Hardware Devices Research Articles

A bridge dynamic response analysis and load recognition method using traffic imaging

As the primary variable load of bridges, vehicle load is an important parameter for bridge health monitoring. However, traditional Weigh-in-Motion (WIM) systems and the commonly used method of placing sensors on the bridge are challenging to apply in load monitoring for many small and medium-sized bridges. Therefore, this paper proposes a bridge vehicle load identification method based on traffic surveillance video data. Leveraging the surveillance video data on the bridge, without introducing additional hardware devices, the displacement of target points is detected through sub-pixel level image detection algorithms, enabling non-contact measurement of bridge structural response through imaging. A spatiotemporal relationship model of structural displacement, vehicle load, and load distribution is established to solve for vehicle load. Finally, model bridge tests under various loading conditions and engineering practice experiments are conducted to validate the feasibility of the method. The results of the model bridge tests show that the structural displacement measured using traffic video measurement has a deviation of less than 10% compared to the measurements obtained using contact displacement sensors (LVDT), and it can accurately reflect the displacement characteristics of the structure. The results of the field tests demonstrate that the average estimation deviation for heavy vehicle loads ranging from 12 to 18 tons is approximately 18%, meeting the engineering requirements. The proposed method can provide load statistical information for the extensive health monitoring of small and medium-sized bridges and offer a new technical pathway for obtaining bridge load information.

Identification of digital device hardware vulnerabilities based on scanning systems and semi-natural modeling

Objectives. The development of computer technology and information systems requires the consideration of issues of their security, various methods for detecting hardware vulnerabilities of digital device components, as well as protection against unauthorized access. An important aspect of this problem is to study existing methods for the possibility and ability to identify hardware errors or search for errors on the corresponding models. The aim of this work is to develop approaches, tools and technology for detecting vulnerabilities in hardware at an early design stage, and to create a methodology for their detection and risk assessment, leading to recommendations for ensuring security at all stages of the computer systems development process.Methods. Methods of semi-natural modeling, comparison and identification of hardware vulnerabilities, and stress testing to identify vulnerabilities were used.Results. Methods are proposed for detecting and protecting against hardware vulnerabilities: a critical aspect in ensuring the security of computer systems. In order to detect vulnerabilities in hardware, methods of port scanning, analysis of communication protocols and device diagnostics are used. The possible locations of hardware vulnerabilities and their variations are identified. The attributes of hardware vulnerabilities and risks are also described. In order to detect vulnerabilities in hardware at an early design stage, a special semi-natural simulation stand was developed. A scanning algorithm using the Remote Bitbang protocol is proposed to enable data to be transferred between OpenOCD and a device connected to the debug port. Based on scanning control, a verification method was developed to compare a behavioral model with a standard. Recommendations for ensuring security at all stages of the computer systems development process are provided.Conclusions. This paper proposes new technical solutions for detecting vulnerabilities in hardware, based on methods such as FPGA system scanning, semi-natural modeling, virtual model verification, communication protocol analysis and device diagnostics. The use of the algorithms and methods thus developed will allow developers to take the necessary measures to eliminate hardware vulnerabilities and prevent possible harmful effects at all stages of the design process of computer devices and information systems.

Connected to the cloud at time of death: a case report

BackgroundOur case report provides the first clinical evaluation of autopsy practices for a patient death that occurs on the cloud. We question how autopsy practices may require adaptation for a death that presents via the ‘Internet of Things’, examining how existing guidelines capture data related to death which is no longer confined to the patient's body.Case presentationThe patient was a British man in his 50s, who came to the attention of the medical team via an alert on the cloud-based platform that monitored his implanted cardioverter defibrillator (ICD). The patient had a background of congenital heart disease, with previous ventricular fibrillation cardiac arrest, for which the ICD had been implanted two years earlier. Retrospective analysis of the cloud data demonstrated a gradually decreasing nocturnal heart rate over the previous three months, falling to a final transmission of 24 beats per minute (bpm). In the patient post-mortem the ICD was treated as medical waste, structural tissue changes precluded the effective evaluation of device hardware, potential issues related to device software were not investigated and the cause of death was assigned to underlying heart failure. The documentation from the attending law enforcement officials did not consider possible digital causes of harm and relevant technology was not collected from the scene of death.ConclusionThrough this patient case we explore novel challenges associated with digital deaths including; (1) device hardware issues (difficult extraction processes, impact of pathological tissue changes), (2) software and data limitations (impact of negative body temperatures and mortuary radio-imaging on devices, lack of retrospective cloud data analysis), (3) guideline limitations (missing digital components in autopsy instruction and death certification), and (4) changes to clinical management (emotional impact of communicating deaths occurring over the internet to members of family). We consider the implications of our findings for public health services, the security and intelligence community, and patients and their families. In sharing this report we seek to raise awareness of digital medical cases, to draw attention to how the nature of dying is changing through technology, and to motivate the development of digitally appropriate clinical practice.

Long-term explantation risk in patients with chronic pain treated with spinal cord or dorsal root ganglion stimulation

ObjectiveTo investigate long-term explantation risks and causes for the explantation of neuromodulation devices for the treatment of chronic pain from different manufacturers.MethodsThis retrospective analysis included patients implanted with a system...

Monitoring and analysis of settlement and deformation status of high-rise buildings based on nonlinear regression

In order to solve the problems of low reliability and poor prediction accuracy in traditional building structure settlement monitoring, the author proposes a monitoring and analysis of high-rise building settlement deformation status based on nonlinear regression. The author collected and wirelessly transmitted building settlement information through various hardware devices such as sensors and GPRS communication modules. The monitoring data collected by sensors were compared and analyzed to determine the settlement situation of the building. An RBF neural network prediction model was constructed for possible settlement points. Then, the leapfrog algorithm is used to optimize the structural parameters of the RBF neural network. The experimental results show that this method can accurately evaluate the possible settlement of building structures in actual environments, and the prediction error is small, with a maximum relative error of 4.83 %, indicating good warning ability. This method achieved the best actual value fitting curve results, verifying its feasibility in settlement prediction. Subsequently, a more widely applicable settlement detection and prediction system for building complex structures will be established based on the proposed method, in order to promote its large-scale application.

Genetically synthesized supergain broadband wire-bundle antenna

High-gain antennas are essential hardware devices, powering numerous daily applications, including distant point-to-point communications, safety radars, and many others. While a common approach to elevate gain is to enlarge an antenna aperture, highly resonant subwavelength structures can potentially grant high gain performances. The Chu-Harrington limit is a standard criterion to assess electrically small structures and those surpassing it are called superdirective. Supergain is obtained in a case when internal losses are mitigated, and an antenna is matched to radiation, though typically in a very narrow frequency band. Here we develop a concept of a spectrally overlapping resonant cascading, where tailored multipole hierarchy grants both high gain and sufficient operational bandwidth. Our architecture is based on a near-field coupled wire bundle. Genetic optimization, constraining both gain and bandwidth, is applied on a 24-dimensional space and predicts 8.81 dBi realized gain within a half-wavelength in a cube volume. The experimental gain is 8.22 dBi with 13% fractional bandwidth. The developed approach can be applied across other frequency bands, where miniaturization of wireless devices is highly demanded.

Exploiting beam search confidence for energy-efficient speech recognition

With mobile and embedded devices getting more integrated in our daily lives, the focus is increasingly shifting toward human-friendly interfaces, making automatic speech recognition (ASR) a central player as the ideal means of interaction with machines. ASR is essential for many cognitive computing applications, such as speech-based assistants, dictation systems and real-time language translation. Consequently, interest in speech technology has grown in the last few years, with more systems being proposed and higher accuracy levels being achieved, even surpassing human accuracy. However, highly accurate ASR systems are computationally expensive, requiring on the order of billions of arithmetic operations to decode each second of audio, which conflicts with a growing interest in deploying ASR on edge devices. On these devices, efficient hardware acceleration is key for achieving acceptable performance. In this paper, we propose a technique to improve the energy efficiency and performance of ASR systems, focusing on low-power hardware for edge devices. We focus on optimizing the DNN-based acoustic model evaluation, as we have observed it to be the main bottleneck in popular ASR systems, by leveraging run-time information from the beam search. By doing so, we reduce energy and execution time of the acoustic model evaluation by 25.6 and 25.9 %, respectively, with negligible accuracy loss.

Development and research of filter models communication and control systems configured for the required set of parameters

Problem statement. A digital filter refers to a hardware or software implementation of a mathematical algorithm that converts a digital signal in a certain way. The classification of digital filters is usually based on the functional characteristics of digital filtering algorithms. Digital filters may have parameters that cannot be implemented in analog filters, do not require periodic monitoring and calibration, and one filter can process several input channels or signals, while the accuracy is limited only by the bit depth used. The implementation of digital signal processing units using programmable logic integrated circuits (FPGAs) will significantly simplify hardware costs and make the most effective use of these digital filter capabilities. Goal. Conducting research in the field of digital signal processing in order to obtain fundamentally new software and hardware architectures, developing an RTL model of a complex functional block (SF block) of a digital signal processing system, its verification both at the logical and physical level. Results. In the course of the scientific research, a software and hardware model of the filter was developed to adjust the parameters during operation, taking into account their architectural advantages and disadvantages. In order to verify and confirm the adequacy of the methodology developed in the course of the study, an SF model was created-a digital signal processing unit. This model has been implemented and physically verified on the basis of a debugging board using FPGAs. Practical significance. The technique presented in this paper will allow the development of software and hardware devices for digital signal processing, with the possibility of their configuration at the software level for the required set of processing parameters, which in turn will significantly save resources spent on development.

Benchmarking parallel programming for single-board computers

Within the computing continuum, SBCs (single-board computers) are essential in the Edge and Fog, with many featuring multiple processing cores and GPU accelerators. In this way, parallel computing plays a crucial role in enabling the full computational potential of SBCs. However, selecting the best-suited solution in this context is inherently complex due to the intricate interplay between PPI (parallel programming interface) strategies, SBC architectural characteristics, and application characteristics and constraints. To our knowledge, no solution presents a combined discussion of these three aspects. To tackle this problem, this article aims to provide a benchmark of the best-suited parallelism PPIs given a set of hardware and application characteristics and requirements. Compared to existing benchmarks, we introduce new metrics, additional applications, various parallelism interfaces, and extra hardware devices. Therefore, our contributions are the methodology to benchmark parallelism on SBCs and the characterization of the best-performing parallelism PPIs and strategies for given situations. We are confident that parallel computing will be mainstream to process edge and fog computing; thus, our solution provides the first insights regarding what kind of application and parallel programming interface is the most suited for a particular SBC hardware.

Closed-loop Control System Design of Wind Rope Hoisting of Transmission Tower

Abstract The traditional wind rope (tricing pendant) hoisting method of power transmission towers mainly relies on manpower to observe the posture of the hanger and adjust the retraction and release speed of the control rope, so as to avoid the scraping of hanger and the assembled tower body. In this paper, the ADAMS/Cable modeling tool is used to establish the transmission tower hoisting dynamic model with the JC27151CG tower as the object, and the reasonable installation position of the tricing pendant retraction system is analyzed. And based on the reasonable installation position, the hoisting strategy and hoisting control system of JC27151CG tower are studied. The main control system hardware of the control device mainly includes Delta PLC, servo driver, AC contactor and DC relay. The main control program is divided into two modes, manual and automatic, with a closed-loop control servo motor system. The manual mode operates the servo motor through buttons to realize forward and reverse rotation. In automatic mode, the system intelligently adjusts the lifting speed and attitude according to the data of the navigation system to ensure a smooth and safe lifting process. The research outcomes not only offer novel theoretical perspectives and methods for the hoisting of power transmission towers but also provide robust technical support for practical engineering operations.

WITHDRAWN: A Technique for Enhancing Respiration Sensing in Interference Scenarios with Wi-Fi Signal

Wi-Fi-based non-contact respiratory monitoring technology plays a significant role in smart elderly care by eliminating the need for users to wear additional hardware devices. Although respiration sensing has shown impressive accuracy in ideal scenarios, achieving precise respiration monitoring in interfering scenarios, such as when other users are engaged in activities, remains challenging. This difficulty arises primarily from the weak nature of the respiratory signal, which is susceptible to interference from activities. To address this issue, this paper introduces the RespEnh system, a respiratory monitoring system that utilizes human location information to mitigate interference. Firstly, we enhance the quality of respiratory signal by leveraging the frequency diversity of Wi-Fi signal, effectively visualizing respiratory patterns even amidst interference. Secondly, we apply short time window shift to remove walking noise in the time domain while preserving respiratory signal characteristics. Finally, we integrate multi-antenna signals to enhance spatial domain performance. Experimental results demonstrate that our system achieves a relative anti-interference performance of 2.4m, even when breathing as far away as 6.4m. It effectively monitors person's respiration, with a detection rate of 80% within an error range of 1bpm.

The rapid generation of E-learning tools under resource constraints approach

Enhancing the efficiency of Ukraine's security and defense sector is one of the key prerequisites for maintaining state sovereignty and achieving victory in the war. This is particularly relevant to the training and education system for specialists of the State Service of Special Communications and Information Protection of Ukraine (SSSCIP). The active phase of Russia's war against Ukraine has significantly exacerbated the contradiction between the necessity to improve the efficiency of this system on one hand and the stringent requirement to economize resources (financial, human, and temporal) on the other. Resolving this contradiction is feasible through the intensive implementation of various e-learning tools and technologies, along with associated pedagogical methodologies. One of the factors limiting the effectiveness of e-learning is the complexity of the traditional process of creating these tools. The negative impact of this factor can be mitigated by developing approaches for the rapid generation of e-learning tools. Thus, there is a need to develop an integrated approach that enables the quick creation of educational content and e-learning tools through the use of innovative technologies. Such tools should save time and enhance the efficiency of learning material assimilation. The primary requirements for this approach include the rational use of limited resources and the adaptability of outcomes to the needs of each specialist. The article proposes an integrated approach to the rapid generation of e-learning tools for studying hardware and software devices and systems. This approach includes the following stages: developing a paper version of the learning material, which is the foundation for further enhancements; supplementing the paper version with an eBook based on H5P, which ensures interactivity and flexibility in learning; introducing a simulator module that allows for the emulation of device operation, stimulates student engagement, and deepens understanding of the material; adding an augmented reality module that visualizes complex concepts and provides opportunities for practical knowledge application. The implementation of the integrated approach to the rapid generation of e-learning tools is demonstrated using the educational module "Fortinet FG-60E Firewall." This module exemplifies how the integrated approach can be applied to prepare specialists in Ukraine's security and defense sector under wartime conditions. Survey results from participants who utilized the developed integrated learning approach indicate its high effectiveness.

Development of a Device and Algorithm Research for Akhal-Teke Activity Level Analysis

This study demonstrated that wearable devices can distinguish between different levels of horse activity, categorized into three types based on the horse’s gaits: low activity (standing), medium activity (walking), and high activity (trotting, cantering, and galloping). Current research in activity level classification predominantly relies on deep learning techniques, known for their effectiveness but also their demand for substantial data and computational resources. This study introduces a combined acceleration threshold behavior recognition method tailored for wearable hardware devices, enabling these devices to classify the activity levels of horses directly. The approach comprises three sequential phases: first, a combined acceleration interval counting method utilizing a non-linear segmentation strategy for preliminary classification; second, a statistical analysis of the variance among these segments, coupled with multi-level threshold processing; third, a method using variance-based proximity classification for recognition. The experimental results show that the initial stage achieved an accuracy of 87.55% using interval counting, the second stage reached 90.87% with variance analysis, and the third stage achieved 91.27% through variance-based proximity classification. When all three stages are combined, the classification accuracy improves to 92.74%. Extensive testing with the Xinjiang Wild Horse Group validated the feasibility of the proposed solution and demonstrated its practical applicability in real-world scenarios.

Overview of AI-Models and Tools in Embedded IIoT Applications

The integration of Artificial Intelligence (AI) models in Industrial Internet of Things (IIoT) systems has emerged as a pivotal area of research, offering unprecedented opportunities for optimizing industrial processes and enhancing operational efficiency. This article presents a comprehensive review of state-of-the-art AI models applied in IIoT contexts, with a focus on their utilization for fault prediction, process optimization, predictive maintenance, product quality control, cybersecurity, and machine control. Additionally, we examine the software and hardware tools available for integrating AI models into embedded platforms, encompassing solutions such as Vitis AI v3.5, TensorFlow Lite Micro v2.14, STM32Cube.AI v9.0, and others, along with their supported high-level frameworks and hardware devices. By delving into both AI model applications and the tools facilitating their deployment on low-power devices, this review provides a holistic understanding of AI-enabled IIoT systems and their practical implications in industrial settings.

Iterative algorithm computational spectrometer based on a single-hidden-layer neural network

Computational spectrometers have great application prospects in hyperspectral detection, and fast and high-precision in situ measurement is an important development trend. The computational spectrometer based on iterative algorithms has low requirements for computational resources and is easy to achieve hardware integration and in situ measurement. However, iterative algorithms are difficult to achieve high reconstruction accuracy due to the ill-posed nature of problems. Neural networks have powerful learning capabilities and can achieve high-precision spectral reconstruction. However, solely relying on neural network algorithms for reconstruction requires higher storage space and computing power from hardware devices, which makes it difficult to integrate large-scale neural network models into embedded systems. We propose using neural networks to alleviate the effect of the problem ill-posedness on the reconstruction results of iterative algorithms, so as to improve the reconstruction accuracy of the iterative algorithm computational spectrometers. First, spectral reconstruction was performed with iterative algorithms using a public spectral dataset. Then, a single-hidden-layer neural network was trained to establish a fitting relationship between the iterative algorithm spectral reconstruction results and the original spectrum. Finally, simulation and experimental results show that the proposed application of neural networks to alleviate the ill-posed problem of the iterative algorithm spectral reconstruction can effectively improve the reconstruction accuracy of iterative algorithm computational spectrometers with low computational resources. The research results may have good potential in achieving fast and high-precision in situ measurements of computational spectrometers.

Automated Backend Allocation for Multi-Model, On-Device AI Inference

On-Device Artificial Intelligence (AI) services such as face recognition, object tracking and voice recognition are rapidly scaling up deployments on embedded, memory-constrained hardware devices. These services typically delegate AI inference models for execution on CPU and GPU computing backends. While GPU delegation is a common practice to achieve high speed computation, the approach suffers from degraded throughput and completion times under multi-model scenarios, i.e., concurrently executing services. This paper introduces a solution to sustain performance in multi-model, on-device AI contexts by dynamically allocating a combination of CPU and GPU backends per model. The allocation is feedback-driven, and guided by a knowledge of model-specific, multi-objective pareto fronts comprising inference latency and memory consumption. Our backend allocation algorithm that runs online per model, and achieves 25-100% improvement in throughput over static allocations as well as load-balancing scheduler solutions targeting multi-model scenarios.

RAD-BNN: Regulating activation distribution for accurate binary neural network

The outstanding performance of deep convolutional neural networks comes from their effective extraction and learning ability. Although binary neural networks (BNNs) have the obvious advantages of low storage and high efficiency over their full-precision counterparts on resource-constrained hardware devices, the accuracy degradation brought by binary quantization is still an unavoidable problem. The activation distribution in BNNs is a key factor affecting network performance. To elevate the accuracy of BNNs, in this paper, we propose to regulate the activation distribution to strengthen the representation ability of BNNs. We first propose an Information Entropy enhancement Basic block (IEBlock) to build a competitive baseline model with higher information entropy of output activation distribution. Specifically, we build the IEBlock by deliberately reorganizing the position of the elements in the normal basic block based on a deep analysis of the information flow. After that, we propose a Depth-aware Activation Distribution Amendment (DADA) module, which learns the interdependencies of feature channels to amend the activation distribution with information loss after binary convolution. Extensive experiments demonstrate that our method effectively improves the information entropy of binary activations and elevates the accuracy of BNNs. Our method has outperformed the state-of-the-art methods on CIFAR-10 and ImageNet datasets. Code is available at: https://github.com/tomorrow-rain/RAD-BNN

Impact of a contract manufacturer's entry on two competing platforms' sourcing strategies

Many platforms, including the likes of Google, Amazon, and Microsoft, are now no longer merely satisfied with providing services or software but are increasingly eager to shape the future landscape of hardware devices. However, contract manufacturers (CMs) who supply hardware devices to such platforms might also produce competing products. This potential competition could prompt these platforms to consider in-house production for their devices. Motivated by this practice, we investigate the sourcing strategies of two competing platforms in the presence of a CM's entry and further explore the interaction between entry and sourcing strategies in a platform supply chain. The platforms select between insourcing the device through in-house production and outsourcing it to the CM. We find that, without entry, the CM's large cost advantage in producing the device relative to the platform induces the platform to choose the outsourcing strategy. In contrast, under entry, the equilibrium sourcing structures hinge upon the extent of the production cost difference between the platform and the CM in producing the device, as well as the degree of the high-efficiency platform's advantage in research and development efficiency over the low-efficiency platform in designing the platform. Particularly, when the CM enters through the platform with low efficiency, the asymmetric sourcing structure in which only the low-efficiency platform outsources is not an equilibrium, whereas the other sourcing structures may exist in equilibrium. Furthermore, in asymmetric sourcing structures, the large production cost difference prevents the CM from entering the market and, more intriguingly, the CM prefers the platforms to adopt the insourcing strategy to gain favourable market entry if the cost difference in producing the device is small. Several extensions are studied to obtain additional insights and demonstrate the robustness of the main findings from the baseline model.

Characterizing the safety profile of vagus nerve stimulation devices for epilepsy from 21,448 manufacturer and user reports.

This study summarizes medical device reports (MDRs) associated with adverse events for vagus nerve stimulation (VNS) devices indicated for epilepsy as reported by the Manufacturer and User Facility Device Experience (MAUDE) database of the US Food and Drug Administration. The MAUDE database was surveyed for MDRs from November 2013 to September 2022 regarding VNS devices for epilepsy. Event descriptions, device problems, correlated patient consequences, and device models were grouped and analyzed in Python. Based on event description, revision surgeries and other unique events were identified. Revenue from VNS device sales was used to approximate growth in their use over time. A total of 21,448 MDRs met the inclusion criteria. High VNS impedance, the most prevalent device malfunction overall (17.0% of MDRs), was the most common factor for 18 of the 102 encountered patient problems and led to 1001 revision surgeries (3371 total revisions). Included in those 18 device malfunctions were 3 of the top 6 occurring patient problems: seizure recurrence (9.9% associated with high impedance; encompassed focal, absence, and grand mal subtypes), death (1.3%), and generalized pain (7.9%). The next 4 top cited device malfunctions-lead fracture (13.7% of MDRs), operational issue (6.6%), battery problem holding charge (4.2%), and premature end-of-life indicator (2.9%)-differed widely in their percentage of cases that did not impact patients (77.4%, 57.3%, 48.9%, and 92.2%, respectively), highlighting differing malfunction severities. Seizure recurrence, the most prevalent patient impact, was the outcome most associated with 32 of the 68 encountered device problems, including high impedance (12.8%), lead fracture (12.2%), operational issue (18.4%), battery problem holding charge (31.2%), and premature end-of-life indicator (8.9%), which comprised the top 5 occurring device problems. In general, MDRs spanned a diverse range including device age, hardware, software, and surgeon or manufacturer error. Trends were seen over time with declining annual MDRs coupled with a rise in the use of VNS devices as gauged by revenue growth. Shifting device and patient problem profiles were also seen in successive models, reflecting engineering updates. This study characterizes the most common and consequential side effects of VNS devices for epilepsy while clarifying likely causes. In addition, the outcomes of 68 distinct device malfunctions were identified, including many not ubiquitously present in literature, lending critical perspective to clinical practice.

UAV payload virtualization based on the unified driving and capability abstraction

As UAV and swarms applications continue to deepen and become increasingly widespread, UAV payloads have characteristics such as diverse types, heterogeneous forms, and complex interfaces. At the same time, it is required that the system can quickly access and efficiently manage payloads. In order to decouple the close relationship between the payloads and the system platforms, and expand the compatibility with heterogeneous payloads, a novel payload device virtualization method is proposed. Firstly, a layered driver model is proposed to decouple the device hardware and device driver, standardize the payload access process, and the driver model is implemented by using the software and hardware. Secondly, a payload abstraction based on "capability" is proposed to decouple load application and device. Finally, specific examples for the verification of the virtualization method are implemented. The present UAV payload virtualization method can not only achieve software and hardware decoupling, but also enhance the compatibility and scalability of system, improving the efficiency of unified payload management.