Miracle-dz/UniFAR-FTUs huggingface dataset:
Particle Filter-Based Indoor Localization and Tracking Method Using Leaky Coaxial Cables

As a key technology, localization has promoted many location-based services and applications. It also benefits various novel systems such as the Internet of Things (IoT), autonomous vehicles, and smart homes and buildings. Smart manufacturing factory, as an important application scenario in Industry 4.0, need effective and stable localization technology as technical support for advanced production. Most of the existing localization methods in wireless communication consider the communication systems using conventional monopole antennas. However, in a factory environment, the influence of metal objects on the shielding and interference of radio waves limits the performance of conventional monopole antennas. It is often necessary to increase the number of access points (AP) with additional complexity and cost to guarantee entire wireless coverage. Leaky coaxial cable (LCX) which can be used as antennas for wireless communication can be employed to solve this problem. As a long and flexible cable, LCX can provide promising wireless coverage for stable wireless communication and localization. This paper is aimed at a particle filter-based localization method using LCX in an indoor environment. The localization scheme uses the time difference of arrival (TDOA) as the indicator for filtering.
Modeling and Analysis of DC Pole-to-Pole Fault in High-Frequency-Bus Based Power Electronic Transformer

Multi-port power electronic transformer (PET) is a key component for future power grid. A kind of PET topology based on high-frequency-bus (HFB) has attracted attention because of its lower cost and higher efficiency. But conventional HFB-PET has not been able to deal with DC short-circuit fault yet. With the presence of the unique HFB structure, traditional DC short-circuit model will result in error. So, a modified DC short-circuit model is needed for fault analysis and protection. The paper proposes a modified short-circuit model for HFB-PET, which explains the mechanism of fault transient process and provides a way to calculate the fault current mathematically. The numerical solution of the model is proved to be faster than circuit simulation one and the result is more accurate than conventional analytical method. The validity and accuracy of the model is verified by comparison with simulation in DSIM 2021a and hardware-in-loop real-time simulation.
Effects of VSI on the Correlation between System Stability and Output Active Power of Wind Farms in Weak Grid

Recently, subsynchronous interactions (SSIs) between wind farms and weak grids attract significant concerns. To investigate the impact of the wind farms output active power on SSI modes in the weak grid, an impedance-based grid-connected system model is established. The dominant factors that affect the correlation between output active power and system stability are analyzed. Based on the small-signal impedance model, the SSI modes of the system is analyzed by the stability analysis results. From the results, the dominant factors affecting SSI instability of the drive permanent magnet synchronous generator (D-PMSG) based wind farms connected to a weak AC grid is presented, including the inner loop, outer loop and phase-locked loop (PLL) parameters of its voltage source inverter (VSI). Meanwhile, the correlation between outer loop and system stability is various at different wind farms output active power level. The stability analysis results are verified by the time-domain simulation in PSCAD/EMTDC.
PCANet Combined with Residual Connection for Hyperspectral Image Classification

This paper proposed a relatively lightweight DL model for the classification of hyperspectral imagery, which is built on the PCANet backbone framework and combined with residual connection. Compared to the conventional CNNs, the proposed DL model (Hereafter dubbed ‘ResPCANet’) has some surprising merits, on the one hand, more simpler of network architecture and less requirements of the labelled samples, as well as faster learning speed of the network. On the other hand, combined with residual skip connection, more diverse and complementary features that derived from different layers can be fused for subsequent classifier construction. Our experiments, carried out on real hyperspectral dataset, revealed the effectiveness of the proposed ResPCANet model, which further demonstrated the superiority of the residual skip connection strategy.
Characterization, Analysis and Optimization of a CMOS RF Energy Harvesting Rectifier System for WSN Application

Wireless Power Transfer has become an important aspect in energy harvesting to supply ultra-low power sensor networks. In remote areas, energy sources become a significant restriction for sensors. This paper presents an analysis and optimization technique for RF Energy harvesting system for Wireless Local Area Network (WLAN) source operating at 2.4GHz to power sensor node applications. The rectifier which is the main component for energy conversion features a multi-stage cross-coupled differential-drive architecture with single-sided biasing and storage capacitors on each stage to help improve the rectifier performance by adding instantaneous RF voltage at each stage. The architecture is implemented in standard 65nm CMOS Technology. The measurements were done by using input RF signal frequency of 2.4GHz for resistive load values of 30k, 100k and 1M Ohm. The proposed design system verification is used to analyze and investigate the effect and importance of layout and package parasitic impedances to the rectifier output voltage, sensitivity, and power conversion efficiency (PCE). Measurement results of $\boldsymbol{\text{Rload}=100\mathrm{k}\Omega}$ and $\boldsymbol{\mathbf{Cload}=10\text{pF}}$ showed an output voltage of 1 V at −10dBm input power and 23.93% PCE.
Diabetic retinopathy detection using a pretrained machine learning model

One of the possible side effects of diabetes is Diabetic Retinopathy. Early diagnosis of diabetic retinopathy is an important step toward healing and can prevent many patients from potential blindness. To identify diabetic retinopathy, practitioners may need an artificial intelligence tool to provide a second opinion to confirm or refute a diagnosis. In this article we use a pretrained machine learning model to find a solution to the issue of low-quality inputs by implementing, in the pre-processing step, the technique of Contrast Limited Adaptive Histogram Equalization to extract more features especially the blood vessels.
3D Printing Antennas for 5G and Millimeter Wave 6G Applications

The use of inexpensive open-source 3D printing technologies for the development of antennas for future communication systems is proposed. Two antennas have been designed and then fabricated using additive manufacturing techniques. The first is a patch antenna developed using a combination of fuse filament fabrication (FFF) for the supporting substrate and direct write to for the metal layers. The patch antenna is integrated into an also 3D printed wearable ring and operates at the 28 GHz band allocated to 5G systems. The second is an aperture coupled dielectric resonator antenna (DRA). It employs a high-performance dielectric substrate which is 3D printed using fuse filament fabrication. The DRA operates at 13.2 GHz and is used as an initial test to assess the technology. The aim is to further develop DRA antennas at millimeter wave frequencies. CST Microwave studio was used for the design of the antennas. Measurements compare well with simulations It illustrates the potential of low-cost open-source 3D printing techniques for the development of antennas for the fifth generation (5G) and future millimeter wave (mmW) sixth generation (6G) wireless communication systems applications.
gem5-ndp: Near-Data Processing Architecture Simulation From Low Level Caches to DRAM

Unlike standard accelerators, the performance of Near-Data Processing (NDP) devices highly depends on the operation of the surrounding system, namely, the Central Processing Unit (CPU) and the memory hierarchy. Therefore, to accurately evaluate the gain provided by such devices, the entire processing system must be considered. Recent proposals redesigned existing architectural simulators to estimate the performance of NDP devices. However, the conclusions that can be drawn from using these frameworks are limited, and they fail to provide full support to simulate these devices (e.g., most simulators do not allow simultaneous operation of the CPU and the NDP device). In this paper, a novel framework (called gem5-ndp) based on the gem5 architectural simulator is proposed, providing full support to the development, validation, and evaluation of novel NDP architectures. To illustrate the process of developing and integrating an NDP device with a processing system using the proposed framework, as well as to demonstrate its viability and benefits, two case studies are also proposed and thoroughly discussed. gem5-ndp significantly improves the performance evaluation confidence of NDP devices with results showing that classical approaches lead to a deviation of up to 54.9 % when compared with results obtained with gem5-ndp.
Compact Integrated Duplex Filtering Antenna with Common Feeding and Radiating SIW Cavities

In this paper, a compact integrated duplex filtering antenna is proposed based on co-shared substrate integrated waveguide (SIW) cavity. Firstly, a dual-mode TE102 and (TE201 modes) rectangular cavity with two input ports is utilized as a common feeding cavity to respectively contribute one transmission poles for each channel. And then, an additional square cavity with a rectangular ring slot is coupled from the lower feeding cavity for both dual-mode excitation and duplexing passband radiation. To verify the proposed concept, a prototype duplex filtering antenna is simulated. The duplex filtering antenna resonates at 7.27 GHz and 8.06 GHz with a low cross-polarization and a high gain of 5.82 dBi and 6.91 dBi, respectively.
Implement a Continuous Learning Model to Detect Different Types of DDoS Attacks with Hierarchical Temporal Memory

This paper presents a machine learning model developed based on Hierarchical Temporal Memory to detect DDoS attacks. The model can support continuous learning, which can allow the model to update new attack patterns in real time. We also design a mechanism to evaluate the confidence s core for each classification to detect uncertain patterns for deeper analysis by traffic engineers. Furthermore, t he modeluses features of packet length distribution and inter-arrival time, which can be extracted with FPGA accelerators for a high-speed network, to classify different types of attacks. Finally, we use the CICDDoS 2019 dataset in our experiments to evaluate the performance and achieve a good result.
Massive MIMO Channel Prediction in Real Propagation Environments Using Tensor Decomposition and Autoregressive Models

Massive multiple-input multiple-output (MIMO) has been widely deployed in practice due to its capability of improving spectral efficiency. In mobility scenarios where users move at relatively high velocities (e.g., 40 km/h), however, massive MIMO faces a severe problem called “channel aging”. This problem leads to performance degradation as outdated and inaccurate channel state information (CSI) is used in massive MIMO beamforming. In the paper, we study time-variant massive MIMO channel characteristics in real propagation environments, and propose channel prediction schemes using tensor decomposition and autoregressive (AR) models to combat channel aging. Specifically, a multi-dimensional massive MIMO channel can be regarded as a higher-order tensor and modeled by the canonical polyadic decomposition (CPD). By applying the CPD, channel components that vary relatively slowly in time are extracted, and their time variations are captured and predicted using AR models. Based on channel data measured at the 2.6 GHz frequency band, we evaluate the prediction accuracy of the CPD-AR scheme and the corresponding performance using zero-forcing (ZF) beamforming. For comparison, a scheme using the multi-dimensional discrete Fourier transform (DFT) and AR prediction is also presented. Simulation results show that both schemes can reduce CSI error by 3–10 dB as compared to the aged CSI, resulting in significant improvements of ZF sum-rates. The research indicates that massive MIMO channels are to a large extent predictable in both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios, and the proposed prediction schemes can effectively alleviate channel aging when users move in relatively high speeds.
An easy way to determine simultaneously the electron density and temperature in high-pressure plasmas by using Stark broadening

This paper discusses the possibility of determining, at the same time, both the electron density and temperature in a discharge produced at atmospheric pressure using the Stark broadening of lines spontaneously emitted by a plasma. This direct method allows us to obtain experimental results that are in good agreement with others previously obtained for the same type of discharge. Its advantages and disadvantages compared to other direct methods of diagnostics, namely Thomson scattering, are also discussed.
Energy Efficiency Optimization in Millimeter-wave Air-to-Ground Links under UAV Wobbling

The power consumption is a critical bottleneck of the unmanned aerial vehicle (UAV) air-to-ground (A2G) link due to the limited on-board battery. Meanwhile, the fast time-varying channel for millimeter-wave (mm-wave) A2G links may lead to the aged channel state information (CSI) under inevitable UAV wobbling. In this paper, we propose two optimization problems to evaluate the energy efficiency performance of the mm-wave UAV A2G link under wobbling. Then, we design two algorithms to maximize the energy efficiency of a mm-wave UAV A2G link by optimizing the transmission time and the UAV hover height subject to bit error probability while considering aged CSI. The numerical results present the spectral-energy efficiency trade-off and the optimum hover height of the mm-wave UAV A2G link under aged CSI.
Impact of Compression on the Performance of the Room Impulse Response Interpolation Approach to Spatial Audio Synthesis

In Virtual Reality (VR) an application where a user is able to move freely in the virtual or physical environment, the 3D sound scene reproduced over headphones, including the acoustic Room Impulse Response (RIR) must be constantly updated. Storing and transmitting the RIR for every possible source and listening position is impractical and hence efficient compression is required. This paper investigates compression and interpolation approaches for synthesising the RIR at new listening positions using a discrete set of compressed directional RIRs (DRIRs) recorded with an Ambisonics B-format microphone. The predicted sound quality and localisation accuracy of Ambisonic sound scenes derived using interpolated, compressed RIRs is based on the Neural Similarity Index Measure (NSIM) applied to the time-frequency phase information derived from reference and synthesised B-format channels and as used in the AMBIQUAL objective spatial audio quality metric. Results show a strong correlation between the compression bitrate and the objective quality scores of B-format Ambisonics audios including interpolated and ground truth DRIRs.
Intelligent Fault Diagnosis Method of Motor Gear Based on Transfer Learning Under Variable Working Conditions

A transfer learning (TL) based intelligent diagnosis method for motor gear faults under variable operating conditions is proposed. Firstly, an improved threshold function is used to eliminate the signal noise caused by gear gap oscillation. Secondly, a feature TL model is developed. The model uses the transfer component analysis (TCA) method to preserve the maximum embedment space of the source and target domain data characteristics. Finally, similar features are mapped to the new space of labeled data transmission. The simulation results show that the method solves the problem of large data volume and computational complexity. At the same time, it improves the diagnosis efficiency of motor gear faults under variable working conditions.
A Multi-Target ISAR Imaging Method Based on Zhang-Suen Thinning and Radon Transform

Achieving high-resolution images of multiple targets is a crucial problem in ISAR imaging. Mono-target imaging methods generally fail to address multi-target imaging formation. To tackle this problem, we propose a multi-target ISAR imaging method based on Zhang-Suen thinning and the Radon transform in this paper. Zhang-Suen thinning is first utilized to extract skeletons of overlapped range profiles. Since Zhang-Suen thinning retains the motion information of multiple targets and reduces the overlap, Radon transform is allowed to estimate range walk more accurately. Subsequently, these aligned targets are isolated in the image domain. We then utilize the Maximum-Correlation Range Alignment (MCRA) and the Phase Gradient Autofocus (PGA) methods to compensate motions of range profiles thoroughly. Through the above procedures, we achieve accurately compensated and completely separated ISAR images of different targets.
Design and study of effect of magnetic flux-barriers on a high-pole Permanent Magnet Synchronous Machine

This paper presents the design of a permanent magnet synchronous machine, with a 28-pole fractional slot concentrated wound machine as reference. The new stator structure is based on the principle of magnetic flux-barriers in the stator core, whose effect on the air-gap flux density harmonics is studied. The output parameters analysis of the two machines indicates that the new stator model presents significant advantages.
Improving Nonlinearity Tolerance of PCS-QAM Digital Multi-Carrier Systems Through Symbol Rate Optimization

We experimentally demonstrate that symbol-rate optimization (SRO) provides nonlinear gains in multicarrier systems, even with PCS modulation and realistic DSP. Optimized carrier phase recovery is crucial to achieving 0.2 dB gain for 1400 km 800G transmission, out of the ∼0.7 dB theoretical maximum gain we measured.
A 28-GHz Current-Mode Outphasing Power Amplifier with Peak PAE of 51 % and OP1dB of 22.89dBm for 5G Applications

This paper presents a 28-GHz current-mode power combining outphasing power amplifer (P A). Each P A cell utilizes source degeneration to achieve high linearity while maintaining high gain and high output power. A current-mode power combiner is optimized for Chireix compensation and minimum insertion loss. Designed in TSMC 40nm LP CMOS process, the proposed outphasing P A achieves a peak power-added efficiency (PAE) of 51 %, a 6-dB power back-off (PBO) PAE of 27.82%, and an output power 1-dB compression point (OP1dB) of 22.89dBm in post-layout simulation results.
Case Study: Defining and Measuring Protection Signal Transfer Speed, Latency, and Reliability Within Digital Trip Circuits

Abstract —The recent Newton‑Evans study of the North American market for substation, automation, and integration systems reveals that 56 percent of respondents plan to use digital trip circuits to replace their legacy analog hardwired trip circuits. However, it is important to recognize that the top three protection system failures identified by the Electric Reliability Organization similarly affect digital trip circuits. It is also important to anticipate and reduce communications errors, hardware malfunctions, and logic and setting errors, which has accounted for 60 percent of misoperations since 2011. This paper includes the standardized methods to evaluate, test, and measure digital trip circuit designs, and it includes test results to compare speed and reliability of typical designs. To understand fault‑clearing time, various trip‑circuit latencies must be evaluated. The trip circuit time is the sum of the protective relay’s decision time, the relay’s physical output contact time assertion, and any additional timing required by auxiliary trip circuits and digital communications paths. The trip‑circuit time can be estimated using theoretical information from the sources of the different components. However, it is good practice to measure the trip circuit time before placing the system in operation, especially if the trip circuit is composed of nondeterministic technologies, such as Ethernet‑based devices and protocols. This paper includes measurements to verify fault‑clearing time calculations.
Frontier and hotspot evolution in Wiskott–Aldrich syndrome: A bibliometric analysis from 2001 to 2021

Background: Wiskott–Aldrich syndrome (WAS) is a rare X-linked primary immunodeficiency disorder. Despite our enormous progress in the strategies used to diagnose, treat, and cure WAS, no bibliometric studies have been performed in this research field. This study explored the trends in WAS research through a bibliometric analysis evaluating relevant literature quantitatively and qualitatively. Methods: The literature concerning WAS from 2001 to 2021 was retrieved from the Science Citation Index Expanded (SCI-expanded) of the Web of Science Core Collection database. Acquired data were then visually analyzed using CiteSpace and VOSviewer. Results: 2036 papers were included in the final analysis. The annual publication outputs reached its peak in 2013 but declined in recent years. The dominant position of the United States in WAS research was quite obvious. Harvard University (USA), University College London (UK), and Inserm (France) were the three most prolific institutions. Adrian J. Thrasher exerted significant publication impact and made the most notable contributions in the field of WAS. Blood was the most influential journal with the highest publication outputs, and nearly all the top 10 journals and co-cited journals belonged to Q1. Immune dysregulation, thrombocytopenia, syndrome protein deficiency, stem cell, mutation, and diagnosis were the keywords with the strongest citation burst. Conclusion: From 2001 to 2021, the United States was a global leader in the WAS research. Collaboration between countries and institutions is expected to deepen and strengthen in the future. Research hotspots included pathogenesis, clinical manifestations, diagnosis, and therapy. Our results suggest a greater understanding of the mechanistic underpinnings of immune dysfunction in WAS patients, the application of targeted therapies for individual complications, and the development of curative approaches, which will remain research hotspots in the future.