arXiv subjects

Jun Zhang

Publications and source records attributed to Jun Zhang.

At least 829 records · Page 46Linked to original sources

arXiv2021

Primary-Auxiliary Model Scheduling Based Estimation of the Vertical Wheel Force in a Full Vehicle System

In this work, we study estimation problems in nonlinear mechanical systems subject to non-stationary and unknown excitation, which are common and critical problems in design and health management of mechanical systems. A primary-auxiliary model scheduling procedure based on time-domain transmissibilities is proposed and performed under switching linear dynamics: In addition to constructing a primary transmissibility family from the pseudo-inputs to the output during the offline stage, an auxiliary transmissibility family is constructed by further decomposing the pseudo-input vector into two parts. The auxiliary family enables to determine the unknown working condition at which the system is currently running at, and then an appropriate transmissibility from the primary transmissibility family for estimating the unknown output can be selected during the online estimation stage. As a result, the proposed approach offers a generalizable and explainable solution to the signal estimation problems in nonlinear mechanical systems in the context of switching linear dynamics with unknown inputs. A real-world application to the estimation of the vertical wheel force in a full vehicle system are, respectively, conducted to demonstrate the effectiveness of the proposed method. During the vehicle design phase, the vertical wheel force is the most important one among Wheel Center Loads (WCLs), and it is often measured directly with expensive, intrusive, and hard-to-install measurement devices during full vehicle testing campaigns. Meanwhile, the estimation problem of the vertical wheel force has not been solved well and is still of great interest. The experimental results show good performances of the proposed method in the sense of estimation accuracy for estimating the vertical wheel force.

eess.SY↗

arXiv2021

Chekanov's dichotomy in contact topology

In this paper we study submanifolds of contact manifolds. The main submanifolds we are interested in are contact coisotropic submanifolds. Based on a correspondence between symplectic and contact coisotropic submanifolds, we can show contact coisotropic submanifolds admit a $C^0$-rigidity, similar to Humilière-Leclercq-Seyfaddini's coisotropic rigidity on symplectic manifolds. Moreover, based on Shelukhin's norm defined on the contactomorphism group, we define a Chekanov type pseudo-metric on the orbit space of a fixed submanifold of a contact manifold. Moreover, we can show a dichotomy of (non-)degeneracy of this pseudo-metric when the dimension of this fixed submanifold is equal to the one for a Legendrian submanifold. This can be viewed as a contact topology analogue to Chekanov's dichotomy of (non-)degeneracy of Chekanov-Hofer's metric on the orbit space of a Lagrangian submanifold.

math.SG↗

arXiv2021

Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach

In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) system in a sophisticated three-dimensional (3D) environment, where the UAV's trajectory is optimized to efficiently collect data from multiple IoT ground nodes. Unlike existing approaches focusing only on a simplified two-dimensional scenario and the availability of perfect channel state information (CSI), this paper considers a practical 3D urban environment with imperfect CSI, where the UAV's trajectory is designed to minimize data collection completion time subject to practical throughput and flight movement constraints. Specifically, inspired from the state-of-the-art deep reinforcement learning approaches, we leverage the twin-delayed deep deterministic policy gradient (TD3) to design the UAV's trajectory and present a TD3-based trajectory design for completion time minimization (TD3-TDCTM) algorithm. In particular, we set an additional information, i.e., the merged pheromone, to represent the state information of UAV and environment as a reference of reward which facilitates the algorithm design. By taking the service statuses of IoT nodes, the UAV's position, and the merged pheromone as input, the proposed algorithm can continuously and adaptively learn how to adjust the UAV's movement strategy. By interacting with the external environment in the corresponding Markov decision process, the proposed algorithm can achieve a near-optimal navigation strategy. Our simulation results show the superiority of the proposed TD3-TDCTM algorithm over three conventional non-learning based baseline methods.

cs.IT↗

arXiv2021

Joint Activity Detection and Data Decoding in Massive Random Access via a Turbo Receiver

In this paper, we propose a turbo receiver for joint activity detection and data decoding in grant-free massive random access, which iterates between a detector and a belief propagation (BP)-based channel decoder. Specifically, responsible for user activity detection, channel estimation, and soft data symbol detection, the detector is developed based on a bilinear inference problem that exploits the common sparsity pattern in the received pilot and data signals. The bilinear generalized approximate message passing (BiG-AMP) algorithm is adopted to solve the problem using probabilities of the transmitted data symbols estimated by the channel decoder as prior knowledge. In addition, extrinsic information is derived from the detector to improve the channel decoding accuracy of the decoder. Simulation results show significant improvements achieved by the proposed turbo receiver compared with conventional designs.

eess.SP↗

arXiv2021

IDRLnet: A Physics-Informed Neural Network Library

Physics Informed Neural Network (PINN) is a scientific computing framework used to solve both forward and inverse problems modeled by Partial Differential Equations (PDEs). This paper introduces IDRLnet, a Python toolbox for modeling and solving problems through PINN systematically. IDRLnet constructs the framework for a wide range of PINN algorithms and applications. It provides a structured way to incorporate geometric objects, data sources, artificial neural networks, loss metrics, and optimizers within Python. Furthermore, it provides functionality to solve noisy inverse problems, variational minimization, and integral differential equations. New PINN variants can be integrated into the framework easily. Source code, tutorials, and documentation are available at \url{https://github.com/idrl-lab/idrlnet}.

cs.LG↗

arXiv2021

Massive Access in Media Modulation Based Massive Machine-Type Communications

The massive machine-type communications (mMTC) paradigm based on media modulation in conjunction with massive MIMO base stations (BSs) is emerging as a viable solution to support the massive connectivity for the future Internet-of-Things, in which the inherent massive access at the BSs poses significant challenges for device activity and data detection (DADD). This paper considers the DADD problem for both uncoded and coded media modulation based mMTC with a slotted access frame structure, where the device activity remains unchanged within one frame. Specifically, due to the slotted access frame structure and the adopted media modulated symbols, the access signals exhibit a doubly structured sparsity in both the time domain and the modulation domain. Inspired by this, a doubly structured approximate message passing (DS-AMP) algorithm is proposed for reliable DADD in the uncoded case. Also, we derive the state evolution of the DS-AMP algorithm to theoretically characterize its performance. As for the coded case, we develop a bit-interleaved coded media modulation scheme and propose an iterative DS-AMP (IDS-AMP) algorithm based on successive inference cancellation (SIC), where the signal components associated with the detected active devices are successively subtracted to improve the data decoding performance. In addition, the channel estimation problem for media modulation based mMTC is discussed and an efficient data-aided channel state information (CSI) update strategy is developed to reduce the training overhead in block fading channels. Finally, simulation results and computational complexity analysis verify the superiority of the proposed algorithms in both uncoded and coded cases. Also, our results verify the validity of the proposed data-aided CSI update strategy.

cs.IT↗

arXiv2021

Does Concentration Drive the Scatter in the Stellar-to-Halo Mass Relation of Galaxy Clusters?

Concentration is one of the key dark matter halo properties that could drive the scatter in the stellar-to-halo mass relation of massive clusters. We derive robust photometric stellar masses for a sample of brightest central galaxies (BCGs) in SDSS redMaPPer clusters at $0.17<z<0.3$, and split the clusters into two equal-halo mass subsamples by their BCG stellar mass $M_*$. The weak lensing profiles $ΔΣ$ of the two cluster subsamples exhibit different slopes on scales below 1 M$pc/h$. To interpret such discrepancy, we perform a comprehensive Bayesian modelling of the two $ΔΣ$ profiles by including different levels of miscentring effects between the two subsamples as informed by X-ray observations. We find that the two subsamples have the same average halo mass of $1.74 \times 10^{14} M_{\odot}/h$, but the concentration of the low-$M_*$ clusters is $5.87_{-0.60}^{+0.77}$, ${\sim}1.5σ$ smaller than that of their high-$M_*$ counterparts~($6.95_{-0.66}^{+0.78}$). Furthermore, both cluster weak lensing and cluster-galaxy cross-correlations indicate that the large-scale bias of the low-$M_*$, low-concentration clusters are ${\sim}10\%$ higher than that of the high-$M_*$, high-concentration systems, hence possible evidence of the cluster assembly bias effect. Our results reveal a remarkable physical connection between the stellar mass within 20{-}30 k$pc/h$, the dark matter mass within ${\sim}$ 200 k$pc/h$, and the cosmic overdensity on scales above 10 M$pc/h$, enabling a key observational test of theories of co-evolution between massive clusters and their central galaxies.

astro-ph.CO↗

arXiv2021

The Audio Auditor: User-Level Membership Inference in Internet of Things Voice Services

With the rapid development of deep learning techniques, the popularity of voice services implemented on various Internet of Things (IoT) devices is ever increasing. In this paper, we examine user-level membership inference in the problem space of voice services, by designing an audio auditor to verify whether a specific user had unwillingly contributed audio used to train an automatic speech recognition (ASR) model under strict black-box access. With user representation of the input audio data and their corresponding translated text, our trained auditor is effective in user-level audit. We also observe that the auditor trained on specific data can be generalized well regardless of the ASR model architecture. We validate the auditor on ASR models trained with LSTM, RNNs, and GRU algorithms on two state-of-the-art pipelines, the hybrid ASR system and the end-to-end ASR system. Finally, we conduct a real-world trial of our auditor on iPhone Siri, achieving an overall accuracy exceeding 80\%. We hope the methodology developed in this paper and findings can inform privacy advocates to overhaul IoT privacy.

cs.CR↗

arXiv2021

An Edge Computing Paradigm for Massive IoT Connectivity over High-Altitude Platform Networks

With the advent of the Internet-of-Things (IoT) era, the ever-increasing number of devices and emerging applications have triggered the need for ubiquitous connectivity and more efficient computing paradigms. These stringent demands have posed significant challenges to the current wireless networks and their computing architectures. In this article, we propose a high-altitude platform (HAP) network-enabled edge computing paradigm to tackle the key issues of massive IoT connectivity. Specifically, we first provide a comprehensive overview of the recent advances in non-terrestrial network-based edge computing architectures. Then, the limitations of the existing solutions are further summarized from the perspectives of the network architecture, random access procedure, and multiple access techniques. To overcome the limitations, we propose a HAP-enabled aerial cell-free massive multiple-input multiple-output network to realize the edge computing paradigm, where multiple HAPs cooperate via the edge servers to serve IoT devices. For the case of a massive number of devices, we further adopt a grant-free massive access scheme to guarantee low-latency and high-efficiency massive IoT connectivity to the network. Besides, a case study is provided to demonstrate the effectiveness of the proposed solution. Finally, to shed light on the future research directions of HAP network-enabled edge computing paradigms, the key challenges and open issues are discussed.

cs.IT↗

arXiv2021

Lévy Walks and Path Chaos in the Dispersal of Elongated Structures Moving across Cellular Vortical Flows

In cellular vortical flows, namely arrays of counter-rotating vortices, short but flexible filaments can show simple random walks through their stretch-coil interactions with flow stagnation points. Here, we study the dynamics of semi-rigid filaments long enough to broadly sample the vortical field. Using simulation, we find a surprising variety of long-time transport behavior -- random walks, ballistic transport, and trapping -- depending upon the filament's relative length and effective flexibility. Moreover, we find that filaments execute Lévy walks whose diffusion exponents generally decrease with increasing filament length, until transitioning to Brownian walks. Lyapunov exponents likewise increase with length. Even completely rigid filaments, whose dynamics is finite-dimensional, show a surprising variety of transport states and chaos. Fast filament dispersal is related to an underlying geometry of ``conveyor belts''. Evidence for these various transport states are found in experiments using arrays of counter-rotating rollers, immersed in a fluid and transporting a flexible ribbon.

cond-mat.soft↗

arXiv2021

Square Peg in a Circular Hole: Choosing the Right Ansatz for Isolated Black Holes in Generic Gravitational Theories

The metric of a spacetime can be greatly simplified if the spacetime is circular. We prove that in generic effective theories of gravity, the spacetime of a stationary, axisymmetric and asymptotically flat solution must be circular if the solution can be obtained perturbatively from a solution in the general relativity limit. This result applies to a broad class of gravitational theories that include arbitrary scalars and vectors in their light sector, so long as their nonstandard kinetic terms and nonmininal couplings to gravity are treated perturbatively.

gr-qc↗

arXiv2021

Feedback Pyramid Attention Networks for Single Image Super-Resolution

Recently, convolutional neural network (CNN) based image super-resolution (SR) methods have achieved significant performance improvement. However, most CNN-based methods mainly focus on feed-forward architecture design and neglect to explore the feedback mechanism, which usually exists in the human visual system. In this paper, we propose feedback pyramid attention networks (FPAN) to fully exploit the mutual dependencies of features. Specifically, a novel feedback connection structure is developed to enhance low-level feature expression with high-level information. In our method, the output of each layer in the first stage is also used as the input of the corresponding layer in the next state to re-update the previous low-level filters. Moreover, we introduce a pyramid non-local structure to model global contextual information in different scales and improve the discriminative representation of the network. Extensive experimental results on various datasets demonstrate the superiority of our FPAN in comparison with the state-of-the-art SR methods.

eess.IV↗

arXiv2021

Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution

Recently, deep convolutional neural network methods have achieved an excellent performance in image superresolution (SR), but they can not be easily applied to embedded devices due to large memory cost. To solve this problem, we propose a pyramidal dense attention network (PDAN) for lightweight image super-resolution in this paper. In our method, the proposed pyramidal dense learning can gradually increase the width of the densely connected layer inside a pyramidal dense block to extract deep features efficiently. Meanwhile, the adaptive group convolution that the number of groups grows linearly with dense convolutional layers is introduced to relieve the parameter explosion. Besides, we also present a novel joint attention to capture cross-dimension interaction between the spatial dimensions and channel dimension in an efficient way for providing rich discriminative feature representations. Extensive experimental results show that our method achieves superior performance in comparison with the state-of-the-art lightweight SR methods.

eess.IV↗

arXiv2021

HMM-Free Encoder Pre-Training for Streaming RNN Transducer

This work describes an encoder pre-training procedure using frame-wise label to improve the training of streaming recurrent neural network transducer (RNN-T) model. Streaming RNN-T trained from scratch usually performs worse than non-streaming RNN-T. Although it is common to address this issue through pre-training components of RNN-T with other criteria or frame-wise alignment guidance, the alignment is not easily available in end-to-end manner. In this work, frame-wise alignment, used to pre-train streaming RNN-T's encoder, is generated without using a HMM-based system. Therefore an all-neural framework equipping HMM-free encoder pre-training is constructed. This is achieved by expanding the spikes of CTC model to their left/right blank frames, and two expanding strategies are proposed. To our best knowledge, this is the first work to simulate HMM-based frame-wise label using CTC model for pre-training. Experiments conducted on LibriSpeech and MLS English tasks show the proposed pre-training procedure, compared with random initialization, reduces the WER by relatively 5%~11% and the emission latency by 60 ms. Besides, the method is lexicon-free, so it is friendly to new languages without manually designed lexicon.

eess.AS↗

arXiv2021

AI Empowered Resource Management for Future Wireless Networks

Resource management plays a pivotal role in wireless networks, which, unfortunately, leads to challenging NP-hard problems. Artificial Intelligence (AI), especially deep learning techniques, has recently emerged as a disruptive technology to solve such challenging problems in a real-time manner. However, although promising results have been reported, practical design guidelines and performance guarantees of AI-based approaches are still missing. In this paper, we endeavor to address two fundamental questions: 1) What are the main advantages of AI-based methods compared with classical techniques; and 2) Which neural network should we choose for a given resource management task. For the first question, four advantages are identified and discussed. For the second question, \emph{optimality gap}, i.e., the gap to the optimal performance, is proposed as a measure for selecting model architectures, as well as, for enabling a theoretical comparison between different AI-based approaches. Specifically, for $K$-user interference management problem, we theoretically show that graph neural networks (GNNs) are superior to multi-layer perceptrons (MLPs), and the performance gap between these two methods grows with $\sqrt{K}$.

cs.IT↗

arXiv2021

Agile wide-field imaging with selective high resolution

Wide-field and high-resolution (HR) imaging is essential for various applications such as aviation reconnaissance, topographic mapping and safety monitoring. The existing techniques require a large-scale detector array to capture HR images of the whole field, resulting in high complexity and heavy cost. In this work, we report an agile wide-field imaging framework with selective high resolution that requires only two detectors. It builds on the statistical sparsity prior of natural scenes that the important targets locate only at small regions of interests (ROI), instead of the whole field. Under this assumption, we use a short-focal camera to image wide field with a certain low resolution, and use a long-focal camera to acquire the HR images of ROI. To automatically locate ROI in the wide field in real time, we propose an efficient deep-learning based multiscale registration method that is robust and blind to the large setting differences (focal, white balance, etc) between the two cameras. Using the registered location, the long-focal camera mounted on a gimbal enables real-time tracking of the ROI for continuous HR imaging. We demonstrated the novel imaging framework by building a proof-of-concept setup with only 1181 gram weight, and assembled it on an unmanned aerial vehicle for air-to-ground monitoring. Experiments show that the setup maintains 120$^{\circ}$ wide field-of-view (FOV) with selective 0.45$mrad$ instantaneous FOV.

cs.CV↗

arXiv2021

Information flow based defensive chain for data leakage detection and prevention: a survey

Mobile and IoT applications have greatly enriched our daily life by providing convenient and intelligent services. However, these smart applications have been a prime target of adversaries for stealing sensitive data. It poses a crucial threat to users' identity security, financial security, or even life security. Research communities and industries have proposed many Information Flow Control (IFC) techniques for data leakage detection and prevention, including secure modeling, type system, static analysis, dynamic analysis, \textit{etc}. According to the application's development life cycle, although most attacks are conducted during the application's execution phase, data leakage vulnerabilities have been introduced since the design phase. With a focus on lifecycle protection, this survey reviews the recent representative works adopted in different phases. We propose an information flow based defensive chain, which provides a new framework to systematically understand various IFC techniques for data leakage detection and prevention in Mobile and IoT applications. In line with the phases of the application life cycle, each reviewed work is comprehensively studied in terms of technique, performance, and limitation. Research challenges and future directions are also pointed out by consideration of the integrity of the defensive chain.

cs.CR↗

arXiv2021

Cores in discrete exchange economies with complex endowments

The core is a traditional and useful solution concept in economic theory. But in discrete exchange economies without transfers, when endowments are complex, the core may be empty. This motivates Balbuzanov and Kotowski (2019) to interpret endowments as exclusion rights and propose a new concept called exclusion core. Our contribution is twofold. First, we propose a rectification of the core to solve its problem under complex endowments. Second, we propose a refinement of Balbuzanov and Kotowski's exclusion core to improve its performance. Our two core concepts share a common idea of correcting the misused altruism of unaffected agents in blocking coalitions. We propose a mechanism to find allocations in the two cores.

econ.TH↗