Hardware Design Research Articles

Damage detection in wind turbine blades using embedded radar networks

To ensure the availability and operational safety of wind turbine blades, regular inspections are required according to specified maintenance intervals. As with all periodic inspections, damage that develops between two inspection dates remains undetected until the next inspection. In recent years, monitoring systems have therefore been developed that enable independent structural health monitoring (SHM) with permanently installed sensors. The most important representatives here include vibration analysis, acoustic emission testing and ultrasonic methods. In addition, it is necessary for an SHM system to distinguish between signal changes resulting from damage or changing environmental and operating conditions. The present work presents a new approach for the in-situ assessment of wind turbine blades via embedding radar networks in the frequency band from 58 to 63.5 GHz. Therefore, 40 FMCW (frequency-modulated continuous wave) radar sensors have been embedded into a wind turbine blade during manufacturing. This contribution provides additional information related to the hardware design of the sensor nodes, its preliminary testing and the specific implementation of the radar network during a fullscale fatigue test of a 31m long wind turbine blade [1]. Damage indicators can be formulated relative to baseline conditions to see the development of fatigue damage over time. Reference: [1] Simon, J.; Kurin, T.; Moll, J.; Bagemiel, O.; Wedel, R.; Krause, S.; Lurz, F.; Nuber, A.; Issakov, V. & Krozer, V., Embedded Radar Networks for Damage Detection in Wind Turbine Blades: Validation in a Full-scale Fatigue Test, Structural Health Monitoring, 2023, https://doi.org/10.1177/14759217231152815

Electron cyclotron emission detection of neoclassical tearing modes for control for ITER.

Successful operation of ITER requires control of magnetic instabilities including neoclassical tearing modes (NTMs) that can degrade confinement and lead to disruption. Low latency detection by electron cyclotron emission (ECE) diagnostics has been demonstrated in a few current experiments. Using a synthetic diagnostic, we demonstrate low latency NTM detection for ITER with plasmas described by ITER IMAS database scenarios and with realistic limitations imposed on the instrumentation by these high temperature scenarios. 2/1 NTMs are detected 430ms after magnetic island seeding and before island locking. The radiometer configuration was optimized using simulation, and the smallest detectable island size was explored. Island sizes of ∼3cm are detectable at the 2/1 surface. The simulated signals incorporate recent physics models for island growth and rotation, which show early locking and continued island growth after locking and before disruption. This work determines limits for ITER ECE spatial resolution imposed by relativistic broadening of channels, which informs hardware design. Real-time detection is demonstrated in hardware that is required by ITER, including on an NI PXI-7853R FPGA system. Development of a synthetic diagnostic and details of the hardware will be discussed.

Distance optimization KNN and EMD based lightweight hardware IP core design for EEG epilepsy detection

Long-term and effective detection of epileptic seizures is a crucial aspect of epilepsy monitoring and treatment. Addressing the resource overhead issue of wearable epilepsy detection devices, this paper proposes a lightweight hardware implementation scheme for epilepsy detection based on a reusable architecture empirical mode decomposition (EMD) and K-Nearest Neighbors (KNN). Firstly, EMD is used to extract epileptic features from electroencephalogram (EEG), optimized through a reusable architecture design and sawtooth transform to reduce hardware resource usage. Subsequently, a KNN classifier with similarity judgment mechanism is designed to improve the recognition efficiency. Implemented on TSMC 65 nm process, the circuit area is 1.91 mm2, operates at 1 V and 20 MHz, with a power consumption of 4.034 mW. Evaluation on the Bonn EEG dataset yielded a classification accuracy of 96 %, sensitivity of 98 %, and a single detection delay of 1.51 ms. The hardware design offers a simple structure, high accuracy, and low resource consumption, making it suitable for wearable epilepsy detection devices.

Multi-Scale Deep Learning-Based University Financial System: Hardware Design and Database Integration

With the increasing scale of college enrollment and the increasing complexity of college teaching management, college finance department should innovate the traditional financial management mode while adapting to the reform of teaching management, and make use of the openness and real-time characteristics of Internet to improve the quality of college financial management and reduce the risk of college financial management. To this end, this paper designs a university financial system based on multi-scale deep learning. In the hardware design, the system adds multiple sensors and scans all the information in the financial database using a coordinator. In the software design, the weights that can connect the financial information of the same attribute are set by establishing a database form; according to the multilayer perceptual network topology, a full interconnection model based on multi-scale deep learning is designed to realize the system’s deep extraction of data. The experimental results show that the financial risk is based on the risk warning capability for university finance, and compared with the system under the traditional design, the university finance system designed in this time has the most categories of financial information parameters extracted.

A digital twin for parallel liquid-state nuclear magnetic resonance spectroscopy

One approach to increasing nuclear magnetic resonance measurement sample throughput is to implement multiple, independent detection sites. However, the presence of radio frequency interference poses a challenge in multi-detector systems, particularly in unshielded coil arrays lacking sufficient electrical isolation. This issue can lead to unwanted coupling of inductive coils, resulting in excitation pulse interference and signal transfer among multiple detection sites. Here we propose a theoretical framework that combines electromagnetic simulation with spin-dynamic calculations. This framework enables the evaluation of coil coupling effects, the design of parallel pulse sequences to mitigate inter-channel coupling, and the separation of composite free induction decays obtained from multiple detectors. The parallel pulse compensation scheme was validated by a 2-channel parallel spectroscopy experiment. These results provide valuable insights for the design of parallel nuclear magnetic resonance hardware and for exploring the limits of parallelization capacity within a fixed magnet system.

Towards canine brain–computer interfaces (BCIs)

Brain–Computer Interfaces (BCIs) have shown remarkable potential in human neuroscience and medicine. Yet, their application with animals, in particular canines, remains largely unexplored. This paper delves into the background, systems overview, implementation details, potential features, prospective use-cases and challenges of canine BCIs. While the focus is on EEG-based BCIs, other modalities such as fNIRS are also discussed. Despite significant challenges, such as hardware design and data inference, I argue that canine BCIs hold potential to significantly push forward our understanding of canine cognition, and facilitate a higher bandwidth of human-canine communication, collaboration, and flourishing.

Sistem Kontrol Dan Monitoring Hidrogen - Oksigen (HHO) Generator Berbasis Internet Of Things (IoT)

Electrolysis is a way to separate water (H2O) into hydrogen (H2) and oxygen (O2) gas by using an electric current through the water. The aim of this research is to create an Internet of Things (IoT) based prototype system that can monitor and control the hydrogen gas (H2) production process using the water electrolysis method. This research was carried out in several stages starting with a technology survey, then continuing with prototype design (including hardware design, software design, and circuit schematic design). This monitoring system device is designed with 3 sensors, namely: MQ-8 gas sensor, ACS712 current sensor, and BME280 pressure-temperature sensor. The MQ-8 sensor is set with a reading scale of 200-1000 ppm (parts per million) with an RMSE value obtained of 0.0288%. The ACS712 current sensor has an accuracy of 88.35% with a reading scale of 0-3.3 amperes, and the BME280 sensor has an accuracy of 94.47% for temperature and 99.37% for pressure with an ambient air reading scale. The results of water electrolysis testing of the entire system show that the system can work well in measuring parameters in accordance with the data sheet provisions, as well as providing flexibility in monitoring for users via smartphone.

Perancangan Sistem Kunci Otomatis Berbasis Qr Code Menggunakan Sensor Gm66

The security system plays an important role for the comfort of human life today. Moreover, in the development of the times which are increasing day by day. In the world of technology, many things are implemented in security systems to increase consumer confidence. One of the main factors of the security system is theft by breaking into the building door. This security problem can be overcome by utilizing existing technology, for example with a QR Code. The design of this device will be carried out using two paths, namely hardware design and software design. This is done so that the design of this automatic lock system can work in accordance with the design which includes a circuit block diagram and circuit realization with the working principles of each circuit that has been designed. From some of the results of testing QR Code-based automatic door locks using the GM66 sensor which can be seen from several tables of test results, it can be concluded that the tool can work properly.

Radio Tomography Imaging using Adjacent Criterion Method to determine the Localization Error

Observed that most setups have limitations in the number of RF nodes due to a limited number of measurements. However, it is well known that the main difficulty in radio tomographic imaging attributes to the uncertainties in the receive signal strength (RSS) measurements of transceivers due to multipath effects, especially, when the environment of interest is much cluttered, and requirements on the larger number of nodes for the performance improvements. However, no study has been conducted to solve the inverse problem and improve the quality of the reconstructed image using a reduced sensor model for Radio tomography system localization. This work focuses on the design and development of a Radio tomography system for human localization that will employ a transceiver sensor arrangement to increase the number of measurements, without making any changes to the hardware design as well as the number of pixels in the sensing domain. An image reconstruction technique namely, Adjacent Criterion Method (ACM) was proposed to enhance the image spatial resolution. A number of experiments were used to evaluate the performance of the system. The results showed that the proposed technique improves the spatial resolution and exhibits more accurate tomograms

Heart Signal Acquisition Based System Autoregressive Identification Models

A system identification (SI) model can be constructed without any prior knowledge of the nature or physics of the relationship that has been involved. It is therefore appropriate to examine the question of (heart rate). In this paper, a simple and efficient hardware design is implemented to acquire the heart signal with the help of several linear models of SI. The relationships between different system identification models are discussed with detailed justification of the aid of these typical types. And then characterize the methods that fit the system structure to measure data input and output, as well as the most basic characteristics of the resulting models. For evaluation, compare between SI models to validate the results.

MPPT constraint conditions based on four-parameter cell model for some usual photovoltaic systems

Hitherto there is no work to specially analyze in detail the question when the maximum power point (MPP) of the photovoltaic (PV) system exists and when it disappears. It has caused the difficulty in the hardware and software designs of the PV system when the maximum power point tracking (MPPT) control must be implemented all the time. To solve this question, in this paper, the MPPT constraint conditions (MPPTCC) of the 12 usual PV systems are proposed under ideal conditions and expressed by the model parameters of the four-parameter cell model in the engineering application. They are the necessary and sufficient conditions to guarantee the existence of the MPPs of these 12 PV systems. Meanwhile, they also disclose the inherent relationships between load (or bus voltage) and cell parameters when the MPP of the PV system always exists. In addition, to apply them in practice, their expressions in practical application are also presented. Finally, some simulation experiments verify the reasonableness and accuracy of these MPPTCC in practical application, and test the influence of the varying weather to the MPPT range. By this work, the possible failure of the MPPT control arising from the occasional disappearance of the MPP of the PV system can be completely avoided. Therefore, the findings in this work can be used as the theory foundation for the hardware design, theoretical research and product installation of the PV system.

NGHIÊN CỨU XÂY DỰNG THIẾT BỊ Y TẾ HỖ TRỢ THEO DÕI TỪ XA CHO BỆNH NHÂN TIM MẠCH

This article focuses on researching and developing a medical support device with functions to monitor various health parameters of patients directly or remotely, such as body temperature, heart rate, and blood oxygen saturation. The implementation method includes designing algorithm diagrams, hardware circuit design, component selection, and building a display application on smartphones or a website interface that allows monitoring the parameters quickly and accurately. Actual testing initially showed that, the device essentially operates quite accurately according to the design and programming with many advantages such as: The system operates stably, meeting design requirements; Results can be monitored visually in a variety of ways and conveniently on LCD screen, App, Web; Each measurement data is saved with data security assurance; The system is evaluated to have minimal errors, low power consumption, and is cost-effective, along with the convenience of result monitoring, making it suitable for various user groups. Furthermore, the research results can serve as a practical model for students in the Biomedical Engineering field at educational institutions where there is currently a shortage of devices catering to learning needs.

FPGA implementation of IIR elliptic filters for de-noising ECG signal

De-noising of ECG signal is very much necessary to monitor the heart health and to diagnose disease. This paper presents a real time de-noising system that efficiently wiped out the noises from corrupted ECG signal and improves its SNR which enhances the interpretation of features present in ECG signal. This proposed system is designed with three Infinite Impulse Response (IIR) elliptic filters in cascaded direct form II structure and is implemented on the FPGA platform. Total 376 ECG samples are taken from the open access public database physiobank. All the architectural level FPGA designs have been developed in Xilinx System Generator (XSG) and implemented on Xilinx Zynq 7000 board. Our design utilized only 3.87 % of the total available resource and consumed 0.321 watt on-chip power. Worst Negative Slack (WNS) for the critical path is 2.101 ns. The positive value of WNS signifies our hardware design implementation run successfully by meeting the required time constraint. To judge the performance of the proposed design, the measurement matrices SNR Improvement, RMSE, PRD, SSNR and SINAD are calculated and achieved 81.11% , 88.89% and 85.41% accuracy for ECG recordings of ECG-ID, MIT-BIH Normal Sinus Rhythm and MIT-BIH Arrhythmia database respectively. The novelty of this proposed denoising system is its robustness, which work satisfactorily for Baseline Wander (BW), Electrode Motion (EM), Power Line Interference (PLI), Muscle or Electromyography (EMG) and as well as Additive White Gaussian Noise (AWGN).

Modular Hardware Design of Pipelined Circuits with Hazards

Modular Hardware Design of Pipelined Circuits with Hazards

A Low Complexity Cooperative Spectrum Sensor for Cognitive –Radio Network Based on Approximate Computing

With the rapid growth of new wireless technologies, the issue of spectrum scarcity is becoming more prominent due to the limited availability of radio spectrum. Unutilized spectrum bands designated for primary users are known as spectrum holes or white spaces. Assigning these white spaces to secondary users can enhance spectrum utilization. Dynamic spectrum access is crucial for sharing licensed spectrum with secondary users, as opposed to static allocation policies which impede effective spectrum utilization. Spectrum sensing aims to detect licensed users in the spectrum to prevent interference from secondary users. Approximate computing has emerged as a strategy to enhance efficiency by leveraging the fault resilience of various applications. It introduces trade-offs in design optimizations, allowing for significant reductions in VLSI circuit area and energy consumption. A cooperative spectrum-sensing (CSS) algorithm based on approximate computing for cooperative cognitive-radio networks is proposed to reduce computational complexity for efficient hardware design while maintaining detection performance. The hardware-efficient architecture of the cooperative spectrum sensor (CSR) based on the CSS algorithm, combined with resource-sharing architectural optimization, outperforms existing implementations by occupying less area, achieving shorter sensing times, and displaying superior hardware efficiency. Keywords—Approximate Computing, Cooperative Spectrum Sensor(CSR), very-large scale integration(VLSI)

Low-Cost Source Measure Unit (SMU) to Characterize Sensors Built on Graphene-Channel Field-Effect Transistors.

This study introduces a flexible and low-cost solution for a source measure unit (SMU), which is presented as an alternative to conventional source meter units and a blueprint for sensor FET drivers. An SMU collects current-voltage (I-V) curves with an additional variable voltage or current and is commonly used to characterize semiconductors. We present the hardware design, interfacing, and test results of our SMU. Specifically, we present representative I-V curve measurements for graphene-channel FETs to demonstrate the SMU's capability to efficiently characterize these devices with minimal noise and sufficient accuracy. This cost-effective solution presents a promising avenue for researchers and developers seeking reliable tools for sensor development and characterization. We demonstrate, with the example of surface illumination, how the sensing behavior of graphene-channel FETs can be characterized without the need for expensive equipment. Additionally, the SMU was validated with known passive and active components, along with probe station integration for semiconductor die-scale connection. The SMU's focus on collecting I-V curves, coupled with its ability to identify device defects, such as parasitic Schottky junctions or a failed oxide, contributes to its utility in quality testing for semiconductor devices. Its low-cost nature makes it accessible for various research endeavors, enabling efficient data collection and analysis for graphene-based and other nanomaterial-based sensor applications.

A Noval Solar Simulator to Explore Envirnoment Impacts On Panel Efficiency

The paper provided a complete hardware design for a solar simulator for versatile environmental conditions. These conditions include shading, humidity, dust, and temperature. In this work, the solar simulator was designed and fabricated to analyze the performance of PV panels with and without air cooling mechanisms in indoor tests. The halogen bulbs with built-in reflector support by the steel structure holder act as natural sunlight. The challenge for a solar simulator is stable uniform solar radiation and illumination. The uniform solar radiation of 7,575 candela was measured in the test area. Several units of PV panels were tested on variable environmental conditions. The air-cooling mechanism with a speed of 200 km/h decreases the surface PV panels with an air-cooling mechanism decreased by 2.3 ˚C. This increases the efficiency of the panel by 6.11 % of the maximum power output based on different fixed solar radiation. They have also compared it by hardware and by simulation method. An overall method and procedure of the measurement by the solar simulator are discussed and proposed

Contraction assessment of abdominal muscles using automated segmentation designed for wearable ultrasound applications

PurposeWearable ultrasound devices can be used to continuously monitor muscle activity. One possible application is to provide real-time feedback during physiotherapy, to show a patient whether an exercise is performed correctly. Algorithms which automatically analyze the data can be of importance to overcome the need for manual assessment and annotations and speed up evaluations especially when considering real-time video sequences. They even could be used to present feedback in an understandable manner to patients in a home-use scenario. The following work investigates three deep learning based segmentation approaches for abdominal muscles in ultrasound videos during a segmental stabilizing exercise. The segmentations are used to automatically classify the contraction state of the muscles.MethodsThe first approach employs a simple 2D network, while the remaining two integrate the time information from the videos either via additional tracking or directly into the network architecture. The contraction state is determined by comparing measures such as muscle thickness and center of mass between rest and exercise. A retrospective analysis is conducted but also a real-time scenario is simulated, where classification is performed during exercise.ResultsUsing the proposed segmentation algorithms, 71% of the muscle states are classified correctly in the retrospective analysis in comparison to 90% accuracy with manual reference segmentation. For the real-time approach the majority of given feedback during exercise is correct when the retrospective analysis had come to the correct result, too.ConclusionBoth retrospective and real-time analysis prove to be feasible. While no substantial differences between the algorithms were observed regarding classification, the networks incorporating the time information showed temporally more consistent segmentations. Limitations of the approaches as well as reasons for failing cases in segmentation, classification and real-time assessment are discussed and requirements regarding image quality and hardware design are derived.

Codesign of reactor-oriented hardware and software for cyber-physical systems

Modern cyber-physical systems often make use of heterogeneous systems-on-chip with reconfigurable logic to provide adequate computing power and flexible I/O. However, modeling, verifying, and implementing the computations spanning CPUs and reconfigurable logic is still challenging. The hardware and software components are often designed by different teams and at different levels of abstraction, making it hard to reason about the resulting computation. We propose to lift both hardware and software design to the same level of abstraction by using the Lingua Franca coordination language. Lingua Franca is based on a sparse synchronous model that allows modeling concurrency and timing while keeping a sequential model for the actual computation. We define hardware reactors as a subset of the reactor model of computation underlying Lingua Franca. We also present and evaluate reactor-chisel, a hardware runtime implementing the semantics of hardware reactors, and an extension to the Lingua Franca compiler enabling reactor-oriented hardware-software codesign.

Design of intelligent controller for obstacle avoidance and navigation of electric patrol mobile robot based on PLC

Currently, the obstacle avoidance control of patrol robots based on intelligent vision lacks professional controller module assistance. Therefore, this paper proposes a design method of intelligent controller for obstacle avoidance and navigation of electrical inspection mobile robot based on PLC control. The controller designs a laser range finder to determine the required position of electrical patrol inspection. Use PLC as the core controller, and combine sensors, actuators, communication module and PLC selection module in the process of hardware design to achieve autonomous navigation and obstacle avoidance functions of the robot. Then design the software including the PLC compiler system and the virtual machine module. Based on the above steps, design the control module of obstacle avoidance navigation, which realizes the key link of robot autonomous navigation. The test results show that the controller can successfully avoid obstacles, improve the efficiency and quality of inspection, and achieve accurate and fast obstacle avoidance navigation for the electrical inspection mobile robot.