100. The healthcare sector is one of the biggest contributors to greenhouse gas emissions. The aim of this study is to quantify and evaluate the carbon footprint of the consultation activity of an ophthalmology department at a single institution in France. The perimeter of the investigation included consultations and excluded the surgeries and the hospitalisation. We calculated the carbon footprint of a single day of practice in our scope of investigation. We included consumption of energy, patient travel, staff travel, pharmaceutics and medical devices purchases, computer hardware, biomedical equipment/exam materials, and waste. We used the official French public database of emission factors: Base Empreinte® of the Environmental and Energy Management Agency of France. The carbon footprint of a single day of our department was estimated at 1 688.65 kgCO2eq. It represents an average of 9.28 kgCO2eq per patient. Energy consumption contributed for 114.80 kgCO2eq (6.8%), travel for 1324.76 kgCO2eq (78.4%), pharmaceutics and medical devices for 208.33 kgCO2eq (12.3%), equipment for 14.38 kgCO2eq (0.9%) and waste for 26.38 kgCO2eq (1.6%). This study highlights the importance of patient travels, and possibly pharmaceutics, in the carbon footprint of hospital ophthalmology practice in France. More studies are needed to establish it at the national or international scale, as well as more carbon footprint analyses on products, especially those of high prices, to increase the accuracy of these studies.

A Review of Recent Hardware and Software Advances in GPU-Accelerated Edge-Computing Single-Board Computers (SBCs) for Computer Vision.

Computer Vision (CV) has become increasingly important for Single-Board Computers (SBCs) due to their widespread deployment in addressing real-world problems. Specifically, in the context of smart cities, there is an emerging trend of developing end-to-end video analytics solutions designed to address urban challenges such as traffic management, disaster response, and waste management. However, deploying CV solutions on SBCs presents several pressing challenges (e.g., limited computation power, inefficient energy management, and real-time processing needs) hindering their use at scale. Graphical Processing Units (GPUs) and software-level developments have emerged recently in addressing these challenges to enable the elevated performance of SBCs; however, it is still an active area of research. There is a gap in the literature for a comprehensive review of such recent and rapidly evolving advancements on both software and hardware fronts. The presented review provides a detailed overview of the existing GPU-accelerated edge-computing SBCs and software advancements including algorithm optimization techniques, packages, development frameworks, and hardware deployment specific packages. This review provides a subjective comparative analysis based on critical factors to help applied Artificial Intelligence (AI) researchers in demonstrating the existing state of the art and selecting the best suited combinations for their specific use-case. At the end, the paper also discusses potential limitations of the existing SBCs and highlights the future research directions in this domain.

Proton Conducting Neuromorphic Materials and Devices.

Neuromorphic computing and artificial intelligence hardware generally aims to emulate features found in biological neural circuit components and to enable the development of energy-efficient machines. In the biological brain, ionic currents and temporal concentration gradients control information flow and storage. It is therefore of interest to examine materials and devices for neuromorphic computing wherein ionic and electronic currents can propagate. Protons being mobile under an external electric field offers a compelling avenue for facilitating biological functionalities in artificial synapses and neurons. In this review, we first highlight the interesting biological analog of protons as neurotransmitters in various animals. We then discuss the experimental approaches and mechanisms of proton doping in various classes of inorganic and organic proton-conducting materials for the advancement of neuromorphic architectures. Since hydrogen is among the lightest of elements, characterization in a solid matrix requires advanced techniques. We review powerful synchrotron-based spectroscopic techniques for characterizing hydrogen doping in various materials as well as complementary scattering techniques to detect hydrogen. First-principles calculations are then discussed as they help provide an understanding of proton migration and electronic structure modification. Outstanding scientific challenges to further our understanding of proton doping and its use in emerging neuromorphic electronics are pointed out.

The application of Verilog in the development of casual games

Verilog is a hardware description language (HDL) that is widely used in digital circuit design and simulation. Its development is closely related to computer science and electrical engineering. Verilog gained popularity in the early 1980s as digital circuit designs became increasingly complex, requiring more efficient circuit design and verification tools. At the same time, rapid advances in computer hardware also stimulated the demand for digital circuit design languages. Furthermore, the popularity and adoption of Verilog highlight the growing necessity for digitisation, automation, and intelligence in modern society. As digital technology continues to advance across various industries, the need for effective and dependable digital circuit design languages is also increasing. This paper delves into the complex process of recreating the timeless arcade classic Pac-Man on the Spartan 3E FPGA platform using hardware description and digital circuit techniques and the Verilog programming language. Through a comprehensive review of existing literature and research, this study investigates the fusion of traditional game design principles with state-of-the-art hardware programming methods, demonstrating the seamless integration of software-driven game mechanics with hardware-based implementation. Through careful design and coding strategies, Pac-Man's basic functionality, such as maze traversal, ghost AI, and pellet consumption, is faithfully replicated using Verilog modules customized for the Spartan 3E FPGA board. By bridging the realms of game development and hardware engineering, this paper not only showcases the versatility of Field Programmable Gate Array (FPGA) technology in entertainment applications but also underscores the interdisciplinary nature of modern computing endeavours.

The development history and future prospects of artificial intelligence in facial recognition technology

In recent decades, with the fast development of various algorithms and the rapid improvement of computer hardware performance, the development of facial recognition technology has been very rapid. This technology used to only appear in science fiction movies, but now it has been widely used in mobile payments, identity recognition, and other fields, and it also has very reliable performance. This article introduces the different facial recognition methods used in different periods of facial recognition technology from the 1960s and 1970s to the present day, to help people understand the development process of facial recognition technology. Among them, the focus is on early methods based on geometric features, grayscale information, and feature extraction, as well as recent applications based on deep learning and convolutional neural networks. In response to some challenges faced by current technology, some speculations on possible future development directions are proposed.

Applications of deep learning in physical oceanography: a comprehensive review

Deep learning, a data-driven technology, has attracted widespread attention from various disciplines due to the rapid advancements in the Internet of Things (IoT) big data, machine learning algorithms and computational hardware in recent years. It proves to achieve comparable or even more accurate results than traditional methods in a more flexible manner in existing applications in various fields. In the field of physical oceanography, an important scientific field of oceanography, the abundance of ocean surface data and high dynamic complexity pave the way for an extensive application of deep learning. Moreover, researchers have already conducted a great deal of work to innovate traditional approaches in ocean circulation, ocean dynamics, ocean climate, ocean remote sensing and ocean geophysics, leading oceanographic studies into the “AI ocean era”. In our study, we categorize numerous research topics in physical oceanography into four aspects: surface elements, subsurface elements, typical ocean phenomena, and typical weather and climate phenomena. We review the cutting-edge applications of deep learning in physical oceanography over the past three years to provide comprehensive insights into its development. From the perspective of three application scenarios, namely spatial data, temporal data and data generation, three corresponding deep learning model types are introduced, which are convolutional neural networks (CNNs), recurrent neural networks (RNNs) and generative adversarial networks (GANs), and also their principal application tasks. Furthermore, this study discusses the current bottlenecks and future innovative prospects of deep learning in oceanography. Through summarizing and analyzing the existing research, our aim is to delve into the potential and challenges of deep learning in physical oceanography, providing reference and inspiration for researchers in future oceanographic studies.

Impacts of Climate Change on Food Security and Sustainable Water Resources in Nigeria

This study evaluates the impacts of climate change on food security and sustainable water resources in Nigeria. Stratified samples were taken from North Central States of Benue and Plateau, and South-South Cross River State and Akwa-Ibom. These four states are among the thirty six states in Nigeria that have been badly hit by climate change. This study used soft and hardware computer applications including German 12 handheld GPS, and Integrated Land and Water Information System (ILWIS), for data collection. Quantitative and qualitative data were collected for the evaluation. The GIS information software package was used to manipulate and perform feature identification, recognition, classification, calculation, and ground-trotting. A structured questionnaire was also used to collect data on some livelihood activities, the influence of livelihoods activities on water resources, food security, and climate change impacts. Data analysis, found that the forest and water bodies are shrunken. Industrial activities such as oil exploration and exploitation, mining, by Multinational Corporations, illegal mining, grazing, unsustainable farming practices, fishing and hunting for bush meats, bush fire, timber exploitation and deforestation have contributed to climate change. The study recommends climate action and climate diplomacy, building local capacity for resilience, engaging the private sector, partnerships and international assistance.

What Does an IMoART Application Look Like? IMoART—An Interactive Mobile Augmented Reality Application for Support Learning Experiences in Computer Hardware

What Does an IMoART Application Look Like? IMoART—An Interactive Mobile Augmented Reality Application for Support Learning Experiences in Computer Hardware

Performance Improvement of Degrading Memristor-Bridge-Based Multilayer Neural Network with Refresh Pulses

Memristors as non-volatile memory devices have been recognized for executing in-memory computation in neuromorphic hardware. In this paper, a multilayer neural network has been developed with memristor-bridges as electrical synapses and trained with modified-chip-in-the-loop technique for an image classification task. Modeling the ideal conduction behavior of memristors by their device-physics inspired analytical model has yielded satisfactory performance. However, repeated voltage cycling degrades the resistance window of memristors by aggregating conductive residuals in filamentary memristors. Therefore, emulation of such nonideality has demonstrated compromised results. To improve the performance, refresh pulses have been introduced to the devices in between write pulses to eradicate the fundamental reason of the degradation — i.e., the residuals. It has been observed that improvement of performance is contingent upon the refreshment frequency, and frequent refreshment has the ability to restore performance to a level closely approaching its ideal emulation.

Pengenalan Perangkat Komputer Dasar sebagai Upaya Meningkatkan Pengetahuan Siswa SDI Waioti Maumere

In today's digital era, basic computer and Microsoft literacy training is very important, both for elementary school students and professional workers. Increasing use is recognized by several sectors, including the banking industry and the education sector. To overcome this, basic computer introduction activities were carried out in the context of outreach to the community, especially elementary school students. From the information found at the SDI Waioti Maumere Elementary School, some students are not yet familiar with current computer technology. So the service carried out aims to provide an introduction to basic computers to elementary school students at SDI Waioti Maumere, including an introduction to computer hardware and an understanding of using Microsoft Word as an effort to improve each student's ability to adapt to today's global world. The three steps in this activity process are preparation, implementation, and assessment. In this activity, the committee succeeded in initiating basic computer and Microsoft Word learning for 36 students divided into two classes, namely A and B.

The ICT Competencies Required for the General Education Teachers from the Computer Teachers’ Viewpoints

This study aims to explore the viewpoints of computer teachers about the ICT competencies required for general education teachers in the State of Qatar. A questionnaire was designed, validated, and administered to a sample consisted of 504 male and female computer teachers. The results revealed the following: There are differences in the levels of importance in the three areas of the general education teachers’ ICT competencies according to the computer teachers’ viewpoints, ordered as follows: Operating System & Software, Internet & E-learning, and Computer Hardware & Accessories. No significant differences were found between the variables of the study: (gender, years of experience, academic qualifications, and education levels of computer teachers). And an interaction between the variables in the area of Operating System & Software was found. A significant difference was found between the male & female in favor of female computer teachers in the area of computer hardware & accessories. No significant difference was found between the other variables of the study in this area, and no significant differences were found between the interactions of the variables of the study in this area. A significant difference was found between years of experience in favor of the computer teachers who have more than 20 years of teaching experience in the area of Internet & E-learning, and no significant difference was found between the other variables of the study in this area. A significant difference was found between the interactions of the variables of qualifications & the years of experience in favor of the years of experience in this area, and no significant differences were found between the interactions of other variables of this study in this area. The results were interpreted and some recommendations and further studies were suggested.

CO2 emission mitigation of a hybrid photovoltaic and cogeneration system in computer hardware manufacturing industry: A case study in Thailand

Abstract In the wake of COP26 and the growing urgency of addressing climate change, achieving carbon neutrality by 2050 has become a central global objective. This imperative extends to industries like computer hardware manufacturing, which are now actively pursuing decarbonization strategies through the strategic adoption of renewable energy sources and energy efficiency enhancements. This research paper assessed the CO2 emission mitigation potential of a hybrid system of photovoltaic (PV) roof and cogeneration where a large factory of computer hardware manufacturing in tropical Thailand was selected as a study site. On one hand, a one-Megawatt photovoltaic system was installed over the roof of the production building to generate electricity from solar radiation to serve the building. On the other hand, a twenty-four-Megawatt cogeneration system of gas engines as the prime mover was used to supply power to meet the building’s electricity demand. Waste heat from the gas engine was used by the absorption chiller to generate chilled water for cooling inside the building. Based on the system equipment specifications, the annual simulation using Thailand’s solar radiation showed that the installed photovoltaic system could generate electricity of 1,412.4 MWhelec/year while the implementation of the absorption chillers for cooling helped to reduce the electrical energy consumed by the traditional electric chiller by 10,211.4 MWhelec/year. In our study case where the CO2 emission of the grid power was 0.4758 kgCO2/kWhelec in the year 2022 and was reduced to 0.350 kgCO2/kWhelec in the year 2050, the total CO2 emission mitigation from the hybrid photovoltaic and cogeneration system with the genset efficiency of 50% and the waste heat recovery of 60% could reduce approximately 207,388.5TonCO2 for over 20 years as compared to the scenario where the grid electricity alone powered the building. These findings underscored the critical role of the proposed hybrid system in addressing the climate crisis and exemplified how the industry could make meaningful strides toward more environmental sustainability.

A TEMPEST Attack Implementation based on Hidden Markov model in Smart Grid

Abstract A Smart Grid contains a large number of terminals to gather voltage, current, and power data. If these terminals are attacked by hackers to steal data and disrupt the operation of the grid, the grid will encounter a huge challenge. And side channel attack is an attack way that uses information signals (such as electromagnetic radiation, and computer hardware running sound) inadvertently released by the computer to decipher. The computer keyboard generates electromagnetic radiation signals during operation, and hackers may acquire your account and password through the keyboard’s tapping sound, resulting in the leakage of important data. This article analyzes the electromagnetic radiation leakage vulnerabilities of keyboards and designs a way to implement a TEMPEST (Transient Electromagnetic Pulse Emanation Surveillance Technology) Attack based on the Hidden Markov model, which can provide a foundation for avoiding potential risk.

Assessment of energy saving and CO2 emission mitigation of low-lift cooling in computer hardware manufacturing industry: A case study in Thailand

Abstract Released heat from production process contributes the largest proportion of cooling load in computer hardware manufacturing industry. According to traditional design practice, chiller plant produces chilled water at a temperature of 7°C to treat the entire load from the production process and from the comfort air-conditioning in buildings. However, the heat from the process is solely the sensible component where high-temperature chilled water (e.g., 15°C) can be used for the heat removal, the chillers equipped with low temperature-lift technology thus offer a great opportunity to improve the plant’s cooling performance. Under the critical global warming situation and the need of decarbonizing industry, this paper investigated the energy saving and CO2 emission mitigation potential of the low-lift technology by which the chiller plant of high-temperature chilled water (15°C) is dedicated for the production process cooling, and the traditional chiller plant of the low-temperature chilled water is used to serve the building air-conditioning. The study site was a large factory of computer hardware manufacturing in Thailand. Through detailed energy auditing, the cooling load profile of the production process was established, and it was used to design the low-lift chiller plant. The annual simulation results showed that in our study case the low-lift chiller plant together with variable motor speed control had 31.6% higher cooling performance than the traditional chiller plant. The plant could reduce the energy consumption by 5,550 MWhelec/year. As the CO2 emission of the grid power in Thailand was 0.47 kgCO2/kWh in 2023 and it was expected to reduce to 0.35 kgCO2/kWh in 2050, the CO2 emissions from implementing the low-lift cooling could be reduced by 49,400 TonCO2 over a course of the 20-year project lifetime. The study results encouraged the low-lift cooling technology as a practical energy efficiency measure in computer hardware manufacturing industry. To our knowledge, while past research focused on the low-lift cooling in building air-conditioning, the study was the first potential assessment project of the low-lift cooling as a deep decarbonization technology for the computer hardware manufacturing industry in Thailand.

Optimizing the utilisation of different computational hardware types for real time simulation

Optimizing the utilisation of different computational hardware types for real time simulation

RockDNet: Deep Learning Approach for Lithology Classification

Analyzing rock and underground layers is known as drill core lithology. The extracted core sample helps not only in exploring the core properties but also reveals the lithology of the entire surrounding area. Automating rock identification from drill cuttings is a key element for efficient reservoir characterization, replacing the current subjective and time-consuming manual process. The recent advancements in computer hardware and deep learning technology have enabled the automatic classification of various applications, and lithology is not an exception. This work aims to design an automated method for rock image classification using deep learning technologies. A novel CNN (Convolution Neural Network) is proposed for lithology classification in addition to thorough comparison with benchmark CNN models. The proposed CNN model has the advantageous of having very low complexity while maintaining high accuracy. Experimental results on rock mages taken from the “digitalrocksportal” database demonstrate the ability of the proposed method to classify three classes, carbonate, sandstone and shale rocks, with high accuracy, and comparisons with related work demonstrated the efficiency of the proposed model, with more than 98% saving in parameters.

Classification of Cardiovascular Arrhythmia Using Deep Learning Techniques: A Review

Deep Learning (DL), an offshoot of Machine Learning (ML) has emerged as a powerful and feasible solution for medical image analysis due to advancements in robust computer software and hardware technologies. It plays a key role in Cardiovascular disease (CVD) diagnosis by detecting anomalies in Electrocardiogram (ECG) signals. Cardiac arrhythmia, which refers to irregular heartbeat, may signal an early symptom of CVD and can lead to fatal outcomes if ignored. Accurate detection of arrhythmia is very challenging even for experts to distinguish between acute and chronic conditions in ECG readings. This triggered the focus of researchers to explore the application of Artificial Intelligence for ECG classification. Traditional machine learning methods use handcrafted features that require domain knowledge. The new era in DL makes the automatic detection of Cardiovascular Disease (CVD) possible. In this paper, an exhaustive review of DL-based techniques for ECG classification has been presented. Research findings in this survey indicate the challenges and issues with arrhythmia detection, such as single lead and multiple lead ECG signals, choice of the size of the training data set, and the number of arrhythmia classes, etc. The study also signifies that there is great scope for improving the performance of arrhythmia prediction models by employing hybrid ensemble learning, time series analysis using Recurrent Neural Network architectures and identification of unexplored classes of arrhythmia.

State Machines for Large Scale Computer Software and Systems

The behavior and architecture of large scale discrete state systems found in computer software and hardware can be specified and analyzed using a particular class of primitive recursive functions. This article begins with an illustration of the utility of the method via a number of small examples and then via longer specification and verification of the “Paxos” distributed consensus algorithm [ 26 ]. The “sequence maps” are then shown to provide an alternative representation of deterministic state machines and products of state machines. Distributed and composite systems, parallel and concurrent computation, and real-time behavior can all be specified naturally with these methods—which require neither extensions to the classical state machine model nor any axiomatic methods or other techniques from formal logic or other foundational methods. Compared to state diagrams or tables or the standard set-tuple-transition-maps, sequence maps are more concise and better suited to describing the behavior and compositional architecture of computer systems. Staying strictly within the boundaries of classical deterministic state machines anchors the methods to the algebraic structures of automata and makes the specifications faithful to engineering practice.