Hardware Operation Research Articles

Internet of Things Simulation Learning Model Based on Virtual Remote Input Output

In our technology-driven world, various methods for teaching in an educational venue or in a simulated environment have been suggested especially for computer and coding education, among which virtual learning is a widely used teaching approach. In particular, IoT (Internet of Things)-related education has been made possible owing to the industrial developments that have occurred in various fields since the Fourth Industrial Revolution. Because it is mostly based on hardware and simple operations taught in a two-dimensional (2D) environment, conventional virtual learning of coding decreases the interest among learners. Therefore, there is a need for a three-dimensional (3D)-based educational approach that can arouse interest in learners. The proposed model allows various IoT systems to be indirectly built; it provides an inexpensive learning method by applying a simulation system in a 3D environment. The model is implemented on IoT devices, real IoT systems, and an IoT simulator based on the Arduino platform, thereby reducing the cost of building an education system while at the same time arousing the interest of learners with its 3D environment for learning. Various education-related content can be provided to learners through such an indirectly developed system. A header file type library is created by analyzing the prototype of the GPIO (general-purpose input/output) function suitable for the IoT device to be used in the virtual IoT simulator; a virtual remote IO module is also implemented. In addition, the physical and electrical characteristics of the peripheral devices to be applied were identified and implemented in a virtual space. Sensors and actuators with various characteristics were implemented to enhance the educational effect, and a test code was written to check the consistency of an operation between the real system and the virtual system. An experiment conducted to check the update cycle of the actual input and output showed that they differ by more than 5,000 fold on average. This is a physical limitation resulting from the difference between a method that directly controls an actual electrical signal with a register and a method of transmitting and receiving signals through a communication device. The actual system motion analysis showed no cases in which the automatic guided vehicle (AGV) deviated from the line and there was a slight difference in the responsiveness when returning to the original position from the deviation.

E-sports organizations with franchised networks: formalization of technological and economic development based on optimal operation and upgrade of the hardware

The paper focuses on the priority given to the technological and economic development of e-sports organizations with franchised networks. Attention is paid to the specificity of the process of timely upgrading of e-sports hardware, which involves the upgrading and introduction of new, more sophisticated and advanced gaming and other devices, which determines the number of e-sports disciplines and available e-sports events for the е-sports arena. The success of franchising networks depends on the quantity of the c-sports hardware, which should be similar to the hardware used by the main arena in order to ensure the functionality of a well-structured state-of-the-art training process for e-sport athletes. The purpose of the study is to present a basis for the technological and economic development of e-sports organizations with a franchising network within the formalized system in terms of finding optimal solutions to the problem of hardware upgrades. The study presents a model of technological and economic development of all e-sports organizations with franchised networks as а content area, posing the rules of operation, regulating the process of hardware upgrades and focusing on the sustainability of development. Dynamic programming methods based on Bellman’s equations and the formalization of the problem of hardware replacement through graphic notations, cloud computing in the AnyLogic environment are used to identify and illustrate the features of such solutions. The result of the research is a description of technological development of e-sports organizations with a franchise network with the use of a model that approximates the optimal way of operation and upgrade of related hardware. This development illustrates the optimal way of operation and hardware upgrade of Blizzard Arena and Overwatch League, represented by participants from the United States, the United Kingdom, Germany, France, South Korea and Ukraine. The franchised Overwatch League includes the main arena (Blizzard Arena), as well as Florida Mayhem Club (Miami-Orlando, USA), New York Excelsior Club (New York, USA), London Spitfire Club (London, UK), Vault Club 15 (Kyiv, Ukraine), Immortals Club (Los Angeles, USA), NRG E-sports Club (Berlin, Germany), Misfits Club (Seoul, South Korea), PS4 Training Base (Beijing, China) and Xbox One Training Base (Paris, France). According to the obtained data, the formalization of the technological and economic development of Blizzard Arena suggests a solution to the problem of finding an optimal strategy relevant to optimization of hardware up to the moment of its transfer to the franchised network. Such formalization is highly relevant. They rely on the possible state of the system proposed in our research. That system state identifies the functional Bellman equations. Naturally, emerges a possibility to significantly reduce investment in the e-sports environment of the main arena and the franchise while controlling the quality and functionality of e-sports hardware. The organizations’ focus on two-stage upgrades will reduce investment in major hardware. The study illustrates the formalization of the techno-economic development of the Blizzard Arena through a two-stage upgrade of the Aerocool Advanced Technologies franchise (primary franchise - from producer, secondary franchise - from franchise Blizzard Arena operator). Based on the specific features of the Bellman equations, the development of the Blizzard Arena must take into account the model which determines the feasibility of hardware transferring to a franchised network during the third period of operation, where it operated as long as franchisees enter the maximum profit area. When using the Bellman equations, we suggest that the arena focus on the moment when the function values will correspond to the replacement state of the hardware and the franchise member on the stability of the environment. The formalization of the technical development will make it possible to orient the e-sport organizations with franchised networks to search for conditions sufficient for optimal operation and upgrade of their hardware. As a result, there will appear an optimized system with a content area, which will provide a stable environment for e-sports events at the arena and athletes’ training in franchise clubs.

Design of monitoring system for redundant communication power supply based on ZigBee

Aiming at the problems of poor maintenance effect and large measurement error of traditional communication power supply monitoring system, a redundant communication power supply monitoring system based on ZigBee is designed. The sensor and power supply cable are installed, the battery working state parameters are collected and cleaned, compressed and fused. The MSP430F149 single chip microcomputer is used to realize the high-speed processing of field information and optimize the configuration of hardware structure. The software maintenance process of the complete redundant communication power supply monitoring system is designed. The logic editing technology is used to correct, adjust and improve the system software. The mathematical model of hardware maintenance is established. The most reasonable monitoring and measurement of redundant communication power supply is calculated by ZigBee method to realize the effective monitoring of communication power supply. The experimental results show that the hardware operation effect of the design system is good, and the measurement error can be effectively reduced.

The Development of Green Software Process Model

Software process and development are the fundamental activities in software engineering. Increasing software usage either develops in-house or outsourcing requires improving the software process accordingly to minimise the adverse effects on the environment. The resources and power consumption controlled by hardware affected the software to move the process that causes high emission of power and energy. Thus, most of the existing work on software development is aimed at the efficiency of hardware operation through CPU, memory, and processor. Although sustainability is still initial in software engineering, the green software process can be achieved through sustainable development that concerns the preservation of the environment. However, there is still a lack of study and effort in the software process that emphasises sustainability perspectives and software waste elimination. Therefore, this study proposes the green factors for the software process that consider the sustainability elements and waste reduction during development. The green factors are the benchmark to measure a sustainable and green software process. Besides, this paper also presents the qualitative interview design and pilot study. The pilot study analysis has demonstrated the reliability of the interview protocol. Therefore, the actual interview and data analysis are currently in progress.

Guidance Simulation and Experimental Verification of Trajectory Correction Mortar Projectile

Integrated methods are used in the modification of trajectory, including improved perturbation impact point deviation prediction, adaptive proportional guidance and adaptive proportional differential guidance, thus improving the firing accuracy of guided mortar shell. The six degrees of freedom of both the trajectory model and the control model were established, and their guidance laws were designed based on the three guidance schemes. Firstly, the perturbation impact point deviation prediction method is improved by setting up a discrimination factor in the rising phase of trajectory based on the principle of traditional perturbation impact point deviation prediction method and in combination with the trajectory characteristics of guided mortar, which further improves the correction efficiency. The adaptive proportional guidance law is designed in the longitudinal plane, while the adaptive proportional differential guidance law is designed in the transverse plane due to the fact that the constant proportional coefficient in the proportional guidance law does not conform to the requirements of actual trajectory. In this paper, Monte Carlo simulation method and ammunition flight test are used to verify the designed guidance law, and the simulation results illustrate that the integrated guidance method is both reasonable and effective. As for the error of the guidance tool and actuator, the method is available for the reduction of impact point deviation and the improvement of accuracy. The circular error probability (CEP) not under control decreases from 126.317m to 10.1284m when control is applied. Besides, the feasibility of the designed guidance law is verified by the flight tests of guided mortar projectile in large, medium and small range respectively from the perspective of engineering application. It can be seen from the test trajectory impact point data that the guidance law is available for the effective correction of trajectory deviation in the actual hardware operation and site environment with reliable guidance. As the outcome, the CEP reaches 10.86m, and the impact point deviation of some guided missiles is within 2m.

Creating Strong Titanium/Titanium Hydride Brown Bodies at Ambient Pressure and Moderate Temperatures.

A simple, low temperature, method, hydrogen-enhanced atomic transport (HEAT), for creating metallic-bonded brown bodies of order 40% bulk density in molds of designed shape from Ti metal particles is introduced. In this initial study 40 micron titanium particles were poured into graphite molds, then heated to temperatures equal to or greater than 650 °C for four hours in a flowing ambient pressure gas mixture containing some hydrogen led to brown body formation that closely mimicked the mold shape. The brown bodies were shown to be dense, metallic bonded, and consisted of primarily Ti metal, but also some TiH. It is postulated that hydrogen is key to the sintering mechanism: it enables the formation of short-lived TiHx species, volatile at the temperatures employed, that lead to sintering via an Ostwald Ripening mechanism. Data consistent with this postulate include findings that brown bodies are formed with hydrogen present (HEAT process) had mechanical robustness and only suffered plastic deformation at high pressure (ca. 5000 Atm). In contrast, brown bodies made in identical conditions, except the flowing gas did not contain hydrogen, were brittle, and broke into micron scale particles under much lower pressure. HEAT appears to have advantages relative to existing titanium metal part manufacturing methods such as powder injection molding that require many more steps, particularly debinding, and other methods, such as laser sintering, that are slower, require very expensive hardware and expert operation.

Optimizing Discharge Efficiency of Reconfigurable Battery With Deep Reinforcement Learning

Cell imbalance in a multicell battery occurs over time due to varying operating environments. This imbalance leads to overall inefficiency in battery discharging due to the relatively weak cells in the battery. Reconfiguring the cells in the battery is one option for addressing the problem, but relevant circuits may lead to severe safety issues. In this article, we aim to optimize the discharge efficiency of a multicell battery using safety-supplemented hardware. To this end, we first design a cell string-level reconfiguration scheme that is safe in hardware operations and also provides scalability due to the low switching complexity. Second, we propose a machine learning-based run-time switch control that considers various battery-related factors, such as the state of charge, state of health, temperature, and current distributions. Specifically, by exploiting the deep reinforcement learning (DRL) technique, we train the complex relationship among the battery factors and derive the best switch configuration in run-time. We implemented a hardware prototype, validated its functionalities, and evaluated the efficacy of the DRL-based control policy. The experimental results showed that the proposed scheme, along with the optimization method, improves the discharge efficiency of multicell batteries. In particular, the discharge efficiency gain is maximized when the cells constituting the battery are unevenly distributed in terms of cell health and exposed temperature.

Phase I Upgrade of the Readout System of the Vertex Detector at the LHCb Experiment

This article describes the high-speed system designed to meet the challenging requirements for the readout of the new pixel VErtex LOcator (VELO) of the upgraded LHCb experiment. All elements of the electronics readout chain will be renewed to cope with the requirement of ~40-MHz full-event readout rate. The pixel sensors will be equipped with VeloPix ASICs and placed at ~5 mm from the Large Hadron Collider (LHC) beams in a secondary vacuum tank in an extremely high and nonhomogeneous radiation environment. The front-end (FE) ASICs with the highest occupancy will have to cope with pixel-hit rates above ~900 Mhits/s using up to four 5.13-Gb/s data readout links. Each module comprises six VeloPix ASICs, wire-bonded to two FE hybrid boards, while a third hybrid will employ a GBTx ASIC as the control interface. High-speed data will reach the wall of the vacuum chamber through low-mass flexible copper tapes. A custom board routes the signals outside the vacuum tank. On the air side, an optical and power board converts the electrical high-speed signals into optical signals for transmission from the underground cavern to the off-detector electronics that process data and send them to a farm of computers for further analysis. Several tests allowing the validation of the system are described here with special emphasis on a test with proton beams that confirms the correct operation of the whole readout hardware.

Assessment of the Wind and Temperature Loads Influence on the PVC Windows Deformation

The PVC windows application experience in various climatic conditions shows that at their operation the windows profile deformations can be observed. One of the reasons for these deformations is the climatic loads influence on the windows – wind pressure, solar radiation, negative outside temperatures. The profile elements deformations affect the window tightness and the normal window hardware operation. The calculated loads combinations on windows in winter and summer were analyzed. For window as external element of building is more critical winter loads combination – the combined effect of wind pressure and negative temperature. The work was done the analysis of the effect of negative temperatures and wind pressure on the PVC window deformation. The studies were performed in laboratory conditions on the basis of two testing centers. Three PVC Windows with different profile thickness were tested. The temperature deformation of the casements and mullions profiles of this windows were analyzed. These deformations have to take into account in the static calculations. But the engineering methods creation for calculating windows for the wind and temperature loads effect requires additional research. They are associated with the study of the joint work of the windows constituent elements (profiles, insulating glass units, hardware).

Enhance Intrusion Detection in Computer Networks Based on Deep Extreme Learning Machine

Networks provide a significant function in everyday life, and cybersecurity therefore developed a critical field of study. The Intrusion detection system (IDS) becoming an essential information protection strategy that tracks the situation of the software and hardware operating on the network. Notwithstanding advancements of growth, current intrusion detection systems also experience dif- ficulties in enhancing detection precision, growing false alarm levels and identifying suspicious activities. In order to address above mentioned issues, several researchers concentrated on designing intrusion detection systems that rely on machine learning approaches. Machine learning models will accurately identify the underlying variations among regular information and irregular information with incredible efficiency. Artificial intelligence, particularly machine learning methods can be used to develop an intelligent intrusion detection framework. There in this article in order to achieve this objective, we propose an intrusion detection system focused on a Deep extreme learning machine (DELM) which first establishes the assessment of safety features that lead to their prominence and then constructs an adaptive intrusion detection system focusing on the important features. In the moment, we researched the viability of our suggested DELMbased intrusion detection system by conducting dataset assessments and evaluating the performance factors to validate the system reliability. The experimental results illustrate that the suggested framework outclasses traditional algorithms. In fact, the suggested framework is not only of interest to scientific research but also of functional importance.

Intelligent system for university legal education based on machine learning

University legal education is of great significance to the personal development and social stability of college students. At present, there are certain problems in the traditional teaching system, which has led to inefficient university legal education. In order to improve the legal teaching effect of the university, based on machine learning and neural networks, this paper integrates and optimizes the original hardware and software and operation process, and further highlights the functions of interconnection and sharing, automatic sensing, real-time recording, interactive feedback, dynamic supervision, and intelligent analysis, which greatly facilitates the evaluation of teaching at all levels. In particular, this study uses big data technology to conduct an intelligent analysis of data completeness, multimedia application rate, system execution, and average test scores, and scientifically evaluates the implementation of basic-level education systems and the effectiveness of education, which can effectively solve the problems of quantitative formalization and qualitative subjectivity of current education evaluation from a technical level. In addition, this study designs a control experiment to analyze the system performance. The research results show that the model proposed in this paper has a certain effect.

P.456 Akathisia during cariprazine treatment: Post-hoc analyses of pooled schizophrenia studies

Rodent behavioral models with an electrophysiological component may require the joint operation of hardware from Med Associates, Inc. (St. Albans, VT) and Tucker-Davis Technologies (TDT; Alachua, FL). Although these manufacturers do produce supplemental hardware for interfacing with each other, investing in such hardware may be untenable for research groups with limited funds who wish to use equipment already in their possession.We designed a printed circuit board (PCB) in KiCad and had it fabricated by Advanced Circuits (Aurora, CO), with components sourced from Digi-Key (Thief River Falls, MN). The PCB provided 8 channels of bidirectional communication for the transmission of signals between Med Associates’ SG-716B SmartCtrl connection panel and TDT’s RZ5D base station. This setup enabled the coordinated operation of programs running separately on each set of hardware.The custom-built PCB facilitated the joint operation of Med Associates and TDT hardware in a go/no-go detection task involving rats with electrical implants in their sciatic nerves.Conventional methods for interfacing Med Associates and Tucker-Davis Technologies rely on the purchase of pre-built hardware whose costs can add up to thousands of dollars. The present method offers a viable alternative that is easily implemented and considerably less expensive (below $200).The present approach provides an inexpensive yet effective alternative to far more costly interfacing solutions offered by Med Associates and Tucker-Davis Technologies.

Statistical Analysis of Multiple Antenna Strategies for Wireless Energy Transfer

Wireless energy transfer (WET) is emerging as a potential solution for powering small energy-efficient devices. We propose strategies that use multiple antennas at a power station, which wirelessly charges a large set of single-antenna devices. The proposed strategies operate without channel state information (CSI), and we attain the distribution and main statistics of the harvested energy under Rician fading channels with sensitivity and saturation energy-harvesting (EH) impairments. A switching antenna strategy, where a single antenna with full power transmits at a time, provides the most predictable energy source, and it is particularly suitable for powering sensor nodes with highly sensitive EH hardware operating under non-LOS (NLOS) conditions while other WET schemes perform alike or better in terms of the average harvested energy. While switching antennas is the best under NLOS, transmitting simultaneously with equal power in all antennas is the most beneficial as LOS increases. Moreover, spatial correlation is not beneficial unless the power station transmits simultaneously through all antennas, raising a tradeoff between average and variance of the harvested energy since both metrics increase with the spatial correlation. Moreover, the performance gap between CSI-free and CSI-based strategies decreases quickly as the number of devices increases.

A 4K-Capable FPGA Implementation of Single Image Haze Removal Using Hazy Particle Maps

This paper presents a fast and compact hardware implementation using an efficient haze removal algorithm. The algorithm employs a modified hybrid median filter to estimate the hazy particle map, which is subsequently subtracted from the hazy image to recover the haze-free image. Adaptive tone remapping is also used to improve the narrow dynamic range due to haze removal. The computation error of the proposed hardware architecture is minimized compared with the floating-point algorithm. To ensure real-time hardware operation, the proposed architecture utilizes the modified hybrid median filter using the well-known Batcher’s parallel sort. Hardware verification confirmed that high-resolution video standards were processed in real time for haze removal.

The structure of a sliding sensory system for a self-adaptive prehensor

The quality of the prehension is determined by the mobility state of the prehension object.The emphasis on sliding within the self-adaptive prehensor, proposed in the paper, is achieved by correlating the parameters generated by the rotation sensor (encoder), the contact sensor and the angular position of the stepper motor.The rotation of the encoder is influenced by the active and passive mechanical components in the prehensor jaws.In this way, it is obtained a 2D sliding sensory system. The sensory system is a complex mechatronic system that can adapt to most industrial robots of different sizes and types, including the cartesian type. The prehension of the objects is accomplished through a sensory technique which integrates all software and hardware operations to achieve the image of the two directions grip. The sensory system has a resistive position sensor in its structure which determines the angular position of the axis of rotation of the execution element. The inductive position sensor is the second sensor of the system. It highlights the mechanical tensionless contact on the prehensile object. The sliding of the object is emphasized by a resistive position sensor. An important role in programming the prehensor is the correlation between the software information from the three sensing elements with the stepper motor that achieves the force required for the prehension.

Design of sparse cosine modulated filter banks based on Hopfield neural network

Cosine modulated filter bank (CMFB) is the most basic and important module in multi-rate signal processing system. Sparse FIR linear-phase CMFB can not only reduce the complexity of hardware design, but also ensure good performance. In this paper, a new design method of CMFB is proposed. First, the set of sparse coefficients of the prototype filter is found by using the orthogonal matching pursuit (OMP) algorithm. Then the Hopfield neural network (HNN) is employed to optimize the non-zero coefficients. The analytical and simulation results demonstrate that the new approach not only ensures that the sparse CMFBs satisfy near perfect reconstruction (NPR), but also improves the efficiency of hardware operation.

An investigation of ramie fiber cross-section image analysis methodology based on edge-enhanced image fusion

Usually the quality of cross sectional fiber image is affected by the slicing and imaging device, some image processing techniques could be used to solve this problem instead of hardware operation. In this paper, a series of edge detection and weighted average image fusion algorithms are used to enhance the ramie fiber image to improve the quality of fiber cross-section image obtained by the traditional section and optical microscope. The cross-section characteristic parameters of ramie fiber could be extracted after the image pre-processing including threshold segmentation, median filtering and corrosion. The experimental results show that this method can be used to solve the identification problem of low contrast ramie fiber cross-section images caused by background blur, improve the accuracy of image recognition and analysis, it provides an effective algorithm basis for the digital measurement of ramie fibers.

Study of Fault Tolerance Methods for Hardware Implementations of Convolutional Neural Networks

The paper concentrates on methods of fault protection of neural networks implemented as hardware operating in fixed-point mode. We have explored possible variants of error occurrence, as well as ways to eliminate them. For this purpose, networks of identical architecture based on VGG model have been studied. VGG SIMPLE neural network that has been chosen for experiments is a simplified version (with smaller number of layers) of well-known networks VGG16 and VGG19. To eliminate the effect of failures on network accuracy, we have proposed a method of training neural networks with additional dropout layers. Such approach removes extra dependencies for neighboring perceptrons. We have also investigated method of network architecture complication to reduce probability of misclassification because of failures in neurons. Based on results of the experiments, we see that adding dropout layers reduces the effect of failures on classification ability of error-prone neural networks, while classification accuracy remains the same as of the reference networks.

On Performance Degradation of Channel State Feedback Due to Latency and a Novel Solution

In millimeter wave (mmWave) systems, the channel state fluctuates faster than that in sub-6GHz systems. Thus, the channel state dependent scheduling requires the faster feedback from a user equipment, which will generate the larger overhead. To reduce the feedback overhead, a prediction based feedback scheme is proposed under the assumption of the imperfect channel states being available at the user equipment. The proposed feedback scheme optimizes the reconstruction accuracy at a given feedback overhead. Within the scheme, the performance degradation caused by the protocol and hardware operation delay can be mitigated. The impact of the proposed scheme on improving the downlink bit rate is studied in this paper.

Control System in Open-Source FPGA for a Self-Balancing Robot

Computing in technological applications is typically performed with software running on general-purpose microprocessors, such as the Computer Processing Unit (CPU), or specific ones, like the Graphical Processing Unit (GPU). Application-Specific Integrated Circuits (ASICs) are an interesting option when speed and reliability are required, but development costs are usually high. Field-Programmable Gate Arrays (FPGA) combine the flexibility of software with the high-speed operation of hardware, and can keep costs low. The dominant FPGA infrastructure is proprietary, but open tools have greatly improved and are a growing trend, from which robotics can benefit. This paper presents a robotics application that was fully developed using open FPGA tools. An inverted pendulum robot was designed, built, and programmed using open FPGA tools, such as IceStudio and the IceZum Alhambra board, which integrates the iCE40HX4K-TQ144 from Lattice. The perception from an inertial sensor is used in a PD control algorithm that commands two DC motors. All the modules were synthesized in an FPGA as a proof of concept. Its experimental validation shows good behavior and performance.