Hardware Design Research Articles

Performance Estimation of Fixed-Wing UAV Propulsion Systems

The evaluation of propulsion systems used in UAVs is of paramount importance to enhance the flight endurance, increase the flight control performance, and minimize the power consumption. This evaluation, however, is typically performed experimentally after the preliminary hardware design of the UAV is completed, which tends to be expensive and time-consuming. In this paper, a comprehensive theoretical UAV propulsion system assessment is proposed to assess both static and dynamic performance characteristics via an integrated simulation model. The approach encompasses the electromechanical dynamics of both the motor and its controller. The proposed analytical model estimates the propeller and motor combination performance with the overarching goal of enhancing the overall efficiency of the aircraft propulsion system before expensive costs are incurred. The model embraces an advanced blade element momentum theory underpinned by the development of a novel mechanism to predict the propeller performance under low Reynolds number conditions. The propeller model utilizes XFOIL and various factors, including post-stall effects, 3D correction, Reynolds number fluctuations, and tip loss corrections to predict the corresponding aerodynamic loads. Computational fluid dynamics are used to corroborate the dynamic formulations followed by extensive experimental tests to validate the proposed estimation methodology.

Optimized power and speed of Split-Radix, Radix-4 and Radix-2 FFT structures

Fast Fourier transform (FFT) is a fundamental building block for digital signal processing applications where high processing speed is crucial. Resource utilization in implementing FFT structures can be minimized by optimizing the performance of multipliers and adders used within the design. FFTs are also widely used in various machine learning algorithms. To achieve increased processor efficiency and reduced resource utilization, we propose a hardware design for Radix-2, Radix-4, and Split-Radix FFT architectures that utilizes a novel parallel prefix adder. This design offers lower power consumption, smaller chip area, and faster operation compared to existing architectures. Our performance analysis focuses on metrics such as power consumption, clock speed, and hardware complexity for Radix-2, Radix-4, and Split-Radix FFT algorithms implemented with the proposed adder. We compare these metrics using our proposed arithmetic structure against existing adder designs. The results indicate that the Split-Radix FFT architecture achieves lower power consumption and smaller chip area compared to Radix-4 and Radix-2 methods. Additionally, the Split-Radix FFT exhibits a higher clock speed. Therefore, based on these findings, the Split-Radix algorithm appears to be a compelling choice for implementation on field-programmable gate arrays due to its high speed and lower power consumption.

Design and Validation of a Monte Carlo Method for the Implementation of Noninvasive Wearable Devices for HbA1c Estimation Considering the Skin Effect.

To diagnose diabetes early or to maintain stable blood glucose levels in diabetics, blood glucose levels should be frequently checked. However, the only way to check blood glucose levels regularly is to use invasive methods, such as pricking the fingertip or using a minimally invasive patch. These invasive methods pose several problems, including being painful and potentially causing secondary infections. This study focuses on noninvasively measuring glycated hemoglobin (HbA1c) using PPG signals. In particular, the study relates to a method and a hardware design technology for removing noise that may be present in a PPG signal due to skin contact with a noninvasive HbA1c measurement device. The proposed HbA1c measurement device consists of the first sensor (PPG sensor) module including an optical barrier and the second sensor (cylindrical sensor) module for removing the skin effect. We have developed a Monte Carlo method to implement accurate, noninvasive HbA1c measurement by considering different skin properties among different subjects. Implementing this model in wearable devices will allow end users to not only monitor their glycated hemoglobin levels but also control diabetes with higher accuracy without needing any blood samples. This will be a groundbreaking advancement in modern wearable medical devices.

Hardware information flow tracking based on lightweight path awareness

Vulnerabilities and Trojans in hardware design may cause sensitive data to be leaked and tampered. Information flow tracking technology can effectively verify the confidentiality and integrity of hardware design. Currently, this technology mainly analyzes the reachability of information flow and lacks fine-grained analysis of information flow paths. It is difficult to find structural defects in information flow paths and malicious sensitive information processes in hardware design. To solve above problem, we propose Path-aware Dynamic Information Flow Tracking (PDIFT) technology, which performs taint tracking and path tracking while sensitive information is propagated. It analyzes the propagation of sensitive information in hardware design with fine-grained taint label propagation logic and inserts path label propagation logic only on basic blocks divided by branch nodes, which greatly simplifies the path tracing overhead compared to the full node sequence tracing on the path. Experiments have shown that compared to CellIFT, PDIFT has a 12.1% increase in static analysis time and a 0.1% increase in dynamic validation time. The average instrumentation area cost of each basic block has increased by 16.4 um2. In terms of detection capability, PDIFT makes up for the limitation of false negatives in traditional taint tracking technology through joint analysis of path labels and taint labels, then detect problems such as insufficient iterations of encryption components and malicious processing of important assets, thereby improving the accuracy of hardware security verification.

Design and Application Research of a UAV-Based Road Illuminance Measurement System

This paper presents a UAV-based road illumination measurement system and evaluates its performance through experiments. The system employs a HUBSAN Zino 2+ UAV, STM32F103RCT6 microcontroller, BH1750 illuminance sensor, and GPS and integrates flight, processing, measurement, cloud platform, obstacle avoidance, communication, and power supply units via the OneNET cloud platform. Both hardware and software designs were implemented, using the Z-score algorithm to handle outliers in illumination data. The system showed a single-point measurement error rate of 1.14% and a MAPE of 5.08% for multi-point measurements. In experiments, the system’s horizontal and vertical illuminance RMSE were 1.92 lx and 1.75 lx, respectively. The real-time visualization interface improved operational efficiency, cutting labor costs by half and time costs by nearly four-fifths. UAV control and monitoring from the roadside ensured safety during measurements. The system’s efficiency and wide measurement range enabled extended experiments, collecting illuminance data across multiple horizontal and vertical planes. This resulted in the creation of both horizontal and innovative vertical-plane illuminance distribution maps. These findings provide valuable data for evaluating road lighting quality, enhancing road traffic safety, and improving road illumination design.

Improved Unsupervised Stitching Algorithm for Multiple Environments SuperUDIS.

Large field-of-view images are increasingly used in various environments today, and image stitching technology can make up for the limited field of view caused by hardware design. However, previous methods are constrained in various environments. In this paper, we propose a method that combines the powerful feature extraction capabilities of the Superpoint algorithm and the exact feature matching capabilities of the Lightglue algorithm with the image fusion algorithm of Unsupervised Deep Image Stitching (UDIS). Our proposed method effectively improves the situation where the linear structure is distorted and the resolution is low in the stitching results of the UDIS algorithm. On this basis, we make up for the shortcomings of the UDIS fusion algorithm. For stitching fractures of UDIS in some complex situations, we optimize the loss function of UDIS. We use a second-order differential Laplacian operator to replace the difference in the horizontal and vertical directions to emphasize the continuity of the structural edges during training. Combined with the above improvements, the Super Unsupervised Deep Image Stitching (SuperUDIS) algorithm is finally formed. SuperUDIS has better performance in both qualitative and quantitative evaluations compared to the UDIS algorithm, with the PSNR index increasing by 0.5 on average and the SSIM index increasing by 0.02 on average. Moreover, the proposed method is more robust in complex environments with large color differences or multi-linear structures.

An energy and area-efficient spike frequency adaptable LIF neuron for spiking neural networks

As Moore's law approaches its limit, conventional computing methods face energy efficiency challenges, prompting the exploration of alternative information processing methods like spiking neural networks (SNNs). Neurons, the fundamental unit of the nervous system, are crucial for efficiently implementing SNNs in hardware, requiring energy and area-efficient design. This paper presents novel circuit designs for the leaky integrate-and-fire (LIF) neuron model at 7 nm FinFET technology, including 8T FinFET LIF Neuron 1, 7T FinFET LIF Neuron 2, 7T FinFET LIF Neuron 3 and 6T FinFET LIF Neuron 4. In the proposed configurations of LIF neuron, the energy per spike is reduced by 95.80 %, 95.89 %, 95.89 %, 96.76 %, and 96.65 % in the 8T FinFET LIF neuron 1 (case 1), 8T FinFET LIF neuron 1 (case 2), 7T FinFET LIF Neuron 2, 7T FinFET LIF Neuron 3, and 6T FinFET LIF Neuron 4, respectively, compared to the lowest energy per spike reported in the literature, with the minimum known energy per spike being 0.67 fJ. The proposed FinFET neurons offer attractive hardware acceleration (spiking frequency in the MHz range), minimal energy per spike (fJ range), simplicity, and area efficiency, making them more biologically plausible due to their adaptable spiking frequency.

Optimized design of hardware accelerator based on machine learning

Hardware accelerators are playing an increasingly important role in the field of computer science and engineering, especially in accelerating deep learning and large-scale data processing. However, the traditional hardware accelerator design often relies on manual adjustment and experience accumulation, which is inefficient. Therefore, the use of machine learning technology to optimize hardware accelerator design has become a new trend. In this paper, a hardware accelerator optimization design method based on machine learning is proposed, including hardware design, algorithm optimization, system-level optimization, and deep learning model compression. Through the research and verification of the actual case, this research proves the effectiveness and feasibility of the hardware accelerator optimization design method based on machine learning. Compared with the traditional design method, this method can find better design scheme in shorter time and has higher performance prediction accuracy. Therefore, this method can significantly improve the design efficiency and performance of hardware accelerators. The results of this study are of great significance for promoting the development of hardware accelerator technology. It can promote scientific research and engineering applications in related fields to make greater progress, and promote the development of artificial intelligence, big data processing and other fields.

Cyborg Children: A Systematic Literature Review on the Experience of Children Using Extended Reality.

This literature review presents a comprehensive and systematic account of research on the experiences of children with extended reality (XR), including VR, AR, and other types of immersive technologies that enhance and augment children's activities. The search on Scopus and Web of Science produced 531 outputs. Content analysis with inter-rater reliability (Krippendorff's α) and Leximancer, a software for text mining, were used for analyzing the material. Four research strands were identified: (1) interventions, treatments, and medical procedures in clinical contexts; (2) teaching and learning enhanced by XR; (3) children's adoption and user experiences; (4) design and prototyping of XR hardware and software for children. The results showed the following findings: (a) studies on children's clinical interventions and treatments using HMD-supported immersive virtual reality comprise the most substantial strand of studies; (b) research in this area, and in teaching and learning studies, has grown dramatically since 2017, while the other areas have been stagnant over the years; (c) AR research is still limited and is mainly applied in educational contexts for design and prototyping; (d) few studies have considered children's perspectives on XR safety issues; (e) research on the use of XR for enhancing social and emotional skills development is underrepresented. Future research should focus on the potential of XR technologies for interventions to enhance children's psychosocial wellbeing and health more broadly. The further implications and study limitations for the fast-developing nature of this transdisciplinary research field are also discussed.

Design of an image processing system for heterogeneous computer architectures

Embedded real-time image processing systems are widely applied in various fields, enhancing visual experience and making life more convenient. With the development of embedded processors and operating systems, as well as in-depth research into image processing algorithms, the effects and speed of image processing have been significantly improved. This paper proposes an embedded image processing system design based on the current technological developments, centered around the Zybo processor. This includes a collaborative hardware and software design, specific hardware circuit design, and the design of a system software platform. The paper also discusses the establishment of a development environment for the Zybo system, the transplantation of the Linux operating system, the design of the file system, and the application of OpenCV library functions in image processing.

Alat Feeding Ayam Otomatis Berbasis Arduino

The increasing population in Indonesia has resulted in increased food needs. One of the factors that affects chicken production is the management of the feeding system. The purpose of this study is to design and create an automatic chicken feeder based on Arduino. Calibrate the Load Cell Sensor. Control the AC motor as a metal screw driver for feeding with a load cell sensor. Determine the amount of chicken feed according to the specified setting point and Connect the MCU Node to the telegram. This study uses the "Research and development" research method. Consisting of literature studies, hardware design, software design, tool design, and data collection, analysis and conclusions. Overall testing was carried out for 2 days starting from 6 am to 4 pm. The capacity of the tank is filled with 3 kg of feed. The setting point for feeding in the morning is 200 grams while in the afternoon it is 300 grams, for feed output there are 2 containers. Testing on the first day at 7 am the weight of the feed coming out of container 1 was 221 grams while in container 2 it was 225 grams, for 4 pm the weight of the feed coming out of container 1 was 352 grams while in container 2 it was 357 grams. Testing on the second day at 7 am the weight of the feed coming out of container 1 was 219 grams while in container 2 it was 221 grams, for 4 pm the weight of the feed coming out of container 1 was 346 grams while in container 2 it was 348 grams. Telegram notification sends a message when the amount of feed remaining is 1405 grams. The results of the weight of the feed coming out are different from the specified setting point value, the amount of feed coming out exceeds the setting point value. This is because the output hole of the feed that comes out through the PVC pipe is too large. An "automatic chicken feed tool based on Arduino" has been successfully created, the tool created is equipped with a load cell sensor that facilitates the process of feeding chickens. Making a system for pouring feed into the hopper automatically by making a conveyor so that it does not need to be done manually. Making automatic chicken feed based on Arduino on a large scale so that it can cover a larger number. Addition of making automatic drinking places on automatic chicken feed based on Arduino.

Design of Intelligent Window Automatic Monitoring System Based on Microcontroller Control

To ensure the safety of residents’ lives and property by using automatic opening and closing of ordinary windows, this article designs an intelligent window automatic monitoring system. The article proposes a software and hardware design scheme for the system, which comprises a microcontroller control module, temperature and humidity detection module, harmful gas detection module, rainfall detection module, human thermal radiation induction module, Organic Light-Emitting Diode (OLED) display module, stepper motor drive module, Wi-Fi communication module, etc. Users use this system to monitor environmental data such as temperature, humidity, rainfall, harmful gas concentrations, and human health. Users can control the opening and closing of windows through manual, microcontroller, and mobile application (app) remote methods, providing users with a more convenient, comfortable, and safe living environment.

(Invited) Enabling Bio-Realistic Artificial Intelligence Hardware with Neuromorphic Nanoelectronics

The exponentially improving performance of digital computers has recently slowed due to the speed and power consumption issues resulting from the von Neumann bottleneck. In contrast, neuromorphic computing aims to circumvent these limitations by spatially co-locating logic and memory in a manner analogous to biological neuronal networks [1]. Beyond reducing power consumption, neuromorphic devices provide efficient architectures for image recognition, machine learning, and artificial intelligence [2]. This talk will explore how low-dimensional nanoelectronic materials enable gate-tunable neuromorphic devices [3]. For example, by utilizing self-aligned, atomically thin heterojunctions, dual-gated Gaussian transistors have been realized, which show tunable anti-ambipolarity for artificial neurons, competitive learning, spiking circuits, and mixed-kernel support vector machines [4,5]. In addition, field-driven defect motion in polycrystalline monolayer MoS2 enables gate-tunable memristive phenomena that serve as the basis of hybrid memristor/transistor devices (i.e., ‘memtransistors’) that concurrently provide logic and data storage functions [6]. The planar geometry of memtransistors further allows multiple contacts and dual gating that mimic the behavior of biological systems such as heterosynaptic responses [7]. Moreover, control over polycrystalline grain structure enhances the tunability of potentiation and depression, which enables unsupervised continuous learning in spiking neural networks [8]. Finally, the moiré potential in asymmetric twisted bilayer graphene/hexagonal boron nitride heterostructures gives rise to robust electronic ratchet states. The resulting hysteretic, non-volatile injection of charge carriers enables room-temperature operation of moiré synaptic transistors with diverse bio-realistic neuromorphic functionalities and efficient compute-in-memory designs for low-power artificial intelligence and machine learning hardware [9].[1] V. K. Sangwan, et al., Matter, 5, 4133 (2022).[2] V. K. Sangwan, et al., Nature Nanotechnology, 15, 517 (2020).[3] M. E. Beck, et al., ACS Nano, 14, 6498 (2020).[4] M. E. Beck, et al., Nature Communications, 11, 1565 (2020).[5] X. Yan, et al., Nature Electronics, DOI: 10.1038/s41928-023-01042-7 (2023).[6] X. Yan, et al., Advanced Materials, 34, 2108025 (2022).[7] H.-S. Lee, et al., Advanced Functional Materials, 30, 2003683 (2020).[8] J. Yuan, et al., Nano Letters, 21, 6432 (2021).[9] X. Yan, et al., Nature, DOI: 10.1038/s41586-023-06791-1 (2023).

A study on design and moving behavior of meal partner robots that can perform eating behavior expression

Dining with people improves our lives and brings a lot of benefits. Nonetheless, many people are compelled to eat by themselves due to the current social situation. We believe that robots can be good meal partners. In this paper, we describe the developed meal partner robots called Mamoru'21 and Mamoru'22. The design of these robots is inherited from a robot for watching over elderly people. Based on previous research that robot's eating behavior expression can improve the mealtime experience, they can perform motions that mimic eating and present food images by monitors. In the first experiment, we confirmed the developed robot could improve the co-eating experience compared to a conventional small humanoid robot, and investigated the keywords for preferable hardware design, such as ‘smaller than a child.’ In the second experiment, we studied the effects of the robot's moving around during mealtime. As a result, no statistically significant negative effects of robot movement during meals were observed in the developed robot's capability. Though this study has some limitations such as shorter experimentation time compared to regular meals, it contributes some insights and knowledge into the behavior and design of meal partner robots.

Low-Cost, Low-Power Edge Computing System for Structural Health Monitoring in an IoT Framework.

A complete low-power, low-cost and wireless solution for bridge structural health monitoring is presented. This work includes monitoring nodes with modular hardware design and low power consumption based on a control and resource management board called CoreBoard, and a specific board for sensorization called SensorBoard is presented. The firmware is presented as a design of FreeRTOS parallelised tasks that carry out the management of the hardware resources and implement the Random Decrement Technique to minimize the amount of data to be transmitted over the NB-IoT network in a secure way. The presented solution is validated through the characterization of its energy consumption, which guarantees an autonomy higher than 10 years with a daily 8 min monitoring periodicity, and two deployments in a pilot laboratory structure and the Eduardo Torroja bridge in Posadas (Córdoba, Spain). The results are compared with two different calibrated commercial systems, obtaining an error lower than 1.72% in modal analysis frequencies. The architecture and the results obtained place the presented design as a new solution in the state of the art and, thanks to its autonomy, low cost and the graphical device management interface presented, allow its deployment and integration in the current IoT paradigm.

Co-designing ab initio electronic structure methods on a RISC-V vector architecture.

Ab initio electronic structure applications are among the most widely used in High-Performance Computing (HPC), and the eigenvalue problem is often their main computational bottleneck. This article presents our initial efforts in porting these codes to a RISC-V prototype platform leveraging a wide Vector Processing Unit (VPU). Our software tester is based on a mini-app extracted from the ELPA eigensolver library. The user-space Vehave and a RISC-V vector architecture implemented on an FPGA were tested. Metrics from both systems and different vectorisation strategies were extracted, ranging from the most simple and portable one (using autovectorisation and assisting this by fusing loops in the code) to the more complex one (using intrinsics). We observed a progressive reduction in the number of vectorial instructions, executed instructions and computing cycles with the different methodologies, which will lead to a substantial speed-up in the calculations. The obtained outcomes are crucial in advancing the porting of computational materials and molecular science codes to (post)-exascale architectures using RISC-V-based technologies fully developed within the EU. Our evaluation also provides valuable feedback for hardware designers, engineers and compiler developers, making this use case pivotal for co-design efforts.

Assemblage Robotic Plants: Individualizations of Many Orders of Magnitude

Abstract This paper focuses on the author’s artistic practice and presents robotic plant creations as assemblages using robotic autonomy unities in a Small-World Network configuration. This artistic production is significantly founded on Gilbert Simondon’s philosophical concepts about Technological Beings. Understanding these communities of robotic structures as individualizations is critical to apprehending them as individuals. The paper also discusses the implications of hardware design, coding, and the concept of artificial life related to the development of such technological organisms.

Hardware Design and Implementation of a High-Precision Optically Pumped Cesium Magnetometer System Based on the Human-Occupied Vehicle Platform

High-precision magnetometers play a crucial role in ocean exploration, geophysical prospecting, and military and security applications. Installing them on human-occupied vehicle (HOV) platforms can greatly enhance ocean exploration capabilities and efficiency. However, most existing magnetometers suffer from low sensitivity and excessively large size. This study presents a high-sensitivity, miniaturized magnetometer based on cesium optically pumped probes. The designed magnetometer utilizes a three-probe design to eliminate the detection dead zone of the cesium optically pumped probe and enable three-dimensional magnetic detection. The proposed magnetometer uses a flux gate probe to detect the three-axis magnetic field and ensure that the probe does not enter the dead zone. The three probes can automatically switch by measuring the geomagnetic elements and real-time attitude of the HOV platform. This article primarily introduces the cesium three-probe optically pump, flux gate sensor, and automatic switching scheme design of the above-mentioned magnetometer. Moreover, it is proven through testing that the core indicators, such as the accuracy and sensitivity of the cesium three-probe optically pumped and flux gate sensor, reach international standards. Finally, the effectiveness of the automatic switching scheme proposed in this study is demonstrated through drone-mounted experiments.

Adaptive spiking, itinerancy, and quantum effects in artificial neuron circuit hardware with niobium–hafnium oxide-niobium memristor devices inserted

To improve artificial intelligence/autonomous systems and help with treating neurological conditions, there is a requirement for the discovery and design of artificial neuron hardware that mimics the advanced functionality and operation of the neural networks available in biological organisms. We examine experimental artificial neuron circuits that we designed and built in hardware with memristor devices using 4.2 nm of hafnium oxide and niobium metal inserted in the positive and negative feedback of an oscillator. At room temperature, these artificial neurons have adaptive a spiking behavior and hybrid non-chaotic/chaotic modes. When networked, they output with strong itinerancy, and we demonstrate a four-neuron learning network and modulation of signals. The superconducting state at 8.1 K results in Josephson tunneling with signs that the hafnium oxide ionic states are influenced by quantum control effects in accordance with quantum master equation calculations of the expectation values and correlation functions with a calibrated time-dependent Hamiltonian. These results are of importance to continue advancing neuromorphic hardware technologies that integrate memristors and other memory devices for many biological-inspired applications and beyond that can function with adaptive-itinerant spiking and quantum effects in their principles of operation.

Development of Precision Controllable Magnetic Field-Assisted Platform for Micro Electrical Machining.

In order to introduce the magnetic field into micro electrical machining technology to explore the influence of magnetic field on micro electrical machining, the development of a precision controllable magnetic field-assisted platform is particularly important. This platform needs to precisely control the spatial magnetic field. This study first completes the hardware design and construction of the magnetic field generation device, using electromagnetic coils with soft iron cores as the sources of the magnetic field. Mathematical models of the magnetic field are established and calibrated. Since the magnetic dipole model cannot effectively describe the magnetic field generated by the electromagnetic coil, this study adopts a more precise description method: the spherical harmonic function expansion model and the magnetic multipole superposition model. The calibration of the magnetic field model is based on actual excitation magnetic field data, so a magnetic field sampling device is designed to obtain the excitation magnetic field of the workspace. The model is calibrated based on a combination of the theoretical model and magnetic field data, and the performance of the constructed setup is analyzed. Finally, a magnetic field-assisted platform has been developed which can generate magnetic fields in any direction within the workspace with intensities ranging from 0 to 0.2 T. Its magnetic field model arrives at an error percentage of 2.986%, a variance of 0.9977, and a root mean square error (RMSE) of 0.71 mT, achieving precise control of the magnetic field in the workspace.