Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective control of TEOS–PECVD thin film depositions

Deposition parameters of tetraethylorthosilicate (TEOS) layers on silicon substrate using Plasma Enhanced Chemical Vapor Deposition (PECVD) method were investigated We design the experiment taking into consideration chamber pressure, substrate temperature, RF Power and mass flow rate (oxygen and TEOS) to asses their influence on deposition rate, film uniformity, refractive index uniformity and film stress. All the results were evaluated for applications that require low temperature processing in order to avoid damage of Microelectromechanical Systems (MEMS) devices.
Electrical Percolation Threshold In Oxidized Single Wall Carbon Nanohorn-Polyvinylpyrrolidone Nanocomposite: A Possible Application For High Sensitivity Resistive Humidity Sensor

The objective of this study is to design and synthetize an oxidized single wall carbon nanohorns (SWCNHs) - polyvinylpyrrolidone (PVP) composite film as potential sensing material for resistive humidity sensor. Raman spectroscopy is used to investigate the nanohybrid formation and components interaction. Comparative results are presented for the percolation threshold values determined for 2 study-cases: carbon nanohorns in PVP film deposited on either rigid (Si / SiO2) or flexible substrate (polyimide). While in the first case, the percolation threshold occurs at a concentration of oxidized SWCNHs in the nanocomposite matrix in the range (0.05-0.1)% (w/w), in the second instance, the concentration of SWCNHs for the percolation threshold is between (2-3.5)% (w/w). Experimental findings are explained in terms of size effects in the geometry of the studied structures. The key role played by the electrical percolation threshold in the resistive sensing capabilities of the synthesized nanocomposite towards water molecules is described.
Frequency Domain Spectroscopy for Non-Uniformly Distributed Moisture Detection in Transformer Bushings

Frequency domain spectroscopy (FDS) has been widely used to evaluate the moisture fault of oil-impregnated paper (OIP) bushings. However, there is no reliable method for the location detection of moisture faults in bushings, which frequently initiate its insulation failure. In this study, we developed a distributed FDS measurement method for OIP bushings with two taps. Three times FDS will be measured to acquire the FDS parameters from 0.001 Hz to 10 kHz between the rod (a) and end foil (c), rod and second end foil (b), and second end foil and end foil at room temperature over time. The results show that the <inline-formula> <tex-math notation="LaTeX">$C_{1mHz} /C_{10kHz}$ </tex-math></inline-formula> ratio of <inline-formula> <tex-math notation="LaTeX">$C_{ab}$ </tex-math></inline-formula> is more sensitive for detection of the inner damp layer. The rate of <inline-formula> <tex-math notation="LaTeX">$C_{1mHz} /C_{10kHz}$ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">$C_{bc}$ </tex-math></inline-formula> is more sensitive to the outer layer damp bushings. The layer ratios of complex capacitance (<inline-formula> <tex-math notation="LaTeX">$V_{12}/V_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$V_{2}/V_{1}$ </tex-math></inline-formula>) are key features for the detection of non-uniformly distributed moisture for a capacitance bushing.
Semi-Analytical Calculation of a Laminated Transverse Flux Machine

In this paper a semi-analytical calculation of a transverse flux machine is presented. First the design of the machine is explained, then the semi-analytical calculation, which is finally compared with a numerical 3D finite element analysis (FEA). The semi-analytical approach is based on a magnetic equivalent circuit, which is solved by nodal analysis and the Broyden-Method. The air gap of the machine is transformed into the magnetic equivalent circuit by conformal mapping using the Schwarz-Christoffel transformation. The comparison to numerical simulation shows that the approach gives good results and is sufficiently accurate for a rough design of the machine.
Non-contact Vital Signals Measurement on a Mobile Rescue Robot

Improving the mobile robot’s ability to perceive the physiological parameter of the wounded during search and rescue missions has attracted the attention from re-searchers. This paper proposed a vision-based vital signals measurement, we detect the human body from video images captured by the camera that mounted on a mobile rescue robot, then track the face. Extract heart rate (HR) and respiration rate (RR) by detecting colour variations of the face skin. We select human posture and face position as experimental variables, the evaluation results show that this method has good consistency with medical monitor, and the overall root mean square error is HR: 1.58, RR: 1.92. This non-contact vital signals detection method will effectively enhance the performance of the mobile robot in search and rescue tasks.
A Novel Sequential Direct Torque Control Scheme for Seven-phase Induction Motors

Traditional direct torque control method suffers from the issues of torque ripple and harmonic currents for multiphase motors. This paper firstly extends the concept of virtual vectors for a seven-phase motor to eliminate harmonic currents. Secondly, the duty cycle optimization is utilized to reduce the torque ripple. Finally, a novel cascade method is proposed to select the voltage vector which can further alleviate the torque ripple. Simulation results verified that the proposed method can effectively reduce dynamic and steady-state torque ripple at both high and low speed. This paper uses a seven-phase motor for an illustration, but it should be mentioned here that the proposed method is applicable for multiphase motors with any number of phases.
Deep Fingerprint Classification in a Low-cost Environment

Fingerprint classification is plays an essential role in human identification mechanisms. For systems with a big fingerprint database, an accurate fingerprint classification algorithm can significantly reduce fingerprint searching and matching time. In this respect, developing an accurate algorithm based on deep learning may be an ideal solution. However, issues on the computational complexity and inadequacy to invest on expensive graphic processing units (GPUs) make the development task becomes challenging. In this paper, we present a deep fingerprint classification algorithm that can achieve better performance compared to the previously reported in the literature without a requirement to invest in GPUs. For these purposes, we modify and train the pre-trained model DenseNet-121 on publicly available GPUs offered by Google’s Colaboratory. The performance of our framework is evaluated on images from the NIST-4 database. In the five-class classification problem, the proposed algorithm is superior to all the competing algorithms, with an average accuracy of 96.3%. When used for the four-class classification scheme, our algorithm can maintain an average accuracy of 97.7%, significantly better than the competing algorithms. The obtained results and the fact that no compulsion to invest in expensive GPUs suggest that our algorithm would be useful for human recognition systems.
Real-time Wind Estimation with a Quadrotor using BP Neural Network

This paper presents an approach based on BP neural network for quadrotors that estimates the wind velocity in real-time based on measurement data of its on-board inertial measurement unit (IMU) and GPS only. The proposed method is a gray box modelling method for the real-time wind estimation, avoids oversimplifications and determination of many parameters in the existing dynamic models or aerodynamic models of quadrotors. The nonlinear functional relationship between the wind velocity and the flight parameters provided by the on-board IMU and GPS is established after the training of the BP network, using the data collected from the quadrotor and an anemometer not far away from the quadrotor, and then applied to estimate the wind velocity in real time only with the outputs of the on-board IMU and GPS when the quadrotor is flying. The simulation results show that the proposed method can achieve wind estimation with a root mean square error (RMSE) less than 0.02 m/s.
An efficient MATLAB implementation of grid maps and laser range finder likelihood models for the autonomous navigation of mobile robots

For the autonomous navigation of mobile robots we need grid maps and probabilistic models to describe the world and the sensors’ measurements. This work presents an efficient Matlabi mplementation of grid maps and laser sensor likelihood models that can be used for both the rapid prototyping of autonomous navigation algorithms and the easy analysis of the impact of the several parameters of these models. The approach makes use of an object oriented approach and of vectorized operations wherever possible in order speed the velocity of execution. The main advantages of this library are the easy with wich the user can implement and analyze autonomous navigation algorithms which is demonstrated in the implementation of a localization algorithm using particle filters, and the low execution times that allow its use in real-time operations.
Small-Signal Modeling of SIMO DC-DC Converters and Comparative Continuous/Discrete-Time Results

Fundamental small-ripple approximation in a well- known averaging technique tends to limit the accuracy of continuous-time (CT) small-signal models of pulse-width- modulated (PWM) DC-DC converters up to 1/10th of the switching frequency. This paper shows that their accuracy significantly degrades even at much lower frequencies for single-inductor- multiple-output (SIMO) DC-DC converters due to higher current ripple with multiple switch configurations. CT small-signal models are derived for a SIDO buck converter, and the transfer functions using circuit averaging and state-space averaging (SSA) techniques are found to be identical. Thereafter, exact discretetime (DT) large-signal models and accurate small-signal models are derived. Both CT and DT small-signal models are compared, and the accuracy of the latter is justified with the actual switch simulation using MATLAB. Frequency responses demonstrate significant inaccuracies using CT small-signal models for varying system parameters. The results will be useful for accurate modeling and stability analysis using DT modeling for SIMO DC- DC converters and also to understand the underlying dynamics.
Small-Signal Approach for Precise Evaluation of Gate Losses in Soft-Switched Wide-Band-Gap Transistors

High-frequency switching is favorable for fast transient response, small size of passive components and superior power density, especially in soft-switching topologies. At high frequencies, power dissipation due to consecutive charging/discharging of gate capacitance is considerably large. As presented in this work, the actual gate charge (QG) of a transistor can be very different from the typical values reported in manufacturer datasheet, which leads to errors in estimation and modeling of gate loss based on datasheets. Furthermore, the reported QG values in datasheets correspond to a hard-switching test condition, and are not a good representative of the losses in soft-switched transistors. Here, we propose a simple method to precisely evaluate gate loss in soft-switched transistors for high-frequency applications. A small-signal input-capacitance measurement is used to derive gate loss in two commercial Gallium-Nitride (GaN) transistors. The estimated losses are then verified by results from accurate thermal modeling based on a matrix of temperatures, when the transistors are driven up to 30 MHz. The results are of great significance to the modeling and accurate measurement of gate losses at high frequencies. It is instrumental to a proper cooling design to avoid device and gate driver thermal runaway and failure. Also, the more accurate gate capacitance measurement enables an accurate dead time adjustment to achieve synchronized turn ON between various transistors in soft-switching topologies.
Analytical Delay Model of virtual CPE for 5G eMBB services in SD-CDN

The high bandwidth and moderate latency requirements of enhanced Mobile BroadBand (eMBB) content in 5G are handled by Content Delivery Network (CDN) components such as customer premises equipment (CPE) in edge, branches in fog, and servers in the cloud. The indispensable goal of 5G network providers is meeting these requirements on the same physical component that reduces operational and capital expenditures (OPEX/CAPEX). However, we’ve believed that different configuration policies of providers can be only handled by Software-Defined CDN (SD-CDN). Moreover; due to the flow characteristics of eMBB and handling packet forwarding by only software-based component in an OpenFlow (OF) switch, obtained packet processing delay in conventional CPE has become challenging. Therefore, we propose SD-vCPE with a novel physical block diagram which includes hardware and software-based OF tables. The analytical models of conventional and SD-vCPE are also derived by using G/G/1 Markov queues, considering general eMBB flow characteristics, and SpeedUp parameters in the software part. According to analytical and simulation results, as the resolution of video content in eMBB is increased, the conventional CPE cannot handle delay in the microsecond level; whereas, proposed SD-vCPE outputs approximately 1-millisecond decrease on packet delay which differentiates according to matching rules in TCAM or CPU. Namely, the proposed analytical model for SD-vCPE results in a delay in an acceptable rate while comparing the simulation results performed in MATLAB.
Human Recognition and Tracking in Narrow Indoor Environment using 3D Lidar Sensor

This paper studies the human recognition, tracking, and clustering method in an indoor environment using a 3D lidar sensor and discusses two major issues in clustering. The first problem is when the Euclidean distance-based clustering is used, where a wall and a person are frequently clustered into one object. The other issue is that there is some noise due to reflective materials such as glass or marble. In order to cluster objects and recognize humans in this environment, we proposed a pre-processing sequence module for clustering. The pre-processing module composed in 5 steps that can remove walls around the robot and reduce the point cloud noise. We embedded this whole process in the robot system and it works while the robot is in motion.
Hybrid Cloud Workflow Scheduling Method With Privacy Data

Privacy protection is an important problem in workflow scheduling, for which scheduling tasks with privacy constraints to reliable resources is essential. In this article, we consider the scheduling problem of Spark applications in a hybrid cloud with deadline and privacy constraints. A scheduling algorithm framework is proposed, which consists of four algorithm components. Candidate strategies are developed for each algorithm component. The components and parameter values are statistically calibrated over a comprehensive set of random instances. The proposed algorithm is compared to modified classical algorithms for similar problems. The experimental results indicate that the proposed algorithm outperforms the compared algorithms under different application scales, deadlines, and private VMs
Medical Image Registration Technique Based on Partial Differential Equation

The radiological examination of congenital clubfoot is used not only in primary differential diagnostics and documentation, but frequently in follow-up as well. To eliminate the radiation risk, ultrasound is a relatively simple, and it is easily available, also, an inexpensive procedure for objective documentation of clubfoot correction. Image registration has a vast range of applications, and it is essential in order to study the evolution of pathology of a patient or to take full advantage of the complementary information coming from multimodal imagery. The purpose of the registration is to determine a transformation source image Is onto target image It. Ideally, one wants to determine a displacement field to improve the evaluation of the anatomy and the mobility of talus and calcaneus in clubfeet. The deformable registration calculates the displacement field, by minimizing energy functional, consisting of a regularization term and sum of square residuals of corresponding two sets of N points, between the two images. The proposed method is successfully applied to estimate displacement vector fields to improve the evaluation of the anatomy and the mobility of talus and calcaneus in clubfeet using a registration multimodal medical method based on Partial Differential Equation (PDE).
GOPE: Geometry-Aware Optimal Viewpoint Path Estimation Using a Monocular Camera

The goal of the optimal viewpoint path estimation is to generate a path to the optimal viewpoint location where the robot can best see the Point of Interest (POI). There are several learning-based methods to find an optimal viewpoint, but these methods are limited to a specific object POI and it is necessary to newly learn in a situation where a new POI is added, and not robust to the environment changes. In this paper, we propose an algorithm that generates a path to the optimal viewpoint by using the geometrical features of the environment in the situation where the target POI is in the field of view. This method makes it easy to add new POIs and is robust to environmental changes because it uses semantic and geometric information. We assume that the robot can make a simple estimation of the geometric characteristics of the surrounding environment by using pretrained networks or by using sensor values. We collected the Kwanjeong street dataset for testing our algorithm. In this dataset, the distance accuracy of our method to reach the optimal viewpoint of the POI achieved 81.8% and 70.9% for template matching accuracy.
Fractional-Order Sliding-Mode Fault-Tolerant Neural Adaptive Control of Fixed-Wing UAV With Prescribed Tracking Performance

In this paper, a fractional-order sliding-mode fault-tolerant tracking control scheme is proposed for a fixed-wing UAV with prescribed performance. The outer-loop position dynamics is first transformed to the second-order nonlinear model. By using neural networks, the unknown nonlinear functions containing actuator faults are identified. Moreover, the minimum learning parameter of neural networks is constructed to reduce the computational burden. Fractional-order calculus is further utilized in the sliding-mode control for improving the fault-tolerant tracking performance. Simulation results are presented to show the effectiveness.
Incremental Delta-Sigma ADCs: A Tutorial Review

In many sensor applications, a high-resolution analog-to-digital converter (ADC) is a key block. The use of an incremental delta-sigma ADC (IADC) is often well suited for such applications. While the energy-efficiency of IADCs has improved by several orders of magnitude over the past decade, the implementation of high performance IADCs, especially in battery-powered systems, is still challenging. This paper presents a tutorial review on energy-efficient IADCs and addresses the progress in this area. This paper describes the fundamentals of IADCs and energy-efficient hybrid IADC architectures. Various design techniques for improving the energy-efficiency of the IADCs are described. This paper is intended to serve as a starting point for the development of a new energy-efficient IADC.
Elimination of Cogging Torque for Axial Flux Permanent Magnet Motors Based on Current Harmonic Injection

Axial-flux permanent magnet (AFPM) disc machines are widely used for many industrial applications such as electrical vehicles, pumps, valve control and robotics due to their high power density and torque capability. One of the key issues of axial-flux permanent magnet machines for such applications is the torque quality and cogging component. Cogging torque causes torque and speed ripples, as well as acoustic noise and vibration, especially in low speed and direct drive applications. Therefore, the machine performance is affected and special precautions need to be taken. This paper focuses on harmonic injection and its effects for cogging torque minimization of axial-flux permanent magnet disc machines. Firstly, dominant harmonic order of cogging is investigated and then harmonic currents are added to the q-axis reference current. The proposed scheme is carried out on a microcontroller based servo driver and the test results confirm the effectiveness of the proposed method. It should be emphasized that this method is feasible to all permanent magnet motors having relatively large cogging torque components.
Vulnerable top-of-permafrost ground ice indicated by remotely
sensed late-season subsidence

Abstract. Ground ice is foundational to the integrity of Arctic ecosystems and infrastructure. However, we lack fine-scale ground ice maps across almost the entire Arctic, chiefly because ground ice cannot be observed directly from space. Focusing on northwestern Alaska, we assess the suitability of late-season subsidence from Sentinel-1 satellite observations as a direct indicator of vulnerable excess ground ice at the top of permafrost. The idea is that, towards the end of an exceptionally warm summer, the thaw front can penetrate materials that were previously perennially frozen, triggering increased subsidence if they are ice rich. For locations independently determined to be ice rich, the late-season subsidence in an exceptionally warm summer was 4–8 cm (5th–95th percentile), while it was lower for ice-poor areas (−1–2 cm). The distributions overlapped by 2 %, demonstrating high sensitivity and specificity for identifying top-of-permafrost excess ground ice. The strengths of late-season subsidence include the ease of automation and its applicability to areas that lack conspicuous manifestations of ground ice, as often occurs on hillslopes. One limitation is that it is not sensitive to excess ground ice below the thaw front and thus the total ice content. Late-season subsidence can enhance the automated mapping of vulnerable permafrost ground ice, complementing existing (predominantly non-automated) approaches based on largely indirect associations with vegetation cover and periglacial landforms. Improved ground ice maps will prove indispensable for anticipating terrain instability in the Arctic and sustainably stewarding its ecosystems.
Energy-Use Internet and Friendly Interaction with Power Grid: A Perspective

The new round of Internet era will completely change the way of energy consumption and greatly improve the efficiency of energy utilization. It is imperative to integrate the energy industrial Internet with the widely known consumption Internet. In order to improve energy efficiency and introduce market competition mechanism, the concept of Energy-Use Internet (EUI) has been proposed in this paper. Within the scope of EUI, energy suppliers, energy service providers, energy aggregators and energy consumers are collectively referred to as energy elements. These energy elements with trading and circulation functions are regarded as the basis of EUI. Using the Internet thinking for reference, a fair and free energy service and trading platform needs to be constructed. The friendly interaction mechanism between EUI and power grid has been extensively studied, mainly focusing on interaction mode, business mode, and regulation from grid company. Finally, some key technologies to realize the perspective of EUI and interaction with power grid are discussed.