Hardware Software Research Articles

The design of LLRF system for STCF storage ring

The Super Tau Charm Facility (STCF) is a new generation of high brightness electron–positron collider planned by China, designed to achieve a center of mass collision energy is 2–7[Formula: see text]GeV and a peak brightness is 0.5–[Formula: see text][Formula: see text]cm[Formula: see text]s[Formula: see text]. The digital low-level RF control system (LLRF), as the essential equipment of the storage ring RF system, is usually responsible for maintaining the stability of the cavity field. This paper presents the design scheme of LLRF for cavity in STCF storage ring, which mainly includes the hardware structure and software design of the system. We perform simulation analysis on the STCF RF system to evaluate the influence of delay and control parameters on the stability of the system and the influence of control parameters on the suppression of two kinds of noise. Desktop testing on the existing prototype demonstrates that the adopted direct-sampling architecture introduces less phase drift compared to the down-conversion architecture. Under the direct-sampling architecture, non-IQ demodulation offers better SNR and stability in both amplitude and phase. Specifically, direct-sampling in non-IQ mode achieves open-loop RMS short-term stability of 0.019% for amplitude and 0.021∘ for phase. Long-term stability in the closed-loop setting measures 0.027% for amplitude and 0.024∘ for phase.

Assessment of Envelope- and Machine Learning-Based Electrical Fault Type Detection Algorithms for Electrical Distribution Grids

This study introduces envelope- and machine learning (ML)-based electrical fault type detection algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an envelope-based detector identifying the anomaly region was improved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, switching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy. The performance of the proposed algorithms is tested in an emulated software–hardware electrical grid testbed using different sample rate meters/relays, such as SEL735, SEL421, SEL734, SEL700GT, and SEL351S near and far from an inverter-based photovoltaic array farm. The performance outcomes demonstrate the proposed model’s robustness and accuracy under realistic conditions.

Decimeter-Level Accuracy for Smartphone Real-Time Kinematic Positioning Implementing a Robust Kalman Filter Approach and Inertial Navigation System Infusion in Complex Urban Environments.

New smartphones provide real-time access to GNSS pseudorange, Doppler, or carrier-phase measurement data at 1 Hz. Simultaneously, they can receive corrections broadcast by GNSS reference stations to perform real-time kinematic (RTK) positioning. This study aims at the real-time positioning capabilities of smartphones using raw GNSS measurements as a conventional method and proposes an improvement to the positioning through the integration of Inertial Navigation System (INS) measurements. A U-Blox GNSS receiver, model ZED-F9R, was used as a benchmark for comparison. We propose an enhanced ambiguity resolution algorithm that integrates the traditional LAMBDA method with an adaptive thresholding mechanism based on real-time quality metrics. The RTK/INS fusion method integrates RTK and INS measurements using an extended Kalman filter (EKF), where the state vector x includes the position, velocity, orientation, and their respective biases. The innovation here is the inclusion of a real-time weighting scheme that adjusts the contribution of the RTK and INS measurements based on their current estimated accuracy. Also, we use the tightly coupled (TC) RTK/INS fusion framework. By leveraging INS data, the system can maintain accurate positioning even when the GNSS data are unreliable, allowing for the detection and exclusion of abnormal GNSS measurements. However, in complex urban areas such as Qazvin City in Iran, the fusion method achieved positioning accuracies of approximately 0.380 m and 0.415 m for the Xiaomi Mi 8 and Samsung Galaxy S21 Ultra smartphones, respectively. The subsequent detailed analysis across different urban streets emphasized the significance of choosing the right positioning method based on the environmental conditions. In most cases, RTK positioning outperformed Single-Point Positioning (SPP), offering decimeter-level precision, while the fusion method bridged the gap between the two, showcasing improved stability accuracy. The comparative performance between the Samsung Galaxy S21 Ultra and Xiaomi Mi 8 revealed minor differences, likely attributed to variations in the hardware design and software algorithms. The fusion method emerged as a valuable alternative when the RTK signals were unavailable or impractical. This demonstrates the potential of integrating RTK and INS measurements for enhanced real-time smartphone positioning, particularly in challenging urban environments.

A Control System Design and Implementation for Autonomous Quadrotors with Real-Time Re-Planning Capability

Real-time (re-)planning is crucial for autonomous quadrotors to navigate in uncertain environments where obstacles may be detected and trajectory plans must be adjusted on-the-fly to avoid collision. In this paper, we present a control system design for autonomous quadrotors that has real-time re-planning capability, including the hardware pipeline for the hardware–software integration to realize the proposed real-time re-planning algorithm. The framework is based on a modified version of the PX4 Autopilot and a Raspberry Pi 5 companion computer. The planning algorithm utilizes minimum-snap trajectory generation, taking advantage of the differential flatness property of quadrotors, to realize computationally light, real-time re-planning using an onboard computer. We first verify the control system and the planning algorithm through simulation experiments, followed by implementing and demonstrating the system on hardware using a quadcopter.

Critical infrastructure monitoring in CBRNe scenarios: a reliable and robust communication network for distributed multimodal sensors

The malicious misuse of CBRNe agents can inflict extensive damage to critical infrastructure and terrorize public society. The effects of such attacks can range from financial or structural drawbacks to significant casualties. Recent terror attacks in Cucuta (2021) or Kabul (2021) demonstrate the need to protect infrastructures such as airports, power plants, and transportation infrastructure. In the face of these modern-day threats, the need for a reliable and effective monitoring system for critical infrastructure has become increasingly important. In a first contribution to the long-term development, we present an initial version of a concept that requires the implementation paradigm of diverse redundancy and secure communication using MQTTS relying on the included cipher suite. An initial set of node types is defined and assigned requirements for implementation. The software tools architecture we propose based on these requirements is designed to support network operators and developers by providing standard features for network management and sensor node implementation. This includes the definition of a secure semi-automatic onboarding process for new sensor nodes, which is presented in detail. We strive for independency from specific hardware platforms, software frameworks, and network technologies to enable an open standard for communication within a critical infrastructure sensor network and also between such networks in the future.

A bioinspired tactile scanner for computer haptics

Computer haptics (CH) is about integration of tactile sensation and rendering in Metaverse. However, unlike computer vision (CV) where both hardware infrastructure and software programs are well developed, a generic tactile data capturing device that serves the same role as what a camera does for CV, is missing. Bioinspired by electrophysiological processes in human tactile somatosensory nervous system, here we propose a tactile scanner along with a neuromorphically-engineered system, in which a closed-loop tactile acquisition and rendering (re-creation) are preliminarily achieved. Based on the architecture of afferent nerves and intelligent functions of mechano-gating and leaky integrate-and-fire models, such a tactile scanner is designed and developed by using piezoelectric transducers as axon neurons and thin film transistor (TFT)-based neuromorphic circuits to mimic synaptic behaviours and neural functions. As an example, the neuron-like tactile information of surface textures is captured and further used to render the texture friction of a virtual surface for “recreating” a “true” feeling of touch.

Backpropagation-Based Learning Techniques for Deep Spiking Neural Networks: A Survey.

With the adoption of smart systems, artificial neural networks (ANNs) have become ubiquitous. Conventional ANN implementations have high energy consumption, limiting their use in embedded and mobile applications. Spiking neural networks (SNNs) mimic the dynamics of biological neural networks by distributing information over time through binary spikes. Neuromorphic hardware has emerged to leverage the characteristics of SNNs, such as asynchronous processing and high activation sparsity. Therefore, SNNs have recently gained interest in the machine learning community as a brain-inspired alternative to ANNs for low-power applications. However, the discrete representation of the information makes the training of SNNs by backpropagation-based techniques challenging. In this survey, we review training strategies for deep SNNs targeting deep learning applications such as image processing. We start with methods based on the conversion from an ANN to an SNN and compare these with backpropagation-based techniques. We propose a new taxonomy of spiking backpropagation algorithms into three categories, namely, spatial, spatiotemporal, and single-spike approaches. In addition, we analyze different strategies to improve accuracy, latency, and sparsity, such as regularization methods, training hybridization, and tuning of the parameters specific to the SNN neuron model. We highlight the impact of input encoding, network architecture, and training strategy on the accuracy-latency tradeoff. Finally, in light of the remaining challenges for accurate and efficient SNN solutions, we emphasize the importance of joint hardware-software codevelopment.

Design and realization of a water quality monitoring system based on the Internet of Things

ABSTRACT Water quality monitoring is a key means of ensuring the safety of water resources, protecting the ecological environment and maintaining public health. This paper describes a water quality monitoring system based on the Internet of Things (IoT), which supports real-time monitoring, remote control and intelligent early warnings. It is concluded that the system can effectively collect, process and display data on water quality. To further improve the level of intelligence of the water quality monitoring system, the ability of machine learning algorithm to analyze and predict the water quality data has also been separately tested. In this paper, the hardware design, software design, test results and algorithm evaluation of the system are described. The actual performance and reliability of the system in practical application under various field conditions have not yet been verified. This new system is compared with other water quality monitoring systems.

Polishing Machine Control System Based on B-Spline Curve Trajectory

In this study, a B-spline trajectory control algorithm is presented, showcasing its ability to achieve smooth trajectory control within an embedded motion control system. Traditional surface machining methods involve converting interpolated trajectories into G-code using numerical control (NC) software, which is then downloaded and executed on the numerical control system. This research introduces a specialized third-degree B-spline interpolation method tailored for curved surface trajectories, aiming to enhance machining efficiency. A significant strength of this project lies in the holistic design of an embedded system, encompassing both hardware circuitry and software logic. Utilizing the cost-effective STM32 architecture, a B-spline discrete velocity interpolation algorithm is implemented, validated through experiments conducted on a polishing machine system. The project involves MATLAB software for data simulations and experiments on a polishing machine using B-spline curve interpolation, confirming the practicality and effectiveness of the third-degree B-spline algorithm within an embedded system.

Optimizing Plant Watering Efficiency via IoT: Fuzzy Sugeno Method with ESP8266 Microcontroller

This research explores automatic plant sprinklers utilizing fuzzy logic with the fuzzy Sugeno method to overcome optimal watering schedules. Developed with ESP8266 microcontroller hardware and Arduino IDE software, the WSN prototype focuses on spinach plants. Watering in the nursery was done twice a day for seedling growth, then reduced to once a day. Soil moisture levels were assessed using a soil analyzer, as well as temperature variations with the HTC-1 device. Python facilitates data analysis, generating fuzzy inference graphs on temperature sensors (10-40°C) and soil moisture values (1-1024 RH). This innovative approach, which integrates fuzzy logic and advanced hardware, offers a promising solution for effective plant care.

Assessing the efficiency of using precision farming technology and remote monitoring of weather conditions in the activities of agricultural enterprises

The object of this study was the technology of precision agriculture and remote monitoring of weather conditions. The task to evaluate the effectiveness of using precision farming and precision irrigation technologies in the activities of agricultural enterprises under different conditions, in particular, different climatic and weather conditions, has been considered. To solve the task, a hardware-software system for smart irrigation and remote monitoring of weather conditions in the activities of agricultural enterprises during the cultivation of agricultural crops was designed and described. Results of the system's performance were analyzed in the activities of the Ukrainian agricultural company, which grew potatoes of various varieties in the Kyiv oblast (Ukraine) from 2021 to 2023. The results show that the average yield of potatoes of different varieties without irrigation for three years of observation was 29.74 t/ha, with irrigation – 48.99 t/ha, and with smart irrigation – 55.26 t/ha. At the same time, in the latter case, water, human, and financial resources were saved. The increase in yield with smart irrigation compared to yield with conventional irrigation over the three years of observation was on average 12.8 %. According to the results of the implementation of the hardware-software system for smart irrigation and remote monitoring of weather conditions in Ukraine, the effect of the possible implementation of this system by agricultural companies in the Republic of Kazakhstan was analyzed. The forecast of the average yield of potatoes for the period from 2024 to 2026 was built based on the model of linear weighted moving average, taking into account corrections in the case of using smart irrigation. Data on potato yield from 1990 to 2023 were chosen as the basis. The use of smart irrigation according to the described technology could increase the yield of potatoes of various varieties on average from 31.71 t/ha to 35.78 t/ha in comparison with the forecast values of yield without irrigation at the level of 19.25 t/ha. This confirms the need to apply the transfer of precision farming technologies to increase the yield of agricultural crops, in particular potatoes, and the productivity of agricultural companies

Design of a Novel RFID Reader for Oilwell Downhole Applications

RFID (Radio Frequency Identification), which transmits control data through electronic tags in a non-contact manner, provides a new approach for efficient and low-cost remote control of oil downhole tools. However, the interference of harsh downhole environments and the high-speed movement of tags seriously affect the performance of the current downhole reader. To effectively address this issue, in this study, a novel downhole RFID reader is presented. By introducing the half-duplex communication protocol to replace the current full-duplex communication protocol in the hardware circuits of the reader, its tag recognition ability can be improved. Then, the corresponding hardware circuits and software programs are designed. Furthermore, a sparse solenoid antenna is adopted to replace the traditional tightly wound solenoid antenna, which can provide a longer reading area range to cope with the high-moving tag, and its total length and spacing parameters between adjacent coils are designed in detail. The test results show that the proposed RFID reader based on a half-duplex communication protocol can communicate with tags normally, and its sparse solenoid antenna provides significantly more tag reading times than traditional tightly wound solenoid antennas under the same antenna inductance.

Using the kano quality model to evaluate the functions of automotive smart head-up displays

The development of novel technologies has resulted in changes in the functions offered by head-up displays (HUDs), which differ considerably from conventional digital dashboard displays. This study aimed to create a scientific framework for automotive services for automobile manufacturers to review their service models and solutions as they transition to developing autonomous vehicles. This study used the two-dimensional Kano quality model to identify and classify quality characteristics of HUDs. A Kano questionnaire survey was conducted to collect data on customer satisfaction with different quality characteristics. These characteristics were then ranked in the order of their importance to consumers, thereby identifying quality characteristics that must be prioritized for providing high-quality services with HUDs. Data were collected on their emphases on and satisfaction with 32 quality characteristics belonging to seven factors across two dimensions, namely tangible specific functions and intangible value-added services. The seven factors were speed and distance support, driver alerts, information and communication, display settings, active matching, automatic systems, and software–hardware integration. A statistical analysis was performed to determine the presence of significant differences in the customers’ perception of the quality characteristics. On the basis of the analysis results, 11 quality characteristics were classified as attractive quality, four as one-dimensional quality, six as must-be quality, seven as indifferent quality, and four as reverse quality. By referencing the classification of quality characteristics in their products, automotive companies can adopt the product improvement priority scheme proposed in this study to effectively enhance customer satisfaction with their services.

Arduino Nano Voice Recorder with MAX9814 Mic

This project involves creating a compact and efficient voice recorder using the Arduino Nano and the MAX9814 microphone amplifier. The device captures and stores high-quality audio, leveraging the Arduino Nano's small form factor and the superior audio sensitivity of the MAX9814. Ideal for various applications, including voice logging, sound monitoring, and DIY audio projects, this setup provides an affordable and accessible solution for enthusiasts and developers interested in audio recording technology. The project includes detailed instructions on hardware assembly and software implementation, ensuring ease of replication and customization.

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.

Robotic technology in Autism intervention: Design and application of the CARS cat-like robot

This study introduces a novel robotic system designed to aid individuals with Autism Spectrum Disorder (ASD) in enhancing their social communication and interaction skills. Incorporating advanced artificial intelligence, sensors, and image recognition technologies, the robot simulates human social interactions and provides tailored feedback, aiming to improve the quality of life for individuals with ASD. Through a detailed examination of hardware design, software functionalities, and data processing mechanisms, this paper evaluates the system's practical applications and identifies ongoing challenges and potential improvements. The findings suggest that while the robotic system significantly enhances social interaction and learning for autistic individuals, challenges such as technological adaptability, cost, and physical design require further innovation. This research contributes to the field of assistive technologies by offering insights into how robotic systems can be optimized to meet the unique needs of the autism community, thus paving the way for more effective therapeutic tools.

Design of a Real-Time Monitoring System for Electroencephalogram and Electromyography Signals in Cerebral Palsy Rehabilitation via Wearable Devices

Cerebral palsy is a disorder of central motor and postural development, resulting in limited mobility. Cerebral palsy is often accompanied by cognitive impairment and abnormal behavior, significantly impacting individuals and society. Time, energy, and economic investment in the rehabilitation process is substantial, yet the rehabilitation outcomes often remain unsatisfactory. Additionally, some patients have limited sensory perception during rehabilitation training, making it challenging to effectively regulate exercise intensity. Traditional evaluation methods are mostly based on recovery performance, lack guidance at the neurophysiological level, and have an unequal distribution of medical rehabilitation resources, which pose great challenges to the rehabilitation of patients. Based on the issues mentioned above, this paper proposes a real-time cerebral signal monitoring system based on wearable devices. This system can monitor and store blood oxygen, heart rate, myoelectric, and EEG signals during cerebral palsy rehabilitation, and it can track and monitor signals during the rehabilitation treatment process. The system includes two parts: hardware design and software design. The hardware design includes a data signal acquisition module, a main control chip (ESP32), a muscle electrical sensor module, a brain electrical sensor module, a blood/heart rate acquisition module, etc. It is primarily for real-time signal data acquisition, processing, and uploading to the cloud server. The software design includes functions such as data receiving, data processing, data storage, network configuration, and remote communication and enables the visual monitoring of data signals. The system can achieve real-time monitoring of electromyography, electroencephalography, and blood oxygen levels, as well as the heart rate of patients with cerebral palsy, and adjust rehabilitation training in real-time during the rehabilitation process. At the same time, based on the real-time storage of the original electromyography and electroencephalography data, it can provide auxiliary guidance for later rehabilitation evaluation and effective data support for the entire rehabilitation treatment process.

IoT traffic classification and anomaly detection method based on deep autoencoders

This study investigates anomaly detection of IoT device traffic using Convolutional Neural Networks (CNN) and Variational Autoencoders (VAE) to enhance the detection capability of security threats in IoT environments. A series of hardware configurations, software environments, and hyperparameters were utilized to optimize the training and testing processes of the models. The CNN model demonstrates robust classification performance, achieving an accuracy rate of 95.85% on the test dataset, effectively distinguishing between different types of IoT device traffic. Meanwhile, the VAE model exhibits proficient anomaly detection capabilities by effectively capturing abnormal patterns in the data using reconstruction loss and KL divergence. The combined use of CNN and VAE models offers a comprehensive solution to cybersecurity challenges in IoT environments. Future research directions include exploring diverse IoT traffic data, practical deployment for validation, and further optimization of model structures and parameters to improve performance and applicability.

The marketing on Big 4 websites of Big Data Analytics in the external audit: Evidence and consequences

Leveraging ubiquitous digital data, advanced hardware, and sophisticated software, Big Data Analytics (BDA) enables unprecedented in-depth examination of business processes. This paper investigates how the Big 4 accounting firms promote their use of technology-enabled analytics in auditing practices on their official websites. We find that all the Big 4 market their audit analytics as offering operational insights that add value for clients. Historically, auditors have sought to share valuable perspectives gained during engagements beyond verifying that clients are in compliance with accounting principles. However, providing these value-adding insights is transitioning from a supplementary benefit to an essential selling point of audits. This behavior likely stems from BDA technology’s inherent duality–facilitating regulatory compliance and business intelligence. Incorporating artificial intelligence into audit firms’ technological arsenal will further enhance their ability to deliver client-valued insights. These developments necessitate further research into how the BDA application impacts auditor independence.

A Hardware–Software Complex for Diagnostics of a Human Being’s Psychophysiological State during the Solution of Cognitive Tasks

A Hardware–Software Complex for Diagnostics of a Human Being’s Psychophysiological State during the Solution of Cognitive Tasks