Miracle-dz/UniFAR-FTUs huggingface dataset:
Practical Evaluation of mm-Wave Communication for Automotive Applications in a Mixed Urban Area of London

Intelligent vehicles or smart cars are considered to be capable of reducing the high mortality rate that occurs on roads globally. The different applications of intelligent vehicles i.e., vehicle-to-vehicle (V2V) or vehicle-to-infrastructure networks (V2I), are collectively called vehicle-to-everything (V2X) communications which has the endorsement of the international standard IEEE 802.11p. It is widely expected that V2X can diminish traffic gridlocks and aid the drivers with information such as road status and weather forecast in order to prevent traffic accidents. The aim of this paper is to generate viable mm-Wave channel parameters for V2V scenario in a specific urban area in London as a benchmark for future development. Thus, a thorough evaluation of the implemented V2V mm-Wave propagation channel is performed so as to determine the main constraint factors, such as the signal fading, shadowing, reflection, refraction, scattering and Doppler effect, that can affect V2V communication and connectivity. We analyse comparatively the simulation results of V2V channel propagation parameters using the geometry-based efficient propagation simulator for V2V communications with the experimental measurements conducted using our testbed setup in Hendon area of London UK, which is a combination of urban and suburban streets, with hill top and express way in terms of roadside environments. It is found that i) the simulation results are consistent with the practical measurement; and ii) the simulation at higher mmWave frequencies shows that signal can be detected up to 200m in the urban area irrespective of it being highly non-line of sight environment.
Magnetic Integration of EMI filter for Grid-Connected Voltage-Source Inverters

The magnetic integration method of single-stage and multi-stage electromagnetic interference (EMI) filter was proposed with EE and EIE-type cores for single-phase grid-connected voltage-source inverter (VSI). Common mode (CM) and differential mode (DM) chokes were all integrated into a single magnetic core unit. The effectiveness of integrated single-stage EMI filter (ISSEF) and multi-stage EMI filter (IMSEF) has been verified by simulation and experiments. Taking a 500 W single-phase inverter as an experiment platform, the conducted EMI noise and core temperature distribution were presented. The EMI filter with integrated structure can significantly reduce system volume with satisfied EMI suppression ability.
I/O Interaction Analysis of Binary Code

The increasing use of closed-source software in everyday life leads to a need for privacy verification of binary code. Unfortunately, analyses of machine code behavior are often imprecise, do not scale, or require expert interaction. We present a novel and fast algorithm that is capable of soundly approximating I/O behavior of binary code. The key idea of our algorithm is that overall I/O behavior can be approximated by analyzing input and output operations. Since most function parameters are defined close to their use, our backward symbolic execution algorithm can quickly recover most meaningful parameters. We show the applicability and performance of our approach by analyzing the coreutils binaries.
Threshold Estimation Models for Linear Threshold-Based Influential User Mining in Social Networks

Influence Maximization (IM) is a popular social network mining mechanism that mines influential users for viral marketing in social networks. Most of the Influence Maximization techniques employ either the independent cascade (IC) or linear threshold (LT) model in the node activation process. In the IC model, all the active in-neighbors are given a single chance to activate a node with a particular probability whereas, in the LT model, a node is activated if the aggregated influence of all the activated in-neighbors is no less than a threshold value. Thus, the threshold plays a significant role in the LT-based influence maximization. In this paper, we comprehensively survey the different threshold values used in various IM models. Based on the survey, we observe that the current studies lack threshold estimation models. Therefore, we develop a system model and propose four threshold estimation models based on influence-weight and degree distribution. The empirical results show that our algorithms generate threshold values that resemble the thresholds used by most IM algorithms along with faster running time. Besides, the proposed models are scalable and applicable to any influence-weight estimation technique and offer narrower threshold ranges rather than the broad ranges used in many existing works.
Compact Millimeter-Wave SIW Circuit that Combines Phase-Shifting and Attenuating Functions

In the 5G networks, millimeter-wave (mmW) devices and circuits with compact size and multiple functions are essential for the development of a large-scale wireless system thanks to their high-level integration features. In this work, we propose and demonstrate a mmW band compact device that simultaneously performs the function of phase-shifter and attenuator. The proposed device is based on a folded SIW scheme with coupling aperture and etched slots designed for the concurrent tuning of amplitude and phase. Theoretical and experimental results are given, which confirm the usefulness of the proposed technique.
Design and Pattern Measurement of a Sub-6GHz 64-Channel Full Digital Massive MIMO System

In this paper, a 64 channel, full digital beamforming massive MIMO system centered at 3.5 GHz is designed for 5G application. The RF bandwidth of the system is 3.4-3.6 GHz, which has the ability to support 200 MHz modulated signal. Besides, a 64 channel synchronized direct digital synthesizer(DDS) system as well as a 64 channel synchronized ADC system are designed for signal generation and capture purpose, respectively. The whole system was calibrated and tested through Over-The-Air (OTA) method. The effective isotropic radiated power (EIRP) of the system after calibration is 43 dBm, making it possible to realize realistic communication. Beam scanning patterns and multibeam patterns were measured, which shows that the system can cover ± 45o region. Simulation results are in good agreement with the measured patterns, which validates the massive MIMO system design.
1 Adaptive defect simulation flow for Defect-oriented Test evaluation

For AMS-RF circuits, functional test is usually considered the best way to test a circuit. By construction, it should detect any fault (i.e. performance loss) and consequently it does not require a-priori validation. However, defect-oriented strategies require an evaluation of the test quality prior to their implementation. This implies resorting to computationally intensive defect simulation campaigns. In this work, we propose an adaptive defect simulation loop that evaluates at each step the defect coverage and the fault escape rate of the test under validation and determines the best way to employ the computational power as a function of the test target metrics. That is to say, if it is better to simulate the performance setup to update the fault escape metric or, conversely, to simulate the proposed test setup to update the defect coverage metric.
Interval Observers for a Class of Nonlinear Systems Using Gaussian Process Models

An interval observer design approach for partially unknown nonlinear systems is developed, where the unknown system component is modeled using Gaussian processes and noisy system measurements. The proposed method is applicable for bounded nonlinear systems where the system uncertainty is described by a Lipschitz continuous function. The interval observer generates a correct estimation error with high probability, and the error bound is decreased by employing new training data points.
Design of a Miniaturized Antenna Based on Split Ring Resonators for 5G Wireless Communications

The design of a high-gain and high-orientation rectangular patch microstrip antenna for 5G communication, which works at 3.5GHz with a relative bandwidth of 2.28% and a gain of 7.43dB, is presented in this paper. By theoretical analysis and electromagnetic simulation design, a new type of miniaturized antenna is designed, in which 10 split ring resonators (SRR) metamaterial structures are symmetrically etched on the radiation patch. Compared with the same microstrip antenna without metamaterial structure, the overall size of the antenna is reduced from 40 * 45 mm to 35 * 40 mm, which is reduced by 22.2%. The loading of metamaterials has an obvious effect on miniaturization of the antenna.
An RC-IGCT for Application at Up to 5.3kV

A 94mm, Reverse Conducting-Integrated Gate Commutated Thyristor (RC-IGCT) for application at high DC voltage, up to 5.3kV has been manufactured and characterized. The design challenges for this type of device operating at such high voltage are to balance the losses to the ability to cool the device while keeping the snappiness of the diode part and the cosmic-ray failure rate to acceptable levels. The snappiness of the diode part has been overcome by implementing a new diode design in this high voltage (10kV) RC-IGCT, without influencing the technology curve under nominal operating conditions for the same silicon thickness. Furthermore, the experimental results show that the technology curve can be improved significantly with the new diode design by thinning the silicon while maintaining the soft reverse recovery behavior of the diode. The newly developed 94mm, 10kV RC-IGCT could be a device of choice for the low switching frequency high voltage power electronics applications, where one device could replace complicated series connection of two 4.5kV or 5.5kV devices per switch position.
Hardware Design of a (2,1,2) Binary Convolutional Encoder Circuit

Convolutional codes are used as error-correction techniques over unreliable noisy channels in communication processes that do not support message retransmission. Many communication devices, such as satellite receivers, use convolutional codes as channel coding techniques that are usually denoted as Forward-Error-Correction (FEC). In this paper, the design of a convolutional encoder with three flip-flops, in CMOS technology, with 1.2 V DC voltage source is done. This circuit is intended to be included in portable devices, powered by rechargeable batteries. A simulation of the designed circuit is presented in order to analyze its behavior in various conditions.
SVD-Based Channel Pruning for Convolutional Neural Network in Acoustic Scene Classification Model

Convolutional Neural Network (CNN) offers promising performance in Acoustic Scene Classification (ASC) tasks. The CNN model, however, often involves a large number of parameters, and thus requires large storage space for the implementation of the model. In this paper, we propose a new method for model pruning based on singular value decomposition (SVD). More specifically, the number of parameters is reduced by a low-rank decomposition method, where a matrix is decomposed into products of three small matrices. As a result, the original convolutional layer is decomposed into three smaller convolutional layers resulting in an overall reduction in the number of parameters involved in the model. The proposed method is evaluated on the dataset of ASC task in DCASE2018. The results illustrate that the proposed approach dramatically reduces the CNN layers size by more than 90% with relatively 1% performance loss and the activity of parameters in convolutional layers increases with performance loss of the compressed model.
Three-Beam Circularly Polarized ME Dipole Antenna Array Fed by 3 × 3 Butler Matrix

A circularly polarized array consisting of magneto-electric (ME) dipole antennas with beam scanning is proposed, which is fed by a substrate integrated waveguide (SIW) 3 × 3 butler matrix. The wide AR beamwidth antenna element is the key to completing the antenna array. In order to ensure the generation of normal beam, the 3 × 3 butler matrix was adopted as the beam-forming network. The proposed structure operates in the range of 27.5GHz ~ 28.5GHz and generates three circularly polarized beams that cover the range of ±40°.
Sharp Roll-Off Bandpass Filter Using Coupled Lines and Stepped-Impedance Stubs

A fifth-order bandpass filter (BPF) using three pairs of coupled lines and two shorted stepped-impedance stubs is proposed in this paper. Five transmission poles (TPs) within the passband and six deep transmission zeros (TZs) from 0 to 2f0 (f0:passband center frequency) can be realized through input admittance calculation. For demonstration, a sharp roll-off BPF prototype centered at 2 GHz with good in-band characteristics (insertion losses and return losses) and high stopband rejections is designed, whose 3-dB fractional bandwidth is of 34%.
Real-Time Single Channel Over-the-Air Data Acquisition for Digital Predistortion of 5G Massive MIMO Wireless Transmitters

In this paper, a single channel over-the-air (OTA) data acquisition approach for real-time calibration of digital predistorter in multiple-input multiple-output transmitters is proposed. By using the data acquired from the far-field OTA tests, the output of each power amplifier (PA) can be virtually reconstructed and thus the linearization reference at the main beam direction can be accurately estimated. Digital predistortion (DPD) can therefore be effectively constructed without direct measurement at PA output. Experimental results demonstrate that the proposed scheme can accurately estimate far-field main beam data and the proposed DPD can achieve excellent linearization performance.
A novel set-based reachability method for optimal robust control of constrained linear systems

This paper addresses finite horizon optimal control of linear dynamical systems affected by an additive bounded disturbance and subject to polyhedral state and input constraints. The goal is to design a static state feedback control law that minimizes a quadratic nominal cost while robustly satisfying the state/input constraints. We propose a novel set-based reachability approach and compare it against two alternative set-based approaches that were proposed for robust model predictive control (MPC) a few years back. All the three approaches offer a computational procedure to the design of a control policy, which is expressed as the sum of a state feedback term and an open loop term. While in the robust MPC methods the feedback term is a-priori fixed and only the open loop term is optimized, in the proposed method both of them are design parameters that are jointly optimally tuned. This is achieved by adopting a zonotopic parametrization of the control law that makes constraints and cost function respectively linear and quadratic in the parameters. As a result, the set-based reachability method provides a feasible solution for tighter constraints than the two alternative set-based methods.
Schizophrenia Classification Using fMRI Data Based on a Multiple Feature Image Capsule Network Ensemble

Automatic diagnosis and classification of schizophrenia based on functional magnetic resonance imaging (fMRI) data have attracted increasing attention in recent years. Most previous studies abstracted highly compressed functional features from the view of brain science and fed them into shallow classifiers for this purpose. However, their classification performance in practical applications is unstable and unsatisfactory. As an acute psychotic disorder, schizophrenia shows functional complexity in fMRI data. Therefore, additional features and deep classification methods are needed to improve classification performance. In this study, we propose a multiple feature image capsule network ensemble approach for schizophrenia classification. The proposed approach proceeds in three steps: 1) extracting multiple image features from the perspective of linear sparse representation, nonlinear multiple kernel representation, and function connection of brain areas respectively; 2) feeding these image features into three specially designed independent capsule networks for classification; 3) obtaining the final results by fusing the outputs of these three deep capsule network using a ensemble approach. To further improve the classification performance, we design a optimization model of maximizing the square of correlation coefficients and propose a weighted ensemble technology based on this model, which is mathematically proved to be solved as a eigenvalue decomposition problem in certain case. Finally, the proposed approach is implemented and evaluated on the schizophrenia fMRI dataset from COBRE, UCLA and WUSTL. From the experimental results, we conclude that the proposed method outperforms some current methods and further improves the accuracy of schizophrenia classification.
WiFi Channel State Information-Based Recognition of Sitting-Down and Standing-Up Activities

Real-time recognition of human activities is an important functionality of smart spaces. It allows a wide range of security and healthcare applications. In this work, we use the Channel State Information (CSI) of WiFi signals to assess the patterns associated with dynamic human activities, including sitting-down and standing-up actions. We preprocess raw signals with both a Hampel filter and low-pass filter. The signals are then segmented into 20-packet labelled sequences. Features including kurtosis, maximum, mean, minimum, maximum peak, skew, standard deviation, and variance are extracted for each sequence, providing feature vectors of 168 variables to enable activity recognition. Features are normalized and a series of classifiers were trained and compared to predict three activity classes: stationary (seated or standing still), sitting-down, and standing-up. Preliminary results on data collected for a single subject achieve a classification accuracy of 98.4% with a medium Gaussian Support Vector Machine (SVM) to distinguish between these three classes.
Get a Grip! Introducing Variable Grip for Controller-Based VR Systems

We propose an approach to facilitate adjustable grip for object interaction in virtual reality. It enables the user to handle objects with loose and firm grip using conventional controllers. Pivotal design properties were identified and evaluated in a qualitative pilot study. Two revised interaction designs with variable grip were compared to the status quo of invariable grip in a quantitative study. The users performed placing actions with all interaction modes. Performance, clutching, task load, and usability were measured. While the handling time increased slightly using variable grip, the usability score was significantly higher. No substantial differences were measured in positioning accuracy. The results lead to the conclusion that variable grip can be useful and improve realism depending on tasks, goals, and user preference.
W-band Dielectric Lens Horn Antenna and FMCW Circuit Module for SAR Imaging Radar

In this paper, a frequency-modulated continuous-wave (FMCW) SAR imaging radar front-end architecture has been proposed including antenna, W-band transceiver, IF receiver and frequency synthesizer. The dielectric lens horn antennas are applied as the Tx and Rx antennas. The transceiver consists of traditional CNC milling waveguide circuit module with Rogers5880 PCB broad. Firstly, the frequency synthesizer generates X-band FMCW signal and feeds into the W-band transmitter, which is transmitted to the target through Tx antenna. The echo is collected by the Rx dielectric lens horn antenna, amplified and down-conversion by the W-band receiver. The I/Q IF signals are finally received by the IF receiver, which is sent to ADC and signal processing module.
Extended Blind Interference Alignment for Superior DoF in Multiple User-Clusters Networks

Interference Alignment (IA) is a promising technique for interference elimination in wireless networks. However, it is difficult for IA to achieve the desired effect in practice since the channel state information at transmitter (CSIT) is always limited. In this paper, an extended blind interference alignment (eBIA) is proposed to enhance the performance of multi-cluster networks without CSIT. The theoretical analysis indicates that the proposed scheme can cancel the inter-cell and intra-cell interferences by relying on the single reconfigurable antenna of users. Meanwhile, this paper gives the relative comparisons with other existing algorithms on degree of freedom (DoF) and sum-rate. The simulation results demonstrate that the proposed scheme does indicate the better performance.
Estimation of the Residual Energy in Battery Electric Vehicles

The driving range of battery electric vehicles (BEVs) has been fairly extended during recent years, as a consequence of little improvements in energy density of lithium-based batteries. Nonetheless, charging stations are not widespread installed in all geographical areas. For these reasons, range anxiety still acts as a barrier when considering to move from traditional fuel vehicles to BEVs. Most of the traditional range estimation methods are untrustworthy because they leverage upon the electric energy drawn by the motor. As a matter of fact, the estimates do not generally match the energy that is actually drawn from the battery, as they do not take into account the non-linearity of battery efficiency and its dependence from the state of charge. In this work, we propose and incorporate a state-dependent battery model, including the non-linear characteristics, into a range estimator that takes into account slope, speed limits as well as traffic information. Our simulation results show that the energy estimation is 10.19% more accurate with respect to the conventional estimation based on linear battery model.
Text Similarity Measurement of Semantic Cognition Based on Word Vector Distance Decentralization With Clustering Analysis

Text similarity measurement, which is a basic task in natural language processing, is widely used in text information mining, news classification and clustering, artificial intelligence, and other fields. This paper proposes a text similarity measure method named word vector distance decentralization (WVDD) which can deal with complex semantic relations, including sentence components, word order and weights for Chinese language. Then, the clustering analysis is performed for the obtained similarity results. A K-means algorithm based on Spark architecture for parallel computing is adopted to accelerate clustering speed here. In experimental verification, the test sets are significant number of customer comments posted on the Jingdong website, which is a comprehensive online shopping mall. F-measure is used to evaluate the accuracy of the results obtained by the proposed method. The superiority of the proposed method is verified and compared with the sentence vector model (Doc2vec) and bag-of-words model. The proposed method can be applied to analyze network language, such as customers’ comments online and web chat data.
Synthesis of Optimal Feedback Controllers from Data via Stochastic Gradient Descent

We propose a policy search method for synthesizing optimal feedback control laws for reference tracking directly from data. During the learning phase, the control law is optimized by using stochastic gradient descent iterations and (optionally) applied to the plant while collecting data. Differently from model-based methods, in which a full model of the open-loop plant is first identified from data, here a simple linear model is recursively identified with forgetting factor for the only reason of computing approximately the gradients required for the descent. We report examples showing that the method recovers the optimal feedback law in case the underlying plant is linear, and outperforms the best control law that is achieved by first identifying an open-loop linear model in case the underlying plant is nonlinear.