Miracle-dz/UniFAR-FTUs huggingface dataset:
Connection Degree Cost and Reward Based Algorithm in Cognitive Radio Networks

Spectrum management is a vital problem in cognitive radio networks. Unfortunately, most current spectrum management algorithms don't consider power control at the same time, which deviates from the original intention for cognitive radio (CR). Thus, a new spectrum management mechanism named Connection degree cost and Reward Based (CRB) algorithm for CR networks is presented in the paper. The new algorithm allocates available resources properly, while guaranteeing quality of service (QoS) for secondary users (SUs). And a new utility function is designed to characterize the system total utility more realistically in the network model. Numerical results validate the new algorithm gains better performance than the existing schemes both in the system total utility performance and in the system fairness performance.
Fusionnet: Multispectral Fusion of RGB and NIR Images Using Two Stage Convolutional Neural Networks

In low light condition, color (RGB) images captured by imaging systems suffer from severe noise causing loss of colors and textures. Near infrared (NIR) images, which tend to ignore interference from external lights, have advantage of capturing invisible information that can not be obtained by regular RGB cameras. In this paper, we propose multispectral fusion of RGB and NIR images using two stage convolutional neural networks (CNNs), called FusionNet. Lack of training data is a huge obstacle to the learning-based fusion. We synthesize noisy RGB images for training by adding multiscale Gaussian noise. We adopt two stage CNNs for RGB-NIR fusion that consists of denoising and fusion. First, we use a compact denoising subnetwork to remove severe noise from the input RGB image. Then, we utilize a fusion subnetwork to recover textures of the denoised RGB image with the help of its corresponding NIR image. We provide a perceptually motivated loss function to ensure color/texture consistency between the input RGB image and the output fusion result. Experimental results show that the proposed method produces natural looking fusion results by successfully recovering colors and textures. Moreover, the proposed method outperforms state-of-the-art fusion methods in terms of visual quality and quantitative measurements.
Selection of Charging Technology for Electric Bus Fleets in Intra-city Public Transport in India

Successful roll-out of an electric bus fleet for public transport in a city hinges on careful planning of charging infrastructure. The paper deals with selection of appropriate charging technology (EVSE) for electric bus fleets for intra-city public transport in Indian cities. The paper presents a review of the key features of the bus transport network in Tier-I and Tier-II cities in India and attempts to develop a model set of e-bus specifications for intra-city operations. It is followed by an examination of the charging technologies currently available in the market. The charging technologies are studied in detail to determine the best charging options for electric buses. The study highlights the importance of maintaining the service level after electrification by selecting electric bus of suitable capacity. The study finds that an intra city public bus fleet can be charged at depots during fleet operating hours and overnight. The study reveals that en-route charging of buses is not necessary in the case of intra-city bus operation in view of the average route-length. The investigation infers that out of the charging options, charging by DC plug-in, DC pantograph or AC plug-in is suitable to charge an e-bus fleet at a depot post completion of trips.
Proposed Conceptual Design Model of Persuasive Game for Upper limb for Stroke Rehabilitation

The gamification of stroke rehabilitation may increase patient’s motivation and engagement towards their rehabilitation activity and hence contributes to faster recovery. Apart from the interactive experience that games offer, it also may be used as a persuasive tool. In this view, games may be used to persuade stroke patients in shaping a good behavior towards their rehabilitation activity. This paper reports our proposed conceptual design model of building persuasive game for stroke patients that follows the Persuasive System Design Model. We incorporate the persuasive software features that includes reduction, tailoring, self-monitoring, and rewards. The proposed game architecture is also discussed in this paper.
Using an Affective Computing Taxonomy Management System to Support Data Management in Personality Traits

Affective Computing is a rather new and multidisciplinary research field that seeks sophisticated automation in emotion detection for later analysis. However, the automated emotion detection and analysis require as well comprehensive data management support, e.g. to keep control of data produced, and to enable its efficient reuse through classification with established terminology. This paper contributes to data management aspects in Affective Computing and to automation support in emotion classification on the basis of a personal traits analysis. Hence, we describe the implementation of a taxonomy management system, derived from requirements of a case study that investigates the relationship between personality and emotions in Affective Computing. The study makes use of machine learning software developed by SenseCare, an EU-funded R&D project that applies Affective Computing to enhance and advance future healthcare processes and systems.
Modeling and Control of an Unmanned Aerial Vehicle with Tilt Rotors Equipped with a Camera

In this work, we present a control law for an unmanned aerial vehicle (UAV) carrying a camera through a two degrees of freedom (2-DOF) gimbal mechanism. The vehicle is an H -shaped quadrotor with an additional mechanism that allows the independent projection, along the longitudinal direction, of the thrust generated by each rotor. This particular characteristic shows useful during the execution of some specific tasks, especially the ones that require high velocity. A control law is designed to deal with the underactuation and nonlinearities behavior of the vehicle, that are expressed mathematically by the Euler-Lagrange formulation. The control structure is arranged in four subsystems, designed to control the dynamics of the UAV position, attitude, rotors inclination, and camera pointing. For the position control, the inverse dynamic is used to linearize the translation subsystem, and then a robust H∞ controller is applied. For the remaining subsystems, the Super-Twisting Sliding Mode Control is applied. We present, through simulation, the performance of the designed control structure, proving that the controlled UAV is stabilized and able to track given trajectories even in the presence of unmodeled dynamics, parametric uncertainties and external disturbances (such as wind gusts).
IOS Mobile APP for Tuberculosis Detection Based on Chest X-Ray Image

Tuberculosis is one of the serious illness in the world. This disease still prevalent in underdeveloped and developing countries. To screen tuberculosis, chest X-ray (CXR) and sputum smear have been used widely. Traditionally, both methods have to be managed by doctors and technicians. To improve the effectiveness of loading of mass CXR interpretation sputum smear reading, we therefore developed an algorithm with the aid of artificial Intelligence (AI). We converted CXR and sputum smear into digital images. Subsequently, using image processing methods, Chest X-Ray (CXR) and sputum smear images automatically read. In this abstract, we only report the CXR reading with custom- designed AI analysis algorithm. The tuberculosis infection usually will result in some white dots in CXR. Caffe Frame work with GoogLeNet Network were used to create a model for CXR classification. The datasets were formed by normal CXR, Tuberculosis Suspect CXR and plural effusion CXR images. The GoogLeNet network accuracy was 98.39%. coreML tools was used to convert the Caffe model into coreML model for IOS mobile App. IOS mobile app successfully classified CXR image on IOS devices.
Carrier-Based PWM Technique for a New Six-to-Three-Phase Multilevel Matrix Converter for Wind-Energy Conversion Systems

In this paper, a new six-phase to three-phase Multilevel Matrix Converter ($6 \times 3$ MMC) topology is proposed. This converter is used to integrate a six-phase generator to a three-phase grid in the medium voltage and multiphase wind energy conversion systems. The proposed topology is based on the indirect configuration of the MC. It consists of two series connected Current Source Rectifiers (CSRs) in the input side and a three-level T-type inverter in output side. Consequently, it has the advantages of conventional Indirect MC (IMC), such as near sinusoidal input and output waveforms, the input displacement factor can be fully controlled and simple commutation with minimum switching losses at the rectifier-stage. Furthermore, the proposed converter can generate a higher output voltage with lower distortion in both output and input sides compared with the conventional IMC due to the nature of the multilevel output. A Carrier-Based PWM (CBPWM) technique is proposed in this paper to modulate the converter switches. The feasibility of the proposed $6 \times 3$ MMC and the CBPWM technique are confirmed using the simulation results.
Determining the Generation Rate of Silicon Solar Cells from Reflection and Transmission Measurements by Fitting an Analytical Optical Model

A common task in silicon solar cell device modelling is fitting an optical model to measured reflection (and transmission) (R+T) spectra, in order to determine the device’ generation rate for subsequent electrical modelling. The analytical light-trapping model introduced by Basore, also used in PC1D, and variants thereof, are often used for this task. This work investigates the accuracy of this approach, by checking the predicted generation resulting from R+T fits on experimental samples optically similar to industry standard PERC solar cells. We find that the simplest investigated model variant shows the overall highest accuracy of the generation current density better than 0.1 mA/cm², due to avoided generalizability errors of more complex models with more fitting parameters ("overfitting") with errors of up to 1 mA/cm².
Energy Efficiency of Multi-Hop Underwater Acoustic Networks Using Fountain Codes

Ensuring low energy consumption is a crucial issue in underwater communications, since battery replacement may be difficult and expensive in these scenarios. We analyze in this paper the energy consumption of a multi-hop underwater acoustic network (UWAN) employing fountain codes. Moreover, in order to reduce this energy consumption we optimize the modulation order assuming differential phase-shift keying ( $M$ -DPSK), as well as the number of hops. We investigate the required number of fountain-encoded packets for different modulation orders. Our results show that, by optimizing the modulation order, the number of hops and the number of fountain packets, we can obtain important energy savings, especially for long distances. In addition, with the proposed scheme the energy consumption decreases significantly compared to the case without fountain codes, reaching up to 40% of savings.
Improvement of Ohmic Behavior of Back Contact in ZnSnP2 Solar Cells by Inserting Cu3P

One of the main issues for the improvement of solar cells is to develop Ohmic contacts with low contact resistance on absorbers, such as CIGS/MoSe2/Mo. In the case of ZnSnP2 (ZTP)-based solar cells, Cu is used as a back electrode. At the Cu/ZTP interface, Cu3P is formed epitaxially on ZTP due to interdiffusion through annealing process and contributes to the improvement of the carrier transport property across the junction. However, the interdiffusion also causes degradation of electrodes and hence the increase in the series resistance. In this study, we adopt DC sputtering of a Cu3P target on the ZTP wafers before the deposition of Cu electrodes to prepare uniform Cu/Cu3P electrodes. As a result, the degradation of electrodes was suppressed and the low series resistance was obtained in the annealing time range from 5 to 30 min. The Cu/Cu3P stacked electrodes could therefore be a robust Ohmic contact for ZTP.
WeightMentor, bespoke chatbot for weight loss maintenance: Needs assessment & Development

Obesity and overweight are significant health risks. Effective communication can improve weight loss maintenance. There are technologies to support communication in weight management, such as text messaging that has been shown to benefit self-reporting and motivation but has its limitations. Having a conversation is an emotional experience, and chatbots are conversation-driven intelligent systems. Chatbots have been reported to increase compliance with health interventions. The aim of this study was to identify the needs of individuals who are maintaining weight loss in order to design and develop WeightMentor, a weight loss maintenance chatbot. This was a needs assessment and technology (chatbot) development study. The needs assessment identified the needs adults aged 18+ who were maintaining weight loss using semi-structure interviews. These needs were used to inform the design and development of a chatbot. Data were analyzed using thematic analysis. Findings identified five key themes: (1) Weight loss maintenance is challenging; (2) Social contact is beneficial but may also reinforce unhealthy habits; (3) Apps should be convenient and support progress tracking; (4) Personal messages should be specific and relevant; (5) Chatbots have potential for weight loss maintenance. Chatbot, WeightMentor, was designed and developed based on findings from the needs assessment and a review of the most popular nutrition apps. WeightMentor operates within Facebook messenger, which is currently one of the most popular social media platform. WeightMentor's purpose is to positively influence the user's emotions while maintaining weight loss. Its functions include self-reporting of diet and physical activity, viewing previous self-reporting trends, and motivational messaging. Chatbots such as WeightMentor has the potential to aid weight loss maintenance. Further research is required to test the usability and effectiveness of this WeightMentor.
Research on Cooperative Control Technology of Lower Extremity Exoskeleton

Lightweight structures and man-machine interface are widely needed for wearable rehabilitation/power assist devices for injured people. Based on a lower extremity exoskeleton driven by the Bowden-cable, a hardware and software platform was established to control DC brushed servo motors and to help patients in rehabilitation training and assisted walking. In terms of control, two control algorithms were designed. Combined with the position control outer loop, the fuzzy PD control algorithm is designed to achieve better trajectory tracking of the Bowden-cable transmission system. The impedance control algorithm provides the assisting effect on human walking and the EMG signal proves the effectiveness of the designed impedance control algorithm.
Use of Decision Tables in Monitoring of Performance Discipline

This paper discusses a variant of a decision-making algorithm for monitoring of performance discipline. The algorithm is based on a decision table that delivers a solution to problem situations that might arise in the course of performance of management's assignments. Practical im-plementation of this algorithm in document management systems can help automate and, as a result, facilitate monitoring of progress in management's assignments.
Fast and Parallel Wireless Communication Backbone Deployment with Networked Robots

Disaster robotics have gained considerable attention in the last decade while providing support for the emergency teams in search and rescue operations. Here, we propose a system to support first responders, such as firefighters and police officers, by establishing a communication network connecting multiple clients. The clients are the access points for the rescue teams. The objective is to deploy robots equipped with communication devices in such a manner that the clients are able to communicate among themselves. We focus on minimizing the deployment time. Our approach allows to combine links when computing the backbone. Then, our proposed method decomposes the problem into a set of local communication networks that when solved connects all clients in a network. Unlike previous works, the deployment is done in parallel, which decreases the time to set up the network. Our method uses realistic communication metrics to determine the connectivity between devices and to deploy the robots in the environment. We show using both simulations and physical experiments the feasibility of our solution.
Two Stream Deep Convolutional Neural Network for Eye State Recognition and Blink Detection

Eye state recognition and blink detection has been an important research problem in various fields like driver fatigue and drowsiness measurement, dry eye detection, video spoofing detection, psychological status analysis and many others. Hence an automated eye state classification and blink detection algorithm which is robust to a variety of conditions is required for this purpose. To this end, we propose a novel approach towards detection of eye blinks from a video stream by classifying the eye state of every frame as open or closed. First the eyes are localized from a frame with robust state-of-the-art facial landmark detectors. Then binary masks of the eyes are computed to capture and focus on how much the eyes are open. We propose a novel two stream convolutional neural network model which is jointly trained with the extracted eye patches, their masks as inputs and the corresponding eye state as output. With the eye state predicted by our network for every frame, we model a Finite State Machine to check for blinks by comparing number of consecutive frames with eyes closed against average human blink duration. Extensive experimentation has been done on a various number of popular benchmark datasets both for eye state classification and blink detection. Our proposed eye state classifier achieves a 3.2% and 3.86% improvement over the state-of-the-art in terms of accuracy and equal error rate (EER). The blink detector achieves a 1–2 % improvement over the state-of-the-art in terms of precision and recall. Hence our algorithm outperforms the existing methods for eye state classification and blink detection to the best of our knowledge.
Active Power Filter with Reduced Number of Semiconductor Switches Based on Synchronous Reference Frame

In this paper a 3-phase shunt active power filter (SAPF) with reduced semiconductor switches based on synchronous reference frame (SRF) is introduced. The power circuit of SAPF contains only 4 switches, forming only two legs to compensate the load currents of two phases. The third leg is replaced by a split capacitor. SRF is used to calculate the reference currents, modifications are done to control the two capacitors voltages in the DC-link. The advantages of these modifications are simplifying the control circuit, reducing the total number of sensors and reducing the total cost of SAPF. Simulation studies using MATLAB/SIMULINK to verify the operation of the SAPF with the modified control circuit. Experimental setup is implanted to confirm the SAPF operation actually.
Ransomware detection using process mining and classification algorithms

The fast growing of ransomware attacks has become a serious threat for companies, governments and internet users, in recent years. The increasing of computing power, memory and etc. and the advance in cryptography has caused the complicating the ransomware attacks. Therefore, effective methods are required to deal with ransomwares. Although, there are many methods proposed for ransomware detection, but these methods are inefficient in detection ransomwares, and more researches are still required in this field. In this paper, we have proposed a novel method for identify ransomware from benign software using process mining methods. The proposed method uses process mining to discover the process model from the events logs, and then extracts features from this process model and using these features and classification algorithms to classify ransomwares. This paper shows that the use of classification algorithms along with the process mining can be suitable to identify ransomware. The accuracy and performance of our proposed method is evaluated using a study of 21 ransomware families and some benign samples. The results show j48 and random forest algorithms have the best accuracy in our method and can achieve to 95% accuracy in detecting ransomwares.
Novel Scheme For Extracting Maximum Power From Façade Based Building Integrated Photovoltaics

Building Integrated Photovoltaics (BIPV) is expected to be a viable option for Near Zero/Zero Energy Buildings (NZEB/ZEB) in urban areas. Facade based BIPV gives an option to the builders/designers to manage human comfort as well as improve aesthetics by including more than one BIPV technology or transparency. However, from the power generation point of view, these PV modules are subjected to non-uniform illumination and partial shading due to the surrounding buildings and structures in most urban scenario. The power output of the modules do not match with the inverter rating as they do not receive direct radiation for most of the day and implementation of vertical orientation minimizes the power generation. Connecting them in series and/or parallel is an option to increase power generation, but it is highly influenced by the shading patterns. In this paper, we propose a scheme of power converters for BIPV in which each module is connected to a dedicated DC-DC power converter and the outputs of several such converters are connected to a grid-tied inverter. This configuration helps to track the maximum global power of the BIPV modules and inject power into the grid even under the partially shaded conditions. The simulation results for a two-module case show that the designed controller can handle sudden changes in radiation level in either or both of the BIPV technologies and stable state MPP(Maximum Power Point) condition is achieved. The impact of the fill factor on the dynamic response of the controller for various BIPV technologies is also highlighted.
Fast Convolutional Neural Network for Real-Time Robotic Grasp Detection

The development of the robotics field has not yet allowed robots to execute, with dexterity, simple actions performed by humans. One of them is the grasping of objects by robotic manipulators. Aiming to explore the use of deep learning algorithms, specifically Convolutional Neural Networks (CNN), to approach the robotic grasping problem, this work addresses the visual perception phase involved in the task. To achieve this goal, the dataset “Cornell Grasp” was used to train a CNN capable of predicting the most suitable place to grasp the object. It does this by obtaining a grasping rectangle that symbolizes the position, orientation, and opening of the robot's parallel grippers just before the grippers are closed. The proposed system works in real-time due to the small number of network parameters. This is possible by means of the data augmentation strategy used. The efficiency of the detection is in accordance with the state of the art and the speed of prediction, to the best of our knowledge, is the highest in the literature.