Hardware Selection Research Articles

Quantitative Speech Assessment in Ataxia—Consensus Recommendations by the Ataxia Global Initiative Working Group on Digital-Motor Markers

Dysarthria is a common and debilitating symptom of many neurodegenerative diseases, including those resulting in ataxia. Changes to speech lead to significant reductions in quality of life, impacting the speaker in most daily activities. Recognition of its importance as an objective outcome measure in clinical trials for ataxia is growing. Its viability as an endpoint across the disease spectrum (i.e. pre-symptomatic onwards) means that trials can recruit ambulant individuals and later-stage individuals who are often excluded because of difficulty completing lower limb tasks. Here we discuss the key considerations for speech testing in clinical trials including hardware selection, suitability of tasks and their role in protocols for trials and propose a core set of tasks for speech testing in clinical trials. Test batteries could include forms suitable for remote short, sensitive and easy to use, with norms available in several languages. The use of artificial intelligence also could improve accuracy and automaticity of analytical pipelines in clinic and trials.

Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review

Real-time object detection based on UAV remote sensing is widely required in different scenarios. In the past 20 years, with the development of unmanned aerial vehicles (UAV), remote sensing technology, deep learning technology, and edge computing technology, research on UAV real-time object detection in different fields has become increasingly important. However, since real-time UAV object detection is a comprehensive task involving hardware, algorithms, and other components, the complete implementation of real-time object detection is often overlooked. Although there is a large amount of literature on real-time object detection based on UAV remote sensing, little attention has been given to its workflow. This paper aims to systematically review previous studies about UAV real-time object detection from application scenarios, hardware selection, real-time detection paradigms, detection algorithms and their optimization technologies, and evaluation metrics. Through visual and narrative analyses, the conclusions cover all proposed research questions. Real-time object detection is more in demand in scenarios such as emergency rescue and precision agriculture. Multi-rotor UAVs and RGB images are of more interest in applications, and real-time detection mainly uses edge computing with documented processing strategies. GPU-based edge computing platforms are widely used, and deep learning algorithms is preferred for real-time detection. Meanwhile, optimization algorithms need to be focused on resource-limited computing platform deployment, such as lightweight convolutional layers, etc. In addition to accuracy, speed, latency, and energy are equally important evaluation metrics. Finally, this paper thoroughly discusses the challenges of sensor-, edge computing-, and algorithm-related lightweight technologies in real-time object detection. It also discusses the prospective impact of future developments in autonomous UAVs and communications on UAV real-time target detection.

Exploring Instruction Set Architectural Variations: x86, ARM, and RISC-V in Compute-Intensive Applications

As computational demands continue to evolve in the modern era, the choice of hardware architecture plays a pivotal role in optimizing the performance of compute-intensive applications. This research paper delves into the exploration and comparison of three prominent hardware architectures: x86, ARM, and RISC-V, within the context of compute-intensive applications. The study begins with a comprehensive overview of these architectures, highlighting their distinctive features, and strengths. Subsequently, we investigate their suitability and adaptability in diverse compute-intensive workloads. Our analysis encompasses a wide spectrum of parameters, including computational throughput, power efficiency, scalability, and architectural flexibility. We scrutinize the architectural intricacies that impact the execution of compute-intensive tasks, shedding light on both the advantages and limitations of each architecture. We used the gem5 simulator to compare these Instruction Set Architectures (ISA). We run different benchmarks on gem5 with different ISA and different configurations and compare the result. Based on these results we predict which architecture is better in which scenario. Gem5 is not a cycle accurate simulator but it’s a model accurate. In conclusion, "Exploring Architectural Variations: x86, ARM, and RISC-V in Compute-Intensive Applications" offers a comprehensive insight into the nuances of hardware selection for compute-intensive workloads. Our findings aid system architects, researchers, and technology enthusiasts in making informed decisions about the most suitable architectural choice for their specific computeintensive applications, ultimately contributing to advancements in computational performance and efficiency

Automatic Piston-Type Flow Standard Device Calibration System

Measurement of flow is crucial for assuring product quality, increasing manufacturing effectiveness, and promoting the development of science and technology. With the advancement of calibration and automation, standard devices using the mass method, volumetric method, and master meter method have limitations, such as low calibration efficiency and automation, large size, and complex operation. Innovations in this area are desperately needed. To realize the automation of calibrating ultrasonic water meters, a piston-type flow standard device calibration system with a high degree of automation, high calibration efficiency, small size, and easy operation was designed. A piston-type flow standard device was designed, the standard device was modeled, the selection of the main hardware and the design of the automated control of the hardware parts were completed; an automation control system adapted to the flow standard device was developed; and, furthermore, a water meter flow point calibration algorithm integrating the start–stop method and the dual-time method, as well as a water meter flow correction algorithm, was devised to improve the efficiency of ultrasonic water meter calibration. An uncertainty assessment of the designed system was completed; the standard uncertainty and expanded uncertainty of the device were 0.013% and 0.026%. Meanwhile, flow calibration tests were conducted, validating the rationality of the automated calibration algorithm for ultrasonic water meters. The results show that ultrasonic water meters calibrated with flow correction have a flow error within ±3% in the “low flow range” and within ±2% in the “high flow range”, with a repeatability of less than 0.05%. This indicates that a piston-type flow standard device, coupled with an automation calibration control system, can efficiently, accurately, and conveniently perform water meter calibration, and the system has good practical value.

Enabling the Ocean Internet of Things with Renewable Marine Energy

Marine renewable energy can play an integral role in reducing the cost and complexity of collecting data from the oceans, and enhancing its exploitation. The processing and transmission of data at sea may be modelled as an `internet of things' (IoT) application. The `thing' is any offshore device which can collect and process data in-situ, while the `internet' represents the medium for transmitting data. IoT is differentiated from other internet based communication paradigms by the constraints on the system. These constraints are typically limited energy budgets and computing power, intermittent and low-bandwidth connectivity, and limited physical access. While land based IoT applications are already well served by a wide selection of hardware and software components, the needs of offshore IoT applications are not well served. Utilizing marine energy can advance the adoption of IoT at sea by providing in-situ energy generation, whilst also benefitting from the same technology. The Sustainable Energy Authority of Ireland funded `BlueBox' project aims to overcome barriers to entry for applying IoT technologies to offshore sensing by developing Ocean IoT (OIoT) hardware and software solutions. Features of the BlueBox system include modular offshore focussed hardware, no-code configuration and control of peripherals (such as sensors and actuators), duplex transmission of data using multiple media, serverless cloud server architecture and an edge computing framework. This paper presents an overview of BlueBox, tank tests for system validation of a prototype wave-energy powered ocean-observing platform, and a discussion of future applications of the technology.

Intramedullary Fibular Strut Allograft and a Small Fragment Nonlocking T-plate for Periprosthetic Lateral Femoral Condyle Fracture: A Case Report

Intramedullary Fibular Strut Allograft and a Small Fragment Nonlocking T-plate for Periprosthetic Lateral Femoral Condyle Fracture: A Case Report

Application of PID Control System in Mecanum Wheelchair

This research centers on the design and implementation of a control system for an electric wheelchair equipped with Mecanum wheels. The study details a comprehensive research methodology starting with the creation of a block diagram to guide system design, hardware selection, and overall implementation. The electric wheelchair system incorporates power resources, input devices, and energy output mechanisms, utilizing a 24 VDC battery and a joystick with a 10K ohm potentiometer connected to an Arduino Due microcontroller. The operational workflow of the system is defined, enabling the wheelchair to respond to joystick commands for forward, left turn, right turn, and other movements. A PID control system is employed to regulate motor movement, enhancing control precision. The Cohen-Coon tuning method is used to determine the PID controller's gain, ensuring efficient closed-loop control. Results from PID controller experiments under P control and PD control are presented, demonstrating the system's responses for different gain values. Optimal performance is observed with a Kp value of 80 and Kd value of 1.2, showcasing improved response speed, reduced rise time, enhanced setting time, and lower percent overshoot. In conclusion, the combined proportional and derivative control system, specifically with Kp = 80 and Kd = 1.2, proves to be effective in enhancing the Mecanum wheelchair's performance. This study provides valuable insights into precise parameter adjustments for optimal control in Mecanum wheelchair applications.

Wearable technologies for health research: Opportunities, limitations, and practical and conceptual considerations

One of the most notable limitations of laboratory-based health research is its inability to continuously monitor health-relevant physiological processes as individuals go about their daily lives. As a result, we have generated large amounts of data with unknown generalizability to real-world situations and also created a schism between where data are collected (i.e., in the lab) and where we need to intervene to prevent disease (i.e., in the field). Devices using noninvasive wearable technology are changing all of this, however, with their ability to provide high-frequency assessments of peoples’ ever-changing physiological states in daily life in a manner that is relatively noninvasive, affordable, and scalable. Here, we discuss critical points that every researcher should keep in mind when using these wearables in research, spanning device and metric decisions, hardware and software selection, and data quality and sampling rate issues, using research on stress and health as an example throughout. We also address usability and participant acceptability issues, and how wearable “digital biomarker” and behavioral data can be integrated to enhance basic science and intervention studies. Finally, we summarize 10 key questions that should be addressed to make every wearable study as strong as possible. Collectively, keeping these points in mind can improve our ability to study the psychobiology of human health, and to intervene, precisely where it matters most: in peoples’ daily lives.

Design and Implementation of Home Automation using Amazon Alexa

Abstract: The design and implementation of a home automation system using Amazon Alexa, a voice-controlled virtual assistant. With the rapid growth of smart home technology, integrating voice control capabilities has become a popular and convenient way to interact with home automation systems. The proposed system leverages the power of Amazon Alexa to enable users to control and monitor various household appliances through voice commands. The system consists of a cloud-based backend for data storage and processing, a smart home hub for device control, and a mobile application for user interaction. The hardware and software components, including devices, sensors, and communication protocols, are carefully selected and integrated into the system. The integration with Amazon Alexa Skills Kit allows the system to understand and process voice commands effectively. A case study is presented to demonstrate the practical application of the system in a typical home environment. The results showcase the system's efficiency, convenience, and user satisfaction. This project contributes to the growing field of home automation and highlights the potential of voice-controlled virtual assistants like Amazon Alexa in improving the functionality and usability of smart homes. The proposed system encompasses a cloud-based backend, a smart home hub, and a mobile application to facilitate seamless control and monitoring of various household appliances and devices through voice commands. The selection and integration of appropriate hardware and software components, including devices, sensors, and communication protocols, are thoroughly discussed. Furthermore, the system is integrated with the Amazon Alexa Skills Kit to enable effective processing of voice commands. A detailed case study is presented, highlighting the system's practical application within a typical home environment. The results demonstrate the system's efficacy, emphasizing its convenience, efficiency, and user satisfaction. This project contributes to the ever-growing field of home automation, showcasing the potential of voice-controlled virtual assistants, such as Amazon Alexa, to enhance the functionality and user experience of smart homes. Future enhancements and research directions are also discussed, paving the way for further advancements in this domain.

Power Circuit Design and Analysis of Controller For High-Power Axial Flux PMSM

Designing a high-power controller having high efficiency for permanent magnet motors is a challenge for developers in recent times and very few techniques are available. Design and analysis of power electronic drive for a high-power axial flux permanent magnet synchronous motor is presented in this paper. The motor under consideration here is having two outer stators and single permanent magnet rotor to drive the shaft. Control schemes and methodologies are the major concentration for research. Present paper explains a method to estimate the operational drive parameters and loss calculation according the selected power switches. In addition to this, thermal modelling is also important for designing a controller for high power machines. The hardware selection for high current switching operations is very critical for reliable and robust operation. Parts like IGBT module, gate driver, DC link capacitor, snubbers, current sensor sensitivities and estimation of losses are also critical along with control logic and processor. The importance of thermal analysis for high current switching drives is discussed and methodology for thermal analysis of power electronic devices is explained. The current work focuses on hardware level design of high-power drive for 150kW axial flux PMS motor.

Three-way decision for three-stage ranking pattern with criterion fuzzy concept

The application of three-way decision models in solving multi-criteria ranking problems has been widely recognized and noticed by researchers in recent years. However, most types of three-way decision models will not be directly usable when there are individual criterion minimum requirements set by decision-maker in a multi-criteria problem. For this problem, a two-stage three-way ranking pattern oriented to criterion fuzzy concept has been proposed. But, when many objects are directly identified as accepted or rejected, using this ranking pattern, there will be some objects for which the ranking results cannot be achieved. In view of this, considering criterion fuzzy concept and three-way decision, a three-stage ranking pattern is proposed to provide a more realistic and reliable ranking in a multi-criteria environment. Firstly, a new risk-loss function is defined according to the level of risk involved in decision behavior. Then, the decision thresholds are calculated. Secondly, a fuzzy description of an object is obtained using a maximum-k-means clustering algorithm. A feasible way to calculate the conditional probability value of an object is provided by this means. Finally, a new three-way ranking rule is constructed and a feasible three-stage ranking pattern is derived. Meanwhile, the feasibility of the new ranking pattern is analyzed by a computer hardware selection problem and the definition of two ranking indices. In addition, according to the experimental results on several data sets, the ranking indices between the three-stage ranking pattern and some existing ranking patterns are mostly higher than 0.8. The effectiveness of three-stage ranking pattern in solving different types of decision problems is provided by this experiment results. Also, the advantages of the three-stage ranking pattern are demonstrated based on the qualitative analysis between several ranking patterns.

Active noise cancellation with FPGA - practical considerations

Active noise cancellation (ANC) has been successfully applied to mitigate noise in many applications, such as in headphones. Besides algorithm development and computer simulation, developing an ANC system involves many practical considerations. From hardware selection to fixed-point implementation, many practical constraints can prevent a good algorithm from functioning properly on the hardware. This paper discusses the development of an ANC prototype system using Field-Programmable Gate Array (FPGA) hardware. The paper also reviews several practical issues that were encountered in the development of the prototype, the symptom and root cause of each issue, and the solutions to mitigate them. The final ANC prototype system successfully cancels noise of both tonal and broadband contents.

Lectura y almacenamiento de recursos bibliográficos empleando tecnología RFID de ultra alta frecuencia con internet de las cosas

Libraries supervisors have the administrative duty of control and keep safe their inventories, such as books, documents, manuals, and theses, facilitating access to their users to this information and helping them loan and return, internal or external, in case it’s permitted. We wanted to design and build a prototype for a device capable of complementing these tasks in a low cost. We go through the processes in the design of a prototype for a simultaneous Radio Frequency Identification Reader using Ultra High Frequencies with Internet of Things technologies for data storage and communication between multiple stations, to do so we use a decision matrix method to select hardware tools, and software programming languages and libraries, needed to build such device, comparing, and weighting the characteristics that fit our needs. The resulting hardware selection was the Raspberry Pi and the RedBoard with a 59,7% and 12,4% respectively, and NodeJS with WebSockets for the software selection with a 53,8%. Finally, we present the designed architecture and a working prototype of the device that was built. Obtained response times from the reader and the database storage were as low as 10 milliseconds and as high as 20 milliseconds.

Automated software and hardware selection systems for enterprise computers’ cybersecurity

Automated software and hardware selection systems for enterprise computers’ cybersecurity

A Design of Automatic Magnetic Properties Measurement System for Under/Postgraduate Open Experimental Project: Effective, Low Cost, and Scalable

Contribution: This research designs an experimental education project of an automatic material magnetism properties measurement system. It explores how the do-it-yourself (DIY), hands-on establishment, and hardware–software integration experiment system could be leveraged to enhance the understanding of the electromagnetism theory and nonelectrical quantity measurement application. Background: Designing and establishing a prototype of an automatic measurement system are essential, inspiring and beneficial to comprehending and consolidating the theoretical principles and simulations in electromagnetism and nonelectrical quantity measurement technology courses. Furthermore, in the vision of batch deployment and flexible group teaching, a cost-efficiency, hard/soft integration system for the electromagnetism property measurement definitely fits the aim of innovation laboratory education. Intended Outcomes: This project is intended to guide the students to finish a comprehensive research-oriented project loop for multiple skills training. It is also intended to encourage student motivation and confidence through theory investigation, hands-on group collaboration, and tunning for performance calibration. Application Design: The design of the proposed project includes the literature review, hardware selection and assembly, system integration, software programming, interface visualization, and system performance calibration. The participants are assessed with both objective exam questions and subjective open survey for training objectives to reveal their increased knowledge, interests, skill confidence, and creative thinking. Findings: The proposed project has been used in 4-year laboratory open experiments with students at different levels from freshmen undergraduates to 1st-year postgraduates. A use case and survey assessment demonstrate tremendous knowledge, skills, and confidence improvement among students in electromagnetism and measurement technology courses.

ENABLING FLEXIBLE AND ADAPTABLE NAVIGATION OF GROUND ROBOTS IN DYNAMIC ENVIRONMENTS WITH LIVE LEARNING

Federated learning is utilized for automated ground robot navigation, enabling decentralized training and continuous model adaptation. Strategies include hardware selection, ML model design, and hyperparameter fine-tuning. Real-world application involves optimizing communication protocols and evaluating performance with diverse network conditions. Federated learning shows promise for machine learning-based life learning systems in ground robot navigation. Research objective: to explore the use of federated learning in automated ground robot navigation and optimize the system for improved performance in dynamic environments. Materials and methods. The research utilizes federated learning to train machine learning models for ground robot navigation. Hardware selection, ML model design, and hyperparameter fine-tuning are employed. Communication protocols are optimized, and performance is evaluated using multiple gaming machine algorithms. Results. The results show that decreasing the learning rate and increasing hidden units improve model accuracy, while batch size has no significant impact. Communication protocols are evaluated, with Protocol A providing high efficiency but low security, Protocol B offering a balance, and Protocol C prioritizing security. Conclusion. The proposed approach using federated learning enables ground robots to navigate dynamic environments effectively. Optimizing the system involves selecting efficient communication protocols and fine-tuning hyperparameters. Future work includes integrating additional sensors, advanced ML models, and optimizing communication protocols for improved performance and integration with the control system. Overall, this approach enhances ground robot mobility in dynamic environments.

INVESTIGATING THE PERFORMANCE INDICES OF YOLO MODELS IMPLEMENTED ON A DPU: A COMPARATIVE ANALYSIS

A comprehensive analysis of the performance indices achieved by implementing various “You Only Look Once” dataset (YOLO) models on a dedicated Deep Learning Processing Unit (DPU). YOLO models are renowned for their real-time object detection capabilities, making them a good choice for a range of applications including autonomous vehicles, surveillance systems, and robotics. In this study, the YOLO models are deployed onto a specialized hardware accelerator, specifically a DPU, to assess their inference speed, accuracy, and power efficiency. By conducting an in-depth comparative evaluation of multiple YOLO variants, including YOLOv3, YOLOv4, YOLOv5, YOLOv6 insights into how each model interacts with the DPU architecture are revealed. The experiments involve benchmarking these models across diverse datasets and varying hardware configurations. The results not only highlight the advantages and limitations of employing DPUs for YOLO-based applications but also help in choosing the most suitable model-DPU combination based on specific performance requirements. This study contributes to the optimization of real-time object detection systems and assists practitioners in making informed decisions regarding the model and hardware selection.

Magnetic resonance imaging in neonates: a practical approach to optimize image quality and increase diagnostic yield.

Magnetic resonance imaging has emerged as a preferred modality in pediatric imaging because of its high soft-tissue contrast and the lack of ionizing radiation. It is important to recognize that despite its many advantages, several challenges to performing neonatal MRI arise from the lack of patient compliance and the small size of the anatomy. This manuscript presents the approach to patient preparation used at the authors' institution, summarizes general principles of image optimization and hardware selection, and reviews common indications across various organ systems. This manuscript also incorporates input from our pediatric-trained MRI technologists, in an attempt to compile a practical guideline covering all major aspects of neonatal MRI, from its execution to its interpretation.

Implementation of integrated BIM-VR into construction management curriculum: lessons learned and development of a decision support system

Several educational institutions and universities have introduced Building Information Modelling (BIM) and Virtual Reality (VR) courses separately in the construction management (CM) programs around the globe, and others are in the process of integrating them into their curricula. However, fewer institutions have included integrated BIM and VR into their courses. The industry needs for integrated BIM-VR are not fully recognised by universities due to the lack of a guideline and cognitive dissonance between academia and industry. As a result, most universities do not have a rigorous strategy for developing BIM-VR teaching topics and plans. This study firstly aims to report on some of the employed processes by university lecturers and academics in an Australian University to integrate BIM-VR education into CM curriculum. The review on the secondary sources indicates that the integration of BIM/VR education requires the consideration of three aspects including: industry needs, course contents, classroom size and software and hardware selection. The second aim of this paper is to develop a decision support system by utilising the PROMETHEE method as one of the multi-criteria decision-making approaches. The findings of the secondary sources and development of the decision support system help the educational institutions and practitioners for their future BIM-VR implementation initiatives.

Swimming Movement Guidance Training System Based on OpenCL Technology

In order to improve the physical quality and even ability of swimmers, this study built OpenCL technology for the swimming movement design system of swimmers, so as to design a series of movements. Combined with the capture system, this paper captures and processes the athletes’ movements, obtains the key position information, and calculates the accurate data. It can provide effective guidance for accurately positioning athletes’ swimming movements, enable users to have a clearer understanding of their own movements, improve users’ movements, and enhance the system operation effect. According to the technology and application requirements of virtual swimming training simulator, the overall design scheme of the device is determined. From the point of view of ensuring the comfort and safety of simulated virtual swimming, the design, installation, and strength verification of the supporting structure are carried out. Hardware design and selection shall be carried out according to the requirements of head position detection and virtual environment presentation, including the design of positioning ball set, camera selection, and VR head display selection. The software functions of head position detection program and virtual scene program are designed according to the requirements of natural human-computer interaction. The designed swimming guidance training system has complete functions and stable operation and can be used as an auxiliary tool for swimming training.