Miracle-dz/UniFAR-FTUs huggingface dataset:
Secrecy Outage Performance of SWIPT Cognitive Radio Network With Imperfect CSI

We consider a cognitive radio (CR) network with simultaneous wireless information and power transfer (SWIPT) system having one base station (BS) with multiple antennas that acts as a secondary transmitter, one desired information receiver (IR) acting as secondary receiver, multiple primary users (PUs) having licenses band of spectrum, and multiple energy harvesting receivers (EHRs). The EHRs harvests energy from the BS and eavesdrop the information from signal. In order to extract the information and then harvest energy from that signal, power splitting is considered in each EHR. On the basis of an imperfect channel state information (CSI) in IR, selection of the best transmit antenna at the BS is made. The effect of path loss on the system is also considered. The derivation of analytical expression in closed-form is provided for the exact secrecy outage probability (SOP) of the network. The derived expression highlights the variation in the secrecy outage of the considered CR SWIPT system relying on imperfect CSI with respect to the system parameters, which is depicted through numerical analysis. Furthermore, we derive the asymptotic SOP and study the effects of different system parameters on the secrecy diversity order and secrecy array gain.
Joint Registration of Multiple Point Sets with Refinement

The recent advances in fast and affordable 3D scanners, such as the Microsoft Kinect, have triggered new developments in 3D reconstruction. However, the geometric alignment of multiple point clouds is still a very challenging task. This paper addresses the problem of registering multiple point sets by building upon the state-of-the-art Joint Registration of Multiple Point Clouds (JRMPC) algorithm. We advance over JRMPC by incorporating the surface normal orientation of each point in the Gaussian Mixture Models (GMM) employed by this method. We formally derive an expectation-conditional maximization algorithm that iteratively estimates the refined model and transformation parameters. Experiments performed on both synthetic, real indoor and outdoor data prove that incorporating the orientation information substantially improves the parameter estimation, allowing for a 50% average reduction of the registration error compared to JRMPC. Our method is compared to other pairwise approaches, demonstrating the best performance on three different realistic datasets.
A Dynamic Heuristic Method for Detecting Packed Malware Using Naive Bayes

In this paper we consider a heuristic malware detection method based on dynamic analysis of API calls. We utilize a naïve Bayes classifier to distinguish between benign and malware samples, and Levenshtein distance is shown to increase the effectiveness of the technique. For comparison, we consider commercial anti-malware products. We provide experimental evidence of the strength of our technique based on a substantial malware dataset. We show that our approach achieves particularly impressive results in detecting packed malware samples.
Space-based quantification of per capita CO2 emissions from cities

Urban areas are currently responsible for ∼70% of the global energy-related carbon dioxide (CO2) emissions, and rapid ongoing global urbanization is increasing the number and size of cities. Thus, understanding city-scale CO2 emissions and how they vary between cities with different urban densities is a critical task. While the relationship between CO2 emissions and population density has been explored widely in prior studies, their conclusions were sensitive to inconsistent definitions of urban boundaries and the reliance upon CO2 emission inventories that implicitly assumed population relationships. Here we provide the first independent estimates of direct per capita CO2 emissions (Epc) from spaceborne atmospheric CO2 measurements from the Orbiting Carbon Observatory-2 (OCO-2) for a total 20 cities across multiple continents. The analysis accounts for the influence of meteorology on the satellite observations with an atmospheric model. The resultant upwind source region sampled by the satellite serves as an objective urban extent for aggregating emissions and population densities. Thus, we are able to detect emission ‘hotspots’ on a per capita basis from a few cities, subject to sampling restrictions from OCO-2. Our results suggest that Epc declines as population densities increase, albeit the decrease in Epc is partially limited by the positive correlation between Epc and per capita gross domestic product. As additional CO2-observing satellites are launched in the coming years, our space-based approach to understanding CO2 emissions from cities has significant potential in tracking and evaluating the future trajectory of urban growth and informing the effects of carbon reduction plans.
Deep Learning Based Beam Allocation in Switched-Beam Multiuser Massive MIMO Systems

This paper purposes beam allocation problem for a massive MIMO system using Deep Neural Network (DNN). We use supervised Machine Learning (ML) algorithm for Beam-Selection and Switching (BSS) that provides best communication performance to the receiver. These beams are narrow and highly directed that are produced by Butler method to achieve high gain. The brute-force and suboptimal search methods are used to allocate beams that have run time complexity but the proposed ML algorithm has training complexity that achieves nearly the same average sum data rate and on the run time it takes only query data to predict beams. Interpretation of BSS is achieved by multiclass classification that is to compare with the labeled training data of beams for prediction. We estimate channel equation according to the position of the user for training and use different KPIs for labeling that helps in the selection of optimal beam for the user. Simulation results show that the proposed scheme is able to predict and allocate beams with the accuracy of 91.6 to 97.7% that provides best communication performance to the receivers. We further explore the effect on the system performance in terms of average sum data rate by increasing or decreasing the number of users compared to beams.
Optimal Current Excitation for the Time-Domain Estimation of the Electrochemical Impedance

For the real-time fault detection of electrochemical cells in automotive applications, time-domain based impedance estimation schemes, together with broad-band current excitation signals, have been developed. Whereas the appropriate choice of the estimator is necessary as to prevent a systematic estimation bias, the choice of the applied excitation signal is of equal crucial importance as thereby the covariance of the estimate, and therefore the resulting accuracy of the estimation scheme, is directly influenced. In this work, an optimization of the applied current excitation signal, based on the Fisher information matrix, is proposed and its influence on the estimation accuracy is investigated.
Dynamic-Scale Graph Convolutional Network for Semantic Segmentation of 3D Point Cloud

Semantic segmentation of 3D point cloud data is a crucial step for various applications such as autonomous driving. Conventional methods for semantic segmentation of point cloud heavily rely on numerous hand-crafted features often derived from the 3D covariance matrix, which are typically time-consuming. In this work, a dynamic scale graph convolutional neural network is proposed to perform semantic segmentation on 3D point cloud without relying on extensive exploitation of hand-crafted features. Using only spatial coordinates, backscattered intensity and spectral information derived from aerial images, our proposed method aims at modeling multi-scale local structural information by combining dynamic scale sampling and multi-scale neighbor graphs. Sampling and neighbor graph construction are both implemented on the fly such that no preprocessing or data augmentation is needed before training. Evaluated using the ISPRS 3D Semantic Labeling Contest, our approach has achieved the performance on par with the current state-of-the-art in terms of average f1-score without hand-crafted features and less computational overhead.
A New Sound Source Localization Approach Using Stereo Directional Microphones

This paper proposes a new Sound Source Localization (SSL) algorithm using the features of 2-channel directional microphones. Noise reduction techniques and voice activity detection (VAD) are used to calculate the root mean square (RMS) values of the signal received on each channel. In the noise reduction stage, band pass filter, improved minima controlled recursive averaging (IMCRA), and multichannel Wiener filter (MWF) are applied to reduce the ego noise from UAV. After data preprocessing and RMS value computation, specific grouping phenomena is observed according to the angle. In this study, we employ K-medoids, one of the clustering techniques, to classify the groups. The data used in this study to investigate the proposed approach are collected as follows: The angle between the speaker and the microphone varies from 0 to 180 degrees with a 60degree increment, and the distance does from 5 to 20 meters. The distance between the microphone and drone is set to 0.3 through 0.8 meters. 50 data are randomly selected for the evaluation. Since the proposed approach is based on the RMS ratios in the speech presence sections, it can be applied to the situation in which the time delay of arrival (TDOA) methods are difficult to be applied.
Generation of expressive motions for a tabletop robot interpolating from hand-made animations

Motion is an important modality for human-robot interaction. Besides a fundamental component to carry out tasks, through motion a robot can express intentions and expressions as well. In this paper, we focus on a tabletop robot in which motion, among other modalities, is used to convey expressions. The robot incorporates a set of pre-programmed motion animations that show different expressions with various intensities. These have been created by designers with expertise in animation. The objective in the paper is to analyze if these examples can be used as demonstrations, and combined by the robot to generate additional richer expressions. Challenges are the representation space used, and the scarce number of examples. The paper compares three different learning from demonstration approaches for the task at hand. A user study is presented to evaluate the resultant new expressive motions automatically generated by combining previous demonstrations.
Systematically Covering Input Structure

Grammar-based testing uses a given grammar to produce syntactically valid inputs. To cover program features, it is necessary to also cover input features—say, all URL variants for a URL parser. Our k-path algorithm for grammar production systematically covers syntactic elements as well as their combinations. In our evaluation, we show that this results in a significantly higher code coverage than state of the art.
Artificial Intelligence Enabled Internet of Things: Network Architecture and Spectrum Access

The explosive growth of wireless devices motivates the development of the internet-of-things (IoT), which is capable of interconnecting massive and diverse "things" via wireless communications. This is also called massive machine type communications (mMTC) as a part of the undergoing fifth generation (5G) mobile networks. It is envisioned that more sophisticated devices would be connected to form a hyperconnected world with the aids of the sixth generation (6G) mobile networks. To enable wireless accesses of such IoT networks, artificial intelligence (AI) can play an important role. In this article, the frameworks of centralized and distributed AI-enabled IoT networks are introduced. Key technical challenges, including random access and spectrum sharing (spectrum access and spectrum sensing), are analyzed for different network architectures. Deep reinforcement learning (DRL)-based strategies are introduced and neural networks-based approaches are utilized to efficiently realize the DRL strategies for system procedures such as spectrum access and spectrum sensing. Different types of neural networks that could be used in IoT networks to conduct DRL are also discussed.
Enhanced Performance of Thermoelectric Generator by Modified H-Bridge Converter

Thermo electric generator (TEG) converts the temperature difference into voltage by See beck effect. Thermal gradient is the heart of thermo electric generator. High electrical conductivity and low thermal conductivity materials must have good thermo electric property. Now development of Nano technology can improve property of thermo electric materials with change in thermal conductivity without affecting electrical property. Thermo electric generator is a dynamic technology which can be used to reduce most of the environmental problem such as global warming due to change in climate. In high voltage applications multilevel inverter produces smooth output voltage. Harmonics are comparatively small. Multilevel inverter produce better performance even they are operated in medium voltage applications. Enhance the efficiency of thermo electric generator by using different types of converters such as full bridge H-bridge and modified H-bridge converter.
Tunable Bandstop Filter Using Distributed Coupling Microstrip Resonators With Capacitive Terminal

This letter presents a varactor-based tunable bandstop filter (BSF) using distributed coupling microstrip resonators with the capacitive terminal. Each distributed coupling microstrip resonator consists of a coupling microstrip line, a varactor, and a capacitive terminal which is a floating pad connected to the cathode of the varactor. The capacitive terminal serves not only as a bias pad for the control voltage but also as an intrinsic capacitive path to ground. Compared to the conventional inductive terminal with the metallized via, the capacitive terminal can be beneficial to realize much higher frequency responses by taking advantage of the capacitance to ground from the floating pad. High stopband suppression level is achieved by cascading multiple distributed coupling microstrip resonators. For demonstration, a tunable BSF prototype with the frequency tuning range of 11.3–16.5 GHz (37%) is designed and fabricated. Measured and simulated results agree well and validate the design principle.
Climate Change Perception in Scientific and Public Sphere

Climate change is one of the major concerns of humankind during this 21st century. Indeed everywhere on the planet, effects climate change is now widely perceptible to everyone. However, while a lot of efforts have been made on the proposal of efficient models able to provide reliable climate projections for the future years, very little work has been done on the global population's feelings regarding the climate changes. Thus in this paper we focus to this social dimension of the problem and we adopt a data analytics approach that aims to study people's perception of climate change. First, we analyze the scientific papers published during the last forty years and we try to understand what are the topics addressed and their evolution. Then, we have collected data on Twitter to understand what are the feelings and the topics addressed by the individuals regarding climate change. To the best of our knowledge, this is the first data analysis approach that highlights the feelings and topics of the global population about climate change and compares them with scientific articles.
Segmentation of Retinal Microvasculature Based on Iterative Self-Organizing Data Analysis Technique (ISODATA)

The modern ophthalmology is completely dependent on digital image processing to find out the remarkable symptoms for diagnosing the severe cardiovascular disease such as hypertensive retinopathy, and transient ischemic attack that are related to the changes of the retinal microvasculature. Employing the image segmentation techniques, the abnormalities in retinal microvasculature like vessel tortuosity, cotton wool spots, and vessel caliber can be extracted which are recognized as the salient symptoms for the abovementioned cardiovascular diseases. In this paper, an automated method for retinal image segmentation has been proposed. The proposed method was developed employing the thresholding based Iterative Self-Organizing Data Analysis Technique (ISODATA) for image segmentation combining with other existing image preprocessing techniques. The performance of the proposed method was evaluated on the healthy patient image set of (High-Resolution Fundus Image Database) HRFID. This newly developed algorithm achieved 94.3% accuracy with 97.86% specificity and 0.0054 standard deviations. The proposed algorithm can be integrated as the computer-aided clinical diagnostic tool to facilitate the ophthalmologist with further evaluation and validation.
Parallelization of Direct-Forcing Immersed Boundary Method Using OpenACC

A parallelization of Poisson equation solver is developed by using the parallel algorithm of red-black SOR method performed on GPU by OpenACC. We use the parallel computing solver to solve the fluid-structure interaction problem with direct-forcing immersed boundary method. The speedup for the parallel computing solver is up to 8.62 times. In addition, we present that the optimization of the red-black SOR (RBSOR) algorithm for the problem with the complex-geometry objects is to allocate the same memory on two variables which is to take advantages of accessing data by random indexes and formatted indexes. The implementation of parallel computing RBSOR with OpenACC is efficient to delegate the simulation of fluid-structure interaction problem on the GPU.
Study on the Effect of Gamma Irradiation on Protocorm-Like Bodies of Dendrobium Hybrid against Fusarium proliferatum and Fusarium oxysporum Authors:

Dendrobiums orchids are prone to diseases caused by Fusarium proliferatum and Fusarium oxysporum. Therefore, gamma irradiation was utilised on Dendrobium hybrid, D5 to produce cultivars that are resistant towards these fungi. Gamma doses of 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 Gray (Gy) were radiated on thin cell layer (TCL) of protocorm* Corresponding author: sreeramanan@usm.my Sangeetha Siva Sangu et al. 130 like bodies (PLB) and were observed for four weeks. Gamma doses of 20 and 30 Gy were optimum for survivability and shoot regeneration of treated PLB. The radio sensitivity (LD50) of the PLB was approximately 63 Gy. Histology and scanning electron microscopy (SEM) analysis showed prominent cell damage and alteration in surface morphology in gamma irradiated PLB. The number of stomata and stomatal aperture decreased where it was 15.47 ± 8.16 μm at 10 Gy but reduced to only 8.24 ± 3.91 μm at 90 Gy. Direct amplification of minisatellite DNA (DAMD) showed high degree of polymorphism in these PLB. When challenged with spore suspension of these fungi, plantlets radiated with 20 and 30 Gy of gamma irradiation showed the least disease symptom, thus, proving resistance towards these fungi. Therefore, this study is a preliminary screening study where the optimum doses of gamma irradiation were selected based on the reaction of radiated TCL of PLB towards the mutagen.
An Overview of Electromagnetic Band-Gap Integrated Wearable Antennas

The recent advancement in the wireless technology has led to the advent of wearable antennas. These antennas are utilized for Wireless Body Area Networks (WBANs) purposes such as health-care, military sportive activities and identification systems. Compared to conventional antennas, wearable antennas operate in an environment which is in highly near proximity to the curved human body. Therefore, the wearable/flexible antenna performance parameters, including reflection coefficient, bandwidth, directivity, gain, radiation characteristic, Specific Absorption Rate (SAR), and efficiency are anticipated to be influenced by the coupling and absorption by the human body tissues. In addition, an electromagnetic band-gap structure is introduced in the wearable/flexible antenna designs to enable and give a high degree of isolation from the human body which also decreases the SAR dramatically. This paper reviews the state-of-the-art wearable/flexible antennas integrated with the electromagnetic band-gap structure on flexible materials concentrating on single and dual-band designs. Besides, it also highlights the challenges and considerations for an appropriate wearable/flexible antenna.
I/O Schedulers for Proportionality and Stability on Flash-Based SSDs in Multi-Tenant Environments

The use of flash based Solid State Drives (SSDs) has expanded rapidly into the cloud computing environment. In cloud computing, ensuring the service level objective (SLO) of each server is the major criterion in designing a system. In particular, eliminating performance interference among virtual machines (VMs) on shared storage is a key challenge. However, studies on SSD performance to guarantee SLO in such environments are limited. In this paper, we present analysis of I/O behavior for a shared SSD as storage in terms of proportionality and stability. We show that performance SLOs of SSD based storage systems being shared by VMs or tasks are not satisfactory. We present and analyze the reasons behind the unexpected behavior through examining the components of SSDs such as channels, DRAM buffer, and Native Command Queuing (NCQ). We introduce two novel SSD-aware host level I/O schedulers on Linux, called A+CFQ and H+BFQ, based on our analysis and findings. Through experiments on Linux, we analyze I/O proportionality and stability in multi-tenant environments. In addition, through experiments using real workloads, we analyze the performance interference between workloads on a shared SSD. We then show that the proposed I/O schedulers almost eliminate the interference effect seen in CFQ and BFQ, while still providing I/O proportionality and stability for various I/O weighted scenarios.
Modelling the Turbulent Flow Inside a FGD Tower Model Using OpenFOAM

This paper deals with the CFD simulation of the flow inside a channel. The model of the fluid flow contains the turbulence modelling using RANS models, especially three different modifications of the k-epsilon turbulence models. The numerical study is based on the experimental setup, used in the research and development of the CFD solver for modelling of the flue gas desulphurization devices. The aim of this study is to compare modifications of turbulent models and show the possibilities of their using for the developed solver. The main results of the research are velocity profiles inside the channel. This paper is focused only to the CFD simulations. The obtained velocity profiles illustrate the differences which correspond to the used turbulent model modification. On the other hand, it is not possible to clearly state which model is correct without the comparison with the experiment. Therefore, the authors assume to make an experimental research and after that to compare the results of CFD with experimental data before the final judgement of the suited model.
Design and Implementation of a high speed 4bit ALU using BASYS3 FPGA Board

An Arithmetic Logic Unit (ALU) is an integral part of a computer processor. It is one of the most frequently accessed modules in a CPU and is utilized during most instruction executions. It has the capability of performing no. of different arithmetic and logic operations such as addition, subtraction, multiplication, bit-shifts and different logic operations. This paper primarily deals with the construction of Arithmetic Logic Unit (ALU) using Xilinx VIVADO 2016.2 and implement them on Field Programmable Gate Arrays (FPGAs) to analyze the design parameters. The main objective of designing the ALU is to develop algorithms in order to achieve an efficient utilization of the available hardware. The measures of the efficiency of an algorithm are speed improvement, less power consumption and better utilization of ALU. In this paper, we have simulated and synthesized the various parameters of ALUs by using VERILOG on Xilinx Vivado2016.2 and BASYS3 FPGA board [1].