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

Yunpeng Qu, Yunlei Zhou, Qiuyun Yang et al.

Electromagnetic (EM) metamaterials have garnered considerable attention due to their capacity to achieve negative parameters, significantly influencing the integration of natural materials with artificially structural media. The emergence of carbon aerogels (CAs) offers an opportunity to create lightweight EM metamaterials, notable for their promising EM shielding or absorption effects. This paper introduces an efficient, low‐cost method for fabricating CAs without requiring stringent drying conditions. By finely tuning the ZnCl2/lignin ratio, the porosity is controlled in CAs. This control leads to an epsilon‐negative response in the radio‐frequency region, driven by the intrinsic plasmonic state of the 3D carbon network, as opposed to traditional periodic building blocks. This approach yields a tunable and weakly epsilon‐negative response, reaching an order of magnitude of −103 under MHz frequencies. Equivalent circuit analysis highlights the inductive characteristics of CAs, correlating their significant dielectric loss at low frequencies. Additionally, EM simulations are performed to evaluate the distribution of the electric field vector in epsilon‐negative CAs, showcasing their potential for effective EM shielding. The lignin‐derived, lightweight CAs with their tunable epsilon‐negative response hold promise for pioneering new directions in EM metamaterials and broadening their application in diverse extreme conditions.

Jiovana S. Gomes, Mateus Grellert, Fabio L. L. Ramos et al.

The pervasive presence of video content has spurred the development of advanced technologies to manage, process, and deliver high-quality content efficiently. Video compression is crucial in providing high-quality video services under limited network and storage capacities, traditionally achieved through hybrid codecs. However, as these frameworks reach a performance bottleneck with compression gains becoming harder to achieve with conventional methods, Deep Neural Networks (DNNs) offer a promising alternative. By leveraging DNNs’ nonlinear representation capacity, these networks can enhance compression efficiency and visual quality. Neural Video Coding (NVC) has recently received significant attention, with Neural Image Coding models surpassing traditional codecs in compression ratios. Therefore, this survey explores the state-of-the-art in NVC, examining recent works, frameworks, and the potential of this innovative approach to revolutionize video compression. We identify that NVC models have come a long way since the first proposals and currently are on par in compression efficiency with the latest hybrid codec, VVC. Still, many improvements are required to enable the practical usage of NVC, such as hardware-friendly development to enable faster inference and execution on mobile and energy-constrained devices.

Sung‐Jae Chang, Seokho Moon, Junhyung Jeong et al.

Abstract GaN‐based high electron mobility transistors (HEMTs) is demonstrated using an extremely thin (≈ 5 nm) h‐BN passivation layer and air spacer, for the first time. The h‐BN passivation layer is grown by metal–organic chemical vapor deposition on top of the AlGaN barrier, followed by GaN‐based HEMTs fabrication. To prohibit the loss and/or damage of the thin h‐BN passivation layer, the SiN is deposited as a protection layer during the device fabrication. When the device fabrication is finalized, the SiN protection layer is removed by buffered oxide etchant, introducing the air spacer under the head of the T‐gate electrode. The electrical properties of the GaN‐based HEMTs applying h‐BN passivation layer and air spacer are measured and compared to the h‐BN/SiN passivated and conventional SiN passivated GaN‐based HEMTs. The difference of the DC characteristics corresponding to the passivation layer in GaN‐based HEMTs is negligible. However, compared to the conventional SiN passivated GaN‐based HEMTs, the RF performance, such as current gain cut‐off frequency and maximum oscillation frequency is improved by 50.3% and 68.5%, respectively, since the parasitic capacitances is reduced by the air spacer formation in GaN‐based HEMTs using a thin h‐BN passivation layer.

Xinyan Xiang, Shuaijie Li, Alisher Duspayev et al.

Rydberg atoms are widely employed in precision spectroscopy and quantum information science. To minimize atomic decoherence caused by dc Stark effect, the electric field noise at the Rydberg atom location should be kept below $\sim 10$ mV/cm. Here we present a simple yet effective electronic circuit, referred to as a clamp switch, that allows one to realize such conditions. The clamp switch enables precise low-noise electric field control while allowing application of fast high-voltage ionization pulses through the same electrode(s), enabling atom detection via electric-field ionization and electron or ion counting. We outline the circuit design and analyze its noise suppression performance for both small and large input signals. In application examples, we employ the clamp switch to reduce the spectral width and increase the signal strength of a Rydberg line by a factor of two, to estimate the electric-field noise in the testing chamber, and to perform electric-field calibration using Rydberg Stark spectroscopy. The clamp switch improves coherence times and spectroscopic resolution in fundamental and applied quantum science research with Rydberg atoms.

Jun Wang, Zhao Wang, Jorge Ayarza et al.

The spatial and temporal control of material properties at a distance has yielded many unique innovations including photo-patterning, 3D-printing, and architected material design. To date, most of these innovations have relied on light, heat, sound, or electric current as stimuli for controlling the material properties. Here, we demonstrate that an electric field can induce chemical reactions and subsequent polymerization in composites via piezoelectrically-mediated transduction. The response to an electric field rather than through direct contact with an electrode is mediated by a nanoparticle transducer, i.e., piezoelectric ZnO, which mediates reactions between thiol and alkene monomers, resulting in tunable moduli as a function of voltage, time, and the frequency of the applied AC power. The reactivity of the mixture and the modulus of a naïve material containing these elements can be programmed based on the distribution of the electric field strength. This programmability results in multi-stiffness gels. Additionally, the system can be adjusted for the formation of an electro-adhesive. This simple and generalizable design opens new avenues for facile application in adaptive damping and variable-rigidity materials, adhesive, soft robotics, and potentially tissue engineering.

Satyappa Kalliguddi, R. F. Bhajantri, Shivaprasad Chalawadi et al.

The electrolyte is an essential element of modern energy storage systems, guiding ion migration between electrodes. Sodium carboxymethyl cellulose (NaCMC) has emerged as a promising green alternative for electrolyte materials. The poly(vinyl alcohol)/NaCMC polymer network has gained popularity as a prominent polymer blend. PVA/NaCMC polymer blends loaded with sodium tungstate salt were prepared as PVA/NaCMC/Na2WO4 polymer blend nanocomposite electrolytes via a solution casting technique. FTIR analysis reveal shifts in band assignments related to OH and CO groups, suggesting Na+ interactions with polar functional groups in PVA and NaCMC, promoting salt dissociation and anion immobilization for efficient cation mobility. Tungstate anions, on the other hand, act as a pivotal nanofiller component that optimizes the polymer blend's microstructure and properties beyond mere ion supply. Tungstate anions disrupt the semi‐crystalline nature of the PVA/NaCMC polymer blend, as confirmed by XRD patterns showing reduced crystallinity with increasing Na2WO4 salt concentration, which increases amorphous domains and free volume for enhanced ion pathways. This leads to improved thermal stability and electrochemical stability. Sodium ions, derived from both NaCMC and the dissociated Na2WO4 salt, serve as the primary mobile charge carriers responsible for ion transport. They facilitate conductivity through a hopping mechanism, where sodium ions migrate between coordination sites in the polymer matrix, particularly in amorphous regions, under an applied electric field. This is evidenced by the observed ionic conductivity of 5 × 10−6 S cm−1 was recorded at room temperature for the PVA/NaCMC blend containing 10 wt% sodium tungstate salt, and rose to 4.4 × 10−4 S cm−1 at 80°C. The temperature dependence of the conductivity followed Arrhenius behavior. An equivalent electric circuit model was used to interpret the EIS data. The dielectric properties were investigated by examining AC conductance spectra, dielectric constants (ε′ and ε″), electric moduli (M′ and M″), and loss tangents. The dielectric permittivity increased in the low‐frequency region owing to electrode polarization effects. The maximum of the loss tangents shifted with increasing temperature, accompanied by an increase in peak height at high frequencies. Sodium‐tungstate‐based polymer blend nanocomposite electrolytes exhibited an enhanced electrochemical stability window (2.57 V), a higher transference number (0.973), and improved ionic conductivity, making them suitable for energy storage device applications.

Ahmad Jalili, Mehdi Gheisari, Jafar A. Alzubi et al.

Wireless Sensor Networks (WSNs) are essential for monitoring operational environments. Efficient selection of cluster heads is crucial in minimizing energy consumption. This study introduces an innovative model for determining the positions of cluster heads in a WSN, using the residual energy of each sensor node as a selection metric. The sensor incorporates a neural network model that has been trained using different significant models to determine the best location for the cluster head. Our model is compared with the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm to showcase its effectiveness, particularly in mobile node scenarios. We employ a feed-forward multilayer network, specifically Multilayer Perceptrons (MLPs), as the neural network technique. The proposed method comprises two stages: the training stage, where the neural network is trained with appropriate models, and the execution stage, where the trained network suggests the cluster head's location based on the sensor's conditions. The results demonstrate that the proposed method can accurately identify suitable locations for cluster heads in a sensor network and is capable of adapting to new models despite environmental changes and variations in input configurations.

Wang Benzheng

The combined system based on multiple MEMS sensors is a miniature measurement system used for dynamic output and display of 3D information about the user's posture. It is mainly used for various Tai Chi movement posture calculation simulation research, wearable devices, etc. This article explores MEMS sensor technology, focusing on MEMS sensor data processing, Tai Chi movement position calculation and fusion calculation positioning algorithm. Due to the high noise characteristics of MEMS sensor devices, time series analysis is used to model MIMU signals and Kalman filtering is optimized. As a research field, simulation of Tai Chi movement appears in the intersection of biomechanics, robotics and computer science. The purpose is to create a computer model to simulate the natural and real body movements of the human body under certain conditions. In addition to creating special effects, Tai Chi movement posture calculation simulation can also be used for operation training and research on body structure. This article first introduces the typical applications of several MEMS sensor combinations, and then introduces the key technology of studying Tai Chi movement simulation. The kinematics and mechanics data of Tai Chi are obtained using biomechanical measurement technology, while the individual simulation of Tai Chi dynamics is realized in a certain mode of the machine. By creating a kinematic model of the human upper limb, and finally creating a flexible machine that imitates the human upper limb, to analyze the kinematic characteristics of the human upper limb, and cleverly realize the imitation of active interaction, the simulation of human movement and the solution of Tai Chi movement posture Simulation.

Prabhat Kumar, Michael Hoffmann, Andy Nonaka et al.

Abstract HfO2– and ZrO2–based ferroelectric thin films have emerged as promising candidates for the gate oxides of next‐generation electronic devices. Recent work has experimentally demonstrated that a tetragonal/orthorhombic (t/o‐) phase mixture with partially in‐plane polarization can lead to negative capacitance (NC) stabilization. However, there is a discrepancy between experiments and the theoretical understanding of domain formation and domain wall motion in these multi‐phase, polycrystalline materials. Furthermore, the effect of anisotropic domain wall coupling on NC has not been studied so far. Here, 3D phase field simulations of HfO2– and ZrO2–based mixed‐phase ultra‐thin films on silicon are applied to understand the necessary and beneficial conditions for NC stabilization. It is found that smaller ferroelectric grains and a larger angle of the polar axis with respect to the out‐of‐plane direction enhances the NC effect. Furthermore, it is shown that theoretically predicted negative domain wall coupling even along only one axis prevents NC stabilization. Therefore, it is concluded that topological domain walls play a critical role in experimentally observed NC phenomena in HfO2– and ZrO2–based ferroelectrics.

Likun Wang, Kyungyee Kim

This study investigated the emotional trends of users on social platforms, considering the event of “the Changsha girl jumping off the Lalamove truck” as a case study. It examined the effects of recommendation algorithms and group social comparison attributes on group emotions across three platforms: Zhihu, Weibo, and Bilibili. Through text mining and emotion analysis algorithms, group reviews were analyzed, and an event-based ARIMA robustness detection model was constructed using time series data. Utilizing the theoretical framework of the social comparison process, the study discovered that the “information database” formed by the recommendation algorithms of social platforms fosters the emergence of emotional group polarization among users. Furthermore, the findings revealed that the audience's social comparison attributes play a role in shaping emotional group polarization. High knowledge attributes tend to inhibit emotional group polarization, while low knowledge attributes tend to promote it. Machine learning algorithms were employed to measure user sentiment in social media platform comments, revealing insights into the causes of group polarization through a comparison of social comparison attributes and algorithm techniques. Future studies must focus on measuring technical information entropy.

Aparna R. Patil, Satish Sampatrao Salunkhe

The classification of knee osteoarthritis is solely based on contextual factors, with image processing algorithms playing a significant role in computer-aided diagnosis (CAD) systems. The inconsistent real-time pre-processing, on the other hand, has a significant impact on the diagnosing process. In this work, a Densely Connected Fully Convolutional Network (DFCN) for knee osteoarthritis classifier based on multiple learning (ML) strategies effectively classify knee osteoarthritis on the basis of risk estimation. Spatial osteoarthritis contextual vectors extracted by identifying the relationship between contextual variables using a machine learning approach. The hidden convolutional layers are used to compute edge interpretation, contextual cues, and input correction. The fused layer, which is simply a concentration of derived features, supports automatic learning of contextual features of osteoarthritis classification. The standard datasets from the Osteoarthritis Initiative (OAI) and the Multicentre Osteoarthritis Study (MOST) are used for experimental purposes to validate the proposed method. The results shows that the proposed DFCN is significantly improves the feature recognition for accurate classification around 94 % which is significantly higher than existing CNN results and flexibility to real-time implementation in the CAD system. It can also be used to automatically detect osteoarthritis types using a lightweight CNN architecture.

Mingxu Du, Yang Chen, Minqiang Mai et al.

Abstract Recently, a novel paradigm of boron‐ and nitrogen‐embedded polycyclic nanographites featuring multiple resonance thermally activated delayed fluorescence (MR‐TADF) has garnered substantial interest due to their extraordinary attributes of efficient narrowband emissions with small full width at half maxima (FWHMs). Despite an array of diverse color tuning strategies, it remains elusive how to effectively manipulate device efficiencies without altering the materials' intrinsic MR‐TADF characteristics. Here, an advanced ‘non‐conjugate fusion’ design methodology was proposed, aimed at dramatically amplifying the horizontal orientations of MR‐TADF emitters while preserving the short‐range charge‐transfer properties. As envisioned, when compared to the classical BCz‐BN mother core, the proof‐of‐concept emitter mICz‐BN achieved an impressively enhanced horizontal dipole ratio (83% vs. 75%) at analogous emission wavelengths (∼486 nm), FWHMs (∼26 nm) and photoluminescence quantum yields (∼93%). Consequently, the external quantum efficiency of the optimized device yielded a performance enhancement of 1.2‐fold (30.5% vs. 25.3%) whilst keeping the spectrum almost unchanged.

Abhishek Sahu, Abhishek Kumar, Anurag Dwivedi et al.

Shrinking of the thickness of silicon on insulator (SOI) has been proposed as a potential solution for scaling down the physical base length of symmetric lateral electrostatically doped bipolar transistors. An ultra-thin body device that utilizes the full SOI thickness has been presented and the performance of the same is investigated in detail. The device features two distinct doping techniques: work function-induced electrostatic doping (WED) and bias-induced electrostatic doping (BED). The proposed design approach leads to significant improvements in gain and cut-off frequency compared to previously reported designs. The resulting devices exhibit peak current gain β values >1100, ft>500 GHz, fmax>1300 GHz. Moreover, these improved device performance matrices get translated into better performance of universal gates with low rise and fall time of ∼1.3 ns, and improved noise margin performance in static random access memory (SRAM) device of 0.43 and 0.41 for WED and BED based devices respectively. Furthermore, the study investigates the reliability of the device concerning breakdown voltage and its response to different temperature conditions. The findings reveal a decline in the β value for WED-based devices when subjected to temperatures exceeding 340 K. In contrast, BED-based devices demonstrate a comparatively smaller variation in β at temperatures above 340 K. These results show the potential of the proposed device for mixed-signal and digital circuit applications.

Stefano Falletta, Andrea Cepellotti, Anders Johansson et al.

Predicting response of materials to external stimuli is a primary objective of computational materials science. However, current methods are limited to small-scale simulations due to the unfavorable scaling of computational costs. Here, we implement an equivariant machine-learning framework where response properties stem from exact differential relationships between a generalized potential function and applied external fields. Focusing on responses to electric fields, the method predicts electric enthalpy, forces, polarization, Born charges, and polarizability within a unified model enforcing the full set of exact physical constraints, symmetries and conservation laws. Through application to $α$-SiO$_2$, we demonstrate that our approach can be used for predicting vibrational and dielectric properties of materials, and for conducting large-scale dynamics under arbitrary electric fields at unprecedented accuracy and scale. We apply our method to ferroelectric BaTiO$_3$ and capture the temperature-dependence and time evolution of hysteresis, revealing the underlying microscopic mechanisms of nucleation and growth that govern ferroelectric domain switching.

Pei Wang, You-Quan Li

We present a phenomenological model for magnetoelectricity in multiferroic materials. The distinctive feature of the model is a two-component complex order parameter that encodes the electric polarization, along with a direct coupling between the polarization and magnetic field. Our model effectively elucidates that a sufficiently strong magnetic field can destroy electric polarization. Furthermore, the transition field strength diminishes with rising temperature, following a power-law relation with the exponent being precisely worked out. At lower field strength, the electric polarization takes a spiral order in the magnetic field, with the spiral wavelength inversely proportional to the magnetic field strength. We anticipate these predictions can be experimentally tested in future studies on multiferroic materials.

Chunmeng Xu, Xiaoqing Song, Pietro Cairoli

Compared to conventional mechanical breakers, solid state circuit breakers (SSCBs) are well-known for the ultra-fast fault clearing speed and the arc-free current interruptions, making them a promising protection apparatus for electric vehicle charging infrastructure (EVCI), electrified ship and aircraft, and railway system applications. With the superior material properties of silicon carbide (SiC), the SiC based SSCBs are expected to achieve a lower conduction loss and a faster fault breaking speed in a smaller form factor. One remaining design challenge of SiC based SSCBs is to maintain the safe device junction temperature under all operation conditions, especially the overload conditions which cause escalated thermal stresses for power semiconductor devices. In this article, the thermal performance of a SiC metal oxide semiconductor field effect transistor (MOSFET) based SSCB is experimentally evaluated under both nominal and overload conditions. Finite element models and thermal network models are constructed to estimate the overload withstand time of the SSCB prototype under a wide range of ambient temperatures. Moreover, the established overload evaluation strategy is applicable to not only SSCBs, but also power converters with a high requirement on their overload withstand capabilities.

Xingyue Wang, Yingzhou Yu, Kaixin Chen et al.

Long-period grating (LPG) stands as a significant wavelength-selective optical apparatus with applications in telecommunications. This device facilitates the selective coupling of light at resonated wavelengths from the core of an optical fiber or waveguide to the cladding, thereby creating a rejection band within the output transmission spectrum. Long-period fiber gratings (LPFGs) have therefore been studied extensively for fiber-optic communication and sensing applications. Nevertheless, achieving high-speed wavelength tuning of the LPG remains a formidable challenge. In contrast to LPFG, fabricating LPG on waveguide chip is more practicable due to the diverse selection of waveguide materials and structures available. This flexibility enables the use of electro-optic materials to activate the long-period waveguide grating (LPWG), thereby achieving high-speed, sensitive, and wide-band wavelength tuning. This report outlines the design electro-optically tunable LPWG utilizing electro-optic polymer (EOP) cladding on silicon nitride waveguide. EOP presents notable advantages, including high electro-optic coefficients, low dielectric constants, and seamless integration with various photonic materials. The modulation of the refractive index of the EOP cladding by an external electric field induces a change in the effective refractive index of the cladding mode, consequently leading to a central wavelength shift in the output transmission spectrum. The simulation results indicate that when the electrode space is 5 µm and the EO coefficient of EOP is 100 pm/V, the tuning efficiency is 0.3 nm/V. This demonstration underscores the efficacy of utilizing electro-optic polymer-based long-period waveguide gratings for rapid and precise wavelength tuning in photonic integrated circuits.

Xie Bing-Hong, Xu Guo-Kai, Xiao Shao-Qiu et al.

Magnetoelectric composites, comprising piezoelectric and magnetostrictive materials, are widely used in magnetic field sensing, energy harvesting, and transducers. This paper establishes a finite element model of a laminated magnetoelectric transducer coupled with magneto-elastic-electric fields based on the constitutive equations of the nonlinear magnetostrictive materials. Then, the resonant magnetoelectric effect under different biased magnetic fields is studied. Based on the equivalent circuit model and the two-port network theory, the magnetoelectric coefficient and the equivalent source impedance of the resonant state are solved entirely for the first time. Introducing optimized L-section matching networks between the magnetoelectric transducer and the load resistor can increase the load power and expand the operating bandwidth. The simulation results are consistent with the data from references, thus confirming the accuracy and effectiveness of the model. The simulation results demonstrate that the magnetoelectric coefficient reaches 51.79V·cm-1·Oe-1@51.4 kHz at a 450 Oe bias magnetic field, and reaches the ultimate output power of -3.01 dBm@50.4 kHz at a 350 Oe bias magnetic field. To ensure the load power, the power increase of 2.30 dB and the bandwidth expansion of 2.27 times are achieved by optimizing the matching network. This paper's nonlinear finite element model takes full account of the magnetoelectric effect in the acoustic resonance state and quantifies the ultimate output power. The magnetoelectric transducer model can achieve high magnetoelectric coefficient, load power, and power density in a small volume, providing a significant advantage in terms of equilibrium. The research results are of great importance in guiding the design and performance improvement of miniaturized magnetically coupled wireless power transfer systems.

Sathiyandrakumar Srinivasan, Deepalakshmi P

With various malware, botnets are the legitimate risk increasing against cybersecurity providing criminal operations like malware dispersal, distributed denial of service attacks, fraud clicking, phishing, and identification of theft. Existing techniques used for detection of botnet, which are suitable only for specific command of botnet and protocol for controlling and do not support botnet detection at earlier stages. In several computer security defense systems, honeypots are deployed successfully by security defenders. As honeypots can attract botnet compromises and expose spies in botnet membership and behaviors of the attacker, they are broadly employed in botnet defense. Thus, attackers whose role is to construct and maintain botnets have to determine honeypot trap avoiding methods. To handle the issues related to botnet attacks, machine learning techniques are used to support detection and prevent bot attacks. An Ensemble Classifier Algorithm with Stacking Process (ECASP) is proposed in this paper to select optimal features fed as input to the machine learning classifiers to estimate the botnet detection performance. As a result, the method achieves proposed achieves 94.08% accuracy, 86.5% sensitivity, 85.68% specificity, and 78.24% F-measure.

Suresh Ponnan, Tikkireddi Aditya Kumar

Core systems with network transactions deployed semiconductor materials to develop wireless networks-on-chip to minimize latency with increased performance. For transmitting data from the source point towards the target point, an appropriate reconfigurable routing method has to be deployed with respect to nodes. For overhead on-chip communication that involves the linking of many cores in a single chip, congestion may occur which has to be eliminated. A marching memory arbitrator is deployed in the path that is prone to congestion which computes the port as a buffer. The static degradation of energy power utilization in the router is solved by using a Marching memory buffer. The secure communication of data can be deployed with hash, identity, and address verification blocks. The traffic is then relaxed by routing arbitrator and then data transmission is done through frequency division multiplexing in the communication channel with reconfigurable routing. The analysis of simulation results is found to have a better throughput, less latency, and reduced power consumption.