Hasil untuk "Electric apparatus and materials. Electric circuits. Electric networks"

Georgios Chatzigiannakis, Anastasia Soultati, Leonidas C. Palilis et al.

ABSTRACT Metal halide perovskite field‐effect transistors (PeFETs) have rapidly gained recognition as leading candidates for next‐generation electronic and optoelectronic technologies, owing to their exceptional optoelectronic properties, facile solution processability, and notable mechanical flexibility. Nevertheless, the practical deployment of high‐performance PeFETs is significantly impeded by persistent challenges, including ion migration, hysteresis effects, and environmental instability, which collectively hinder their widespread adoption. This review offers a thorough and up‐to‐date overview of recent progress in the field of PeFETs, with particular emphasis on advances in material engineering, device architecture optimization, and innovative processing techniques designed to enhance device performance. The discussion encompasses the fundamental physics governing charge transport in perovskite semiconductors, with a focus on the influence of defect chemistry, interface engineering, and stability considerations. Special attention is devoted to a comparative analysis of tin‐based and lead‐based PeFETs, elucidating their respective charge transport mechanisms, benefits, and limitations. The review concludes by identifying the principal challenges and outlining future research directions that are essential for realizing the full potential of perovskite transistors in delivering high‐speed, flexible, and cost‐effective electronic devices.

Sihem Djebbri, Samir Ladaci

This paper deals with the power control of the cascaded doubly-fed induction generator (CDFIG) for wind energy applications. using a fractional order proportional integral (FOPI) controller within a field oriented control (FOC) scheme. The FOPI controller has a similar structure to the classic proportional-integral (PI) controller but FOPI controller considers non integer integration orders in determining the control action. This allows obtaining responses of the controlled system with different characteristics such as oscillations, stability and rise time. In this study FOPI strategy is simulated within a FOC scheme using a mathematical model of cascaded doubly-fed induction generator (CDFIG).    The system can be an attractive alternative to conventional double output wound rotor induction generators. Cascaded doubly-fed induction generator examined in the paper consists of two identical wound rotor induction machines having their rotors mechanically and electrically coupled.    The system employs two cascaded induction machines to eliminate the brushes and copper rings in the traditional DFIG.         In this case, Cascaded induction generators require lower maintenance. In CDFIG both stators of connected machines are accessible. The control strategy for flexible power flow control is developed. The independent control of the active and reactive power flows is achieved.

Songang Peng, Zhi Jin, Yao Yao et al.

Eram Fatima Siddiqui, Tasneem Ahmed, Sandeep Kumar Nayak

The aim of today's healthcare services is to provide high quality and real-time facilities and treatment options for their patients and give a patient-centric experience with full support. IoT-Based Healthcare System have improved the quality of healthcare services by enhancing its diagnosis and decision-making accuracy. On the basis of data collected from different medical Bio Sensors and Machine Learning techniques, a patient mortality and treatment can be improved with the help of current medical condition and historical Medical Health Records. In the paper a Decision Tree method has been proposed which will firstly acquire real-time medical parameter-based data from the patient through multiple BS. This data will be fed into the already trained Decision Trees in order to classify the patient into Low Risk/Normal/High Risk Category. Mobile Edge Computing technology is used in collaboration with BS in order to provide ultra-latent computation of BS-generated data and transform it into real-time decision. After severity categorization of the patient, a definite task offloading decision, whether to go for no offloading/Edge Offloading/Collaborative Edge Offloading mode will be taken. This will be done in order to facilitate severe patient with prompt treatment in case of any exigency. The proposed method outperformed Energy-Efficient Internet of Medical Things to Fog Interoperability of Task Scheduling, Optimized Latency Fog Computing and Intelligent Multimedia Data Segregation methods with a total of 88 % of improved system's performance.

Zhou Tian, Deng Yi

The psychological health problems of students are directly related to the stability and development of society. With the development of society and the fierce competition in education, students are facing increasing psychological pressure, leading to increasingly prominent mental health problems. This not only seriously affects students' lives and studies, but also has a negative impact on the entire society. This article develops a student mental health support system based on artificial intelligence and big data analysis, through research and analysis of artificial intelligence and big data analysis. Then, based on the needs of students' mental health support and crisis prediction, corresponding algorithms and models are designed to apply artificial intelligence and big data analysis to the students' mental health support system, and relevant experiments and tests are conducted. The research results indicate that through this system, students can receive personalized mental health support and guidance, and can predict the possibility of student mental health crises. This system has achieved significant results in providing students with mental health support and predicting crises.

Yuan Gao

With the rapid development of artificial intelligence technology, the application of image algorithm in multimedia vision technology has made remarkable progress. In the field of sports, human health is an important concern, so it is of great significance to apply artificial intelligence image algorithm to the research of human health in sports. This paper uses computer vision and image processing technology, combined with artificial intelligence algorithm, to analyze and process the multimedia image of the movement. Through the acquisition and preprocessing of moving images, the key information and features of the movement are extracted. Then, deep learning algorithms and pattern recognition technology are used to analyze and evaluate the posture, movement and body state of the exercisers. Finally, according to the analysis results, personalized health advice and guidance are provided. The results show that the method can accurately identify and analyze the posture, movement and body state of the athletes, provide personalized health advice and guidance, help the athletes to improve the training effect, avoid sports injuries, and improve the level and quality of sports.

Xi Pan, Tingsheng Zhao

In super high-rise buildings, the integration of large tower cranes with climbing platforms can enhance the construction efficiency. However, this integration mode significantly increases the load on the tower crane support system (TCSS). Current low-precision manual construction methods can lead to uneven pressures at the support points, potentially causing structural damage and serious accidents. This study proposes a method for monitoring, providing warnings, and optimizing deviations in TCSS pressure to ensure safe and controlled operations. Large-load sensors have been developed to monitor the pressure on TCSS in super-high-rise building projects. The pressure tests revealed that the uneven load distributions at the support points differed significantly from the simulation results. The safety of large integrated construction equipment can be ensured by considering the uneven distribution coefficients of the support pressure and improving the design accuracy of the support points.

Delaram Rashadfar, Brandi L. Wooten, Joseph P. Heremans

In ferroelectric materials, an electric field has been shown to change the phonon dispersion sufficiently to alter the lattice thermal conductivity, opening the possibility that a heat gradient could drive a polarization flux, and technologically, also opening a pathway towards voltage-driven, all solid-state heat switching. In this report, we confirm the validity of the theory originally developed for Pb(Zr,Ti)O_3 (PZT) on the ferroelectric relaxor 0.67Pb[Mg_(1/3)Nb_(2/3)]O_3-0.33PbTiO_3 (PMN-33PT). In the theory, the change in sound velocity and thermal conductivity with electric field relates to the piezoelectric coefficients and the Gruneisen parameter. It predicts that in PMN-33PT the effect should be an order of magnitude larger, and of opposite sign as in PZT; this is confirmed here experimentally. The effects are measured on samples never poled before and on samples that underwent multiple field sweep cycles and passed through two phase transitions with change in temperature. The thermal conductivity changes are linked to variations in the piezoelectric coefficients and can be as large as 8-11% at room temperature and above. To date, this is the only means of heat conduction modulation that utilizes changes in the phonon spectrum. While this technology is in its infancy, it offers another path to future active thermal conduction control.

Yihao Guo, Anne Limburg, Jesse Laarman et al.

Using electric field induced second harmonic generation (E-FISH), we performed direction-resolved absolute electric field measurements on single-channel streamer discharges in 70 mbar (7 kPa) air with 0.2 mm and 2 ns resolutions. In order to obtain the absolute (local) electric field, we developed a deconvolution method taking into account the phase variations of E-FISH. The acquired field distribution shows good agreement with the simulation results under the same conditions, in direction, magnitude and in shape. This is the first time that E-FISH is applied to streamers of this size (> 0.5cm radius), crossing a large gap. Achieving these high resolution electric field measurements benefits further understanding of streamer discharges and enables future use of E-FISH on cylindrically symmetric (transient) electric field distributions.

M.Gomathy Nayagam, B. Vijayalakshmi, K. Somasundaram et al.

The term ''smart agriculture'' describes how farming is carried out in the modern day as technology develops. Application of diverse insect protection and agricultural production tactics is crucial for crop monitoring. The structure as it is now has problems. A particular core Graphical Processing Unit (GPU) is used to manage the numerous sensors connected for crop surveillance and pest management. A Parallel and Distributed Simulation Framework (PDSF) with the Internet of Things (IoT) is proposed for pest management and agricultural monitoring tools. It lessens the pressure on a certain GPU, evenly distributes the workload over all available GPUs at simultaneously, and delivers data to the dashboards even when it's broken. The procedure will decrease system performance. In the PDSF multi-threading paradigm, each GPU unit distributes workloads to specific additional cores. To carry out the various tasks, the four levels of this system—crop management, pest identification and control, output activities, and input functional areas—are distributed among them. The information is processed simultaneously and handled in an efficient and controlled manner. The proposed system improves the performance measures of 98.65%.

Amine Slassi, Linda‐Sheila Medondjio, Andrea Padovani et al.

Abstract The choice of the ideal material employed in selector devices is a tough task both from the theoretical and experimental side, especially due to the lack of a synergistic approach between techniques able to correlate specific material properties with device characteristics. Using a material‐to‐device multiscale technique, a reliable protocol for an efficient characterization of the active traps in amorphous GeSe chalcogenide is proposed. The resulting trap maps trace back the specific features of materials responsible for the measured findings, and connect them to an atomistic description of the sample. The metrological approach can be straightforwardly extended to other materials and devices, which is very beneficial for an efficient material‐device codesign and the optimization of novel technologies.

Chuan Yu Han, Sheng Li Fang, Yi Lin Cui et al.

Abstract For the first time, a configurable NbOx memristor is achieved that can be configured as an artificial synapse or neuron after fabrication by controlling the forming compliance current (FCC). When the FCC ≤ 2 mA, the memristors exhibit the resistive‐switching (RS) property, enabling multiple types of synaptic plasticity, including short‐term potentiation, paired‐pulse facilitation, short‐term memory, and long‐term memory. When the FCC ≥ 3 mA, the memristors can be electroformed and exhibit the threshold switching (TS) property with excellent endurance (>1012), thus achieving various biological neuron characteristics, such as threshold‐triggering, strength‐modulation of spike frequency, and leaky integrate‐and‐fire. This enables the successful implementation of a spiking Pavlov's dog that employs the spikes as information carrier by connecting an RS NbOx memristor as artificial synapse and a TS memristor as artificial neuron in series. Furthermore, a fully NbOx memristors‐based single‐layer spiking neural network is simulated. It is first found that, due to the forgetting property of synapse, the recognition accuracy for the Modified National Institute of Standards and Technology handwritten digits is increased from 85.49% to 91.45%. This study provides a solid foundation for the development of neuromorphic machines based on the principles of the human brain.

Madhu Kumar Vanteru, K.A. Jayabalaji, Suja G. P et al.

The development of the Internet of Things (IoT) has recently revived interest in IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). For the enormous IP-based detection technologies in the future IoT, 6LoWPAN mobile compatibility is still in its infancy. A significant 6LoWPAN use of IoT, the hospital's wireless technology, which continuously monitors patients' vital signs, regardless of whether they are moving. To track accurate patient positions, communication between patient nodes and the hospital network needs to be maintained through sensor network. In addition, it must offer automatic switching and improve the power consumption of devices. In this research, a hospital structure based on 6LoWPAN with a Media Access Control (MAC) design for patient data was offered. Our preliminary numerical results indicate a decrease in the expenses associated with transfers on the mobile router, which generally represents a bottleneck in such a network. Biological ECG signals are routed via low pan before transmission via routing algorithms to the gateway network.

Lei Cai, Lianfeng Yu, Wenshuo Yue et al.

Abstract Humans are complex organisms made by millions of physiological systems. Therefore, physiological activities can represent physical or mental states of the human body. Physiological signal processing is essential in monitoring human physiological features. For example, non‐invasive electroencephalography (EEG) signals can be used to reconstruct brain consciousness and detect eye movements for identity verification. However, physiological signal processing requires high resolution, high sensitivity, fast responses, and low power consumption, hindering practical hardware design for physiological signal processing. The bionic capability of memristor devices is very promising in the context of building physiological signal processing hardware and they have demonstrated a handful of advantages over the traditional Von Neumann architecture system in accelerating neural networks. Memristor networks can be integrated as a hardware system for physiological signal processing that can deliver higher energy efficiency and lower latency compared to traditional implementations. This review paper first introduces memristor characteristics, followed by a comprehensive literature study of memristor‐based networks. Physiology signal processing applications enabled by these integrated memristor networks are also presented in this review. In summary, this paper aims to provide a new perspective on physiological signal processing using integrated memristor networks.

William Bontems, Daniel Dzahini

This paper presents a design methodology for a low-power, low-chip-area, and high-resolution successive approximations register (SAR) analog-to-digital converter (ADC). The proposed method includes a segmented capacitive DAC (C-DAC) to reduce the power consumption and the total area. An embedded self-calibration algorithm based on a set of trimming capacitors was applied alongside a dynamic element matching (DEM) procedure to control the inherent linearity issues caused by the process mismatch. The SAR ADC and each additional algorithm were modeled in MATLAB to show their efficiency. Finally, a simple methodology was developed to allow for the fast estimation of signal-to-noise ratios (SNRs) without any FFT calculation.

Gergely Endrodi, Gergely Marko

The phase diagram and the equation of state of QCD is investigated in the presence of weak background electric fields by means of continuum extrapolated lattice simulations. The complex action problem at nonzero electric field is circumvented by a novel Taylor expansion, enabling the determination of the linear response of the thermal QCD medium to constant electric fields -- in contrast to simulations at imaginary electric fields, which, as we demonstrate, involve an infrared singularity. Besides the electric susceptibility of QCD matter, we determine the dependence of the Polyakov loop on the field strength to leading order. Our results indicate a plasma-type behavior with a negative susceptibility at all temperatures, as well as an increase in the transition temperature as the electric field grows.

Prakash K. Sonwalkar, Vijay Kalmani

Due to the radio range of the network; suspicious transmission; unattended nature; and easier access; Wireless Sensor Networks are vulnerable to malicious users and physical attacks. Hence security is a must in these cases. The development of a WSN for a secured temperature measurement system could be a feasible answer to these issues. The temperature signals are collected by sensors and transmitted to an Advanced RISC Microprocessor (ARM) via Wireless Fidelity (Wi-Fi) technology, which has been proven to transfer data accurately and reliably. The data is then stored in memory that is controlled by the microprocessor. The functionality is created using an ARM 9-based Samsung S3C2440 Controller with the Linux operating system. The security system is incorporated using the host MCUs and the temperature values are converted to digital form using the ARM processor's ADC (S3C2440). The processor is connected to the console terminal through UART, which sends data to the system on a regular basis with security data being monitored and transferred to other Wi-Fi-equipped devices through a USB-based Wi-Fi module, and the temperature readings are continuously monitored via this wireless sensor network. We perform the validation of our study through MATLAB simulations. Through MATLAB simulations we measure the total energy dissipated, throughput, and the lifetime of the sensor nodes. Overall, in this research, we develop an improved security in WSN system with application in ARM controller based secured temperature monitoring system. This paper paves a direction towards further research in physical security in Wireless Sensor Networks.

A. Arivoli, Devdatt Golwala, Rayirth Reddy

COVID-19 continues to threaten the world with its impact and severity. This pandemic has created a sense of havoc and shook the world stretching the medical fraternity to an unimaginable extent, who are now facing fatigue and exhaustion. Due to the rapid increase in cases all across the globe demanding extensive medical care, people are hunting for resources like testing facilities, medical drugs and even hospital beds. Even people with mild to moderate infection are panicking and mentally giving up due to anxiety and desperation. To combat these issues, it is necessary to find an inexpensive and faster way to save lives and bring about a much-needed change. The most fundamental way through which this can be achieved is with the help of radiology which involves examination of Chest X rays. They are primarily used for the diagnosis of this disease. But due to panic and severity of this disease a recent trend of performing CT scans has been observed. This has been under scrutiny since it exposes patients to a very high level of radiation known to increase the probability of cancer. As quoted by the AIIMS Director, one CT scan is equivalent to around 300–400 Chest X-rays. Also, it is relatively a much costlier testing method. Hence, in this report, we have presented a Deep learning approach which can detect covid 19 positive cases from Chest X ray images. It involves creation of a Deep learning based Convolutional Neural Network (CNN) using Keras (python library) and integrating the model with a front-end user interface for ease of use. This leads up to the creation of a software which we have named as CoviExpert. It uses the sequential Keras model which is built layer by layer. All the layers are trained independently to make independent predictions which are then combined to give the final output. 1584 images of Chest X-rays of both COVID-19 positive and negative patients have been used as training data. 177 images have been used as testing data. The proposed approach gives a classification accuracy of 99%. CoviExpert can be used on any device by any medical professional to detect Covid positive patients within a few seconds.

Ryotaro Fujiwara, Hiroki Shimizu

New multi-point methods, the six-point method of 3-2-1 sensor layout and the nine-point method of 3 × 3 sensor layout, have been proposed as the two-dimensional profilometry for a machined flat surface. Monte Carlo simulations were carried out and properties of proposed methods were examined. As a result, it was shown that the six-point method, the minimum configuration that calculates the planar shape with only six displacement sensors, can obtain the equivalent result to that of the simple nine-point method. In the nine-point method with averaging, it was confirmed that the maximum and average values of the standard deviation of the reconstructed profile were reduced to 68% and 81%, respectively compared with simple nine-point method. The improved nine-point method that averages the pitching error to improve the data connection accuracy also proposed and this method reduced standard deviation, but effectiveness is limited.

Aris Hartono, Unan Yusmaniar Oktiawati

The development of information and communication technology, especially in this digital era, demands communication takes place quickly and the management of network connectivity efficiently and effectively. In line with this, PT Indonesia Comnets Plus have built the Metro Ethernet network which is an ethernet network technology implemented in a metropolitan area (big cities). The company provides an internet network due to providing the best quality network is compulsory. Hence, we need a Metro Ethernet network monitoring system with the aim of being able to know the performance and problems efficiently and in real time. One of the open source based applications used for network monitoring is Zabbix. This study aims to create and test the performance of Raspberry Pi in the Metro Ethernet network monitoring system at PT Indonesia Comnets Plus with the case study of DISKOMINFO in Garut Regency using the Zabbix application. In monitoring, the SNMP (Simple Network Monitoring Protocol) protocol is needed which will send network problems in the form of triggers. Trigger will evaluate the data collected and represent the current state of the system. Trigger contains two statuses, "OK" and "PROBLEM", to determine the threshold for what is "acceptable" data. Therefore, if the incoming data exceeds an acceptable state, the trigger status changes to "PROBLEM", the trigger status is recalculated every time the Zabbix server receives a new value. Zabbix server will send trigger status to electronic mail (e-mail) and Telegram application as notification for network administrators.