Miracle-dz/UniFAR-FTUs huggingface dataset:
Attitude Estimation for a Deorbiter CubeSat

This paper investigates the attitude estimation ca pabilities of a debris-removing nanosatellite called deorbite CubeSat. The spacecraft is designed based on the utilizatioi of commercially-available components with long space heritage which are embedded in an eight-unit form factor. The attitud. estimation machinery employed in this work is a discrete time, quaternion-based, extended Kalman filter, which utilize measurements provided by a three-axis rate sensor, five su sensors, and a three-axis magnetometer. To obtain a linear state space model, gravity gradient and magnetic disturbance torque are included in the plant model, and the model is linearizec with respect to the process noise and the states, namely the inertial angular velocities and the quaternions. measurement noises are modelled based on zero-mean Gaussian distributions and are quantified based on the performance of the state-of-the art, commercial-of-the-shelf devices. A Monte Carlo simulatioi is created to analyze the performance of the estimator agains various initial angular velocities and quaternions, both in the sunlit and in the eclipsed portions of the orbit. In light o the results, the accuracy of the deorbiter CubeSat's attitud knowledge is discussed.
A Novel Vessel Velocity Estimation Method Using Dual-Platform TerraSAR-X and TanDEM-X Full Polarimetric SAR Data in Pursuit Monostatic Mode

In this paper, we demonstrate that the spaceborne dual-platform TerraSAR-X (TSX) and TanDEM-X (TDX) pursuit monostatic mode full polarimetric (full-pol) synthetic aperture radar (SAR) data with a time lag can be used to monitor maritime traffic. For single polarization (single-pol) SAR data, the performance of vessel velocity estimation is mainly determined by 2-D cross correlation of SAR intensity data. As the sea clutter is changing dynamically during the TSX/TDX data acquisition, the correlation between two dual-platform images decreases significantly. We may get unstable or incorrect estimations of vessel velocity, especially under a higher wind condition. For solving this problem, we propose an object-oriented polarimetric likelihood ratio test (PolLRT) method based on the complex Wishart distribution. The proposed method makes PolLRT statistics of the detected target pixels for eliminating the effect of varied sea clutter. Two pairs of full-pol SAR data sets covering the Strait of Gibraltar acquired by dual-platform TSX/TDX in pursuit monostatic mode with a time lag of approximately 10 s are selected for the experiments. The experimental results demonstrate that the proposed PolLRT method has a better performance than that of the classical normalized cross correlation (NCC) method with VV polarization SAR data and the mutual information (MI) method with full-pol SAR data. Specifically, under the lower wind condition, the correct estimation rate of the NCC, the MI, and the proposed PolLRT methods are 85.7%, 57.1%, and 100%, respectively; under the relatively higher wind condition, the correct estimation rate of the above three methods are 48.8%, 23.2%, and 90.1%, respectively.
Toward Controllable Morphogenesis in Large Robot Swarms

Morphogenetic engineering aims to achieve functional, self-organized but controllable structures in human-designed systems. Controlling the structures is crucial if they are to be used for real-world applications. Building on previous work on morphogenesis, in this letter, we present a new algorithm, with controllability at its core, for large swarms of simple robots where morphogenesis occurs without self-localization, predefined map, or preprogrammed robots. Controllability is achieved through three parameters that influence the morphogenesis process and create a rich morphospace of quantitatively different shapes. The algorithm was tested in over 2000 simulations and three times on real swarms of 300 kilobots. Swarms were able to grow shapes using only local communication, and regrow missing parts when manually damaged. Extra simulations also demonstrated swarms adapting to an obstacle in the environment by getting around it. Results were compared with our previous work on morphogenesis to show how controllability allowed richer shapes. This letter represents a step into designing a controllable morphogenesis algorithm toward more functional swarms for real-world applications.
LoRa-based Mesh Network for IoT Applications

In this paper, we study LoRa-based private networks for IoT applications. First, we consider the problems that may arise when LoRa devices are deployed in a private network with special operation scenarios (other than commercial networks) based on the standard LoRaWAN. We propose an improved LoRa protocol that can replace LoRaWAN. The proposed LoRa protocol overcomes the shortcomings of the existing LoRaWAN and can be effectively deployed in private networks of various applications. The proposed LoRa protocol employs mesh networking to improve the network coverage and a new multiple access scheme (other than Aloha) to reduce the data collision rate.
Polarimetric Radar for Automotive Self-Localization

In order to enable highly automated driving, high demands are placed on the sensors perceiving the environment around the vehicle. Today’s radar sensors have advantages over other sensor types because they are robust in adverse weather conditions and enable an instantaneous velocity estimation. Newly available automotive radar sensors with polarimetric capabilities provide even richer information on the perceived objects in the environment including, for example, shapes and orientations. This work shows how the additional information can be incorporated into gridmaps that are especially useful for vehicle self-localization tasks. Experiments have been carried out to demonstrate real world results.
Local App Classification using Deep Neural Network based on Mobile App Market Data

Due to the spread of smartphones, mobile applications (app) have been widely used in daily life. Several apps which provide real-world related information (called "local apps") are useful for not only tourist but also residents. There is a category that seems to contain local apps in app market such as "Travel & Local" in Google Play, but many local apps are categorized into other categories. Thus, we present a method to classify local apps based on app market data using deep neural network (DNN). We leverage the fact that each app is manually labeled by developer to pre-train the DNN. In addition, we create features from an app market data because app markets involve multi-modal data such as app name, category and number of installs. We conducted an experiment on a real-world dataset crawled from Google Play to validate the effectiveness of the proposed method. Our evaluation shows that the proposed method outperforms the baseline method by 5.5% regarding F1 score.
Development Cost Optimization for Multi-Functional Mixed-Criticality Embedded Systems

In some industrial embedded systems, functions with different safety integrity levels (SILs) are integrated into the same hardware platform to share resources, and usually, the schedulability of applications should be met first. Meanwhile, development costs’ optimization of applications is also an important requirement, because some industries, such as automotive, are cost-sensitive for the mass market. To minimize the development costs of applications while satisfying their schedulability, an optimization method is presented based on a key path tabu search algorithm combined with a message movement strategy. The key path tabu search algorithm uses the tree-based tabu list to find a schedulable solution with low development cost. The message movement strategy is proposed to significantly reduce the development cost caused by the recursive elevation of the SILs of tasks for the sharing of partitions. The results of experiments using real-life benchmark and simulated benchmark show that the proposed method is effective.
Robust Nonlinear Output Feedback Control of a 6-DOF Quadrotor UAV

A robust nonlinear output feedback control method is presented, which achieves asymptotic position and attitude regulation for a six degrees of freedom (6-DOF) quadrotor unmanned aerial vehicle (UAV). The control law is designed to compensate for uncertainty in the quadrotor system dynamic model, including input-multiplicative parametric uncertainty. To reduce the computational requirement in the closed-loop system, constant feedforward estimates of the input-multiplicative uncertainty are utilized in lieu of adaptive parameter estimates. In order to avoid the high-gain feedback requirement that is characteristic of standard sliding mode observer methods, the proposed control method utilizes a bank of dynamic filters, which operates as a velocity estimator in the closed-loop system. Simulation results are provided to demonstrate the performance of the attitude control method using the Quanser 6-DOF QBall 2 system test bed.
A Novel Web Application Framework for Ubiquitous Classification of Fatty Liver Using Ultrasound Images

Medical imaging techniques are being profoundly used for diagnosis of many diseases. In many of the remote areas in the developing nations, patients who live in rural areas are facing vital health disparities compared to the general population. In such scenarios, eHealth can offer promising solutions. The eHealth especially aims at developing digital applications for offering high accuracy diagnosis even in remote areas. Also, the integration of eHealth with advanced technologies such as deep learning and artificial intelligence will further improve the diagnostic accuracy and also reduces the duration. In this paper, we develop a novel low-cost and easily scalable eHealth architecture comprising of a web application which enables clinicians in fatty liver classification using ultrasound images. The developed web application framework uses a deep learning model (using CNN) for accurate classification of fatty liver using ultrasound images. The clinician in a remote location with a moderate internet connectivity can upload the scanned ultrasound image to the developed web application and the application identifies if any abnormality is present. From the performance analysis, it is observed that the developed model achieves an accuracy of 91.37%. Also, regarding latency the developed classification model predicts the abnormality presence in less than 20 ms. However, including the network latency, it is observed that the developed eHealth architecture predicts with a latency of less than 150 ms using moderate network connectivity.
Optimal Resource Block Assignment and Power Allocation for D2D-Enabled NOMA Communication

A novel joint optimization framework for device-to-device (D2D)-enabled non-orthogonal multiple access (NOMA) networks is proposed. Our objective is to maximize the performance of the D2D communication by jointly optimizing the resource block (RB) assignment and the power allocation, by considering the SIC decoding order of the NOMA-based cellular user equipments (CUEs). We invoke the distributed decision making (DDM) framework to decouple the formulated problem into two sub-problems. For the RB assignment sub-problem with integer variables, we propose a differential evolution (DE) algorithm to obtain the optimal NOMA CUE group and RB assignment for D2D pairs. For power allocation sub-problem with continuous variables and decoding order variables, we first use a heuristic algorithm to optimize the power allocation for NOMA-based CUEs with given D2D power allocation. We prove that the power allocation for the NOMA-based CUEs is the optimal solution. We then invoke the successive convex approximation (SCA) and DE to find the sub-optimal power allocation of the D2D pairs. The numerical results validate the feasibility, fast convergence, and flexibility of the proposed algorithm, and the performance with our algorithm outperforms the conventional OMA technology in terms of energy efficiency and sum rate.
A Comprehensive Survey on Broadcasting Emergency Messages

Broadcasting official and emergency information is seen as a principle role for controlling crisis situations. In fact, it has a great importance to ensure the communication between network nodes (vehicles, passengers, etc.). This paper presents a survey that examines the existing studies of broadcasting protocols. Our survey follows the guidelines of systematic literature reviews (SLR). It provides a comparison of the existing approaches based on some criteria such as communication technologies and simulation tools. Finally, we highlight some recommendations and possible future researches which need further investigations.
Low-Cost Portable ECG

Common contemporary ECG units are computer-based devices that can be connected to the computer network to enable simultaneous monitoring of several patients in, e.g., intensive care units. Typically, these devices are relatively large, heavy, and powered from the wall socket. As a result, the movement of a patient is limited, even in cases when the patient's physical condition does not bind him/her to the bed. This paper describes proof-of-concept portable device for electrocardiography which can measure three elemental ECG leads, is battery powered and transmits measured data into a central data collection unit via WiFi. Therefore, the patient can leave the bed for a reasonable distance, while the physician can continue monitoring the patient's health condition. It is easy to quickstart usage and mass production of the presented prototype. We have tested the hardware and developed the necessary software. The system is based on ADAS1000 from Analog Devices as an ECG analogue front-end. Measured data are processed by STM32L0 MCU and sent to the data collection unit using the ESP8266 WiFi module.
The POSEIDON Project: Microgrid in Port Areas to Improve Energy Efficiency by the Integration of RES, Flexible Loads and Smart Mobility

In the paper, a general overview and the first results of the POSEIDON project are presented. The project aims at defining optimal control strategies of microgrids in the port area, which include the management of electric vehicles with public charging stations, energy storage systems installed on boats, flexible loads and Renewable Energy Sources (RES). The planning studies will allow evaluating, by considering the use of vehicle-to-grid (V2G) and boat-to-grid (B2G) paradigms, the positive impact of both electric vehicles and boats in terms of better exploitation of RES, energy efficiency and reduction of the polluting emission.
A Low-Complexity Implementation Scheme for PCM/FM Based on MLSD

In this paper, a low-complexity noncoherent maximum likelihood sequence detection (MLSD) scheme for PCM/FM is proposed. In the proposed method, the criterion of noncoherent MLSD is derived when the carrier phase is unknown, and then a novel Viterbi algorithm (VA) with modified state vector is designed to simplify the implementation of noncoherent MLSD. Both analysis and experimental results show that the proposed approach has lower computational complexity and does not need accurate carrier phase recovery, which overcomes the shortage of traditional MLSD method. What’s more, compared to the traditional MLSD method, the proposed method also achieves the same detection performance.
Emotion Detection IoT enabled Edge-node for Citizen Security

In this paper, we have proposed a novel architecture for citizen security using deep learning based edge computing device. The proposed system comprises of an end- device which can be connected to every street light pole in an area. The gateway device can be connected at a large distance from the end-devices. LoRa physical layer is used as the main communication interface which enables large distance transmission. CNN based algorithm is used to identify the expression of the person who pressed the panic button on the device to reduce false alarm. A very low complex Convolutional Neural Network (CNN) architecture has been developed, to recognize the expression on the end device itself. A data logging server is used for logging the node-id everytime it is pressed. An android application is also developed which shows the real-time location of the pressed node and provides navigation routes to reach that node.
MyGitIssues Web Application as a Solution in Dealing with Issues on GitHub

There is a huge increase of the number of people that are using GitHub as a service for managing projects, sharing codes and providing open source solutions. The need for tools or applications for dealing with this service is also increased. Therefore, the initiative is to adapt to the trend by developing a web application MyGitIssues, which will differentiate from others with a good front-end, back-end and effectiveness in dealing with issues on GitHub. Consequently, the main idea of this paper is to give an overview of proposed MyGitIssues web application, which will be able to list issues from a specific GitHub repository. MyGitIssues application is supported by the possibilities of the new Web (Real time web) through WebSockets and WebHooks and presents a solution in simplifying the process of dealing and managing different repositories.
Adaptive Scene-Matching Algorithm based on Frequency Pattern Analysis for Aerial Vehicle

Vision-based navigation can be classified primarily into relative and absolute navigation. Absolute navigation can estimate stable position solution of the vehicle. However, the performance of the feature detection algorithm greatly varies depending on the environment, and the matching accuracy may not be guaranteed. This represents navigation accuracy and can lead to serious navigation errors if an appropriate feature detector is not used. Therefore, in this study, we propose an algorithm that uses optimized feature information detector through frequency analysis of input image. To verify the performance of the proposed algorithm, we used the data obtained from flight experiments. The proposed algorithm prevents the occurrence of mismatching in flight experiments.
User Grouping Based Multi-Layer Precoding for Multi-Cell 3D MIMO System with Statistical CSI

In this paper, we propose a multi-layer precoding scheme for multi-cell three-dimensional multiple-input multiple-output (3D MIMO) system with large-scale uniform planar antenna array at the base station (BS). Based on a lower bound of the average signal-to-leakage-plus-noise ratio (SLNR) and taking the fairness of scheduled users into account, we propose a user grouping algorithm exploiting only statistical channel state information (CSI). Then we apply the user grouping algorithm to the multi-layer precoding framework. The proposed scheme greatly reduces the CSI required by the BS with using only users’ statistical CSI. Simulation results show that the proposed scheme can achieve good fairness and high sum rate.
Self-interference Cancellation for Simultaneous Transmit and Receive Applications

As the demand for frequency spectrum increases with new technological advancements in radar and communications, the frequency spectrum is becoming increasingly limited and overcrowded. This is driving research in Simultaneous Transmit and Receive (STAR) systems as there is a growing need for systems with higher spectral efficiency. Conventional systems operate in half-duplex mode, either transmitting and receiving at different times, over different frequency bands, or using other multiple access techniques. This paper presents a practical STAR demonstrator system developed at the University of Cape Town.
Impact of outdated CSI on the secrecy performance of dual-hop networks using cooperative jamming

This paper studies the problem of secure dual-hop AF network in the presence of eavesdropper. We study the secrecy performance of partial relay selection with outdated channel state information (CSI). We propose a system secrecy where two sources and destination simultaneously transmit the legitimate and the jamming signal respectively. The selected relay forwards the combined signal to the legitimate receiver and the eavesdropper. We derive a closed-form expression for lower bound on the ergodic secrecy capacity (ESC) under different assumptions of the eavesdropper channel gains. We verify that the system performance is degraded as the correlation coefficient decreases. Simulation results are considered to verify the exactness of the proposed mathematical analysis.
Low-complexity joint channel estimation and symbol detection for OFDMA systems

In this paper, we propose a joint channel estimation and symbol detection (JCESD) algorithm relying on message-passing algorithms (MPA) for orthogonal frequency division multiple access (OFDMA) systems. The channel estimation and symbol detection leverage the framework of expectation propagation (EP) and belief propagation (BP) with the aid of Gaussian approximation, respectively. Furthermore, to reduce the computation complexity involved in channel estimation, the matrix inversion is transformed into a series of diagonal matrix inversions through the Sherman-Morrison formula. Simulation experiments show that the proposed algorithm can reduce the pilot overhead by about 50%, compared with the traditional linear minimum mean square error (LMMSE) algorithm, and can approach to the bit error rate (BER) performance bound of perfectly known channel state information within 0.1 dB.
A Gamification Model For E-Learning Platforms

The use of gamification in education has been viewed as an innovative approach to introduce benefits of games in a non gaming context. Instructively, for a successful gamification process in learning, motivational theories and behavioral outcomes for the learner have to be incorporated in design framework. Observably gamification projects particularly within the education domain for e-learning platforms have encountered challenges key amongst them, emanating from lack of appropriate gamification design framework. Subsequently the study sought through systematic literature review to investigate types of gamification design frameworks. 16 design frameworks were evaluated and only 2 frameworks were designed for gamification within an e-learning environment. However the two frameworks did not address motivational affordances, behavioral Outcomes and evaluations rigor. In view of this the study proposes a framework identifying games elements as motivational affordances that influence behavioral outcome of the learner.
High Gain Ultrawide Band Vivaldi Antenna Design for Mini/Micro Satellite Synthetic Aperture Radar Applications

In this paper, Vivaldi antenna array design with high gain and directivity, which works in ultra-wide band, is presented especially for Synthetic aperture radar applications in small surveillance satellites. The proposed antenna structure has been modified to improve the parameters that make up the radiation characteristic of an antenna, such as directivity, gain, return loss (S11), voltage standing wave ratio (VSWR), and antenna efficiency. Firstly, the design parameters of a single antenna element are optimized using Genetic Algorithms (GA). The optimized Vivaldi antenna structure in 44⨯49 mm dimensions offers high antenna gain and return loss(S11)below −10 dB in X-band (8−12 GHz) and Ku-band (12−18 GHz) frequency range. As a substrate, Rogers Duroid 5880 with a 2.2 dielectric constant and 0.508 mm thickness, which can be easily found on the market and preferred at high frequencies, has been used. The antenna structure, which is optimized for use in high resolution synthetic aperture radar applications, is designed as a planar antenna array. The planar antenna array (4⨯4)is formed with 16 elements. The distance between the elements and the amplitude of current is optimized by GA in order to enhance antenna gain by minimizing side lobe levels (SLL) and the grating lobes in operating band width. In this way, the side lobe level below −20 dB, the average 18 dB gain during the operating frequency and the highest 19.4 dB antenna gain at 10 GHz have been obtained. The measurement and simulation results are in agreement and the proposed antenna has been proven to be suitable for high resolution SAR applications.