Miracle-dz/UniFAR-FTUs huggingface dataset:
An decoding parameter optimization method for LDPC codes in 5G NR based on 3D EXIT chart

Low-density parity-check (LDPC) codes are channel code of fifth-generation(5G) New Radio(NR), and it is important to explore low-complexity decoding algorithms that are easy to implement on hardware. In order to realize 5G ultra-reliable low latency communication (URLLC), low-complexity decoding of short packets is necessary. For quasi-cyclic LDPC (QC-LDPC) codes in 5G NR, the layer offset min-sum (LOMS) algorithm and the layer normalized min-sum (LNMS) algorithm are two efficient algorithms with low complexity. The performance of these two simplified decoding algorithms is significantly affected by the values of the above two decoding parameters. In this paper, the 3D extrinsic information transfer (EXIT) chart is introduced as an analysis tool to indicate the performance of different values for the offset and normalization parameters. The simulation results show that with different values of the above two factors, the changing of the output information of EXIT chart is consistent with that of the block error rate (BLER) in decoding simulations. In addition, it is shown that this analysis method can be extended to the decoding with fixed-point inputs.
Asymmetric Metaconductors Featuring Wideband Skin Effect Suppression

Metaconductors with near-zero permeability is an incentive for the development of high-frequency passive components due to effective suppression of skin effect. In this work, an asymmetric multilayered structure is proposed to broaden the bandwidth of skin effect suppression while preserving a sufficient lower frequency limit of permeability compensation. Coplanar waveguide (CPW) transmission lines consisting of two ferromagnetic layers alternatively stacking with Cu layers were designed and fabricated. Near-zero permeability is obtained at two separated frequencies, which enables broadband eddy current canceling and resistance reduction. The measured results manifest a resistance reduction up to 46% at 11.1 GHz, compared to a Cu transmission line with the same dimensions, and a broad skin effect suppression band between 7.5 and 30.0 GHz, which is sufficient to cover the important 5G applications.
Linear Antenna Array Parameter Estimation using a Neuro-computational Approach

The power of estimating after learning as provided by artificial neural network has helped a lot of researchers to determine solution to many a problem in antenna research. The solutions to most of the problems involved unwieldy and lengthy computations. In this paper, the relationship of beamwidth and gain with respect to the number of antenna elements in a linear antenna system is modelled using a neuro-computational model. The model uses a multi-layer perceptron network. It was found that the network accurately modeled the relationship. The mean square error (MSE) was found to be of the order of 10−9.
AptBLE: Adaptive PHY Mode based on K-means Algorithm in Bluetooth5

Since Bluetooth5 standard released in 2016, its usage in commercial electronic products has been increased rapidly and substantially. Comparing to BLE 4.2, Bluetooth5 supports three PHY modes, respectively 2M, 1M and Coded PHY mode, providing a higher throughput and a wider range. Whereas there is a trade-off between its throughput and coverage. When the connection is established, the PHY mode is commonly pre-configured and fixed. This rigid design limits the flexibility in offering dynamic throughput and coverage. Therefore, we propose a method termed AptBLE, that switches the PHY mode in Bluetooth5 adaptively by considering the Received Signal Strength Indicator (RSSI) level. Specifically, we optimise the RSSI threshold for different PHY modes using the K-means clustering algorithm. Moreover, based on AptBLE, we further enable the Data Length Extension (DLE) feature and term the improved method as AptBLEM. We implement AptBLE (M) on the boards and test in indoor environment. The experimental results show that, AptBLE is more flexible, robust and outperforms the original fixed PHY mode in terms of throughput and transmission range. Furthermore, AptBLEM can triple the throughput than AptBLE, with a maximum throughput value in 1035Kbps and 42m range in indoor environment.
Design and Evaluation of a Low Energy Bluetooth Sensor Node for Animal Monitoring

New trends and advancements in communication and sensing technologies in Precision Farming (PF) offer opportunities to farmers for a continuous and real-time monitoring applications, which are mainly based on the use of Wireless Sensor Networks (WSNs). Animal monitoring and supervision is one critical aspect in PF, where various types of sensing and control systems are employed. However, available solutions for animal monitoring portrait a compromise between performance and system efficiency in terms of energy consumption, memory allocation, hardware size, and cost. In this work, an energy-efficient, lightweight, and cost effective wireless sensor node for animal monitoring is designed. The designed system is compact and small in size with a dimension of $26\times 33\ mm$. During its active state, the current consumption of the designed sensor node using ADXL is in the range from 3 to 9 mA at a sampling frequency of 12 Hz.
Medical Imaging-based Techniques for Alzheimer's Disease Detection: A Survey

Alzheimer's disease (AD) detection corresponds one of the most powerful and challenging tasks in medical imaging processing. This paper describes the survey of recent AD detection techniques in the last ten years. The process of AD detection can include different stages such as preprocessing, feature extraction, feature selection, dimensionality reduction, segmentation and classification. In this survey, we review the recent findings and future trends and we set out the various types of AD detection techniques with their major contributions. The performances of the most important AD detection techniques are compared and discussed according to the applied algorithms, approaches and databases (e.g. OASIS, ADNI and MIRIAD).
DC-DC Converter With Low Input Current Ripples in Hybrid Switched-Capacitor and Boost Topology

This paper presents a novel concept of a hybrid DC-DC converter composed of a switched capacitor (SC) unit and a switch-mode boost converter. In the proposed system, the major part of energy is transferred via the SC converter, and the boost converter is incorporated into this topology to improve the input power quality by reducing the current ripples. Therefore, the required passive filter is partly replaced by the power electronic components. To achieve the effect of a significant input current improvement, the boost converter operates with low power, low voltage stresses and with the use of a low-inductance choke.
Modeling Funneling Effect With Generalized Devices for SPICE Simulation of Soft Errors

Recent advances in CMOS scaling have made circuits more and more sensitive to errors and dysfunction caused by ionizing radiation, even at ground level, requiring accurate modeling of such effects. Besides generation, transport, and collection of radiation-induced excess carriers, another phenomenon, called funneling, has to be modeled for an accurate prediction of soft errors. The funneling effect occurs when the radiation track crosses a space charge region and generates excess carriers with a density higher than the doping close to it. These carriers distort the electric field of the space charge region, deeply changing the transport mechanism, from diffusion in a field-free semiconductor to drift. The objective of this work is to include funneling as part of the generalized lumped devices model in order to obtain a complete tool for SPICE-compatible simulations of single-event effects (SEEs). The latter approach has been recently proposed to simulate radiation-induced charges in the silicon substrate and is based on the so-called generalized lumped devices that simulate charge generation, propagation, and collection using standard circuit simulators. The generalized devices are here extended to include funneling and used to simulate an alpha particle impinging on the bulk of nMOS and pMOS transistors. The results obtained are validated with TCAD numerical simulations. Finally, an static random-access memory (SRAM) struck by an alpha particle is analyzed. The model predicts that the occurrence of a soft error, i.e., flipping of memory state, may depend on whether or not there is funneling. This justifies the need for accurate modeling of funneling phenomena to predict SEEs in ICs.
A Reconfigurable Battery Supercapacitor Hybrid Energy System with Active Balancing for Vehicle Applications

The concept of reconfigurable energy systems has become more and more popular in recent years for electric vehicle applications, which can largely enhance battery performance and extend battery lifetime. Active balancing is an advanced function realized by battery management systems (BMSs) to improve energy efficiencies and avoid potential over-charging risks. This paper proposed a reconfigurable hybrid energy system (HESS) that consists of one supercapacitor module, n lithium-ion modules, one DC/DC converter, and 3n switches, to enhance energy system performance with fast active balancing. The developed algorithms are to find out all possible configurations for a given lithium-ion module number and module section number, and the best configuration is selected based on the fastest balancing speed requirement. The proposed topology and algorithms are finally verified by simulations under standard vehicle driving cycles.
Neighbor-Assisted Localization for Massive MIMO 5G Systems

In this paper, we propose a multi-stage localization technique that enables to determine the position of a Target User Equipment (T-UE) with harsh channel conditions through the assistance of neighboring User Equipments (UEs). The proposed approach allows a reduced-complexity localization by minimizing the search space through multistage treatment. A neighbor discovery processing is first performed to identify the two nearest neighboring UEs among a set of UEs in the vicinity. The so selected neighbors are used as anchors (A-UE). Then, based on the signal strength, the distances separating the two A-UEs to the T-UE are determined. The distance estimates are used to plot two potential solutions of the Angle of Arrival (AoA) of T-UE. To distinguish the correct AoA estimate, oriented beamforming is performed around short arcs centered on the two AoA candidates. The user's position is then deduced based on the estimates of the AoA and the distance to the A-UEs. Our approach exploits the capabilities of neighbor discovery and oriented beamforming to provide an accurate position estimate for UEs experiencing harsh channel conditions that render direct localization at the base station difficult.
Saturated Iron Core Superconductor and Thyristor based DC Fault Current Limiter for HB-MMC

Last three decades have witnessed exponential growth in necessities of limiting DC fault current and instant restoration of High Voltage DC power systems. In this paper, Half Bridge Modular Multilevel Converter (HB-MMC) has been investigated for DC fault current. Half Bridge Modular Multilevel Converter is unable to clear DC fault current due its free-wheeling diodes, therefore it is indispensible to confine DC fault current. For limiting DC fault current and instant restoration of system, Thyristor based DC fault current limiter and Saturated Iron Core Superconductor fault current limiter (SI-SFCL) is analyzed with Hybrid DC Circuit Breaker (HDCCB). The Thyristor based fault current limiter consists of thyristor, resistor and an inductor. SI-SFCL consists of primary, secondary and superconducting coil. The Thyristor based DC fault current Limiter confine fault current to 2.5 kA from 14 kA which show 82% suppression of DC fault current, while SI-SFCL confine fault current to 3.5 kA from 14 kA which shows 75% suppression of DC fault current. The restoration time achieved from TB-FCL with HDCCB is 28 ms and from SI-SFCL with HDCCB is 140 ms. A PSCAD/EMTDC is used to design and simulate HB-MMC, SI-SFCL, TB-FCL and HDCCB
A Completely Autonomous TEG-Based Node for Thermal Gradient Measurements

This paper presents a fully autonomous measurement system based on thermoelectric generators (TEGs). The system implements a battery-less device that, by using a TEG, harvests energy from a thermal gradient and it is able to perform measurements with the same transducer. The device implements a conditioning circuit to harvest energy using an ultra-low-power system on chip (SoC) provided by STMicroelectronics. The SoC plays the role of sensing the harvested energy to activate a Bluetooth Low Energy radio as soon as the harvested energy is enough for transmission. The measuring method exploits the proportionality between the thermal gradient and the frequency of the transmitted beacons. The reading of the measurement data uses a convenient time-domain readout approach. Results and experiments evince the suitability of the proposed solution with the possibility to adopt and disseminate the device in various environments and for application in an industrial scenario.
Development and Investigation of Inverter-Fed IPMSM Drive for Electric Vehicles

This paper is devoted to development and investigations of an inverter-fed interior permanent magnet synchronous motor (IPMSM) drive for battery electrical vehicles (BEV). The main purposes are comparative investigation of two torque and speed control algorithms: vector control (FOC) and model predictive control (MPC) both with and without maximum torque per ampere algorithm. The theoretical considerations are illustrated by stationary and dynamic performance tests of the 20 kW IPMSM laboratory model.
Robust Active Fault Tolerant Control for Five-Phase PMSM against Speed Sensor Failures

The speed sensor is one of the most important parts of the five-phase Permanent Magnet Synchronous Motor (PMSM) drive, and the most sensitive sensor to the external or internal perturbation. In this framework, this paper is devoted to the Active Fault Tolerant Control (AFTC) of the five-phase PMSM. Based on a switching system between a real and virtual sensor, which is the Unknown Input Observer (UIO), and between two different control techniques, the system can maintain its high performance and efficiency. The PMSM is mainly controlled by PI regulator, but with the presence of high uncertainty or under a fault condition, the regulation system commute to the sensorless control mode with the Backstepping regulator. Simulated results justify the effectiveness of the proposed AFTC used to drive the five-phase PMSM under different operation condition.
Considering Sync Word and Header Error Rates for Performance Assessment in LoRa System

This paper deals with a general and complete analysis of the frame error rate in LoRa system, not only considering the payload error rate, but also the sync word (carrying network information) and the header (carrying control information) error rates. It is proved that, in the high signal-to-noise ratio (SNR) range, it is more likely to reject a LoRa frame due to an erroneous sync word estimation rather than an erroneous header or payload decoding, due to the fact that it is not coded. The theoretical results are then verified through simulations.
Characteristics of Pseudospark Discharge in Particle-in-Cell Simulations

In this article, the characteristics of the pseudospark (PS) discharge were studied through particle-in-cell simulations with the consideration of the external circuit. The different discharge stages, including the Townsend discharge, hollow cathode discharge, and super-dense glow, were characterized by the discharge voltage and current. The sources of electrons at different discharge stages were analyzed, with the electrons generated from the ionization of the background gas playing the dominant role. The electron densities at the Townsend discharge, hollow cathode discharge, and super-dense glow discharge stages were in the range of 1013, 1018, and 1020 m−3, respectively. The energy of the electron beam extracted at the anode aperture at different discharge phases was exported and postprocessed. The electron beams generated at the three stages have an energy spread of 10%, 20%, and 40%, respectively. The hollow cathode discharge phase has balanced parameters in beam energy, energy spread, and electron density in contrast to the Townsend discharge and super-dense glow discharge stages.
Analytical Study on a 700 V Triple RESURF LDMOS With a Variable High-K Dielectric Trench

A novel 700 V triple REduced SURface Field (RESURF) lateral double-diffused MOSFETs (LDMOS) with a variable high-<inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> (VHK) dielectric trench for smart power applications is proposed and studied by TCAD simulations. Compared with conventional triple RESURF (CTR) LDMOS, the new structure features a composite high-<inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> (HK) dielectric trench embedded in the drain edge. First, a higher HK dielectric layer is in the upper trench to suppress the high electric field (<inline-formula> <tex-math notation="LaTeX">${E}$ </tex-math></inline-formula>-field) under the drain by dielectric RESURF. Second, a lower HK dielectric is at the bottom of the trench to promote the depletion of the <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula>-buffer layer and <inline-formula> <tex-math notation="LaTeX">${P}$ </tex-math></inline-formula>-substrate, which increases the <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula>-buffer doping concentration and thus reduces ON-resistance. The overall vertical bulk <inline-formula> <tex-math notation="LaTeX">${E}$ </tex-math></inline-formula>-field distribution is modulated by the <inline-formula> <tex-math notation="LaTeX">${E}$ </tex-math></inline-formula>-field peak generated at the position of varying <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> dielectric, which greatly improves breakdown voltage (BV). An analytical model of BV and vertical <inline-formula> <tex-math notation="LaTeX">${E}$ </tex-math></inline-formula>-field taking account of the influence of the VHK dielectric trench is presented. Simulation results show that the proposed VHK TR LDMOS is able to obtain a 30.2% higher BV and a lower 15.4% <inline-formula> <tex-math notation="LaTeX">${R}_{ \mathrm{\scriptscriptstyle ON}, \text {sp}}$ </tex-math></inline-formula> than the CTR LDMOS. Moreover, the figure of merit (BV<sup>2</sup>/<inline-formula> <tex-math notation="LaTeX">${R}_{ \mathrm{\scriptscriptstyle ON}, \text {sp}}$ </tex-math></inline-formula>) of VHK TR LDMOS has doubled further breaking the lateral silicon limit.
Sparse Learning Based Implantable Medical Device Transmission against Eavesdropping

Due to the restrictions of computing resources and energy supply, the wireless communication security of recently emerging implantable medical devices (IMD) is still an open issue. In order to ensure privacy and security and reduce transmission power consumption, we propose a physical layer security (PLS) scheme called Sparse Learning based Encryption and Recovery (SLER) to compress and encrypt the sparse IMD sensing signal at the transmitter, and recover the sparse signal at the receiver. The encryption stage of the SLER scheme at the transmitter is conducted by a joint compression and encryption algorithm based on compressed sensing, which only requires simple matrix addition and multiplication operations. The decryption stage at the receiver adopts a pre-trained deep neural network that evolved from the conventional iterative reconstruction method, i.e., approximate message passing (AMP), to implement accurate signal recovery by learning the sparse features of the received IMD sensing signal. Simulations are performed that have verified the security performance of the SLER scheme and proved the prominent accuracy of sparse signal recovery compared with other state-of-the-art sparse approximation algorithms.
Analysis of heat transfer in GAAFET Transistor with linear doping based on single-phase lag model

The self-heating effect in gate all around FET transistors (GAAFET) is investigated in this paper. The finite element method is used to analyze the heat conduction based on the 2d single-phase lag (SPL) equation. New devices with linear doping profile in the source and drain region have been taken into consideration. The study has carried out different structures, triple material surrounding gate (TMSG), dual material surrounding gate (DMSG), and single material surrounding gate (SMSG) structure. The simulation results were compared to conventional devices with a constant doping profile. The influence of heat flux time lag values in temperature distribution has also been discussed. Results show that the linear doping profile and the right choice of the gate work function or the heat flux lag $\tau\mathrm{q}$ parameters provide better energy management profile to improve performance of technology applications
Two-Dimensional Binary Z-Complementary Array Pairs

One-dimensional (1-D) Golay complementary pair (GCP) and its extension Z-complementary pair (ZCP) with additional zero autocorrelation zone property have been widely studied in the literature. Recently, the investigation of 1-D GCPs have been extended to two-dimensional (2-D) and higher dimensional Golay complementary array pairs (GCAPs). In this article, the concept of zero correlation zone (ZCZ) is applied to 2-D GCAP and the 2-D Z-complementary array pair (ZCAP) is presented. Several constructions of ZCAPs are proposed in this article and the existence of ZCAPs is discussed as well. Based on the proposed constructions, 2-D ZCAPs are shown to exist for all sizes. Furthermore, an upper bound on the rectangular ZCZ size of the 2-D ZCAP is derived when both dimensions are odd. The proposed constructions can include ZCAPs which achieve the upper bound.
The Classification of Power Quality Disturbances using Statistical S-Transform and Probabilistic Neural Network

This article compares the ability of the Probabilistic Neural Network in the classification of several Power Quality Disturbances (PQD) using statistical parameters. The objective is to investigate the effectiveness of the classifier in modeling the low-dimensional feature vectors describing several PQD disturbances. In the process, several statistical parameters such as the mean, RMS value, standard deviation, skewness, Kurtosis, form factor, Crest factor, Energy, normalized entropy, log entropy, and Shannon entropy have been extracted using the Feature vectors of the well-known Stockwell Transform (ST). The statistical coefficients corresponding to ten-PQDs have been fetched and fed to the chosen PNN for efficient modeling. A comparison of the recognition accuracy of the PQDs has been made to that of the conventional statistical parameters extracted directly from the synthetic raw signals. The ST statistical parameters have shown to outperform with an average recognition accuracy of 92.6%. On the contrary, the conventional statistical parameters have provided a lower accuracy of 79.5%. In the case of PNN, the number of hidden layer neurons is made equal to the number of training data. A suitable selection of the spread factor leads to better recognition accuracy as revealed from our results.