Miracle-dz/UniFAR-FTUs huggingface dataset:
Survey on Low Power Wide Area Networks in IoT

Communication technologies, aimed at providing effective wireless communication, form one of the cornerstones of the wide adoption of Internet of Things (IoT). Low-Power Wide-Area (LPWA) wireless communication technologies are particularly suited for implementation of Machine-to-Machine (M2M) communication in usage contexts where low data rate and low device mobility are frequently combined with a requirement for (extremely) low-power consumption and long range. In this paper, we identify the main objectives put in front of the LPWA technologies. We follow by describing how those objectives are met in the three most widespread LPWA technologies today: NB-IoT, LoRaWAN, and Sigfox. The usability of the described technologies is summarized for different contexts, based on use-case scenarios from different IoT application domains.
Critical Masses for Unreflected Metal Spheres

Calculated critical masses of bare metal spheres for 28 actinide isotopes, using the SCALE/XSDRNPM one-dimensional, discrete-ordinates system, are presented. ENDF/B-VI, ENDF/B-VII, and JENDL-3.3 cross sections were used in the calculations. Results are given for isotopes of uranium, neptunium, plutonium, americium, curium, californium, and for one isotope of einsteinium. Calculated k values for these same nuclides are also given. We show that, for non-threshold or low-threshold fission nuclides, a good approximation for the nuclide k is the value of nubar at 1 MeV. A plot of the critical mass versus k values is given for 19 nuclides with A-numbers between 232 and 250. The peaks in the critical mass curve (for seven nuclides) correspond to dips in the k curve. For the seven cases with the largest critical mass, six are even-even nuclides. Neptunium-237, with a critical mass of about 62.7 kg (ENDF/B-VI calculation), has an odd number of protons and an even number of neutrons. However, two cases with quite small critical masses, 232U and 236Pu, are also even-even. These two nuclides do not exhibit threshold fission behavior like most other even-even nuclides. The largest critical mass is 208.8 kg for 243Am and the smallest is 2.44 kg for 251Cf.more » The calculated k values vary from 1.5022 for 234U to 4.4767 for 251Cf. A correlation between the calculated critical mass (kg) and the fission spectrum averaged value of is given for the elements U, Np, Pu, Am, Cm, and Cf. For each of the five elements, a fit to the data for that element is provided. In each case the fit employs a negative exponential of the form mass = exp(A + B ~ ln( ) The values of A and B are element dependent and vary slightly for each of the five elements. The method described here is mainly applicable for non-threshold fission nuclides (15 of the 28 nuclides considered in this paper). There are three exceptions, 238Pu, 244Cm, and 250Cf, which all exhibit threshold fission behavior. « less
A Polynomial Model for the System of Systems Graphical Representation

This paper tackles the problematic of multilevel modeling a large-scale integrated systems which are commonly named system of systems. These systems implies the collaboration of several complex independent systems in order to achieve a common goal. Modeling such systems is challenging mainly due to the high computational complexity generated by the representation model. In the present work, we use hypergraphs in order to model the multilevel aspects of SoS. As well, given the high complexity of such model, a decomposition method by means of the hypergraph tree-based acyclicity property is proposed. The approach is formulated as a constraint satisfaction problem, where the purpose is to spot for tractable classes of hypergraph which yields to a resolution in a polynomial time.
Iris Recognition using Convolutional Neural Network

The design of a pragmatic user authentication system is vital to provide accurate detection of personal identity. Iris recognition as a form of biometric identification technology has been actively researched for decades and is gaining wider popularity considering the increasing awareness of personal privacy. The rise of artificial intelligence provides a great opportunity to further elevate the penetration of iris recognition in safeguarding one's private data. Convolutional neural network is a practical algorithm that is highly suitable for image processing and pattern recognition, its effectiveness and flexibility have seen it being applied in many fields. This study focuses on the development of an iris recognition system based on convolutional neural network with high precision and efficiency. A total of iris samples from 20 individuals with both sides of the eyes included are used to train the deep recognition system. The model shows an early sign of underfitting and little convergence with inadequate number of training epoch. However, as the training epochs are increased, the trained model managed to achieve a testing accuracy of 99%.
Globally Optimal Camera Orientation Estimation from Line Correspondences by BnB algorithm

This letter is concerned with the problem of estimating camera orientation from a set of 2D/3D line correspondences, which is a major part of the Perspective-n-Line (PnL) problem. There are some cases that usually occur in real applications for PnL: the input line correspondences are corrupted by mismatches (a.k.a. outlier correspondences). The RANdom SAmple Consensus (RANSAC) algorithm is the de facto standard for solving outlier-contaminated PnL problems. However, RANSAC is a non-deterministic algorithm, which means that it produces a reasonable result only with a certain probability. Therefore, a PnL algorithm that could obtain a certifiably optimal solution from outlier-contaminated data is a matter of priority for some safety-critical applications. In this letter, we take a big step towards this goal by investigating globally optimal camera orientation estimation algorithms. Firstly, we decouple the rotation and translation estimation of a PnL problem by considering the geometrical property of the PnL problem. The Branch-and-Bound (BnB) algorithm is applied and it globally searches the entire rotation space to obtain the optimal camera orientation. To investigate the performance of our method, we tested the proposed algorithm on both synthetic and real data, and the results show that our algorithms can obtain the optimal camera orientation and are more robust than several state-of-the-art PnL methods.
A Dynamic Pricing Model for Virtual Machines in Cloud Environments

In recent years, cloud computing enjoys rapid development as a business service computing model. Being a kind of important services, virtual machines (VMs) play an important role for Infrastructure as a Service (IaaS) in a cloud service environment. Reasonable pricing of virtual machines can reflect a good selling and buying relationship between the supply and the demand. Hence, it is necessary to explore a more ideal pricing model, that is, both service providers and users can achieve their best benefits. However, current methods hardly meet the need. To solve the problem, a dynamic pricing model for virtual machines is proposed, which involves both users’ demand and cloud service providers’ benefits. Based on the model, a hybrid optimization search algorithm is designed. Simulation experimental results show that the proposed model and algorithm outperform its peers, i.e., one based on static pricing and another based on Particle Swarm Optimization, in terms of service providers' profit, user’s demand, and completion time of tasks.
Enhancing Tokenization by Embedding Romanian Language Specific Morphology

this paper addresses the significance, complexity and approaches of Romanian tokenization, the first step in NLP. A strategy is proposed for tokenizing the Romanian text with various specificity: standard (literal language), archaic and medical. The strategy is extending existing tokenization models for English, Spanish and Portuguese with Romanian features. Moreover, we attempt to address the Romanian ambiguities caused by the complexity of the grammar and provide support for further Romanian NLP processing. The approach proved that there is no general solution for tokenization and that diverging tokenization strategies for all languages can lead to considerably better results. With the current approach we obtained a high-quality tokenization on all three Romanian formats as follows: <97.01% precision, 96.84% recall> for standard sources, <98.10% precision, 97.03% recall > for archaic sources and <94.73% precision, 95.36% recall > for medical sources.
Resolution of a Routing and Wavelength Assignment Problem by Independent Sets in Conflict Graphs

In an optical network, a Scheduled Lightpath Demand (SLD) is a connection demand between two nodes, during a certain time and with a certain wavelength. We consider the following NP-hard Routing and Wavelength Assignment problem dealing with SLDs: given a set of SLDs and a number W of wavelengths, maximize the number of SLDs to which we can assign a lightpath (i.e. a routing path and a wavelength) without exceeding the number W of available wavelengths. The constraints are: a same wavelength must be assigned all along the routing path of any SLD; at any time, a given wavelength on a given edge of the network cannot be used to satisfy more than one SLD. To solve this problem, we study an approach stating the problem as the successive searches of independent sets in some conflict graphs. Moreover, we improve this approach thanks to a post-optimization method. The experimental results show that this model and the post-optimization method are quite efficient to provide a large number of routed SLDs.
Behavioral Modeling and STDP Learning Characteristics of a Memristive Synapse

In this work, we study the feasibility of using memristor as a synapse in spiking neural networks. A simple voltage controlled model is selected and the effect of several parameters on the I-V characteristics and memristance are studied. Next, the ability of the device to perform synaptic behaviors has been investigated. The Spike-Timing-Dependent Plasticity (STDP) learning algorithm is applied to a circuit model which contains LIF pre and post-synaptic neurons and the memristor as a synapse. Components are modeled in verilog-A, and the circuit is simulated in HSPICE. Continuous and analog weight change of synapse is demonstrated. The two most important characteristics of the synapse, i.e the maximum voltage over the memristor and the STDP characteristics, have been extracted from simulations. These results confirm that the proposed memristor model is an attractive candidate for complex spiking neural networks.
A Multi-threaded Particle-in-cell Approach for Kinetic Plasma Simulations

Particle-in-cell (PIC) simulations for plasma physics model self-consistently plasma phenomena at kinetic scales. Such simulations focus on the individual trajectories of a very large number of particles in self-consistent and external electric and magnetic fields, and are of great importance in astrophysics and space sciences. We developed a multi-threaded application to simulate such phenomena. This article describes the parallel performance of our application on a high-performance multicore system
A 1.2GHz adaptive bandwidth type-I PLL using a digital lock detector

This paper introduces a 1.2GHz adaptive bandwidth Type-I PLL with wide bandwidth. PLL operates in wide bandwidth mode during transient to have a fast settling time, and in narrow bandwidth mode during phase lock to more efficiently suppress the sidebands at fref, 2fref, etc, at VCO output. The PLL operating mode selection is done utilizing the digital lock detector circuit. The bandwidth of proposed PLL in transient is 5–10 times greater than the bandwidth of traditional Type-I PLL. Operating with a reference frequency of 10MHz, the proposed PLL provides a loop BW of 5MHz in transient, and a spur level of −53dBc. The presented PLL exhibits an in-band phase noise of -118 dBc/Hz at 1MHz offset. PLL total power consumption is 2.5mW.
Bayesian Optimized Long-Short Term Memory Recurrent Neural Network for Prognostics of Thermally Aged Power Mosfets

Power semiconductor switches such as MOSFET are widely used in electric vehicles, electric trains, and power conversion systems. The prognostics and health monitoring (PHM) and reliability of the MOSFETS are critical in power electronic systems. The ON-state resistance of the MOSFETS is the primary and significant precursor of failure. In this paper, we present a data-driven approach to predict the change in ON-state resistance using Long-Short Term Memory (LSTM) Recurrent Neural Network (RNN). The experimental data are obtained by power cycling the MOSFET in thermal stress. The LSTM network's hyperparameters are optimized using Bayesian Optimization, which improves results and reduces time to develop the model. Our proposed Bayesian Optimized LSTM RNN (BO-LSTM RNN) gives good prediction accuracy using less number of layers. The model's performance is also analyzed using a small percentage of data used to train the LSTM RNN. The proposed model is also tested on different MOSFETS degradation data to prove the universality of the model using the same hyperparameters.
Actor-Critic Algorithm for Optimal Synchronization of Kuramoto Oscillator

This paper constructs a reinforcement learning (RL) based algorithm of Actor-Critic (AC) for the optimal synchronism of the Kuramoto oscillator. This is accomplished through the Ott-Antonsen ansatz framework for the dynamics of large interactive unit networks. Besides, this approach reduces the infinite-dimensional dynamics to phase space flow, i.e., low dimensional dynamics for certain systems of globally coupled phase oscillators. The resulting Hamiltonian-Jacobi-Bellman (HJB) expression is extremely difficult to solve in general, therefore this paper introduces the AC method for learning approximate optimal control laws for the Kuramoto oscillator model. RL has been contemplated as one of the efficient methods to solve optimal control of non-linear systems. For a collection of non-homogeneous oscillators, the states are elucidated as phase angles, which is the modification of the model for a coupled Kuramoto oscillator. An admissible initial control policy for the Kuramoto oscillator model is designed and solved using RL giving an approximate solution of the optimal control problem. Finally, local synchronism of the coupled Kuramoto oscillator model is supported through simulations analysis.
Control System Synthesis Based on Optimal Trajectories Approximation by Symbolic Regression for Group of Robots*

The paper considers the solution of the problem of optimal control system synthesis. It is proposed to solve this problem based on the approximation of the set of optimal trajectories using symbolic regression methods. At the first step the optimal control problem is solved for various initial states; at the second step symbolic regression method is used to approximate the obtained set of optimal trajectories. In the suggested approach the proximity of the solution to the optimal one is determined by the accuracy of the approximation. A computational experiment of solving the applied problem of optimal control system synthesis for a group of car-like mobile robots in space with dynamic and static phase constraints is presented. The experiment showed that the found synthesized control function allows to move robots by the trajectory close to the optimal one for any initial state from a given domain.
Far Field, Suspended Trapping of a Single Dielectric Nanoparticle using Annular Slit-ring Plasmonic Zone-plate Lens

This paper demonstrates far field, trapping of a single dielectric nanoparticle using a Plasmonic Zone Plate Lens. This is achieved by beaming a linearly polarized light through series of concentric annular slit-rings formed in Gold, leading to creation of a sub-diffraction focal spot in the far field which can be used as a hotspot to trap the nanoparticle in it, suspended. A stable trap is formed in the far field using this structure and a 100-nm Polystyrene particle is successfully trapped using this technique. Forces are calculated in both x and z directions and the stability of the trap is further proved by calculating the potential well for x and z directions at location of the focal spot.
SOC and SOH Estimation for a Lithium-Ion Battery Using a Novel Adaptive Observer Based Approach

By the development in the electric vehicles (EVs) industry, estimating the state of health (SOH) and state-of-charge (SOC) has become more important for Lithium batteries. This paper presented an adaptive observer based on a novel transfer function based technique to simultanuously estimate the SOH and SOC of a Lithium-ion battery. An electrical model is used for obtaining the battery dynamics. Observer design is performed by simply linearizing the system and observer dynamics and then, obtaining the transfer function from the true parameter to its estimation. A convergence analysis is carried out using the linear criterion. The effectiveness of the presented observer is affirmed by using the simulation results.
Finger Vein Segmentation from Infrared Images Using Spectral Clustering: An Approach for User Indentification

Among biometric systems for user identification, finger vein patterns captured in the infrared spectrum have shown to be relevant for identifying users; and, in this way to provide a high level and low-cost security system. Unfortunately, the extraction of these vascular patterns is affected by many factors such as the capture device, light variations, force exerted on the finger, tissues, and bones with different morphology, finger position, etc. Therefore in this paper, we propose Spectral Clustering for the vein pattern extraction task from infrared images. To do so, the Spectral Clustering memory requirements for a large number of samples are attacked considering small disjoint partitions of the image and comparing resulting clusters in order to joint them avoiding the need for further expensive post-processing steps. Results are presented in terms of user classification error rates, showing that a good performance can be obtained by means of the proposed method.
Reset Method Based Unknown Input Observer Design for Continuous-Time T-S Fuzzy System

This proposed study deals with the observer design with combination of unknown input method and reset strategy for continuous-time Takagi-Sugeno (T-S) fuzzy systems. Reset strategy is utilized to reset the states of the observer due to a proper reset law in order to enhance the performance of the existing observers. By using the reset strategy, the settling time is decreased, hence, the error estimation converges to zero faster. The stability analysis of the estimation error is studied and require conditions for existence of the recommended observer are addressed. All feasible parameters of this study are derived by solving Linear Matrix Inequality (LMI) technique. Furthermore, the proposed strategy can be covered single input-single output (SISO) and multi input-multi output (MIMO) T-S fuzzy systems. Finally, an inverted pendulum with DC motor via gear train is simulated to examine the effectiveness of the devised strategy.
Observer-based Stabilization for a Class of Chaotic Systems in the Presence of Disturbance and Nonlinear Function: an LMI Approach

This paper presents the state feedback controller design based on observer for a class of chaotic systems in the presence of disturbance and Lipschitz nonlinearities via linear matrix inequality approach. The effect of external disturbances and nonlinear Lipschitz perturbation on the system stability is reduced. The central merits of the recommended technique are Lyapunov closed-loop stability, the convergence of estimation error to zero, and the elimination of the effect of disturbances and nonlinearities. Furthermore, the accuracy and efficiency of the proposed method is graphically illustrated with a numeric example
Human Gait Model Identification Approach Based on Foot Trajectory

Identification of gait parameters is one of the most important tasks allowing to estimate stability of walking aid devices usage case and biped robots. The estimation approach has been proposed and used to estimate model parameters using the foot trajectory. This method uses only foot coordinates of captured data and shows fast estimation. Efficiency of proposed approach is demonstrated by simulation as well as experimental results in comparison with another well-known method. An approach of the data preprocessing is applied to make motion captured data suitable for estimation. Estimated parameters of the model could be applied to estimation of walking robots stability and human walking in case of aid devices usage.
Non-intrusive Load Monitoring Using Water Consumption Patterns

In this paper, we tackle the problem of non-intrusive load monitoring (NILM). The purpose of algorithm NILM, is to disaggregate the total power consumption of a house-hold into individual consumption of appliances by analyzing changes in the power signal using analytical methods. One of the main challenges in this field is the existence of appliances consuming nearly-equal power. Different studies tried to extract and define specific features for these appliances to overcome this challenge. In this research, we incorporate the water consumption patterns of appliances into our analysis to separate otherwise-indistinguishable appliance. More precisely, we perform NILM via an event-based multi-label classification method in which water consumption patterns are employed to improve accuracy. To demonstrate the efficiency of the proposed method, numerical results are provided for four appliances of AMDP dataset.
A multi-objective and multi-decision maker approach for the balancing market in distribution grids in presence of aggregators

A multi-objective optimization problem is here formalized for a distribution grid in which are present both traditional generators and aggregators of microgrids, buildings, or general local prosumers. At the lower level, generators and aggregators want to maximize their profit, taking into account the limits of the flexibility they can sell. At the higher level, the distribution system operator (DSO) receives power/price bids from market participants and, based on technical and economic considerations, decides which bid to accept. For the lower-level decision problem, the Karush Khun Tucker (KKT) conditions are developed and included as constraints within the higher-level optimization problem. The resulting optimization problem is applied to a modified IEEE 13-bus system.
Reliability on Deep Learning Models: A Comprehensive Observation

This paper provides a comprehensive observation to examine the reliability of deep learning (DL) models. First, we will briefly introduce the essential background and kernel techniques in deep learning, such as downsampling and nonlinear discontinuity. Each of them may have some relation to the reliability of DL models. Then we discuss the inherent structural flaws of deep learning and the risk of unreliability that can result from it. Subsequently, we discuss various ways of generating adversarial samples that affect the DL model’s reliability and corresponding preventive measures. Finally, we complete this observation by identifying current challenges and future trends for research.