Miracle-dz/UniFAR-FTUs huggingface dataset:
Extraction of the Trench Sidewall Capacitances in an n-Type 4H-SiC Trench Metal– Oxide–Semiconductor Structure

In this work, a method was investigated to extract trench sidewall and trench bottom capacitances of a SiC trench metal–oxide–semiconductor (MOS) structure. Five groups of 4H-SiC trench MOS capacitors were designed and fabricated, with various trench bottom widths and trench mesa widths. High-frequency capacitance–voltage (HFCV) measurements at 100 kHz were performed on these trench MOS structures. The relationships between trench MOS capacitances and the widths of trench bottom as well as trench mesa were studied. As expected, under the same bias voltage, the measured trench MOS capacitances were proportional to the trench bottom widths and trench mesa widths. Based on this, the contributions of capacitances from the bottom, mesa, and sidewall of trench in trench MOS structure were studied systematically. The oxide thicknesses at different locations in trench were extracted. The <inline-formula> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> characteristics of the MOS capacitors from trench sidewall and trench bottom could also be deduced, from which the flat-band voltage and the charges in oxide of these two MOS capacitors could be subsequently calculated and analyzed. This method provides a convenient and precise technology to monitor process control in SiC trench MOSFETs manufacturing.
One-Class Learning Towards Synthetic Voice Spoofing Detection

Human voices can be used to authenticate the identity of the speaker, but the automatic speaker verification (ASV) systems are vulnerable to voice spoofing attacks, such as impersonation, replay, text-to-speech, and voice conversion. Recently, researchers developed anti-spoofing techniques to improve the reliability of ASV systems against spoofing attacks. However, most methods encounter difficulties in detecting unknown attacks in practical use, which often have different statistical distributions from known attacks. Especially, the fast development of synthetic voice spoofing algorithms is generating increasingly powerful attacks, putting the ASV systems at risk of unseen attacks. In this work, we propose an anti-spoofing system to detect unknown synthetic voice spoofing attacks (i.e., text-to-speech or voice conversion) using one-class learning. The key idea is to compact the bona fide speech representation and inject an angular margin to separate the spoofing attacks in the embedding space. Without resorting to any data augmentation methods, our proposed system achieves an equal error rate (EER) of 2.19% on the evaluation set of ASVspoof 2019 Challenge logical access scenario, outperforming all existing single systems (i.e., those without model ensemble).
Data Driven State Reconstruction of Dynamical System Based on Approximate Dynamic Programming and Reinforcement Learning

A data-driven approximation formulation for the state reconstruction problem of dynamical systems is presented in this paper. Without the assumption of an explicit mathematical model, the Hamilton–Jacobi–Bellman (HJB) based approach of a data-driven state reconstruction design method for monitoring and output-feedback control of dynamical systems is presented. The proposed state reconstruction design is based on a dynamic programming approach. To evaluate the proposed state reconstruction, computational experiments are conducted using only dynamical system model output data. The sensitivity of the algorithm parameters is also analyzed and discussed. The performance evaluation is analyzed in terms of the error metrics of the discrete linear quadratic regulator with output feedback under the value iteration algorithm through a reinforcement learning strategy.
BottleNet: Hiding Network Bottlenecks Using SDN-Based Topology Deception

The robustness of a network’s connectivity to other networks is often highly dependent on a few critical nodes and links that tie the network to the larger topology. The failure or degradation to such network bottlenecks can result in outages that may propagate throughout the network. Unfortunately, the presence of the bottlenecks also offers opportunities for targeted link flooding attacks (LFAs). Researchers have proposed a new and promising defense to counter LFAs, referred to as topology deception. This strategy centers on hindering the discovery of bottlenecks by presenting false trace responses to adversaries as they perform topological probing of the target network. Even though the goal of topology deception centers on obscuring critical links, node dependencies can be exploited by an adversary. However, current approaches do not consider a wide range of metrics that may reveal important and diverse aspects of network bottlenecks. Furthermore, existing approaches create a simple form of virtual topology, which is subject to relatively easy detection by the adversary, reducing its effectiveness. In this paper, we propose a comprehensive topology deception framework, which we refer to as BottleNet. Our suggested approach can analyze various network topology features both with respect to static and dynamic metrics and then use this information to identify bottlenecks, finally producing complex virtual topologies that are resilient to adversarial detection.
3D Printed Perfusable Renal Proximal Tubule Model With Different Extracellular Matrix Compositions

This paper describes the design of a chip modeling the proximal renal tubule, namely the nephron segment that is most targeted by drug toxicants. The tubule model is based on a 3D convoluted geometry whose printing parameters and Pluronic concentration were optimized to achieve a diameter of <inline-formula> <tex-math notation="LaTeX">$320~\mu \text{m}$ </tex-math></inline-formula> without discontinuities. A perfusion system guaranteeing a <inline-formula> <tex-math notation="LaTeX">$1~\mu \text{L}$ </tex-math></inline-formula>/min flow was also integrated. Moreover, the tubule model was integrated into a gel recapitulating the composition and the elastic properties of the human kidney extracellular matrix. Different formulations containing Matrigel, Collagen I and transglutaminase were investigated and compared in terms of rheometric properties and renal cell viability and ability to form a compact layer on them. Optimal results were found with a matrix composed of Matrigel and Collagen I (especially in the ratios 1:1), with the addition of 2.7% transglutaminase and 0.9% CaCl<sub>2</sub>.
Analysis of Coal Combustion in Oxy-fuel Conditions through Pulsed Feeding Experiments in an Entrained Flow Reactor

Coal combustion is investigated in both air and oxy-fuel conditions by feeding pulses of coal particles in an entrained flow reactor able to provide high residence times. The radiant energy emitted in the range from UV to IR was collected by several photodiodes operating with high acquisition rate. The signal analysis showed the capability of the technique to capture the passage of coal streams and to identify different phenomena (e.g., volatiles ignition, char oxidation). The spatial arrangement of the photodiodes was planned in order to allow also deriving quantitative information, such as particle velocity, ignition delay, and devolatilization time, from the correlation of more signals. The ignition delay was found to be higher in oxy-fuel conditions than in air, mainly because the larger specific heat of the oxy-fuel environment. The proposed diagnostics may help the qualification of advanced experimental apparatus as entrained flow reactors, with the purpose to make them suited for heterogeneous kine.
Deep Learning Based Predictive Power Allocation for V2X Communication

As an essential technology of the fifth generation communication (5G), Vehicle-to-Everything (V2X) has attracted wide attention lately. A well-designed power allocation scheme can decrease the interference among terminals to yield a significant performance gain for V2X communication. Accurate channel state information (CSI) feedback, which is crucial to power allocation, is hard to be tracked due to fast time-varying channel in V2X scenario. This paper investigates power allocation problem under delayed CSI feedback in V2X system. We focus on maximizing sum throughput of V2X system while meeting Quality of Service (QoS) constraint requirement of each link. First of all, a power allocation scheme utilizing projective constraint analysis (PCA) is proposed to guarantee reliability of V2X network and improve system throughput, namely PCA-PA. Subsequently, we develop a Deep Neural Network (DNN) based predictive power allocation algorithm, namely DNN-PPA. This algorithm aims to address delayed CSI feedback in V2X system utilizing the normalized data obtained from PCA-PA scheme. Furthermore, a V2X communication model compliant with the 3rd Generation Partnership Project (3GPP) standard is simulated to validate the performances of PCA-PA and DNN-PPA. Simulation results illustrate that PCA-PA has superior performances compared to existing power allocation approaches and the throughput performance of DNN-PPA is impressively close to optimal solution under delayed CSI feedback.
Impacts of Different Charging Strategies on the Electric Vehicle Battery Charger Circuit Using Phase-Shift Full-Bridge Converter

This paper compares the influences of two different charging strategies on the performance of the Electric Vehicle (EV) charging system, including the EV battery, the Battery Management System (BMS), and the power electronic converter. Firstly, the two types of EV charging strategies are explained. Herein, the characteristics, the influences on the EV battery, and the respective requirements for the hardware and communication of the BMS and power electronic converter are summarized. Secondly, a simulation case study is conducted to investigate the influences of the two charging strategies on the performance of a typical power electronic converters employed as a back-end converter of a fast EV charger. A modular 50kW DC-DC EV charger constructed with five parallel-connected 10kW Phase-Shift Full-Bridge (PSFB) DC-DC converter is modelled with two different charging strategies, and two comparison metrics are proposed for a benchmark: the charging-cycle efficiency and the installation utilization rate. The results show that these two comparison metrics are influenced by the charging strategies of the EV.
Modelling of Six-Phase Electric Drive with PI and PD Fuzzy Controllers

The paper presents model of field-oriented control six-phase induction drive with fuzzy controllers. Two types of fuzzy controllers were designed and implemented in the model. PD and PI fuzzy controllers, with the same per unit membership functions and 49 rules were used to replace conventional PI controller. Simulation results of controlled parameters indicate that both controllers create robust systems.
Comparison of LORETA and CSP for Brain-Computer Interface Applications

Motor imagery (MI) is the most used paradigm in Brain-Computer Interface (BCI). As in other BCI paradigms, it is divided into feature extraction and classification. In this study, we implemented two feature extraction methods, one based on the well-known Common Spatial Pattern (CSP) algorithm and the other based on a not so well-known electroencephalogram (EEG) source localization technique called Low-Resolution Brain Electromagnetic Tomography (LORETA). We evaluated 30 right-handed subjects from an EEG data set made publicly available through Giga Science, where participants performed MI of left- and right-hand movements. After feature extraction with CSP and LORETA, MI tasks classification were carried out using the Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Naïve Bayes (NB) and Multi-Layer Perceptron (MLP) algorithms. Finally, we evaluated classification performance with all possible combination of classifiers and feature extraction methods. For all classifiers, the CSP feature extraction method performed better than LORETA. The best classification accuracy for the LORETA method was 71.2% and for the CSP method was 94.2%, both achieved with SVM.
Effect of Feature Dimension on Classification of Speech Emotions

This paper analyses both the static and temporal dynamics of the spectral features in classifying speech emotions. Initially, different frame-level spectral techniques such as the Linear Prediction Cepstral Coefficients (LPCC), Perceptual LP coefficients (PLP), and Mel-Frequency Cepstral Coefficients (MFCC) have been examined. Further, these spectral features are extracted using Wavelet Analysis (WA) for a better emotional portrayal. The extracted feature sets remain high-dimensional and overload the recognizer with redundant features, large memory space, and slower response. To alleviate these issues and fetch more discriminating parameters, the applicability of Vector Quantization in clustering the data has been explored. Machine learning algorithms such as the Gaussian Mixture Model (GMM), the Probabilistic Neural Network (PNN), and the Multilayer Perceptron (MLP) have been simulated with the derived feature sets for their effectiveness in classifying speech emotions. While the GMM has been efficient in classifying the frame-level feature dimension, the NN-based classifiers outperform the GMM for low feature dimensions as revealed from our results.
Simulation of Fault Operation of a 13-level CHB Multilevel Converter for Power Coolant Pump

This paper discusses the simulation of the cascaded H-bridge (CHB) multilevel converter for power coolant pump with the demand of high reliability and robustness. Multi-level converters are designed to control medium-voltage motors, energy conversion, active power filtering, and many other applications. First, an overview of the CHB converter, the method of control, and operation under fault conditions is described. The next part deals with the requirements of the application of the converter and the parameters of the simulation system. Then the 13-level multilevel converter is simulated during operation under normal conditions and during operation under abnormal conditions. If the cell is damage, it is bypassed by an auxiliary switch. The simulated voltages and torques were compared with each other. As a result, the converter is able to operate even with three faulty cells without interrupting the operation of the cooling pump.
Retrieval of Ultraviolet Diffuse Attenuation Coefficients From Ocean Color Using the Kernel Principal Components Analysis Over Ocean

Underwater ultraviolet radiation (UVR), which plays a significant role in photobiological and photochemical processes, is one of the key factors in marine ecosystems. A new algorithm KpcaUV, based on kernel principal component analysis (KPCA) and multiple linear regression (MLR), was proposed in this study for the retrieval of the UVR diffuse attenuation coefficient <inline-formula> <tex-math notation="LaTeX">$ {K_{\mathrm {d}}(\lambda)}$ </tex-math></inline-formula> from remote sensing reflectance <inline-formula> <tex-math notation="LaTeX">$ {R_{\mathrm {rs}}(\lambda)}$ </tex-math></inline-formula> in the global ocean. KPCA can be applied in all areas that principal components analysis (PCA) can be used. More importantly, KPCA can help mapping data into high dimensions and reducing the nonlinearity between inputs and outputs, which will improve the performance and robustness of algorithms when deriving large dynamic ranges parameters. Compared with SeaUVc, which is one of the most successful <inline-formula> <tex-math notation="LaTeX">$ {K_{\mathrm {d}}(\lambda)}$ </tex-math></inline-formula> retrieval algorithms in UVR, the results showed that KpcaUV (with <inline-formula> <tex-math notation="LaTeX">$ {R^{2}}$ </tex-math></inline-formula>: 0.970 and RMSE: 14.0%) performed similar to SeaUVc (with <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {R^{2}}$ </tex-math></inline-formula>: 0.963 and RMSE: 15.6%) when implemented with high-quality data. Nevertheless, KpcaUV was more robust and consistent than SeaUVc when implemented on the satellite images with different levels of quality control. The RMSD of SeaUVc had a significant reduction from 26.8% (QA ≥ 0.6) to 12.7% (QA = 1.0), and the RMSD of KpcaUV varied less than SeaUVc from 14.6% (QA ≥ 0.6) to 10.1% (QA = 1). Hence, considering its good nonlinear-problem-solving ability and robustness when applied to multiple satellites, KpcaUV proposed by this study can be used to obtain <inline-formula> <tex-math notation="LaTeX">${K_{\mathrm {d}}(380)}$ </tex-math></inline-formula> for the continuous observation of the large area.
One-dimensional photonic crystal surface plasmon nanolaser for on-chip optical interconnects

Optical devices can beyond the diffraction limit through surface plasmon technology and realize on-chip optical interconnection. A qualified on-chip light source needs to meet the following requirements: smaller size, lower power consumption and higher output power. In our study, a nanolaser is designed based on surface plasmon, and a 1D photonic crystal structure is combined with the etching periodic and taper holes in the transverse waveguide. We simulated the Q factor, Purcell factor, mode volume, and waveguide coupling efficiency of resonantor, and analyzed the output characteristics and modulation response characteristics of the laser. Studies have shown that the combo surface plasmon 1D photonic crystal nanobeam cavity has excellent performance, the mode volume is only 0.15 (λR/n)3, and the Q factor of 808, the Purcell factor of 106 and the waveguide coupling efficiency of 75% are achieved. The nanolaser we designed works at a wavelength of 1535nm, with a 3-dB bandwidth at a threshold pump rate of 134GHz, and produces an output power of more than 10μW at an injection current of 17μA, which meets the energy requirements of the nanometre chip light source. This ultra-small nanometre light source with low power consumption and high modulation rate provides a new solution for the next generation of on-chip optical interconnection.
An overview of supercapacitors for integrated PV – energy storage panels

One limitation of photovoltaic energy is the intermittent and fluctuating power output, which does not necessarily follow the consumption profile. Energy storage can mitigate this issue as the generated power can be stored and used at the needed time. Integrating energy storage directly in the PV panel provides advantages in terms of simplified system design, reduced overall cost and increased system flexibility. Incorporating supercapacitors directly in the PV panel on module or cell level raises some challenges regarding the electrical integration, such as charge controlling for the capacitors, capacitor matching, as well as internal power electronics layout. Physical integration of graphene supercapacitors with solar cells, at module- or cell-level presents challenges related to physical dimensioning, thermal management and life expectation of the entire system. The main goal of this article is to review the supercapacitor technologies and perform a comparison between the available supercapacitors in the market and selecting the most suitable type for developing supercapacitor-based integrated PV – energy storage systems, to achieve optimal electrical and physical integration.
Improved “Augmented MVDR Spectrum-Based Frequency Estimation Algorithm” for Unbalanced Power Systems

This article proposes mymargin an improved augmented iterative minimum variance distortionless response (AI-MVDR) frequency estimation algorithm which resolves a significant performance degradation of the AI-MVDR algorithm at off-nominal frequency of unbalanced power signals. This problem of the AI-MVDR algorithm is caused by the omitted terms in computing the augmented covariance matrix which is suggested to compensate the unbalance effect. In this article, a new frequency estimator is analytically derived considering the effect of the omitted terms by replacing a fixed nominal frequency parameter with a variable frequency parameter in obtaining the augmented covariance matrix. The performance of the derived equation is also verified through computer simulations.
N-Type TiO2-Based Reusable Sensor for Photon-Assisted Cd(II) Ion Detection

Exponential growth of industrialization has triggered the need of a cheap and reusable water quality monitoring system to maintain drinkable water standards. This present work demonstrates n-type hierarchical flower-like nanostructured TiO2 on p-Si/SiO2 substrate to fabricate compact photon-assisted resistive sensor for Cd(II) ions detection in wastewater. The sensor presented a maximum response of 2.2 times for 50 ppm Cd(II) ions with a fast response and recovery times of 3.5 and 3.8 s, respectively, under white light irradiation. The limit of detection was calculated as ~100 ppb. Reusability study showed a maximum recovery of ~95.45% after three wash cycles along with a high degree of selectivity toward Cd(II) ions over six other common heavy metal ion contaminants. A mathematical model has been put forward relating the surface potential of TiO2 and sensor response which depended highly on surface defects. The effect of light on sensor performance has also been expressed mathematically and its dependence on the heterogeneity of the sensing layer has been explained. Hence, the proposed sensor may be utilized for continuous water quality monitoring over its conventional counterparts in the market.
Vehicle Detection System for Smart Crosswalks Using Sensors and Machine Learning

Cities are transforming into smart areas thanks to several key technologies involving artificial intelligence (AI), 5G or big data aimed at improving the lives of their inhabitants with new services (e.g., transport systems, including road safety). In this field, the paper describes how to improve vehicle detection through several machine learning techniques applied to smart crosswalks. As a main advantage, this approach avoids readjusting labels in classic fuzzy classifiers that typically depends on the system location and road conditions. To address this, various AI methods were evaluated with data taken from real traffic pertaining to roads in Spain and Portugal. The machine learning techniques were random forest (RF), extremely randomized trees (extra-tree), deep reinforcement learning (DRL), time series forecasting (TSF), multi-layer perceptron (MLP), k-nearest neighbor (KNN) and logistic regression (LR). The results were validated through a receiver operating characteristic (ROC) analysis, obtaining the best performance in RF with a true positive rate (TPR) of 96.82%, false positive rate (FPR) of 1.73% and accuracy (ACC) of 97.85%. This was followed by DRL and TSF, while MLP and LR presented the worst outcomes.
3D CNN for Human Action Recognition

Recognizing different human actions from still images or videos is an important research area in the computer vision and artificial intelligence domains. It represents a key step for a wide range of applications including: human-computer interaction, ambient assisted living, intelligent driving and video surveillance. However, unless the many research works being involved, there are still many challenges ahead including: the high changes in human body shapes, clothing and viewpoint changes and the conditions of system acquisition (illumination variations, occlusions, etc). With the emergence of new deep learning techniques, many approaches are recently proposed for Human Action Recognition (HAR). Compared with conventional machine learning methods, deep learning techniques have more powerful learning ability. The most wide-spread deep learning approach is the Convolutional Neural Network (CNN/ConvNets). It has shown remarkable achievements due to its precision and robustness. As a branch of neural network, 3D CNN is a relatively new technique in the field of deep learning. In this paper, we propose a HAR approach based on a 3D CNN model. We apply the developed model to recognize human actions of KTH and J-HMDB datasets, and we achieve state of the art performance in comparison to baseline methods.