Related papers: Low-frequency noise parameter extraction method fo…
Scattering mechanisms in graphene are critical to understanding the limits of signal-to-noise-ratios of unsuspended graphene devices. Here we present the four-probe low frequency noise (1/f) characteristics in back-gated single layer…
We report on the influence of low gamma irradiation (10^4 Gy) on the noise properties of individual carbon nanotube (CNT) field-effect transistors (FETs) with different gate configurations and two different dielectric layers, SiO2 and…
Low-frequency $1/f^{\gamma}$ noise is ubiquitous, even in high-end electronic devices. For qubits such noise results in decrease of their coherence times. Recently, it was found that adsorbed O$_2$ molecules provide the dominant…
Narrowing the performance gap between optimal and feasible detection in inter-symbol interference (ISI) channels, this paper proposes to use graph neural networks (GNNs) for detection that can also be used to perform joint detection and…
The graph Hilbert transform (GHT) is a key tool in constructing analytic signals and extracting envelope and phase information in graph signal processing. However, its utility is limited by confinement to the graph Fourier domain, a fixed…
In physical-layer security schemes, radio frequency fingerprint (RFF) identification of WiFi devices is susceptible to receiver differences, which can significantly degrade classification performance when a model is trained on one receiver…
We investigate the influence of low-dimensionality and disorder in phonon transport in ultra-narrow armchair graphene nanoribbons (GNRs) using non-equilibrium Greens function (NEGF) simulation techniques. We specifically focus on how…
Deep learning based approaches has achieved great performance in single image super-resolution (SISR). However, recent advances in efficient super-resolution focus on reducing the number of parameters and FLOPs, and they aggregate more…
A low complexity computational model of the current-voltage characteristics for graphene nano-ribbon (GNR) field effect transistors (FET), able to simulate a hundred of points in few seconds using a PC, is presented. For quantum capacitance…
We introduce a data-driven approach for extracting two-level system (TLS) parameters-frequency $\omega_{TLS}$, coupling strength $g$, dissipation time $T_{TLS, 1}$, and the pure dephasing time $T^{\phi}_{TLS, 2}$, labelled as a 4-component…
One-atom thick crystalline layers and their vertical heterostructures carry the promise of designer electronic materials that are unattainable by standard growth techniques. In order to realize their potential it is necessary to isolate…
In-time particle trajectory reconstruction in the Large Hadron Collider is challenging due to the high collision rate and numerous particle hits. Using GNN (Graph Neural Network) on FPGA has enabled superior accuracy with flexible…
Scanning Electron Microscopes (SEM) with low energy electron sources (accelerating voltage of less than 1000V) have important application requirements in many application scenarios. Tunneling junction can potentially achieve low-voltage and…
The dependence of random telegraph noise (RTN) amplitude on the gate overdrive in a junctionless field-effect transistor (FinFET) with rectangular and trapezoidal channel (fin) cross sections manufactured using silicon-on-insulator…
Low-frequency noise with the spectral density S(f)~1/f^g (f is the frequency and g~1) is a ubiquitous phenomenon, which hampers operation of many devices and circuits. A long-standing question of particular importance for electronics is…
Graph neural networks (GNNs) have been proven to be effective in various network-related tasks. Most existing GNNs usually exploit the low-frequency signals of node features, which gives rise to one fundamental question: is the…
Anomaly detection is widely used to distinguish system anomalies by analyzing the temporal and spatial features of wireless sensor network (WSN) data streams; it is one of critical technique that ensures the reliability of WSNs. Currently,…
Convolutional neural networks (CNNs) have become widely adopted in gravitational wave (GW) detection pipelines due to their ability to automatically learn hierarchical features from raw strain data. However, the physical meaning of these…
The vehicular-to-everything (V2X) technology has recently drawn a number of attentions from both academic and industrial areas. However, the openness of the wireless communication system makes it more vulnerable to identity impersonation…
Varieties of noises are major problem in recognition of Electromyography (EMG) signal. Hence, methods to remove noise become most significant in EMG signal analysis. White Gaussian noise (WGN) is used to represent interference in this…