Hardware Development Research Articles

Inclusive Innovation through Arduino Embedded Systems and ChatGPT

This article aims to highlight the possibilities of inclusive innovation provided by the ChatGPT Artificial Intelligence Model through the development of projects that apply the Internet of Things (IoT) together with Arduino Embedded Systems. Inclusive innovation, considered as a path towards sustainable development, refers to the creation of new goods and services conceived for, by and with those who have been marginalized from the mainstream of development, particularly those people living in poverty who coexist with the higher income strata in society. Today, innovation drives the generation of new technological projects in need and demand. Through the Internet of Things (IoT) and Arduino Embedded Systems, which are designed to fulfill one or more functions through microcontrollers in real time, significant popularity has been achieved in the field of education and applied research. It is in these fields where the ChatGPT Model plays a crucial role in the progress of these areas. The methodology implemented consists of a Participatory Action Research with a qualitative approach, which was developed in the following cycles: 1. Planning of the intervention, 2. The intervention instrument selected was the Field Diaries on Digital Boards. As a result of this research, the development of Digital Educational Resources that contribute to the educational processes in the development of Free Software and Hardware was achieved. With this, the development of Digital Educational Resources for the Computer Engineering program was achieved.

Finite Control Set Model Predictive Control (FCS-MPC) for Enhancing the Performance of a Single-Phase Inverter in a Renewable Energy System (RES)

A single-phase five-level T-type topology has been investigated in this article. This topology has emerged as a viable option for renewable energy systems (RES) due to its inherent benefits. The finite control set model predictive control (FCS-MPC) strategy has been implemented to this topology in order to improve the performance and overall reliability of the system. This control strategy empowers the inverter to predict future behavior based on a discrete set of control signals, enabling precise modulation and high-speed response to system dynamics. In the realm of RES, integration of FCS-MPC with multilevel inverters (MLIs) holds great potential to enhance energy conversion efficiency, grid integration, and overall system reliability. The article is structured to present an overview of the evolving landscape of power electronic systems, and the advantages of FCS-MPC. This paper provides a comprehensive analysis of the FCS-MPC control strategy applied to the single-phase five-level T-type topology. The study covers various aspects including the theoretical framework, hardware development, and experimental evaluation. It is obvious from the analysis that this inverter topology is reliable. Several redundant states make it fault-tolerant which helps in maintaining the output voltage at the same level even in the fault conditions. Additionally, the results show that the output load voltage is maintained at the same level irrespective of load change. Also, output load voltage has maintained the high-quality sinusoidal characteristics as the total harmonic distortion (THD) is very low. With all these features, this system is suitable within the framework of RES.

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AbstractThe cover image focuses on neuronal circuit motif with specialized excitatory–inhibitory connectivity pattern. The neuronal circuit is an advanced functional unit of the brain beyond neurons and synapses. Neurons do not function in isolation and are linked to ensembles or circuit motifs that process specific types of information, enables multidimensional signal processing in the information flow of the brain. The authors demonstrate a core processor that can be employed to construct commonly used neuronal circuits and further perform bio‐realistic neuromorphic computing. Exploring the working principle, physical configuration, scalable design, and extensive signal‐processing capabilities of core processing neuron is crucial for advancing hardware development for brain‐inspired integrated neuromorphic systems. image

Software and Hardware Development of Madrasah Ibtidaiyah Student's Fitness Evaluation based on Hexagonal Obstacle Microcontroller (Evabugar Mikrohexo)

Software and Hardware Development of Madrasah Ibtidaiyah Student's Fitness Evaluation based on Hexagonal Obstacle Microcontroller (Evabugar Mikrohexo)

SLAV-Sim: A Framework for Self-Learning Autonomous Vehicle Simulation.

With the advent of autonomous vehicles, sensors and algorithm testing have become crucial parts of the autonomous vehicle development cycle. Having access to real-world sensors and vehicles is a dream for researchers and small-scale original equipment manufacturers (OEMs) due to the software and hardware development life-cycle duration and high costs. Therefore, simulator-based virtual testing has gained traction over the years as the preferred testing method due to its low cost, efficiency, and effectiveness in executing a wide range of testing scenarios. Companies like ANSYS and NVIDIA have come up with robust simulators, and open-source simulators such as CARLA have also populated the market. However, there is a lack of lightweight and simple simulators catering to specific test cases. In this paper, we introduce the SLAV-Sim, a lightweight simulator that specifically trains the behaviour of a self-learning autonomous vehicle. This simulator has been created using the Unity engine and provides an end-to-end virtual testing framework for different reinforcement learning (RL) algorithms in a variety of scenarios using camera sensors and raycasts.

Atomic-scale imaging of electron sensitive zeolites with aberration corrected transmission electron microscopy

The spatial resolution of transmission electron microscopes (TEMs) and scanning transmission electron microscopes (STEMs) has been drastically improved by introducing aberration correction. However, observable range in electron sensitive zeolites are still limited due to electron irradiation damages. Nevertheless, atomic resolution imaging of some zeolites is currently being realized by various developments in electron microscopic hardware, such as high sensitivity cameras. On the other hand, surveying the status of TEM and STEM imaging is very important for further progress in the structural analysis of zeolites. Here, we demonstrate and compare the high-resolution imaging of zeolites with several kinds of imaging modes.

A Secure Key Expansion Algorithm for Present

The efficiency of a cryptographic algorithm in terms of security depends on the resistance against cryptanalytic attacks. Besides the complexity of the encryption algorithm, the key plays an essential role in the security against cryptanalytic attacks. The strength and complexity of the encryption algorithm do not suffice and serve the fundamental purpose of security if the key is compromised at any stage. So, apart from the cryptanalytically robust encryption algorithm, a strong key schedule is also essential to thwart possible attacks against a particular algorithm. PRESENT, a lightweight encryption technique with a simple design, is resistant to linear and differential attacks but has a weak key schedule and is susceptible to cryptanalytic attacks. This paper proposes an efficient key scheduling algorithm for PRESENT lightweight encryption technique resistant against cryptanalytic attacks. Statistical tests examine the proposed key schedule’s cryptographic strength on the properties of subkeys produced in the key generation mechanism. The tests prove the efficiency of the proposed key schedule in terms of cryptanalytic attacks. Implementation and comparison of the efficacy of the proposed key schedule in terms of security and implementation costs are illustrated with the PRESENT-80 key schedule and the PRESENT-128 key schedule. The key scheduling algorithm is implemented on the ARM Cortex M3-based NXP LPC 1857 and 1768 hardware development platforms. The results indicate the efficiency of the proposed algorithm in terms of security, performance, and power consumption across the two hardware platforms.

Optically detected magnetic resonance with an open source platform

Localized electronic spins in solid-state environments form versatile and robust platforms for quantum sensing, metrology and quantum information processing. With optically detected magnetic resonance (ODMR), it is possible to prepare and readout highly coherent spin systems, up to room temperature, with orders of magnitude enhanced sensitivities and spatial resolutions compared to induction-based techniques, allowing for single spin manipulations. While ODMR was first observed in organic molecules, many other systems have since then been identified. Among them is the nitrogen-vacancy (NV) center in diamond, which is used both as a nanoscale quantum sensor for external fields and as a spin qubit. Other systems permitting ODMR are rare earth ions used as quantum memories and many other color centers trapped in bulk or 2-dimensional host materials. In order to allow the broadest possible community of researchers and engineers to investigate and develop novel ODMR-based materials and applications, we review here the setting up of ODMR experiments using commercially available hardware. We also present in detail the dedicated collaborative open-source interface named Qudi and describe the features we added to speed-up data acquisition, relax instrument requirements and extend its applicability to ensemble measurements. Covering both hardware and software development, this article aims to overview the setting of ODMR experiments and provide an efficient, portable and collaborative interface to implement innovative experiments to optimize the development time of ODMR experiments for scientists of any backgrounds.

A systematic review of hardware technologies for small-scale self-driving cars

Autonomous vehicle (AV) technology has the potential to revolutionize the transportation and logistics industry, making it more efficient and safer. However, testing such technologies is often limited by time, space, and cost constraints. Therefore, in recent years, several initiatives have emerged to test autonomous software and hardware on scaled vehicles. In order to provide guidance for future research, this systematic literature review was conducted to provide an overview of the literature surrounding small-scale self-driving cars, summarizing the current autonomous platforms deployed and focusing on the hardware developments in this field. Through the use of databases such as Web of Science, Scopus, Springer Link, Wiley, ACM Digital Library, and the TRID, 38 eligible studies that present small-scale testing of self-driving cars were identified and reviewed. The results indicated that publications on the topic are relatively new, with only the last four years showing an increase in the number of publications. Additionally, most papers only presented preliminary results, highlighting the potential for further research and development in the field. Research papers predominantly focused on software rather than hardware.

Opto-Electronic Hybrid Network Based on Scattering Layers.

Owing to the disparity between the computing power and hardware development in electronic neural networks, optical diffraction networks have emerged as crucial technologies for various applications, including target recognition, because of their high speed, low power consumption, and large bandwidth. However, traditional optical diffraction networks and electronic neural networks are limited by long training durations and hardware requirements for complex applications. To overcome these constraints, this paper proposes an innovative opto-electronic hybrid system that combines optical diffraction networks with electronic neural networks. Using scattering layers to replace the diffraction layers in traditional optical diffraction networks, this hybrid system circumvents the challenging training process associated with diffraction layers. Spectral outputs of the optical diffraction network were processed using a simple backpropagation neural network, forming an opto-electronic hybrid network exhibiting exceptional performance with minimal data. For three-class target recognition, this network attains a classification accuracy of 93.3% within a substantially short training time of 9.2 s using only 100 data samples (training: 70 and testing: 30). Furthermore, it demonstrates exceptional insensitivity to position errors in scattering elements, enhancing its robustness. Therefore, the proposed opto-electronic hybrid network presents substantial application prospects in the fields of machine vision, face recognition, and remote sensing.

A Model of Big Data Architecture on the Base of FIWARE Components

The intensive development of hardware and software tools provides many opportunities for solving various real-world tasks, such as working with large data sets. This, in turn, is a powerful incentive and opportunity for the development of different approaches and tools for solving them. After discussing several well-known BigData architectures a model of a BigData architecture is designed. The architecture is organized in three sections: edge section, processing-and-storage section and application section. The model will be implemented using FIWARE components.

RANCANG BANGUN PERANGKAT KERAS SISTEM SMART LAMPU PENERANGAN JALAN UMUM BERBASIS INTERNET OF THINGS GUNA MENDUKUNG IMPLEMENTASI SMART CITY

Public Street Lighting (PJU) is a lighting system designed to provide street lighting at night with the aim of increasing comfort for pedestrians and motorists passing through. However, the obstacles faced regarding PJU lights at this time are that when carrying out monitoring and control officers are still checking and visiting the location of every whereabouts of PJU lights. So that to make it easier for officers to manage PJU lights in this study, they will conduct discussions related to the design and development of hardware that is applied to PJU lights. The methods used in the development of this system are design, development, and testing. To build hardware using components such as ESP32 as a microcontroller, PZEM004Tv30 sensor, DHT22 sensor, IC RTC DS3231, Relay 30 A, IC LM2596 5V, IC AMS1117 3.3V, GSM SIM808 Module and LDR sensor, while for implementing the protocol using the MQTT protocol and Antares as the MQTT platform. The results of tests carried out on each circuit block in hardware such as the DHT22 sensor have an accuracy range of ~0.1 – 0.2, the PZEM004Tv30 sensor has an accuracy range of ~0.3 – 0.5, Regulator IC LM2596 5V and AMS1117 3.3V each has an appropriate output voltage of ~5V and ~3.3V, and the CH340C IC Driver IC can work and read properly.

User Perspectives on Branching in Computer-Aided Design

Branching is a feature of distributed version control systems that facilitates the "divide and conquer" strategy present in complex and collaborative work domains. Branching has revolutionized modern software development and has the potential to similarly transform hardware product development via CAD (computer-aided design). Yet, contrasting with its status in software, branching as a feature of commercial CAD systems is in its infancy, and little research exists to investigate its use in the digital design and development of physical products. To address this knowledge gap, in this paper, we mine and analyze 719 user-generated posts from online CAD forums to qualitatively study designers' intentions for and preliminary use of branching in CAD. Our work contributes a taxonomy of CAD branching use cases, an identification of deficiencies of existing branching capabilities in CAD, and a discussion of the untapped potential of CAD branching to support a new paradigm of collaborative mechanical design. The insights gained from this study may help CAD tool developers address design shortcomings in CAD branching tools and assist CAD practitioners by raising their awareness of CAD branching to improve design efficiency and collaborative workflows in hardware development teams.

Optimising the quantum/classical interface for efficiency and portability with a multi-level hardware abstraction layer for quantum computers

Steady progress is being made in the development of quantum computing platforms based on different types of qubit technologies. Each platform requires bespoke strategies to maximise the efficiency of the quantum/classical interface when operating close to the qubits. At a higher level, however, a shared interface allowing portability of quantum algorithms across all the available quantum platforms is preferred. Striking the right balance between portability and performance of the algorithm as implemented on quantum hardware remains a major challenge for this field. Here, we propose a quantum hardware abstraction layer (QHAL) providing a multi-level intermediate representation of the quantum stack. A collaborative effort between software specialists and quantum hardware developers operating on four major qubit technologies (photonics, silicon, superconducting and trapped ions) led to the identification of a minimum common set of instructions and metadata allowing the QHAL to interact efficiently with multiple platforms. Access to the stack from the higher levels increases latency yet minimises the amount of hardware architecture parameters to be handled by the algorithm developer, thus simplifying code development and reducing security threats from misuse or malicious access for hardware developers. Access to the stack from the lowest—closest to the qubits—level provides the highest hardware responsiveness, suitable for algorithms requiring minimum latency for data and instruction transfer. With respect to existing quantum assembly languages, the QHAL extends further down in the stack by defining an application-binary interface to interact with the quantum hardware. By defining a standard representation of the quantum stack, a common reference framework is provided to both software and hardware developers which would ensure future integration of their R&D efforts.

Best Practice in Open Source Scientific Designs Pre-Covid-19

Before the COVID-19 pandemic, open source hardware development grew steadily. However, the pandemic rapidly accelerated the global acceptance of open source hardware solutions. While open source software has long been vital in the ICT sector, this concept now extends to physical hardware production. Notably, the medical and energy fields have prominently embraced this shift, utilizing 3D printing to actualize open source designs. This study explores pre-pandemic Open Source Scientific practices, aiming to extract insights for a stronger future open source framework. Through an in-depth case study, we illuminate best practices, exemplary applications, and propose a framework for upcoming open source hardware projects. Additionally, this paper categorizes key areas of open source hardware innovation, outlining key sections and areas, thus offering a comprehensive view of the open source hardware landscape.

4D radar simulator for human activity recognition

AbstractMillimetre‐wave radar has been widely used in health monitoring and human activity recognition owing to its improved range resolution and operation in a variety of environmental conditions. With the MIMO antenna array, 4D radar is increasingly employed in autonomous driving, while its application in assisted living is recent and therefore the value added compared to the increase in signal processing and hardware requirements is still an open question. A model for 4D Time‐division multiplexing (TDM) multiple‐input‐multiple‐output (MIMO) frequency‐modulated Continuous wave radar is established using the human activities from the HDM05 motion capture dataset. The simulator produces an end‐to‐end simulation, including four human motions (jumping Jack, kick, punch, and walk), signal time of flight, noise, MIMO signal processing, and classification. Different pre‐processing and point cloud‐based methods are compared to obtain an average classification accuracy of 90% with PointNet. This study simulates a specific 4D TDM MIMO radar configuration to benchmark signal pre‐processing algorithms, which can also assist other researchers to generate range‐Doppler‐time (range‐Doppler time) point cloud data sets for human activities testing different radar configurations, array configurations, and activities saving valuable time in human resources and hardware development before prototyping to assess expected performances.

Assessment of steel bridges – cracking the code

This article has been developed from a presentation given at the IStructE conference on 'Structural rehabilitation' in December 2022. It gives a view, based on my career over 30 years, of the way in which our assessment codes can fall short in carrying out a structural assessment of steel bridges. I have taken three differing types of bridge form, scale and purpose, and explained the assessment problems encountered and the method by which a better solution was found using computer methods of analysis. The bridges chosen are the M5 Avonmouth Bridge, a steel box-girder structure; a single-span railway bridge over the River Wey, originally of wrought iron, but subsequently strengthened in steel; and, finally, a lattice-girder truss footbridge. Considering the ongoing pace of development in hardware and software power, and the relative falling cost of computer analysis, the suggestion is made that upcoming assessment codes should make better use of computer tools available.

High-fidelity virtual reality simulation training in enhancing competency assessment in orthopaedic training.

Surgical competence is the ability to successfully apply academic knowledge, clinical skills and professional behaviour to inpatient care. Along with ensuring patient safety, the ability to communicate effectively, collaborative teamwork and probity, and achieving satisfactory competencies form the fundamental principles of good medical practice. Current strategies to develop surgical competencies include a range of formative and summative assessments. The cancellation of traditional face-to-face meetings and training opportunities during the COVID-19 pandemic had a profound impact on the delivery of medical education and opportunities to achieve surgical competencies. Simulation learning has been used since before the pandemic to deliver surgical training across all grades and specialities, including orthopaedic surgery. Simulation-based training provides a safe, controlled environment to develop skill acquisition. Simulated surgery using virtual reality has evolved following developments in software and hardware. This article explores the role of high-fidelity virtual reality simulation to assess competencies in orthopaedic training in the post-COVID-19 era, and examines whether simulation could be used within the curriculum to augment and improve training.

An extensive benchmark study on biomedical text generation and mining with ChatGPT.

In recent years, the development of natural language process (NLP) technologies and deep learning hardware has led to significant improvement in large language models (LLMs). The ChatGPT, the state-of-the-art LLM built on GPT-3.5 and GPT-4, shows excellent capabilities in general language understanding and reasoning. Researchers also tested the GPTs on a variety of NLP-related tasks and benchmarks and got excellent results. With exciting performance on daily chat, researchers began to explore the capacity of ChatGPT on expertise that requires professional education for human and we are interested in the biomedical domain. To evaluate the performance of ChatGPT on biomedical-related tasks, this article presents a comprehensive benchmark study on the use of ChatGPT for biomedical corpus, including article abstracts, clinical trials description, biomedical questions, and so on. Typical NLP tasks like named entity recognization, relation extraction, sentence similarity, question and answering, and document classification are included. Overall, ChatGPT got a BLURB score of 58.50 while the state-of-the-art model had a score of 84.30. Through a series of experiments, we demonstrated the effectiveness and versatility of ChatGPT in biomedical text understanding, reasoning and generation, and the limitation of ChatGPT build on GPT-3.5. All the datasets are available from BLURB benchmark https://microsoft.github.io/BLURB/index.html. The prompts are described in the article.

WIRELESS BLACK BOX FOR VEHICLE USING IOT

The project aims to develop a wireless black box system for vehicles using IoT technology. The system will collect data from various sensors installed in the vehicle, such as GPS, accelerometer, gyroscope, and camera, and transmit the data wirelessly to a cloud-based data storage and processing system. The data will be processed and analyzed using advanced data analysis and visualization techniques to provide insights into vehicle performance and driver behavior. The system will also provide driver feedback in real-time, helping to improve driving behavior and reduce the risk of accidents. The project will involve the design and development of hardware and software components, as well as testing and deployment of the system in vehicles. The wireless black box system has the potential to revolutionize the transportation industry by providing real-time data on vehicle performance and driver behavior, which can be used to improve safety, reduce fuel consumption, and optimize fleet management