Advances In Hardware Research Articles

Virtual Reality-Driven Army Training Simulator

Abstract: This study presents the VRATS system, a modern military training tool that uses advanced VR hardware and software to simulate various operational scenarios. VRATS offers AI-driven opponents, instantaneous performance feedback, and data analytics for skill enhancement. VRATS promotes decision-making, leadership, and adaptation, reducing injury risks, cutting costs, and improving training efficacy

The application of reinforcement learning algorithms in intelligent games

The ongoing advancement of computer hardware has spurred the need for high-quality gaming experiences. The pressing need for intelligent games, the proven efficacy of reinforcement learning in various fields, the advancement of deep reinforcement learning technology, and the desire for customized gaming experiences collectively propel the ongoing exploration and implementation of reinforcement learning in intelligent games.This study use algorithms sourced from the literature review to examine multiple facets of reinforcement learning, including the modeling of game settings, reward design, the utilization of deep learning approaches, and adversarial learning. The utilization of reinforcement learning in intelligent games holds immense importance. Initially, it enhances the gaming encounter and renders intelligent games more demanding and tailored. Furthermore, the game gains complexity and intrigue by the implementation of intelligent adversaries capable of assimilating and adjusting to the player's tactics. This not only fosters the advancement of AI technology through research and development, but also broadens the utilization of reinforcement learning in other domains, like autonomous driving and smart manufacturing. Furthermore, tailored encounters, cutting-edge game design, and utilization in schooling are likewise significant contributions, rendering reinforcement learning a pivotal element in propelling technological advancement and augmenting user experience.

Real-Time Dynamic Intelligent Image Recognition and Tracking System for Rockfall Disasters.

Taiwan, frequently affected by extreme weather causing phenomena such as earthquakes and typhoons, faces a high incidence of rockfall disasters due to its largely mountainous terrain. These disasters have led to numerous casualties, government compensation cases, and significant transportation safety impacts. According to the National Science and Technology Center for Disaster Reduction records from 2010 to 2022, 421 out of 866 soil and rock disasters occurred in eastern Taiwan, causing traffic disruptions due to rockfalls. Since traditional sensors of disaster detectors only record changes after a rockfall, there is no system in place to detect rockfalls as they occur. To combat this, a rockfall detection and tracking system using deep learning and image processing technology was developed. This system includes a real-time image tracking and recognition system that integrates YOLO and image processing technology. It was trained on a self-collected dataset of 2490 high-resolution RGB images. The system's performance was evaluated on 30 videos featuring various rockfall scenarios. It achieved a mean Average Precision (mAP50) of 0.845 and mAP50-95 of 0.41, with a processing time of 125 ms. Tested on advanced hardware, the system proves effective in quickly tracking and identifying hazardous rockfalls, offering a significant advancement in disaster management and prevention.

Social-Aware Group Display Configuration in VR Conference

Virtual Reality (VR) has emerged due to advancements in hardware and computer graphics. During the pandemic, conferences and exhibitions leveraging VR have gained attention. However, large-scale VR conferences, face a significant problem not yet studied in the literature -- displaying too many irrelevant users on the screen which may negatively impact the user experience. To address this issue, we formulate a new research problem, Social-Aware VR Conference Group Display Configuration (SVGD). Accordingly, we design the Social Utility-Aware VR Conference Group Formation (SVC) algorithm, which is a 2-approximation algorithm to SVGD. SVC iteratively selects either the P-Configuration or S-Configuration based on their effective ratios. This ensures that in each iteration, SVC identifies and chooses the solution with the highest current effectiveness. Experiments on real metaverse datasets show that the proposed SVC outperforms 11 baselines by 75% in terms of solution quality.

Quantum Computing in Machine Learning - The Future of Quantum Computing

In order to solve challenging optimization issues, analyze data effectively, and improve the capabilities of current machine learning algorithms, quantum computing has the potential to revolutionize the area of machine learning. The proposed work examines the fundamental ideas and methods of quantum computing—including quantum gates, quantum circuits, and quantum algorithms—as they relate to machine learning in this abstract. Various quantum computing applications in machine learning, including quantum neural networks, quantum support vector machines, and conventional methods influenced by quantum mechanics are also discussed. A review of state-of-the-art in quantum computing for machine learning, including recent advancements in quantum hardware and software has been done and the future prospects of this fascinating area has been examined.

Economic and Environmental Assessment of Technologies Optimizing the Execution of Long Trips for Electric Vehicles

Further advances in hardware and software features are needed to optimize battery and thermal management systems to allow for the execution of longer trips in electric vehicles. This paper assesses the economic and environmental impacts of the following features: eco-charging, eco-driving, smart fast charging, predictive thermal powertrain and cabin conditioning, and an advanced heat pump system. A Total Cost of Ownership (TCO) and externalities calculation is carried out on two passenger cars and one light commercial vehicle (LCV). The energy consumption data from the vehicles are based on experiments. The analysis shows more benefits for the LCV, while the smart fast-charging feature on the car shows a slight increase in TCO. However, negative results did not contribute significantly compared to the ability to install a smaller battery capacity for similar use.

CAR and CSTR Cardiac Computed Tomography (CT) Practice Guidelines: Part 1 Coronary CT Angiography (CCTA).

Imaging the heart is one of the most technically challenging applications of Computed Tomography (CT) due to the presence of cardiac motion limiting optimal visualization of small structures such as the coronary arteries. Electrocardiographic gating during CT data acquisition facilitates motion free imaging of the coronary arteries. Since publishing the first version of the Canadian Association of Radiologists (CAR) cardiac CT guidelines, many technological advances in CT hardware and software have emerged necessitating an update. The goal of these cardiac CT practice guidelines is to present an overview of the current evidence supporting the use of cardiac CT in various clinical scenarios and to outline standards of practice for patient safety and quality of care when establishing a cardiac CT program in Canada.

Enhancing Network Security Through Router-Based Firewalls: An Investigation into Design, Effectiveness, and Human Factors

A firewall serves as a crucial computer program designed to block malicious software, thereby safeguarding the user's system. It helps prevent the unauthorized dissemination of personal information and the incursion of harmful viruses. Taking inspiration from China's "Great Firewall"—a sophisticated system utilizing advanced software and hardware to automatically filter, censor, and monitor internet content—this research aims to devise a router-based firewall system capable of autonomously shielding users' computers from viruses and unauthorized websites. A comprehensive questionnaire was deployed to unearth the predominant sources of information leakage among computer users and ascertain the role of firewalls within computer systems. The analytical findings underscore that the majority of information leakage security risks stem from a significant lack of personal privacy awareness and a deficient understanding of firewall security systems. The proposed firewall system harnesses router-based control to address the limitations inherent in PC software-based firmware systems. This innovation enhances the control of viral threats and the prevention of user privacy leaks. Serving as a tangible barrier, the router-based firewall obstructs the route of viruses or malevolent programs before reaching the user's computer. This strategic design substantially reduces user intervention, ensuring the continuous operation of the firewall to steadfastly protect the user's computing environment.

Challenges and Opportunities for Twenty First Century Bayesian Econometricians: A Personal View

Abstract This essay is about Bayesian econometrics with a purpose. Specifically, six societal challenges and research opportunities that confront twenty first century Bayesian econometricians are discussed using an important feature of modern Bayesian econometrics: conditional probabilities of a wide range of economic events of interest can be evaluated by using simulation-based Bayesian inference. The enormous advances in hardware and software have made this Bayesian computational approach a very attractive vehicle of research in many subfields in economics where novel data patterns and substantial model complexity are predominant. In this essay the following challenges and opportunities are briefly discussed, including the scientific results obtained in the twentieth century leading up to these challenges: Posterior and predictive analysis of everything: connecting micro-economic causality with macro-economic issues; the need for speed: model complexity and the golden age of algorithms; learning about models, forecasts and policies including their uncertainty; temporal distributional change due to polarisation, imbalances and shocks; climate change and the macroeconomy; finally and most importantly, widespread, accessible, advanced high-level training.

A Lightweight CNN Based on Memristive Stochastic Computing for Electronic Nose

Gas detection plays different roles in different environments. Traditional algorithms implemented on electronic nose for gas detection and recognition have high complexity and cannot resist device drift. In response to the above issues, we propose a convolutional neural network based on memristive Stochastic Computing (SC), which combines the characteristics of small devices and low power consumption of memristor devices, as well as the fast and fault-tolerant random calculation speed. It can effectively utilize hardware advantages, recognizing gases by electronic nose. The experimental results show that for two different gas sensor array datasets, the accuracy of the proposed method can achieve the level of 99%. When using memristive SC for deduction, the accuracy decreases by less than 1%, but in drift data, the accuracy can be improved by about 3%. Finally, the improvement in area, power, and energy compared to inference in GPU (NVIDIA Geforce RTX 3060 Laptop) is 1104X, 48X, and 9X, respectively.

Motion-compensated diffusion encoding in multi-shot human brain acquisitions: Insights using high-performance gradients.

To evaluate the utility of up to second-order motion-compensated diffusion encoding in multi-shot human brain acquisitions. Experiments were performed with high-performance gradients using three forms of diffusion encoding motion-compensated through different orders: conventional zeroth-order-compensated pulsed gradients (PG), first-order-compensated gradients (MC1), and second-order-compensated gradients (MC2). Single-shot acquisitions were conducted to correlate the order of motion compensation with resultant phase variability. Then, multi-shot acquisitions were performed at varying interleaving factors. Multi-shot images were reconstructed using three levels of shot-to-shot phase correction: no correction, channel-wise phase correction based on FID navigation, and correction based on explicit phase mapping (MUSE). In single-shot acquisitions, MC2 diffusion encoding most effectively suppressed phase variability and sensitivity to brain pulsation, yielding residual variations of about 10° and of low spatial order. Consequently, multi-shot MC2 images were largely satisfactory without phase correction and consistently improved with the navigator correction, which yielded repeatable high-quality images; contrarily, PG and MC1 images were inadequately corrected using the navigator approach. With respect to MUSE reconstructions, the MC2 navigator-corrected images were in close agreement for a standard interleaving factor and considerably more reliable for higher interleaving factors, for which MUSE images were corrupted. Finally, owing to the advanced gradient hardware, the relative SNR penalty of motion-compensated diffusion sensitization was substantially more tolerable than that faced previously. Second-order motion-compensated diffusion encoding mitigates and simplifies shot-to-shot phase variability in the human brain, rendering the multi-shot acquisition strategy an effective means to circumvent limitations of retrospective phase correction methods.

Метод оценки факторов, влияющих на вибрацию судового дизеля, вызванную работой цилиндропоршневой группы

The efficiency of the use of the marine fleet is associated with a reduction in the cost of transportation, the need for which is steadily increasing. A significant share of the total transportation costs is occupied by the costs associated with the operation of ships, therefore, reducing these costs is a reserve for reducing the cost of maritime transportation. The problem of increasing the efficiency of the operation of marine vessels is closely related not only to reducing the cost of it, but also to reducing the time for maintenance and repair of ships. The existing system of scheduled preventive maintenance and repair provides for the maintenance and restoration of the normal technical condition of the vessel and its elements with a pre-planned frequency. The transfer of ships to work with extended repair periods is possible only when solving a whole range of engineering and technical measures and scientific research aimed at improving the quality of repairs, the level of technical operation, improving logistics, etc. An essential role in this is played by the use of modern methods and means to determine the technical condition of shipboard devices and mechanisms, primarily engines of shipboard power plants (SEUs), without disassembling them. High vibration loads of SEU parts lead to fatigue stresses, accelerate wear of the contacting surfaces, reducing the reliability and service life of marine diesel engines, therefore monitoring of their vibration activity is important, especially during operation. The safe operation of ships of the transport fleet largely depends on the reliability of marine diesel engines. Technical means of monitoring their operability in the process of work are currently insufficiently effective and need to be improved. It is relevant not only to develop new, more advanced and mobile hardware and software, but also to search for new scientific and methodological approaches to using the information contained in the spectrum of vibration parameters. Marine diesel internal combustion engines are complex systems that involve many variable parameters. The study of such engines requires the use of methods that can be used to systematically investigate the influence of various factors on their operation, in particular, random experimental schemes are used, since they provide systematic control over variable parameters and maximize the accuracy of estimates. The results of the application of the method of ranking the factors influencing the vibration of a marine diesel engine are presented in order to establish the most significant of them. A mathematical model is presented that allows calculating current values and predicting changes in the most significant indicators, among which the most important is the size of the gap between the piston and the cylinder sleeve.

Current state and prospects of edge computing within the Internet of Things (IoT) ecosystem

The burgeoning growth of the Internet of Things (IoT) has prompted a paradigm shift in computing architectures, leading to the emergence and rapid evolution of edge computing. This review delves into the current state and prospects of edge computing within the IoT ecosystem, exploring its significance, challenges, and future potential. Edge computing, characterized by decentralized data processing at or near the source of data generation, has gained substantial traction owing to its ability to address critical concerns such as latency, bandwidth consumption, and privacy issues inherent in centralized cloud-based systems. By enabling data processing closer to the point of collection, edge computing minimizes latency and ensures real-time responses, making it indispensable for latency-sensitive applications like industrial automation, autonomous vehicles, and healthcare. The integration of edge computing with IoT devices has facilitated the creation of distributed computing architectures capable of handling massive data volumes generated by interconnected devices. This convergence enables efficient data aggregation, analysis, and decision-making at the network's edge, reducing the burden on centralized cloud infrastructure and optimizing resource utilization. Despite its numerous advantages, edge computing faces several challenges, including resource constraints, security vulnerabilities, and interoperability issues. Resource-constrained edge devices often lack the computational power and storage capacity required for complex analytics, necessitating innovative approaches to resource management and workload distribution. Moreover, the distributed nature of edge environments introduces new security risks, necessitating robust security measures to safeguard sensitive data and mitigate cyber threats. Looking ahead, the prospects of edge computing within the IoT ecosystem are promising, with advancements in edge hardware, software, and networking technologies driving innovation and adoption. Edge computing is poised to play a pivotal role in enabling the next wave of IoT applications, ranging from smart cities and autonomous systems to immersive experiences and personalized services. However, realizing the full potential of edge computing requires concerted efforts from industry stakeholders to address existing challenges and foster ecosystem-wide collaboration. The convergence of edge computing and IoT holds immense potential to revolutionize industries, reshape computing architectures, and empower a new era of intelligent, responsive, and decentralized systems.

Autonomous Robotic Capabilities in Space Exploration: From Mars to Beyond

The historical timeline of automated systems functioning in space settings spans around 75 years, whereas the existence of practical automated equipment to facilitate astronomical observation dates back over 200 years. Over time, physical servomechanisms have seen advancements in both hardware and software capabilities, enabling their successful operation on the Martian surface and even during journeys to the heliopause, which marks the outer boundary of interstellar space. Contemporary spaceflight operations exemplify a growing capacity to provide decentralized coordination across several automated systems, intricate communication networks, and diverse communities of scientists and engineers. This article examines the impact of autonomous robotic functionalities on space explorations, specifically emphasizing the investigation of Mars' surface. The article examines the use of robotic systems in past missions, specifically focusing on their application in activities like as the identification and analysis of water ice deposits, study of geological features, and the deployment of sensor devices. The article further emphasizes the achievements of autonomous operations in missions conducted in Earth's orbit, as well as the progress made in developing autonomy for operations in close vicinity to minor celestial bodies. This research explores the Chang'e 4 lunar mission and the OSIRIS-REx mission as instances of autonomous exploration and sample gathering on minor celestial bodies. The research also encompasses the exploration of prospective autonomy in forthcoming expeditions to oceanic realms and distant locales.

Multimodal Imaging in Oncology: Challenges and Future Directions

Multimodal imaging, a vital tool in oncology, combines various imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) to offer valuable insights for tumor detection, diagnosis, staging, and treatment planning. Despite its advantages, challenges persist in image registration, fusion, optimization of algorithms, hardware advancements, and the integration of artificial intelligence (AI) and computer vision techniques. This paper delves into the current state of multimodal imaging in oncology, addresses the challenges faced, explores potential solutions, and underscores future developments to augment diagnostic and therapeutic capacities, including the application of AI, computer vision, and big data analytics to enhance the efficiency and effectiveness of multimodal imaging in cancer diagnosis and treatment. Additionally, it highlights how AI-powered algorithms can automate image analysis, assist in tumor segmentation, and provide predictive insights, ultimately improving the precision and speed of diagnosis and treatment planning in oncology.

Efficient bead-on-plate weld model for parameter estimation towards effective wire arc additive manufacturing simulation

AbstractDespite the advances in hardware and software techniques, standard numerical methods fail in providing real-time simulations, especially for complex processes such as additive manufacturing applications. A real-time simulation enables process control through the combination of process monitoring and automated feedback, which increases the flexibility and quality of a process. Typically, before producing a whole additive manufacturing structure, a simplified experiment in the form of a bead-on-plate experiment is performed to get a first insight into the process and to set parameters suitably. In this work, a reduced order model for the transient thermal problem of the bead-on-plate weld simulation is developed, allowing an efficient model calibration and control of the process. The proposed approach applies the proper generalized decomposition (PGD) method, a popular model order reduction technique, to decrease the computational effort of each model evaluation required multiple times in parameter estimation, control, and optimization. The welding torch is modeled by a moving heat source, which leads to difficulties separating space and time, a key ingredient in PGD simulations. A novel approach for separating space and time is applied and extended to 3D problems allowing the derivation of an efficient separated representation of the temperature. The results are verified against a standard finite element model showing excellent agreement. The reduced order model is also leveraged in a Bayesian model parameter estimation setup, speeding up calibrations and ultimately leading to an optimized real-time simulation approach for welding experiment using synthetic as well as real measurement data.

Computational label-free microscope through a custom-built high-throughput objective lens and Fourier ptychography

Label-free microscopy directly images live samples without fluorescent markers, providing a panoramic view of biological structures and functions. However, pursuing high-throughput and high-content live cell imaging requires both higher spatial resolution and larger field of view, which are limited by the optical system. In this study, we custom-built a 5▪/0.35 objective with a field number of 28 mm, providing a space-bandwidth product of 34 megapixels, which is four times greater than that of commercial high-throughput objectives such as the Nikon Lambda series (4▪/0.2, 10▪/0.45, 20▪/0.75). Furthermore, we incorporated Fourier ptychography microscopy (FPM) into the system, achieving a synthetic numerical aperture of 0.72, suitable for digital pathology and long-term live cell observation. To improve the FPM performance, we proposed a phantom-based calibration method for quantitative correction of illumination angle errors. Additionally, this method can also serve as an initial step for the on-line calibration. The remarkable capabilities of our 5▪/0.35 objective have been demonstrated, as well as the effectiveness of computational label-free microscopy. Furthermore, by combining our original FPM with more advanced hardware and algorithms, our FPM can achieve higher performance.

Analyze and evaluate Apple’s investment potential in the current financial market

This research is centered on Apple, Inc., a prominent American-based multinational corporation renowned for its preeminent role in the design, manufacture, and distribution of advanced electronic devices alongside a diverse array of internet services. Established in 1976 by Steve Jobs and Steve Wozniak (Alex, 2015), Apple has risen to become the world’s largest company, boasting a market capitalization of $3 trillion and a listing on NASDAQ while occupying a significant position within the S&P 500 index (Bob, 2023). Apple’s influence on computing and mobile technology is unparalleled, exemplified by the widespread adoption of its macOS and iOS operating systems in personal computers and mobile devices, contributing to the global popularity of Apple products. Apple is a frontrunner in the computer sector, known for its advanced systems and hardware. MacOS is one of the three major computer operating systems, alongside Microsoft and Linux.

Paediatric Thoracic Imaging in Cystic Fibrosis in the Era of Cystic Fibrosis Transmembrane Conductance Regulator Modulation.

Cystic fibrosis (CF) is one of the most common progressive life-shortening genetic conditions worldwide. Ground-breaking translational research has generated therapies that target the primary cystic fibrosis transmembrane conductance regulator (CFTR) defect, known as CFTR modulators. A crucial aspect of paediatric CF disease is the development and progression of irreversible respiratory disease in the absence of clinical symptoms. Accurate thoracic diagnostics have an important role to play in this regard. Chest radiographs are non-specific and insensitive in the context of subtle changes in early CF disease, with computed tomography (CT) providing increased sensitivity. Recent advancements in imaging hardware and software have allowed thoracic CTs to be acquired in paediatric patients at radiation doses approaching that of a chest radiograph. CFTR modulators slow the progression of CF, reduce the frequency of exacerbations and extend life expectancy. In conjunction with advances in CT imaging techniques, low-dose thorax CT will establish a central position in the routine care of children with CF. International guidelines regarding the choice of modality and timing of thoracic imaging in children with CF are lagging behind these rapid technological advances. The continued progress of personalised medicine in the form of CFTR modulators will promote the emergence of personalised radiological diagnostics.

Dynamic Modelling, Process Control, and Monitoring of Selected Biological and Advanced Oxidation Processes for Wastewater Treatment: A Review of Recent Developments.

This review emphasizes the significance of formulating control strategies for biological and advanced oxidation process (AOP)-based wastewater treatment systems. The aim is to guarantee that the effluent quality continuously aligns with environmental regulations while operating costs are minimized. It highlights the significance of understanding the dynamic behaviour of the process in developing effective control schemes. The most common process control strategies in wastewater treatment plants (WWTPs) are explained and listed. It is emphasized that the proper control scheme should be selected based on the process dynamic behaviour and control goal. This study further discusses the challenges associated with the control of wastewater treatment processes, including inadequacies in developed models, the limitations of most control strategies to the simulation stage, the imperative requirement for real-time data, and the financial and technical intricacies associated with implementing advanced controller hardware. It is discussed that the necessity of the availability of real-time data to achieve reliable control can be achieved by implementing proper, accurate hardware sensors in suitable locations of the process or by developing and implementing soft sensors. This study recommends further investigation on available actuators and the criteria for choosing the most appropriate one to achieve robust and reliable control in WWTPs, especially for biological and AOP-based treatment approaches.