Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual Approach to Support Analysis of Optimum-Path Forest Classifier

Optimum-path forest (OPF) is a graph based classifier in which the training process computes optimum-path trees rooted by prototype instances. Thus, one or more optimum-path trees represent each class and the testing process is based on identifying which optimum-path tree would contain a test sample. Usually, OPF performance is analyzed based on measures computed from training and testing process, such as f-score and correct classification rate (accuracy). This paper proposes an approach based on visualization to support understanding of OPF training and testing processes. The visual approach uses multidimensional projection techniques to reduce the feature space dimensionality and to generate graphical representation from instances similarities. As a result, one can visualize, analyze and understand each step of OPF classifier: generation of the minimum-spanning tree, prototypes choosing, computation of optimum-path trees, and test samples classification. The experiments show that our approach is useful to understand how the prototypes are chosen, to identify what are the best prototypes, to visualize how the training dataset size influences the OPF performance, to analyze how a weak feature space can impact the OPF performance, and to identify some insights about OPF classifier as a whole.
Investigation on the electrical, thermal and mechanical properties of silicone rubber nanocomposites

Addition of alumina nano particles to silicone rubber (SR) has shown a significant improvement in electrical, mechanical and thermal properties of the SR nanocomposites. Static contact angle and water droplet-initiated corona inception voltage exhibited a marginal change on inclusion of nano alumina into SR matrix. Alumina filler improved the corona ageing resistance of the composites; however, a marginal reduction in the rate of hydrophobicity recovery is observed. Laser induced breakdown spectroscopy proved as an effective tool to rank the performance of the SR nano composites. Variation of crater depth with energy of laser pulse and number of laser pulses follows the exponential relation. Plasma temperature calculated by optical emission spectroscopy is in line with the amount of damage caused to the surface of the specimen by water droplet-initiated discharge. Dielectric response spectroscopic studies indicate that inclusion of nano fillers has resulted in lower interfacial polarization and lower imaginary permittivity. It is observed through dynamic mechanical analysis that the composites have higher storage modulus and the activation energy compared to base SR.
Face Anti-spoofing using Eyes Movement and CNN-based Liveness Detection

Biometric authentication is more and more popular these days. Among authentication techniques, face recognition is the most widely used technique. Face anti-spoofing is the core of the biometric system. Face Anti-spoofing is about prevention of spoofing attack by detecting the face image is live or not before feeding it to the system. In this paper, we propose two streamed line approaches for face anti-spoofing. First approach is detecting of eyes movement and second approach is CNN-based liveness detection by extracting the local features. Experiment demonstrates on comparison of previous works with HTER (Half Total Error Rate) over three datasets: NUAA imposter dataset, Replay Attack and OWN replay dataset which is created in this paper.
A Shoulder-Surfing Resistant Image-Based Authentication Scheme with a Brain-Computer Interface

With the increasing availability of consumer brain-computer interfaces, new methods of authentication can be considered. In this paper, we present a shoulder surfing resistant means of entering a graphical password by measuring brain activity. The password is a subset of images displayed repeatedly by rapid serial visual presentation. The occurrence of a password image entails an event-related potential in the electroencephalogram, the P300 response. The P300 response is used to classify whether an image belongs to the password subset or not. We compare individual classifiers, trained with samples of a specific user, to general P300 classifiers, trained over all subjects. We evaluate the permanence of the classification results in three subsequent experiment sessions. The classification score significantly increases from the first to the third session. Comparing the use of natural photos or simple objects as stimuli shows no significant difference. In total, our authentication scheme achieves an equal error rate of about 10%. In the future, with increasing accuracy and proliferation, brain-computer interfaces could find practical application in alternative authentication methods.
Multivariable Generalization of BMRAC Algorithm by means of Global HOSM Differentiators with Dynamic Gains

This paper presents an extension of the Binary Model Reference Adaptive Control (BMRAC) for uncertain multivariable (square) systems with non-uniform arbitrary relative degrees, using only output feedback. The BMRAC algorithm is a robust adaptive strategy which has the good transient and robustness properties of sliding mode control with the important advantage of preserving a continuous control signal free of chattering, inherited from classical adaptive systems. Unlike most of the publications which consider plants with uniform relative degree one, here we circumvent the arbitrary relative degree obstacle using a multivariable generalization of global finite-time differentiators with dynamic gains and higher-order sliding modes. State-norm observers are employed to obtain an upper bound for the unmeasured state and to update the gains of the proposed robust and exact multivariable differentiator. Global and exact output tracking is guaranteed without requiring stringent symmetry assumptions on the plant High-Frequency Gain matrix, usually assumed in other works in the literature.
Car Plate Character Recognition Via Semi-Supervised Learning

Car plate character recognition can be difficult since sample images might suffer perspective distortion, motion blur and poor lighting. Moreover, there are many character pattern variations such as size, font and color. Some related works are based on supervised approach and take a lot of training and labeled data to build an effective classification. Motivated by a graph's capacity to model the underlying manifold and in the semi-supervised learning (SSL) ability to use few labeled data, this paper employs a graph-based SSL approach for character classification. We use four datasets: the first one was artificially generated by symbol rotation, blurring and contrast shifting. The second corresponds to Chars74K computer characters with font variations. The third and fourth are digits obtained from real car plates from the USA and Brazil. Classification experiments, based on artificial and real data, show that SSL' s accuracy on a 10% labeled dataset is statistically comparable to approaches such as classical supervised algorithms k-NN, Naive Bayes, Decision Tree, Neural Networks and SVM.
A Weighted Ordered Probit Collaborative Kalman Filter For Hotel Rating Prediction

A successful recommender system interacts with users and learns their preferences. This is crucial in order to provide accurate recommendations. In this paper, a Weighted Ordered Probit Collaborative Kalman filter is proposed for hotel rating prediction. Since potential changes may occur in hotel services or accommodation conditions, a hotel popularity may be volatile through time. A weighted ordered probit model is introduced to capture this latent trend about each hotel popularity through time. It is demonstrated by experiments that such model of hotel popularity trends reinforces the performance of Collaborative Kalman filter, yielding more accurate potential recommendations.
Stereological study of heart volume in male rats after exposure to electromagnetic fields

چ هديک هنيمز : جاوما نيا يلامتحا تارثا و تسا هدراذگ شيازفا هب ور هرمزور يگدنز رد زورما عماوج رد سيطانغمورتكلا جاوما هدننكديلوت لياسو دربراك تدش اب يتح اه ي مادنا رب نيياپ م نيققحم هجوت دروم ندب ياه ي اذل ،دشاب ريثأت ق تارفح و ينلاضع هراويد تارييغت رب جاوما نيا يشرازگ هك بل تفرگ رارق رضاح قيقحت هجوت دروم ،دشن تفاي نآ دروم رد . شور و داوم اه : دادعت ۲۴ داژن رن ييارحص شوم رس راتسيو هب راهچ شيامزآ هورگ , کي لرتنك هورگ دهاش هورگ کي و ) هورگ ره رد ۴ ناويح ( دندش ميسقت . هورگ رد شيامزآ ياه ۲ فلتخم تدش ۱ / ۲ يليم و لاست ۳ / ۴ ليم ي تدـش رـه رد و دش هتفرگ رظن رد سيطانغمورتكلا ناديم زا لاست هورگ كي ،ناديم ۱۵ رگيد هورگ و موادم زور ۶ تفرگ رارق سيطانغمورتكلا جاوما ضرعم رد موادم زور . هورگ دهاش يواسم نامز اب اب هورگ ياه آ ارق شوماخ ناديم رد يلو شيامز ر تفرگ لرتنک هورگ یور ربو ه ي لمع چ ي تروص یتا تفرگن . و جراخ ناويح بلق ،يريگرارق تدم مامتا زا دعب تماخض هب يتفاب لايرس عطاقم ۷ گنر و هيهت رتموركيم نيليسکوتامه يزيمآ ‐ دش ماجنا نيزوئا . هورگ ره رد مجح و ينلاـضع هراوـيد ،بـلق لك زا هدافتسا اب ينطب نيب هراويد و پچ نطب و تسار نطب هرفح شور و ديدرگ هبساحم هيلاواك م ياطخ ؤ دمآ تسدب كيژولويرتسا رث . هتفاي اه : م جياتن ؤ ينعم فلاتخا دوجو مدع دي هورگ نيب راد ياه ۱۵ يم لرتنك هورگ اب سيطانغمورتكلا جاوما ضرعم رد زور دشاب . هورگ رد يلو ۶ تدش اب سيطانغمورتكلا جاوما ضرعم رد زور ۱ / ۲ يليم ينعم شهاك نطب ود ره ينلاضع هراويد مجح ،لاست هورگ رد و هتشاد راد ۶ تدش اب زور ۳ / ۴ يليم ينعم شيازفا ينطب نيب هراويد مجح ،لاست يم ناشن راد دهد . يريگ هجيتن : ً لاامتحا ،تدم هاتوك تروصب و نيياپ تدش اب سيطانغمورتكلا جاوما لاكيدار داجيا اب نوخ اي و دشر رب دازآ ياه تلاـضع يناـسر م بلق ؤ ز تدم لوط شيازفا يلو دنرث تشگرب تارثا ناديم ضرعم رد يريگرارق نام يم ديدپ ار ناديم رد يريگرارق زا لبق تلاح هب ريذپ هك دروآ ً لاامتحا يم قورع و بلق و باصعا متسيس نيب لماعت زا يشان دشاب . يديلک ناگژاو : لاکيدار ،يژولويرتسا ،بلق ،سيطانغمورتکلا ناديم دازآ ياه
Univiariate Signal Preprocessing Methodology for Fault Detection in Semiconductor Manufacturing Process

Many studies using sensor signals have been conducted in the field of fault detection and classification (FDC) for semiconductor manufacturing processes. This is because sensor signals generated in the semiconductor production process provide important information for predicting quality and yield of the finished product. However, as the process becomes more sophisticated and refined, normal and abnormal data with similar shape appears. They only show delicate differences and it is difficult to classify them using general classification algorithms. The purpose of this research is to present a preprocessing methodology for improving classification performance. The methodology consists of four steps based on signal segmentation and clustering methods. The experimental results illustrate the better performance of the proposed procedure.
Wearable Sport Activity Classification Based on Deep Convolutional Neural Network

This paper develops a wearable sport activity classification system and its associated deep learning-based sport activity classification algorithm for accurately recognizing sport activities. The proposed wearable system used two wearable inertial sensing modules worn on athletes’ wrist and ankle to collect sport motion signals and utilized a deep convolutional neural network (CNN) to extract the inherent features from the spectrograms of the short-term Fourier transform (STFT) of the sport motion signals. The wearable inertial sensing module is composed of a microcontroller, a triaxial accelerometer, a triaxial gyroscope, an RF wireless transmission module, and a power supply circuit. All ten participants wore the two wearable inertial sensing modules on their wrist and ankle to collect motion signals generated by sport activities. Subsequently, we developed a deep learning-based sport activity classification algorithm composed of sport motion signal collection, signal preprocessing, sport motion segmentation, signal normalization, spectrogram generation, image mergence/resizing, and CNN-based classification to recognize ten types of sport activities. The CNN classifier consisting of two convolutional layers, two pooling layers, a fully-connected layer, and a softmax layer can be used to divide the sport activities into table tennis, tennis, badminton, golf, batting baseball, shooting basketball, volleyball, dribbling basketball, running, and bicycling, respectively. Finally, the experimental results show that the proposed wearable sport activity classification system and its deep learning-based sport activity classification algorithm can recognize 10 sport activities with the classification rate of 99.30%.
Observer-Based Adaptive Tracking Control of Wheeled Mobile Robots With Unknown Slipping Parameters

Based on wheeled mobile robots (WMRs) with unknown longitudinal slipping parameters, an adaptive control strategy for a tracked mobile robot is presented, in which the longitudinal slipping of the left and right wheels are described by two unknown parameters. The kinematic model of mobile robot with wheels’ slipping is derived from the motion model of mobile robot without wheels’ slipping. Employing the Lyapunov direct method, an adaptive nonlinear feedback control law that compensates for the longitudinal slipping is proposed to achieve an objective of tracking a given trajectory. The orientation angle observer is designed to estimate the immeasurable orientation angle of the robot by employing the coordinate information. Asymptotic stability of the closed-loop system is ensured by choosing an appropriate Lyapunov function. Numerical and experimental results show the effectiveness of the proposed control approach.
Imaging Photoplethysmography: Signal Waveform Analysis

Imaging photoplethysmography (iPPG) is used to remotely measure the blood volume pulse using a camera. Traditionally, it has been employed to estimate heart and respiratory rates, and recently blood pressure and oxygen saturation. Biomedical engineering researchers have become increasingly interested in iPPG because of its several advantages as comfort, non-invasive, simple, non-stressful and inexpensive characteristics. Moreover, the results of the waveform signal analysis obtained by the conducted studies on contact PPG sensors made the use of iPPG a more promising technique for early screening of various atherosclerotic and cardiovascular pathologies. iPPG waveform has not been studied before. In this paper we process the iPPG signal obtained using a camera to extract its waveform features such as areas and time parameters. These features will make possible a non-contact estimation of arterial stiffness and early diagnosis of many cardiovascular pathologies. The obtained results show a high correlation with those obtained by an approved contact PPG sensor.
A Novel Handheld Diffuse Optical Spectroscopy System Implementation Based on High-Accuracy Multidimensional Imaging Reconstruction Model for Human Breast Healthcare Applications

Diffuse Optical Tomography (DOT) is a relatively novel, nonionizing and noninvasive technique for breast tumor imaging. Due to the diffusive nature of light propagation, the inverse problem is highly ill-posed. In this study, we propose the modified Beer-Lambert’s law that reduces the number of unknown values to reduce the ill-posedness issue. An integrated mobile DOT system is proposed. The system comprises a front-end circuit and an integrated digital signal processor. The sources and the detectors are distributed hexagonal geometry for increasing the spatial resolution. The processor included the front-end controller, the iteration algorithm engine and wireless communication controller were implemented on FPGA. The functionalities of the proposed system were validated using a breast phantom. Extensive experimental results illustrated to show the performance of the proposed system. The depth of the tumor detected by the system is 20mm.
Adaptive Robust Motion Control of an Ironless Permanent Magnet Linear Synchronous Motor with Dead-zone Compensation*

The ironless permanent magnet linear synchronous motor (ILPMLSM) is widely used in precision applications because of its advantages, such as direct drive, zero cogging and attractive force, and high speed and acceleration potential. In most of the existing control schemes, the adaptive robust control algorithm has special advantages in dealing with parameter uncertainties and uncertain nonlinearities of the linear motor systems. However, the previously ignored nonparametric uncertainty (noise and disturbance) is widespread in industrial applications. Furthermore, even small uncertainties may produce parameter drift possibly leading to instability of the control system, when the reference signals are not persistently exciting. In this paper, a nonlinear dynamic model of an ILPMLSM system including both the traditional various uncertainties and the previously ignored nonparametric uncertainty is given, leading to a better understanding of the parameter drift and system instability caused by the noise and disturbance. Additionally, an adaptive robust control scheme with dead-zone compensation is proposed. The dead-zone technique is employed to improve the discontinuous projection adaptation law. The approach of dead-zone compensation adaptive robust control is applied to synthesize the system controller for both accurate parameter estimation and a guaranteed robust performance to various uncertainties. Comparative experiments show that the proposed approach can achieve fast and accurate trajectory tracking, robustness, and stability against uncertainties mentioned above.
Measurement of PM2.5 Mass Concentration Using an Electrostatic Particle Concentrator-Based Quartz Crystal Microbalance

Particulate matter (PM) is one of the most critical air pollutants, and various instruments have been developed to measure PM mass concentration. Of these, quartz crystal microbalance (QCM) based instruments have received much attention. However, these instruments are subject to significant drawbacks: particle bounce due to poor adhesion, need for frequent cleanings of the crystal electrode, and non-uniform distribution of collected particles. In this study, we present an electrostatic particle concentrator (EPC)-based QCM (qEPC) instrument capable of measuring the mass concentration of PM<sub>2.5</sub>(PM smaller than 2.5 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>), while avoiding the drawbacks. Experimental measurements showed high collection efficiencies (~99% at 1.2 liters/min), highly uniform particle distributions for long sampling periods (up to 120 min at 50 <inline-formula> <tex-math notation="LaTeX">$\mu \text{g}/\text{m}^{3})$ </tex-math></inline-formula>, and high mass concentration sensitivity [0.068(Hz/min)/(<inline-formula> <tex-math notation="LaTeX">$\mu \text{g}/\text{m}^{3})$ </tex-math></inline-formula>]. The enhanced uniformity of particle deposition profiles and mass concentration sensitivity were made possible by the unique flow and electrical design of the qEPC instrument.
Super Twisting-Based Position Control of PMLSM with Load Force Observer

High-precise and high-dynamic control is the most important requirement of permanent magnet linear synchronous motor (PMLSM) system. The main problem is to suppress the disturbances existing in PMLSM system. Sliding mode control (SMC) is one of the most robust control algorithms now, it can achieve high response speed and high robustness at the same time. But chattering problem is the main weakness of SMC. To solve that, higher order SMC algorithms have been developed. Super-twisting sliding mode control (STSMC) is a kind of second-order sliding mode control algorithm and has attract wide attention now. This paper presents a position controller for PMLSM based on STSMC, and demonstrates the advantages of this controller by comparing it with normal SMC controllers. Then a disturbance observer based on STSMC is presented, and with the observation result, the disturbance can be compensated, and the robustness of system can be further improved.
Inductive Wireless Power Transfer at 100MHz with Wide Load Range and Constant Output Current

Power conversion at 100MHz is important to miniaturization and integration, which is critical in wireless power transfer (WPT) for Peer-to-Peer (P2P) charging in consumer electronics and Internet of Things (IoT). Design and implementation at this frequency is challenging in selecting active devices, passive components, gate drivers, and circuit topologies along with compensation networks, which are crucial for WPT systems capable of a wide load range.This paper articulates the design of a 100MHz inductive wireless power transfer system using current-mode class D converters both for the transmitter and receiver. An equivalent model for the symmetric CMCD WPT system is derived and used to show purely resistive input impedance, wide load range, and constant output current with the tuned compensation networks obtained from the previously published current-controlled-voltage-source (CCVS) model. Uniqueness of solution and sensitivity to parameters is analyzed for the compensation networks, which provides a guide for hardware designers. Finally, a prototype of a 100MHz symmetric CMCD WPT system is built and used to verify the characteristics with the designed compensation networks under different loading conditions and input voltages. A prototype of a symmetric CMCD WPT system was demonstrated to achieve 2.7W output power, nearly 70% end-to-end drain efficiency, and nearly 60% total efficiency when including gating loss.
Electrode Shape Optimization of Electrostatic Chuck for Uniform Force Distribution using Continuum Sensitivity Analysis

This paper proposes electrostatic chuck electrode design method for uniform chucking force distribution. To design the chuck, we combine continuum sensitivity analysis and adaptive level set method. Continuum sensitivity analysis derives the sensitivity at the electrode surface, and adaptive level set method clearly demonstrates the changed shape. The design objective is to obtain uniform force in each segment of the wafer, which is calculated using virtual work method. To show the feasibility of the design method, this paper designs coulomb type bipolar electrostatic chuck with two electrodes.
Realistic Folded Surface Modeling from Sketching

We propose a method that allows a user to create folded surfaces with a good sense of reality from simply sketches directly drawing curves on the surface of a cloth 3D model. The user can obtain a one-shot image of a simple cloth fold simulation result with a good sense of reality through our method in a short time, even for the one who has little or no knowledge of the cloth apparel cutting structure and the physical properties of the fabric. The method consists of a simulation method that allows the user to make a preliminary adjustment to their cloth model, a reconstruction method to design the folded surface by sketching on the surface, and a refinement method to modify the shape of the folded surface.
A Method of Analyzing the Impact of the Wiring Parameters on the Electromagnetic Coupling to PCB Inside Electronic Equipment

This paper proposes a method of field-TL-circuit joint simulation method to analyze the impact of wiring parameters on the electromagnetic coupling to PCB inside electronic equipment. The coupling mechanism of PCB is established on the basis of the multiconductor transmission theory and field-TL coupling theory. Then, the field-TL-circuit joint simulation technique is applied to analyze electromagnetic coupling model of PCB. Finally, a model of electronic equipment with a piece of PCB under three different wiring paths is constructed to discuss the EM interference issue of PCB. The calculated results indicate that the sensitive circuit should be kept away from strongly interfering wiring area and reasonable wiring can reduce the radiated electromagnetic interference.