Data Processing Unit Research Articles

Design of ITER neutron flux monitor system back-end electronics

The primary aim of the International Thermonuclear Experimental Reactor (ITER) Neutron Flux Monitor (NFM) system is to diagnose the total neutron emission rate and fusion power of the plasma fusion reaction. The neutron flux is measured directly by the system and the fusion power can be calculated according to the neutron flux result. The NFM system is composed of detectors and back-end electronics. This paper focuses on the work of NFM#7 back-end electronics design and development. The back-end electronics consist mainly of four parts: Firstly, the pre-amplifiers are used to charge integrate and amplify the signal of fission chambers. Secondly, the output of pre-amplifiers will be transmitted to Signal Processing Unit (SPU) for analog signal conditioning, data processing, and measurement result uploading. Thirdly, pre-amplifiers and fission chambers have different electricity sources. Pre-amplifiers receive electricity generated from low voltage power whilst fission chambers receive electricity generated from high voltage power. Lastly, the Instrument and Control (I&C) is designed as the interface between NFM hardware and ITER central system and its functions are configuration & control, monitoring, calibration, data communication & archiving and clock synchronization. All the four parts are well integrated and tested in neutron field. The system design also follows the guidelines and standards of ITER.

IoT based Optimal Liquid Metal Pipeline Damage Detection Using Hybrid Soft Computing Techniques

The pipeline plays an important role in transporting liquid metals over long distances. However, due to the harsh conditions on the construction site, the pipelines are always exposed to structural damage due to corrosion, damage, etc. These pipelines are often vulnerable to natural and third-party events such as explosions, earthquakes, explosions, drilling and vehicle traffic. Methods for monitoring liquid metal pipes include the use of continuous pipe assessments and pipe integration management systems. One of the biggest challenges faced by liquid metal pipeline system in the past has been the real-time monitoring of seamless pipes at certain locations. Previous studies of liquid metal pipeline monitoring have rarely focused on real-time wireless data transmission and data monitoring in the Internet of Things (IoT) operating system. In this paper, we propose an optimal liquid metal pipeline damage detection using IoT sensor platform and hybrid soft computing techniques. The proposed work consists of two fold systems such as data collection and data processing unit. In data collection unit, we utilize IoT sensors deployment with the multimedia sensors to gather liquid metal pipeline images which improve the detection accuracy. In data processing unit, we introduce a hybrid cat hunting based neural network (hybrid CHNN) to detect and localize the pipeline damages/cracks to avoid unwanted leakage and accidents. Finally, we evaluate the performance of our proposed hybrid CHNN detector with the different test samples and the simulation results are compared with the existing stateof- art pipeline damage detectors in terms of accuracy, precision, Recall and F-measure.

Optimized design of battery pole control system based on dual-chip architecture.

At present, the global demand for lithium batteries is still in a high growth state, and the traditional lithium battery pole mill control system is still dominated by ARM (Artificial Intelligence Enhanced Computing), DSP (Digital Signal Processing), and other single-chip control methods. There are problems such as poor anti-interference ability and insufficient real-time online analysis of production data. This paper adopts the dual-chip control system architecture based on "ARM+DSP", starting from the mechanical characteristics and operating signal features of the pole mill. The hardware system adopts a three-unit joint control hardware structure, which separates the control unit from the data processing unit and improves the operation of the system. The software system adopts fuzzy PID algorithm to realize deflection control and tension control, and verifies that the Fuzzy PID (Proportion Integration Differentiation) control algorithm can effectively improve the anti-interference ability of the deflection system and tension system. The results show that the data loss rate is low with the SPI communication between DSP and ARM. The tension error of the "ARM+DSP" control system does not exceed 5%, and the deviation of the correction band is within ±4mm. The dedicated dual-chip hardware architecture effectively improves the robustness and operation efficiency of the pole mill, solves the problem of low tension control accuracy, and provides a theoretical basis for the application of the dual-roll mill.

The Real-Time Prediction of Product Quality Based on the Equipment Parameters in a Smart Factory

Product quality is an important part of enterprise competitiveness. Product processing is the key process of quality formation. In smart factories, the improvement of data acquisition and processing capability provides a basis for data-based quality control. In order to reduce the occurrence of product quality problems, we abstracted the product processing process as a data processing unit, abstracted the process of changing the product quality state as a process of the processing quality characteristics data, divided the measured value of quality characteristics into three states according to the fluctuation of the measured value of product quality characteristics, and then the classification model of process equipment parameters was established. The experimental results show that the error rate of the real-time dynamic prediction of quality characteristics based on equipment parameters was acceptable, and its prediction could be used as a reference in real production. The research could be applied in product quality prediction, production process simulation, digital twin and other fields.

Bio-Inspired Electronic Eyes and Synaptic Photodetectors for Mobile Artificial Vision

Conventional imaging and data processing devices are not ideal for mobile artificial vision applications, such as vision systems for drones and robots, because of the heavy and bulky multilens optics in the camera modules. Furthermore, the physically isolated image data processing units of conventional systems induce large power consumption and data latency. For mobile artificial vision applications, electronic eyes, including neuromorphic ones, have been developed inspired by biological eyes and neural networks. Here, we summarize the development of such bio-inspired electronic eyes and synaptic photodetectors (PDs). Bio-inspired electronic eyes, typically consisting of curved image sensor arrays, enable aberration-free imaging and module size miniaturization in addition to other advantageous optical features, such as wide field-of-view and deep depth-of-field. Furthermore, photodetecting devices with synaptic properties can efficiently enhance image contrast because of photon-triggered synaptic plasticity. Therefore, the signal-to-noise ratio of the acquired image can be enhanced, which facilitates efficient image recognition for machine vision. A brief summary of the remaining challenges and prospects concludes this review.

Design of FFNN architecture for power quality analysis and its complexity challenges on FPGA

As we all know, power quality (PQ) issues are a major concern these days. Field programmable gate array (FPGA) are essential in PQ analysis, particularly in smart meters for data processing, storage, and transmission. One of the most significant advantages of FPGA is its reconfigurability, with vast hardware resources that can be used to implement complex as well as time-critical data processing units. Because the FPGA architecture supports fixed point arithmetic, data loss occurs in the data path unit, necessitating the realization of the PQ event detection module, and classification model to be more accurate than software implementation algorithms. The majority of the work reported, with feed forward neural network (FFNN) structure occupying large number of multipliers and adders for classification, most of the work reported has not addressed to minimize the data path resources for FFNN instead have addressed in improving classification accuracy. Based on these issues, this paper addresses the implementation challenges in FFNN architecture design by proposing improved and fast architectures. The proposed FFNN architecture design using optimum resources. The FFNN based classifier are designed to perform PQ event detection and classification with 99.5% accuracy. The FFNN processor operates at maximum frequency of 238 MHz.

Optical Microfibers for Sensing Proximity and Contact in Human-Machine Interfaces.

The monitoring of proximity-contact events is essential for human-machine interactions, intelligent robots, and healthcare monitoring. We report a dual-modal sensor made with two functionalized optical microfibers (MFs), which is inspired by the somatosensory system of human skin. The integrated sensor with a hierarchical structure gradationally detects finger approaching and touching by measuring the relative humidity (RH) and force-triggered light intensity variations. Specifically, the RH sensory part shows enhanced evanescent absorption, achieving a sensitive RH measurement with a fast response (110 ms), a high resolution (0.11%RH), and a wide working range (10-100%RH). Enabled by the transition from guided modes into radiation modes of the waveguiding MF, the force sensory part exhibits a high sensitivity (6.2%/kPa) and a fast response (up to 1.5 kHz). By using a real-time data processing unit, the proximity-contact sensor (PCS) achieves continuous detection of the full-contact events, including finger approaching, contacting, pressing, releasing, and leaving. As a proof of concept, the electromagnetic-interference-free PCS enables a smart switch system to recognize the proximity and contact of bare/gloved fingers. Moreover, skin humidity detection and respiration monitoring are realized. These initial results pave the way toward a category of optical collaborative devices ranging from human-machine interfaces to multifunctional on-skin healthcare sensors.

A novel utilization-aware and power-delay-aware intelligent DMA controller for video streaming used in AI applications

PurposeIn real-time entertainment processing applications, processing of the multiple data streams demands high efficient multiple transfers, which leads to the computational overhead for system-on-chip (SoC), which runs the artificial intelligence algorithms. High-performance direct memory access controller (DMAC) is incorporated in SoC to perform the multiple data transfers without the participation of main processors. But achieving the area-efficient and power-aware DMAC suitable for streaming the multiple data remains to be a daunting challenge among the researchers.Design/methodology/approachThe purpose of this paper to provide the DMA operations without intervention of central processing unit (CPU) for bulk video data transmissions.FindingsThe proposed DMAC has been developed based on the hybrid advanced extensible interface (AXI)-PCI bus subsystem to handle the multiple data streams from the video sources. The proposed model consists of bus selector module, user control signal, status register, DMA-supported address and AXI-PCI subsystems to achieve better performance in analysing the video frames.Originality/valueThe extensive experimentation is carried out with Xilinx Zynq SoC architecture using Very High Speed integrated circuit hardware description language (VHDL) programming, and performance metrics such as utilization area and power are calculated and compared with the other existing DMA controllers such as Scatter-DMA, Gather-DMA and Enhanced DMA. Simulation results demonstrate that the proposed DMAC has outperformed other existing DMAC in terms of less area, less delay and power, which makes the proposed model suitable for streaming multiple video streams.

Securing InfiniBand Networks with the Bluefield-2 Data Processing Unit

Interest in securing InfiniBand networks with encryption is growing. However, the performance benefit realized by InfiniBand’s use of Direct Memory Access (DMA) to bypass the kernel and avoid intervention from host Central Processing Units (CPUs) is at odds with IP datagram encryption techniques. Encryption forces data through the CPU before transmission and decryption, incurring multiple clock cycles. The Bluefield-2 Data Processing Unit (DPU) is Nvidia-Mellanox’s latest system on chip that combines a high-performance, programmable processor, network interface card (NIC), and flexible hardware accelerators. This research characterizes the Bluefield-2’s capability to accelerate IPsec encryption in hardware. Results show that the Bluefield-2’s hardware accelerators are capable of supporting a secure IPsec tunnel with a throughput of nearly 16 Gb/s. Offloading IPsec encryption operations to the hardware accelerators on the Bluefield-2 is a promising method for adding confidentiality, integrity, and authentication to InfiniBand networks.

Optimizing Distributed DNN Training Using CPUs and BlueField-2 DPUs

The deep learning (DL) training process consists of multiple phases—data augmentation, training, and validation of the trained model. Traditionally, these phases are executed either on the central processing units or graphics processing units in a serial fashion due to lack of additional computing resources to offload independent phases of DL training. Recently, Mellanox/NVIDIA introduced the BlueField-2 data processing units (DPUs), which combine the advanced capabilities of traditional application-specific-integrated-circuit-based network adapters with an array of ARM processors. In this article, we explore how to take advantage of the additional ARM cores on the BlueField-2 DPUs. We propose and evaluate multiple novel designs to efficiently offload the phases of DL training to the DPUs. Our experimental results show that the proposed designs are able to deliver up to 17.5% improvement in overall DL training time. To the best of our knowledge, this is the first work to explore the use of DPUs to accelerate DL training.

Realization of Wireless Sensors and Intelligent Computer Aided Teaching in Physical Education and Training

Wireless sensors integrate a variety of high-tech, powered by engines to form network nodes, and randomly distributed sensors and data processing units form a network through a certain rule. The wireless sensor can directly transmit the collected digital signal to the computer for analysis and processing. Intelligent computer refers to a computer that can simulate and expand artificial intelligence. It is a dynamically developing concept and always represents the most advanced level of computer development. This article aims to study the role of wireless sensors and intelligent computer-assisted teaching in physical education and training. It is hoped that wireless sensors and intelligent computer technology will assist in physical training. This article briefly summarizes the status quo of the use of wireless sensors, briefly introduces the design and use of various wireless sensors, and briefly explains their differences. This article explains the theoretical basis and principles of use of intelligent computers. Aiming at the shortcomings of traditional multimedia courseware in the teaching process, an application idea is proposed, and a modular design method is adopted to design a computer-assisted teaching system based on integrated ware. The test results of this paper show that 40% of the students were very satisfied with the intelligent PC-assisted educational delivery system in PE lessons, 67% were satisfied with the usage of the intelligent CC-AS in PE lessons and 22% disagreed with the usage of the intelligent CC-AS in PE lessons; 14.2% of the teachers often used the intelligent CC-AS in PE lessons and 30% of teachers occasionally used ICDS in PE lessons and another 12% never used ICDS in PE lessons.

Telemedicine in Cardiology: Modern Technologies to Improve Cardiovascular Patients' Outcomes-A Narrative Review.

The registration of physical signals has long been an important part of cardiological diagnostics. Current technology makes it possible to send large amounts of data to remote locations. Solutions that enable diagnosis and treatment without direct contact with patients are of enormous value, especially during the COVID-19 outbreak, as the elderly require special protection. The most important examples of telemonitoring in cardiology include the use of implanted devices such as pacemakers and defibrillators, as well as wearable sensors and data processing units. The arrythmia detection and monitoring patients with heart failure are the best studied in the clinical setting, although in many instances we still lack clear evidence of benefits of remote approaches vs. standard care. Monitoring for ischemia is less well studied. It is clear however that the economic and organizational gains of telemonitoring for healthcare systems are substantial. Both patients and healthcare professionals have expressed an enormous demand for the further development of such technologies. In addition to these subjects, in this paper we also describe the safety concerns associated with transmitting and storing potentially sensitive personal data.

Real Estate Advisory Drone (READ): system for autonomous indoor space appraisals, based on Deep Learning and Visual Inertial Odometry

The present paper describes the development of a mobile platform as a support of the real estate appraisal procedure. Currently, the estate evaluation is performed by an expert that manually collects data, performs measurements, and grabs pictures of the inspected unit to finally evaluate its commercial value. The READ project aims at automatizing this process by developing a solution based on a mobile unit (drone or tablet) able to navigate the indoor environment and record data, which will be later processed on the cloud. To accomplish all these tasks, the platform is equipped with cameras, a LiDAR sensor, and a data process unit, with the goal of 1) understanding its motion and localization; 2) reconstructing a 3D map of the inspected space; 3) performing image-based analyses applying AI algorithms enabling the identification of the indoor space (e.g. bedroom or kitchen), the counting and the classification of furniture objects, and the detection of building imperfections or frauds. Tests have been performed in different scenarios providing promising results, laying the foundations for bringing these technologies into a real operational context.

The Design of Low-concentration Dust Detection System Based on Mie Scattering Theory

According to the requirement of low-concentration dust emission in China, this paper designed a low-cost and high-precision dust detection system based on Mie scattering. Through theoretical analysis, the relationship between the intensity of scattered light and the concentration of soot is obtained. The laser emitting unit, signal receiving unit and STM32 data processing unit are designed. The driving principle of laser modulation and the principle of selective signal amplification of weak signal are analyzed. The host computer program is written with LabVIEW software, and the test data are displayed on the front panel of the host computer. The experimental results show that this system has high sensitivity, fast response, good repeatability, and can be measured online for a long time, which means this system has good practicability.

A Preliminary Study on Flight Characteristics of the Longhorn Date Palm Stem Borer Jebusaea hammerschmidtii (Reiche 1878) (Coleoptera: Cerambycidae) Using a Computerized Flight Mill

The longhorn date palm stem borer, Jebusaea hammerschmidtii (Reiche), is a highly destructive beetle of edible date palm Phoenix dactylifera L. The flight capabilities and dispersal potential of this beetle are unknown, which hinders the planning for its proper management in date palm groves. In this study, the flight propensity of this insect pest was investigated using a computerized flight-testing system. The flight system consisted of a flight mill, a digitally controlled testing chamber, and a data logging and processing unit with a USB digital oscilloscope connected with a laptop. A total of 40 field-collected beetles of unknown sex and mating history were used in the experiments; about 34% of this number failed to fly on the flight mill. The relationship between temperature treatments (°C) and flight speed (m/min), cumulative flight time (min), and cumulative flight distance (km) of the test beetles were studied using regression equations with correlation coefficients (R2) of 0.91, 0.98, and 0.98, respectively. The maximum cumulative distance flown by the beetle was 11.5 km at a temperature of 35 °C, and a minimum distance of 2.4 km was recorded at 45 °C. The flight threshold of the beetle was 20 °C, at which flight activities ceased utterly. The velocity of the beetle increased with increasing temperature and reached a maximum of 107 m/min at 40 °C, before starting to decline. The obtained information on the flight characteristics of the J. hammerschmidtii may aid in understanding the dispersal of this pest in date palm plantations and in setting up management strategies.

RETRACTED ARTICLE: Computer development based embedded systems in precision agriculture: tools and application

This article has been retracted from publication in the Taylor & Francis journal, Acta Agriculturae Scandinavica, Section B — Soil & Plant Science. Following publication, concerns were raised by multiple third-parties around the content of the special issue and the decision-making process. Following an investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editor-in-Chief, it was confirmed that this article included in Special Issue titled “ Envisage Computer Modelling and Statistics for Agriculture”, guest edited by Gunasekaran Manogaran was not peer-reviewed appropriately, in line with the Journal's peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines.

Random Partition Based Adaptive Distributed Kernelized SVM for Big Data

In this paper, we present a distributed classification technique for big data by efficiently using distributed storage architecture and data processing units of a cluster. While handling such large data, the existing approaches consider specific data partitioning techniques which demand complete data be processed before partitioning. This leads to an excessive overhead of high computation and data communication. The proposed method does not require any pre-structured data partitioning technique and is also adaptive to big data mining tools. We hypothesize that an effective aggregation of the information generated from data partitions by subprocesses of the complete learning process can lead to accurate prediction results while reducing the overall time complexity. We build three SVM based classifiers, namely one phase voting SVM (1PVSVM), two phase voting SVM (2PVSVM), and similarity based SVM (SIMSVM). Each of these classifiers utilizes the support vectors as the local information to construct the synthesized learner for efficiently reducing the training time and ensuring minimal communication between processing units. In this context, an extensive empirical analysis demonstrates the effectiveness of our classifiers when compared to other existing approaches on several benchmark datasets. However, among existing methods and three of our proposed (1PVSVM, 2PVSIM, and SIMSVM) methods, SIMSVM is the most efficient. Considering MNIST dataset, SIMSVM achieves an average speedup ratio of 0.78 and minimum scalability of 73% when the data size is scaled up to 10 times. It also retains high accuracy (99%) similar to centralized approaches.

Experimental Demonstration and Evaluation of BCH-Coded UWOC Link for Power-Efficient Underwater Sensor Nodes

Mobile nodes can encounter many challenges in underwater environments during communication due to turbidity, suspended particles, small bubbles, and turbulence. These factors cause absorption and scattering, ultimately corrupting optical signals. Error-correcting codes can be used to correct certain corrupted bits and thus reduce the errors in a channel. In this work, a Bose-Chaudhuri-Hocquenghem (BCH) (31, 16)-coded underwater wireless optical communication (UWOC) system is proposed to enhance the communication performance between power-efficient sensor nodes. The proposed BCH-coded system needs a reasonable amount of computing capability and is powered by a battery, enabling the node to have an on-site data processing unit and untethered communication. The encoder and decoder algorithms of the BCH code are implemented on the Embedded C software and coded to run on an Atmel ATmega128A micro-controller. The system&#x2019;s performance is evaluated by emulating the effects of scattering and absorption, noise due to surrounding and ambient light, turbidity, air bubbles, and turbulence in a natural underwater environment. Moreover, a 0.5 Mbps BCH-coded link achieves a 93.5% PSR, which is 6% better than that of the uncoded system, at a moderate turbidity level of 64 NTU, in the presence of weak turbulence (induced by a pump at a 2.5 <i>L/min</i> displacement rate) and air bubbles (generated by an aerating jet at an airflow rate of about 1.2 <i>L/min</i>).

Two-Chamber Unit for Detecting Parameters in the Neutron Flux

The detection unit contains two neutron detectors — fission chambers KNT-31 or KNT 31–1, in which the radiator sensitive to neutron radiation is 235U (concentration of 235U in the radiator is more than 90%). The detection unit also includes two radiation- resistant preamplifiers (primary electronic equipment), which are connected to the cable transmission line to the secondary equipment, which captures the moment of pulses from the reaction of neutrons with the fission chamber radiator and save information about the time of these pulses. Further processing of the intervals between neutron events (intervals between pulses) can be done according to individual methods of the experimenter. The presence of a data processing unit type B-471 significantly expands the capabilities of the experimenter on the conclusions of statistical analysis of data and forecasting the development of emergencies in fuel-containing materials of the Shelter object. The presence of time intervals in the nanosecond range of values with sufficient probability can be classified as pulses of registration of instantaneous fission neutrons, and a change in their number may indicate a change in the effective multiplication factor of fuel containing materials. The analysis of the statistical properties of the flow of neutron event recording intervals by the electronic equipment makes it possible to predict the occurrence of a self-sustaining chain reaction. This is the main task of the Shelter object nuclear safety system. The proposed configuration of the CPI is promising and can be expanded by the number of fission chambers and electronic neutron detection units for more accurate diagnosis and prediction of the state of fissile nuclear materials.

Developing the Test Bench Software for the Technical Inspection of Volumetric Hydraulic Drives

Introduction. The article describes the test bench specialized software, developed for technical inspection of domestic and foreign volumetric hydraulic drives in repair enterprises and service centers. The results of bench tests using a hydraulic bench and software are presented. Materials and Methods For the application software development, G graphical programming language of the Laboratory Virtual Instrumentation Engineering Workbench (Lab- View) programming environment developed by the National Instruments Company has been used. The reliability of the results obtained has been confirmed by a series of bench tests of domestic and foreign volumetric hydraulic drives. Results. There has been developed and implemented new test bench software for the technical inspection of hydraulic drives of different models from domestic and foreign manufacturers in repair plants and service centers. The software is used to capture, process and store data from the sensors during testing, to calculate and output measurement data, to conduct nonlaboratory analysis of the testing process in graphical, textual and video formats, to calibrate sensors, and to store the test results in the electronic library system. The specialized software runs under the Microsoft Windows 7 x86 (32-bit) operating system and is installed on a personal computer of the hydraulic bench data processing unit. The developed software is characterized by the ergonomics of the user interface, the ability to control all the parameters of diagnosing the tested volumetric hydraulic drives. Discussion and Conclusion. Specialized software and the test bench permit high accurate monitoring of all technical condition parameters of the most common domestic and foreign volumetric hydraulic drives in accordance with the requirements of the manufacturers in the repair enterprises and service centers.