Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Model-Based Receding Horizon Control of Interconnected Renewable-Based Power Micro-grids for Effective Control and Optimal Power Exchanges

This paper proposes an Adaptive Model-based Receding Horizon Control Scheme (AMRHCS), which allows taking into consideration the uncertainty power production of renewable energy resources, demand response, the variance of real-time electricity price, load demand and as well as respecting the special constraints for optimal performance and economic benefits of the micro-grids. The essence of this study is to propose a control scheme for the interconnected power microgrids so as to minimize the operating costs of the individual micro-grid, the power purchased by each micro-grid, the pollutant gas emissions, energy procured from the host grid and from the other micro-grids. Therefore, in order for these objectives to be achieved, an adaptive MPC controller is utilized to locate the best patterns for power exchange and state of energy storage system among micro-grids. More so, to investigate the optimal control action of the interconnected power micro-grids under the proposed control framework, we iteratively formulated a finite horizon Mixed Integer Linear Programming (MILP) problem. The MATLAB simulation results demonstrated the superiority of the proposed control technique in terms of excellent performance and economic benefits of the micro-grids.
Estimation of Backscattered Signals of Frequency-coded Chipless RFID Tag

In this paper, we present an approximated method in order to estimate a frequency-coded chipless RFID tag response. This study can broaden the theoretical understanding of the backscattered signal from a chipless tag in the frequency spectral and time domain, therefore, a detection process can be improved. Both simulation and measurement results are compared in order to validate the concept. Moreover, it has been concluded that the response of a frequency-coded tag is similar to the response of a multi-band filter.
H-Plane Loaded Sine Waveguide TWT in G-Band

In this paper, a G-band sine waveguide travelling wave tube (SWGTWT) is designed. Sinusoidal H-plane loading is incorporated to enhance the interaction impedance of the structure. Various cold circuit parameters are estimated by analyzing a single period of the structure with periodic boundary condition. Input and output coupler are designed to minimize the reflection from the structure. Particle-in-cell (PIC) simulation is carried out to compute beam wave interaction. The simulation predicts more than 58 W of output power at 220 GHz. The gain and electronic efficiency are estimated to be 31.1 dB and 3.27%, respectively
Research on Real-time Learning Prediction Method Based on Spark

Based on the research of real-time prediction and big data processing platform, an effective solution is proposed to solve the shortcomings of current real-time learning prediction in engineering application. By analyzing learners’ learning behaviors related to a certain course, learners’ learning behaviors can be divided into three categories in terms of time and space: online learning behaviors, offline learning behaviors and performance of relevant basic courses. Based on the parallel computation and binary logistic regression algorithm in Spark framework, the off-line learning prediction model is created. In the real-time environment, large scale real-time learning prediction can be realized based on Spark Streaming and kafka. With the increase of learning behaviors data, the scalability problem of prediction scheme can be solved by expanding Spark cluster nodes. The advantages of the proposed scheme have been verified in the practical application of smart campus.
A Robust Performance Degradation Modeling Approach Based on Student’s t-HMM and Nuisance Attribute Projection

Performance degradation assessment (PDA) is of great significance to ensure safety and availability of mechanical equipment. As an important issue of PDA, the robustness of the trained model directly affects the assessment efficiency and restricts its application in practice. This paper proposes a robust modeling approach based on Student’s t-hidden Markov model (Student’s t-HMM) and nuisance attribute projection (NAP). NAP can remove nuisance attributes caused by individual differences from the feature space. Student’s t-HMM utilizes the finite Student’s t-mixture models (SMMs) to describe the observation emission densities associated with each hidden state, which can be more tolerant towards outliers than conventional HMMs. Based on these two techniques, the proposed method is supposed to be more robust and can assess the performance degradation process of new objects based on data of tested objects. The superiority of the proposed approach is evaluated using the vibration data from the accelerated life tests of bearings and the public XJTU-SY Bearing Datasets. The results demonstrate the robustness and effectiveness of the proposed approach for PDA modeling of rolling element bearings.
Design of A Compact Four-way Filtering Wilkinson Power Divider with Satisfactory Performance

A compact four-way filtering power divider (FPD) integrating conventional Wilkinson power divider with only one pair of triple-mode resonators is presented in this paper. Comparing with other existing four-way FPDs, it brings out not only very compact size, flexible design freedom, but also nice output return loss, sharp frequency selectivity as well as good port-to-port isolation. Four demonstration, a prototype FPD operating at 2.4 GHz has been designed, fabricated and measured. Both simulated and measured results are provided with reasonable agreement.
A PVT Variation Tolerant 24-GHz FMCW Signal Generator for Automobile Radar Applications

We have developed a process, voltage, and temperature (PVT) variation tolerant 24-GHz FMCW generator MMIC for automobile radar featuring phase noise variation of 3.3 dB and output power variation of 1.9 dB under all PVT conditions using a 0.18-µm SiGe BiCMOS process. In our design, in order to increase the PVT tolerance, a voltage-controlled oscillator (VCO) utilizes a temperature proportional bias and a transformer-coupled output configuration, and a driver amplifier utilizes a 2-step coarse and a 32-step fine power tuning adjustment function. The measurement results of the implemented signal generator MMIC demonstrated low phase noise and output power variations under all PVT conditions while generating desired waveforms for automobile radar.
Efficiency Enhancement of GaN Doherty Power Amplifier at Large Power Back-Off with Virtual Short Stub Technique

We propose a new efficiency enhancement technique of Doherty power amplifiers (DPAs) using virtual short stub. In this technique, the peaking amplifier branch was utilized as an inductive short stub at output power combing point at low power level. Using this technique, it is possible to transform the low-power load impedance seen from the carrier amplifier into higher impedance than that of classical DPAs. This leads to efficiency enhancement at larger power back-off than conventional DPAs (>6dB). The relation of efficiency enhanced level and the circuit topology for DPAs with the virtual short stub are analytically derived. The proposed technique was experimentally verified by fabricating a 3.5 GHz 40 Watt Doherty power amplifier using GaN HEMTs. An average drain efficiency of 51% and adjacent power leakage ratio of −50 dBc was obtained using 10MHz LTE test signals with 8.5dB peak-to-average power ratio.
Compact Broadband Four-Port MIMO Antenna for 5G and IoT Applications

This work presents a novel compact multiple-input multi-output (MIMO) antenna which can be applied in the fifth generation (5G) network and internet of things (IoT). It is fed by four coaxial cables and consists of four spatial orthogonal antenna elements where two of them are in the same plane and the other two are in another plane. Each antenna is realized with different inverted L-monopoles and grounded stubs. Different L-monopoles create respective resonant frequencies. Long grounded stubs can produce extra resonance frequency modes. The broadband characteristic is enabled by multiple resonant frequencies. The isolation between both antenna elements is enhanced with the $\pi$-shaped structure. The short ground stubs near feeding ports can improve the antenna impedance matching. The proposed MIMO antenna occupies the size of $56\ \mathrm{mm} \times 56 \mathrm{mm} \times 35.3\ \mathrm{mm}$, and operates at the frequency range from 2 to 6 GHz, covering 5G sub-6-GHz network communication channel and other multiple communication bands, like LTE (2.2-3.8 GHz), Bluetooth (2.4 GHz), WLAN (2.4 and 5.1-5.8 GHz), WiMAX (2.3-5.7 GHz), and ISM bands (2.4/5.2/5.8 GHz). With the isolation larger than 15 dB between each antenna element pair, the envelope correlation coefficient (ECC) is lower than 0.04 across the operating bandwidth. Also, the realized gain and radiation efficiency are calculated to be larger than 1.5 dBi and 85%, respectively.
A Comparative Study of the Joint Neuro-Fuzzy Friction Models for a Triple Link Rotary Inverted Pendulum

In this paper, Neuro-Fuzzy Friction Estimation Models (NFFEM) are developed to estimate the joint friction coefficients in a Triple Link Rotary Inverted Pendulum (TLRIP) system and compared with an Adaptive Friction Estimation Models (AFEM). The different versions of AFEMs and NFFEMs are generated based on each of the following friction estimation models: Non-Conservative Friction Model (NCFM), Linear Friction Model (LFM), and Non-Linear Friction Model (NLFM). The aim of this study is to obtain joint friction models which depend on both velocity and acceleration in a large range of motion trajectory that involves difficult and sudden large changes. In the proposed NFFEMs, joint velocities and accelerations of the TLRIP are used as the input variables of the Neuro-Fuzzy system trained by using a Radial Basis Function Artificial Neural Network (RBANN). Several experiments are conducted on TLRIP system to verify the NFFEMs. In order to determine the estimation performance of the friction models, total Root Mean Squared Errors (RMSE) between position simulation results obtained from each joint friction model and encoders in the experimental setup are computed. Based on the position RMSEs, the NFFEMs produces much better estimation results than the AFEMs. Among NFFEMs, the neuro-fuzzy nonlinear friction model (NFNLM) gives the best results.
Towards Prediction of Landslide Susceptibility using Random Forest for Kalutara District, Sri Lanka

Landslide susceptibility prediction has been a topic of interest in the world of research for many years. The prime objective of this study is to explore the integration of geospatial techniques such as Geographic Information Systems(GIS) with machine learning techniques to get refined and improved models with better landslide prediction accuracy. In this paper, a comprehensive study on landslide susceptibility in Kalutara district, Sri Lanka was carried out for a total of 84 landslide occurrences under 12 conditioning factors; slope, aspect, geology, hydrology, soil type, soil thickness, land use, landform, Sediment Transport Index(STI), Topographic Wetness Index(TWI), Stream Power Index(SPI), and rainfall. The results obtained by analyzing the 12 landslide conditioning factors using the Frequency Ratio and Information Gain Ratio are used to construct a landslide susceptibility prediction model using the Random Forest algorithm.
Recognizing Hazard Perception in a Visual Blind Area Based on EEG Features

Many potential hazards are encountered during daily driving in mixed traffic situations, and the anticipatory activity of a driver to a hazard is one of the key factors in many crashes. In a previous study using eye-tracking data, it was reliably recognized whether the eyes of a driver had become fixated or pursued hazard cues. A limitation of using eye-tracking data is that it cannot be identified whether the anticipatory activity of a driver to hazards has been activated. This study aimed to propose a method to recognize whether the psychological anticipation of a driver had been activated by a hazard cue using electroencephalogram (EEG) signals as input. Thirty-six drivers participated in a simulated driving task designed according to a standard psychological anticipatory study paradigm. Power spectral density (PSD) features were extracted from raw EEG data, and feature dimensions were reduced by principal component analysis (PCA). The results showed that when a driver detected a hazard cue, the alpha band immediately decreased, and the beta band increased approximately 300 ms after the cue appeared. Based on performance evaluation of the support vector machine (SVM), k-nearest neighbor (KNN) method, and linear discriminant analysis (LDA), SVM could detect the anticipatory activity of the driver to a potential hazard in a timely manner with an accuracy of 81%. The findings demonstrated that the hazard anticipatory activity of a driver could be recognized with EEG data as input.
Transaction Protection in Corporate Networks Based on Distributed Ledger Technology

In this article, we describe the use of distributed ledger technology based on blockchain technology without mining to protect various kinds of transactions in corporate networks. The relevance of this topic comes from the rapid developments in the digitalization of the economy and other spheres of human activity. The digital economy has taken a strong position worldwide. Data are processed and transmitted electronically. At the same time, it becomes vulnerable to attacks of criminals and competitors during transfer and storage processes.The article proposes an approach to the protection of corporate transactions based on a distributed ledger with elements of blockchain technology in comparison with traditional methods.
Comparative Analysis of Load Compensation Techniques in Low-Voltage Distribution Networks Using Passive Components

Non-linear loads have become a common load penetrating domestic and commercial spheres. These types of loads are harmonic polluting sources and negatively affect the level of supply quality, that is both the voltage and current quality. As new mitigation strategies emerge from researchers in the field it is important to assess how these mitigation strategies perform. This paper compares the performance of delta-star compensating networks and later establishes a hybrid mitigation strategy that combines passive filtration to such a network in a three-phase four-wire supply network that is supplying an unbalanced and harmonically distorted load. Passive filtration which is low cost and simple is shown to be effective especially in the scenario where the load behavior is well understood and fixed. The study of the network at the point of common coupling makes it possible to scale down the quantity of the filters that are needed for implementation making their use practical and feasible. The hybrid mitigation strategy adopted to solve the unbalance and harmonic distortion in the network offers some advantages like localised compensation, ease of use and simplicity.
A 13.56 MHz Wireless Power Transfer System with Bidirectional Data Link and Closed-loop Power Control for Implantable Neuromuscular Stimulator

This paper presents a 13.56 MHz wireless power transfer (WPT) system for fully implantable neuromuscular stimulator. Bidirectional wireless data link and closed-loop power control functions are incorporated into the WPT system for stable power delivery to the implant. The external power transmitter (PTX) module includes a DC-DC converter and a Class-E power amplifier (PA) for tunable high efficiency power transmission. The forward link command is modulated on the carrier by amplitude-shift keying (ASK) modulation. The implantable power receiver (PRX) module receives power and data simultaneously from the coupling inductors. PTX and PRX are implemented with discrete components and integrated circuit, respectively. Measurement results showed over 90% Class-E PA efficiency and 42% PRX power conversion efficiency.
Fourth-order Resonator based Current Reuse Injection-Locking Frequency Divider with Peaking Inductor

A current reuse (CR) injection-locking frequency divider (ILFD) with low power and wide locking range is proposed. The current reused core is used to decrease the current consumption compared to the conventional ILFD, current-mode logic (CML) and regenerative divider. The fourth-order resonator using a transformer with peaking inductor is employed to overcome narrow locking range of the conventional ILFD. The simulated locking range of the proposed CR-ILFD is from 25.7 to 42.5 GHz at 0-dBm input power and the power consumption of the core is 1.88 mW with 1-V supply voltage. The CR-ILFD was implemented in a 65-nm CMOS technology.
Self-inductance of Multi-turn Round Inductive Coils with Production-specific Geometric Features

nowadays, inductive wireless power transfer is actively used as a power source for medical implants. However, to ensure patient safety, the use of this method requires accurate calculations of the magnetic coupling of the coils and their characteristics. This paper presents an analysis of the influence of jumpers between turns on the self-inductance calculation of multi-turn round inductive coils with geometric productions features. The self-inductance of multi-turn circular coils was calculated in MATLAB and compared with experimental results, obtained using a precise digital RLC meter. The discrepancy between the results of theoretical and experimental calculations was shown and discussed.
Machine Vision Based Rice Disease Recognition by Deep Learning

Bangladesh is a land of agriculture, where people consumes rice as the main meal for three times a day. Rice is undoubtedly the most cultivated crop in Bangladesh. Like every other crops, rice also gets affected by a lots of diseases. These diseases differ from region to region and season to season. Although a number of implementation of different technology in agricultural field is increasing at an enormous rate, the farmers of our country still depends on the ancient techniques of disease identification. By keeping this very thing in our mind, we have conducted this research where we have tried to develop a model which can recognize rice diseases by deploying machine learning. We have worked with six main disease that is commonly seen in the paddy fields of Bangladesh. Authentic dataset of these six diseases were collected very carefully so that our model can render us the highest accuracy rate. BRRI(Bangladesh Rice Research Institute) has assisted us a lot in this matter. Three vastly popular pre-trained models of CNN such as Inception -v3, MobileNet-v1 and Resnet50, have been used to carry out this research. Necessary augmentation and scaling was done in the dataset before employing them. The research yields gratifying outcome. Hence, it proves that how effectively machine learning can collide with the agriculture. This research will pave machine learning techniques a path to enter in the agricultural sector of our country as well as help the young generation immensely who will enter into the agriculture in the future.
Terahertz Absorber Design Adopting Metallic FSS in Sub-Skin-Depth Thickness

Resistive surfaces are frequently used as essential parts constituting planar absorbers, where electromagnetic energy will be dissipated as heat. In this paper, we propose a realization of controlled surface resistance for metallic FSS at terahertz frequencies based on sub-skin-depth thickness for circuit analog (CA) absorber design. The Drude model is applied to accurately determine the frequency dependent features of metal in the terahertz range. The proposed single-FSS-layer absorber is firstly synthesized via equivalent circuit modeling, and then verified with silver and lead square ring in fullwave simulations. With readily available materials and easily accessible fabrication techniques, the absorber design adopting simple square ring patterns is predicted to achieve a relative bandwidth of approximately 94% with a performance robust to oblique incidence.
An Antipodal Vivaldi Antenna Array with Improved Beam Synthesis Accuracy and Scanning Performance

An 8-element antipodal Vivaldi antenna (AVA) array operating at 24.35-29 GHz for future 5G millimeter-wave (mmWave) communication applications is introduced. Multinotch structures are implemented to obtain better isolation when AVA elements are used to assemble into array. This new technique gives more freedom to improve beam synthesis accuracy and scanning performance. In addition, it can also enhance the gain of AVA array from 10.7-13 dB to 12.4-14 dB in the frequency band of future 5G mm Wave communication. It is a relatively small antenna with eight feeds of AVA array, 2.27 λg × 6.16 λg× 0.08 λg in size.
A K-band Temperature Compensated Transmitted Power Detector

In this paper, we present a K-band temperature compensated transmitted power detector embedded within a power amplifier. A dummy detector is added by the detector without input power to compensate variation due to different temperatures. We also adopt the de degeneration technique in the power detector to enhance its linearity. At OP1dB, the measured linearity error is 0.61 dB, 0.57 dB, 0.47 dB, and 0.39 dB at 25°C, 50°C, 75°C, and 100°C, respectively. The measured dynamic range within ±0.5 dB linearity error is larger than 15 dB. The dc power consumption is 0.098 mW for the power detector core from the 1.8-V power supply. In addition, we verified the transfer curve and linearity error under different temperatures. From the measured results, our proposed power detector can truly eliminate power measurement variation due to temperature change.
Ultra Wideband Antenna for Future 5G

An ultra-wideband miniature antenna based on circular patch with circular slots has been presented for future generation mm-wave indoor wireless applications. The proposed miniature antenna is fed by probe feed and, the maximum realized gain and the total efficiency throughout the three bands are 7.7 dBi in an upper higher band and 97% in the lower band, respectively. This proposed antenna covers the seven bands (five bands are in the existing allocation to mobile, and the other two bands are to be considered for allocation) for 5G higher bands and lower band covers uplink Ku-band (14GHz - 14.5GHz) for satellite communication. The proposed antenna in ultra-wideband has the impedance bandwidth of 31.8 GHz and the fractional bandwidth of 60.61%.
Employing Machine Learning techniques on Sentiment Analysis of Google Play Store Bangla Reviews

This article offers an in-depth insight on a number of existing methodologies to perform sentiment analysis using text classification on Bangla dataset. Although the rapidly developing machine learning algorithms are showing promising results, the viability of those methods for non-English languages such as Bangla is yet to be fully explored. This research aims to fill in some of those existing research gaps through proper implementation of machine learning techniques where words are converted into feature vectors via implementation of TF-IDF algorithm on data crawled from Google play store, the largest Android application market. Many significant algorithms staring linear algorithms like Naïve Bayes, Linear Support Vector Machine (SVM) are implemented. An in-depth comparison is also made among the results of various existing algorithms. The experimental results indicate that even the base-line algorithms, after proper pre-processing, can show promising results on our Bangla dataset. Naïve Bayes, Support Vector Machine and Logistic Regression has shown very promising results (accuracy score of 0.75 on average) even with the data limitation. An Ensemble method is also proposed with Adaptive Boosting technique showing an accuracy score of 0.7639 with five-fold applied. SVM has the best accuracy score of 0.7648 among all the algorithms when five-fold is applied and Gradient Boosting has the best accuracy score of 0.7695 when five-fold is not applied.