Hardware Functions Research Articles

Dynamical memristive neural networks and associative self-learning architectures using biomimetic devices.

While there is an abundance of research on neural networks that are "inspired" by the brain, few mimic the critical temporal compute features that allow the brain to efficiently perform complex computations. Even fewer methods emulate the heterogeneity of learning produced by biological neurons. Memory devices, such as memristors, are also investigated for their potential to implement neuronal functions in electronic hardware. However, memristors in computing architectures typically operate as non-volatile memories, either as storage or as the weights in a multiply-and-accumulate function that requires direct access to manipulate memristance via a costly learning algorithm. Hence, the integration of memristors into architectures as time-dependent computational units is studied, starting with the development of a compact and versatile mathematical model that is capable of emulating flux-linkage controlled analog (FLCA) memristors and their unique temporal characteristics. The proposed model, which is validated against experimental FLCA LixNbO2 intercalation devices, is used to create memristive circuits that mimic neuronal behavior such as desensitization, paired-pulse facilitation, and spike-timing-dependent plasticity. The model is used to demonstrate building blocks of biomimetic learning via dynamical memristive circuits that implement biomimetic learning rules in a self-training neural network, with dynamical memristive weights that are capable of associative lifelong learning. Successful training of the dynamical memristive neural network to perform image classification of handwritten digits is shown, including lifelong learning by having the dynamical memristive network relearn different characters in succession. An analog computing architecture that learns to associate input-to-input correlations is also introduced, with examples demonstrating image classification and pattern recognition without convolution. The biomimetic functions shown in this paper result from fully ion-driven memristive circuits devoid of integrating capacitors and thus are instructive for exploiting the immense potential of memristive technology for neuromorphic computation in hardware and allowing a common architecture to be applied to a wide range of learning rules, including STDP, magnitude, frequency, and pulse shape among others, to enable an inorganic implementation of the complex heterogeneity of biological neural systems.

Cloud-Edge Architecture With Virtualized Hardware Functionality for Real-Time Diagnosis of Transients in Smart Grids

Edge Cloud is providing unprecedented opportunities for IoT and WAMS (Wide Area Monitoring Systems) in electrical grid operation. It is an orchestrated environment able to address low latency events through appropriate edge-cloud computing configurations.Transient State Estimation (TSE) is a key monitoring tool for capturing a reliable knowledge of the Smart Grid status in real-time, given the impediments introduced by the increasing penetration of Distributed Energy Resources in the energy mix. Frequency response anomalies, large scale transients, and voltage swings can be captured by TSE for real time or post failure data analytics. This work presents a cloud edge framework for the efficient calculation of TSE which, albeit its benefits, demands high computational resources at the edge (close to the measurement units) along with ultra low latency communications. The framework enables TSE as a service through the coordination of Virtual Machines (VMs) running on virtualized infrastructure and other non-virtualized physical nodes. In order to support the stringent time requirements, part of the TSE is offloaded to dedicated hardware acceleration units (FPGA). The proposed TSE framework is validated using an IEEE 30-bus, and the results show a significant superiority in terms of total latency compared to conventional cloud and edge deployments.

Detailed Study on Electronic Data Energy Consumption Monitoring System Based on the Construction of Internet of Things

Background: Buildings, in the traditional sense, are becoming increasingly incapable of meeting modern humans’ pursuit of high-quality living and working environments. With the increasing pace of urban development, modern buildings are becoming increasingly popular. Objective: We investigate an electronic data energy consumption monitoring system based on the construction of Internet of Things for the airport building. Aim: A model based on Dynamic Programming Genetic Algorithm (DPGA) is proposed to generate parameter and service models based on user scenarios. Methods: Two definitions are presented for the communication format between the middleware and the Wireless Sensor Network (WSN); the software and hardware functions of the nodes of the system's WSN are designed, and part of the node is implemented. Finally, the specific implementation of the application program interface and data interface between the modules of the middleware system are described considering the internal environment of a typical office building as an example. The deployment plan of system nodes in specific environments and the division of similar areas are discussed Results: The platform can strictly monitor and manage energy-consuming equipment. Conclusion: The proposed model can be used to achieve reasonable distribution of energy consumption, energy saving, and humanised and automated energy consumption monitoring functions in the office areas of large office buildings in modern cities.

Runtime Testability on Autonomous System

More than ever, autonomous systems that are critical to everyday life, social infrastructure, and social security are increasingly dependent on sophisticated hardware computer systems. As the computer hardware systems become increasingly complex and semiconductor manufacturing technology becomes continuously miniaturized (especially in several nanometer technologies), there is a corresponding increased vulnerability to system malfunction caused by runtime hardware defects. Detecting defects that will be generated in postdeployment time is impossible in the predeployment verification. This is the reason why some lifetime testabilities are necessary, especially for autonomous systems requiring extremely high-reliable functionalities. This article proposes a hardware architecture that extends the pre-deployment mass production verification (such as the scan-test) to the lifetime of the hardware systems, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">to guarantee the mass production verification level integrity for the hardware systems in their lifetime</i> . In addition, this article explains how to recover hardware functionalities compromised by runtime defects.

Optimized algorithms and architectures for fast non-cryptographic hash functions in hardware

The design and implementation of eight novel non-cryptographic hash functions is proposed in this paper. Those can suitably be employed in networking and security applications that require fast lookup and/or counting architectures. Our approach is inspired by the design of the existing non-cryptographic hash function Xoodoo-NC, which is constructed through the concatenation of several Xoodoo permutations. In a similar way, this work concatenates several rounds of symmetric-key ciphers to construct non-cryptographic hash functions. The goal is to achieve high performance while maintaining the avalanche properties. Meeting the avalanche criteria is necessary to yield a significant change in the output of hash functions even for the slightest change in the input. The number of rounds required to achieve satisfactory avalanche scores for each cipher is simulated. We implement the corresponding non-cryptographic hash functions on an FPGA and we synthesize them on ASIC to evaluate the occupied resources and the performance. This paper evaluates the performance and area of ASIC implementations optimized for high operating speed. This evaluation is done on the 65 nm TSMC technology. One of the proposed non-cryptographic hash functions, namely PHOTON-NC, outperforms all previously proposed non-cryptographic hash functions in terms of throughput and latency on FPGA, in exchange for an acceptable increase in FPGA resources, while Xoodoo-NC proves to have the highest performance on ASIC.

Reliability Assessment of the Configuration of Dynamic Uninterruptible Power Sources: A Case of Data Centers

The number of data centers worldwide is increasing year by year, mostly because of the development of cloud services and applications. In the near future, the rate of construction of data centers will grow, with a corresponding increase in their electrical energy consumption. The requirements of the reliability of the electrical power supply of data centers are one of the highest among industrial power consumers, since uninterrupted power supply is critically important for the continuous functioning of server hardware. The assessment of electrical power supply reliability is one of the most important parts of the design process of data centers. However, the speed of the development of new power equipment does not always make it possible to use classical probabilistic and statistical methods for reliability assessment. Therefore, the development of new methods for reliability assessment based on alternative approaches, which can eliminate the disadvantages of probabilistic and statistical methods, are of great interest. This paper discusses the alternative method for analyzing the reliability of electrical power supply for the case of data centers. The method defines the reliability through the internal information of the system that characterizes the system’s topology, flows of information, energy, and matter in the system.

Global trigger versatile module for ATLAS phase-II upgrade

The Global Trigger Versatile Module (GVM) has been developed within the framework of the ATLAS detector Phase-II upgrade. The GVM acts as an auxiliary hardware component that can be used for development, testing and operational purposes within and beyond the ATLAS Global Trigger in projects requiring high bandwidth and processing capabilities. The module is designed to host an advanced Xilinx Ultrascale+ VU13P FPGA and Finisar BOA optical modules, running at high data rates up to 25.8 Gb/s, as well as other hardware resources needed for the Global Trigger, located on a high-density PCB, optimized for high-speed data transmission. A testing program of the Global Trigger Versatile Module includes verification of the main hardware functionality of the module, performance evaluation of the high-speed optical modules and the FPGA, and Global Common Module development firmware tests.

The level of digital literacy ability of elementary school students

Digital literacy skills are mandatory for students to master so that they can process information wisely. The purpose of this research is to determine the level of digital literacy of elementary school students. The research method used in this research is quantitative. The sample of this research is 86 elementary students in Laweyan Sub-district, Surakarta, Indonesia. The data collection technique used is a test that validated by an expert. The data analysis used in this research is descriptive statistical analysis. The result of this study shows that the students’ level of ability in digital literacy is good with the fulfillment of five indicators, namely the use of technology reaches 79.75% or students can analyze the functions of hardware and software in technology, the management of information on digital media reaches 70% or the students can search for valid data sources, social networking reaches 82.25% or students can design and participate in digital technology, online safety reaches 83.33% or students can protect their personal data, and students can identify impacts of technology with percentage reaches 72%. The result of this research can be used as a reference to measure the level of students' digital literacy. Keywords: Digital literacy, students, elementary school

Development of a pulse-width-discriminating 3He position-sensitive detector system

In many neutron scattering experiments, 3He-gas position-sensitive detectors (PSDs) are employed to obtain high-quality data. However, the exact position where a neutron is detected cannot be determined if two or more neutrons are simultaneously captured at different positions. This results in noise and degrades the quality of the data. In particular, such noise is a serious source of spurious scattering in inelastic neutron scattering instruments equipped with a large number of long PSDs recently developed at pulsed neutron sources. Herein, we introduce a pulse-width-discriminating PSD system that monitors the pulse width and height of the collected data. The system utilizes previously developed neutron-readout boards and removes instances of two or more simultaneous captures from the data to significantly improve the performance of PSDs. We also propose a new program to monitor the pulse width from PSD data using a hardware function implemented for other purposes. We confirm that the noise decreases to a level almost equal to that of the background. Although the developed program is applied to an inelastic scattering experiment, it is applicable to other types of experiments in which mispositioned signals should be eliminated as noise.

Innovative research on urban public art design from the view of interactive experience

with the continuous progress of social technology, people’s demand in the material, spiritual experience and living environment has been further promoted. The urban infrastructure and other hardware functions have been achieve.Therefore, people began to develop urban public art design, in order to further promote the urban beautification environment, and ensure the quality of people’s living environment. Nowadays, urban public art design has drawn the social development attention. This paper put forward the combination of technology and art, interactive experience enhancement, public participation and information design innovation direction., through the analysis to urban public art design innovation research on the perspective of interactive experience from the domestic and foreign cases.

КОНЦЕПТУАЛЬНАЯ И ТЕОРЕТИКО-МНОЖЕСТВЕННАЯ МОДЕЛИ ЗАДАЧИ ФУНКЦИОНИРОВАНИЯ И ПРИМЕНЕНИЯ СИСТЕМОТЕХНИЧЕСКИХ РЕШЕНИЙ ДЛЯ ПРОБООТБОРА ДОННЫХ ОТЛОЖЕНИЙ

The modernization of limnological equipment used for the sampling of bottom sediments is systematically considered. Conceptual and set-theoretical models of the functioning and application of bottom grab sampling are proposed. A classification of existing samplers is given, their distinctive characteristics are considered, and a historical overview of upgrades is carried out. The task of designing acceptable system engineering solutions for bottom grab sampling is reduced to finding options that meet functional and ergonomic criteria. The structure and functions of bottom grab sampling software and hardware are considered taking into account the cost of the entire life cycle of the equipment. The importance of taking into account the ergonomics and safety of the use of the measuring instruments and sampling equipment during their design and modernization is emphasized. The version of the bottom grab sampler is distinguished by the use of sampling quality analysis when automating the operation of buckets. Bottom grab sampling was successfully tested during an expedition on Lake Ladoga. The collected material and the changes in acceleration at the time of the landing of the bottom grab sampler on the bottom sediment is used to configure the structure and train the artificial neural network, on the basis of which the on-board system for recognizing types of bottom surfaces.

An overview of smart grid smart meters with fault detection

Smart meters (SM) arrangement play an essential role in Smart Grid (SG), serving as advanced energy meters crucial for monitoring and managing energy consumption. This paper provides an in-depth exploration of the significance, functions, and technological aspects of smart meters within the context of the SG. Smart meters acquire data from endusers' load devices, offering a comprehensive measurement of consumers' energy consumption. The obtained information is then communicated to utility companies and system operators, facilitating efficient energy management. The integration of control devices and a number of sensors, supported by a dedicated communication infrastructure, enhances the reliability and functionality of smart meters. The paper summarizes hardware model key facets and functions of smart meters, offering a comprehensive view of their capabilities.

AutomationShield: An Open-Source Hardware and Software Initiative for Control Engineering Education

This paper presents the AutomationShield open-source initiative and framework, which offers small and affordable devices suitable for take-home experiments in the education of future control engineers. Every device embodies a dynamic feedback system based on different physical phenomena. These devices, so-called shields, are meant to be installed on Arduino or similar development boards with the Arduino R3 compatible mechanical and electronic layout. An application programming interface and instructional examples in C/C++, MATLAB, and Simulink come with the devices. This interface serves hardware functions, so that the user may focus on creating embedded applications for automatic control. Hardware design and program codes are published under the open-source paradigm, and thus may be reproduced and built upon by anyone. In the paper we also focus on integration of the shields into the educational process and describe the experience and feedback gained so far.

Application of Deep Learning in Histopathology Images of Breast Cancer: A Review.

With the development of artificial intelligence technology and computer hardware functions, deep learning algorithms have become a powerful auxiliary tool for medical image analysis. This study was an attempt to use statistical methods to analyze studies related to the detection, segmentation, and classification of breast cancer in pathological images. After an analysis of 107 articles on the application of deep learning to pathological images of breast cancer, this study is divided into three directions based on the types of results they report: detection, segmentation, and classification. We introduced and analyzed models that performed well in these three directions and summarized the related work from recent years. Based on the results obtained, the significant ability of deep learning in the application of breast cancer pathological images can be recognized. Furthermore, in the classification and detection of pathological images of breast cancer, the accuracy of deep learning algorithms has surpassed that of pathologists in certain circumstances. Our study provides a comprehensive review of the development of breast cancer pathological imaging-related research and provides reliable recommendations for the structure of deep learning network models in different application scenarios.

Research on Sleep APP Design Based on Visual Communication Analysis

This research focused on the current demand for sleep products, analyzed the demand for healthy sleep products in today's society and recognized the importance of further visual communication for users of sleep products. This research also analyzed the function and structure of sleep hardware and software products, quantitatively analyzing their TGI index and penetration rate. The relationship between them and the visual communication of information was explored. The current situation of the visual communication part of the sleep APP products was analyzed. The design strategy of highlighting the visual communication focus, fitting the usage scenario and enhancing the branding of the product was proposed. This research provided a future direction for the design of health software and hardware products.

Simple, inexpensive, and ergonomic phantom for quality assurance control of MRI guided Focused Ultrasound systems.

The popularity of Magnetic Resonance guided Focused Ultrasound (MRgFUS) as a beneficial therapeutic solution for many diseases is increasing rapidly, thus raising the need for reliable quality assurance (QA) phantoms for routine testing of MRgFUS systems. In this study, we propose a thin acrylic film as the cheapest and most easily accessible phantom for assessing the functionality of MRgFUS hardware and software. Through the paper, specific QA tests are detailed in the framework of evaluating an MRgFUS preclinical robotic device comprising a single element spherically focused transducer with a nominal frequency of 2.75MHz. These tests take advantage of the reflection of ultrasonic waves at a plastic-air interface, which results in almost immediate lesion formation on the film at a threshold of applied acoustic energy. The phantom offered qualitative information on the power field distribution of the FUS transducer and the ability to visualize different FUS protocols. It also enabled quick and reliable assessment of various navigation algorithms as they are used in real treatments, and also allowed for the assessment of the accuracy of robotic motion. Therefore, it could serve as a useful tool for detecting defects in system's performance over its lifetime after establishing a baseline while concurrently contributing to establish QA and calibration guidelines for clinical routine controls.

КИБЕРБЕЗОПАСНОСТЬ СЕТЕВОГО ПЕРИМЕТРА ОБЪЕКТА КРИТИЧЕСКОЙ ИНФОРМАЦИОННОЙ ИНФРАСТРУКТУРЫ

The purpose of this paper is an analytical pre-project study of possible technological aspects of countering external computer attacks on critical network infrastructure. This will make it possible to specify the tasks for further resolving this problem in the aspect of developing the necessary software and hardware. The practical implementation of such tasks is an urgent and rather unconventional problem due to various factors of change in the classical concept of the network perimeter as a physical boundary of the information infrastructure, which becomes virtual and, therefore, requires the use of new approaches to the development of technical solutions. Based on statistical data on the number and quality of computer incidents, the study provides a justification for the relevance of the above problem, and gives an overview of widely used technical means for protecting the classic network perimeter, such as firewalls and systems for detecting attacks and intrusions. A comparative analysis of modern technological trends in their development, referred to in publications as «Threat Detection and Response», «Extended Detection and Response», is carried out. However, despite the powerful software and hardware functionality of these solutions, their common drawback is indicated as the lack of adequate counteraction to computer attacks with a remote mode of the user work. In this regard, the latest concept of virtual network perimeter protection, referred to by the authors as «Cybersecurity Mesh» («cybersecurity network»), is detailed. It is this methodology that seems to be the most promising for the development of appropriate technological solutions to ensure the cybersecurity of the perimeter of the critical information infrastructure. The paper might be useful to specialists working on the security of critical information infrastructure facilities, as well as to employees of educational classes in the implementation of appropriate training, retraining and advanced training programs for such specialists.

Genetic analysis instrumentation innovations built into the SeqStudio ™ Flex Genetic Analyzers

The new Applied Biosystems™ SeqStudio ™ Flex Series Genetic Analyzer have improved the benchmark for research use only for Capillary Electrophoresis (CE) by providing innovative approaches to enhanced hand-free operation, flexibility, ease of use, data quality and connectivity. This newly designed 8 or 24 capillary system supports fragment sizing and DNA sequencing applications providing scientists with medium throughput technology for use in research applications. The steps from system set-up to size or base-called data have been simplified with hardware functionality and user-friendly software enhancements designed into this new CE system.We will discuss innovations related to ease of use including one button start-up, an on-board computer with touchscreen, intuitive software, easier to use capillary arrays, and desktop and cloud-based plate manager software. Innovations related to increased flexibility include continuous plate loading, automated plate linking that be able to maintain traceability from sample to result when using barcoded plates, urgent sample reprioritization, fragment and sequencing samples be run on the same plate, and multi-user support will also be discussed. In addition, gold standard fragment analysis and sequencing data quality has been enhanced through innovative algorithms providing autospectral calibrations, and off-scale recovery of data for fragment analysis. Innovative service and support functionality including remote troubleshooting with instrument system login capability, and on-board instrument help videos are included. Finally, we will touch upon new connectivity, which includes Thermo Fisher connect for remote monitoring, analysis, and data sharing as well as other functionality such as voice commands and Wi-Fi capability that the system will provide. The following summarizes highlights from the developmental validation performed to demonstrate the functionality of SeqStudio ™ Flex Series Genetic Analyzer.

Small-world spiking neural network with anti-interference ability based on speech recognition under interference

The external interference can hamper the normal function of neuromorphic hardware under complex noise environment. Therefore, the study of brain-like models with anti-interference ability is a crucial issue. A bio-brain has the advantage of self-adaptability. The existing brain-like models are lack of bio-interpretability. Drawing from the advantage of bio-brain, the purpose of this paper is to investigate the anti-interference ability of brain-like models with bio-interpretability. In this study, we construct a spiking neural network with a small-world topology (SWSNN) as a brain-like model, in which Izhikevich neuron models and synaptic plasticity models with time-delay are used to represent the nodes and edges of the network, respectively. Then, the anti-interference ability of our SWSNN against different external noise is investigated, and its mechanism is explored. Further, by taking the speech recognition task as the case study, we verify the anti-interference ability of the SWSNN. Our simulation results indicate that: (i) The anti-interference ability of SWSNN is better than that of SNNs with other topologies. (ii) The discussion of neural information processing of the SWSNN under interference implies that the dynamic regulation of synaptic plasticity is an intrinsic factor of the anti-interference, and the topology is a factor that affects the anti-interference at the level of performance. (iii) Taking the speech recognition task as a case study, the SWSNN under different external noises still have almost the same high speech recognition accuracy, compared with the SWSNN without interference, and SWSNN yields better anti-interference ability than SNNs with other topologies in terms of the speech recognition accuracy. In addition, the anti-interference ability of our SWSNN framework is superior to that of existing liquid state machine (LSM) framework. Our simulation results lay a preliminary foundation for the neuromorphic hardware with robustness under complex noise environment.

Gigabit Modbus user datagram protocol fieldbus network integrated with industrial vision communication

In the era of Industry 4.0, the industrial internet of things (IIoT) is expected to transform traditional manufacturing. Machine vision, as the key enabling technology, can reduce maintenance and costs and improve production efficiency. This paper proposes an innovative machine vision system that uses the Gigabit Modbus user datagram protocol fieldbus. The protocol uses hardware and cross-layer function controllers to achieve edge computing with real-time image transmission. Results demonstrated that our innovative high-performance industrial machine vision solution can meet the requirements of IIoT applications. Moreover, the system considerably reduces the real-time response time and has superior usability compared with existing Ethernet fieldbus solutions.