Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrochemical Impedance Spectroscopy of Li-Ion battery on-board the Electric Vehicles based on Fast nonparametric identification method

Electrochemical Impedance Spectroscopy (EIS) is commonly used for the diagnosis of electrochemical energy accumulators, for example Li-Ion batteries in Electric and Hybrid Vehicles. Measuring the impedance in a wide frequency range, it is possible to investigate on the modifications of the internal electrochemical cell process, evaluating its current state of life. EIS test consists of the excitation of the battery, or cell, with a defined current signal, and then measuring the cell voltage, the frequency response of the system is computed. The classical EIS test, based on the excitation with a frequency controlled sine wave in input, requires expensive instruments and long time test procedures; therefore it has many problems on the integration on the embedded systems. In this paper, a pseudo random square signal, which shows a simply hardware implementation, is used as excitation signal input on the cell for the impedance evaluation. The effectiveness of the method has been validated based on simulation test, in order to obtain good results in terms of impedance estimation accuracy, minimizing time duration and energy consumption.
New in the Theory of Periodic Structures

New results in the theory of periodic structures (PSs), obtained on the basis of stepped transmission line model, are presented. It is shown that PSs peculiarly transform the wave impedance, inducing unusual properties of the wave medium. Stepped transmission line model makes it possible to determine the fundamental relationships of physics and mathematics for PSs. Model of impedance delta-inhomogeneities allows to get simple analytical expressions for PS characteristics. Two-phase PSs for which band gap frequencies are twice lower than those for traditional single-phase PSs are considered.
Adaptive Jaya Optimization Technique for Economic Load Dispatch Considering Valve Point Effect

ELD with the help of Jaya Optimization Technique has been proved as one of the better options. Along with the cost curves or cost coefficients valve point effect has also very important impact in ELD. So in this paper, for ELD with valve point effect has been optimized with the help of Adaptive Jaya Optimization Technique. 13 thermal unit has been taken to get the optimal power output and optimal cost, which has been compared to the other optimization techniques' results. Advantage of this proposed optimization technique has been shown in the current work.
Analysis of Logic Gates for Generation of Switching Sequence in Symmetric and Asymmetric Reduced Switch Multilevel Inverter

Analysis of logic gates for the switching sequence operation of reduced switch multilevel inverter (MLI) is introduced in this paper. Two variants of MLI with reduced switches are considered for the analysis of the logic gates to obtain proper output voltage level. One MLI is with the symmetrical voltage, and the other is with the asymmetrical voltage. The analysis of the proposed logical operation is presented through the single-phase seven-level output voltage for both symmetrical and asymmetrical inverters. Input pulse pattern for the operation of the logic gates are chosen from the multi carrier pulse width modulation (PWM) techniques as phase disposition (PD), phase opposition disposition (POD), and alternate POD (APOD). The analysis for the generation of the required pulse pattern to operate the switches in MLI is presented as logical equations. The simulation work is performed and evaluated with the MATLAB/Simulink. The real-time simulation is performed for the required pulse pattern and generated with the support of dSPACE 1104. The THD comparative analysis is analyzed with a modulation index and various PWM methods.
Analysis of Coupled Thermo-Hydro-Mechanical Simulations of a Generic Nuclear Waste Repository in Clay Rock Using Fiber Surfaces

The use of clean and renewable energy and the abandoning of fossil energy have become goals of many national and international energy policies. But even when once accomplished, mankind has to take charge of the relics of the current energy supply system. For example, due to its harmful effects, nuclear waste has to be isolated from the biosphere safely and for sufficiently long times. The geological subsurface is considered as a promising option for the deposition of such by-or end products. In order to investigate the long-term evolution of a repository system, a multiphysics simulation was performed. It combines the structural mechanics of the host rock, the fluid dynamics of formation fluids, and the thermodynamics of all materials resulting in a highly multivariate data set. A visualization of such multiphysics data challenges the current methodology. In this article, we demonstrate how an analysis of a carefully selected subset of the variables in attribute space allows to visualize and interpret the simulation data. We apply a fiber surface extraction algorithm to explore the relationships between these variables. Studying the temporal evolution in attribute space, we found a regionally bulge that could be identified as an effect of the nuclear waste repository because it can be clearly separated from the natural geophysical state prior to waste disposal. Furthermore, we used the extracted fiber surface as a starting point to examine the distribution of other variables inside this area of the physical domain. We conclude this case study with lessons learned from the visualization as well as the geotechnical side.
Face Biometric Identity Authentication System

With the popularity of payment by smartphone, mobile financial transactions security becomes serious. This paper takes smartphone as application background, combineing with several algorithms to propose a face identity authentication system. Firstly, samples are preprocessed. Then, Haar and AdaBoost algorithms are adopted for face detection, features of face area are extracted by LBP operator. Finally, Euclidean distance is used for feature matching to achieve authentication. The test results show that preprocessing suppresses the illumination influence effectively, face detection accuracy reach to 97.2% and 96.6% respectively, system authentication accuracy can be as high as 98.86%, FRR and FAR are kept at a low value of 1.14% and 0.89%, the average authentication time takes 271∼327ms. In summary, the face identify authentication system proposed in this paper has better robustness and higher authentication accuracy while maintaining good timeliness.
Metallic Source/Drain Ge-Based Charge-Trapping Memory Cells

Schottky barrier source/drain produces particular ambipolar conduction and strong hotcarrier generation in CMOS devices. This work presents a new metallic source/drain Ge-based charge-trapping cells for memory applications. Two-dimensional simulations were employed to elucidate the source-side injection programming of Ge-based memory cells and discuss the differences of cell characteristics between the Ge and Si cells.
A Novel Tensor CS-Based NOMA MIMO System for the Downlink of Massive Mission-Critical MTC in 5G and Beyond

The introduction of 5G and beyond systems brings new challenges for efficiently supporting the random access of a massive number of users with ultra-reliable low-latency requirements. To address these challenges, we propose a novel compressed sensing (CS)-based nonorthogonal multiple access (NOMA) multiple-input multiple-output (MIMO) scheme, called the CS-NOMA MIMO scheme, for the downlink of massive mission-critical machine-type communication (mmcMTC). By taking advantage of tensor CS techniques, a novel tensor CS-based packet (frame) structure, called TCS packet (frame), is proposed for the CS-NOMA MIMO scheme to further reduce the transmit time intervals (TTIs), resulting in smaller latency than that using the conventional packet (frame) structure. In the TCS packet (frame) structure, the CS signatures are assigned not only to different users but also to different time slots and different symbols of a time slot, thus achieving a user, time slot, and symbol overload system. We present a sufficient condition for the construction of the CS signatures that ensure effective deployment of tensor CS-based multiuser detection (CS-MUD) in user receivers. Furthermore, we consider the case of imperfect channel state information (CSI) and present an upper bound for the performance of the proposed scheme. Finally, the simulation results show that the proposed scheme can achieve high user overload (up to 5) and time slot overload (up to 3) when the activity ratio of users is 1%. When the time slot overload is 3, the proposed scheme with the TCS packet (frame) shortens time slots by at least 2/3 times that of the typical grant-based NOMA schemes. In addition, the block error rate (BLER) of the proposed scheme with the TCS packet (frame) is much lower than that with the conventional packet (frame), even less than 10−9 by repeat transmission when the time slot overload is 3.
A Grey-box Model Based on Unscented Kalman Filter to Estimate Thermal Dynamics in Buildings

Buildings are responsible of about 40% of primary energy consumption. The widespread diffusion of Internet-of-Things devices provide allow collecting large amount of energy related data such as indoor air-temperature and power consumption of heating/cooling systems. Collected information can be used to develop data-driven models to learn building characteristics and to forecast indoor temperature trends. In this paper, we present a Grey-box model to estimate thermal dynamics in buildings based on Unscented Kalman Filter and thermal network representation. The proposed methodology has been applied to different implementation of building thermal networks to test their accuracy in temperature prediction. Results show the accuracy of the proposed methodology in predicting indoor temperature trends up to next 24-hours with a maximum error of 1.50°C.
Demutiplexing of Nyquist-OTDM Signal Based on Temporal Magnification Aided Optical Sampling With Enhanced Performance

Demultiplexing of high-speed Nyquist optical time-division multiplexed (N-OTDM) signal requires ultra-narrow local sampling pulse to avoid the severe inter-symbol-interference (ISI) between each tributary. Unfortunately, generation of such narrow sampling pulse is difficult or usually need complex setup configure at the receiver side, which would cause ineffective cost in application scenarios such as short-reach optical communication or PON applications. In this paper, we propose and demonstrate demultiplexing of N-OTDM signal based on temporal magnification followed by coherent optical sampling to enable broader sampling pulsewidths while reducing the ISI. The target tributary of the N-OTDM signal is temporally magnified and further demultiplexed through coherent optical sampling using Gaussian-shaped or Nyquist-shaped pulses. With the aid of temporal magnification based on a time-lens, the sampling window can be extended during the coherent detection progress at the receiver side. Gaussian sampling pulses with a pulsewidth up to 10.4 ps are enabled for the demultiplexing of a 160 Gbaud N-OTDM signal. Compared with demultiplexing based on Nyquist pulses (i.e., coherent matched sampling) without temporal magnification, the proposed scheme features a better performance with pulsewidths greater than 6 ps. If temporal magnification is combined with coherent matched sampling, Nyquist sampling pulses with a pulsewidth up to 12.4 ps are made possible for the demultiplexing. Therefore, the proposed scheme based on temporal magnification could significantly release the pulsewidth requirement for N-OTDM demultiplexing while mitigating the ISI.
AC Resistance Evaluation, Measurements and Savings of Innovative Bare Conductors

The continuously growing electrical power demand is more and more faced by the increase of generation from renewable energy sources. Given the difficulties in building new lines, the development of new technologies for conductors is desirable in order to increase the ampacity and the efficiency of the existing overhead lines. For this purpose high temperature low sag conductors, optical phase conductors and conductors composed by different strands shape and dimensions it has been developed and are currently a main research topic. Substitution of traditional overhead lines conductors with innovative ones can play a key role in the reinforcement of transmission and distribution electric grids, allowing at the same time high environmental benefit, as reduction of CO2 emissions. In order to predict accurately economic and environmental savings, it is important to demonstrate that the value of the estimated and calculated ac resistance is almost equal to the ac resistance in operating conditions. The present paper summarizes the variable of interest in the ac resistance evaluation for general stranded bare conductors, then a general ac resistance measurement process is defined and applied, measured values are compared with estimated values computed by means of codes. An approximated evaluation of economic and environmental savings due to the re-conductoring operation is carried out and the actualized costs of the energy losses for traditional and innovative conductors are computed and compared, with the aim to show the benefits of innovative conductors.
Terahertz-Enabled Wireless System for Beyond-5G Ultra-Fast Networks: A Brief Survey

With 5G Phase 1 finalized and 5G Phase 2 recently defined by 3GPP, the mobile communication community is on the verge of deciding what will be the Beyond-5G (B5G) system. B5G is expected to further enhance network performance, for example, by supporting throughput per device up to terabits per second and increasing the frequency range of usable spectral bands significantly. In fact, one of the main pillars of 5G networks has been radio access extension to the millimeter-wave bands. However, new envisioned services, asking for more and more throughput, require the availability of one order of magnitude more spectrum chunks, thus suggesting moving the operations into the THz domain. This move will introduce significant new multidisciplinary research challenges emerging throughout the wireless communication protocol stacks, including the way the mobile network is modeled and deployed. This article, therefore, provides a brief survey of the challenges and opportunities of THz band operation in wireless communication, along with some potential applications and future research directions.
Nanoscale CMOS Implications on Analog/Mixed-Signal Design

AMS design in remaining CMOS nodes is tedious & about managing technology-imposed non-idealities. AMS area scaling 0.8-0.9x per node vs. 0.5x for logic & SRAM. Parasitics a LDEs only get worse, will ultimately limit scaling. Digital-friendly AMS design inspires new performance, power & integration levels. AMS design at inflection towards more automation to reduce design iterations.
Proposal of Effective Orthogonal and Hexagonal Hierarchical Structures for Disc Queries

The Fixed-Radius Nearest Neighbors (FRNN) query is a common task in scientific areas dealing with multidimensional point clouds (PCs) such as computer graphics, image processing, geographic information systems, machine learning and pattern recognition. We especially focus on 2D points in this paper. The FRNN means search all the points within defined radius for tasks when local properties of data are crucial. As this procedure is time consuming, an effective accelerating structure indexing the points is required. The standard methods are based on the orthogonal grids subdividing the space into uniform cells or hierarchies of orthogonal grids. However, several papers highlight the regular hexagonal grids for their superior theoretical metrics, e.g. better coverage of query discs, uniform distance to all six neighbors or lower number of passed cells. This paper provides a comparison of effectiveness of orthogonal and hexagonal hierarchies and traditional algorithms for FRNN disc queries. To do this, we propose a novel hierarchical method based on the hexagonal space-filling curve called the Node-Gosper curve which defines the hierarchical location of points in the hexagonal grids and their linear order. This method is tested with similar structure based on the orthogonal Z-order curve to show the real features of both approaches in practice.
Modeling and Control of Multi-Port DC/DC Converter

A comprehensive mathematical modeling and control design for an isolated multi-port DC/DC converter for photovoltaic (PV) generation systems is discussed in this paper. The adopted converter is a modified flyback converter consisting of three ports, namely, a photovoltaic (PV) module input port, a bi-directional battery port, and an isolated output port. The output port is receiving its demanded power without any interruption, while the power extracted from the PV module is maximized adopting a maximum power point tracking (MPPT) controller. Moreover, to improve the overall efficiency of the system, synchronous rectification switches are used on the secondary side of the isolation transformer. Set of simulations using MATLAB/Simulink are done to show the effectiveness of the proposed converter under different operating conditions.
Privacy Shield securing privacy in social networks

Privacy is so important for social networks. Social networks are uncertain in terms of privacy. Now a days, privacy breach is a major problem. Data privacy will be lost when it is distributed in a social network. This is because of multi distributing the data without taking control of data. To deal with these problems, Securing privacy in Social Networks has been proposed based on privacy control over data. An efficient system named, Privacy Shield is designed to preserve privacy of data which can be managed by shared users in social networks. A third party server called Eco Server is used to do this. User privileges are administered by controlling the contents shared in social networks by the eco server. Base64 algorithm is used for data encryption.
A First Literature Review On Enterprise Reference Architecture

Enterprise Reference Architectures have been increasingly emerging as new standardized architectural description artefacts suitable to provide a frame of reference for a particular business domains. Used in an appropriate way, they can be a useful tool for improving enterprise architecture management practices. Whilst from a practitioners perspective several instances of such architectures have been created over the past years, little research on such artefacts has been done to date. Hence, academia still lacks a comprehensible overview of prior literature on Enterprise Reference Architectures, despite the relevance of literature reviews to knowledge advancement in any scientific field. To close this gap, in this paper we present a primer literature review on Enterprise Reference Architectures conducted following general guidelines proposed for undertaking information systems reviews. Similarly to precedent contributions addressing enterprise architecture oriented topics, we introduce a novel classification framework based on Gregor‘s theory types of information systems to structure and summarize former research. Major findings from significant studies on the topic are then identified, analysed and mapped into the referred framework. Based on the analysis and results of the review, brief suggestions to stimulate further research on the design, improvement and application of Enterprise Reference Architectures are also derived.
Distributed Deep Convolutional Neural Networks for the Internet-of-Things

Severe constraints on memory and computation characterizing the Internet-of-Things (IoT) units may prevent the execution of Deep Learning (DL)-based solutions, which typically demand large memory and high processing load. In order to support a real-time execution of the considered DL model at the IoT unit level, DL solutions must be designed having in mind constraints on memory and processing capability exposed by the chosen IoT technology. In this article, we introduce a design methodology aiming at allocating the execution of Convolutional Neural Networks (CNNs) on a distributed IoT application. Such a methodology is formalized as an optimization problem where the latency between the data-gathering phase and the subsequent decision-making one is minimized, within the given constraints on memory and processing load at the units level. The methodology supports multiple sources of data as well as multiple CNNs in execution on the same IoT system allowing the design of CNN-based applications demanding autonomy, low decision-latency, and high Quality-of-Service.
A non-equilibrium theoretical framework for statistical physics with application to turbulent systems and their predictability

A new theoretical approach to non-equilibrium statistical systems has recently been proposed by the author, a co-author and others. It is based on a variational principle which is associated with the discrepancy of a path through thermodynamical space to one following Liouvillean evolution. In this contribution the approach is extended in such a way that it can be applied to a wide range of practical non-equilibrium statistical systems such as those arising in turbulence but also to a general class of statistical physics models. The new methodology allows for application to autonomous dynamical systems generalizing the previous work which applied only to Hamiltonian systems. Furthermore it provides a general analysis of near equilibrium conditions which allows for a natural analysis of predictability limits in turbulent systems. Finally it describes a method is described for the numerical calculation of far from equilibrium thermodynamical trajectories.
The Information Loss Problem: An Analogue Gravity Perspective

Analogue gravity can be used to reproduce the phenomenology of quantum field theory in curved spacetime and in particular phenomena such as cosmological particle creation and Hawking radiation. In black hole physics, taking into account the backreaction of such effects on the metric requires an extension to semiclassical gravity and leads to an apparent inconsistency in the theory: the black hole evaporation induces a breakdown of the unitary quantum evolution leading to the so-called information loss problem. Here, we show that analogue gravity can provide an interesting perspective on the resolution of this problem, albeit the backreaction in analogue systems is not described by semiclassical Einstein equations. In particular, by looking at the simpler problem of cosmological particle creation, we show, in the context of Bose–Einstein condensates analogue gravity, that the emerging analogue geometry and quasi-particles have correlations due to the quantum nature of the atomic degrees of freedom underlying the emergent spacetime. The quantum evolution is, of course, always unitary, but on the whole Hilbert space, which cannot be exactly factorized a posteriori in geometry and quasi-particle components. In analogy, in a black hole evaporation one should expect a continuous process creating correlations between the Hawking quanta and the microscopic quantum degrees of freedom of spacetime, implying that only a full quantum gravity treatment would be able to resolve the information loss problem by proving the unitary evolution on the full Hilbert space.