Miracle-dz/UniFAR-FTUs huggingface dataset:
Theoretical study of signal and geometrical properties of Two-dimensional UWB-based Indoor Positioning Systems using TDoA

This paper presents an introductory yet comprehensive study of the combined signal and geometrical properties of Indoor Positioning Systems (IPSs) based on ultra-wideband (UWB) technology. These IPSs consist of a network of more than three transmitting anchors and a (tagged) single receiving object to be localised. The specific algorithm used in this paper is the Time Difference of Arrival (TDoA) with round-robin scheduling. The analysis is structured in a systematic manner in order to lay the foundations for the optimal number, location and orientation of anchors aiming for maximum precision, and also for maximum size of the working area with a desired prescribed precision.
Deep Architectures for Abstractive Text Summarization in Multiple Languages

Abstractive text summarization is the task of generating a novel summary given an article, not by merely extracting and selecting text to produce a summary, but by actually creating and understating the given text to produce a summary. LSTM seq2seq encoder-decoder with attention models have proved successful in this task, but they suffer from some problems. In this work, we would go through multiple models to try and solve these problems, beginning with simple seq2seq with attention models to going to Pointer-Generator, to using a curriculum learning approach called Scheduled-Sampling, till we reach the new approaches of combining reinforcement learning with seq2seq. We have applied these models on multiple datasets for multiple languages, English and Arabic. We have also introduced a new novel method of working with agglutinative languages, it is a preprocessing technique that is applied to the dataset which increases the relevancy of the vocabulary, which effectively increases the efficiency of the text summarization without modifying the models, we call this technique advanced cleaning, we have applied it to the Arabic dataset, and it can then be applied to any other agglutinative language. We have built these models in Jupiter notebooks to run seamlessly on Google colaboratory.11https://medium.com/@theamrzaki22https://github.com/theamrzaki/text_summurization_abstractive_methods
Identification and Multi-Objective H∞ Control Design for a Quadruple Tank System

This paper presents the grey-box modeling and Linear matrix inequality (LMI)-based $H$∞ controller design for a quadruple tank system with time-delay dynamics. Firstly, the minimum and non-minimum phase dynamics of the quadruple tank system are modeled separately using grey-box identification technique and the obtained model is validated with the real-time data. Secondly, we design an LMI based multi-objective controller with pole placement and $H$∞ norm reduction. Finally, the controller performance is validated using real-time experiments on the quadruple tank Laboratory setup.
A follow-up study of coping strategies of compensation claimants reporting an occupational injury associated with return to work/disability benefits in the subsequent year

Abstract Purpose: To describe coping strategies of claimants reporting an occupational injury associated with return to work/disability benefits in the subsequent year. Method: We included 5,782 respondents of a questionnaire (Brief COPE) at time of reporting an occupational injury to the National board of Industrial Injuries. We used register data on benefits from the Danish Ministry of Employment. Multivariate logistic regression was used with long-term disability benefit as outcome as a proxy of not returning to work. The analyses were adjusted for factors associated with disability benefits (severity of injury and social factors (education)). Results: A total of 714 (12.3%) received long-term disability benefits. A coping strategy of acceptance of the injury at time of report increased responders’ possibilities of returning to work, and use of self-distraction, and support increased risk of long-term disability benefits. Claimants with reported accident were more likely to accept the injury than claimants with an occupational disease. There were only little differences in coping strategies according to compensation decision and return to work. Conclusion: Respondent claimants who at time of report accepted the injury and their situation had increased possibilities of returning to work in the subsequent year.
Integration of Solar Photovoltaic (PV) Systems with CCM Inverters into VCM Droop-Controlled Islanded AC Microgrids

Nowadays, photovoltaic (PV) systems and microgrids are two of the main technologies in electrical engineering researches. Both are important to realize different types of energy management, feeding islanded loads with high reliability, efficiency, social benefits, low cost and environmental impact. This paper assess the possibility of connecting a PV system to a droop-controlled islanded AC microgrid without modified advanced controls, additional components or the use of batteries in a way that only simple standard commercial equipment are used. Two inverters operating with Voltage Control Mode (VCM) employing the droop control method to form an islanded AC microgrid is approached. These inverters function as grid forming converters giving voltage references in order to integrate them with a conventional solar PV system in Current Control Mode (CCM), consolidated technologies which are easily found on the market, since they are used for grid-tie. The proposed complete system is analysed for a typical day, with real measured variations of irradiance and temperature. The computational results of the VCM and CCM inverters integrated is exposed, showing its feasibility and its limitations.
Modified Level-Shifted PWM Technique With Active DC Capacitors Voltages Balancing for Nine-level Packed E-Cell (PEC9) Inverter

In this paper, a modified Level-Shifted PWM technique is integrated to an active capacitor voltage balancing method to apply for the recent introduced compact single-phase multilevel converter so-called as Packed E-Cell (PEC9) topology. PEC9 is composed of a single main DC source, single auxiliary DC link including two connected capacitors in series as well as six power switch and one back-to-back switch forms bidirectional switch which nine level voltage waveforms is achievable at the output AC terminal. PEC9 benefits the advantages such as reduced component, simultaneous charging and discharging capacitors with redundant states led to have an active capacitors voltage balancing algorithm. The modified Level-Shifted PWM is integrated with active capacitor voltage balancing technique to regulate the capacitor voltage to one quarter of DC input voltage so as PEC generates nine-level voltage waveform. According to the proposed modified Level-Shifted PWM, the appropriate switching states from the redundant states are selected in the specified level which guarantees balancing PEC9 DC capacitors. The proposed modulation technique has been tested on PEC9 using Matlab/Simulink and simulation results validate its excellent performance in active DC capacitor voltage balancing.
The Research about Film and Television Information Management and Integrated Service

China's film industry has made a vigorously progress in recent years, However, in the production of film-making, the related preparatory work is still mainly offline. Practical information systems are urgently needed to promote information exchange and serve film and television practitioners. Firstly, this paper puts forward a design scheme of film and television information management and integrated service system. Secondly, it introduces an online script copyright trading method combining subjective and objective evaluation. Finally, it introduces the innovations of this system. The system satisfies the needs of various types of practitioners in film preparation and designs a script evaluation method based on subjective and objective evaluation, which provides a new opportunity for the development of film and television industry.
A PSO Based PIDF Controller for Multiarea Multisource System Incorporating Dish Stirling Solar System

The comprehensive evaluation of Automatic Generation Control (AGC) scheme is performed for a multi-area multisource model with implementation of a proportional integral derivative controller with derivative Filter (PIDF). Optimal parameters of the controller are extracted by applying stochastic population motivated Particle Swarm Optimization (PSO) technique. The study of Dish Stirling Solar Thermal System (DSTS) is utilized in the proposed three area system which highlights the significance of renewable energy in the AGC scheme. Appropriate generation rate constraints are considered for thermal and hydro plants. Better tie line power on load perturbation and dynamic response of frequency is achieved with PIDF by regulating the real power output of the generators. Various conventional controllers like proportional integral (PI) and proportional integral derivative (PID) are employed in the multi-area system and the performances are compared to the output of the designed PIDF controller.
A Self-Driving Car Implementation using Computer Vision for Detection and Navigation

In recent years, lidar has been used as primary sensors for self-driving cars, however, due to their high expense, it becomes infeasible for mass production. Hence, we present the working of a self-driving car prototype that relies upon a cheaper alternative, viz. cameras. The primary objective of our prototype is to navigate safely, quickly, efficiently and comfortably through our virtual environment using computer vision. We have performed detection of lanes, traffic cars, obstacles, signals, etc. and have used the concept of stereo vision for depth calculation. Trajectory planning and steering control have also been implemented. Experimental results show that camera-based self-driving cars are viable and thus our paper can provide a foundation for all future real-world implementations.
Closed-Form Solutions for Core and Winding Losses Calculation in Single-Phase Boost PFC Rectifiers

This work proposes closed-form solutions for core and winding losses for the boost inductor of a single-phase boost PFC converter built in a iron powder toroid core. The core losses expressions are derived based on the analytical loop separation procedure according to the iGSE method. The winding losses are computed based on a set of equations for the approximated Fourier series of the boost inductor current waveform, which is derived in this work. The overall procedure to obtain the Fourier series coefficients are demonstrated, which is used to estimate the winding losses related to the ac resistance for each current harmonic.
Multi-Mode Traffic Demand Analysis Based on Multi-Source Transportation Data

Automatic vehicle identification (AVI) data, Integrated Circuit (IC) card data and Global Positioning System (GPS) data offer an emerging and promising source of information for analysis of traffic problems. Research on insights and information from AVI data for transport analysis has made little progress in developing specific applications especially. The emergence of multi-source data provides us with a new perspective for multi-mode transportation. This paper proposes a multi-mode traffic demand forecasting method based on AVI data, metro IC card data, and taxi GPS data. The paper extracts traffic origins and destinations (OD) information of travelers from the multi-source data and uses the extracted data for traffic zone division. Finally, a multi-mode traffic forecasting model is established on this basis. GPS data of taxi trips are selected as the clustering data and k-means algorithm was adopted to divide traffic zones in Shenzhen. Moreover, the research applies the principle of convex hull to outline the boundary of the cell. Additionally, this paper establishes the multi-mode transportation forecasting model by integrating the correlation between various transportation modes into the deep learning model for prediction. The results show that the multi-mode demand forecasting model has higher accuracy and better forecasting results comparing it with the single-mode demand forecasting model which is referring to the conventional four-step procedure. The result demonstrates that effective traffic and travel data can be obtained from multi-source data, providing an opportunity to improve the analysis of complex travel patterns and behaviors for travel demand modeling and transportation planning. Furthermore, the substantive contribution of this research is that it provides strong empirical evidence for the existence of correlation among multi-mode travels and travel demand.
Forecasting Electricity Consumption using ARIMA Model

Autoregressive integrated moving average, ARIMA, is a popular technique, which is used to fit time series data for prediction and forecasting. This paper proposes ARIMA models with different sets of parameters for forecasting electricity consumption. The three ARIMA models, which are quite good and robust to develop a reliable model, are investigated to forecast electricity consumption for providing the required level of performance. The best fitted model, effective and reliable approach, and network structure are determined according to the prediction performance. For this purpose, we use synthetic dataset and electricity consumption data in industries at Guangdong province in China. The experimental results show that the ARIMA(1,1,1) has high precision, stable predictions and suitable for predicting electricity consumption. The forecasting results are essential to manage the required electricity demand in various kind of industries and other sectors.
RGB-D Camera based 3D Object Pose Estimation and Grasping

It is a great challenge to grasp 3D objects in unstructured environment. This task is closely related with object recognition, pose estimation, hand-eye calibration and grasp strategy planning. This paper focuses on the 6-DoF pose estimation and hand-eye calibration problems. Based on the point cloud provided by the RGB-D sensor, Viewpoint Feature Histogram (VFH) descriptor is used to localize the object by comparing the scene and model library. Instead of using a pan-tilt platform to build the template library, an industrial robot with in-hand camera is programmed to collect point clouds from different view angles. Distances between scene point cloud and the model point clouds are evaluated to find a group of candidate poses. The poses are further refined by aligning those point cloud pairs using Iterative Closest Point (ICP) algorithm. Although standard VFH descriptor is invariant to scale, it is sensitive to viewpoint variance, which may lead to irrational results. In order to improve the robustness, effect of the translational offset and number of pose candidates are evaluated. The hand-eye calibration process is formulated into an AX=ZB problem and solved by using quaternion rotation and least squares method. A series of experiments are performed with a RGB-D sensor and an industrial robot. The results verify that the method is effective to estimate the object poses. Considering the accuracy of the used sensor, it is proved that the proposed method has acceptable robustness and accuracy.
Trajectory Generation for a Hydraulic Mini Excavator using Nonlinear Model Predictive Control

Autonomous excavators have great potential to improve safety and to increase productivity. A common task for an excavator is to grade. Considering an autonomous grading system, a basic requirement is to perform straight line movements of the tool-center-point in the cartesian space. In this paper, a nonlinear model predictive controller is used to generate straight line trajectories of the tool-center-point considering actuator constraints. The performance is verified using a validated physical simulation model. The results show that the tool-center-point follows straight lines between the desired points without violating the constraints. Furthermore, the resulting trajectories are near time-optimal.
Improving Success of Sending From Two Mutually Hidden 802.15.4 Nodes

This paper presents an interesting result obtained for IEEE 802.15. 4 nonbeacon-enabled wireless personal area networks. We consider a network in which two mutually hidden nodes send one data frame to the central node. By using the statistical model-checking capability of the PRISM model checker, we have analysed the probability that the central node successfully receives the data frame of both nodes. For larger data frame sizes, this probability is equal to zero. We show that an additional node within the range of both mutually hidden nodes which transmits one data frame like the latter considerably improves this probability both in the case of using the medium access control protocol with acknowledgements and without them.
Using micro/mini PLC/PAC in the Edge Computing Architecture

A programmable logic controller (PLC) is the core of any industrial automation. For many years, PLC is used mostly to solve logical control tasks. Increasing processor performance and memory capacity, PLCs have the ability to handle a number of different and more complex tasks that are no longer just logical type. This kind of controller is the Programmable Automation Controller (PAC). In industrial control systems are present different kinds of architecture and concepts for integration in enterprise information systems. Edge computing architecture is one of them. In this paper is described a comparison of performance some of the micro/mini PLC/PAC in order to find how of micro/mini PLC/PAC can fit in the Edge Computing Architecture.
Combining Facial Behavioral Cues, Eye Movements and EEG-Based Attention to Improve Prediction of Reading Failure

Educational data mining can extract useful information from students' reading data to predict their reading performance. This study aims to present a model for the identification of students who are likely to fail in reading. To improve predictive performance, multimodal data, facial behavioral cues, eye movements, and EEG-based attention, are combined to predict reading performance. Experimental results revealed that integrating students' external subconscious behaviors and internal cognitive states was complementary to reading performance prediction. Also, the results demonstrated that adding internal data (EEG-based attention) presented a significant improvement in prediction effectiveness beyond using external data (facial behavioral cues and eye movements) alone. Hence, applying EEGbased attention could play an important role in raising the level of prediction quality. The findings contribute to the possibility of promoting students' self-regulated learning and presenting appropriate feedback by instructors. From this, the exploring results support the potential for the improvement of learning engagement in reading.
Complex Pole Placement Control for a Three-Phase Voltage Source Converter

In this work, a complex-valued controller for a three-phase VSC with an LC output filter is proposed. The system is first transformed into its complex representation with the consequent order reduction and simpler analysis. The proposed complex controller places all the poles at desirable locations in the complex plane. Unlike classical controllers with an inner current-control loop and outer voltage-control loop, the proposed controller considers the complete dynamics of the system, thus ensuring the closed-loop stability. Furthermore, the complex representation facilitates the pole placement in the complex plane compared to the real domain. The performance of the proposed controller is validated through simulations and experiments.
A Semantic Collaborative Clustering Approach Based on Confusion Matrix

In this paper we discuss about a new images retrieval technique based on clustering. We argue that images don’t have an intrinsic meaning, but they can receive different interpretation. These images can complicate documents retrieval. However, users need a quick and direct access to documents. To answer this requirement, we propose a retrieval approach which use a collaborative clustering technique based on Confusion matrix.
Multi-focus image fusion using pixel level deep learning convolutional neural network

Image Fusion is an application of digital image processing. Image Fusion is a phenomenon of amalgamating the substantial features from similar pair of images into a single image, where the fused image will be of superior quality than either of the source images. Through this research work, we propose a Pixel level Deep learning method using a 3-Channel convolutional neural network to fuse two multi focus images obtain a high definition or high quality fused image. Size reduction of the image is performed before the fusion procedure in order to highly reduce the computational time and make the method more immune to noise. In the proposed method, source images are decomposed into pixels using the deep learning framework. After feature extraction, appropriate weights are assigned to all pixels. Then averaging and max pooling of pixel values of both the source images are done to get the resultant features of the fused image. Then smoothening filter is used to minimize noise in the fused image. Experimental results show that the proposed fusion technique demonstrates better PSNR value and less computational time.
Bionic Design of a 7-DOF Human-arm-like Manipulator Actuated by Antagonized Pneumatic Artificial Muscles

Humanoid robot has a wide application area and thus attracts a large number of researchers to study it. In this paper, we designed and implemented a human-arm-like manipulator with seven Degrees of Freedom (DOFs) according to the human-arm model of KIT motion capture database. DOF configurations of the robot are similar to those of human-arm, and the mechanical structure is designed to make the motion of every DOF, which is actuated by a pair of antagonized pneumatic artificial muscles (PAMs), independent from each other. Bowden cables are elaborately deployed to make the motion of adjacent DOFs decoupling from each other in favor of the control of the robotic arm. As a result, the robot can achieve motions as the same as those of the human arm. We validate it via simulations using the KIT human motion capture data, and suggest that this design scheme of the robotic arm can be used to improve the performance of humanoid robot in the human-centered environment as a cooperative robot or in the tele-manipulation application as a homogeneous slave robot.
Wheelchair Free Hands Navigation Using Robust DWT_AR Features Extraction Method With Muscle Brain Signals

Researchers try to help disabled people by introducing some innovative applications to support and assess their life. The Brain-Computer Interface (BCI) application that covers both hardware and software models, is considered in this work. BCI is implemented based on brain signals to be converted to commands. To increase the number of commands, non-brain source signals are used, such as eye-blinking, teeth clenching, jaw squeezing, and other movements. This paper introduced a low dimensions robust method to detect the eye-blinks and jaw squeezing; so that the method can be applied to drive a wheelchair by using five commands. Our approach is used Discrete Wavelet Transform with Autoregressive to extract the signal’s features. These features are classified by using a linear Support Vector Machine (SVM) classifier. The present method detects every testing sample using a small training set to test and drive a powered wheelchair. The proposed method is fully implemented practically based on binary-coded commands.