Miracle-dz/UniFAR-FTUs huggingface dataset:
Outage-Constrained Secrecy Energy Efficiency Optimization for CRNs With Non-Linear Energy Harvesting

In this paper, we investigate a robust transmit design in underlay energy harvesting (EH) cognitive radio networks (CRNs), where the malicious energy receivers (ERs) can be treated as potential eavesdroppers (Eves) due to their ability of wiretapping the desired information. Considering a general case of statistical Eves’ channel state information (CSI), to transmit signals in a secret and energy-efficient manner, an outage-constrained secrecy energy efficiency (OC-SEE) optimization problem is formulated to design transmit beamforming vectors. The original problem is non-convex and challenging to solve. To make it more tractable, we first rewrite this problem by semi-definite relaxation (SDR), and Bernstein-type inequality (BTI) approach is resorted to conservatively approximate the probabilistic constraints. Then, based on the fractional programming theory, an equivalent parametric reformulation is proposed to convert the original problem into a subtractive form. Furthermore, by introducing auxiliary variables, with the aid of Sphere Bounding method, the tractable solution of this OC-SEE maximization problem can be obtained through solving a two-stage optimization problem. Finally, numerical results are provided to validate the efficacy of the proposed design.
Method and Analysis of TOA-Based Localization in 5G Ultra-Dense Networks with Randomly Distributed Nodes

With the ever-growing number of networked devices and a higher likelihood of having line-of-sight communication most of the time, location estimation of a blind node in the 5th generation Ultra-Dense Networks (UDNs) system has gained considerable attention in recent years. One of main factors for accurate location estimates in 5G UDN is node randomness. Several location methods and their performance analyses have been addressed for localization in 5G UDN. Although the distribution of reference nodes (RNs) is considered in the literature, the information on spatial node distribution is only used to evaluate the average performance and is not utilized in the location methods. In this paper, a Cramer–Rao lower bound (CRLB) and three location estimators including the iterative, closed-form, and hybrid algorithms are proposed for localization in 5G UDN with randomly distributed RNs. Both range measurements and prior information on spatial node distribution are utilized for the proposed location methods and CRLB. Moreover, some characteristics of the CRLB for 5G UDN localization are derived in this paper. Detail comparison between the proposed CRLB and the previous performance study on CRLB for 5G UDN is given. Theoretical analysis proves that the proposed CRLB for the case with randomly distributed RNs is smaller than the average CRLB for the case with fixed location RNs. The top and bottom bounds of the proposed CRLB in the cases with low and high signal noise ratios are also given. Performance evaluation shows that the proposed methods perform better than the conventional methods only based on range measurements and can asymptotically attain the CRLB.
Review of Power Spatio-Temporal Big Data Technologies for Mobile Computing in Smart Grid

Mobile computing adds computing, storage, processing and other functions in wireless network ends to provide customized and differentiated services, so that it can be widely applied in the fields of internet of things, video, medical treatment, retail and so on. Recently, power spatio-temporal big data (PSTBD) technology of smart grid based on mobile computing has experienced explosive growth. This paper emphasizes the specific requirements, technologies, applications, and challenges of the current PSTBD for mobile computing in smart grid. Based on current development status of PSTBD companies in representative countries in the world, we introduce PSTBD technology based on the characteristics of mobile computing based smart grid, and conduct a comprehensive investigation and analysis of relevant articles in this field. After comparing the differences between the traditional and the PSTBD based platform in the aspects of important features, platform goal, and platform architecture, we describe the key technologies and algorithms of the current PSTBD in detail. Then, based on the requirements of each link and field of the power grid, the typical application of PSTBD technology in various aspects of smart grid application based on mobile computing is discussed. Finally, the development direction and challenges of PSTBD are given. Through data analysis and technical discussion, it hopes to provide technical support and decision support for relevant practitioners in the PSTBD field.
Depression Prevalence in Postgraduate Students and Its Association With Gait Abnormality

In recent years, an increasing number of university students are found to be at high risk of depression. Through a large scale depression screening, this paper finds that around 6.5% of the university postgraduate students in China experience depression. We then investigate whether the gait patterns of these individuals have already changed as depression is suggested to associate with gait abnormality. Significant differences are found in several spatiotemporal, kinematic and postural gait parameters such as walking speed, stride length, head movement, vertical head posture, arm swing, and body sway, between the depressed and non-depressed groups. Applying these features to classifiers with different machine learning algorithms, we examine whether natural gait analysis may serve as a convenient and objective tool to assist in depression recognition. The results show that when using a random forest classifier, the two groups can be classified automatically with a maximum accuracy of 91.58%. Furthermore, a reasonable accuracy can already be achieved by using parameters from the upper body alone, indicating that upper body postures and movements can effectively contribute to depression analysis.
DeepAir: Air Quality Prediction using Deep Neural Network

Air pollution is a common cause for several major health hazards and climate change. Air quality predictors are required to plan human activities in a given geographic locality and minimize the negative effects of pollution. We look at the problem of predicting a sequence of future pollutant concentrations. We propose a solution based on Long Short-Term Memory (LSTM) networks, which are deep neural networks that are known to perform well on sequential prediction problems. We conduct adequate number of experiments to select the best possible features and perform hyperparameter optimization using the Python package Talos. We employ the root mean square error (RMSE) as our performance measure. The presented predictor achieves an RMSE of less than 1.2 for the pollution dataset obtained from Indira Gandhi International Airport at New Delhi, India. To the best of our knowledge, this is the highest level of prediction accuracy reported, so far in the literature, for this dataset.
Design and Characterisation of Simple Planar Metamaterial Structure with Double Negative Properties

This paper proposes a novel planar metamaterial structure which displays double negative (DNG) properties in popular bands such as S and C. DNG refers to materials which show simultaneously negative permeability and permittivity. The proposed structure is developed through simulation using HFSS and its characterization is done through various methods such as S-parameter retrieval method and cavity perturbation method. The results of simulated and measured S parameters are used for the retrieval of various material properties. Further verification of obtained results is carried out through cavity perturbation, and the observed results using various methods complement each other confirming the DNG behavior of the newly designed material.
On-Chip Inductor for Millimeter-wave Regime

We discuss transmission line inductor, single coil inductor, 2.5-turn inductor and multi-layer inductor, which can be used in millimeter-wave circuits. We present a multi-layer inductor implemented in 0.13 μm one-poly and seven-metal (1P7M) BiCMOS SiGe process and propose a modified double-π model for on-chip multi-layer inductor. Self-resonance frequency (SRF) beyond 100 GHz were obtained based on the measurement results, demonstrating that the multi-layer inductor can be used for millimeter-wave applications with compact size and high inductance density.
New Tools and Methods in Selection of Air Traffic Controllers Based on Multimodal Psychophysiological Measurements

Comprehensive multimodal psychophysiological measurements and smart data analysis based on wearable and low-cost technologies could enhance traditional air traffic controller (ATC) selection process. Many recent studies in neuro-cognitive science and stress resilience illustrated effectiveness of these multimodal measurements and appropriate metrics in comprehensive assessment of ATCs’ mental states, such as cognitive workload, cognitive decline, attention deficit, fatigue, emotional and behavioural problems, etc. Accordingly, this article is focused on innovation efforts in ATC selection protocols based on a set of comprehensive stimuli and corresponding multimodal psychophysiological measurements. The concept of enhancement of ATC selection process presented in this article includes complex physiological, oculometric and speech measurements and appropriate metrics. From these multimodal measurements during specific stimulation tasks, which include different versions of acoustic startle stimuli, airblasts, semantically relevant aversive images and sounds, different versions of Stroop tests, visual tracking test, a complex set of multimodal-multidimensional features is computed as predictors of ATC candidates’ future performance, like: stress resilience, workload capacity, attention, visual performance, working memory etc. Such cost-effective, more objective, non-invasive preliminary measurements, lasting no longer than 45 minutes may have good discriminative power and might be used in ATC selection processes as enhancement of current selection procedures. Comprehensive analysis of presented multimodal features during different experimental conditions might also be very useful in selection processes of other stressful professional jobs, like first responders, pilots, astronauts etc.
Influence-Time-Proximity Driven Locations Recommendation Model: An Integrated Approach

Location Based Social Networks (LBSNs) like Twitter, Foursquare or Instagram are a very good source of extracting human generated data in the form of check-ins, location and social relationships among users. Location data bridges the gap between the physical and digital worlds and enables a deeper understanding of users preference and behaviour. There are many underlying patterns in this type of dataset of human mobility which are utilised for applications like recommender systems. The current approaches involve extracting data from user-item rating, GPS trajectories, or other forms of data, whereas we focus on an integrated model considering factors like user's interest, social influence, time and proximity. There is metadata associated with this dataset which tells us about the whereabouts of the user, with emphasis on types of places. This paper proposes an integrated location recommendation model that considers users' interests, their friends influences, time and seasonality factors, and users' willingness to visit distant locations. We integrate all these parameters to generate a ranked list of locations which will be recommended to the user. Experiments are performed on a real-world dataset which show that our proposed model is effective in the stated conditions.
Performance estimation of Thulium doped fiber amplifiers in S-Band including Amplified Spontaneus Emission: Ion-Ion interaction analysis

Optical amplification and communication can be potentially provided by Thulium-doped fiber amplifiers (TD-FAs) in S and near-C bands (1460 to 1565) nm. They can prove to be a worthy alternative to existing Erbium-doped fiber amplifiers (EDFAs) for which communication focuses at 1550 nm in dense wavelength division multiplexing (DWDM) systems. Optical conversion efficiency of TDFAs is dependent on parameters like Thulium ion ($Tm^{3+}$) concentration, pump power, fiber length, dopant area, signal wavelength, and ion-ion interaction mechanisms (IM). This work primarily focus on theoretical modeling and simulation of IM effects involving Homogeneous up-conversion (HUC) and Pair induced quenching (PIQ) on TDFA gain characteristics. The beneficial aspects of IM effects were perceived in selection of optimum pump power (450 mW)and TDFA gain (20.32 dB). This Paper also includes the findings on optimum doping radius ($1\ \mu m$), TDFA length (100 $m$), and an added advantage of single wavelength (1050 nm) pump.
Implmeneting a Fuzzy Logic Controller to Improve the Performances of an Off-Grid Photovoltaic Generator

In this work, a power generation system based on a PV module disconnected from the grid has been developed. In which a solar controller MPPT based on the perturbation and observation algorithm is developed using Fuzzy Logic Controllers (Fuzzy Logic Controllers, FLC) approach. The efficiency of this control method is confirmed by simulation under Matlab Simulink.
Low Speed Estimation of Sensorless Direct Torque Controlled Induction Motor Drive Using Extended Kalman Filter with Adaptive Speed Adjustment Technique

This paper mainly focuses on the accurate estimation of speeds ranging from very low speed to rated speed to control sensorless Direct Torque Controlled (DTC) Induction Motor Drives (IMD). Extended Kalman Filter (EKF) is used to estimate the speed from the noisy data based on state space method and recursive algorithm. Previous studies suggest the development of a new state space model for estimation in EKF, with load torque as an input variable and not as an estimated quantity. In order to eliminate the dependency on load torque, this paper projects the derivation of a novel adaptive speed adjustment equation and its use for speed estimation in EKF. The new state space model developed is validated using MATLAB-Simulink platform for speeds ranging from low speed to rated speed at rated torque and various other torque conditions.
Cooperative Behavior by Multi-agent Reinforcement Learning with Abstractive Communication

Reinforcement learning (RL) is a major area of machine learning that aims to develop intelligent agents that are able to adapt in random environments appropriately. In this regard, RL has shown good results when applied to complex tasks such as playing video games. In addition, in multi-agent environments, RL has shown strong potential especially with the recent developments. However, there exist few studies that focus on developing cooperation among learning agents. In general, cooperative behavior among learning agents shows higher performance than independent agent behavior. Therefore, in this research, we focus on the cooperative behavior on Predator-Prey game in a continuous space, which is widely used as one of the typical simulation of Multi-agent environment. Especially we focus on predators that their goal is to catch a prey. We propose Leader-Follower model as the organization of predators, and investigate how they cooperate with each other to achieve their goal considering the prey’s policy using a model of RL. The results of our work indicate that a communication between Leader and Followers affects high performance. In addition, we acquire an interesting result as a process of achieving their goal. We investigate the movement locus of them in three cases which is different reward settings, and in each case, they take different policy depending on the reward. We visualize the movement of locus, and discuss about their cooperation and effectiveness.
Emergency Management using Social Networks

The popularity of social networks make them most efficient to integrate into the Emergency Management process. Posts on social networking sites can help people by ensuring timely detection of an emergency. Often during the situations of a natural disaster, there is an information chasm created between the affected and the unaffected area that further compounds the confusion and chaos. In this paper, we examine the various challenges that exist while attempting to integrate social networks and Emergency Management and trace the state-of-art techniques that exist in various domains that come together for this Emergency Management system. We propose an end-to-end framework that takes public posts from social networking sites and converts it into a structured format that makes the information actionable. A summarization technique may be applied to the acquired information post mining of social media feed to convert everything into a text message that can be released into various social platforms. To increase the reach of this post and to warrant better public participation in the crisis in a timely manner, we apply influence maximization techniques and monitor the diffusion process of this generated post through a diffusion modelling technique that we propose. We conduct experiments to analyze the performance of this model and of the influence maximization process and conclude with an analysis of the experiments and the observed results and list out improvements that we intend to incorporate in future versions of this work.
Document Image Classification: Towards Assisting Visually Impaired

Our work aims to enable the visual information in a document to be accessible by the visually impaired or the blind people. The blind people prefer arts over science subjects in higher education because conveying equations and algorithms to them is seen as difficult. They should not be deprived of acquiring knowledge due to physical disabilities. In this work, we identify different types of images in an algorithm textbook as graphs, algorithm images, equations, and network flow diagrams with 98% accuracy. The Convolution Neural Networks used can predict new data instances with 99% accuracy except for recurrence tree images. We want to extend our work to extract the textual information in these images and produce image descriptions as alt text. This can produce a better speech output to assist the blind people.
Optimized Low Density Superposition Modulation for 5G Mobile Multimedia Wireless Networks

The explosive growth of mobile multimedia services and applications are increasing the demand of access ability for the recent 5G networks. Non-orthogonal multiple access (NOMA) techniques have been recently proposed for 5G to improve access efficiency through allowing multiple users to share the same spectrum resources in a non-orthogonal way. Low Density Superposition Modulation (LDSM) is one of the NOMA techniques with potential to support high spectral efficiency and massive connectivity. In this paper, we utilize Bare-bone Particle Swarm Optimization (BBPSO) algorithm with extrinsic information transfer (EXIT) chart to design degree distribution of LDSM. An optimized design of LDSM scheme has better convergence performance compared with conventional sparse code multiple access (SCMA) and pattern division multiple access (PDMA) scheme over fading channel. Furthermore, the designed irregular LDSM can significantly improve the overall system performance and edge user performance under the near-far effect scenario. Simulation results validate that the proposed optimized LDSM can achieve about 1.0 dB gains compared to both SCMA and PDMA schemes.
Cluster Head Selection in Wireless Sensor Network Using Bio-Inspired Algorithm

Routing and Clustering are the two major issues in Wireless Sensor Networks (WSNs) as these measures play a vital role during data transmission. Small battery-powered sensor nodes have an unadorned impact on communication protocols due to severe energy constraints. In clustering, cluster heads are often overloaded with heavy traffic than other members of the cluster. This creates a hotspot problem on a cluster head near to the base station. This is the primary reason to select a proper cluster head in a clustered based routing protocol. In this paper, we have applied the bio-inspired algorithm such as Genetic Algorithm to handle the energy-specific issues in Wireless Sensor Networks. By doing so, we have selected an energy-efficient cluster head that creates an energy-optimized environment leading to a longer Network lifetime. The Proposed Protocol is experimented through a Java-based Custom Simulator, which shows its superiority over traditional computing model in LEACH and K-Means Clustering.
Compact Ka band Bias Tee Network Using Substrate Integrated Coaxial Line technology

This paper presents Ka- band bias tee network designed in a Substrate Integrated coaxial line environment. The proposed circuit operates in the frequency range 31–35 GHz. A series of butterfly stub is used to realize inductor. To implement capacitor, interdigital structure is introduced in the middle layer of SICL. The size of this proposed circuit design is $1.1 \lambda\mathrm{g}\times 0.72 \lambda\mathrm{g}$, where $\lambda\mathrm{g}$ is guided wavelength at centre frequency of 33 GHz. The proposed results exhibit 0.1 dB insertion loss along with DC isolation 22dB with good impedance matching. The proposed circuit is very much suitable for Ka band active components application.
A Soft Metric for Assessing the Compliance of WLAN Devices

The goal of this paper is to derive a soft metric for assessing the compliance of the medium access mechanism of commercially available Wi-FiTM certified units with the IEEE 802.11 standard. The metric is used in conjunction with data collected through a practical testbed based on a Universal Software Radio Peripheral (USRP). The metric derived in this paper captures three compliance aspects: 1- the statistical distribution of the backoff random number that underlies the medium access protocol used by the WiFi Unit Under Test (UUT); 2- the accuracy of the length of the silence period between frames; and 3- the adherence to prescribed transmission opportunity limits set by the standard. The assessment of the first two aspects relies on the Kullback-Leibler distance between probability distributions, whereas the assessment of the third aspect relies on evaluating the probability with which transmission thresholds set by the standards are exceeded. The soft metric proposed in this work is in the form of a single scalar, which enables the UUTs to be ranked based on their level of compliance.
SLAM algorithms implementation in a UAV, based on a heterogeneous system: A survey

Simultaneous localization and mapping (SLAM) is a solid problem in robotics, this problem can be solved using an algorithm dedicated to the resolution of this problem. In this context we find large variety algorithms like FastSLAM2.0, GraphSLAM, EKFSLAM, VoSLAM, ORBSLAM and other. The drone by its ease and speed to implement allows realizing various types of aerial mapping increasingly used in many sectors of professional activities such as building, public works or agriculture. In this study we propose an implementation of the VoSLAM algorithm in a UAV, based on a heterogeneous system using the OpenCL parallel programming. In the agricultural cities, for agricole process parameters controlling, we can use the heterogeneous DE1-Soc architecture of VoSLAM algorithm based on the OpenCL parallel programming. This architecture permits the control in real-time of the humidity based on a treatment of image to transmit thereafter its data.