Road safety is a critical concern worldwide, with millions of lives lost and countless injuries sustained in traffic accidents annually. To address this pressing issue, a costeffective and reliable solution is propose...
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In recent years, emerging nations have shifted their focus to renewable energy sources for electricity production. This shift has happened in both industrialised and underdeveloped countries. The percentage of electri...
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Machine learning-based detection of false data injection attacks (FDIAs) in smart grids relies on labeled measurement data for training and testing. The majority of existing detectors are developed assuming that the a...
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Machine learning-based detection of false data injection attacks (FDIAs) in smart grids relies on labeled measurement data for training and testing. The majority of existing detectors are developed assuming that the adopted datasets for training have correct labeling information. However, such an assumption is not always valid as training data might include measurement samples that are incorrectly labeled as benign, namely, adversarial data poisoning samples, which have not been detected before. Neglecting such an aspect makes detectors susceptible to data poisoning. Our investigations revealed that detection rates (DRs) of existing detectors significantly deteriorate by up to 9-29% when subject to data poisoning in generalized and topology-specific settings. Thus, we propose a generalized graph neural network-based anomaly detector that is robust against FDIAs and data poisoning. It requires only benign datasets for training and employs an autoencoder with Chebyshev graph convolutional recurrent layers with attention mechanism to capture the spatial and temporal correlations within measurement data. The proposed convolutional recurrent graph autoencoder model is trained and tested on various topologies (from 14, 39, and 118-bus systems). Due to such factors, it yields stable generalized detection performance that is degraded by only 1.6-3.7% in DR against high levels of data poisoning and unseen FDIAs in unobserved topologies. Impact Statement-Artificial Intelligence (AI) systems are used in smart grids to detect cyberattacks. They can automatically detect malicious actions carried out bymalicious entities that falsifymeasurement data within power grids. Themajority of such systems are data-driven and rely on labeled data for model training and testing. However, datasets are not always correctly labeled since malicious entities might be carrying out cyberattacks without being detected, which leads to training on mislabeled datasets. Such actions might degrade the d
The 5G New Radio (NR) network rollout calls for innovative methods to satisfy the growing demand for low latency, large data speeds, and consistent connectivity. Focussing on integrating several channels and signals, ...
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The increasing demand for high dynamic range (HDR), power efficiency, and high resolution has driven the adoption of single-exposure dual conversion gain (DCG) techniques. This paper proposes an adaptive split current...
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As deep learning techniques such as Convolutional Neural Networks(CNNs)are widely adopted,the complexity of CNNs is rapidly increasing due to the growing demand for CNN accelerator system-on-chip(SoC).Although convent...
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As deep learning techniques such as Convolutional Neural Networks(CNNs)are widely adopted,the complexity of CNNs is rapidly increasing due to the growing demand for CNN accelerator system-on-chip(SoC).Although conventional CNN accelerators can reduce the computational time of learning and inference tasks,they tend to occupy large chip areas due to many multiply-and-accumulate(MAC)operators when implemented in complex digital circuits,incurring excessive power *** overcome these drawbacks,this work implements an analog convolutional filter consisting of an analog multiply-and-accumulate arithmetic circuit along with an analog-to-digital converter(ADC).This paper introduces the architecture of an analog convolutional kernel comprised of low-power ultra-small circuits for neural network accelerator *** is an essential component of the analog convolutional kernel used to convert the analog convolutional result to digital values to be stored in *** work presents the implementation of a highly low-power and area-efficient 12-bit Successive Approximation Register(SAR)*** most other SAR-ADCs with differential structure;the proposed ADC employs a single-ended capacitor array to support the preceding single-ended max-pooling circuit along with minimal power *** SARADCimplementation also introduces a unique circuit that reduces kick-back noise to increase *** was implemented in a test chip using a 55 nm CMOS *** demonstrates that the proposed ADC reduces Kick-back noise by 40%and consequently improves the ADC’s resolution by about 10%while providing a near rail-to-rail dynamic rangewith significantly lower power consumption than conventional *** ADC test chip shows a chip size of 4600μm^(2)with a power consumption of 6.6μW while providing an signal-to-noise-and-distortion ratio(SNDR)of 68.45 dB,corresponding to an effective number of bits(ENOB)of 11.07 bits.
The paper focuses on the design, measurement, and performance analysis of a high-gain cross-orthogonal series fed two dipole antenna (STDA) arrays with side-wall reflectors. The antenna is specifically designed for 4G...
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Tip-enhanced Raman spectroscopy(TERS)imaging is a super-resolution imaging technique that features the merits of both surface-enhanced Raman spectroscopy(SERS)and scanning probe microscopy(SPM),such as the high chemic...
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Tip-enhanced Raman spectroscopy(TERS)imaging is a super-resolution imaging technique that features the merits of both surface-enhanced Raman spectroscopy(SERS)and scanning probe microscopy(SPM),such as the high chemical sensitivity from the former and the nanoscale spatial resolution from the *** advantages make TERS an essential nanospectroscopic characterization technique for chemical analysis,materials science,bio-sensing,*** probes,the most critical factor determining the TERS imaging quality,are expected to provide a highly confined electromagnetic hotspot with a minimized scattering background for the generation of Raman signals with high spatial *** two decades of development,numerous probe design concepts have been proposed and *** review provides a comprehensive overview of the state-of-the-art TERS probe designs,from the working mechanism to the practical *** start with reviewing the recent development of TERS configurations and the corresponding working mechanisms,including the SPM platforms,optical excitation/collection techniques,and probe preparation *** then review the emerging novel TERS probe designs,including the remote-excitation probes,the waveguide-based nanofocusing probes,the metal-coated nanofocusing probes,the nanowire-assisted selective-coupling probes,and the tapered metal-insulator-metal *** discussion focuses on a few critical aspects,including the surface-plasmon-polariton(SPP)hotspot excitation technique,conversion efficiency,working frequency,and *** the end,we review the latest TERS applications and give a perspective on the future of TERS.
Purpose: Permanent prostate seed implant brachytherapy is widely adopted as a successful treatment method for prostate cancer. While radiographic planar fluoroscopy images are a well-established method to determine th...
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We have realized efficient photopatterning and high-quality ZrO_(2)films through combustion synthesis and manufactured resistive random access memory(RRAM)devices with excellent switching stability at low temperatures...
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We have realized efficient photopatterning and high-quality ZrO_(2)films through combustion synthesis and manufactured resistive random access memory(RRAM)devices with excellent switching stability at low temperatures(250℃)using these *** synthesis reduces the energy required for oxide conversion,thus accelerating the decomposition of organic ligands in the UV-exposed area,and promoting the formation of metal-oxygen networks,contributing to *** analysis confirmed a reduction in the conversion temperature of combustion precursors,and the prepared combustion ZrO_(2)films exhibited a high proportion of metal-oxygen bonding that constitutes the oxide lattice,along with an amorphous ***,the synergistic effect of combustion synthesis and UV/O_(3)-assisted photochemical activation resulted in patterned ZrO_(2)films forming even more complete metal-oxygen *** devices fabricated with patterned ZrO_(2)films using combustion synthesis exhibited excellent switching characteristics,including a narrow resistance distribution,endurance of 103 cycles,and retention for 105 s at 85℃,despite low-temperature *** synthesis not only enables the formation of high-quality metal oxide films with low external energy but also facilitates improved photopatterning.
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