Hardware System Research Articles

Number Plate Recognition Smart Parking Management System Using IoT

This study aims to address the urban vehicle parking issues by proposing a solution using Automatic Number Plate Recognition (ANPR) through image processing and a sensor-based hardware system. Integrating these technologies forms a Smart Parking Management System (SPMS) to automate parking processes and enhance the parking experience. The study aims to create an efficient system that eliminates manual vehicle registration and optimizes space utilization. ANPR and IoT-based sensors help users identify the available slots and pay only for the actual parking duration, which will help to minimize the fixed billing rates. The proposed ANPR system processes vehicle number plates at entry, ensuring seamless identification and eliminating manual registration. IoT sensors monitor real-time slot occupancy, transmitting data to a web admin panel. This panel provides insights such as entry and exit times, total parking duration, and billing costs, facilitating efficient management and remote monitoring. The ANPR-based SPMS reduces reliance on manual processes, streamlining entry procedures. By dynamically assessing slot availability through IoT sensors, users can locate unoccupied spaces quickly, which enhances user convenience. The web admin panel allows administrators to monitor the system remotely, ensuring smooth operations and maintaining accurate records. This study introduces a comprehensive solution to urban parking challenges by integrating ANPR and IoT technologies. The SPMS improves efficiency, reduces human resource needs, and enhances user experience with flexible billing based on actual duration. The combination of hardware and software provides a foundation for effective urban parking management.

Design and evaluation of a liquid chromatographic column oven with adaptive and precise temperature control

High performance liquid chromatography (HPLC) is a key analytical technique that is used in a number of fields. Improving the separation efficiency, stability, and universality of HPLC has been a continuing analytical-chemistry focus. In chromatographic separation, factors such as the composition and ratio of the mobile phase, the type of stationary phase, and the dimensions of the chromatographic column significantly affect the separation efficiency. In addition, the temperatures of the chromatographic column and mobile phase are also important for achieving separation. The column oven is usually used to stably control the column temperature in the HPLC separation system. Indeed, highly accurate temperature control ensures superior separation performance, short analysis times, and repeatability. In this study, we innovatively improved the traditional column oven by combining a variety of temperature-control algorithms to deliver continuous and highly accurate temperature control in the wide 4-90 ℃ range, and by exploring a new chromatographic-method development route. Instead of focusing on the complex hardware system, we optimized the software to achieve highly stable and accurate (±0.1 ℃) temperature control. Temperature-control performance was further improved by optimizing the structure of the thermal insulation and employing reliable and environmentally friendly thermal-insulation materials. Additionally, the thermal conduction of the heat-source device is discussed based on the heat-transfer principle with the aim of improving the performance of the column oven. The improved column oven delivered significantly enhanced chromatographic-separation repeatability and stability thereby reliably guaranteeing the development of highly efficient chromatographic analysis methods.

A Carpometacarpal Thumb Tracking Device for Telemanipulation of a Robotic Thumb: Development, Prototyping, and Evaluation

Hand−tracking systems are widely employed for telemanipulating grippers with high degrees of freedom (DOFs) such as an anthropomorphic robotic hand (ARH). However, tracking human thumb motion is challenging due to the complex motion of the carpometacarpal (CMC) joint. Existing hand−tracking systems can track the motion of simple joints with one DOF, but most fail to track the motion of the CMC joint, or to do so, there is a need for expensive and intricately set up hardware systems. This research introduces and realizes an affordable and personalizable tracking device to capture the CMC joint Flexion/Extension and Abduction/Adduction motions. Tracked human thumb motion is mapped to a robot thumb in a hybrid approach: the proposed algorithm maps the CMC joint motion to the first two joints of the robot thumb, while joint mapping is established between the metacarpophalangeal and interphalangeal joints to the last two joints. When the tracking device is paired with a flex glove outfitted with bend sensors, the developed system provides the means to telemanipulate an ARH with a four-DOF thumb and one-DOF underactuated digits. A three-stage framework is proposed to telemanipulate the fully actuated robot thumb. The tracking device and framework were evaluated through a device operation and personalization test, as well as a framework verification test. Two volunteers successfully personalized, calibrated, and tested the device using the proposed mapping algorithm. One volunteer further evaluated the framework by performing hand poses and grasps, demonstrating effective control of the robot thumb for precision and power grasps in coordination with the other digits. The successful results support expanding the system and further evaluating it as a research platform for studying human–robot interaction in grasping tasks or in manufacturing, assistive, or medical domains.

Mechano-gated iontronic piezomemristor for temporal-tactile neuromorphic plasticity

In bioneuronal systems, the synergistic interaction between mechanosensitive piezo channels and neuronal synapses can convert and transmit pressure signals into complex temporal plastic pulses with excitatory and inhibitory features. However, existing artificial tactile neuromorphic systems struggle to replicate the elaborate temporal plasticity observed between excitatory and inhibitory features in biological systems, which is critical for the biomimetic processing and memorizing of tactile information. Here we demonstrate a mechano-gated iontronic piezomemristor with programmable temporal-tactile plasticity. This system utilizes a bicontinuous phase-transition heterogel as a stiffness-governed iontronic mechanogate to achieve bidirectional piezoresistive signals, resulting in wide-span dynamic tactile sensing. By micro-integrating the mechanogate with an oscillatory iontronic memristor, it exhibits stiffness-induced bipolarized excitatory and inhibitory neuromorphics, thereby enabling the activation of temporal-tactile memory and learning functions (e.g., Bienenstock–Cooper–Munro and Hebbian learning rules). Owing to dynamic covalent bond network and iontronic features, reconfigurable tactile plasticity can be achieved. Importantly, bridging to bioneuronal interfaces, these systems possess the capacity to construct a biohybrid perception-actuation circuit. We anticipate that such temporal plastic piezomemristor devices for abiotic-biotic interfaces can serve as promising hardware systems for interfacing dynamic tactile behaviors into diverse neuromodulations.

Retention mechanism in slalom chromatography: Perspectives on the characterization of large DNA and RNA biopolymers in cell and gene therapy.

Significant progress has been made in the last two decades in producing small (<2μm), high-purity, and low-adsorption particles, columns and system hardware, for ultra-high pressure liquid chromatography (UHPLC). Simultaneously, the recent rapid expansion of cell and gene therapies for treating diseases necessitates novel analytical technologies for analyzing large (>2 kbp) plasmid double-stranded (ds) DNA (which encodes for the in vitro transcription (IVT) of single-stranded (ss) mRNA therapeutics) and dsRNAs (related to IVT production impurities) biopolymers. In this context, slalom chromatography (SC), a retention mode co-discovered in 1988, is being revitalized using the most advanced column technologies for improved determination of the critical quality attributes (CQAs) of such new therapeutics. In this review, we first recall non-exhaustively the main currently available analytical techniques (enzyme-linked immunosorbent assay (ELISA), agarose gel electrophoresis (AGE), pulse field gel electrophoresis (PFGE), capillary gel electrophoresis (CGE), mass photometry (MP), anion-exchange chromatography (AEX), ion-pairing reversed-phase liquid chromatography (IP-RPLC), hydrophobic interaction chromatography (HIC), size-exclusion chromatography (SEC), hydrodynamic chromatography (HDC), highly converging flow ultra-filtration (HCF-UF), asymmetrical flow field-flow fractionation (AF4), mass spectrometry (MS), and atomic force microscopy (AFM)) for analyzing mixtures containing large nucleic acid biopolymers, while assessing their strengths and weaknesses. We then focus comprehensively on the SC technique, report on its past applications since its birth, and review in detail the history and evolution of the proposed retention mechanisms accounting for the observations made in SC. This includes and emphasizes the latest physico-chemical insights (shear rates in packed HPLC columns, entropic elasticity and relaxation of dsDNA, dsRNA, and mRNA biopolymers) governing the retention behavior of such biopolymers in SC. Finally, based on the recent advancements in understanding the fundamentals of retention in SC, we provide some perspectives and recent proof-of-concept for the analytical characterization by SC of large dsDNAs (plasmid digests, polymerase chain reaction (PCR) verification), the separation of supercoiled/circular and linear dsDNAs (plasmid linearization), the isolation and quantification of large dsRNAs impurities present in mRNA samples produced by IVT, and the differentiation between dsRNA conformers.

How has the concept of health system software been used in health policy and systems research? A scoping review.

Understanding health systems as comprising interacting elements of hardware and software acknowledges health systems as complex adaptive systems (CAS). Hardware represents the concrete components of systems, whereas software represents the elements which influence actions and underpin relationships, such as processes, values and norms As a specific call for research on health system software was made in 2011, we conducted a qualitative scoping review considering how and for what purpose the concept has been used since then. Our overall purpose was to synthesise current knowledge and generate lessons about how to deepen research on, and understanding of, health system software. The review consisted of two phases: first, for the period 2011-2023, all papers which explicitly used the concept of health system software were identified and mapped; second, drawing on a sub-set of papers from phase 1, we explored how the concept was purposively used within research. The databases Pubmed, Scopus, EBSCOhost, Web of Science and Google Scholar were systematically searched using a strategy developed by a skilled librarian. In Phase 1, data were extracted from 98 papers. Our analysis revealed that a third of the papers used the software concept rather superficially; a third used it to conceptualise the importance of selected software elements; and a third used it in examining a specific health system experience, such as preparedness or resilience. In Phase 2, our analysis confirmed that researchers have found value in pro-actively using the software concept within studies, demonstrating two patterns of use. However, limited understanding of how to investigate interactions amongst hard- and software elements was also revealed. Future health policy and systems research should purposively investigate hardware-software interactions, in order to gain greater understanding of the complex, adaptive nature of health systems, understand their operations, and to institutionalise thinking that considers health systems as CASs.

Improvement of Internet of Things (IoT) Interference Based on Pre-Coding Techniques over 5G Networks

The advent of 5G technology has revolutionized wireless communication, offering unprecedented data rates, reduced latency, and enhanced connectivity. A critical component driving these advancements is Multiple-Input Multiple-Output (MIMO) technology. MIMO utilizes multiple antennas at both the transmitter and receiver ends to improve communication performance. In the context of the Internet of Things (IoT), MIMO plays a pivotal role in enhancing network efficiency, reliability, and capacity and can improve system capacity and reduce interference between different users. By leveraging MIMO, IoT devices can achieve higher data throughput and better signal quality, even in challenging environments. This is particularly important for IoT applications that require real-time data transmission and low latency, such as smart cities, autonomous vehicles, and industrial automation. Additionally, MIMO technology helps in mitigating interference and improving spectrum utilization, making it an essential enabler for the massive connectivity demands of IoT networks in the 5G era. However, due to the high channel dimension, complex channel estimation and precoding algorithms in the system, the system hardware and software overhead will increase. The precoding algorithms of massive MIMO systems are divided into three types: digital, analog and hybrid. The three types of precoding algorithms are summarized and compared, and the advantages and disadvantages of different precoding algorithms and applicable scenarios are summarized. The channel estimation schemes are divided into training estimation and blind estimation, and the advantages and disadvantages of the two types of schemes are summarized. It is pointed out that the reasonable use of the channel sparsity of massive MIMO can improve the quality of channel estimation and reduce the estimation overhead.

Using Arduino Uno and EMG Electrodes for Electromyographic Signal Measurement and Servo Control

This paper explores the integration of Arduino Uno with surface electromyography (sEMG) electrodes for measuring muscle electrical activity and controlling servo motors based on these signals. By analyzing electromyographic signals from various hand postures, such as opening and closing, signal variations were thoroughly evaluated. The study implements a servo control mechanism that adjusts the servo motors position based on computed root mean square (RMS) voltage values derived from EMG signals. The experimental results demonstrate that the servo control system effectively mirrors hand movements, providing a feasible solution for biomedical applications and human-machine interfaces. This research highlights the potential for using affordable hardware for sophisticated control systems and suggests future enhancements to improve system accuracy and applicability.

Low-power edge detection based on ferroelectric field-effect transistor

Edge detection is one of the most essential research hotspots in computer vision and has a wide variety of applications, such as image segmentation, target detection, and other high-level image processing technologies. However, efficient edge detection is difficult in a resource-constrained environment, especially edge-computing hardware. Here, we report a low-power edge detection hardware system based on HfO2-based ferroelectric field-effect transistor, which is one of the most potential non-volatile memories for energy-efficient computing. Different from the conventional edge detectors requiring sophisticated hardware for the complex operation such as convolution and gradient, the proposed edge detector is analogue-to-digital converter free and loaded into a multi-bit content addressable memory, which only needs one 4 × 4 ferroelectric field-effect transistor NAND array. The experimental results show that the proposed hardware system is able to achieve efficient image edge detection at low power consumption (~10 fJ/per operation), realizing no-accuracy-loss, low-power and analogue-to-digital-converter-free hardware system, providing a feasible solution for edge computing.

Feasibility study of patient-specific four-dimensional in vivo tracking system for high-dose-rate brachytherapy: Experimental evaluation.

High-dose-rate (HDR) brachytherapy using Iridium-192 as a radiation source is widely employed in cancer treatment to deliver concentrated radiation doses while minimizing normal tissue exposure. In this treatment, the precision with which the sealed radioisotope source is delivered significantly impacts clinical outcomes. This study aims to evaluate the feasibility of a new four-dimensional (4D) in vivo source tracking and treatment verification system for HDR brachytherapy using a patient-specific approach. A hardware system was developed for the experiments, featuring a high-resolution compact gamma camera with a redesigned diverging collimator, enhanced detector, and precision control system. The collimator was redesigned to improve spatial resolution by reducing the hole size and increasing the hole array, while reducing the pixel size of the detector and increasing the number of pixels. The performance was evaluated using Monte Carlo simulations, which demonstrated significant improvements in spatial resolution. Experiments were conducted in a controlled setup using a phantom to simulate clinical conditions. The phantom was positioned at various distances from the gamma camera (327.30, 377.30, and 427.30mm) and imaged at multiple angles. The accuracy of the system was tested in four different cases: three with fixed distances and one employing a multi-focusing method. The multi-focusing method allows the gamma camera to adjust its focus based on the anatomical characteristics of individual patients, thereby enhancing source-tracking accuracy. The performance of the system was evaluated under these four different scenarios. The Euclidean distance and three-dimensional gamma analysis were used to evaluate tracking accuracy and dose distribution. The redesigned collimator demonstrated significant improvements (compared to the previous design) in the spatial resolution of the gamma camera, showing 34.21% and 23.46% enhancements in the horizontal and vertical profiles, respectively. These improvements in gamma camera resolution are crucial for enhancing the tracking system's accuracy. The experimental results demonstrated varying degrees of accuracy across different cases, reflecting the performance of the system under different conditions. The average Euclidean distance errors were Case 1 (327.30mm): 1.358mm; Case 2 (377.30mm): 1.731mm; Case 3 (427.30mm): 1.973mm; and Case 4 (multi-focusing): 1.527mm. The gamma pass rates for the four cases were:- Case 1: 86.39%; Case 2: 75.28%; Case 3: 72.22%; and Case 4: 81.67% (1mm/1%). For the 2mm/2% criterion, the gamma pass rates were 97.11, 94.72, 92.38, and 96.78% for Cases 1, 2, 3, and 4, respectively. Case 4 (multi-focusing) showed an improvement over Case 3, with a 22.6% reduction in the average Euclidean distance error and a 13.1% increase in the gamma pass rate (1mm/1%). These results demonstrate that the new 4D in vivo source tracking and treatment verification system for HDR brachytherapy is feasible and has potential clinical benefits.

Design and validation of the PCIe card in a readout system for MAPS based telescope

MAPS (Monolithic Active Pixel Sensors) are emerging as the leading technology for inner tracking detectors in future particle physics and nuclear physics experiments. At CCNU (Central China Normal University), the MIC series MAPS ASICs are currently under development. To facilitate the evaluation of these chips and the readout of telescopes with multiple modules, a flexible chip readout and data acquisition system is being developed. This system comprises a front-end kit and a back-end PCIe-based system called PiDAQ. The PiDAQ card, PDQ142, is built with the AMD Versal Prime series FPGA VM1402. Key features of this board include: PCIe Gen4×8 bus supports throughput up to 14.76 GB/s;on-board memory modules support data buffering with an average throughput exceeding 37.7 GB/s. Furthermore, the board is designed to interface with up to eight front-end modules in speed up to 28 Gb/s per lane, making it ideal for future telescope applications. This paper details the design, hardware, firmware, and preliminary validation of the PDQ142 system.

Prototype design of a digital Low-Level RF system for S3FEL S-band Transverse Deflecting Cavities

Transverse Deflecting Cavities (TDCs) are generally adopted for electron beam diagnosis. Three sets of S-band and two sets of X-band TDCs are planned at Shenzhen Superconducting Soft X-ray Free Electron Laser (S3FEL) to accurately measure the temporal distribution of ultra-short electron bunches. The microwave system of one TDC consisting of a Low-Level Radio-Frequency system (LLRF), a solid-state amplifier, a klystron, and several waveguide couplers is operated in pulse mode with a maximum repetition rate of 50 Hz. Its microwave stabilities for amplitude and phase are required to be better than 0.05%/0.05∘ (RMS). This article will introduce the prototype design of the hardware, firmware, and software of the digital LLRF system for S-band TDCs. We use a homemade local oscillator and commercial cards based on the MicroTCA standard in hardware design. The firmware design will use an IQ demodulation and a reference-tracking algorithm to eliminate the measurement noise and drift. The software design is based on the Experimental Physics and Industrial Control System (EPICS), achieving data acquisition, slow control, and interface display functions. This technical report will also show some preliminary test results.

Internet of Things Technology for an Autonomous Photovoltaic Solar Energy System Monitoring

This work presents a Photovoltaic (PV) monitoring system using the Internet of Things (IoT). It provides a detailed overview of the key steps involved in designing the IoT-based PV monitoring system, examining the specific architectural components, including different system layers, potential software and hardware platforms, and communication network technologies used in the IoT-integrated PV system. Additionally, it discusses the existing challenges and issues of IoT-based PV monitoring systems. Furthermore, it proposes a low-cost monitoring system designed to monitor a PV system consisting of a PV module, a DC-DC boost converter, and a load.

IoT (internet of things) microclimate monitoring system using node-red platform at plant factory

Abstract Indonesia faces significant challenges in the agricultural sector, such as limited productive land and climate change, which threaten food security. The plant factory emerges as a relevant solution by integrating hydroponic technology and automation to enable year-round controlled food production. This study aimed to develop an IoT-based microclimate monitoring system using the Node-RED platform within a plant factory. The system allowed for real-time temperature, humidity, and light intensity monitoring. The methodology involves designing a system utilizing an Arduino Mega 2560 microcontroller, DHT22 and GY-49 sensors, and an ESP01S WiFi module. The collected data was transmitted via the MQTT protocol and displayed on the Node-RED dashboard. The results demonstrated that this system effectively monitored climate parameters and could enhance the efficiency and productivity of the plant factory, represented by all sensors functioning correctly. The DHT22 sensor has been calibrated with a standard Thermohigrometer measuring instrument with a temperature RMSE value of 1.01 and a humidity RMSE of 5.88. Likewise, the GY-49 sensor has been calibrated with a standard lux meter. In calibrating the GY-49, the equation entered in the coding is 0.978 * sensor reading + 0.891, and the resulting RMSE value is 57.91. During the testing phase, the datalogger hardware system can run 100%. Consequently, this system significantly supports sustainable modern agriculture and national food security.

Enhancing Reliability in Embedded Systems Hardware: A Literature Survey

Enhancing Reliability in Embedded Systems Hardware: A Literature Survey

2023 cyberbiosecurity summit underscores challenges associated with cybersecurity and the rapidly growing bioeconomy

ABSTRACT The 2 November 2023 Cyberbiosecurity Summit at Georgia Tech Research Institute’s (GTRI) headquarters in Atlanta united cybersecurity and biotechnology stakeholders to address safeguarding of bio-based industries. Goals included identifying cyberbiosecurity vulnerabilities and establishing prevention best practices for emerging biotech hardware, software, data and bio-based systems. The Summit highlighted risks and explored mitigation strategies for a safer bioeconomy. Key takeaways include: (1) as genome databases proliferate globally, it becomes more likely that these databases will become targets for cyberbiosecurity attacks; (2) the biotech community should not view cybersecurity as an impediment to research, but rather as a national security and global health imperative for proactive investment and workforce training; (3) the bioeconomy depends on a vulnerable supply chain for software; and (4) mapping of a biological attack surface, inspired by traditional cybersecurity assessments, would facilitate identification of vulnerabilities of cyber-bio-physical systems and the development of protective measures for the bioeconomy.

Design and Implementation of Least Mean Square Adaptive Filter Using Verilog

Adaptive filters are ability of adaptation to an unknown environment. These filters have been used widely because of its capable of operating in an unknown system and low power implementation of hardware. Adaptive filters have great range of signal processing and control operations for the tracking time variations of input statistics and Robust to the noise immunity. These filters used various Areas like Noise cancelling (interface cancelling), system identification, inverse modelling and echo predication. Adaptive filters structures have the adaptive algorithms to perform the time variations of the input statics and Robust to the noise cancelling. The most popular algorithm is LMS (Least Mean Square) it produces the least mean square of error signal in the adaptive filter to minimize noise power. Adaptive filter structures follow the two algorithms RLS and LMS. RLS algorithms excellent performance with increased complexity and the filter coefficients that minimize waited linear least squares cost function relating to the input signals. It requires infinite memory for error signal. LMS algorithms are simplest to understand and describe the hardware of the system compare to the RLS (Recursive Least Square). LMS algorithms are follows the stochastic gradient descent method to minimize the error signal and de-nosing task. It estimates the gradient vector from the input data and LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector which leads to minimum mean square error. It doesn’t require correlation functions for calculations. The main aim of the project is to design the LMS algorithm based adaptive filter using Verilog HDL to reduce the power consumption, hardware complexity and improving the noise cancelling for the adaptive filter on the FGPA boards. An important challenge in the LMS adaptive filters design implementation of structural model in the Verilog HDL for image processing to target the noise cancelling, power and hardware complexity. Tool use for implementation the structural model of LMS filter is vivado tool and Xilinx software for FPGA board implementation.

Intelligent Gesture Recognition Gloves for Real-Time Monitoring in Wireless Human-Computer Interaction.

Flexible sensors mimic the sensing ability of human skin, and have unique flexibility and adaptability, allowing users to interact with intelligent systems in a more natural and intimate way. To overcome the issues of low sensitivity and limited operating range of flexible strain sensors, this study presents a highly innovative preparation method to develop a conductive elastomeric sensor with a cracked thin film by combining polydimethylsiloxane (PDMS) with multiwalled carbon nanotubes (MCNT). This novel design significantly increases both the sensitivity and operating range of the sensor (strain range 0-50%; the maximum tensile sensitivity of this sensor reaches 4.97), marking a breakthrough in flexible sensor technology. The sensor can also be fabricated easily and inexpensively which is suitable for large-scale production, indicating its significant application potential. Combined with a dedicated hardware and software system, the sensors enable real-time monitoring of the finger status and accurate gesture recognition. Furthermore, machine-learning algorithms were used to identify American Sign Language gestures with an accuracy of up to 97%. This innovative human-machine interface provides important support for the future development of intelligent interactive systems, and is of great significance in promoting the progress of advanced human-computer interaction technologies.

Hardware and software system for data collection, detection, classification of surface defects and visualization of results based on computer vision for large-diameter pipes

Hardware and software system for data collection, detection, classification of surface defects and visualization of results based on computer vision for large-diameter pipes

Pixel‐Level Hardware Strategy for Large‐Scale Convolution Calculation in Neuromorphic Devices

AbstractFor convolution neural networks, increasing the performance of hardware computer systems is crucial in the era of big data. Benefiting from the neuromorphic devices, producing the convolutional calculation at the crossbar array circuit has become a promising approach for high‐performance hardware computer systems. However, as computation scales, this hardware system faces the challenge of low resource utilization efficiency and low power efficiency. Here, a novel pixel‐level strategy, leveraging the dynamic change of electron concentration as the process of convolution calculation, is proposed for high‐performance hardware computer systems. Compared with the crossbar array circuit‐based strategy, instead of at least four devices, raised the power efficiency to 413% and decreased the training epochs to 38%. This work presents a novel physics‐based approach that enables highly efficient convolutional calculation, improves power efficiency, speeds up convergency, and paves the way for building complex convolution neural networks with large‐scale convolutional computation capabilities.