BZPEERReading, trust and collaboration for research
Menu

Discover

GraphFollow connections around a paper, topic or saved view.

Workspaces

HomePick up where your research flow left off.InboxHandle requests, replies and notifications from one place.CollectionsCurate reading lists, evidence sets and shareable dossiers.ResearchSee your private provenance graph, trail and imports.CollaborationMove from discovery into focused discussion rooms.PublishDraft, preview and publish full-text research articles.

Network

ExpertsFind specialists worth following or asking for help.CommunitiesJoin research circles organized around shared work.PartnersSee external organizations active around the same topics.

Opportunities

ListingsBrowse roles, calls and collaborations with clearer fit signals.

Account

Log inReturn to your reading and collaboration workspace.Create accountStart saving work, following people and joining teams.
Log inCreate account

Researcher profile

Jianhua Zhang

Jianhua Zhang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
eess.SPInformation Theorymath.ITcond-mat.mtrl-scicond-mat.str-eleess.SYMachine Learningmath.NAphysics.app-phphysics.comp-phphysics.ins-detphysics.opticsSystems and Control

Trust snapshot

Quick read

Trust 21 - EmergingVerification L1Unclaimed author
10works
0followers
13topics
4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Identity and collaboration

How to connect with this researcher

Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.

Log in to claim

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

Research graph

See the researcher in context

Open full explorer

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

10 published item(s)

preprint2026arXiv

On Existence of Girth-8 QC-LDPC Code with Large Column Weight: Combining Mirror-sequence with Classification Modulo Ten

Quasi-cyclic (QC) LDPC codes with large girths play a crucial role in several research and application fields, including channel coding, compressed sensing and distributed storage systems. A major challenge in respect of the code construction is how to obtain such codes with the shortest possible length (or equivalently, the smallest possible circulant size) using algebraic methods instead of search methods. The greatest-common-divisor (GCD) framework we previously proposed has algebraically constructed QC-LDPC codes with column weights of 5 and 6, very short lengths, and a girth of 8. By introducing the concept of a mirror sequence and adopting a new row-regrouping scheme, QC-LDPC codes with column weights of 7 and 8, very short lengths, and a girth of 8 are proposed for arbitrary row weights in this article via an algebraic manner under the GCD framework. Thanks to these novel algebraic methods, the lower bounds (for column weights 7 and 8) on consecutive circulant sizes are both improved by asymptotically about 20%, compared with the existing benchmarks. Furthermore, these new constructions can also offer circulant sizes asymptotically about 25% smaller than the novel bounds.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Three-dimensional imaging of individual carbon atoms

Carbon is fundamental to science and technology due to its diverse bonding configurations, structural versatility, and essential role in defining the mechanical, chemical, electronic, and quantum properties of materials. However, direct three-dimensional (3D) imaging of individual carbon atoms remains a long-standing challenge. Here, we use twisted bilayer graphene (TBG) as a model system and demonstrate ptychographic atomic electron tomography (pAET) for determining the 3D atomic coordinates of individual carbon atoms with a precision of 0.11 angstrom. The resulting 3D atomic model uncovers chiral lattice distortions driven by van der Waals interactions that exhibit meron-like and skyrmion-like structural textures. These findings provide direct insight into the interplay between 3D chiral lattice deformation and electronic properties in moire-engineered carbon systems. Beyond TBG, pAET offers a versatile approach for 3D atomic-scale imaging of carbon-based and other light-element materials that are central to advances in physics, chemistry, materials science, and nanotechnology.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Transmission Mask Analysis for Range-Doppler Sensing in Half-Duplex ISAC

In this paper, we analyze the periodic transmission masks for MASked Modulation (MASM) in half-duplex integrated sensing and communication (ISAC), and derive their closed-form expected range-Doppler response $\mathbb{E}\{r(k,l,ν)\}$. We show that range sidelobes ($k\neq l$) are Doppler-invariant, extending the range-sidelobe optimality to the 2-D setting. For the range mainlobe ($k=l$), periodic masking yields sparse Doppler sidelobes: Cyclic difference sets (CDSs) (in particular Singer CDSs) are minimax-optimal in a moderately dynamic regime, while in a highly dynamic regime the Doppler-sidelobe energy is a concave function of the mask autocorrelation, revealing an inevitable tradeoff with mainlobe fluctuation.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Variational Matrix-Learning Fourier Networks for Parametric Multiphysics Surrogates

Multiphysics simulation is critical for system-technology co-optimization (STCO) in chiplet-based design, but repeated finite-element solutions of PDE-governed problems are computationally expensive in parametric design exploration. This paper proposes a variational matrix-learning Fourier network (VMLFN) for efficient parametric multiphysics surrogate modeling. VMLFN constructs a log-space sine neural representation with randomly sampled spectral frequencies, frequency-dependent decay regulation, and embedded Dirichlet boundary conditions. With fixed hidden-layer parameters, the output-layer weights are determined by reformulating the governing PDEs into variational weak forms and enforcing the stationarity condition of the resulting energy functional. This converts physics-informed training into a linear matrix-solving problem, requiring only first-order derivatives and avoiding both high-order automatic differentiation and penalty-coefficient tuning. A heuristic frequency-scanning algorithm is further introduced to select a problem-adaptive maximum frequency that covers the dominant spectral range of the target problem. The proposed method is validated on heat conduction, solid mechanics, and Helmholtz wave propagation problems. Results from five benchmark cases demonstrate that VMLFN delivers accurate full-field predictions with substantial speedup over conventional physics-informed neural networks and repeated finite-element simulations.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Physics-informed Graphical Neural Network for Power System State Estimation

State estimation is highly critical for accurately observing the dynamic behavior of the power grids and minimizing risks from cyber threats. However, existing state estimation methods encounter challenges in accurately capturing power system dynamics, primarily because of limitations in encoding the grid topology and sparse measurements. This paper proposes a physics-informed graphical learning state estimation method to address these limitations by leveraging both domain physical knowledge and a graph neural network (GNN). We employ a GNN architecture that can handle the graph-structured data of power systems more effectively than traditional data-driven methods. The physics-based knowledge is constructed from the branch current formulation, making the approach adaptable to both transmission and distribution systems. The validation results of three IEEE test systems show that the proposed method can achieve lower mean square error more than 20% than the conventional methods.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Capacity Analysis of Holographic MIMO Channels with Practical Constraints

Holographic Multiple-Input and Multiple-Output (MIMO) is envisioned as a promising technology to realize unprecedented spectral efficiency by integrating a large number of antennas into a compact space. Most research on holographic MIMO is based on isotropic scattering environments, and the antenna gain is assumed to be unlimited by deployment space. However, the channel might not satisfy isotropic scattering because of generalized angle distributions, and the antenna gain is limited by the array aperture in reality. In this letter, we aim to analyze the holographic MIMO channel capacity under practical angle distribution and array aperture constraints. First, we calculate the spectral density for generalized angle distributions by introducing a wavenumber domain-based method. And then, the capacity under generalized angle distributions is analyzed and two different aperture schemes are considered. Finally, numerical results show that the capacity is obviously affected by angle distribution at high signal-to-noise ratio (SNR) but hardly affected at low SNR, and the capacity will not increase infinitely with antenna density due to the array aperture constraint.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

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.

0 citations0 reviewsNo code linkedOpen access