Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of N-port Radial Dividers in Substrate Integrated Waveguided Technology

Modern microwave and millimeter-wave systems require high quality and high-density integration circuits. Also, two of the most critical requirements of these systems are reduced size and cost. The implementation of combining technologies systems seeks to provide better performance in terms of efficiency, size, and price. In this sense, this paper focuses on the analysis, design, and implementation of N– ports radial dividers in the Ku band using Substrate Integrated Waveguide (SIW) technology, bringing together the advantages of radial dividers and the high efficiency of SIW technology. The design and analysis of three power dividers (one 10-port and two 8-port) are presented in SIW technology with transitions in microstrip, using two different substrates. Both simulation and experimental results show good performance agreement.
Fuzzy adaptive integral sliding mode synchronization of a class of chaotic systems

Synchronization between two uncertain different chaotic systems is investigated by using adaptive fuzzy integral terminal sliding mode control. The proposed method can guarantee the convergence of the synchronization errors in some finite time. In addition, the singular problem which usually occurs in traditional sliding mode control, can also be avoided. Finally, some simulation studies are presented to show the effectiveness of the proposed method.
Free Energy of Association of the K+:18-Crown-6 Complex in Water: A New Molecular Dynamics Study

We present a study of free energy profile, or potential of mean force (pmf), for the association of K[sup +] to 18-crown-6 in water. The counterion Cl[sup -] is included, and all long-range interactions are computed via the Ewald summation method. The present results indicate that the minimum free energy is located at the crown ether center of mass and similar to the complex observed in the solid state or simulated in vacuo. In addition, the crown ether retains its D[sub 3d] geometry with a small fluctuation throughout the simulations. The calculated binding free energy, and the corresponding association constant, compare reasonably with experimental measurements made on the same system. 14 refs., 5 figs., 2 tabs.
Demand Responsive Power Flow Controller Providing Resistive Load Perspective Regulation in Cooperation with Small Generation Units

The paper deals with a concept of power flow controller which regulates active power flow at point of connection by pulse-width modulated switching of resistive load in installations with small scale power generation and non-simultaneously fitted consumption. The controller prototype employs a commercially available DC solid-state relay and the design describes limiting factors of its operation from power quality and reliability point of view. The prototype performance is compared to other commercial solutions, utilizing AC solid-state relays equipped with zero-cross switching control strategy, in terms of its impact on supply voltage variations and control dynamics. As it is demonstrated, the developed controller providing heating element input power regulation exhibits promising features for future technically mature demand response solutions.
Development of Auto Tracking and Target Fixing Gun using Machine Vision

In this modern world, protecting ourselves and our belongings has become inevitably important. The already existing security systems like surveillance cameras can identify the mishaps but cannot prevent or react to it. This project aims to add more to the surveillance and specifically proposed for restricted areas. The Raspberry Pi 4 is used as the base processor for the image processing applications and several test cases have been exploited in considerations for different areas. A 8MP NoIR v2 camera is used along with the IR LEDs for enabling a proper vision even during no light and twilight conditions. Python 3 is used as a primary source for obtaining the desired machine learning results. The gun turret is mounted on the servo motor which points to the person of suspicion by using the co-ordinates fed to the servo. Based on the training dataset accuracy the module can be operated either in Automatic (Full control) or in Semi Automatic Mode (Asks for trigger through a web application to take an action.).
Grasping System for Industrial Application Using Point Cloud-Based Clustering

In recent years, numerous studies have been conducted on the robot grasping using deep learning, which requires a lot of data and training time. This study proposes a grasping algorithm that does not require data collection and training. In addition, the hardware of the proposed system is simply configured for a quick application in industrial fields. This algorithm is performed through clustering and grasping analysis based on point clouds. First, the point cloud obtained from the 3D camera is clustered, and the cluster most similar to the 3D CAD model is selected. Next, using the selected cluster, the object pose and the grasping pose are estimated. Finally, the target object is grasped through the estimated grasping pose, and the grasped object is loaded with a predetermined pose in consideration of the object pose. In order to evaluate the performance of the proposed algorithm, the grasping and loading of the target object with a product used on the actual industrial site and the loading jig of the object were tested. The algorithm showed the success rate of 95% in grasping, transporting and loading experiments.
Digital Processing of the Ultrasonic Signal for Mobile Devices in the Transmission of Information

Currently, broadband wireless has largely dominated RF systems using a variety of technologies, including popular commercial protocols such as IEEE802 and Bluetooth. In this work, we consider the study of the effect of direct exposure to ultrasonic waves on an active dielectric using an ultrasound sensor. This provides several advantages over existing methods, especially to ensure safety - it is effectively blocked by most barriers and has a limited distribution range, which makes it very difficult to organize interception from outside the premises. The use of ultrasonic vibrations is one of the simplest and most effective methods for processing an ultrasonic signal. The advantage of this method is that ultrasound is not audible to the human ear, therefore, it does not have a negative mental effect on a person and by increasing the radiation power, a larger suppression area can be achieved. As a result, in the ultrasonic range, beats of two signals occur, at which a periodic change in the amplitude of the total signal occurs. The experiment uses a conventional mobile phone, which miraculously generates a difference frequency of 2 kHz from mixed ultrasonic signals with frequencies of 18 kHz and 20 kHz. The spectral conversion of an ultrasonic signal to a low frequency sound resembles the effect of the appearance of radio sound. The use of beats of ultrasonic vibrations allows you to create a simple and effective processing of an ultrasonic signal.
Design of Matching Network in High Frequency Converter Considering Flexible Aircore Inductor Effect

In this paper, the design method of matching network in a 10MHz DC / DC converter based on flexible aircore inductor is proposed. The mathematical model and inductance calculation method of flexible planar circular spiral coil under different bending situations is analyzed. The design method of T-matching and π-matching networks under variable inductance are depicted in detail to ensure resistive or weak inductive equivalent input impedance of the matching network, which helps to guarantee soft-switching characteristics and high efficiency within a wide bending range. A 32V input, 9V/6.3W output prototype is proposed in the laboratory and the experimental results verify the feasibility of the proposed optimal design methodology.
Mechanical Condition Identification and Prediction of Spring Operating Mechanism of High Voltage Circuit Breaker

Spring operation mechanism is widely used in high voltage circuit breakers, and its reliability is related to the ability of the circuit breaker breaking fault current. During the life cycle of spring operating mechanism, stress relaxation, metal fatigue, and any other mechanical defects are easily occurring. And the mechanical performance of the circuit breaker will be influenced by the above defects. Therefore, identifying and predicting the mechanical conditions of the spring operation mechanism can improve the reliability of the circuit breaker. In the present paper, the 252 kV circuit breakers are used as test objects. Firstly, the spring stress relaxation test, the life-cycle test, and the failure simulated test of 252 kV circuit breakers are carried out. Secondly, a multi-body dynamics simulation model of the experimental prototype is established. Thirdly, support vector machine, random forest, and deep neural network are used in the condition identification of the circuit breaker to compare their performances. Then, the prediction model of spring in stress relaxation test is built, however, the model is not suitable for the life-cycle test of repeat close-open operation. Finally, the remaining useful life prediction model is proposed by using Wiener Process.
Body Trajectory Generation Using Quadratic Programming in Bipedal Robots

The preview control walking method, which is commonly used in bipedal walking, requires jerk and ZMP errors as cost functions to generate body trajectory. Since the two inputs are dependent, optimization to form body trajectory is performed simultaneously with weight factors. Therefore, it is often seen that the resulting body trajectory rapidly changes on velocity according to the weight factors. This eventually requires a torque actuator in order to perform such action. In order to overcome this problem, we apply a method used on a quadruped to a bipedal robot. Since, it only targets to minimize the acceleration of the body trajectory, the body does not require rapid speed change. Also, this method can eliminate the computation time needed for preview control referred to preview time. When applying a quadruped robots walking method that has a relatively large support polygon than that of a bipedal robot, stability deterioration may occur. Therefore, we approached to secure ZMP constraints with relatively small support polygon area as within bipedal robots. In this paper we propose a body trajectory generation method that guarantees real-time stability while minimizing acceleration.
Dynamics Analysis on T chaotic systems with absolute

In this paper, an absolute T Chaotic system is constructed, based on 3D T chaotic systems. Analysis of bifurcation diagram, Lyapunov exponent and Phase portraits of the system by using Matlab, thus showing the sensitivity of a new chaotic system to system parameters. Finally, in order to further study the influence of the double parameter change on the system, using space bifurcation diagram has carried on the analysis of system dynamics, which reveals the abundant dynamics characteristic and realization of the chaotic system to the further.
Analysis of Vibration Characteristics of Twelve-Phase Permanent Magnet Synchronous Motor Under Different Fault Tolerance Strategies

Vibration characteristics of the twelve-phase permanent magnet synchronous motor (PMSM) with one phase winding open circuit under different fault-tolerance strategies are analyzed in this paper. The fault-tolerant strategies involved in the study include the complete set of winding removal method, the two-phase magnetic potential compensation method, and the minimum copper loss method. Firstly, the magnetomotive force (MMF) harmonic expressions of twelve-phase PMSM under normal operation, one-phase open circuit fault, and three types of fault-tolerant strategies are derived using the synthetic current method. Secondly, the theoretical model and finite element model are used to analyze the electromagnetic force (EMF) distribution mechanism of the motor under normal operation, one-phase winding open and three fault-tolerant strategies. Finally, a three dimensional structure model of the motor was established, and the vibration response under different operating conditions and different working conditions was calculated. A comprehensive fault-tolerant strategy using the complete set of winding removal method in the high-speed domain and the minimum copper loss method in the low-speed domain is proposed to minimize the vibration of the full-speed domain of the faulty motor.
Analysis of Multiple Phase-shift Control for Full-bridge CLLC Resonant Converter Based on Improved Fundamental Harmonic Approximation Method

To solve the light-load problem of CLLC resonant converters, this paper proposes a comprehensive analysis of multiple phase-shift control based on improved fundamental harmonic approximation (FHA) method. A precise multi-harmonic approximated AC impedance model is firstly built to derive the analytical relations between the output voltage gain and rms values of resonant currents of CLLC converters and three phase-shifts. Then, the influences of different phase-shifts on voltage gain and rms values of resonant currents are investigated with simulation verifications. Finally, an optimal dual phase-shift control achieving both effective voltage regulation and satisfying power efficiency is concluded and verified on a 18~25V/400V, 200W GaN based CLLC resonant converter prototype.
A Collaborative Filtering Model based on Matrix Factorization and Trust Information

Collaborative filtering (CF) approach is the most efficiency technique for employing as the recommendation system engine. One of the notable types of CF techniques are Singular Value Decomposition (SVD) based techniques. Off-line learning recommendation models use the historical user rating to extract the knowledge. Nevertheless, the difficulties of CF approach are caused from the data sparsity and cold start problems. Aiming to deal with these problems, this research proposed a novel collaborative filtering model relied on matrix factorization technique by incorporate user’s trust information into the explicit historical rating scores of users on the items for generating the rating scores prediction model, called Trust-rRSVD. The empirical experiment was established. The accuracy results shown that Trust-rRVSD performed better than the other techniques along with better mitigating the above problems.
Legged balance on moving table by reinforcement learning

Balancing is one of the most essential ability for legged robots. From maintaining posture on unstable surfaces to withstanding sudden force disturbances, balancing is widely used in rescue robots. Among these various balancing issues, this study focused on balancing on moving plates. The study is applicable when the robot needs to perform an operation on an earthquake or shaking ship environment. In addition, since ZMP(Zero Moment Point) is used for the control, the performance is exhibited not only when an external force is applied such as wind force, but also an inertial force is exerted. In this study, reinforcement learning was used so that the proposed algorithm can avoid redundancy issues and kinematic singularities which cause problems in traditional method. The simulation results show how this new approach can improve the robot’s balancing ability.
Presentation Attack Detection using Heart Rate from Amplified Color Videos and Pulse Oximeter Information

The constantly growing digital era makes the development of biometric identification systems increasingly necessary. This situation also impulses the detection and development of vulnerabilities, for this reason, it is necessary to complement the biometric systems to improve their level of confidence. This paper presents a system to perform vitality detection using heart rate pulse from amplified color videos using Eulerian Video Magnification algorithms. An pulse oximeter is used to trigger the system and the acquired information is compared with the gathered from the videos. The result of such comparison is used to establish the vitality state of the sample. The experimental results shows that the maximum difference between the video and the pulse oximeter information does not overpass ten beats per minute (BPM) with an average of 4.466 BPM.
Fuzzy Social Force Model for Robot Soccer Navigation: A Preliminary Report

This paper proposes an adaptive control strategy to improve the performance of the Social Force Model (SFM) based mobile soccer robot navigation using the Fuzzy Inference System (FIS). The combination of FIS dan SFM is then called a Fuzzy Social Force Model (FSFM). In the FSFM strategy, FIS is used to adaptively adjust the parameters of SFM based on the stimulus from the external condition, namely the obstacle’s relative distance to the robot, d, and its direction, γ, so that the robot’s reactivity and responsiveness can be automatically adjusted. Currently, we adjust only one parameter, namely obstacle’s gain value, k. Obstacle’s gain value will control the amount of forces produced by the closest obstacle. We tested our proposed method using a realistic 3D simulator, called V-Rep, by utilizing an omnidirectional robot model from Festo, namely Robotino. Our experimental results show that our proposed FSFM can work well by always successfully finishing all of the trials with less collision with obstacles. The comparison results with the fixed-parameter method prove that our proposed method is better and very promising to be implemented for real robot applications.
ECG Signal Acquisition Wearable Device base on Single Chip Microcontroller Solution

The proposed of this paper is a signal acquisition wearable device base on single chip microcontroller solution for Electrocardiography (ECG) monitor. Traditional ECG monitor used complex circuits to detect ECG signal that improved detection 60Hz environment noise and skin interference. We proposed an easy to detect ECG signal mothed which used Texas Instruments MCU MSP430I series that feature have 24bit sigma delta ADC, and the low power consumption saved battery energy that has satisfactory performance in wearable device fields. Additional, we design a discrete digital filter to improve environment noise and skin interference in microcontroller. In the experimental results, we presented a single chip ECG wearable device monitor that signal shows satisfactory output $response$.
Bayesian and Neural Inference on LSTM-Based Object Recognition From Tactile and Kinesthetic Information

Recent advances in the field of intelligent robotic manipulation pursue providing robotic hands with touch sensitivity. Haptic perception encompasses the sensing modalities encountered in the sense of touch (e.g., tactile and kinesthetic sensations). This letter focuses on multimodal object recognition and proposes analytical and data-driven methodologies to fuse tactile- and kinesthetic-based classification results. The procedure is as follows: a three-finger actuated gripper with an integrated high-resolution tactile sensor performs squeeze-and-release Exploratory Procedures (EPs). The tactile images and kinesthetic information acquired using angular sensors on the finger joints constitute the time-series datasets of interest. Each temporal dataset is fed to a Long Short-term Memory (LSTM) Neural Network, which is trained to classify in-hand objects. The LSTMs provide an estimation of the posterior probability of each object given the corresponding measurements, which after fusion allows to estimate the object through Bayesian and Neural inference approaches. An experiment with 36-classes is carried out to evaluate and compare the performance of the fused, tactile, and kinesthetic perception systems. The results show that the Bayesian-based classifiers improves capabilities for object recognition and outperforms the Neural-based approach.
MixNet: A Better Promising Approach for Chromosome Classification based on Aggregated Residual Architecture

Chromosome classification is an important way of clinical genetic diagnosis. However, it is usually done manually by skilled cytologists, which requires extensive experience, domain knowledge, and tedious manual work. In this paper, we tackle the task of chromosome classification utilizing deep learning techniques based on aggregated residual architecture. We proposed a novel chromosome classification method named MixNet. Firstly, we design a deep learning-based chromosome classification framework that consists of a chromosome encoding backbone and an adaptive header. Secondly, we design the chromosome backbone utilizing the aggregated residual architecture and propose the adaptive header by aggregating pooling layers to classify chromosome latent features. Lastly, we propose a training algorithm to fine-tune MixNet. The experimental results demonstrate that our proposed method yields a mean classification accuracy of 98.73% with 0.81 standard deviations on our G-band clinical dataset and a mean classification accuracy of 96.50% with 1.87 standard deviations on public Q-band dataset. To our best knowledge, it is the state-of-the-art result of the public Q-band dataset.
Optimal Electricity Trading Strategy for a Household Microgrid

The recent integration of distributed generators (DGs) and renewable energy sources (RESs) into the power system led to the manifestation of a significant number of household microgrid (MG) systems in the electricity market. However, in most of the cases, the DGs in the MG system are passive and are not equipped by their own controllers, thus their integration increases the fluctuation in the power system and brings challenges to its management and control. To address these challenges, this paper proposes a novel electricity trading strategy for a household MG. This is achieved by formulating a nonlinear stochastic control problem that will then be solved such that the profit through electricity trading is maximised. To solve this optimisation problem, a gradient descent method based on compressive sensing is applied. Finally, some numerical examples are given to illustrate the effectiveness of the proposed control method. The results from the simulation experiments indicate that the proposed electricity trading strategy achieves the target and satisfies all constraints by controlling the energy router (ER) with the energy storage component.