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

Ramzy Rammouz, Vasileios Adamopoulos, Ivan D. Castro Miller et al.

Recent efforts have focused on wireless ingestible sensing capsules, but challenges remain in miniaturization, sensor integration, and energy efficiency. This paper presents GISMO-A, an ingestible capsule integrating a custom-designed application-specific integrated circuit (ASIC) for low-power biochemical sensing. The ASIC enables pH and oxidation-reduction potential (ORP) measurements at an average power consumption of <inline-formula> <tex-math notation="LaTeX">$172~\mu $ </tex-math></inline-formula>W, representing a 70% reduction compared to the previously published GISMO capsule. GISMO-A supports a 6-second measurement interval, resulting in a threefold increase in data density relative to GISMO. Validated through in-vitro and in-vivo experiments, GISMO-A represents a significant advancement in the design of energy-efficient, miniaturized GI Tract sensing systems.

Shah Zahid Yousuf, Sreenivasulu Mamilla, N.V.L. Narasimha Murty

In this study, Pt/BiFeO3/Al2O3/ITO heterostructures are fabricated using a sputtering technique, and the role of the interface barrier in influencing the resistive switching (RS) mechanism is investigated. Al2O3/BFO heterostructures are successfully fabricated with uniform granular morphology on a commercial ITO substrate. These devices exhibit self-rectifying analog memristive behavior, positioning them as promising candidates for neuromorphic computing applications. Devices incorporating an Al2O3 layer show intrinsic rectifying characteristics and distinct analog resistance states. Al2O3/BFO devices demonstrate better performance, primarily due to the accumulation and migration of oxygen vacancies (O∗∗). These bilayer devices present two clear switching behaviors: filamentary switching and area-dependent switching. Through systematic exploration of devices with varying device sizes, area-dependent switching, driven by interface barriers, emerges as the dominant mechanism with different resistance loads at different device features. Conductive atomic force microscopy (CAFM) is employed to examine switching behavior at the nanoscale, offering critical insights for future nanoscale device applications. The conduction mechanism in Al2O3/BFO devices is also analyzed to better understand the charge transport process. These devices exhibit stable endurance up to 9 × 105 cycles at room temperature, along with excellent projected data retention of up to 10 years at 85 °C with minimal resistance variation.

Zhaoguo Wu, Qian Wang, Qianbo Xiao et al.

Han Wui Then, M. Radosavljevic, S. Bader et al.

We demonstrate a 50 nm DrGaN technology fabricated in a 300 mm GaN-on-Silicon process combining E-mode high-k dielectric GaN MOSHEMT with integrated 3D monolithic Si PMOS by layer transfer. The DrGaN consists of a channel-length 50 nm GaN MOSHEMT power transistor with figure-of-merit (FOM) of 1.1 (mΩ-nC)-1 and total width of 470.59 mm, integrated with a CMOS gate driver comprising a 27.19 mm wide 180 nm Si PMOS and 49.54 mm wide 130 nm GaN NMOS. In this work, we employed a gate-last 3D monolithic integration process, where the high temperature activation steps for the Si PMOS transistors are completed before the gate dielectric of the GaN MOSHEMT transistors is deposited.

Charles Bechara, Guy Friedrich, Christophe Forgez et al.

This paper introduces an on-board, low-cost Electrochemical Impedance Spectroscopy technique for battery diagnostics, utilizing numerical simulations and experimental results. EIS, commonly used in laboratories to assess charge transfer and diffusion in electrochemical cells, is ideal for monitoring battery performance, state of charge, and health. However, traditional EIS equipment is too large and expensive for automotive use. The proposed system offers a compact, cost-effective solution for electric vehicles. We discuss the challenges and trade-offs for accurate on-board measurements, based on simulations. Prototype results are then compared with laboratory EIS measurements on 260 Ah Li-NMC EV pouch cells, demonstrating that embedded spectroscopy achieves precise results, even for high-capacity, low-impedance cells. The study highlights the potential of this technique as a reliable and effective method for battery diagnostics in automotive applications.

Houssein El Housseiny, Suzanne Fery‐Forgues, Marc Ternisien et al.

Abstract Blue fluorescent materials play a crucial role in the advancement of blue organic light‐emitting diodes (OLEDs) with high stability and high color purity. Fluorescent molecules known as Solution and Solid‐State Emitters (SSSEs) garner significant interest due to their unique optical properties. Most fluorophores exhibit strong emission in solution and typically lose their emissive properties in the solid state due to the emergence of intermolecular interactions in the aggregated phase. In contrast, SSSEs retain their fluorescence both in solution and in the solid state, making them highly valuable for various applications. This study focuses on 2‐phenyl‐naphth[2,3‐d]oxazole (Nzx) that exhibits SSSE‐like properties. This fluorescent molecule is characterized by deep blue emission near the UV range (300–450 nm). It is investigated with the goal of developing stable and high‐quality blue fluorescent OLEDs. To achieve this, Nzx is integrated as both an emissive layer and a dopant in OLED devices. The stability of the OLEDs under electrical stress is analyzed, and the device structure along with the doping concentration is investigated to optimize OLED performance. As a result, near deep blue‐emitting devices with an emission wavelength of 438 nm and an external quantum efficiency (EQE) of 1% are successfully achieved.

Ping Wang, Feng Chen, Yuhe Yang et al.

Abstract Spintronics has been extensively explored over the past decades, focusing primarily on the spin characteristic of the electron, while the orbital feature of the electron has been conventionally assumed to be quenched by the crystal field effect. Recently, studies have unveiled a fascinating discovery that orbital current, originating from orbital effects, can be generated in materials with weak spin‐orbit coupling by applying electric fields, enabling the manipulation of the ferromagnetic magnetization and induction of terahertz emission. This review highlights recent achievements in orbital effects, materials, and devices, beginning by discussing the mechanisms underlying orbital effects, e.g. the orbital Hall effect, orbital Rashba‐Edelstein effect, inverse orbital Hall effect, and inverse orbital Rashba‐Edelstein effect. Subsequently, a wide range of materials exhibiting orbital effects are classified and the orbital sources in them are identified. Furthermore, the review introduces the orbital torque devices and the orbital terahertz emitters, summarizing the in‐depth mechanisms of the orbital torque, orbital torque efficiency, and orbital diffusion length across various material structures. Additionally, the review presents strategies for enhancing orbital torque efficiency and driving magnetization switching. These efforts aim to explore the potential applications for orbitronic memory devices, computing components, and terahertz emitters.

Iyed DERAR, Reda GUERNANE

This study presents a funnel-based motion planning algorithm for a car-like robot, utilizing a dynamic model to capture the robot's motion. The funnel-based planner addresses the obstacle avoidance problem and dynamically updates the path to guide the robot to its goal. The proposed algorithm's performance is evaluated in a dynamic environment, and with a dynamic goal where re-planning capabilities are demonstrated. The results indicate that funnel planner provides robust navigation even in uncertain conditions.

Muskan Chawla, Surya Narayan Panda, Vikas Khullar et al.

The lack of vocal emotional expression is a major deficit in social communication disorders. The current scenario of artificial intelligence focuses on collaborative training of deep learning models without losing data privacy. The primary objective of this paper is to propose a federated learning-based classification model to identify and analyze the emotional capabilities of individuals with vocal emotion deficits. The methodology has developed a collaborative and privacy-preserved approach using federated learning for training the deep learning models. The proposed methodology utilizes Mel-frequency Cepstral Coefficients (MFCC) to preprocess audio recordings. The four datasets (RAVDESS, CREMA, TESS, SAVEE) including emotion-based classified audio recordings were collected from open sources. The collected audio recordings are 3 s each and the total data set has 668376 audio files with happy - 175119 files, sad – 172611 files, angry – 176346 files, and normal - 144300 files. Further, the input audio was pre-processed to generate MFCC features. The study began with extracting features from multiple pre-trained DL models as its base model. Then, the performance of the federated learning (FL) model was tested on independent and identically distributed (IID) and non-IID data. Further, this paper presents a federated deep learning-based multimodal system for verbal communication emotions classification that uses audio datasets to meet data privacy requirements by DL on the FL ecosystem. As per the findings, the federated learning trained model provides nearly similar parametric results in comparison to base model training. For IID data, the model had 99.71 % validation accuracy, precision (99.73 %), recall (99.69 %), and validation loss (0.01). The FL architecture with non-IID data outperformed these measures with validation accuracy (99.97 %), precision (99.97 %), recall (99.97 %), and least loss (0). Hence the acquired results support the utilization of federated learning ecosystem-based trained models with identically and non-identically distributed audio features from emotion identification without losing parametric results. In conclusion, the proposed techniques could be applied to identify verbal emotional deficits in individuals and could support developing emerging technological interventions for their well-being.

Youngwoo Jeon, Soobin Sim, Doyoon Shin et al.

Abstract Recent studies on top‐emitting structure, which is designed to enhance the color purity and outcoupling efficiency of quantum‐dot light‐emitting diodes (QLEDs), employ commercially unviable methods owing to limited options for applying the hole injection layer through solution processes on the bottom electrode. In this study, all‐solution‐processable conventional top‐emitting QLEDs (TQLEDs) are successfully fabricated by introducing a polyethylenimine (PEI) interlayer, doping isopropyl alcohol (IPA) into the hole‐injection layer (poly (3,4‐ethylenedioxythiophene):poly(4‐styrenesulfonate), PEDOT:PSS), and using the dynamic spin‐coating method. The increased hole injection resulting from the tuned anode‐HIL interface by the PEI and IPA‐doped HIL, coupled with the enhanced outcoupling efficiency and full width at half maximum (FWHM) derived from the optimized cavity length through simulation, realizes a red InP QLED with high efficiency and color purity. The optimized TQLED exhibits a maximum current efficiency and FWHM of 28.04 cd A−1 and 36 nm, respectively, which are threefold higher and 8 nm narrower than those of bottom‐emitting QLEDs, marking the highest current efficiency ever reported for top‐emitting red InP QLEDs.

S. Jeevitha, J. Joel, N. Sathish Kumar et al.

Myocardial Infarction otherwise called heart attack occurs in human beings when blood flow decreases or stops to a part of the heart which in turn damages the heart muscle. Prediction of abnormalities in cardio arrhythmia disease is done by using standard 12-lead Electrocardiography (ECG) signals, which also detects Posterior Myocardial Infarction (PMI). The QRS complex is the merged output of different parts of graphical deflection seen on a typical Electro Cardio Gram (Electrocardiography). The main purpose of the paper is to monitor and analyze particularly the Rpeak upward deflections from the QRS complex. Denoising the ECG signal is done by butter worth filter. The denoised signals are used to detect R peaks and image plotting is done by segmentation. R peak images are used to classify the abnormalities in Myocardial Infarction (MI) with the help of the CNN image processing technique. The publicly available PTB diagnostic dataset is used to classify the abnormalities in PMI. The detection of the R peaks is used to guide Cardiologists must advance the Percutaneous Coronary Intervention treatment. Prediction has been done using probability weighted average method. Troponin level has been calculated to evaluate a person's health condition which also supports in close prediction of diseases and abnormalities. From experimental analysis of proposed Probability weighted average method in troponin level (PWAMT), the accuracy scores in the ensemble model were found to be 86 % respectively. The running of algorithm took 250 s–300 s to execute the program and display the prediction results.

Teruo Matsushita

Presents a listing of reviewers who contributed to this publication in 2023.

Jingyi Lu

Traffic flow prediction is a very important research field in intelligent transportation system. The traditional prediction methods have a very wide application in traffic flow prediction. However, in the short-term traffic flow prediction, due to the complexity of its influencing factors, the traditional prediction methods cannot predict the short-term traffic flow well. In this paper, the short-term traffic flow prediction model is constructed by using the short-term and short-term memory network, and the modal aliasing problem is solved by using the variational modal decomposition. From the experimental results, the method proposed in this paper is very suitable for short-term traffic flow prediction, and can achieve good prediction effect and accuracy.

Maciej Klein, Krzysztof Blecharz, Bryan Wei Hao Cheng et al.

Abstract Unbalanced mobility and injection of charge carriers in metal‐halide perovskite light‐emitting devices pose severe limitations to the efficiency and response time of the electroluminescence. Modulation of gate bias in methylammonium lead iodide light‐emitting transistors has proven effective in increasing the brightness of light emission up to MHz frequencies. In this work, a new approach is developed to improve charge carrier injection and enhance electroluminescence of perovskite light‐emitting transistors by independent control of drain–source and gate–source bias voltages to compensate for space‐charge effects. Optimization of bias pulse synchronization induces a fourfold enhancement of the emission intensity. Interestingly, the optimal phase delay between biasing pulses depends on modulation frequency due to the capacitive nature of the devices, which is well captured by numerical simulations of an equivalent electrical circuit. These results provide new insights into the electroluminescence dynamics of AC‐driven perovskite light‐emitting transistors and demonstrate an effective strategy to optimize device performance through independent control of the amplitude, frequency, and phase of the biasing pulses.

Xianglin Gao, Yilin Bao, Zhijun Chen et al.

Abstract Since the discovery of conducting polyacetylene in the 1970s, intrinsically conducting polymers (ICPs) have attracted great attention because of their interesting structure, properties, and applications. Notably different from conventional conductors such as metals and doped semiconductors, ICPs have high mechanical flexibility and are light weight. In addition, their properties can be easily tuned by controlling the doping level, modifying the chemical structure, or forming composites with organic or inorganic materials. Their application in bioelectronics is particularly interesting because they have good biocompatibility and good mechanical matching with biological tissues. In this article, the methods to increase the mechanical stretchability of ICPs are first reviewed because high stretchability is often required for bioelectronic applications while pristine ICPs generally have limited stretchability. The application of ICPs as stretchable electrodes for epidermal biopotential detection and neural interfaces is discussed. Then, the employment of ICPs as the electrodes or sensing material of mechanical sensors is reviewed. They also have important application in controllable drug delivery. Last, their applications in the wearable energy harvesting and storage devices including thermoelectric generators and supercapacitors are also covered.

Jonathan Wittemeier, Muhammed Ali Yildirim, Nils Pohl

Radar systems got very popular in sensing applications in the last two decades besides the traditional military sector. Nowadays, many applications favor multiple-input multiple-output (MIMO) radar over phased-array radar. Here, time-division multiplexing (TDM) and code-division multiplexing (CDM), like a phase-modulated continuous wave (PMCW), are well-known techniques. However, every method needs special components on the MMIC. In this article, a <inline-formula><tex-math notation="LaTeX">$\text{125}\,\text{GHz}$</tex-math></inline-formula> vector modulator (VM) circuit is presented, which can operate as a switchable amplifier in TDM systems, as a binary-phase modulator in CDM systems, and as a phase-shifter in phased-array systems. Based on simulations and <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula>-parameter measurements, the VM itself and the three different operating modes are analyzed. We also present a technique to separate coupler imperfections from the <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula>-parameter measurements to analyze the VM separately. We designed the VM with the B11HFC silicon-germanium technology (<inline-formula><tex-math notation="LaTeX">$f_{t} / f_{max} = 250/370\, \text{GHz}$</tex-math></inline-formula>), using both HBTs (heterojunction bipolar transistors) and CMOS transistors. Inside the VM, two cross-connected power amplifiers (PAs) are fed by an in-phase (I), and two cross-connected PAs are fed by a quadrature-phase (Q) signal. The four PAs are controlled by a 4-bit interface to switch them on or off, thus generating output signals in the range of <inline-formula><tex-math notation="LaTeX">$0^\circ$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$360^\circ$</tex-math></inline-formula>.

Jay R. Sheth, Linsheng Zhang, Xiaochuan Shen et al.

This article introduces a current-mode multiphase digital transmitter with a single-footprint transformer-based asymmetric Doherty output network. The proposed multiphase architecture overcomes the bandwidth expansion associated with the polar power amplifier (PA), while still achieving relatively constant output power and drain efficiency (DE) profiles. Additionally, to achieve efficiency enhancement in deep power back-off (PBO), and to simultaneously achieve a compact form factor, an asymmetric series Doherty output matching network using a transformer-within-transformer structure is also proposed. A proof-of-concept eight-phase digital transmitter using the proposed single-footprint Doherty network is implemented in a general-purpose 65-nm CMOS process. The transmitter achieves more than 20-dBm output power <inline-formula> <tex-math notation="LaTeX">$(P_{\mathrm{ out}})$ </tex-math></inline-formula> and more than 31&#x0025; DE from 4.5 to 6.7 GHz. At 8-dB PBO, it achieves a DE of 23&#x0025; and 24&#x0025; at 6.5 and 7.0 GHz, which corresponds to a <inline-formula> <tex-math notation="LaTeX">$1.76\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$1.93\times $ </tex-math></inline-formula> improvement compared to normalized class B PA, respectively. The transmitter also achieves a 21&#x0025; DE and an average <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm{ out}}$ </tex-math></inline-formula> of 14 dBm with an r.m.s. error vector magnitude <inline-formula> <tex-math notation="LaTeX">$({\mathrm{ EVM}}_{\mathrm{ rms}})$ </tex-math></inline-formula> of 4.1&#x0025; for a 20-MSym/s 64-quadrature amplitude modulation waveform at 6.5 GHz.

Ioannis Zografopoulos, Charalambos Konstantinou

The decentralization of modern power systems challenges the hierarchical structure of the electric grid and necessitates automated schemes to manage adverse conditions. This work proposes an adaptive isolation methodology that can divide a grid into autonomous islands, ensuring stable and economical operation amid deliberate or unintentional abnormal events. The adaptive isolation logic is event-triggered to prevent false positives, enhance detection accuracy, and reduce computational overhead. A measurement-based stable kernel representation (SKR) triggering mechanism initially inspects distributed generation controllers for abnormal behavior. The SKR then alerts an ensemble classifier to assess whether the system behavior remains within acceptable operational limits. The event-triggered adaptive isolation framework is evaluated using IEEE RTS-24 and 118-bus systems. Simulation results demonstrate that the proposed framework detects anomalous behavior with 100% accuracy in real-time, i.e., within 22msec. Supply-adequate partitions are identified outperforming traditional islanding detection and formation techniques while minimizing operating costs.

Shagun Sharma, Kalpna Guleria, Sunita Tiwari et al.

Alzheimer's disease (AD) is one of the most prevalent types of dementia, which primarily affects people over age 60. In clinical practice, it is a challenging task to identify AD in its early stages, and there are currently very few reliable diagnostic systems available for the identification. Additionally, clinical studies for AD medications have a high risk of failure, and currently, there is no confirmed cure. There are various stages of AD: very mild demented, mild, and moderate. It is challenging to identify these stages, due to which the very mild demented case worsens and results in a complete health loss along with weak memory and makes it unable to perform daily tasks without the assistance of others. Early identification of mild cases can help patients to guide additional medical care to stop the disease's progression and avoid brain damage. Recently, there has been a substantial amount of interest in applying deep learning (DL) for early AD recognition. The limitations of these algorithms are that they cannot detect changes in the brain networks in patients with mild demented functional working brain networks. However, to stop AD growth, various scientists and researchers are striving to build methods for early identification by using MRI images. In this article, for early diagnoses of AD, two MRI datasets containing 6400 and 6330 images have been used, and the DL algorithm is utilized by applying a neural network classifier with a VGG16 feature extractor for the early diagnosis of AD, which results in the outcome in the form of accuracy, precision, recall, AUC and F1-score as (90.4%, 0.905, 0.904, 0.969, and 0.904), and (71.1%, 0.71, 0.711, 0.85, and 0.71) for dataset 1 and dataset 2, respectively. Furthermore, the results are compared with previous studies, which concluded the proposed model performs better. Lastly, this article is applicable to identify various machine learning (ML) and DL approaches that can be utilized to study AD stage identification.