Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Max-Min Convolutional Network for Hyperspectral Image Classification

Convolutional neural networks (CNNs) are a noteworthy tool for the classification of hyperspectral images (HSIs). CNNs apply non-linear activation functions to learn data patterns. One of them is the rectified linear unit (ReLU), which is a piece-wise linear function with a value which is the input if positive and zero otherwise. As a result, it is computationally efficient and tends to show good convergence behaviour. Nonetheless, its performance suffers from the so-called dying ReLU effect. This is usually managed by introducing more convolution layers increasing the depth of the model followed by a ReLU non-linearity layer that may hamper the convergence of network and produce a low classification accuracy due to data degradation. In order to alleviate these issues and transmit more information after the activation layer from the convolutional block, this paper develops a new end-to-end supervised feature learning framework called MaxMin-CNN, which works with sub-cubes of the original HSI data and successively applies 3-D MaxMin convolutional filters to improve the discrimination ability of the obtained spectral-spatial features by doubling the feature maps over all the convolutional layers. The new model gradually increases the heterogeneity of high-level spectral-spatial features across the MaxMin convolutional layers, enhancing the performance of HSI classification and reducing the model depth while preserving the classification performance. In order to validate the model, we report experiments over three widely used HSI datasets: Indian Pines, University of Pavia and University of Houston. The results reveal that the proposed MaxMin-CNN achieves a classification comparable to state-of-the-art classification models.
A Circularly Polarized Fabry-Perot Cavity Antenna For Millimeter-Wave Applications

This paper presents the design of a wideband circularly polarized Fabry-Perot cavity antenna for millimeter-wave (MMW) applications. The circular polarization is achieved by utilizing a corner cut patch with a diagonal slot, while the Fabry-Perot cavity is realized by placing a half-wavelength thick dielectric superstrate above the radiator for gain and bandwidth enhancement. Simulated and measured results show that the proposed design has a wide-bandwidth of 27.6 % (25 – 33 GHz) 27.6%) for |S11| < -10 dB. The antenna also offers a stable gain with a maximum value of 14.1 dBi, and a wide 3-dB axial ratio bandwidth ranging from 26 – 31.3 GHz (17%). This operational bandwidth of the antenna covers the proposed entire global MMW spectrum (26 – 29.5 GHz) for 5G applications.
An Analytical Approach of Discrete-Time Modeling of Fixed and Variable Frequency Digital Modulation

This paper proposes an analytical approach of discrete-time (DT) large-signal and small-signal modeling of digitally controlled DC-DC converters operated under different fixed-frequency and variable-frequency modulation techniques using uniform and event-based sampling. The proposed framework (i) provides a mathematical basis for existing approximate DT small-signal modeling and (ii) further improves accuracy, (iii) allows natural extensions to a wider class of fixed and variable frequency digital modulators, sampling techniques and converter topologies, and finally provides (iv) DT large-signal models for nonlinear analysis and high performance digital controller design. Boost converter design cases studies are considered, and the usefulness of variable frequency modulation is demonstrated using simulation and experimental results over its fixed-frequency counterpart. The proposed approach provides a basis to develop, analyze, and design of future high performance digital control.
Flexible carbon nanotube-based electrolyte-gated field-effect transistor for spermidine detection

In this work, we realized a flexible electrolyte-gated field-effect transistor (EG-FET) based on a spray-coated semi-conducting single-walled carbon nanotube (SWCNTs) network. The device was functionalized with anti-spermidine antibodies for a specific detection of polyamine spermidine - a well-known chemical indicator of food quality. The spray-coated SWCNTs films were modified with a bifunctional molecule, which binds to the nanotubes via non-covalent π−π interactions and leaves a free NHS-ester group for amide coupling of the antibodies, ensuring controlled immobilization. Transfer and output curves were recorded for device characterization, showing a typical p-type behavior with an on–off ratio of 190 A/A and a threshold voltage around 0 V. The calibration curve of the fabricated EG-FET immunosensor shows a linear detection range for spermidine from 0.001 to 100 nM, with 99.83% coefficient of determination.
SBST Tool Competition 2021

We report on the organization, challenges, and results of the ninth edition of the Java Unit Testing Competition as well as the first edition of the Cyber-Physical Systems Testing Tool Competition. Java Unit Testing Competition. This year, five tools, Randoop, UtBot, Kex, Evosuite, and EvosuiteDSE, were executed on a benchmark with (i) new classes under test, selected from three open-source software projects, and (ii) the set of classes from three projects considered in the eighth edition. We relied on an improved Docker infrastructure to execute the tools and the subsequent coverage and mutation analysis. Given the high number of participants, we considered only two time budgets for test case generation: thirty seconds and two minutes. Cyber- Physical Systems Testing Tool Competition. Five tools, Deeper, Frenetic, GABExplore, GAB Exploit, and Swat, competed on testing self-driving car software by generating simulation-based tests using our new testing infrastructure. We considered two experimental settings to study test generators' transitory and asymptotic behaviors and evaluated the tools' test generation effectiveness and the exposed failures' diversity. This paper describes our methodology, the statistical analysis of the results together with the contestant tools, and the challenges faced while running the competition experiments.
A Novel Method to Reduce Specific Contact Resistivity of TiSix/n+-Si Contacts by Employing an In-Situ Steam Generation Oxidation Prior to Ti Silicidation

Due to extremely tight Contacted Poly Pitch (CPP) in sub-10 nm nodes, specific contact resistivity (<inline-formula> <tex-math notation="LaTeX">$\rho _{{\text {c}}}$ </tex-math></inline-formula>) has become the main concern for further improvement of transistor performance. Both the reduction of Schottky barrier heights (SBHs) and the enhancement of surface dopant concentration (<inline-formula> <tex-math notation="LaTeX">$\text{N}_{{\text {s}}}$ </tex-math></inline-formula>) are considered as the most effective approaches to reduce <inline-formula> <tex-math notation="LaTeX">$\rho _{{\text {c}}}$ </tex-math></inline-formula>. In this work, a novel <italic>in-situ</italic> steam generation (ISSG) oxidation process prior to Ti silicidation is proposed to achieve an enhanced <inline-formula> <tex-math notation="LaTeX">$\text{N}_{{\text {s}}}$ </tex-math></inline-formula> value, thanks to the segregation of P at the surface of <inline-formula> <tex-math notation="LaTeX">$\text{n}^{+}$ </tex-math></inline-formula>-Si. Refined Transmission Line Model (RTLM) structures were fabricated to extract the <inline-formula> <tex-math notation="LaTeX">$\rho _{{\text {c}}}$ </tex-math></inline-formula> accurately and reliably. The dopant redistribution of P was characterized by both secondary ion mass spectrometry (SIMS) and spreading resistance probe (SRP). Compared to control samples, the <inline-formula> <tex-math notation="LaTeX">$\rho _{{\text {c}}}$ </tex-math></inline-formula> value for samples with 4 nm ISSG SiO<sub>2</sub> is reduced from <inline-formula> <tex-math notation="LaTeX">${2.91}\times {10}^{-{8}}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">${1.90}\times {10}^{-{8}} \Omega $ </tex-math></inline-formula>-cm<sup>2</sup>, and this is attributed to the enhancement of <inline-formula> <tex-math notation="LaTeX">$\text{N}_{{\text {s}}}$ </tex-math></inline-formula> value.
Snake-SLAM: Efficient Global Visual Inertial SLAM using Decoupled Nonlinear Optimization

Snake-SLAM is a scalable visual inertial SLAM system for autonomous navigation in low-power aerial devices. The tracking front-end features map reuse, loop closing, relocalization, and supports monocular, stereo, and RGBD input. The keyframes are reduced by a graph-based simplification approach and further refined using a novel deferred mapping stage to ensure a sparse yet accurate global map. The optimization back-end decouples IMU state estimation from visual bundle adjustment and solves them separately in two simplified sub problems. This greatly reduces computational complexity and allows Snake-SLAM to use a larger local window size than existing SLAM methods. Our system implements a novel multistage VI initialization scheme, which uses gyroscope data to detect visual outliers and recovers metric velocity, gravity, and scale. We evaluate Snake-SLAM on the EuRoC dataset and show that it outperforms all other approaches in efficiency while also achieving state-of-the-art tracking accuracy.
Evaluation of Different Machine Learning Algorithms for Classification of Sleep Apnea

The syndrome of cessation of breathing with recurrent attacks for 10 seconds or more as a result of narrowing or obstruction of the upper respiratory tract is called sleep apnea (SA). As a result of not treating SA, serious problems such as hypertension, heart diseases, obesity and nervous disorders can occur. In recent years, studies of automatic diagnosis and prediction of SA have become popular. In this study, heart rate variability (HRV) signals were obtained using R peak information from from electrocardiography signals divided into one-minute segments. Time and frequency domain features were determined from HRV signals and apnea classification was made from the determined features by using five different machine learning algorithms. In this study, the highest accuracy was obtained from the Random Forest algorithm with 85.26%, the highest sensitivity was obtained from the K-Nearest Neighborhood algorithm with 78.08%, and the highest selectivity was obtained from the Random Forest algorithm with 91.4%.
The Action Consistency of Casting in Virtual Environment*

Interactive techniques in Virtual Reality (VR) is rapidly developing in the recent years. However, the consistency of the interaction activities in VR and real-world remains a subject to be further studied. In this work, we study the consistency of casting action between VR and real-world. In our experiments, we let participants cast a ball to a walking human virtual agent, which is driven by a motion capture system in the virtual environment. By comparing to the casting accuracy in real-world, we found that the participants' performance under various of the experimental configurations is consistently reduced in VR. We also found that the moving ‘speed’ factor of the casting target barely affected the accuracy in VR, which was different in the real-world environment. This study will provide an important experimental reference to develop future VR systems based on casting actions.
Demonstrating a Bayesian Online Learning for Energy-Aware Resource Orchestration in vRANs

Radio Access Network Virtualization (vRAN) will spearhead the quest towards supple radio stacks that adapt to heterogeneous infrastructure: from energy-constrained platforms deploying cells-on-wheels (e.g., drones) or battery-powered cells to green edge clouds. We demonstrate a novel machine learning approach to solve resource orchestration problems in energy-constrained vRANs. Specifically, we demonstrate two algorithms: (i) BP-vRAN, which uses Bayesian online learning to balance performance and energy consumption, and (ii) SBP-vRAN, which augments our Bayesian optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient— converge an order of magnitude faster than other machine learning methods—and have provably performance, which is paramount for carrier-grade vRANs. We demonstrate the ad-vantages of our approach in a testbed comprised of fully-fledged LTE stacks and a power meter, and implementing our approach into O-RAN’s non-real-time RAN Intelligent Controller (RIC).
Object detection, 3-D coordinate extraction and pose estimation for an autonomous medical robot

The presence of autonomous robots is increasing in every field by the day. Robots require computer vision to interact with the surrounding environment. Computer vision is an area of study which deals with the algorithms which interpret the data from images or videos. One scenario in which robots could be helpful is in the medical field. This paper presents a set of algorithms which cater to the needs of a medical robot. An object detection algorithm is trained for the objects expected in medical environment. The detected object’s 3-D coordinates are extracted. To find the 2-D orientation of the object a novel approach based on edge detection and linear regression is described and implemented. These algorithms help the robots to serve in the medical environment where the contagious diseases are a threat to healthcare workers as seen in the COVID-19 pandemic.
Research and Implementation of ORB-SLAM in Drastically Changing Scenes

ORB-SLAM2 based on the feature point method performs well in most scenes, but in scenes with weak textures and rapid environmental changes, it will fail to initialize and lose tracking. Based on ORB-SLAM2, this paper implements a fast point-line feature SLAM method. Feature points are extracted from non-key frames for matching, and line features are extracted from key frames for matching, so as to improve the tracking speed of SLAM and the weak texture environment. Robustness in.
Small Signal Modeling and Discontinuous Stable Regions of Grid-connected Inverter Based on Pade Approximation

The small signal stability of grid-connected inverter is of great importance, and the state space model is one of the most efficient modeling methods to analyze the small disturbance stability of grid-connected inverters. The proposed small signal modeling of the grid-connected inverter system takes the time delays caused by digital control into consideration, and converts the time delay factor into a rational polynomial with the pade approximation method, so as to establish the state space model of the entire system. Eigenvalue analysis method is used to compare the small disturbance stability of the grid-connected inverter system with or without considering time delays under different environments. Numerical simulation results show that in the grid-connected inverter system with time delays considered, when the strength of the power grid changes, the system has discontinuous stable regions and the small signal model of the system with time delay considered is more accurate and more sensitive to changes in the controller parameters. Finally, Matlab/Simulink simulation results verify the correctness of the proposed theoretical analysis.
Power Electronics-based Switched Supercapacitor Bank Circuits with Enhanced Power Delivery Capability for Pulsed Power Applications

Supercapacitors (SCs) are one of the finest alternatives to deal with transient load demands in various pulsed power applications. However, they lack in utilizing their entire energy due to their linear discharge characteristics. Furthermore, the series connection of SCs in a bank offers lower storage capacity that restricts their total power delivery duration. To circumvent these issues, several switching circuits with a timely transition are employed in the SC banks. This paper deals with the state-of-the-art SC bank switching architectures and assesses their performance through simulation studies in the PLECS software platform. The key performance of each configuration in terms of energy utilization, power delivery duration, and terminal voltage variations are holistically evaluated. Finally, the distinct features of each topology are highlighted.
Emotion Recognition from Body Movements with AS-LSTM

With the development of artificial intelligence, people's demand for emotional interaction in virtual reality experience is becoming higher and higher. When the traditional emotion recognition method is used in virtual reality emotion recognition, it has some problems, such as tedious wearing, high demand on image clarity, inaccurate emotion recognition in motion, etc. Therefore, we propose a stack LSTM network based on attention (AS-LSTM) for emotion recognition from whole body movements in VR environment. According to the importance degree, different attention values are set for the feature sequences data of each joint point in the frame sequence of human body motion by adding attention mechanism to the basis of traditional LSTM network, which will build a particular distribution of attention, focus on the key joint points affecting emotion recognition, and reduce the invalid information. Then the proposed method can improve the learning ability of network and emotion recognition accuracy. Moreover, the equipment is simple and easy to operate, which provides users a more immersive emotional interaction experience. One can observe, this method achieves higher recognition accuracy on classification of seven kinds of emotions (happy, sad, fear, anger, surprised and disgust) compared with other deep learning methods in VR. In addition, the accuracy of emotion recognition in six categories (happy, sad, fear, anger, surprise, disgust) and four categories (happy, sad, fear, and anger) is also improved.
Road Sign Instance Segmentation By Using YOLACT For Semi-Autonomous Vehicle In Malaysia

This paper applies the object segmentation method to road sign recognition by using You Only Look At CoefficienTs (YOLACT). YOLACT achieves speeds higher than 30 fps in real-time with high performance in terms of precision and reliability. However, YOLACT performance is affected by the different situations in the image such as lighting, weather, and different angle on the traffic signs. This research trains and applies the image preprocessing on different angles (such as 90 degrees left and right) and environments to increase the recognition performances. This is important to ensure that the method used can be safely executed in an autonomous vehicle. For preprocessing, the backbone network uses ResNet-101 and is used in the YOLACT system to verify the performance of autonomous vehicles. Four types of traffic signs are used to validate our method. The result shows all traffic signs were successfully identified, with no mislabeled and accuracy exceeding 95%.
URL Phishing Detection using Machine Learning Techniques based on URLs Lexical Analysis

Phishing URLs mainly target individuals and/or organizations through social engineering attacks by exploiting the humans’ weaknesses in information security awareness. These URLs lure online users to access fake websites, and harvest their confidential information, such as debit/credit card numbers and other sensitive information. In this work, we introduce a phishing detection technique based on URL lexical analysis and machine learning classifiers. The experiments were carried out on a dataset that originally contained 1056937 labeled URLs (phishing and legitimate). This dataset was processed to generate 22 different features that were reduced further to a smaller set using different features reduction techniques. Random Forest, Gradient Boosting, Neural Network and Support Vector Machine (SVM) classifiers were all evaluated, and results show the superiority of SVMs, which achieved the highest accuracy in detecting the analyzed URLs with a rate of 99.89%. Our approach can be incorporated within add-on/middleware features in Internet browsers for alerting online users whenever they try to access a phishing website using only its URL.
Design of a One-to-Four Isolated DC-DC Converter Using a 13.56 MHz Resonant Air-Core Transformer

This paper showcases the design and development of a DC-DC converter with one input and four outputs using a high frequency resonant air-core transformer. The transmitter to receivers air-gap is 25 mm. Practical tuning equations were derived for multiple receivers which allow the converter to be optimised for overall efficiency and unity power factor at the transmit coil (i.e. zero reflected reactance). Experiments were conducted using two receive coil structures, one with four equally shaped adjacent coils in a single PCB, and the other with four differently-shaped coils featuring overlapping traces to maximise the coupling factor with the transmitter and minimise the coupling factor between the receivers. The two structures were tested and compared using the same transmitter, driven by a single-ended 13.56 MHz Class EF inverter. Single-ended Class D rectifiers were implemented at the receive side. Experiments were performed, first with equal AC test loads, and afterwards with the addition of the rectifiers and buck converters to regulate each of the four output voltages to 15 V independently. The results of the experiments implementing adjacent coils demonstrate that equal distribution of power can be achieved by modifying the tuning capacitances at the receivers with the AC loads; however, when the voltage-regulating buck converters were introduced at each output, it was only with the coil structure with overlapping traces that the required power of 10 W at each output was achieved.
Design and Implementation of a Stepped Air-Gap Inductor for Buck Converters

This paper proposes the design and implementation of an inductor with a stepped air-gap for a buck converter with improved feedback control. Typically, a power converter needs to maintain stable operation during any load transient. When designing the compensator for a converter according to the traditional method for continuous conduction mode (CCM), the same compensator is employed from light load to full load, which leads to poor response at some operating points, especially at light load. To achieve a better system response, a stepped air-gap inductor is proposed to increase the inductance at light load, which is analyzed and compared with a traditional inductor. The proposed stepped air-gap inductor reduces the influence of the magnetic flux leakage on the winding. The effect of having two different inductance values on the controller design is discussed, and the conditions for discontinuous conduction mode (DCM) at light load with the stepped air-gap inductor are outlined. A 48-V to 12-V buck converter rated for 60 W is built and tested to verify the proposed stepped air-gap inductor. With the proposed stepped air-gap inductor, experimental results show that undershoot and overshoot were improved 50% and settling time was decreased to 20% and 30% during light-to-full load transients, compared to the traditional inductor. Hence, the proposed stepped air-gap inductor can effectively improve system response.
Generalized Model Predictive Algorithm for Single-Phase-Grid Connected Multilevel Inverters

Control of single-phase two-level grid connected inverters are easy with conventional controllers. Whereas, the conventional control of multilevel inverters is a challenging problem and hard to implement in digital signal processors (DSP). Model predictive control (MPC) is a sophisticated control system that is highly consistent with recent DSPs. Hence, this work presents a generalized MPC algorithm for single-phase grid connected multilevel inverters. The total harmonic distortion of injected grid current and filter requirement with the variation of number of voltage levels are analyzed with the generalized MPC algorithm. The generalized algorithm operation is verified on a 13-level multilevel dc-link inverter simulation in MATLAB/Simulink.