Hardware Platform Research Articles

A Mixed-Criticality Traffic Scheduler with Mitigating Congestion for CAN-to-TSN Gateway

The network architecture that Time-Sensitive Networking (TSN) is used as the backbone network and the Controller Area Network (CAN) serves as the intra-domain network is considered as the CAN-TSN interconnection network architecture, which has gained considerable attention within industrial embedded networks, such as spacecraft, intelligent automobiles, and factory automation. The architecture employs the CAN-TSN gateway as a central hub for transmitting and managing a significant volume of communications between the CAN domains and TSN. However, the CAN-TSN gateway faces a high congestion challenge due to the rapid growth in data volume, making it difficult to effectively support different time planning mechanisms provided by TSN. In this paper, we propose a two-stage mixed-criticality traffic scheduler. The scheduler in the first stage adopts a Message Optimization Algorithm (MOA) to aggregate multiple CAN messages into a single TSN message (including the aggregation of critical and non-critical CAN messages), which reduces the number of CAN messages requiring transmission. In the second stage, the scheduler proposes a Message Scheduling Optimization Algorithm (MSOA) to schedule critical TSN messages. This algorithm reassembles all the critical CAN messages (within the un-schedulable TSN messages) to generate new TSN messages for rescheduling. Experimental results show that our proposed scheduler effectively improves the acceptance ratio of critical and non-critical CAN messages and outperforms the state-of-the-art message scheduling method in terms of acceptance ratio while improving the bandwidth utilization and the number of schedule table entries. We further construct a hardware platform to evaluate the performance of MSOA. The consistency between practical results and theoretical results shows the effectiveness of MSOA.

Collection skeletons: Declarative abstractions for data collections

Modern programming languages provide programmers with rich abstractions for data collections as part of their standard libraries, e.g., Containers in the C++ STL, the Java Collections Framework, or the Scala Collections API. Typically, these collections frameworks are organised as hierarchies that provide programmers with common abstract data types (ADTs) like lists, queues, and stacks. While convenient, this approach introduces problems which ultimately affect application performance due to users over-specifying collection data types limiting implementation flexibility. In this article, we develop Collection Skeletons which provide a novel, declarative approach to data collections. Using our framework, programmers explicitly select properties for their collections, thereby truly decoupling specification from implementation. By making collection properties explicit, immediate benefits materialise in forms of reduced risk of over-specification and increased implementation flexibility. We have prototyped our declarative abstractions for collections as a C++ library, and demonstrate that benchmark applications rewritten to use Collection Skeletons incur little or no overhead. We also show how Collection Skeletons help shielding the application developer from parallel implementation details, either by encapsulating implicit parallelism or through explicit properties that capture the requirements of parallel algorithmic skeletons. We observe performance improvements across most of the 17 benchmarks resulting from the use of Collection Skeletons before trying to parallelise those benchmarks, while also enhancing performance portability across three different hardware platforms.

A Multiport Current-Fed IIOS Dual-Half-Bridge Converter for Distributed Photovoltaic MVDC Integration System

The input-independent output-series (IIOS) dc-dc converter structure possesses the capability to achieve high voltage gain, single-stage power conversion and multiport independent control, which is suitable for distributed photovoltaic (PV) power collection system. However, if the multiport input powers are different, the output voltage of each submodule (SM) will be uneven, which might lead to power degradation and device damage. In order to address SM output voltage imbalance problem, a voltage self-balancing output-series dual-half- bridge (OSDHB) converter based on multi-winding coupled-inductor (MWCI) is developed in this paper. By multiplexing the SM secondary side switches in OSDHB, the unbalanced power between SMs can be directly transmitted to the target SM, without the requisition for transmission one by one. And all power switches possess zero-voltage switching characteristic. Therefore, the proposed IIOS-DHB features high efficiency, low cost, simple control and more adaptive to distributed PV power generation system. The topological configuration, operating principle and self-balancing performance are presented. Finally, the full-scale simulations and three submodules down-scale experiment hardware platform have been conducted to verify the theoretical analysis of the proposed IIOS-DHB.

Research on Online Measurement Method for Small Workpiece Circular Holes Based on Machine Vision

<p>In modern manufacturing, due to the narrow format, short size, and complex surface structure of small-sized workpieces, automatic detection of surface defects, dimensions, etc. on small-sized workpieces faces difficulties, making it difficult to meet the requirements of fully automated production lines. Especially, there is no good solution to the problem of high-precision automatic measurement of geometric features of circles and arcs on small-sized workpieces. This article takes the engine end cover circular hole thermal component as the research object, builds a hardware platform for image acquisition of small-sized workpieces based on machine vision, proposes an online size measurement method for small-sized circular holes, and realizes the measurement of the position size and aperture size of small-sized product shell workpieces. In order to achieve the above methods, this article builds a visual detection hardware system and describes a detailed machine vision hardware selection scheme. Then, the images collected by the system are preprocessed, including image denoising, image correction, and image feature extraction. Then, for the processed images, an improved Hough transform fitting method is used to obtain the online size of features. In order to visualize the results, the detection system and software are designed according to the actual application scenario. The overall scheme of this article is efficient and reasonable, and the detection result error is within a reasonable range.</p> <p> </p>

小型农用液压底盘远程驾驶控制系统的设计与试验

Aiming at the adaptability and safety problems of agricultural machinery in hilly and mountainous areas, the remote driving control system of agricultural full hydraulic chassis is designed based on ARM-Linux platform. The whole remote driving system is composed of Web upper computer, server system and chassis drive system. According to the requirements of chassis operation, the STM32F407 is used as the lower computer to realize the running control and motion status monitoring of the chassis. Taking the I.MX6ULL as the hardware platform, the Linux as the software platform, and 4G communications as the Web Server, the remote driving of the chassis is realized through Web pages on the computer. It can be seen from the test results that the minimum RTT delay from the Web page driving to the lower computer is 170 ms; the maximum RTT delay is 1310 ms, and the average RTT delay is 222.75 ms. The real-time interactivity of the control system meets the needs of remote driving of the agricultural machinery. The research provides a theoretical basis and technical reference for the development of the remote driving system of the agricultural machinery.

Sign Language Recognition System

Abstract: Sign language serves as a primary mode of communication for the Deaf and hard-of-hearing community. This paper presents a Sign Language Recognition System (SLRS) designed to facilitate seamless communication between individuals proficient in sign language and those who may not share this proficiency. The system employs a multi-faceted approach, integrating computer vision, machine learning, and signal processing techniques to accurately interpret and recognize sign language gestures. The methodology involves data collection from diverse signing styles, preprocessing to enhance data quality, feature extraction capturing key aspects of sign language expressions, and the selection and training of machine learning models. The system aims to represent sign language gestures effectively, providing a foundation for real-time recognition. Integration into hardware or software platforms ensures practical applications in various settings, including education, employment, and public spaces.

Evaluation of Robot Motion Trajectory Based on Selected Mapping Algorithms

This paper presents the concept of a remotely controlled mobile robot that generates a two-dimensional map of its surroundings. The hardware platform developed relies on the LINUX operating system with the Robot Operating System (ROS) to function properly. The authors focused on discussing the robot's hardware and presenting the software used. In line with the assumptions made, the robot is capable of generating a two-dimensional digital map of its surroundings, as well as of recording images of those surroundings. The robot relies on lidar odometry for identifying its position, meaning that the developed algorithm calculates the vehicle's location based on data from a laser scanner. The main sources of environment-related information acquired by the robot include the following: RPLidar A3M1 laser scanner by Slamtec (generating a digital map) and a 5mpx HD OV5647 camera (capturing images of the surroundings). These devices are coupled with the Raspberry Pi 3B on-board computer via a CSI interface.

Iot-based flood monitoring and early warning system for proactive security measures

The project introduces an IoT-based flood monitoring system using Arduino Uno, soil moisture sensor, LCD, buzzer, Wi-Fi module (e.g., ESP8266), and ThingSpeak cloud platform. It monitors soil moisture levels, issuing alerts when a predefined threshold indicative of potential flooding is reached. The soil moisture sensor measures moisture content and is displayed on an LCD screen for real-time monitoring. A buzzer provides audible alerts for high moisture levels. A Wi-Fi module enables wireless communication, transmitting data to the ThingSpeak cloud platform for centralized storage and analysis. Users access real-time and historical data through ThingSpeak's interface. The system configures the Wi-Fi module to connect to a network, securely sending data using a unique API key. ThingSpeak enables visualization, analysis, and alert setup based on soil moisture levels, offering a comprehensive flood monitoring solution. This cost-effective system, integrating open-source hardware and cloud platforms, enhances accessibility and deployment ease and is suitable for various environmental monitoring applications.

Design of Regenerative Braking System and Energy Storage with Supercapacitors as Energy Buffers

Vehicles are part of urban area transport and are subjected to variable loads as they traverse the city with varying slopes and stop-and-go traffic. Electric Vehicles (EVs) can be a good option because of their high efficiency under stop-and-go conditions and ability to gain energy from braking. However, limited battery energy makes EVs less efficient and degrades their lifetime. In contrast to a Li-Ion battery, supercapacitors work well under high power charge and discharge cycles. However, their high cost and low energy density prevent them from being viable replacements for batteries. Due to the slow charging and discharging process of batteries, they have a low power density, but a high energy density compared to the supercapacitor. In this paper, we discussed our system design consisting of both a battery and a supercapacitor. The main aim is to design and develop a scheduling algorithm to optimize energy flow between the battery, supercapacitor, and motor. We further described an analogue-based control methodology and algorithm for the supercapacitor, augmented battery-powered motoring process. This is in addition to a charge controller designed to optimize the supercapacitor bank's current-based charge-discharge profile. The system design and tests are developed on PSPICE and a hardware platform.

FEATURES OF CREATION OF THE SMART MONITORING SYSTEMS FOR MICROGRIDS

In this paper, an analysis of the main requirements for control systems in Microgrid is conducted. The main objectives of the implementation of Microgrid systems were determined and, based on the performed analysis, the requirements for Smart-monitoring systems were systematized. The components of Smart-monitoring systems, which include: communication software platform, hardware communication platform and types of equipment of DG sources for Microgrid systems were analyzed. It is proposed to build Smart-monitoring systems for Microgrid systems on the basis of the SGAM model, taking into account various models of aggregation of different types of DG sources within the framework of Microgrid systems. The proposed structure of the Smart-monitoring system for Microgrid systems with various DG sources makes it possible to effectively aggregate DG sources and prosumers and to carry out effective dispatching of generating capacities based on market mechanisms of their interaction with consumers and among themselves.

Cross-platform adaptation of algorithmic editing techniques

The research relevance is determined by the rapid development of technology and the growing need for efficient data processing on various platforms. The study aimed to address methods that would enable the use of data editing algorithms on various operating systems and hardware platforms. A methodology was developed for studying cross-platform adaptation technologies, including cross-compilation, virtualisation, the use of universal libraries and Application Programming Interface, as well as methods for testing and optimising algorithm performance. The study addressed various approaches to implementing cross-platform compatibility, including the use of cross-compilation, virtualisation and containerisation. The main technical challenges are managing resources, optimising performance and ensuring compatibility with hardware from different platforms. The principles included selecting the most appropriate technology for the task at hand, considering performance and security requirements, and ensuring effective integration of existing systems and infrastructure. The workflows are focused on creating modular and extensible solutions that can easily adapt to changes in the technological environment and user requirements. In the context of this study, artificial intelligence software plays a key role in improving the efficiency and accuracy of data processing across different platforms. The results showed that artificial intelligence software can automate various stages of the video and audio editing process. Artificial intelligence is used to analyse large amounts of content data, such as video files, images and audio recordings. The study determined that artificial intelligence is increasingly relevant in various aspects of movie production. Artificial intelligence can analyse scripts, predict their potential success and suggest improvements using data from previous films and their commercial success

Industrial Internet of Things Gateway with OPC UA Based on Sitara AM335X with ModbusE Acquisition Cycle Performance Analysis.

This article presents the hardware and software architectures used to implement the Modbus Extension (ModbusE) IIoT gateway, the performance of the acquisition cycle at the PRU real-time programmable core level, the acquisition cycle communication flow-dispatcher-OPC UA server (Linux)-OPC UA client (Windows) as well as the performance analysis of data communications between the IIoT ModbusE gateway and the OPC UA client (Windows). In order to be able to implement both the ModbusE acquisition cycle and the OPC UA server, the BeagleBone Black Board was chosen as the hardware platform. This board uses the Sitara AM335x processor (Texas Instruments (TI), Dallas, TX, USA) from Texas Instruments. Thus, the acquisition cycle was implemented on the PRU0 real-time core, and the OPC UA server, running under the Linux operating system, was implemented on the ARM Cortex A8 processor. From the analysis of the communication speed of the messages between the OPC UA client and the ModbusE servers, it was found that the ModbusE acquisition cycle speed was higher than the acquisition speed of the OPC UA client.

An open-source FACS automation system for high-throughput cell biology.

Recent advances in gene editing are enabling the engineering of cells with an unprecedented level of scale. To capitalize on this opportunity, new methods are needed to accelerate the different steps required to manufacture and handle engineered cells. Here, we describe the development of an integrated software and hardware platform to automate Fluorescence-Activated Cell Sorting (FACS), a central step for the selection of cells displaying desired molecular attributes. Sorting large numbers of samples is laborious, and, to date, no automated system exists to sequentially manage FACS samples, likely owing to the need to tailor sorting conditions ("gating") to each individual sample. Our platform is built around a commercial instrument and integrates the handling and transfer of samples to and from the instrument, autonomous control of the instrument's software, and the algorithmic generation of sorting gates, resulting in walkaway functionality. Automation eliminates operator errors, standardizes gating conditions by eliminating operator-to-operator variations, and reduces hands-on labor by 93%. Moreover, our strategy for automating the operation of a commercial instrument control software in the absence of an Application Program Interface (API) exemplifies a universal solution for other instruments that lack an API. Our software and hardware designs are fully open-source and include step-by-step build documentation to contribute to a growing open ecosystem of tools for high-throughput cell biology.

Using Arduino to develop research competencies of students in school physics education

Arduino is a popular hardware and software platform that enables the development of various engineering projects, especially in physics and computer science. Arduino can be used as a powerful tool to foster the research competencies of secondary and high school students and stimulate their interest and creativity in science and technology. In this paper, we present some examples of research projects that involve using Arduino in the context of school physics education. These projects include designing and implementing measuring devices and installations for studying physical phenomena, such as uniformly accelerated motion, free fall, pendulum motion, etc. We also show how Arduino can improve existing projects and create new ones based on the students’ engineering ideas. We demonstrate the results of the experiments conducted by the students using their Arduino-based devices and discuss the benefits and challenges of using Arduino in school physics education.

Dynamic modelling and a dual vector modulated improved model predictive control with auto tuning feature of active front‐end converters for distributed energy resources

AbstractThe operational performance of grid‐connected active front‐end converters (AFEs) faces challenges arising from the intricate interplay among phase‐locked loop (PLL) non‐linearities, grid impedance, and conventional control strategies, resulting in compromised stability. This study introduces a refined approach to dynamic model predictive control (MPC) by integrating recursive least squares (RLS) for the precise estimation of physical model parameters, thereby addressing stability concerns. Unlike conventional methodologies, the proposed enhanced RLS‐based MPC approach, equipped with an auto‐tuning feature, allows for the design of controllers without a prerequisite understanding of exact external dynamics. Notably, this technique exhibits exceptional disturbance rejection capabilities. The evaluation of the cost function at each sampling interval facilitates the determination of optimal switching states based on predicted variables. Gate pulses for the switches of the AFEs are generated accordingly. Employing a simulation platform, the proposed control structure's performance across varied conditions is comprehensively assessed, encompassing alterations in grid impedance and system non‐linearities. The method adeptly integrates inherent non‐linearities within the system, showcasing exceptional robustness in diverse dynamic scenarios. To further substantiate the efficacy of the proposed control system over conventional approaches, simulation results are validated using a laboratory hardware platform equipped with Typhoon HIL and dSPACE real‐time emulators, providing tangible evidence of the proposed control system's effectiveness in real‐world hardware setups. The multifaceted approach, encompassing precise parameter estimation, predictive control, auto‐tuning, disturbance rejection, robust design, and real‐time evaluation, collectively establishes a resilient foundation for enhancing and maintaining the overall stability of the system across diverse operating scenarios.

Advanced MPC Control Strategy for Active Front-End Converters in Distributed Energy Resources

The stability of grid-connected active front-end converters (AFEs) is often compromised by the intricate interplay between inherent converter nonlinearities and grid dynamics. This article presents a novel approach to dynamic model predictive current control (MPC) by leveraging recursive least squares (RLS) to accurately estimate the properties of a physical model. This estimation mitigates stability issues, setting the stage for improved control. Unlike conventional methods, our proposed RLS-based MPC, enriched with an auto-tuning feature, empowers controller development without the upfront need for precise external dynamics. This not only ensures high disturbance rejection but also maintains a high-fidelity control performance, rendering the approach versatile for applications where obtaining or predicting precise external dynamics is challenging. At each sampling interval, a cost function is applied to predicted variables to discern optimal switching states. Through extensive simulation studies covering diverse grid impedance changes and system nonlinearities, we evaluate the controller’s effectiveness. To underscore its superiority over traditional controls, simulation results are validated on a laboratory hardware platform equipped with Typhoon HIL and dSPACE real-time emulators, further attesting to the robustness and practical viability of our proposed approach.

Highly parallel and ultra-low-power probabilistic reasoning with programmable gaussian-like memory transistors

Probabilistic inference in data-driven models is promising for predicting outputs and associated confidence levels, alleviating risks arising from overconfidence. However, implementing complex computations with minimal devices still remains challenging. Here, utilizing a heterojunction of p- and n-type semiconductors coupled with separate floating-gate configuration, a Gaussian-like memory transistor is proposed, where a programmable Gaussian-like current-voltage response is achieved within a single device. A separate floating-gate structure allows for exquisite control of the Gaussian-like current output to a significant extent through simple programming, with an over 10000 s retention performance and mechanical flexibility. This enables physical evaluation of complex distribution functions with the simplified circuit design and higher parallelism. Successful implementation for localization and obstacle avoidance tasks is demonstrated using Gaussian-like curves produced from Gaussian-like memory transistor. With its ultralow-power consumption, simplified design, and programmable Gaussian-like outputs, our 3-terminal Gaussian-like memory transistor holds potential as a hardware platform for probabilistic inference computing.

AraDQ: an automated digital phenotyping software for quantifying disease symptoms of flood-inoculated Arabidopsis seedlings

BackgroundPlant scientists have largely relied on pathogen growth assays and/or transcript analysis of stress-responsive genes for quantification of disease severity and susceptibility. These methods are destructive to plants, labor-intensive, and time-consuming, thereby limiting their application in real-time, large-scale studies. Image-based plant phenotyping is an alternative approach that enables automated measurement of various symptoms. However, most of the currently available plant image analysis tools require specific hardware platform and vendor specific software packages, and thus, are not suited for researchers who are not primarily focused on plant phenotyping. In this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis.ResultsHere, we present the Arabidopsis Disease Quantification (AraDQ) image analysis tool for examination of flood-inoculated Arabidopsis seedlings grown on plates containing plant growth media. It is a cross-platform application program with a user-friendly graphical interface that contains highly accurate deep neural networks for object detection and segmentation. The only prerequisite is that the input image should contain a fixed-sized 24-color balance card placed next to the objects of interest on a white background to ensure reliable and reproducible results, regardless of the image acquisition method. The image processing pipeline automatically calculates 10 different colors and morphological parameters for individual seedlings in the given image, and disease-associated phenotypic changes can be easily assessed by comparing plant images captured before and after infection. We conducted two case studies involving bacterial and plant mutants with reduced virulence and disease resistance capabilities, respectively, and thereby demonstrated that AraDQ can capture subtle changes in plant color and morphology with a high level of sensitivity.ConclusionsAraDQ offers a simple, fast, and accurate approach for image-based quantification of plant disease symptoms using various parameters. Its fully automated pipeline neither requires prior image processing nor costly hardware setups, allowing easy implementation of the software by researchers interested in digital phenotyping of diseased plants.

Circulating power flow restricted operation of the isolated bi-directional dual-active bridge DC-DC converter for battery charging applications

This paper presents the Non-Circulating Power Flow (NCPF) enabled Dual Active Bridge (DAB) converter controller strategy. The proposed DAB controller eliminates the non-circulating power flow for all the four conventional modulation algorithms, i.e., Single Phase Shift (SPS), Extended Phase Shift (EPS), Dual Phase Shift (DPS) and Triple Phase Shift (TPS) modulation schemes. This leads to lesser current stress and power loss across the DAB converter switches due to reduced peak inverse current. The proposed controller strategy finds various applications, such as in a Solid-State Transformer (SST) enabled microgrid, or for Electric Vehicle (EV) Energy Storage Unit (ESU: battery, supercapacitor) integrations, etc. The proposed NCPF operation reduces battery (and supercapacitor) current stress by upto 71.8 %, and improves operative environment, thus fostering its health and lifecycle. In NCPF mode, if the rating of installed switches (current and voltage ratings) remains unchanged, its power transfer capability is marginally reduced by mere 10.0 %. In addition to this, the operating range (i.e., controlling range of phase shift (Φ) and modulation index (m)) remains unaffected. This paper summarizes the DAB NCPF operation in restricted power flow mode as a function of Φ and m through 3-D and 4-D graphs. Finally, working of NCPF DAB is tested via both simulation and hardware platforms. Comparative results are shown for conventional and proposed modulation schemes on hardware platform, to showcase the elimination of circulating current in both forward and reverse DAB operating modes. It is inferred that proposed controller is worthy of real-world implementations.

An integrated energy storage framework with significant energy management and absorption mechanism for machine learning assisted electric vehicle application

Regarding environmental friendliness, low maintenance needs, and statuses as a renewable technology, Hybrid Electric Vehicles (HEV) have become more and more popular around the world. In this, the energy management system is crucial for the effective storage of power and regulation of the energy flow system. As a result, Hybrid Energy Storage Systems (HESS) has increased interest due to their superior capabilities in system performance and battery capacity when compared to solo energy sources. Additionally, the primary problem interaction applications, including such battery electric vehicles, are the energy storage system. Multiple energy storage technologies, including battery packs, flywheels, super-capacitors and fuel cells, are combined into a HESS due to their complementing properties. The goal of this setup is to make renewable energy sources more reliable by storing power generated from intermittent sources or by providing backup energy generation from traditional energy sources. A HESS could be utilized as an alternate energy storage system to help them make up for their lack of power density. HESS needs a smart Energy Management System (EMS) to function properly since it combines the dynamic characteristics of a battery and a SuperCapacitor (SC). The motive of the study is to suggest an actual power management control system to accomplish these objectives. The plan is built using a wavelet transform, deep learning mechanism, and fuzzy logic together. A useful tool for separating the various frequency elements of a load's power requirements to reflect the properties of a battery or supercapacitor is the wavelet transform. It is challenging to immediately apply it in a system, though. Because of this, the traditional optimization-model-based facility energy management system encounters substantial difficulties with online forecast and calculation. To solve this problem, the paper proposes a ML technique dependent on a Long Short-Term Memory (LSTM). The suggested control system structure allows for the separation of the offline and online stages of the LSTM technique. The LSTM is being used to map states (inputs) to decisions (outputs) based on system training during the offline stage. As a result, the supercapacitor receives an online calculation and distribution of the high-frequency power requirement. The SOC of the supercapacitor is kept within the appropriate range via fuzzy logic control. To evaluate the efficacy of the suggested energy management control technique, a 70 V battery with 92 V supercapacitor hybrid energy storage devices for hardware platforms have been created.