Miracle-dz/UniFAR-FTUs huggingface dataset:
Theoretical and Experimental Investigation of HMSIW-Based High-Speed Data Transmission System Using QPSK Scheme

Half-mode substrate integrated waveguide (HMSIW) is a high-pass transmission line, which can be easily integrated into planar circuits. Compared with SIW, HMSIW features wider relative bandwidth and smaller size. The quadrature phase-shift keying (QPSK) scheme can double the signal transmission rate compared with a simple mixing scheme. In this paper, an HMSIW with 8–24-GHz frequency band is designed and fabricated, and then a high-speed data transmission system using the QPSK scheme is implemented based on the HMSIW. The mathematic modeling of the system is provided to analyze time-domain response and crosstalk of the system, and then accurate control of the amplitude and the phase is realized by the introduction of amplifier and phase shifter. Hence, the amplitude attenuation caused by the loss of the components and phase differences has been compensated, and the crosstalk caused by the phase differences has been eliminated. The above-mentioned modeling is validated by experimental results. Comparisons are also made between the system with and without the amplifier/phase shifter. It is shown that the system can transmit the signal with 15-Gb/s data rate by one physical channel. A high data rate and low bit error rate are obtained in the proposed system.
A Soft-Switching Step-Down PFC Converter With Output Voltage Doubler and High Power Factor

In this paper, a new soft-switching bridgeless single-phase power factor correction (PFC) converter is presented and analyzed. Employing an auxiliary switch, the input current dead angle that is the main drawback of the existing buck-type PFCs is omitted, and thus, the power factor (PF) is improved, which is the main contribution of the paper. Proposed PFC converter operates under discontinuous conduction mode (DCM) and draws sinusoidal input current from power supply inherently. All switches and diodes are turned on and off under soft switching, which leads to low switching losses and elimination of diode reverse recovery problems. Also, minimum numbers of semiconductor devices are in the power flow path that reduce the conduction losses. A 120-W laboratory prototype is implemented and experimental results verify the validity of theoretical analysis and show efficiency of 92.1%. In addition, total harmonic distortion (THD) of 3.3% is achieved and the input current harmonics complies with IEC61000-3-2 Class D requirements.
A Data-Driven Voltage Control Approach for Grid-Connected Wind Power Plants

Due to increasing penetration of wind power plants (WPP), power grids are facing significant power quality challenges. To achieve stable and robust power system operation, reactive power plays a vital role. Reactive power needed to compensate voltage fluctuations at the point of interconnection (POI) of the WPP varies with a short circuit ratio (SCR). The reactive power capability of a WPP is also limited and largely depends on various operating conditions. In a physical system, it is difficult to find the correlation among critical parameters for voltage control. In this paper, a data-driven voltage control approach is proposed for a grid-connected WPP. Two regression models are developed through surface fitting using MATLAB curve fitting toolbox: one model based on simulation data is to determine the required reactive power for grid voltage compensation; another model based on field measurement data is to determine the WPP reactive power characteristics. The reactive power compensation device is capacitors in the study. Two controllers, a central WPP controller and a capacitor controller, are designed, their effectiveness is validated through several case and sensitivity studies.
Energy Efficiency Optimization in Energy Harvesting Incremental Relay System

In this paper, according to the energy efficiency (EE) definition based on packet transmission, we analyze the energy efficiency in energy harvesting incremental relay system with energy harvesting constraints. Our purpose is to maximize energy efficiency of source node while making full use of relay’s harvested energy. First, we consider the energy efficiency of point-to-point link with multilevel quadrature amplitude modulation (MQAM) and derive the energy efficiency definition based on packet transmission. Next, the energy efficiency optimization problem in incremental relay system is formulated. To solve the problem, we discuss two factors that affect energy efficiency: transmission power and packet length. Furthermore, we propose a joint transmission power and packet length iterative optimization algorithm and give an intuitive analysis of convergence. Finally, simulations have been carried out to verify the benefits of the proposed algorithm, which can significantly improve energy efficiency compared with only transmission power optimized scheme or only packet length optimized scheme.
High Speed Infrared Probe for Doped Layers Characterization: Experiment and Ray Tracing Study

Measurement of infrared (IR) reflectance and transmittance are proven methods for rapid inline process monitoring of doped layers formation. This work presents two experiments where multiple layer properties can be simultaneously determined using appropriate IR measurements: 1) separation of the sheet resistances of front and rear diffused layers, derived from IR front incident reflectance and transmittance data, and; 2) resolution of the sheet conductance and thickness of front and rear ITO layers, derived from IR front incident reflectance, transmittance and rear incident reflectance data. Both of these scenarios are further articulated using ray tracing modelling combined with sensitivity analysis. Finally, this type of simulation is also used to predict the ability of the IR interactions to resolve phosphorus diffused layer surface concentration independently of the sheet resistance. The simulation shows that reliable resolution is achievable, as long as the surface active dopant concentration exceeds 2 x1019cm-3.
Target Location Using Dual-Beam Directional Modulated Circular Array

A new concept directed toward the location of an object within a sector of space is introduced, by using a combination of the properties associated with directional modulation when implemented on a circular antenna array. In particular, we show that a circular antenna array can be made to project orthogonal data streams on orthogonal spatial beams and, through this process, create a temporal and spatial interference pattern that can be used for target location. This is demonstrated by the way of simulation of a two-beam 16-element circular antenna array carrying quadrature phase shift keying data.
Development of Methods and Procedures of the Analysis of Corporate Social Responsibility of Manufacturing Companies

In this article development of methodology of the analysis of corporate social responsibility on the basis of methods, procedures and analytical indicators covering all spheres of corporate social responsibility (economic, ecological and social) is proposed. At the same time authors proceeded from necessity to provide interrelation of procedures and indicators with tactical and strategic objectives of corporate activities. As a result of implementation of author's approach it is shown that the quality of conclusions is determined by volume of the initial information provided in the public reporting and also policy of the company in the sphere of disclosure of information on corporate social responsibility.
Planar Broadband Huygens’ Metasurfaces for Wave Manipulations

Electrically thin and effectively 2-D material composites, metasurfaces, have been widely exploited for the manipulation of electromagnetic waves. For many applications, it is desired to transform incident waves of a specific frequency range keeping the metasurface invisible at other frequencies. Such a frequency-selective response can be achieved based on subwavelength Huygens’ inclusions. However, their fabrication requires sophisticated processes due to the 3-D geometry. Here, we propose a planar Huygens’ meta-atom with the goal to open a way to realize broadband invisible metasurfaces with topologies suitable for the conventional printed circuit board fabrication technology. We synthesize and analyze, both numerically and experimentally, three different metasurfaces capable of polarization and amplitude transformations of incident waves.
Micro-Structured Fiber as Temperature Sensor in a Loop Architecture

A temperature sensor built by a micro-structured fiber (MSF) into a Sagnac interferometer scheme is reported. The optical waveguide is provided by a two-hole fiber (THF) which are located asymmetrically from the core. A linear response to thermal effects is observed and the sensitivity ∼ 2.22 nm/°C was reached using a 2m-long of THF. The main advantage of the system proposed is a linear response, the use of a MSF with a simpler transverse geometry, non-use a special filled mechanism for the holes and a simple experimental array.
Querying in Internet of Things with Privacy Preserving: Challenges, Solutions and Opportunities

IoT is envisioned as the next stage of the information revolution, enabling various daily applications and providing better service by conducting a deep fusion with cloud and fog computing. As the key mission of most IoT applications, data management, especially the fundamental function-data query, has long been plagued by severe security and privacy problems. Most query service providers, including the big ones (e.g., Google, Facebook, Amazon, and so on) are suffering from intensive attacks launched by insiders or outsiders. As a consequence, processing various queries in IoT without compromising the data and query privacy is an urgent and challenging issue. In this article, we propose a thing-fog-cloud architecture for secure query processing based on well studied classical paradigms. Following with a description of crucial technical challenges in terms of functionality, privacy and efficiency assurance, we survey the latest milestone-like approaches, and provide an insight into the advantages and limitations of each scheme. Based on the recent advances, we also discuss future research opportunities to motivate efforts to develop practical private query protocols in IoT.
Chance Quality-of-Service Driven Resource Allocation Scheme in Multicast Networks

In view of the unavoidable existence of CSI error in real networks, this paper adopts a method of guaranteeing the quality-of-service (QoS) of users statistically. Aiming at minimizing the total power consumption in the multicast network scenario, we establish the chance QoS constraints based resource allocation problem in this paper. The effective bandwidth (EB) and effective capacity (EC) theory is used to deal with the chance constraint, with the help of probability theory, we transform the chance delay- QoS constraint into the lower-bounded EC constraint. Thus, the original resource allocation problem can be deduced as the mixed-integer non-linear programming (MINLP) tacking the joint sub-channel and the power allocation problem. Adopting the Lagrange dual method, the optimal solution is approached. Simulation results reinforce the effectiveness of the proposed schemes and show that the proposed algorithm achieves better energy saving performance than that of traditional unicast networks.
Modeling and Analysis of Harmonic Currents of GSC Caused by Harmonic Grid Voltage and Dead Time

This paper deduces a mathematic model of grid side converter(GSC) to quantitatively analyze the harmonic currents caused by two factors, i.e., grid background harmonic voltages and dead time effect. The impedance of GSC respectively corresponding to these two factors is built and the harmonic currents can be precisely calculated. The model also explains the coupling phenomenon of two kinds of harmonic currents and the generation mechanism of the phenomenon. Finally, the comparison of simulation results and calculation results validates the effectiveness of the model.
All-at-once Decomposition of Coupled Billion-scale Tensors in Apache Spark

As the scale of unlabeled data rises, it becomes increasingly valuable to perform scalable, unsupervised data analysis. Tensor decompositions, which have been empirically successful at finding meaningful cross-dimensional patterns in multidimensional data, are a natural candidate to test for scalability and meaningful pattern discovery in these massive real-world datasets. Furthermore, the production of big data of different types necessitates the ability to mine patterns across disparate sources. The coupled tensor decomposition framework captures this idea by supporting the decomposition of several tensors from different data sources together. We present a scalable implementation of coupled tensor decomposition on Apache Spark. We introduce nonnegativity and sparsity constraints, and perform all-at-once quasi-Newton optimization of all factor matrix parameters. We present results showing the billion-scale scalability of this novel implementation and also demonstrate the high level of interpretability in the components produced, suggesting that coupled, all-at-once tensor decompositions on Apache Spark represent a promising framework for large-scale, unsupervised pattern discovery.
A New 3D CNN-based CAD System for Early Detection of Acute Renal Transplant Rejection

Recently, diffusion-weighted magnetic resonance imaging (DW-MRI) has been explored for non-invasive assessment of renal transplant function. In this paper, a computer-aided diagnostic (CAD) system based on a 3D convolutional neural network (CNN) is developed to assess renal transplant functionality using diffusion MRI-derived markers extracted from (3D + b-value) DW-MRI. Our framework performs the following image processing steps: (i) 3D DW-MRI kidney segmentation using a level-set approach guided by shape and visual appearance features; (ii) feature extraction, namely, voxel-wise apparent diffusion coefficients (3D ADCs) of the segmented DW-MRIs are estimated at different b-values (i.e. gradient field strengths and duration); and (iii) renal transplant status classification, while the extracted 3D ADCs are used as input to train and test a 3D CNN-based classifier to evaluate renal transplant status. The proposed CAD system achieved a 94% accuracy, a 94% sensitivity, and a 94% specificity using a leave-one-subject-out cross-validation scenario in distinguishing non-rejection (NR) from acute rejection (AR) renal transplants. These preliminary results hold a strong promise that the presented CAD system is of a high reliability to non-invasively diagnose renal transplant status.
Comparing the Performance of Neural and Statistical Sentence Embeddings on Summarization and Word Sense Disambiguation

We analyzed the performance of two sentence embeddings: SIF (Smoothed Inverse Frequency) created using weighted GloVe word embeddings, and sent2vec, trained using a neural network. Using these sentence embeddings without modification, we compared and contrasted their performance on extractive text summarization and word sense disambiguation using existing methods tailored for sentence embeddings. We find that our results are better than the simplest baselines and approach competitive baselines for both these tasks, proving that sentence embeddings are to some extent successful in capturing the structure of language.
Estimating the School Dropout Trend by Using Data Mining Methods

The knowledge discovery in the education and training process is very important in terms of raising the quality of the education and training activities. Nowadays, there are a number of applications in the field of education and training that enable giving high-dimensional data such as questionnaires, exam results and guidance tests. It is easier to improve the education and training processes by analyzing data from these large data sets. In this study, a software with graphical interface that can be used in the problem of educational data mining is designed. In addition, this software has been used to estimate the school failure by using data mining methods and to compare the performance of these methods on a data set from the UCI data warehouse.
CBIR for Herbs Root Using Color Histogram and GLCM Based on K-Nearest Neighbor

Ginger, kencur, kunyit, temulawak, and temu hitam are herbs root that is widely used as a spice and herbal medicine in Indonesia. Visually the shape and color of herbs root are quite similar so that the common people difficult distinguish. The problem can be solved with the help of Content-Based Image Retrieval (CBIR). There are three main modules in CBIR namely pre-processing, feature extraction and identification. The study proposed the Color Histogram and Gray Level Co-occurrence Matrix (GLCM) as feature extraction to identify these types of herbal remedies. While the identification stage used K-Nearest Neighbor (KNN) and the calculation of the distance closest to the Euclidean distance. The amount of data used as training data is 250 images, while data testing used 25 images. The results of this study the system can recognize the image of herbs root in accordance with the type with the highest accuracy obtained.
Improving Software Fault Prediction With Threshold Values

Software fault prediction has been studied by many researchers to assess the quality of software and to predict where faults may appear in future. However, the performance of fault prediction degrades because of many reasons including unlabeled instances or data imbalance, i.e., modules that contain faults are minority. The data imbalance is common in fault data where the majority of software modules are marked as non-faulty. However, part of these modules are still fault-prone but faults are uncovered yet. Threshold values are used to identify the modules that are complex and more fault prone. The fault prediction models are combined with the use of threshold values to improve the prediction performance. Fault prediction models are built in two phases. First, threshold values are used to spot the most fault prone modules. The modules that have metrics larger than thresholds and were fault free are classified as medium, while modules with faults are classified as high. Second, the new data are used to build prediction models using five machine learning models. Five classifiers were built for ten software systems. We have found improvements in the classification performance of all classifiers when compared with traditional classification.
Performance Comparison of Compressed Sensing Algorithms for DOA Estimation of Multi-band Signals

Direction of arrival (DOA) estimation of radio waves is used in many fields such as positioning of radio communication terminals and radar systems, etc. In recent years, as a highly accurate estimation method, some techniques using compressed sensing have been proposed. There are several algorithms to solve solutions for the compressed sensing technique. The authors have used Half-Quadratic Regularization (HQR) method to reconstruct original signals so far. In addition, Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) and Orthogonal Matching Pursuit (OMP) are also typically known as compressed sensing algorithms. In this paper, we compare the performance of HQR, FISTA, and OMP for DOA estimation of multi-band signals. The results indicate that HQR achieves the best performance in the three algorithms although the computational load is large. OMP with less computational load is useful when slightly larger estimation errors are allowable. Additionally, It is shown that compressed sensing works reasonably well even with a small number of snapshots.
Faster-than-Nyquist Signaling Based Adaptive Modulation and Coding

In this paper, an adaptive modulation and coding (AMC) scheme based on faster-than-Nyquist (FTN) signaling is considered, where FTN signaling is implemented with multi- layer superposition coded modulation (SCM). Compared to the traditional AMC schemes, the proposed AMC scheme adjusts the number of layers K and the FTN compression factor $\tau$ rather than the code rates and modulation orders. Simulation results show that the proposed AMC scheme is able to achieve a wide range of spectral efficiency. Furthermore, the achievable spectral efficiency approaches the ultimate capacity of the shaping pulse, and its performance can outperform the constrained capacity of orthogonal signaling with the same spectral efficiency.
Cache-Enabled Adaptive Bit Rate Streaming via Deep Self-Transfer Reinforcement Learning

Caching and rate allocation are two promising approaches to support video streaming over wireless networks. However, existing rate allocation designs do not fully exploit the advantages of the two approaches. This paper investigates the problem of cache-enabled video rate allocation. We establish a mathematical model for this problem, and point out that it is difficult to solve it with traditional dynamic programming. Then we propose a deep reinforcement learning approach to solve it. Firstly, we model the problem as a Markov decision problem. Then we present a deep Q-learning algorithm with a special knowledge transfer process to find out an effective allocation policy. Finally, numerical results are given to demonstrate that the proposed solution can effectively maintain high-quality of service. We also investigate the impact of critical parameters on the performance of our algorithm.
Cooperative Joint Estimation and Localization using Mobile Multi-agent Systems: A Minimum Energy Estimator Approach

This paper investigates the problem of joint localization and target tracking using mobile multi-agent networks. We consider the challenge situation that each agent only has partially observation of the target system and also needs to estimate its relative location in order to utilize meaningfully the target observation. Using a minimum-energy filtering approach, we propose optimal distributed filtering algorithms for local state and target state estimation for network agents that utilize local, relative and communicated measurements that can be obtained at general event times. Numerical examples illustrate the performance of the algorithms for the task of estimating the evolution of a gas concentration in a field due to a gas leak using multi-agent systems.
Noise Robust End-to-End Speech Recognition for Bangla Language

Robust speech recognition system is crucial for real-world applications and speech signal generally contains considerable amount of noise. We propose an end-to-end deep learning approach leveraging current progresses in Automatic Speech Recognition system to recognize continuous Bangla speech for noisy environments. We improve the robustness of our model through data augmentation and deep model architecture. We evaluate our model on an internal and two available datasets. We achieve impressive result on both clean read and noisy speech data. Our model achieves 10.65 % and 34.83 % CER on CRBLP (clean read) and Babel (noisy) respectively, which is state of the art for continuous Bangla speech recognition to the best of our knowledge. For our internal data set, we achieved 12.31 % and 9.15 % CER on clean and noisy speech respectively.