Miracle-dz/UniFAR-FTUs huggingface dataset:
Load Frequency Control of Single Area Hybrid Power System Using Fuzzy-PID (FPID) Controller

This paper focuses on the load frequency control of a single area hybrid power system which is a conjugation of conventional and renewable energy sources. Dish-stirling solar thermal system (DSTS) plant is used as a renewable energy source and the thermal, diesel plant are used as a conventional energy source. Genetic algorithm has been implemented for optimizing the gain parameters of the Proportional Integral Derivative (PID) controller to attain frequency control in hybrid power system. Based on the simulation analysis, the productiveness of the PID controller is measured with the proposed intelligent controller (Fuzzy-PID). The comparative performance analysis shows that Fuzzy-PID controller realization is better than the traditional PID controller. In the proposed hybrid system, control action of automatic voltage regulator (AVR) influences the automatic load frequency control (ALFC) loop.
A smart System for Fake News Detection Using Machine Learning

Most of the smart phone users prefer to read the news via social media over internet. The news websites are publishing the news and provide the source of authentication. The question is how to authenticate the news and articles which are circulated among social media like WhatsApp groups, Facebook Pages, Twitter and other micro blogs & social networking sites. It is harmful for the society to believe on the rumors and pretend to be a news. The need of an hour is to stop the rumors especially in the developing countries like India, and focus on the correct, authenticated news articles. This paper demonstrates a model and the methodology for fake news detection. With the help of Machine learning and natural language processing, author tried to aggregate the news and later determine whether the news is real or fake using Support Vector Machine. The results of the proposed model is compared with existing models. The proposed model is working well and defining the correctness of results upto 93.6% of accuracy.
Impact of Photovoltaic Module Performance Degradation Rate on LCOE of Utility Scale Systems

This work examines the impact of different photovoltaic (PV) module degradation rates on the system level degradation for PV systems with DC-AC ratios ranging from 1.0-1.6, typical of the utility scale PV market. Annual module degradation rates ranging from 0.3%-0.7% are simulated in sites with high and low solar resource availability in the United States. Results are presented evaluating the influence of site irradiance, DC-AC ratio, and module degradation rate when predicting degradation of utility scale PV system performance. The diminished influence of module degradation rate in typical utility PV system configurations is analyzed and quantified as it pertains to the levelized cost of energy (LCOE).
On the Modeling of Multistable Memory States Using Memristor

Memristor is a fundamental circuit element which has promising application in neuromorphic computing. In this paper, linear Strukov model is used for showing how the internal state varies in a memristor when driven by a sinusoidal excitation source. Simulation results show, memristor achieves higher conductance state with increase in number of cycles of excitation source. This property of memristor mimics strengthening of synapses. The effect of modifying control parameter in the Jogelkar window on the weight of the synaptic strength is also investigated. Two state memristor based logic generator circuit model is proposed, where logic states are defined by the number of cycles of the excitation source.
Enhancing Power Supply Performance using Parallel Flyback Converters

Increasing of ac/dc power supply in smart grids, losses, power factor, and harmonic problems arise due to the presence of the converter. Therefore, this paper introduces a new topology of dual flyback converters operated in parallel to correct the power factor and reduces the harmonic and the losses. The first flyback converter is utilized as dc/dc stage used to regulate the output voltage by adjusting the duty cycle. While the second flyback converter is utilized as ac/dc stage, used for power factor correction and reducing the harmonics. The second flyback converter generates the input current to be in phase with the input voltage by regulating the variable frequency and switching period keeping the switching on-time fixed. This topology improves the voltage regulation even if the input voltage is reduced by approximately 60%, small circuit size, and reducing the total harmonic distortion. Besides, the circuit efficiency is increased by operating the dual converters at discontinuous current mode also recovering the stored energy in the leakage inductance of the coupled inductor. Finally, a prototype platform which has the ability to deal with universal input $80 \sim 260 \mathrm{V}_{\mathrm{rms}}$ is carried out. The measurements have verified the theoretical analysis to verify the effectiveness of the proposed system.
Predictive Direct Torque Control of Permanent Magnet Synchronous Generators (PMSGs) without Weighting Factors

Conventional predictive direct torque control (PDTC) of permanent magnet synchronous generators (PMSGs) with weighting factors suffers from extensive calculation burden and weighting factor tuning work. This paper proposes three predictive direct torque control techniques for PMSGs without weighting factors. The first proposed control strategy is based on the torque and reactive torque. Reactive torque has the same order of magnitude as torque, which eliminates the need of weighting factor in the cost function. The second proposed control strategy is based on estimating the q-axis reference current from the demanded torque and setting the d-axis reference current to zero, which achieves the maximum torque per ampere (MTPA) operation. Then, according to the deadbeat principle the reference voltage vector (VV) is directly computed from the reference current vector. The cost function design for this strategy includes only the reference voltage vector and the candidates one. The third proposed method is based on two separate cost functions, one is for the torque and another is for d-axis current. The proposed three control strategies eliminates the need of weighting factors and therefore, no tuning work is required.
A Self-Calibrating Off-Time Controller for WSN/IoT Synchronous Non-Inverting Buck-Boost DC-to-DC Converter Application

Inappropriate turning-off of synchronous switches in DC-to-DC converters operating in discontinuous conduction mode (DCM) degrades the overall efficiency of the converter due to the power losses in either body-diode conduction or reverse inductor current. This paper presents a self-calibrating off-time controller using a digitally-controlled delay element (DCDE) for synchronous DC-to-DC converter. An up-down counter that is controlled by the polarity of the inductor current is utilized to drive the binary-weighted DCDE. The programmable DCDE is used to generate a self-calibrating off-time pulse to turn-off the synchronous switches very close to the zero-crossing of the inductor current. The design and simulation of the proposed method is implemented using 65nm CMOS Technology process in Synopsys Custom Designer tool. When implemented, the measured accuracy of the detection is in order of less than $\pm 10mA$. The average power consumption of the proposed system is less than 10 uW and is expected to be much lower at lighter loads.
Android Malware Classification Using XGBoost On Data Image Pattern

The popularity of Android smartphone has encouraged cybercriminals to develop malware targeting this platform. In the third quarter of 2018, the number of Android malware has increased by 40% when compared with the same quarter of year 2017. The traditional malware analysis techniques need to be improved where it must be able to detect new malware quickly and accurately. This paper proposed an Android malware classification by using 8-bit grayscale images where the images features will be extracted using GIST descriptor and later be classified using XGBoost machine learning algorithm. Classes.dex and its data section will be extracted from the Android APK files before they were converted into images. k-Nearest Neighbours and Random Forest machine learning algorithms will also be used to compare the accuracy performance. The experiments show classification using data section performed better than classification on full classes.dex files in all three machine learning algorithms.
A Low-Noise, Low-Power, and Chopper-Stabilized, Current-Feedback Instrumentation Amplifier for Current Sensing Application

This paper presents a low-noise, low-power, and chopper-stabilized current-feedback instrumentation amplifier (CFIA) for current sensing application. To achieve high common-mode-rejection ration (CMRR), the CFIA employs a three-stage topology with nested-Miller compensation. Through chopping both the input and intermediate stages of the amplifier, an input-referred noise power spectral density (PSD) of 22.7 nV/$\sqrt{}$Hz is achieved at 3 mHz. To reduce chopper ripple, the amplifier employs a continuous time AC-coupled ripple reduction loop. The proposed amplifier is implemented in a SMIC 0.18-$\mu$m CMOS process, the CFIA shows a simulated CMRR of 130 dB at a supply voltage of 3.3 V with a current of 200 $\mu$A.
Radar emitter identification based on discriminant joint distribution adaptation

Aiming at the problem that the existing radar emitter identification method cannot effectively identify the radar signal unknown to the prior knowledge base, this paper introduces the joint distribution adaptation algorithm in transfer learning into the field and adds data label to the algorithm optimization item. And a radar emitter identification method based on discriminant joint distribution adaptation is proposed. The method firstly maps different radar signal data to the same hidden space by using the discriminant joint distribution adaptation algorithm to reduce the distribution discrepancy between the radar signal known by the prior knowledge base and the radar signal unknown to the prior knowledge base. Then the support vector machine is trained by using the radar signal data with known tags in the hidden space, and the trained model is used to identify the radar signal. Simulation experiments show that the proposed method can effectively improve the recognition of radar signals unknown to the prior knowledge base.
Intrusion Detection Using Deep Learning and Statistical Data Analysis

Innovation and creativity have played an important role in the development of every field of life, relatively less but it has created several problems too. Intrusion detection is one of those problems which became difficult with the advancement in computer networks, multiple researchers with multiple techniques have come forward to solve this crucial issue, but network security is still a challenge. In our research, we have come across an idea to detect intrusion using a deep learning algorithm in combination with statistical data analysis of KDD cup 99 datasets. Firstly, we have applied statistical analysis on the given data set to generate a simplified form of data, so that a less complex binary classification model of artificial neural network could apply for data classification. Our system has decreased the complexity of the system and has improved the response time.
Rehabilitation Posture Correction Using Neural Network

Rehabilitation treatment is the process which helps people to restore their functioning. Nowadays poor body posture and forward neck problem has become a greater issue all around the world. At home or in the workplace, everyday activities such as force and repetition can play a part in causing these problems. Many people have this postural abnormality, which is most common in aging, working, and old-aged groups. For this, a low-price, reliable, and precise system for in-home rehabilitation is required. In this paper, the device for an in-home rehabilitation is designed for patients especially outpatients (who can’t visit daily or afford a personal trainer) for correcting their improper postures by giving visual feedback of postural data in real-time using LabVIEW software. Our system analyzes the patient’s whole-body posture by extracting the human skeleton called skeleton tracking which gives positional information for the 20 prime skeleton’s joint points that make up the human body using a Kinect sensor. We designed the Pattern Recognition Neural Network algorithm using MATLAB script function in LabVIEW to examine the tracked human skeleton. To verify that the posture is accurate or not, we have collected the data about 896 samples of exercising postures for both conditions to train the network. We performed 70% training, 15% validation and 15% testing of the network which results in classifying the right class of postures. Further testing will also be done.
An Open-Source Lightweight Timing Model for RapidWright

Access to detailed timing information for FPGA resources is essential to achieving the highest performance. Yet, for commercial FPGAs, much of this information is not published or available. At the same time, deploying large, fine-grained timing datasets adversely affects the speed of timing-driven place and route algorithms. We propose a nimble timing model for RapidWright that delivers high fidelity timing approximations while enabling faster algorithms through a frugal memory footprint. By leveraging a combination of architectural knowledge, repeating patterns and extensive analysis of Vivado timing reports, we obtain a slightly pessimistic, lumped delay model within 2% average accuracy of Vivado for UltraScale+ devices. We validate the results with over 240 designs and the proposed model shows high fidelity to Vivado with a Spearman's value of 0.99. By open sourcing the proposed model and describing the process, we empower the community to leverage and extend this work for customized domains, other device families, and additional accuracy.
3D Modeling of the Rotationally Symmetric Objects Using Kinect

With the appearance of RGB-D cameras, 3D reconstruction becomes convenient. Previous studies focused on the objects with strong structural features. But for the objects with weak structure, such as rotationally symmetric objects. There is no solution. In this paper, we propose a method to model the rotationally symmetric objects. In this method, we scan the object in one view and the point cloud is projected to the 2D space. Then, we extract the boundary of the point cloud. And the Least Square is adopted to fit the extracted boundary to improve the accuracy of the boundary. At last, we rotate the fitting boundary and get the model. To verify the effectiveness of our method, several experiments have been performed to evaluate the error of the models. In addition, we use it to reconstruct the object with the hole on one side. And the results show the efficiency and computational complexity of our method.
SGDB: A Sports Gene Database for Visualization of Sports Effects on Human Skeletal Muscle Gene Expression

The relationship between physical exercise and human health has been widely recognized by sports-related professionals and the public. However, the molecular mechanism behind sports remains unclear. A microarray sequencing technology has been widely used to detect gene changes after physical exercise and researchers have accumulated a great deal of data. These data provide a large amount of information for researchers to understand the relationship between genetics and exercise and study changes in genes after different forms of exercise, and their relations with human, gender and age. We integrate eight published gene expression datasets from human skeletal muscle and construct an online web server called SGDB standing for Sports Gene DataBase. SGDB can be used to search for genes that are expressed after exercise and no exercise. One can determine the effects of physical exercise on gene expression while allowing for visualization of changes in female and male. In addition, different forms of exercise and relationships with age are also available. Currently, SGDB contains 54613 annotated gene probes based on the Affymetrix Human Genome U133 Plus 2 Affay (GPL570). It has become one of the most important resources for researchers to study sports and genes. The online access address for SGDB is http://www.sportsgene. top.
A Modified Asymmetric Switched-Capacitor Multilevel Inverter Topology with voltage boosting Capability

In this paper, a modified asymmetric switched-capacitor based MLI topology employing Switched-Capacitor DC/DC converter (SCC) as a level generator is proposed. The topology has the ability of self-voltage boosting and self-balancing of capacitors voltage. Moreover, it does not require an auxiliary circuit or capacitor voltage balancing algorithm. The comparison of this topology with other existing reduced device topologies in terms of numbers of gate driver circuits, power electronic switches, diodes, and capacitors has been done. To verify the feasibility and performance of this topology, the simulation is performed in MATLAB®/Simulink 2017b platform.
Decision Templates based Ensemble Classifiers for Automobile Insurance Fraud Detection

In this paper, we have removed the class imbalance problem using SMOTE and used Decision Templates ensemble technique for efficiently detecting the auto insurance fraud. We employed three supervised classifiers namely, Support Vector Machine, Multilayer Perceptron and K-nearest Neighbors for classification purpose. The final classification results are obtained by aggregating the results obtained from the classifiers using Decision Templates technique. Our model has been experimentally evaluated with a real –world automobile insurance dataset.
Non-stop Handover of Parcel to Airborne UAV Based on High-speed Visual Object Tracking

Although research on physical distribution using unmanned aerial vehicles (UAVs) has seen increasingly significant interest, the task of automatically loading a parcel onto a UAV has not been researched adequately. In this study, to design an automatic UAV delivery system, we achieved the task of non-stop handover of a parcel to an airborne UAV. For the handover task, we developed a novel tracking system with highspeed, multi-camera vision using cameras with different frame rates. Through sensor fusion, the proposed system overcomes the problem associated with tracking far away objects by using high-speed cameras with short exposure times. The proposed system demonstrates that it is feasible to combine both highspeed object tracking (1000 fps) and distant object tracking.
Enhanced Sensorless Model Predictive Control of Induction Motor Based on Extended Kalman Filter

This paper presents an enhanced method for a self-sensing (Sensorless) Model Predictive Control (MPC) of Induction Motor (IM) supplied form two-level using state-space model. For the self-sensing process, the controller needs a proper position and speed observer. In this paper, the position and speed of the rotor are estimated based on Extended Kalman Filter (EKF). The proposed EKF is executed to achieve a small error in the estimation parameters of IM in transient and steady-state operation. Model predictive control uses all the mechanical and electrical variables to calculate a cost function for each switching state of the inverter. The voltage that achieves the lowest errors (lowest cost function) is selected to be applied in the next sampling interval. High level of flexibility is obtained using the proposed control technique over a wide speed range with a fast dynamic response. The proposed methodology is validated by simulation results to clarify the ability of the proposed control algorithm in different speed area.
A Metasurface-Based Low-Profile Wideband Circularly Polarized Patch Antenna for 5G Millimeter-Wave Systems

This paper presents the design and realization of a metasurface-based low-profile wideband Circularly Polarized (CP) patch antenna with high performance for Fifth-generation (5G) communication systems. The antenna consists of a modified patch, sandwiched between an array of <inline-formula> <tex-math notation="LaTeX">$4\times4$ </tex-math></inline-formula> symmetrical square ring Metasurface (MTS) and a ground plane. Initially, the intrinsic narrow bandwidth of the conventional patch antenna is increased using a diagonal rectangular slot. For further performance enhancement, the additional resonances and CP radiations are achieved for wideband operation in terms of impedance and Axial Ratio (AR) by effective excitation of surface waves propagating along the MTS. The stacking of MTS on the modified patch without any air gap resulted in an overall compact size of <inline-formula> <tex-math notation="LaTeX">$1.1\lambda _{0} \times 1.1\lambda _{0} \times 0.093\lambda _{0}$ </tex-math></inline-formula>. Simulated and measured results show that the MTS-based antenna offers a wide impedance bandwidth ranging from 24 – 34.1 GHz (34.7%) for <inline-formula> <tex-math notation="LaTeX">$\vert \text{S}_{11}\vert < -10$ </tex-math></inline-formula> with a maximum gain of 11 dBic and a 3-dB AR bandwidth of 24.1 – 29.5 GHz (20.1 %). Moreover, the proposed antenna has a smooth gain response with a small variation in its gain (9.5 – 11 dBic) and a stable left-hand CP radiation in the desired frequency range. The operating bandwidth of this antenna is covering the proposed entire global millimeter-wave spectrum (24.2 – 29.5 GHz) for 5G communication systems.
Adaptive Control of an Unbalanced Two-Axis Gimbal for Application to Inertially Stabilized Platforms

Inertially stabilized platforms are subject of interest to different engineering areas, such as telecommunications, robotics and military systems. The objective is to maintain the attitude of a desired object constant despite the movements of a host vehicle. This paper deals with the problem of stabilizing a platform using a two degree of freedom gimbal as mechanical actuator. Mechanical unbalances are considered and a MIMO version of the Binary Model Reference Adaptive Controller is employed. The algorithm employs a newly proposed differentiator based in high-order sliding modes that is global and exact. This differentiator can also be used for monitoring and estimation purposes in robotics systems. Simulation results are presented using as inputs the experimental data acquired from a vehicle going through a circuit with ground obstacles.