Hardware System Research Articles

A heterogeneous ultrasound open scanner for the real-time implementation of computationally demanding imaging methods.

Ultrasound open scanners have recently boosted the development and validation of novel imaging techniques. They are usually split into hardware- or software-oriented systems, depending on whether they process the echo data using embedded FPGAs/DSPs or a GPU on a host PC. The goal of this work was to realize a high-performance heterogeneous open scanner capable of leveraging the strengths of both hardware and software-oriented systems. The elaboration power of the 256-channel ultrasound advanced open platform (ULA-OP 256) was further enhanced by embedding a compact co-processing GPU system-on-module (SoM). By carefully avoiding latencies and overheads through low-level optimization work, an efficient PCIe communication interface was established between the GPU and the processing devices onboard the ULA-OP 256. As a proof of concept of the enhanced system, the high frame rate color flow mapping technique was implemented on the GPU SoM and tested. Compared to a previous DSP-based implementation, higher real-time frame rates were achieved together with unprecedented flexibility in setting crucial parameters such as the ensemble length (EL). For example, by setting EL=64 and a continuous-time high-pass filter, the flow was investigated with high temporal and spatial resolution in the femoral vein bifurcation (frame rate = 1.1 kHz) and carotid artery bulb (4.3 kHz), highlighting the flow disturbances due to valve aperture and secondary velocity components, respectively. The results of this work promote the development of other computational-expensive processing algorithms in real-time and may inspire the next generation of ultrasound high-performance heterogeneous scanners.

Comparison of Clinical and Radiographic Outcomes Between Solid Headless and Headed Screws in the Treatment of Zone II and III Fifth Metatarsal Fractures in Elite Athletes

Background: Zone II and III fifth metatarsal (5-MT) fractures among athletes are typically managed with percutaneous fixation following anatomic reduction. However, screw head discomfort and refracture after bone union can occur because of the loads placed on the foot during play. Several hardware systems that use a smaller screw head compared to traditional hardware systems have been developed to minimize the rate of postoperative hardware complications. This study compares clinical and radiographic outcomes of 5-MT fractures in elite athletes treated with a solid headless screw vs a solid headed screw. We hypothesized that the headless screw would be associated with faster union rates, faster clearance times, and lower incidence of symptomatic hardware compared to the headed screw. Methods: Athletes competing at a collegiate level or higher treated for a zone II or III 5-MT fracture between 2016 and 2022 by 2 surgeons fellowship-trained in foot and ankle orthopaedics were screened. Operative notes were reviewed to determine the hardware system used. Subjects were divided based on the hardware system used during operation: headed screw and headless screw. Time to radiographic union, time to full clearance, and return to competition were determined. Postoperative complications, including nonunions, need for revision, need for hardware removal, and refractures were also noted. Results: Forty eligible patients (44 feet) were identified. The solid headed screw group included 20 patients (21 fractures), and the solid headless screw group included 20 patients (23 fractures). Average time to union for the headed screw group was 11.78 (range, 5.86-19.00) weeks; average time to union for the headless screw group was 11.65 (range, 6.00-22.57) weeks ( P = .93). Nineteen out of twenty (95%) patients were able to return to competition in both groups. Average time to return to competition for the headed screw group was 26.9 (range, 10.00-47.86) weeks, while average time for the headless screw group was 21.2 (range, 6.86-55.00) weeks ( P = .55). The overall complication rate for the headed screw was 23.8%, which was not statistically different from the overall complication rate for the headless screw of 13.0% ( P = .35). Conclusion: In this relatively small sample of elite athletes undergoing operative fixation of a 5-MT fracture, fixation using either a headless or headed screw system had similar good outcomes in regard to times to union, return to competition, and complication rate regardless of solid screw head type used. Level of Evidence: Level III, retrospective cohort study.

A control strategy of DC transmission system for offshore wind power based on hybrid converters

Abstract As the DC transmission system of modular multilevel converters for offshore wind power suffers problems like large volume, heavy weight, difficulty in platform transportation, and high cost, a DC transmission scheme for offshore wind power based on diode rectifier units and modular multilevel converters is put forth in the present work; Meanwhile, the features of active-power coordination, the DC harmonic current, and the AC harmonic current of the hybrid converter in this proposed scheme are analyzed to propose control strategies for active power coordination control, DC harmonic current suppression, and AC harmonic current suppression of the hybrid converter, respectively. The performance and feasibility of the proposed strategies are verified by testing the hardware in the loop real-time simulation system for transmission of offshore wind power through hybrid converters based on RTDS. It is found that the proposed strategies could improve the stability, flexibility, and reliability of the system.

Zynq FPGA for hardware co-simulation of Takagi-Sugeno neuro-fuzzy for MPPT algorithm incorporating sensorless wind speed estimation in grid-connected wind system

This paper presents a hardware implementation on the Field Programmable Gate Array (FPGA) Zed-Board of a Maximum Power Point Tracking (MPPT) incorporating pitch angle control system and sensorless wind speed estimation using the Takagi-Sugeno (TS) Adaptive Neuro-Fuzzy Inference System (ANFIS). The described approach is applied specifically to a grid-connected Permanent Magnet Synchronous Generator-based Variable Speed Wind Turbine (VSWT). Firstly, the paper proposes an estimator-based TS-ANFIS model for real-time wind speed estimation, addressing challenges with conventional anemometers, such as precision, and susceptibility to adverse weather conditions. The estimated wind speed guides the calculation of an optimized mechanical speed for MPPT control. Secondly, an MPPT-based TS-ANFIS controller is introduced to achieve the maximum power point, integrating pitch angle control to prevent turbine failures in high wind speeds. Finally, the paper emphasizes the hardware implementation on the FPGA Zed-Board, leveraging its parallel processing capabilities to enhance control system quality by reducing sampling periods and loop delays. Validation includes simulations in Matlab/Simulink using the Xilinx system generator and hardware co-simulation on the FPGA Zed-Board. A comparative analysis highlights the contributions and advancements of the proposed models and controllers compared to recent schemes in the field. Indeed, the proposed MPPT-based TS-ANFIS approach effectively maximizes the power extracted from the VSWT, achieving an estimated average efficiency of 99.84 %. In contrast, PID methods show average efficiencies of 92.63 %. Additionally, compared to other published works, the proposed MPPT-based TS-ANFIS method demonstrates a rapid response time of 0.001 s and lower static error at 0.02 %. Furthermore, the proposed WSE-based TS-ANFIS model exhibits superior performance and effectiveness, yielding a root mean squared error (RMSE) of 0.0085381, a determination coefficient (R2) value of 0.99985, and a Pearson correlation coefficient (r) value of 0.99996. Moreover, the proposed hardware implementation of these approaches maintains lower power usage, operating at a high frequency of 80.38 MHz and achieving a high throughput of 2572.16 Mbps.

Comprehensive monitoring system for high voltage cables based on the ground current signal of the cable metal sheath

Abstract This paper has presented a comprehensive monitoring system for high voltage cables based on the ground current signal of the cable metal sheath. The system can collect and process the power frequency AC current and three high-frequency partial discharge signals in real time synchronously, which realize the synchronous detection of leakage current, relative dielectric loss, harmonic, partial discharge, and improve the integration of the hardware system.

CSNS-II multi-harmonic LLRF upgrade and commissioning

The second phase of the China Spallation Neutron Source (CSNS-II) will employ a dual-harmonic RF system to mitigate the significant space charge effects associated with 500 kW beam power. To address this, three magnetic alloy loaded cavities, characterized by their wideband and high-gradient properties, will be integrated as multi-harmonic cavities in the upgrade project. Additionally, the current LLRF control system hardware, based on the CPCI platform, is encountering issues related to discontinuation and performance limitations. This paper presents the testing and verification of hardware upgrades and multi-harmonic control system algorithms for the magnetic alloy loaded cavity LLRF control system, ensuring the effective implementation of the multi-harmonic control algorithm within the new hardware framework.

Lightweight Deep Learning Algorithm for Sorting Medical Waste Embedded System

The recent COVID-19 pandemic has led to an increased number of hospitalized patients, and in some countries, the overwhelming problem has even caused the collapse of the healthcare system. Thus, the associated waste generated and inadequate waste disposal may have deleterious impacts on public health and the surrounding environment. This paper aims to design and implement an automatic medical waste identification and sorting mechanism that can distinguish the items disposed of via a series of image processing procedures. Specifically, the system can classify, sort, and calculate the disposed items based on their visual appearance. To establish this, both software and hardware systems have been devised. Particularly, the vision system for object detection and classification is constructed based on several popular pre-trained convolutional neural networks. Upon verifying the type of waste (i.e., general infection, dangerous infection, and general garbage), an automatic sorting mechanism will be triggered to dispose of the object in the corresponding garbage bin. The experimentation has been validated on a total of self-collected 2025 images from 3 categories, and the best accuracy attained is 99.34% when adopting GoogLeNet as the backbone architecture.

Design and Implementation of USB Key System Based on Dual-Factor Identity Authentication Protocol

With the increasing demand for information security, traditional single-factor authentication technology can no longer meet security requirements. To this end, this paper proposes a Universal Serial Bus (USB) Key hardware and software system based on a two-factor authentication protocol, aiming to improve the security and reliability of authentication. This paper first analyzes the current status and technical principles of USB Key-related research domestically and internationally and designs a two-factor authentication protocol that combines impact/response authentication and static password authentication. The system consists of a host computer and a USB Key device. The host computer interacts with the USB Key through a graphical user interface. The Secure Hash Algorithm 1 (SHA-1) and MySQL database are used to implement the authentication function. Experimental results show that the designed two-factor authentication protocol can effectively prevent replay attacks and information tampering, and improve the security of authentication. If the corresponding USB Key is not inserted, the system will prompt that the device is not found. Once the USB Key is inserted, user identity is confirmed through two-factor verification, which includes impact/response authentication and static password authentication.

IMPLEMENTATION OF NATURAL EXPERIMENTS IN PHYSICS USING COMPUTER VISION

Laboratory experiments in physics are a fundamental basis for studying physical phenomena occurring in nature and a methodological tool that provides visibility of the learning process and conducting experiments is important for the formation of students’ scientific worldview, deep understanding of physical laws and increasing interest in the study of physics. Existing in universities and schools, in addition to traditional ones, modern tools, technologies and approaches, such as virtual reality, augmented reality, computer modelling, online laboratories, virtual laboratory and others, are additional tools for improving the quality of the learning process and teaching techniques, which do not replace full-scale experiments, but only supplement them. In our opinion, for better learning, laboratory installations in physics are needed, with the help of which students can carry out real-life experiments and can broadcast them using innovative computer technologies for distance learning. To implement this task, we reviewed and analysed existing laboratory installations, identified their advantages and disadvantages, and then designed and developed alternative digital experimental set-ups for studying physics phenomena in laboratory conditions of educational institutions based on computer vision technology and presented the results of the study in this article. In carrying out the research tasks, effective methods of conducting scientific research were used, such as theoretical substantiation of the issue, experimental testing of the developed hardware and software systems and computer final processing of experimental data. In summary, the research described in the paper presents an innovative mechanism for integrating object tracking based on computer vision to improve the quality of measurements and new ways of conducting physics experiments. The mechanical laboratory complexes we have developed consist of hardware and software parts. The software part consists of server and client parts. The hardware consists of the main part - the scene, where the physical process takes place, i.e. where a physical object is located, such as a mathematical pendulum, an inclined plane, etc., with the help of which many physical phenomena and processes in mechanics can be demonstrated, and an additional part where a microcomputer and a camera are located. The operating principle of the laboratory installation is based on the use of computer vision technology, i.e. a system for monitoring the ongoing physical process, consisting of a digital camera for image processing, object identification and data export, and a microcomputer for processing experimental data. The use of the experimental installations in the process of teaching physics is a new model of teaching with a promising future in secondary and higher education, and the installations themselves will become tools for offline and online learning, due to the use of computer vision technology, revealing new opportunities and approaches to teaching.

IoT Integrated Smart Parking Zone and Website with Stand-alone System and Wireless Technology

The research project works to digitalize the full parking management system, transform the manual parking methods, solve traffic and road congestion problem and show live updates of the designated parking slots. It is ecofriendly and eliminates human interaction. It also offers personal and corporate business earnings. In the project, anyone can book time slots for parking following their convenient schedule, pay bills via the online payment system, control the entrance while entering and exit gate in the parking area with the vehicle using the website system. Users do not have to use any extra identity instrument, like- RFID Cards, QR Codes etc. The system will verify them by online credentials. Moreover, people can see the live slot updates for better security. The project collaborated with 3 major technology parts which are practical hardware, software and database system. NodeMCU, Ultrasonic Sensors, Servo motors, and other technical components are used in the hardware part. PHP, HTML, JavaScript, CSS etc. codes are implemented for the website development. Firebase Realtime Database and MySQL are integrated for the cloud and local database management with the hardware and software parts for building the IoT technology environment and infrastructure.

Development and validation of a robotic system for milling individualized jawbone cavities in oral and maxillofacial surgery

ObjectivesThis study aimed to develop and validate a robotic system capable of performing accurate and minimally invasive jawbone milling procedures in oral and maxillofacial surgery. MethodsThe robotic hardware system mainly includes a UR5E arm (Universal Robots, Denmark) and the binocular positioning system (FusionTrack 250, Atracsys LLC, Switzerland). The robotic software (Dental Navi 3.0.0, Yakebot Technology Ltd., China) is capable of generating cutting tool paths based on three-dimensional shape description files, typically in the stereolithography format, and selected cutting tool parameters, as well as designing surgical accessories. Fully impacted supernumerary tooth models in the maxilla were fabricated using software and three-dimensional printing. Following the planning of a customized cavity to fully expose the tooth, maxillary bone milling was performed on both the robot and static guide groups (n = 8). After milling, all models underwent scanning for assessment. ResultsIn the experiment with fully buried supernumerary tooth models in the maxilla, the root mean square, translation error, over-removal rate, and maximum distance were significantly smaller in the robot group compared to the static guide group. Moreover, the overlap ratio and Dice coefficient were significantly greater in the robot group. No statistically significant differences were observed between the two groups in terms of the rotation error (P = 0.80) or under-removal rate (P = 0.92). ConclusionsThis study has developed a robotic system for milling individualized jawbone cavities in oral and maxillofacial surgery, and its accuracy has been preliminarily verified to meet clinical requirements. Clinical significanceThe robotic system can achieve precise, minimally invasive, individualized jawbone milling in a variety of oral and maxillofacial surgeries, including tooth autotransplantation, surgical reshaping for zygomatic fibrous dysplasia, removal of fully impacted supernumerary or impacted teeth, and endodontic microsurgery, among other relevant clinical applications.

Fault identification of rolling bearing based on improved salp swarm algorithm

Due to the rapid development of industrial manufacturing technology, modern mechanical equipment involves complex operating conditions and structural characteristics of hardware systems. Therefore, the state of components directly affects the stable operation of mechanical parts. To ensure engineering reliability improvement and economic benefits, bearing diagnosis has always been a concern in the field of mechanical engineering. Therefore, this article studies an effective machine learning method to extract useful fault feature information from actual bearing vibration signals and identify bearing faults. Firstly, variational mode decomposition decomposes the source signal into several intrinsic mode functions according to the actual situation. The vibration signal of the bearing is decomposed and reconstructed. By iteratively solving the variational model, the optimal modulus function can be obtained, which can better describe the characteristics of the original signal. Then, the feature subset is efficiently searched using the wrapper method of feature selection and the improved binary salp swarm algorithm (IBSSA) to effectively reduce redundant feature vectors, thereby accurately extracting fault feature frequency signals. Finally, support vector machines are used to classify and identify fault types, and the advantages of support vector machines are verified through extensive experiments, improving the ability of global search potential solutions. The experimental findings demonstrate the superior fault recognition performance of the IBSSA algorithm, with a highest recognition accuracy of 97.5%. By comparing different recognition methods, it is concluded that this method can accurately identify bearing failure.

Adaptive Hybrid Deep Learning Based Effective Channel Estimation in MIMO-Noma for Millimeter-Wave Systems with an Enhanced Optimization Algorithm

The channel estimation is crucial in the “millimeter Wave (mmWave) Massive Multiple-Input MultipleOutput (MIMO) and Non-Orthogonal Multiple Access (NOMA)” devices. Hybrid beamforming techniques are employed nowadays to minimize the complexity and equipment price. However, the absence of digital beam forming in mmWave affects the dynamic range and accuracy of the channel estimation. Previous research has concentrated mainly on predicting narrow-band mmWave channels using deep learning networks, as the wideband channels of mmWave create a considerable range and noise issue. Accurate channel estimation in the MIMO system is challenging because of the increased number of antennas and radio frequency (RF) chains. MIMO system communications using mmWave are frequently chosen because of their massive spectrum resources. Therefore, it is essential to tackle the obstacles obtained in the standard channel estimation framework by developing a MIMO-NOMA network with the help of deep learning methods Hence, this paper proposes an efficient hybrid deep learning model for channel estimation in MIMO-NOMA for mmWave systems. At first, the channel estimation is carried out using the Adaptive Hybrid Deep Learning (AHDL) model, where it combines both Autoencoder and Recurrent Neural Network (RNN). Here, the parameters are optimized using the Improved Red-tailed Hawk Algorithm (IRHA). Later the hardware cost and system complexity are reduced by performing the hybrid beam-forming process. Numerical results show that the proposed channel estimation and pilot estimation process outperforms the state-of-the-art approaches.

Spatial coding metastructure for single-sensor impact region recognition

Abstract The rapid development of aircraft has created a strong demand for structural health monitoring, but current methods that rely on multiple sensor fusion suffer from complex hardware systems. Computational sensing with metastructures provides a promising approach to reduce sensing cost, but the lack of calibrated information makes it challenging to identify impact regions. In this study, we propose a concept of spatial coding metastructure for impact region recognition with a single sensor. Owing to the multi-order local resonance effect, the metastructures are capable of producing multiple vibration modulations over a wide frequency band. We demonstrate that the frequency-dependent vibration modulation effects of the metastructures on different test regions are distinguishable, a characteristic referred to as spatial coding. This characteristic enables impact regions to be accurately recognized with only a single sensor by using machine learning methods. Our work not only presents promising application prospects for condition monitoring of aircraft and other mechanical systems, but also inspires the development of safer and more efficient systems in various industries.

Flexible endoscope manipulating robot using quad-roller friction mechanism

A robotic system for manipulating a flexible endoscope in surgery can provide enhanced accuracy and usability compared to manual operation. However, previous studies require large-scale, complex hardware systems to implement the rotational and translational motions of the soft endoscope cable. The conventional control of the endoscope by actuating the endoscope handle also leads to undesired slack between the endoscope tip and the handle, which becomes more problematic with long endoscopes such as a colonoscope. This study proposes a compact quad-roller friction mechanism that enables rotational and translational motions triggered not from the endoscope handle but at the endoscope tip. Controlling two pairs of tilted rollers achieves both types of motion within a small space. The proposed system also introduces an unsynchronized motion strategy between the handle and tip parts to minimize the robot’s motion near the patient by employing the slack positively as a control index. Experiments indicate that the proposed system achieves accurate rotational and translational motions, and the unsynchronized control method reduces the total translational motion by up to 88% compared to the previous method.

Pixel-wise Phase Map Fusion Technique for High Dynamic Range 3-D Shape Measurement

Abstract Digital fringe projection technologies have been extensively applied in 3-D shape measurements. However, it is still limited to achieve accurate 3-D reconstruction of high reflective surfaces. To circumvent this problem, we propose a complete framework to realize pixel-by-pixel phase post-compensation of overexposed points, including the construction of hardware system and the guidelines of algorithm design. First, a virtual binocular vision system is established. Second, based on the fundamentals of projective and spatial transformation, three robust algorithms with low computational complexity are devised to generate a fused phase map that overcomes the information loss due to overexposure. Furthermore, experiments are conducted to evaluate the performance of the proposed method. Qualitative experiment results show that the presented technique achieve a similar surface coverage compared with the other two mainstream methods, but is superior to them in terms of measurement efficiency (13 images vs. 144 images, 13 images vs. 32 images). Quantitative experimentation further verifies the feasibility of our method in precise 3-D measurement, with sphere diameter errors of 0.0367mm and 0.0361mm, spherical centroid distance error of 0.0269mm, and plane distance error of 0.0186mm.

A smartphone-based multichannel magnetoelastic immunosensor for acute aortic dissection supplementary diagnosis

Some biomarkers of acute aortic dissection (AAD) can be used for the potential supplementary diagnosis of AAD, such as C-reactive protein (CRP), smooth muscle myosin heavy chain (SmMHC), and D-dimer (D-D). However, the current measurement methods for common markers primarily rely on sophisticated instruments. The operation process is complicated, and the reagents used are expensive. To provide chronic disease monitoring and home self-examination services for potential AAD patients in real time, we developed a smartphone-based multichannel magnetoelastic (ME) immunosensing device to detect protein levels. Our immunosensor reduced the aforementioned restrictions and demonstrated excellent performance for the supplementary diagnosis of AAD. In this paper, we successfully combined the intelligent terminal with the hardware system to sample the resonance frequency shift (RFS) on the multichannel ME immunosensor. According to the target detection objects with their respective antibodies in the immune binding response, multiple experiments were conducted to detect multiple groups of samples, and we found that a CRP concentration, a SmMHC concentration, and a D-D concentration in the range of 0.1−100μg/mL, 1−4ng/mL, and 0.25−5μg/mL were linearly proportional to the RFS of the ME immunosensor, respectively. For CRP, SmMHC, and D-D, the sensitivities were 13.37Hz/μg∙mL‐1, 155.19Hz/ng∙mL‐1, and 332.72Hz/μg∙mL‐1, respectively, and the detection limits were 2.634×10‐3μg/mL, 1.155×10‐2ng/mL, and 3.687×10‐3μg/mL, respectively. The experiments demonstrated that the accuracy and stability of our device were comparable to those of the vector network analyzer (VNA, Calibration instrument).

A comprehensive survey of UPPAAL‐assisted formal modeling and verification

AbstractUPPAAL is a formal modeling and verification tool based on timed automata, capable of effectively analyzing real‐time software and hardware systems. In this article, we investigate research on UPPAAL‐assisted formal modeling and verification. First, we propose four research questions considering tool characteristics, modeling methods, verification means and application domains. Then, the state‐of‐the‐art methods for model specification and verification in UPPAAL are discussed, involving model transformation, model repair, property specification, as well as verification and testing methods. Next, typical application cases of formal modeling and verification assisted by UPPAAL are analyzed, spanning across domains such as network protocol, multi‐agent system, cyber‐physical system, rail traffic and aerospace systems, cloud and edge computing systems, as well as biological and medical systems. Finally, we address the four proposed questions based on our survey and outline future research directions. By responding to these questions, we aim to provide summaries and insights into potential avenues for further exploration in this field.

A Toolpath Generator Based on Signed Distance Fields and Clustering Algorithms for Optimized Additive Manufacturing

Additive manufacturing (AM) methods have been gaining momentum because they provide vast design and fabrication possibilities, increasing the accessibility of state-of-the-art hardware through recent developments in user-friendly computer-aided drawing/engineering/manufacturing (CAD/CAE/CAM) tools. However, in comparison to the conventional manufacturing methods, AM processes have some disadvantages, including the machining precision and fabrication process times. The first issue has been mostly resolved through the recent advances in manufacturing hardware, sensors, and controller systems. However, the latter has been widely investigated by researchers with different toolpath planning perspectives. As a contribution to these investigations, the present study proposes a toolpath planning method for AM, which aims to provide highly continuous yet distance-optimized solutions. The approach is based on the utilization of the signed distance field (SDF), clustering, and minimization of toolpath distances among cluster centroids. The method was tested on various geometries with simple closed curves to complex geometries with holes, which provides effective toolpaths, e.g., with relative distance reduction percentages up to 16.5% in comparison to conventional rectilinear infill patterns.

Using Hough transform to define track boundaries as part of machine vision by train traffic-tracking hardware and software systems

Aim. In the context of machine vision, the problem of detected object boundary definition is usually solved using semantic segmentation that requires a high computational resource. Its application increases the complexity and the cost of the implemented hardware and software systems. This paper proposes an alternative method for defining the boundary of a segmented object, i.e., a railway track, for a train traffic tracking system. Methods. Since a railway track in an image can be represented by an n-th order polynomial, it is suggested to solve the problem of track boundary detection by using approximations in the form of straight lines. It is suggested using the Hough transform to detect straight lines. The former’s parametric space will be arranged in accordance with the problem to be solved. Conclusions. The proposed approximation will allow abandoning semantic segmentation and reduce the computational complexity and load.