Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality Parameters in IMS/NGN Networks

The Next Generation Network (NGN) architecture, including elements of the IP Multimedia Subsystem (IMS) concept, is a proposition of a telecommunication network dedicated to the needs of current and future information society. The main goal of NGN is to provide Quality of Service (QoS), for which proper network design is necessary with respect to among others standardized call processing performance parameters, including expected values of Call Set-up Delay (E(CSD)) and Call Disengagement Delay (E(CDD)). These aspects are the subject of the paper, in which analytical and simulation traffic models of IMS/NGN are described, allowing assessment of E(CSD) and E(CDD). As the simulation model reflects the phenomena taking place in real IMS/NGN network, it is used as a reference for evaluation of quality of the analytical results, obtained using different queuing system models. Consequently, the queuing system models most appropriate for engineering applications are indicated. Moreover, application of the proposed analytical traffic model for design of IMS/NGN service stratum resources is presented and discussed.
Effective Input Dataset Identification Methodology for Accurate Prediction of Local PV Power Production

Local photovoltaic (PV) systems are playing a considerable role globally as a power resource and constituent element of the smart grid. Nevertheless, PVs may cause significant problems to the electric grid. This is due to the high variability of the PV power that is instigated by intermittent environmental conditions. Accurate prediction of PV power is very important to operate power grids containing high penetration of PVs. Most prior approaches have focused on forecasting the collective quantity of solar power generation at national or regional scale and disregarded the local PVs that are installed mainly for local electric supply. This paper devises an effective input dataset identification methodology (IDIM) to find the most significant and non-repetitive input variables for accurate prediction of the power production of local PVs. In the devised methodology, the Binary Genetic Algorithm (BGA) is used for the input variable identification and Support Vector Regression (SVR) is employed for evaluating the fitness of the input datasets. The devised methodology is implemented and validated based on actual local PVs (building rooftop PVs) located in the Otaniemi area of Espoo, Finland. The results are compared with those obtained by conventional counterparts and manifest outperformed performances.
Variable-Parameter T-Circuit-Based IPT System Charging Battery With Constant Current or Constant Voltage Output

Load-independent output characteristics of inductive power transfer (IPT) systems are increasingly popular in battery charging. This paper proposes a novel variable-parameter T-circuit (VT) for an IPT system charging a battery with constant current (CC) or constant voltage (CV) output. The VT can transfer a CC/CV input to a CC or CV output by using an ac switch and a passive component (inductor or capacitor). An IPT system with a VT for CC–CV charging can reduce the number of passive components and ac switches. Besides, the proposed VT merits more design freedom of charge current/voltage with the constraints imposed by the loosely coupled transformer parameters compared to that of the traditional one. In addition, there are three kinds of VTs for various IPT charging systems with different requirements. A 400 W laboratory-scale prototype with a 150 mm air gap was built to verify the theoretical analyses. Both electronic load and lead-acid battery are utilized to verify the charging profile of the proposed method. The experimental results of the IPT system indicate that the fluctuation of the charging current in CC mode is less than 2%, and the change rate of charging voltage in CV mode is within 2.9%. The maximum overall efficiency 93.93% of the charging system is achieved from a dc 110 V input to a dc 100 V output.
An Integrated Readout for Current Sensing based on a Σ∆ Modulator with Switched Capacitor Feedback

This work presents a current input continuous-time sigma-delta modulator (C-CTΣ∆M) for biomedical application such as electrochemical impedance spectroscopy. Low noise and low area current input digitizers are required to implement high spatial resolution multi electrode arrays. In this work, the use of a switched capacitor with resistor D/A converter (SCR-DAC) for the C-CTΣ∆M is proposed for this purpose. SCR-DACs offer the realization of large resistances on small area, which leads to a very small area consumption combined with good noise performance. A C-CTΣ∆M with an SCR-DAC prototype is fabricated in a 180-nm CMOS process. The implemented ADC occupies an area of only 120 × 200 µm2. In order to increase the dynamic range (DR) of the C-CTΣ∆M, two modes have been implemented. In the low noise configuration an integrated in band noise of 157-pArms in the signal band width of 100 kHz with a peak SNDR of 62 dB is achieved. In the high DR configuration a peak SNDR of 72 dB with an MSA of -4.1 dB corresponding to 9.1 µA is measured. The complete C-CTΣ∆M consumes 2 mW from a 3-V supply and achieves a total DR of 95 dB.
Synergetic Node of Edge Computing and Hybrid Fibre-Wireless (FiWi) Access Networks for IoT

The fast-evolving Internet of Things (IoT) requires reliable and flexible networks and low-latency computing to connect billions or even trillions of edge devices and deal with the vast amount of data generated by them at high speed. This situation poised to induce a significant change to the current network architecture and cloud computing. This paper proposes a new design of fiber-wireless (FiWi) node which integrated with computing, storage for IoT, and virtualization is introduced to manage the networking and computing resources. Analytical results demonstrate that its effectiveness on switching reaches the level of hardware switches and can provide low-latency cloud service for IoT applications based on low-cost and low-power computing devices. Additionally, a use case using the FiWi access networks as a sensor is given to show the advantages of the FiWi ECN.
Learning Methods for RSSI-based Geolocation: A Comparative Study

In this paper, we investigate machine learning approaches addressing the problem of geolocation. First, we review some classical learning methods to build a radio map. In particular, these methods are splitted in two categories, which we refer to as likelihood-based methods and fingerprinting methods. Then, we provide a novel geolocation approach in each of these two categories. The first proposed technique relies on a semi-parametric Nadaraya-Watson estimator of the likelihood, followed by a maximum a posteriori (MAP) estimator of the object’s position. The second technique consists in learning a proper metric on the dataset, constructed by means of a Gradient boosting regressor: a k-nearest neighbor algorithm is then used to estimate the position. Finally, all the proposed methods are compared on a data set originated from Sigfox network. The experiments show the interest of the proposed methods, both in terms of location estimation performance, and of ability to build radio maps.
Broadband Silicon TM-Pass Polarizer using a Slot-Assisted Periodic Waveguide

We demonstrate a compact (21 µm × 0.5 µm) silicon TM-pass polarizer based on a slot-assisted periodic waveguide. Measured results show low insertion loss for the TM mode (average 0.7 dB) and high TE loss (average 41.3 dB) over a 100 nm wavelength range, centered at 1.55 μm.
A review on Hyperspectral Image Classification using SVM combined with Guided Filter

The classification of hyperspectral remote detecting picture is testing assignment in present time. In this survey hyperspectral picture characterization utilizing SVM joined with the guided filter to get the spatial data viably and de-commotion the grouping result by edge-preserving smoothing property. In the paper [1], spatial data for hyperspectral picture classification gathered using morphological profile and local binary pattern. To, begin with, image classifier is utilized to group every pixel and after that apply the guided filter for edge-smoothing property otherwise called edge-preserving filtering(EPF) to the outcome which is utilized to enhance classification exactness. When it is contrasted with existing customary classification techniques, it may provide better classification exactness. It will be beneficial for future remote-detecting image classification.
Medical Applications of Image Reconstruction Using Electromagnetic Field in Terahertz Frequency Range

The possibility of terahertz imaging in medical applications is investigated by the design and implementation of an electromagnetic system. Numerical analysis based on the transmission line matrix method is used to obtain the shape or form of the biological object. The effect of different discretization parameters on the convergence of the transmission line matrix method is studied in detail. Exact and efficient dielectric profile image reconstructions of three dimension lossy scatterers from different complex cross sections using synthetic scattered field data are presented. The influence of antenna spacing and the suitability of the simplified transmission line matrix model for system evaluation are examined.
Model-based Optimization of a Low-dimensional Modulation Filter Bank for DRR and T60 Estimation

Amplitude Modulation Spectrum (AMS) features can be implemented as a cascade of two filter banks whereas the filter bandwidths can be optimized for a particular application. In this work we train AMS-based features using a combination of a model-based optimization (MBO) approach and feature selection for full-band DRR and full-band T60 estimation. MBO replaces the computational complex data-based cost function by approximating a less complex surrogate model and thus reduces the time needed for training. We evaluate our approach on the publicly available ACE challenge corpus and achieve with only five features the best RMSE in the DRR estimation task using the single microphone configuration and upper mid-range performance for T60 estimation. The computational complexity of our algorithm is much lower than all other submitted algorithms.
The Interlanguage Speech Intelligibility Benefit Disappears Under Noisy Conditions

We investigate the influence of language familiarity on speaker identification in noisy conditions, measured by an adapted version of the Speech Perception in Noise (SPIN) test, with special attention for the differences between English native listeners (L1) and non-native listeners (L2) whose native languages are Chinese and French. We find that for speaker identification in noisy conditions there is no interlanguage speech intelligibility benefit, according to which non-native listeners are better able to comprehend an L2 speaker than a native speaker.
Unsupervised-Learning Power Control for Cell-Free Wireless Systems

This paper studies the viability of feedforward neural networks (NNs) for centralized power control in the uplink of cell-free wireless systems with matched-filter reception. The formulation relies only on large-scale channel behaviors as inputs, without the need for user location information, and on unsupervised learning, to avoid the onerous precomputation of training data that supervised learning would necessitate for every system or environment modification. Two different power control objectives are entertained, and for both of them the NN closely approximates the optimum solutions produced by convex solvers while vastly reducing the complexity, thereby opening the door to power control implementations for very large systems.
Simulation as a Service for Cooperative Vehicles

Simulating connected vehicles in realistic environments is a computationally expensive task, particularly when large numbers of traffic participants are involved. To cope with exploding hardware requirements it can become indispensable to tackle such simulations in a divide and conquer manner. A promising approach breaking down a simulation scenario in a set of small tasks is the spatial subdivision of the simulated area. Thereby, each spatial sector is simulated by a dedicated sub-simulator enabling efficient distribution of the overall simulation across multiple machines. However, a distributable and easy-to-use simulation solution providing automated scalability and hiding the distribution complexity from the simulation engineer requires a service-based simulator architecture equipped with interfaces for simulation control, integration, and resource management. In this work, building upon previous results we propose a cloud-ready simulation infrastructure providing automotive engineers with the ability to analyze large scale connected traffic scenarios without caring about the execution environment. As a consequence, the proposed solution can be integrated seamlessly into existing continuous integration environments supporting agile research and development processes for cooperatively interacting vehicles.
Dynamic Configuration of Optical Physical Layer Using SDN and Optical Network Description Language

Optical networks, which were once considered to be static, are now becoming more flexible and dynamic. Software-defined networking (SDN) has an essential role in the provisioning and management of these networks. There is also a requirement to describe the heterogeneous network resources in a unified manner to the SDN controller. In this paper, we develop a novel semantic framework called Optical Network Description Language (ONDL) which is used to describe entities in the optical network. We show its applicability by emulating a physical layer topology and its control using the developed SDN controller based on ONDL. For any network event, we obtain the response time as <100 ms and observe that the total time to configure a device is dependent upon the individual node configuration time.
Event-triggered Output Feedback Load Frequency Control for Multi-area Power Systems

This paper is concerned with the event-triggered output feedback load frequency control (LFC) problem for multi-area power systems(M-aPS). A new linear matrix inequality (LMI) condition is established for the event-triggered controller design. Compared with previous controller design conditions, the sufficient condition in this paper is convex and much less conservative. Besides, the proposed design method can avoid calculating the generalized inverse and remove the equality constraint. Finally, a three-area power system example is given to demonstrate the effectiveness of the proposed approach.
Automatic Measurement of Speech Breathing Rate

The speech breathing rate has been used for the early prediction of disease and detection of emotions. Most of the breath detection equipment are contact based. Here, we try to detect the speech breathing rate from speech recordings. Cepstrogram matrix is used as the feature for classifying the speech frames as breath or non-breath. The classifier used is the support vector machine (SVM) with a radial basis function (RBF) kernel. The classifier output is post-processed to join breathing segments which are closely spaced and remove breaths of small duration. The speech breathing rate is calculated from the breath to breath interval. The algorithm has been tested on a student evaluation database. When tested, the algorithm yields an F1 Score of 89% and root mean square error (RMSE) of 4.5 breaths $\min\min$ for the speech-breathing rate. The breath segments have been validated by keenly listening to speech recordings and viewing thermal videos.
Crosstalk Delay and Stability Analysis of MLGNR Interconnects on Rough Surface Dielectrics

The crosstalk delay and frequency stability of MLGNR interconnects on dielectric materials with rough surfaces are investigated. Two interacting parallel interconnect lines (aggressor and victim) are considered. ABCD matrix model of two port networks is employed to obtain an equivalent single conductor (ESC) circuit model for decoupled victim line. The effect of substrate surface roughness is appropriately included in ESC model. The most common dielectric materials (SiO2, BN and SiC) are considered. By setting up a driver-interconnect-load (DIL) system the crosstalk delay and stability analysis of MLGNR and Cu interconnects are performed. Results show that the crosstalk delay and the stability of MLGNR interconnects oppositely response to the surface roughness of underlying dielectric material. Namely, dielectric materials with rougher surfaces translate to longer crosstalk delays while more stable systems. While MLGNR interconnects on SiO2 have longer crosstalk delay in compare to Cu counterpart, MLGNRs on BN and SiC have shorter crosstalk delay with respect to Cu ones. Therefore, to hold advantage of MLGNR interconnects on SiO2 over Cu counterpart either the SiO2 surface must be more precisely polished or MLGNRs with higher doping concentrations shall be used.
Estimation of the Bit Error Rate (BER) for Uplink Millimeter-Wave Line-of-Sight Communications

This paper proposes a theoretical analysis for evaluating the statistical distribution of the bit error rate (BER) for a millimeter-wave uplink communication involving a mobile terminal (MT) and a base station (BS). The analysis is focused on the line-of-sight scenario, whose investigation provides useful insights on the maximum performance achievable by forthcoming fifth-generation (5G) wireless networks. The developed model accounts for path-loss attenuation and directional antenna gains assuming a noise-limited operating regime. The location of the MTs around the 5G BS is described by a Poisson point process, while the statistic of the BER is estimated by considering the phase-shift keying (PSK) and the quadrature amplitude modulation (QAM) schemes. The analytical results are finally exploited to discuss the dependence of the error rate from the cell radius and the modulation order in a mmWave context.
Using the SCADE Toolchain to Generate Requirements-Based Test Cases for an Adaptive Cruise Control System

In the last years, model-driven engineering has gained a lot of traction, especially in industrial domains, such as automotive or avionics. Various tools which support model-driven engineering, e.g. SCADE or MATLAB/Simulink, have developed over the years in fully fledged integrated development environments, with strong capabilities for the modeling of complex software systems. Model-driven engineering tools are mature enough so that the model created with them are amenable to formal analysis for the purpose of verification and validation. Acceptance testing is a validation method by which a system is tested extensively against legal and customer requirements, before it is allowed in series production. Due to the inherent complexity of automotive systems, large requirements catalogues have become usual in this domain. Checking that a complex automotive software system conforms to an extensive requirements catalogue is a task which cannot be managed manually anymore. In this paper, we design a workflow for test engineers to construct test cases from formalized requirements and examine the quality of tests via mutant testing within the SCADE toolchain. We construct an academic case study based on a prototypical adaptive cruise control system and evaluate our workflow on it. We report on results and lessons learned.
An Instantaneous Frequency Identification Algorithm for Time-Varying Frequency Signals

A high-precision instantaneous frequency (IF) identification algorithm is proposed in this paper. The algorithm is based on the relationship between the signal phase and time, and uses Taylor’s expansion to parameterize the time-varying frequency. It can accurately identify the IF of various frequency-modulated (FM) signals. The simulation results show that high identification precision can be obtained when the frequency of the signal is stable or modulated by linear and nonlinear functions. Compared to the conventional IF identification algorithms Hilbert transform (HT) and Wigner-Ville distribution (WVD), the proposed algorithm performs better in accuracy. Besides, the proposed algorithm is not sensitive to the amplitude-modulation behavior, and it shows better precision than the Direct Quadrature (DQ) algorithm, which is an IF identification algorithm of the amplitude-modulated signal. The calculation amount of the proposed algorithm is not related to the sampling rate. Increasing the sampling rate can make the identification precision higher without changing the calculation speed. It can provide the theoretical basis for the IF identification of a grid-connected inverter and other automatic devices in the power system.
Quality and Success in Open Source Software: A Systematic Mapping

As the number of available Open Source Software (OSS) and the interest they attract are increasing, numerous product attributes are provided to developers and users for evaluating the quality and success of an OSS. Accordingly, various articles in the literature assess the quality and success of OSS, by using different quality attributes and metrics and different approaches. Though this variety can be considered as a positive indicator of research interest and maturation on one side, it creates a kind of jungle in defining and understanding the terms 'quality' and 'success' on the other side. Based on this challenge, in this study, we targeted a systematic mapping (SM) of the articles on quality and success of OSS. More than 474 articles have appeared in this area between the years 2002 and 2017, and the final pool of 128 articles is obtained by defining and applying inclusion and exclusion criteria. SM was employed to develop a classification scheme and categorized the existing body of articles with respect to five research questions (RQs) on: contribution and research types, quality criteria and metrics, success criteria and metrics, the relation of quality and success, and demographics. We observed that the majority of the articles assess the concept of quality as 'code quality', whereas the concept of success is mostly perceived as 'market success' and/or 'developer activity'. Moreover, the metrics of 'contributing developers/users', and the quality attribute of 'functionality' are the quality criteria most employed in the assessment of success.
Techno-economic study of hydrogen production using PV, wind power and battery storage

Hydrogen is regarded by many scientists as the energy fuel of the future, provided that it is produced by nonpolluting renewable energy systems (RES) such as photovoltaics and wind turbines. The majority of studies focusing on hydrogen production with RES have shown that such installations are not yet feasible, at least from an economic perspective, primarily due to significant capital expenditures. Conversely, in this paper, we show that the hydrogen technology can already deliver some interesting investment opportunities. In the present paper, we address a system comprising a 15 MW PV park, wind power, battery storage and a 1 MW PEM electrolyzer. Our study comprises two parts. (i) First, we present the technical analysis of the system. We show how the rate of hydrogen production, here using the term utilization, changes in function of the wind power and the energy capacity of a Lithium-ion battery. (ii) Second, we present the economic analysis. In particular, we provide assessments regarding the payback period of the installation. The results of the study let us conclude that (a) to maximize utilization, it is necessary to combine both source and storage components in a hybrid topology, and that (b) in the best scenario, though the utilization is limited at 65.5%, with a hydrogen price at 6 €/kg, the return of investment can be below the 6 years.