Miracle-dz/UniFAR-FTUs huggingface dataset:
An Infrared High classification Accuracy Hand-held Machine Learning based Breast-Cancer Detection System

Breast cancer is the leading type of cancer among women in the 3rd world countries with < 50% survival rate. However, its early diagnosis can lead to cost-effective and successful treatment. Traditional breast cancer screening tools like mammography and MRI are not readily available to the population in low-income countries. Thermography (infrared imaging) is an FDA approved adjunct screening tool which can be an alternative solution. We present here the architecture of thermography based, application-specific Digital Back End (DBE) processor for a handheld off the shelf portable and intelligent screening device. A thermal image of the thorax taken by an infrared camera is pre-processed to get the regions of interest. To achieve efficient hardware implementation texture features are carefully selected, which are then fed to a dual classifier based on trained Linear Support Vector Machine (LSVM) and convolutional neural network (CNN) to decide the decision boundary. The proposed system achieves an overall sensitivity and specificity of 90.06% and 91.8%, respectively, with efficient hardware implementation by exploiting proposed classifier.
Neutral-Point Oscillation Suppression for Vienna Rectifier Based on Small DC-Link Capacitor

This paper replaces the traditional electrolytic capacitors with film capacitors on the DC side to increase system reliability, working life and power density of Vienna rectifier. However, the neutral-point oscillation would become more severe and higher frequency voltage ripples cannot be neglected due to the small DC-link film capacitors. A simplified strategy of feed-forward zero-sequence component injection is presented in this paper to solve this problem. Based on the mathematical average model, the feed-forward injection is adopted to suppress severe oscillation of the neutral point, while the closed-loop control is adopted to eliminate the DC offset in case of load unbalance. An accurate analytical expression of the neutral-point oscillation's each frequency harmonics is derived to accurately calculate the feed-forward duty cycle. The effectiveness of the utilized approach is verified on a simulation and laboratory prototype.
Rotordynamic Analysis of a Permanent Magnet Synchronous Motor Considering Nonlinear Unbalanced Magnetic Pull

This paper investigates the nonlinear dynamic behavior of a rotordynamic model for permanent magnet synchronous motor (PMSM) incorporating unbalanced magnetic pull (UMP) existing in the air gap between the rotor and stator. The system model comprises a Jeffcott rotor mounted on symmetrically located bearings. The nonlinear UMP is estimated based on the Maxwell stress tensor theory. The harmonic balance approximations and the numerical integration method are applied to solve the governing equations and obtain the steady-state response of the rotor as well as force transmissibility between rotor and bearing. The effects of UMP magnitude, mass unbalance and system damping level on the rotordynamic behavior and vibration transmission characteristics are examined systematically. The results show that the UMP has softening effect on the steady-state frequency response of the rotor. Multiple solutions can be found near resonance frequency of the rotor, demonstrating the complex dynamic behavior of the rotor such as jump phenomenon within the system. The UMP magnitude, mass unbalance and system damping level have large effect on force transmissibility from rotor to the bearing near resonance peak frequency. A small mass eccentricity and large system damping will benefit suppressing the vibration magnitude and force transmission from the rotor to the bearing. These results give a deeper understanding of the effects of UMP on the vibration behavior and vibration transmission within rotordynamic system in PMSM and hence benefit better designs with improved dynamic characteristics.
DC-link Voltage Control Method for High Speed Motors powered by Z-source Inverter

In order to widen the flux-weakening range of motors, this paper derived and proposed a DC-link voltage control strategy for high speed permanent magnet motor drive systems powered by Z-source inverter. In this control strategy, the DC-link voltage varies with the output voltage and remains optimal at all times, not only in steady state, but also in dynamic state. And shoot-through duty ratio and modulation index, the two main control variables in the Z-source inverter, are under unified control to ensure the stability of the system, while achieving fast rising and reducing the loss. In addition, due to the versatility of the control methods, an optimal output control realized during motor starts can be achieved. Besides, various of conventional voltage Z-source inverter space vector pulse width modulation strategies can be applied to this technology. Simulations and experiments verified the effectiveness of the proposed strategy.
Fractal Markers: A New Approach for Long-Range Marker Pose Estimation Under Occlusion

Squared fiducial markers are a powerful tool for camera pose estimation in applications such as robots, unmanned vehicles and augmented reality. The four corners of a single marker are enough to estimate the pose of a calibrated camera. However, they have some limitations. First, the methods proposed for detection are ineffective under occlusion. A small occlusion in any part of the marker makes it undetectable. Second, the range at which they can be detected is limited by their size. Very big markers can be detected from a far distance, but as the camera approaches them, they are not fully visible, and thus they can not be detected. Small markers, however, can not be detected from large distances. This paper proposes solutions to the above-mentioned problems. We propose the Fractal Marker, a novel type of marker that is built as an aggregation of squared markers, one into another, in a recursive manner. Also, we proposed a novel method for detecting Fractal Markers under severe occlusions. The results of our experiments show that the proposed method achieves a wider detection range than traditional markers and great robustness to occlusion.
Application of the Topological Interference Management Method in Practical Scenarios

While the topological interference management (TIM) problem was originally studied in a partially connected network, the novelity of this paper appears in exploring its performance in practical scenarios, where path losses exist. In this work, we also define the conditions under which TIM should be applied in order to save processing time when mobility is taken into account. Numerical results show the implication of these scenarios in constraining the degrees-of-freedom (DoF) achieved.
Deceiving Face Presentation Attack Detection via Image Transforms

Presentation attacks can provide unauthorized access to the users and fool face recognition systems for both small scale and large scale applications. Among all the presentation attacks, 2D print and replay attacks are very popular due to their ease and cost-effectiveness in attacking face recognition systems. However, over the years, there are several successful presentation attack detection algorithms developed to detect 2D print and replay attacks. Generally, 2D presentation attacks are detected using the presence or absence of micro patterns which distinguish a real input from an attacked input. However, if a smart attacker digitally "pre-processes" the image using intensity transforms and then performs 2D presentation attack, differences between real and attacked samples due to the micro-patterns would be minimized. In this paper, for the first time, we show that simple intensity transforms such as Gamma correction, log transform, and brightness control can help an attacker to deceive face presentation attack detection algorithms. Experimental results demonstrate that the smart attacker can increase the error rate of the hand-crafted as well as deep learning based presentation attack detectors.
Modulation and Control of an ANPC/H-Bridge Hybrid Inverter for Ship Electric Propulsion Drives

Multi-phase open-winding motors are widely used in ship electric propulsion systems. In order to improve the power density, efficiency and harmonic performance, this paper presents an ANPC/H-Bridge inverter with hybrid utilization of Si IGBTs and SiC MOSFETs. Each phase of this inverter consists of two three-level ANPC bridges and all the phases are powered by a common DC-link. Each three-level ANPC bridge includes four Si IGBTs operated at fundamental frequency and two SiC MOSFETs operated at carrier frequency. A neutral-point potential balance method is also proposed. Simulation results are presented to demonstrate the validity of this voltage balancing method.
An Approach to Recommendation of Verbosity Log Levels Based on Logging Intention

Verbosity levels of logs are designed to discriminate highly diverse runtime events, which facilitates system failure identification through simple keyword search (e.g., fatal, error). Verbosity levels should be properly assigned to logging statements, as inappropriate verbosity levels would confuse users and cause a lot of redundant maintenance effort. However, to achieve such a goal is not an easy task due to the lack of practical specifications and guidelines towards verbosity log level usages. The existing research has built a classification model on log related quantitative metrics such as log density to improve logging level practice. Though such quantitative metrics can reveal logging characteristics, their contributions on logging level decision are limited, since valuable logging intention information buried in logging code context can not be captured. In this paper, we propose an automatic approach to help developers determine the appropriate verbosity log levels. More specially, our approach discriminates different verbosity log level usages based on code context features that contain underlying logging intention. To validate our approach, we implement a prototype tool, VerbosityLevelDirector, and perform a case study to measure its effectiveness on four well-known open source software projects. Evaluation results show that VerbosityLevelDirector achieves high performance on verbosity level discrimination and outperforms the baseline approaches on all those projects. Furthermore, through applying noise handling technique, our approach can detect previously unknown inappropriate verbosity level configurations in the code repository. We have reported 21 representative logging level errors with modification advice to issue tracking platforms of the examined software projects and received positive feedback from their developers. The above results confirm that our work can help developers make a better logging level decision in real-world engineering.
Improving Sentiment Analysis in Twitter Using Sentiment Specific Word Embeddings

Most existing continuous word representation learning algorithms usually only capture the syntactic information in the texts while ignoring the sentiment relations between words. These represenations are not sufficiently effective for sentiment analysis as, in many cases, words with similar syntactic context, having neighboring word vectors can bear opposite sentiment polarity. In this paper, we present a weighted average word embeddings method which incorporates sentiment information in the continuous representation of words based on an adapted version of the delta TFIDF measure. Majority voting was then applied to determine the final polarity involving three machine learning classifiers notably, Support Vector Machine, Maximum Entropy and Naïve Bayes. Our experiments show promising results and a significant improvement over unweighted embeddings as well as traditional Term Frequency-Inverse Document Frequency (TFIDF).
Multiple Topics Misinformation blocking in Online Social Networks

Misinformation prevention has received much attention due to its important role to user community. However, recent studies ignore the influence of the topics of misinformation in the process of information dissemination. In fact, the spread of propagation of misinformation depends on their topics. Therefore, in order to improve the effectiveness of preventing false information, we need to consider the effect of topics in information dissemination.In this paper, we study the problem of misinformation blocking which considers topics of misinformation sources by removing a set of nodes, called MTMB problem. We show that MTMB is NP-hard and the objective function is a monotone and submodular function. Based on that, we propose a Greedy Algorithm (GA), which provides a approximation ratio of $\left( {1 - 1/\sqrt e } \right)$. We further propose a Scalable Greedy Algorithm (SGA), an efficient algorithm based on speeding up the GA by effective estimating the objective function. Experiments are conducted on networks showing the effectiveness and running time of the proposed algorithms which outperform other methods.
Decision Variable Learning

Many alternatives to traditional Multi-Objective Optimization Algorithms are emerging due to the increasing number of Multi-Objective Problems with a high degree of complexity, such as Many-Objective Problems. Among these alternatives, the methods known as surrogate stand out. These methods seek to construct new models for the objective functions based on the data obtained previously from the actual objective functions. The input of these models are a set of vectors in decision variable space and the output are the values of the objective functions. In this work, the Decision Variable Learning (DVL) algorithm is proposed, which presents an inverse idea to traditional surrogates. In the DVL, machine learning models will be used to learn the behavior of the decision variables in Many-Objective Optimization Problems. In this context, we have enough information to learn from the objective vectors, to predict near optimal solution in decision variable space. DVL algorithm will be evaluated using benchmark problems and its results will be compared with the NSGA-III algorithm.
Control of 1-DOF Exoskeleton based on Neural Network Regression Analysis and Wavelet Transform of MES

Improvement of human locomotor performance through external devices such as exoskeletons is a field of active research. This paper presents the design and implementation of an exoskeleton of one degree of freedom (DOF) to assist the flexion and extension of the upper limb. The exoskeleton is controlled by signals from force sensors and myoelectric signals (MES), achieving a reduction of the muscle activity of the user. The MES are captured from the triceps and biceps muscle groups. Subsequent digital signal processing comprises: for feature extraction of signals the time-frequency Wavelet transform is performed, and its following regression analysis is done by an artificial neural network (ANN). We propose a speed control scheme of the exoskeleton from the aforementioned signals, which is executed in real time, achieving a reduction of the biceps muscle activity up to 94%.
Review of Potential Methods for Handover Decision in V2V VANET

Vehicular ad hoc network (VANET) enables the vehicles to communicate each other. This technology could lead the future of transportation system by enabling the safety application, traffic management, and passenger entertainment. However, due to the fast moving nodes and very dynamic environment, VANET has some challenges waiting to be solved. One of the problems is related with vehicle to vehicle (V2V) handover decision. In this paper, the problem related to V2V handover decision is investigated. Furthermore, several potential methods to solve the problem are presented. The implementations of those methods in VANET are surveyed to assess the potential. Finally, the advantages and disadvantages of each method related to the implementation for V2V handover decision are presented in discussion.
A Survey and Comparison of Trajectory Classification Methods

In trajectory classification tasks, the challenge is to find the subtrajectories or a set of features that better discriminate the class. However, there is a lack of robustness in existing methods, as they do not use the same datasets or do not compare their results with the state of the art. In this paper we survey the state of the art in trajectory classification and compare existing methods using two different classifiers over several datasets with different characteristics, guiding the best scenarios for using each technique.
Multi-Mobile Primary and Secondary Users Spectrum Sensing Effect in Cognitive Radio Networks

In this paper, we investigate the mutual mobility effect for independent multiple primary users and numerous secondary users on the spectrum sensing capability of secondary users. The proposed system model adopts two different mobility models; namely, the random walk (RWL) model and random waypoint (RWP) model with varying numbers of primary mobile users and secondary mobile users in the network. Closed-form expressions for the detection capability and normalized sensing capacity were derived. The results show that the increase in the number of independent mobile users following a random mobility model has an impact on the total sensing capacity over unoccupied bands.
ANTRL as a Development Platform for the Series DSL for the Learning Process

Recently, domain-specific language has become more popular in the IT market. Therefore, there are several variants of the domain-oriented programming languages used in the modern world. A variant of domain-specific language that accepts wind farm modeling language at the input and GPSS code at the output to model the queuing system has been proposed in this paper. The ANTLR grammar rules for the implementation of wind farm modeling language have been formed.
Brain Tumor Segmentation Using Machine Learning Classifier

Using brain magnetic resonance Images a machine based software is developed for segmentation and to classify tumor type as benign and malignant. In this study, the MRI image enhanced using contrast improvement technique, double thresholding is done using morphological operations and skull striping process is used mainly to remove unwanted non cerebral tissues from MR image, The brain tumor segmentation is done in a slice of Magnetic Resonance (MR) Image where massive abnormal cells are localized and tumor region that are sliced are segmented by machine learning classifiers like KNN. fuzzy C-mean. k-means. Feature are derived using GLCM and those features are trained in such a way it produce accurate segmentation of tumor region is done in less computation time and therefore, in proposed system, features derived from the GLCM. so that the segmentation of tumor region using triple technique K-means. KNN and FCM can be done accurately and efficiently. Accuracy and error rate is calculated for brain MRI image using triple technique Means, FCM and KNN.
Stylized Line Drawing of 3D Models using CNN

Techniques to render 3D models like hand-drawings are often required. In this paper, we propose an approach that generates line-drawing with various styles by machine learning. We train two Convolutional neural networks (CNNs), of which one is a line extractor from the depth and normal images of a 3D object, and the other is a line thickness applicator. The following process to CNNs interprets the thickness of the lines as intensity to control properties of a line style. Using the obtained intensities, non-uniform line styled drawings are generated. The results show the efficiency of combining the machine learning method and the interpreter.
Rotor Position Compensation Method for PMSM based on Bus Current Precision Measurement

For permanent magnet synchronous motor (PMSM) control system based on sliding mode observer (SMO), the estimated values of the motor speed and the rotor position is always deviated because of variable of the PMSM parameters in actual operation. The existing optimization method is online parameter identification while the method increases the difficulty of motor control. The accuracy of its identification result directly affects the optimization effect. In order to optimize the observer's performance to obtain the rotor position better, in this paper, the regulator was constructed with rotor position compensation as the control target and the change rate of bus current caused by rotor position error as deviation by deducing the mathematical relationship between rotor position error and bus current of PMSM. Simulation model in MATLAB/Simulink and experiments results in dSPACE show high reliability of the new method and improvement of the motor efficiency.
Direct Torque Control for Three-Phase Open-End Winding Permanent Magnet Synchronous Motor Based on Zero-Sequence Current Suppression

Three-phase open-end winding permanent magnet synchronous motor (OEW-PMSM) with common dc bus, which has advantages of good controllability, high driving power and strong fault-tolerant ability, is becoming more and more popular. Direct torque control (DTC) has simple structure and rapid dynamic response, which is a competitive control strategy. However, DTC of this drive system is difficult to implement for that the zero-sequence current suppression should be also taken into consideration besides the control of electromagnetic torque and stator flux amplitude. To address this problem, a novel DTC strategy based on zero-sequence current suppression is proposed in this paper. Different from the conventional DTC, one more hysteresis controller is introduced to suppress the zero-sequence current. In addition, the optimal switching table has been modified based on the synthesized virtual voltage vectors. Simulation research on this strategy has been done, and the results show that the electromagnetic torque, stator flux amplitude and zero-sequence current can be controlled at the same time.
Automatic Design Environment using High-level Synthesis for Reconfigurable 3D Sound Processor

This paper proposes an automatic design environment for a previously reported three-dimensional (3D) sound processor. The reconfigurable 3D sound processor has a super-directional modulation intellectual property (IP) and a 3D sound processing IP and is intended to be adapted for consumer applications. This processor can reproduce realistic 3D sound in a small sound field within an arbitrary space using ultrasound. The processor is designed using high-level synthesis as the design methodology. The proposed automatic design environment can synthesize the final hardware for the 3D processing IP based on an input description of several design parameters, such as the finite and infinite impulse response filter configurations and the order and coefficients of each filter. The application of the proposed automatic design environment was shown to reduce the design period and cost of 3D processing IP to less than one-tenth of those of the original design procedure.
Review of cellular radio network cell placement design, from traditional to artificial intelligence based approaches

This work reviews the current state of knowledge about radio access network (RAN) base transceiver station (BTS) placement, first describing classic approaches, and then concluding with automated placement planning without the supervision of human domain expert. BTS placement locations (either optimal, or sub-optimal) are chosen during the planning process, so that the maximization of desired RAN quality metrics is achieved.Future automated cell placement will mainly concern BTS placement of small cells (low power, short range cells) as BTSs of this type will be on larger scale employed in future (5G and beyond) multi-tier RANs as a complement to macrocell (high power, long range) BTSs. While small cell BTSs are characterized by easy and low-cost deployment, it is assumed that macrocells will not be relocated by RAN operators as a part of transition to 5G RAN, as their relocation would be both impractical and financially unfavourable.