arXiv subjects

Jianhua Zhang

Publications and source records attributed to Jianhua Zhang.

159 records · Page 9Linked to original sources

arXiv2022

How to Define the Propagation Environment Semantics and Its Application in Scatterer-Based Beam Prediction

In view of the propagation environment directly determining the channel fading, the application tasks can also be solved with the aid of the environment information. Inspired by task-oriented semantic communication and machine learning (ML) powered environment-channel mapping methods, this work aims to provide a new view of the environment from the semantic level, which defines the propagation environment semantics (PES) as a limited set of propagation environment semantic symbols (PESS) for diverse application tasks. The PESS is extracted oriented to the tasks with channel properties as a foundation. For method validation, the PES-aided beam prediction (PESaBP) is presented in non-line-of-sight (NLOS). The PESS of environment features and graphs are given for the semantic actions of channel quality evaluation and target scatterer detection of maximum power, which can obtain 0.92 and 0.9 precision, respectively, and save over 87% of time cost.

eess.SP↗

arXiv2022

Computer Vision Aided mmWave Beam Alignment in V2X Communications

Visual information, captured for example by cameras, can effectively reflect the sizes and locations of the environmental scattering objects, and thereby can be used to infer communications parameters like propagation directions, receiver powers, as well as the blockage status. In this paper, we propose a novel beam alignment framework that leverages images taken by cameras installed at the mobile user. Specifically, we utilize 3D object detection techniques to extract the size and location information of the dynamic vehicles around the mobile user, and design a deep neural network (DNN) to infer the optimal beam pair for transceivers without any pilot signal overhead. Moreover, to avoid performing beam alignment too frequently or too slowly, a beam coherence time (BCT) prediction method is developed based on the vision information. This can effectively improve the transmission rate compared with the beam alignment approach with the fixed BCT. Simulation results show that the proposed vision based beam alignment methods outperform the existing LIDAR and vision based solutions, and demand for much lower hardware cost and communication overhead.

eess.SP↗

arXiv2022

Frequency-Angle Two-Dimensional Reflection Coefficient Modeling Based on Terahertz Channel Measurement

Terahertz (THz) channel propagation characteristics are vital for the design, evaluation, and optimization for THz communication systems. Moreover, reflection plays a significant role in channel propagation. In this letter, the reflection coefficient of the THz channel is researched based on extensive measurement campaigns. Firstly, we set up the THz channel sounder from 220 to 320 GHz with the incident angle ranging from 10° to 80°. Based on the measured propagation loss, the reflection coefficients of five building materials, i.e., glass, tile, aluminium alloy, board, and plasterboard, are calculated separately for frequencies and incident angles. It is found that the lack of THz relative parameters leads to the Fresnel model of non-metallic materials can not fit the measured data well. Thus, we propose a frequency-angle two-dimensional reflection coefficient model by modifying the Fresnel model with the Lorenz and Drude model. The proposed model characterizes the frequency and incident angle for reflection coefficients and shows low root-mean-square error with the measured data. Generally, these results are useful for modeling THz channels.

eess.SP↗

arXiv2022

Adversarial Training-Aided Time-Varying Channel Prediction for TDD/FDD Systems

In this paper, a time-varying channel prediction method based on conditional generative adversarial network (CPcGAN) is proposed for time division duplexing/frequency division duplexing (TDD/FDD) systems. CPcGAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information (CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI. The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.

eess.SP↗

arXiv2021

Accurate Object Association and Pose Updating for Semantic SLAM

Current pandemic has caused the medical system to operate under high load. To relieve it, robots with high autonomy can be used to effectively execute contactless operations in hospitals and reduce cross-infection between medical staff and patients. Although semantic Simultaneous Localization and Mapping (SLAM) technology can improve the autonomy of robots, semantic object association is still a problem that is worthy of being studied. The key to solving this problem is to correctly associate multiple object measurements of one object landmark by using semantic information, and to refine the pose of object landmark in real time. To this end, we propose a hierarchical object association strategy and a pose-refinement approach. The former one consists of two levels, i.e., a short-term object association and a global one. In the first level, we employ the multiple-object-tracking for short-term object association, through which the incorrect association among objects whose locations are close and appearances are similar can be avoided. Moreover, the short-term object association can provide more abundant object appearance and more robust estimation of object pose for the global object association in the second level. To refine the object pose in the map, we develop an approach to choose the optimal object pose from all object measurements associated with an object landmark. The proposed method is comprehensively evaluated on seven simulated hospital sequences1, a real hospital environment and the KITTI dataset. Experimental results show that our method has an obviously improvement in terms of robustness and accuracy for the object association and the trajectory estimation in the semantic SLAM.

cs.CV↗

arXiv2021

Collaborative Visual Inertial SLAM for Multiple Smart Phones

The efficiency and accuracy of mapping are crucial in a large scene and long-term AR applications. Multi-agent cooperative SLAM is the precondition of multi-user AR interaction. The cooperation of multiple smart phones has the potential to improve efficiency and robustness of task completion and can complete tasks that a single agent cannot do. However, it depends on robust communication, efficient location detection, robust mapping, and efficient information sharing among agents. We propose a multi-intelligence collaborative monocular visual-inertial SLAM deployed on multiple ios mobile devices with a centralized architecture. Each agent can independently explore the environment, run a visual-inertial odometry module online, and then send all the measurement information to a central server with higher computing resources. The server manages all the information received, detects overlapping areas, merges and optimizes the map, and shares information with the agents when needed. We have verified the performance of the system in public datasets and real environments. The accuracy of mapping and fusion of the proposed system is comparable to VINS-Mono which requires higher computing resources.

cs.RO↗

arXiv2021

Identification of mental fatigue in language comprehension tasks based on EEG and deep learning

Mental fatigue increases the risk of operator error in language comprehension tasks. In order to prevent operator performance degradation, we used EEG signals to assess the mental fatigue of operators in human-computer systems. This study presents an experimental design for fatigue detection in language comprehension tasks. We obtained EEG signals from a 14-channel wireless EEG detector in 15 healthy participants. Each participant was given a cognitive test of a language comprehension task, in the form of multiple choice questions, in which pronoun references were selected between nominal and surrogate sentences. In this paper, the 2400 EEG fragments collected are divided into three data sets according to different utilization rates, namely 1200s data set with 50% utilization rate, 1500s data set with 62.5% utilization rate, and 1800s data set with 75% utilization rate. In the aspect of feature extraction, different EEG features were extracted, including time domain features, frequency domain features and entropy features, and the effects of different features and feature combinations on classification accuracy were explored. In terms of classification, we introduced the Convolutional Neural Network (CNN) method as the preferred method, It was compared with Least Squares Support Vector Machines(LSSVM),Support Vector Machines(SVM),Logistic Regression (LR), Random Forest(RF), Naive Bayes (NB), K-Nearest Neighbor (KNN) and Decision Tree(DT).According to the results, the classification accuracy of convolutional neural network (CNN) is higher than that of other classification methods. The classification results show that the classification accuracy of 1200S dataset is higher than the other two datasets. The combination of Frequency and entropy feature and CNN has the highest classification accuracy, which is 85.34%.

cs.AI↗

arXiv2021

Consistency-Aware Graph Network for Human Interaction Understanding

Compared with the progress made on human activity classification, much less success has been achieved on human interaction understanding (HIU). Apart from the latter task is much more challenging, the main cause is that recent approaches learn human interactive relations via shallow graphical models, which is inadequate to model complicated human interactions. In this paper, we propose a consistency-aware graph network, which combines the representative ability of graph network and the consistency-aware reasoning to facilitate the HIU task. Our network consists of three components, a backbone CNN to extract image features, a factor graph network to learn third-order interactive relations among participants, and a consistency-aware reasoning module to enforce labeling and grouping consistencies. Our key observation is that the consistency-aware-reasoning bias for HIU can be embedded into an energy function, minimizing which delivers consistent predictions. An efficient mean-field inference algorithm is proposed, such that all modules of our network could be trained jointly in an end-to-end manner. Experimental results show that our approach achieves leading performance on three benchmarks.

cs.CV↗

arXiv2020

Potential of attosecond coherent diffractive imaging

Attosecond science has been transforming our understanding of electron dynamics in atoms, molecules and solids. However, to date almost all of the attoscience experiments have been based on spectroscopic measurements because attosecond pulses have intrinsically very broad spectra due to the uncertainty principle and are incompatible with conventional imaging systems. Here we report an important advance towards achieving attosecond coherent diffractive imaging. Using simulated attosecond pulses, we simultaneously reconstruct the spectrum, 17 probes and 17 spectral images of extended objects from a set of ptychographic diffraction patterns. We further confirm the principle and feasibility of this method by successfully performing a ptychographic coherent diffractive imaging experiment using a light-emitting diode with a broad spectrum. We believe this work clear the way to an unexplored domain of attosecond imaging science, which could have a far-reaching impact across different disciplines.

physics.optics↗

arXiv2019

Localized Singlets and Ferromagnetic Fluctuations in the Dilute Magnetic Topological Insulator Sn$_{0.95}$Mn$_{0.05}$Te

The development of long-range ferromagnetic (FM) order in dilute magnetic topological insulators can induce dissipationless electronic surface transport via the quantum anomalous Hall effect. We measure the magnetic excitations in a prototypical magnetic topological crystalline insulator, Sn$_{0.95}$Mn$_{0.05}$Te, using inelastic neutron scattering. Neutron diffraction and magnetization data indicate that our Sn$_{0.95}$Mn$_{0.05}$Te sample has no FM long-range order above a temperature of 2 K. However, we observe slow, collective FM fluctuations ($<$~70 $μ$eV), indicating proximity to FM order. We also find a series of sharp peaks originating from local excitations of antiferromagnetically (AF) coupled and isolated Mn-Mn dimers with $J_{\rm AF}=460$~$μ$eV\@. The simultaneous presence of collective and localized components in the magnetic spectra highlight different roles for substituted Mn ions, with competition between FM order and the formation of AF-coupled Mn-Mn dimers.

cond-mat.str-el↗

arXiv2019

A general representation of dynamical systems for reservoir computing

Dynamical systems are capable of performing computation in a reservoir computing paradigm. This paper presents a general representation of these systems as an artificial neural network (ANN). Initially, we implement the simplest dynamical system, a cellular automaton. The mathematical fundamentals behind an ANN are maintained, but the weights of the connections and the activation function are adjusted to work as an update rule in the context of cellular automata. The advantages of such implementation are its usage on specialized and optimized deep learning libraries, the capabilities to generalize it to other types of networks and the possibility to evolve cellular automata and other dynamical systems in terms of connectivity, update and learning rules. Our implementation of cellular automata constitutes an initial step towards a general framework for dynamical systems. It aims to evolve such systems to optimize their usage in reservoir computing and to model physical computing substrates.

cs.NE↗

arXiv2019

Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions

The new demands for high-reliability and ultra-high capacity wireless communication have led to extensive research into 5G communications. However, the current communication systems, which were designed on the basis of conventional communication theories, signficantly restrict further performance improvements and lead to severe limitations. Recently, the emerging deep learning techniques have been recognized as a promising tool for handling the complicated communication systems, and their potential for optimizing wireless communications has been demonstrated. In this article, we first review the development of deep learning solutions for 5G communication, and then propose efficient schemes for deep learning-based 5G scenarios. Specifically, the key ideas for several important deep learningbased communication methods are presented along with the research opportunities and challenges. In particular, novel communication frameworks of non-orthogonal multiple access (NOMA), massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are investigated, and their superior performances are demonstrated. We vision that the appealing deep learning-based wireless physical layer frameworks will bring a new direction in communication theories and that this work will move us forward along this road.

eess.SP↗

arXiv2017

Overview of Millimeter Wave Communications for Fifth-Generation (5G) Wireless Networks-with a focus on Propagation Models

This paper provides an overview of the features of fifth generation (5G) wireless communication systems now being developed for use in the millimeter wave (mmWave) frequency bands. Early results and key concepts of 5G networks are presented, and the channel modeling efforts of many international groups for both licensed and unlicensed applications are described here. Propagation parameters and channel models for understanding mmWave propagation, such as line-of-sight (LOS) probabilities, large-scale path loss, and building penetration loss, as modeled by various standardization bodies, are compared over the 0.5-100 GHz range.

cs.IT↗

arXiv2017

The Role of Data Analysis in the Development of Intelligent Energy Networks

Data analysis plays an important role in the development of intelligent energy networks (IENs). This article reviews and discusses the application of data analysis methods for energy big data. The installation of smart energy meters has provided a huge volume of data at different time resolutions, suggesting data analysis is required for clustering, demand forecasting, energy generation optimization, energy pricing, monitoring and diagnostics. The currently adopted data analysis technologies for IENs include pattern recognition, machine learning, data mining, statistics methods, etc. However, existing methods for data analysis cannot fully meet the requirements for processing the big data produced by the IENs and, therefore, more comprehensive data analysis methods are needed to handle the increasing amount of data and to mine more valuable information.

cs.OH↗

arXiv2011

Vibrational modes and lattice distortion of a nitrogen-vacancy center in diamond from first-principles calculations

We investigate vibrational properties and lattice distortion of negatively charged nitrogen-vacancy (NV) center in diamond. Using the first-principles electronic structure calculations, we show that the presence of NV center leads to appearance of a large number of quasilocalized vibrational modes (qLVMs) with different degree of localization. The vibration patterns and the symmetries of the qLVMs are presented and analyzed in detail for both ground and excited orbital states of the NV center. We find that in the high-symmetry ($C_{3v}$) excited orbital state a pair of degenerate qLVMs becomes unstable, and the stable excited state has lower ($C_{1h}$) symmetry. This is a direct indication of the Jahn-Teller effect, and our studies suggest that dynamical Jahn-Teller effect in the weak coupling regime takes place. We have also performed a detailed comparison of our results with the available experimental data on the vibrations involved in optical emission/absorption of the NV centers. We have directly demonstrated that, among other modes, the qLVMs crucially impact the optical properties of the NV centers in diamond, and identified the most important groups of qLVMs. Our results are important for deeper understanding of the optical properties and the orbital relaxation associated with lattice vibrations of the NV centers.

cond-mat.mtrl-sci↗