Miracle-dz/UniFAR-FTUs huggingface dataset:
Aspects of Next Generation Sensor/Radar Networks

Distributed sensor/radar networks offers several benefits over monostatic systems. For instance, as targets are observed from different aspects angles the detection performance and estimation accuracy of position and speed (Doppler) is improved. Furthermore, a sensor network is able to cope with an increased number of targets. Due to this the interest in such systems has increased over the past years. Moreover, steep improvements in technology and data processing have generated new aspects and fields of application to emerge.
Hierarchical anomaly based intrusion detection and localization in IoT

In IoT systems, WSNs and Gateways are exposed to many attacks. WSNs are usually exposed to different types of intrusions like node compromise and denial of service attacks. IoT gateways that connects WSN to the Internet are exposed to all conventional IP attacks. In this paper, we propose an anomaly detection approach using support vector machines (SVM) for WSN intrusion detection, and deep learning technique for gateway intrusion detection. We propose a detection protocol that dynamically executes the on-demand SVM classifier in a hierarchical way whenever an intrusion is suspected. We combine machine learning classification with a statistical approach for malicious node localization. This novel approach allows finding a compromise between intrusion detection efficiency and resource overhead for WSN and gateway security.
Trajectory Planning for Moving Target Tracking from a UAV

This paper presents an optimal control-based solution to the problem of standoff tracking of a moving ground target using a single UAV with limited motion and sensing capabilities. The UAV is a tactical fixed-wing aircraft equipped with a gimbaled camera. The resulted optimal trajectory minimizes the weighted sum of the control effort and the slant range error between the UAV and the ground target. The roll angle is used as the control variable. Firstly, a receding horizon method, which requires a short amount of future ground target movement knowledge, is proposed to solve the trajectory planning problem. Secondly, a more efficient approach is evaluated by discretizing the continuous roll angle for a real-time and easy on-board implementation.Numerical simulations are performed to verify the feasibility and benefit of the discretized trajectory planning approach. The results show trajectory solutions that are similar to the optimal ones with significantly less computational effort, exhibiting robustness to wind and disturbances. When contrasted with other guidance strategies, it outperforms heuristics solutions since it takes benefits of future target location knowledge.
A Seven Bit Silicon Optical True Time Delay Line for Ka-Band Phased Array Antenna

A seven-bit optical true time delay unit with a delay tuning increment of 1.42 ps was designed and fabricated on silicon photonics platform, which can be utilized in Ka-band phased array antenna systems. The delay unit consists of eight thermo-optical switches and seven waveguide delay lines with different lengths. By manipulating the states of the thermo-optical switches, 128 stages of discrete time delays varying from 0 to 191.37 ps has been obtained with low average power consumption of 178.77 mW. Besides, the integrated delay unit was fabricated on the silicon-on-insulator chip that has a very compact footprint of 7.4 mm × 1.8 mm. In addition, as the waveguide delay dependence on the modulation frequency is very limited and can be neglected, the proposed delay unit should process a large bandwidth that allows it to be utilized in microwave applications with abundant bandwidth.
Application of Non-Orthogonal Multiple Access in Wireless Sensor Networks for Smart Agriculture

Agriculture is one of the main economic industries of a country. Application of information technologies in agriculture, smart agriculture, aims to realize precision control of irrigation, fertilizer, diseases, and insect pests prevention in the growing of crops. For the sake of obtaining the interest data, wireless sensor networks (WSNs) are used to collect the interest data in the farm field and send the obtained data to the servers via wireless communication. Since the WSNs usually operate in the unlicensed spectrum, the available resource elements (REs) are scarce especially when a large number of sensor nodes are deployed in the farm field. To accommodate more sensor nodes and prolong the lifetime of the WSNs in agriculture, relay-aided non-orthogonal multiple access is introduced into the uplink transmission stage of the direct transmission from the sensor nodes to the sink node. Non-orthogonal multiple access (NOMA) can transmit multiple symbols simultaneously on the same RE by splitting them in the power domain and distinguish them according to diverse power levels of different symbols. The average sum data rate and outage probability of the relay-aided NOMA in uplink transmission are theoretically analyzed. The numerical simulation results show that the WSNs with relay-aided NOMA outperforms the traditional OMA scheme in uplink transmission in WSNs in agriculture.
Rigorous Error Modeling for sUAS Acquired Image-Derived Point Clouds

The use of small unmanned aircraft systems to collect 3-D topographic data for a variety of geophysical applications has exploded in the last decade. These data are often produced from imagery using structure-from-motion (SfM) algorithms with or without ground control. While the accuracy of these data products has been assessed in a number of efforts, comparatively little focus has been placed on estimating spatially varying ground-space uncertainties in final data products using rigorous error propagation methodology and uncertainty estimation. Uncertainty estimates of the final 3-D products would provide users a more complete understanding of the quality of the products and therefore the applicability of the product. This paper illustrates an end-to-end workflow, using direct geopositioning and SfM techniques to create 3-D topographic point clouds with rigorously propagated accuracy estimates. Included is a method to quantify uncertainty in dense matching and incorporate those contributions into the final ground covariance. The uncertainty estimates are captured as metadata in the point cloud file using standard formats for exploitation. These estimates are critical to the downstream analysis, exploitation, and fusion of data sets; especially in scenarios where ground truth is not an option. The testing of this end-to-end process is described; data from a field campaign were processed without using mensurated control, and the resultant point clouds were compared to surveyed points to evaluate the predicted accuracy. Predicted accuracies bounded observed errors in the horizontal and vertical components. Future enhancements are also proposed.
Indoor Localisation of IoT Devices by Dynamic Radio Environment Mapping

Radiolocation within indoor environments is a challenge due to the dynamic variations caused by movement of objects and the complex multipath radio propagation. Therefore techniques that are suitable for indoor scenarios need to be robust to the multipath effects, which leads to additional power consumption and complexity. For instance, Ultra-wideband transmission can overcome multipath impact at the expense of requiring at least three access points in range with high bandwidth transmission. Such approaches are not feasible in commodity and low power IoT device tracking using legacy transmitter devices, such as WiFi or Bluetooth. Therefore, the proposed approach to address this problem is to selectively use Angle of Arrival (AoA) capabilities in access points, with the need for only two, to adaptively take measurements as compatible devices move around the environment in order to update radio maps used for optimising this process. In doing so important inferences about the dynamic environment can be exploited to adapt the measurement intervals to reduce the measurements and processing required and conserve power.
Software System of Multipath Adaptive Routing in Networks of Distributed Data Centers

Fast growth and development of information systems and technologies associated with the transfer and processing of big data set enterprises to use data centers (DC). To solve the problems of adaptive routing in networks of distributed DC and to reconfiguration theirs parameters and structures new adaptive control and data processing algorithms are applied, as well as multipath adaptive routing algorithms. In this work a mathematical model and enhanced algorithm of multipath adaptive routing in networks of distributed data centers are proposed. On the basis of the proposed algorithm a software system for visualizing the processes of data flows management in DC networks is developed.
Unified Topic-Based Semantic Models: A Study in Computing the Semantic Relatedness of Geographic Terms

Over the last decades, a multitude of semantic relatedness measures have been proposed. Despite an extensive amount of work dedicated to this area of research, the understanding of their foundation is still limited in real-world applications. In this paper, a unifying approach representing topic-based models is proposed and from which the state-of-the-art semantic relatedness measures are divided into two distinct types of topic-based and ontology-based models. Regardless of extensive researches in the field of ontology-based models, topic-based models have not been taken into account considerably. Consequently, the unified approach is able to highlight equivalences among these models and propose bridges between their theoretical bases. On the other hand, presenting a comprehensive unifying approach of topic-based models induces readers to have a common understanding of them despite the differences and complexities between their architecture and configuration details. In order to evaluate topic-based models in comparison to ontology-based models, comprehensive experiments in the application of semantic relatedness of geographic phrases have been applied. Empirical results have demonstrated that not only topic-based models in comparison to ontology-based models confront with fewer restrictions in the real world, but also their performance in computing semantic relatedness of geographic phrases is significantly superior to ontology-based models.
Two Dimensional Markov Chain Approximation for MPTCP over HetNets: Performance Evaluation

In multimedia delivery, especially for mobile users, the Multi-Path Transmission Control Protocol (MPTCP) can ameliorate the sustainability of multimedia sessions continuity. MPTCP establishes a number of sub-flows equals to the number of the available wireless active connections so, the chance of service interruptions decreases. This paper investigates the performance evaluation of using MPTCP in heterogeneous wireless networks (HetNets). Through this evaluation, we propose two dimensional Markov model for MPTCP seamless handover between two wireless connections. Then, we implement the handover algorithm using MPTCP open source Linux Kernel. Moreover, an implementation for Intelligent Transportation System (ITS) platform is conducted in order to valorise the MPTCP aspect in such real time platforms. The overall results show that MPTCP outperforms classical methodologies of handover.
A Perceptually Inspired New Blind Image Denoising Method Using $L_{1}$ and Perceptual Loss

Recently, discriminative learning-based denoising methods have received much attention and have been studied to a large extent because of their high denoising performance with significantly shorter inference time compared to model based denoising methods. In this paper, we consider a perceptually motivated blind image denoising problem, which removes various levels of noise from an observed noisy image with a single model and produces visually pleasant images. It is well known that very few blind image denoising methods are available and they have shown the limited quality of restored images because they sacrifice the fine image details during the denoising process due to over-smoothing, resulting in visually unpleasant images. To overcome these problems, we propose a novel loss function that encourages the network to restore noise free images by focusing on the perceived visual quality. Then, the proposed loss function is adopted to an encoder-decoder network with skip connections that produces visually pleasant images with highly preserved fine image details. Further, this method is robust and constantly performs well for several unknown noise levels. Our extensive experimental results show the superiority of the proposed method both quantitatively and qualitatively.
CUPID pre-CDR

CUPID is a proposed future tonne-scale bolometric neutrinoless double beta decay ($0\nu\beta\beta$) experiment to probe the Majorana nature of neutrinos and discover Lepton Number Violation in the so-called inverted hierarchy region of the neutrino mass. CUPID will be built on experience, expertise and lessons learned in CUORE, and will exploit the current CUORE infrastructure as much as possible. In order to achieve its ambitious science goals, CUPID aims to dramatically reduce the backgrounds in the region of interest introducing a high efficiently $\alpha$/$\beta$ discrimination techniques, also demonstrated by the CUPID-0 and CUPID-Mo experiments, and using a high transition energy double beta decay nucleus, $^{100}$Mo. This document describe the main concepts related with the design of the CUPID experiment and indicates the projected sensitivities and the global scientific goal of the experiment.
Genetic Algorithms Using Hadoop MapReduce

Data-Intensive Computing (DIC) played an important role for large data set utilizing the parallel computing. DIC model shown that can process large amount of data like petabyte or zettabyte day to day. So these have some sorts of attempts to checkout that how DIC will support the Evolutionary(Genetic) Algorithms. Here we have shown step by step explanation that how the Genetic Algorithms(GA), with different implementation form, will be interpret into Hadoop MapReduce framework. Here the results will give details as how Hadoop is best choice to impel genetic algorithm on large dataset problem and shown how the speedup will be increased using parallel computing. MapReduce is designed for large volume of data set. It is introduced for BigData Analysis and it is used a lots of algorithms like Breadth-First Search, Traveling Salesman problems, Finding Shortest Path problem etc. In this framework two key factor, Map and reducer. The Map which is parallely divided the data into many cluster and each cluster the data is form of key and value. The output of map phase data will goes into intermediate phase where data will be shuffling and sorting. Then using the partitioner for dividing the data parallely in different cluster according to the user. The number of cluster are depends on the number of reducers. The reducers will taking all iteration of data give the results in form of values. In This thesis we also show that we compare our implementation with implementation presented in existing model. These two implementation are compare with ONEMAX (bit counting) PROBLEM. The comparison criteria between two implementation are fitness convergences, stability with fixed number of node, quality of final solution, cloud resource utilization and algorithms scalability.
Spectral Lag for a Radiating Jet Shell with a High-energy Cutoff Radiation Spectrum

Recent research shows that the spectral lag is closely related to the spectral evolution in gamma-ray bursts (GRBs). In this paper, we study the spectral lag for a radiating jet shell with a high-energy cutoff radiation spectrum. For the jet shell with a cutoff power-law spectrum, the spectral lag monotonically increases with the photon energy and levels off at a certain photon energy. It is the same for the jet shell with a Band cutoff spectrum (Bandcut). However, a turnover from the positive lags to negative lags appears in the high-energy range for the jet shell with a Bandcut, which is very similar to that observed in GRB 160625B. The dependence of the spectral lags on the spectral shape/evolution is studied in detail. In addition, the spectral lag behavior observed in GRB 160625B is naturally reproduced based on our theoretical outcome.
LinTO : Assistant vocal open-source respectueux des données personnelles pour les réunions d'entreprise

This paper presents the first results of the PIA "Grands D\'efis du Num\'erique" research project LinTO. The goal of this project is to develop a conversational assistant to help the company's employees, particularly during meetings. LinTO is an interactive device equipped with microphones, a screen and a 360$^\circ$ camera, which allows to control the room, query company's information system, helps facilitate the meeting and provides an environment to aid minute writing. Distributed according to an open model that respects private data LinTO is the first open-source enterprise's assistant designed to comply with the GDPR requirements.
Creating photo-realistic works in a 3D scene using layers styles to create an animation

Creating realist objects in a 3D scene is not an easy work. We have to be very careful to make the creation very detailed. If we don't know how to make these photo-realistic works, by using the techniques and a good reference photo we can create an amazing amount of detail and realism. For example, in this article there are some of these detailed methods from which we can learn the techniques necessary to make beautiful and realistic objects in a scene. More precisely, in this paper, we present how to create a 3D animated scene, mainly using the Pen Tool and Blending Options. Indeed, this work is based on teaching some simple ways of using the Layer Styles to create some great shadows, lights, textures and a realistic sense of 3 Dimension. The present work involves also showing how some interesting ways of using the illuminating and rendering options can create a realistic effect in a scene. Moreover, this article shows how to create photo realistic 3D models from a digital image. The present work proposes to present how to use Illustrator paths, texturing, basic lighting and rendering, how to apply textures and how to parent the building and objects components. We also propose to use this proposition to recreate smaller details or 3D objects from a 2D image. After a critic art stage, we are able now to present in this paper the architecture of a design method that proposes to create an animation. The aim is to create a conceptual and methodological tutorial to address this issue both scientifically and in practice. This objective also includes proposing, on strong scientific basis, a model that gives the possibility of a better understanding of the techniques necessary to create a realistic animation.
Modeling and Performance Analysis of Spatially Distributed LTE-U and Wi-Fi Networks

To access an unlicensed channel Wi-Fi follows Listen Before Talk (LBT) mechanism whereas LTE-U adopts ON-OFF duty cycled mechanism to fairly share the channel with Wi-Fi. These contrasting mechanisms result in quite different performance for Wi-Fi and LTE-U based on their relative deployment and density in the environment. In this work, we present an analytical model for characterization of achievable throughputs of Wi-Fi and LTE-U networks in spatially distributed high-density scenarios. The proposed model is used to study how LTE-U and Wi-Fi coexist with each other in different deployment scenarios. Our extensive simulation results prove it to be a reliable model for estimating throughput of both Wi-Fi and LTE-U. We record a very good accuracy in throughput estimation and the mean normalized error is less than 1% for 40-node scenario in which 50% of nodes belong to each of Wi-Fi and LTE-U. Finally, we use the analytical model to conduct coexistence studies of LTE-U and Wi-Fi.
Density Conversion between 1D and 3D Stellar Models with 1DMESA2HYDRO3D

We present 1DMESA2HYDRO3D, an open-source, Python-based software tool that provides an accessible means of generating physically motivated initial conditions (ICs) for hydrodynamical simulations from 1D stellar structure models. We test 1DMESA2HYDRO3D on five stellar models generated with the MESA stellar evolution code and verify its capacity as an IC generator with the Phantom smoothed particle hydrodynamics code. Consistency between the input density profiles, the 1DMESA2HYDRO3D-rendered particle distributions, and the state of the distributions after evolution over 10 dynamical timescales is found for model stars ranging in structure and density from a radially extended supergiant to a white dwarf.
Quantum Computing: Lecture Notes

This is a set of lecture notes suitable for a Master's course on quantum computation and information from the perspective of theoretical computer science. The first version was written in 2011, with many extensions and improvements in subsequent years. The first 10 chapters cover the circuit model and the main quantum algorithms (Deutsch-Jozsa, Simon, Shor, Hidden Subgroup Problem, Grover, quantum walks, Hamiltonian simulation and HHL). They are followed by 2 chapters about complexity, 4 chapters about distributed ("Alice and Bob") settings, and a final chapter about quantum error correction. Appendices A and B give a brief introduction to the required linear algebra and some other mathematical and computer science background. All chapters come with exercises, with some hints provided in Appendix C.
On equivariantly uniform stability and Yau-Tian-Donaldson conjecture for singular Fano varieties

Let $X$ be a $\mathbb{Q}$-Fano variety and ${\rm Aut}(X)_0$ be the identity component of the automorphism group of $X$. Let $\mathbb{G}$ denote a connected reductive subgroup of ${\rm Aut(X)}_0$. We prove that if $X$ is $\mathbb{G}$-uniformly K-stable, then it admits a Kahler-Einstein metric. The converse of this result holds true if $\mathbb{G}$ is a maximal torus of ${\rm Aut}(X)_0$, or is equal to ${\rm Aut}(X)_0$ itself. These results give the first equivariantly uniform versions of Yau-Tian-Donaldson conjecture for arbitrary singular Fano varieties. A key new ingredient is a valuative criterion for the $\mathbb{G}$-uniform K-stability.
Semi-device-independent information processing with spatiotemporal degrees of freedom

Nonlocality, as demonstrated by the violation of Bell inequalities, enables device-independent cryptographic tasks that do not require users to trust their apparatus. In this article, we consider devices whose inputs are spatiotemporal degrees of freedom, e.g. orientations or time durations. Without assuming the validity of quantum theory, we prove that the devices' statistical response must respect their input's symmetries, with profound foundational and technological implications. We exactly characterize the bipartite binary quantum correlations in terms of local symmetries, indicating a fundamental relation between spacetime and quantum theory. For Bell experiments characterized by two input angles, we show that the correlations are accounted for by a local hidden variable model if they contain enough noise, but conversely must be nonlocal if they are pure enough. This allows us to construct a "Bell witness" that certifies nonlocality with fewer measurements than possible without such spatiotemporal symmetries, suggesting a new class of semi-device-independent protocols for quantum technologies.