arXiv subjects

Ze Zhang

Publications and source records attributed to Ze Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2025

RAIL: An Accurate and Fast Angle-inferred Localization Algorithm for UAV-WSN Systems

Location information is a fundamental requirement for unmanned aerial vehicles (UAVs) and other wireless sensor networks (WSNs). However, accurately and efficiently localizing sensor nodes with diverse functionalities remains a significant challenge, particularly in a hardware-constrained environment. To address this issue and enhance the applicability of artificial intelligence (AI), this paper proposes a localization algorithm that does not require additional hardware. Specifically, the angle between a node and the anchor nodes is estimated based on the received signal strength indication (RSSI). A subsequent localization strategy leverages the inferred angular relationships in conjunction with a bounding box. Experimental evaluations in three scenarios with varying number of nodes demonstrate that the proposed method achieves substantial improvements in localization accuracy, reducing the average error by 72.4% compared to the Min-Max and RSSI-based DV-Hop algorithms, respectively.

cs.NI↗

arXiv2025

Topology Partitioning-based Self-Organized Localization in Indoor WSNs with Unknown Obstacles

Accurate indoor node localization is critical for practical Wireless Sensor Network (WSN) applications, as Global Positioning System (GPS) fails to provide reliable Line-of-Sight (LoS) conditions in most indoor environments. Real-world localization scenarios often involve unknown obstacles with unpredictable shapes, sizes, quantities, and layouts. These obstacles introduce significant deviations in measured distances between sensor nodes when communication links traverse them, severely compromising localization accuracy. To address this challenge, this paper proposes a robust range-based localization method that strategically identifies and severs obstructed communication paths, leveraging network topology to mitigate obstacle-induced errors. Across diverse obstacle configurations and node densities, the algorithm successfully severed 87% of obstacle-affected paths on average. Under the assumption that Received Signal Strength Indicator (RSSI) provides accurate distance measurements under LoS conditions, the achieved localization accuracy exceeds 99.99%.

cs.NI↗

arXiv2025

Refined Geometry-guided Head Avatar Reconstruction from Monocular RGB Video

High-fidelity reconstruction of head avatars from monocular videos is highly desirable for virtual human applications, but it remains a challenge in the fields of computer graphics and computer vision. In this paper, we propose a two-phase head avatar reconstruction network that incorporates a refined 3D mesh representation. Our approach, in contrast to existing methods that rely on coarse template-based 3D representations derived from 3DMM, aims to learn a refined mesh representation suitable for a NeRF that captures complex facial nuances. In the first phase, we train 3DMM-stored NeRF with an initial mesh to utilize geometric priors and integrate observations across frames using a consistent set of latent codes. In the second phase, we leverage a novel mesh refinement procedure based on an SDF constructed from the density field of the initial NeRF. To mitigate the typical noise in the NeRF density field without compromising the features of the 3DMM, we employ Laplace smoothing on the displacement field. Subsequently, we apply a second-phase training with these refined meshes, directing the learning process of the network towards capturing intricate facial details. Our experiments demonstrate that our method further enhances the NeRF rendering based on the initial mesh and achieves performance superior to state-of-the-art methods in reconstructing high-fidelity head avatars with such input.

cs.GR↗

arXiv2025

A highly sensitive, self-adhesive, biocompatible DLP 3D printed organohydrogel for flexible sensors and wearable devices

With the increasing demand for personalized health monitoring, wearable sensors have gained attention in medical diagnostics and physiological tracking. Hydrogels, known for their mechanical properties and similarity to biological tissues, are ideal for flexible sensing. However, conventional hydrogels face challenges in stability, biocompatibility, adhesion, and long-term comfort, especially in dynamic conditions.This study presents a highly sensitive, self-adhesive, and biocompatible organohydrogel fabricated via DLP 3D printing. By integrating an entanglement-dominated crosslinking mechanism with chemical and physical crosslinking, the hydrogel achieves high elasticity, mechanical strength, and durability. Methacrylic anhydride-grafted \k{appa}-carrageenan serves as the primary network, with optimized grafting rates enhancing tensile properties and strain modulation. The copolymer network of MA-kappa-CA and ACMO benefits from steric hindrance effects, improving swelling integrity and long-term stability.Experimental results confirm sustained adhesion and structural integrity under prolonged skin exposure, making it suitable for extended wear. The hydrogel exhibits excellent tensile resilience, flexibility, and strain-sensing capabilities. In vitro studies validate its biocompatibility, supporting its biomedical potential. Furthermore, its integration into wearable smart devices demonstrates promise for cervical spine monitoring and sports rehabilitation. A CNN-based system enables real-time, multi-channel analysis of cervical motion, proving its viability as a high-sensitivity flexible sensor for health monitoring and injury prevention.The proposed DLP 3D-printed hydrogel offers significant applications in flexible electronics, wearable sensors, and biomedical technologies, paving the way for next-generation health-monitoring systems.

physics.app-ph↗

arXiv2024

Hyper-sampling imaging

In our research, we have developed a novel mechanism that allows for a significant reduction in the smallest sampling unit of digital image sensors (DIS) to as small as 1/16th of a pixel, through measuring the intra-pixel quantum efficiency for the first time and recomputing the image. Employing our method, the physical sampling resolution of DIS can be enhanced by 16 times. The method has undergone rigorous testing in real-world imaging scenarios.

physics.optics↗

arXiv2024

Photosensitive PEEK Ink Enables Digital Light Processing 3D Printed High-performance Small Architected-Plastics

Polyetheretherketone (PEEK), as a semi-crystalline high-performance engineering plastic, has demonstrated good application prospects since its introduction. The ability of PEEK to be fabricated in complex architecture is a major limitation due to the inherent shortcomings of material extrusion 3D printing technology in terms of low resolution, low surface quality, and interlayer bonding. We propose a novel PEEK ink processing process based on digital light processing (DLP) 3D printing, which is based on high solid content PEEK ink to achieve green bodies with high accuracy, and one-step sintering to enhance the crystallinity of PEEK. We have investigated the processing mechanism of this process and constructed perfect process parameters in terms of mouldability, printing accuracy, material thermal properties, and PEEK crystallinity. Furthermore, the material and architecture performance of the proposed process was evaluated in terms of comprehensive thermal performance (including heat resistance of the substrate, thermal stability, surface energy after heat treatment, and coefficient of static friction and coefficient of kinetic friction), mechanical performance, and corrosion resistance (20 wt% hydrochloric acid, 20 wt% sodium hydroxide, 99 wt% acetone, and 99.5 wt% chloroform). The process is a bold extension of PEEK processing methods to utilize the properties of PEEK in more flexible and efficient applications.

physics.app-ph↗

arXiv2023

3D Programming of Patterned Heterogeneous Interface for 4D Smart Robotics

Shape memory structures are playing an important role in many cutting-edge intelligent fields. However, the existing technologies can only realize 4D printing of a single polymer or metal, which limits practical applications. Here, we report a construction strategy for TSMP/M heterointerface, which uses Pd2+-containing shape memory polymer (AP-SMR) to induce electroless plating reaction and relies on molecular dynamics, which has both shape memory properties and metal activity and information processing power. Through multi-material DLP 3D printing technology, the interface can be 3D selectively programmed on functional substrate parts of arbitrary shapes to become 4D electronic smart devices (Robotics). Microscopically, this type of interface appears as a composite structure with a nanometer-micrometer interface height, which is composed of a pure substrate layer (smart materials), an intermediate layer (a composite structure in which metal particles are embedded in a polymer cross-linked network) and a pure metal layer. The structure programmed by TSMP/M heterointerface exhibits both SMA characteristics and metal properties, thus having more intelligent functions (electroactive, electrothermal deformation, electronically controlled denaturation) and higher performance (selectivity of shape memory structures can be realized control, remote control, inline control and low voltage control). This is expected to provide a more flexible manufacturing process as platform technology for designing, manufacturing and applying smart devices with new concepts, and promote the development of cutting-edge industries such as smart robots and smart electronics.

cs.RO↗

arXiv2023

FedEdge AI-TC: A Semi-supervised Traffic Classification Method based on Trusted Federated Deep Learning for Mobile Edge Computing

As a typical entity of MEC (Mobile Edge Computing), 5G CPE (Customer Premise Equipment)/HGU (Home Gateway Unit) has proven to be a promising alternative to traditional Smart Home Gateway. Network TC (Traffic Classification) is a vital service quality assurance and security management method for communication networks, which has become a crucial functional entity in 5G CPE/HGU. In recent years, many researchers have applied Machine Learning or Deep Learning (DL) to TC, namely AI-TC, to improve its performance. However, AI-TC faces challenges, including data dependency, resource-intensive traffic labeling, and user privacy concerns. The limited computing resources of 5G CPE further complicate efficient classification. Moreover, the "black box" nature of AI-TC models raises transparency and credibility issues. The paper proposes the FedEdge AI-TC framework, leveraging Federated Learning (FL) for reliable Network TC in 5G CPE. FL ensures privacy by employing local training, model parameter iteration, and centralized training. A semi-supervised TC algorithm based on Variational Auto-Encoder (VAE) and convolutional neural network (CNN) reduces data dependency while maintaining accuracy. To optimize model light-weight deployment, the paper introduces XAI-Pruning, an AI model compression method combined with DL model interpretability. Experimental evaluation demonstrates FedEdge AI-TC's superiority over benchmarks in terms of accuracy and efficient TC performance. The framework enhances user privacy and model credibility, offering a comprehensive solution for dependable and transparent Network TC in 5G CPE, thus enhancing service quality and security.

cs.CR↗

arXiv2023

TextShield: Beyond Successfully Detecting Adversarial Sentences in Text Classification

Adversarial attack serves as a major challenge for neural network models in NLP, which precludes the model's deployment in safety-critical applications. A recent line of work, detection-based defense, aims to distinguish adversarial sentences from benign ones. However, {the core limitation of previous detection methods is being incapable of giving correct predictions on adversarial sentences unlike defense methods from other paradigms.} To solve this issue, this paper proposes TextShield: (1) we discover a link between text attack and saliency information, and then we propose a saliency-based detector, which can effectively detect whether an input sentence is adversarial or not. (2) We design a saliency-based corrector, which converts the detected adversary sentences to benign ones. By combining the saliency-based detector and corrector, TextShield extends the detection-only paradigm to a detection-correction paradigm, thus filling the gap in the existing detection-based defense. Comprehensive experiments show that (a) TextShield consistently achieves higher or comparable performance than state-of-the-art defense methods across various attacks on different benchmarks. (b) our saliency-based detector outperforms existing detectors for detecting adversarial sentences.

cs.CL↗

arXiv2022

Group frame neural network of moving object ghost imaging combined with frame merging algorithm

The nature of multiple samples to extract correlation information limits the applications of ghost imaging of moving objects. A novel multi-to-one neural network is proposed and the concept of "batch frame" is introduced to improve the serial imaging method. The neural network extracts more correlation information from a small number of samples, thus reducing the sampling ratio of the ghost imaging technique. We combine the correlation characteristics between images to propose a frame merging algorithm, which eliminates the dynamic blur of high-speed moving objects and further improves the reconstruction quality of moving object images at a low sampling ratio. The experimental results are consistent with the simulation results.

eess.IV↗

arXiv2022

Pulsed Laser Ejection of Single-Crystalline III-V Solar Cells From GaAs Substrates

Like many optoelectronics, the highest quality III-V solar cells start out as thin single-crystalline multilayers on GaAs substrates. Separating these device layers from their growth substrate enables higher performing devices and wafer reuse, both of which are critical for III-V solar cell viability in a terrestrial market. Here, we remove rigidly-bonded, lattice-matched, 16 mm$^2$ x 3.5 um thick GaAs devices off a GaAs substrate using a 10 ns, unfocused Nd:YAG laser pulse. The pulse is selectively absorbed in a lower-bandgap, lattice-matched, crystalline layer below the device, driving a quasi-two dimensional ablation event that ejects the crystalline multilayer from the substrate. After minutes of selective wet-chemical etching and front contact deposition, our champion 0.1 cm$^2$ device showed a (17.4 +/- 0.5) % power conversion efficiency and an open-circuit voltage of 1.07 V, using AM1.5 direct (1000 W m$^{-2}$) with no anti-reflection coating. We show that the performance is comparable to similar solar cells produced via conventional substrate dissolution processes. We discuss unique process characteristics and opportunities, such as the potential to separate wafer-sized thin film solar cells per laser pulse.

cond-mat.mtrl-sci↗

arXiv2022

Non-neglectable entropy effect on sintering of supported nanoparticles

Sintering refers to particle coalescence by heat, which has been known as a thermal phenomenon involving all aspects of natural science for centuries. It is particularly important in heterogeneous catalysis because normally sintering results in deactivation of the catalysts. In previous studies, the enthalpy contribution was considered to be dominant in sintering and the entropy effect is generally considered neglectable. However, we unambiguously demonstrate in this work that entropy could prevail over the enthalpy contribution to dominate the sintering behavior of supported nanoparticles (NPs) by designed experiments and improved theoretical framework. Using in situ Cs-corrected environmental scanning transmission electron microscopy and synchrotron-based ambient pressure X-ray photoelectron spectroscopy, we observe the unprecedent entropy-driven phenomenon that supported NPs reversibly redisperse upon heating and sinter upon cooling in three systems (Pd-CeO2, Cu-TiO2, Ag-TiO2). We quantitatively show that the configurational entropy of highly dispersed ad-atoms is large enough to reverse their sintering tendency at the elevated temperature. This work reshapes the basic understanding of sintering at the nanoscale and opens the door for various de-novo designs of thermodynamically stable nanocatalysts.

cond-mat.mtrl-sci↗

arXiv2021

Towards High Fidelity Face Relighting with Realistic Shadows

Existing face relighting methods often struggle with two problems: maintaining the local facial details of the subject and accurately removing and synthesizing shadows in the relit image, especially hard shadows. We propose a novel deep face relighting method that addresses both problems. Our method learns to predict the ratio (quotient) image between a source image and the target image with the desired lighting, allowing us to relight the image while maintaining the local facial details. During training, our model also learns to accurately modify shadows by using estimated shadow masks to emphasize on the high-contrast shadow borders. Furthermore, we introduce a method to use the shadow mask to estimate the ambient light intensity in an image, and are thus able to leverage multiple datasets during training with different global lighting intensities. With quantitative and qualitative evaluations on the Multi-PIE and FFHQ datasets, we demonstrate that our proposed method faithfully maintains the local facial details of the subject and can accurately handle hard shadows while achieving state-of-the-art face relighting performance.

cs.CV↗

arXiv2020

Measuring the Unique Identifiers of Topological Order Based on Boundary-Bulk Duality and Anyon Condensation

A topological order is a new quantum phase that is beyond Landau's symmetry-breaking paradigm. Its defining features include robust degenerate ground states, long-range entanglement and anyons. It was known that $R$- and $F$-matrices, which characterize the fusion-braiding properties of anyons, can be used to uniquely identify topological order. In this article, we explore an essential question: how can the $R$- and $F$-matrices be experimentally measured? By using quantum simulations based on a toric code model with boundaries and state-of-the-art technology, we show that the braidings, i.e. the $R$-matrices, can be completely determined by the half braidings of boundary excitations due to the boundary-bulk duality and the anyon condensation. The $F$-matrices can also be measured in a scattering quantum circuit involving the fusion of three anyons in two different orders. Thus we provide an experimental protocol for measuring the unique identifiers of topological order.

quant-ph↗

arXiv2020

Application-Driven Synthesis and Characterization of Hexagonal Boron Nitride on Metal and Carbon Nanotube Substrates

Hexagonal boron nitride (h-BN) is unique among two-dimensional materials, with a large band gap (~6 eV) and high thermal conductivity (>400 W/m/K), second only to diamond among electrical insulators. Most electronic studies to date have relied on h-BN exfoliated from bulk crystals; however, for scalable applications the material must be synthesized by methods such as chemical vapor deposition (CVD). Here, we demonstrate single- and few-layer h-BN synthesized by CVD on single crystal platinum and on carbon nanotube (CNT) substrates, also comparing these films with h-BN deposited on the more commonly used polycrystalline Pt and Cu growth substrates. The h-BN film grown on single crystal Pt has a lower surface roughness and is more spatially homogeneous than the film from a polycrystalline Pt foil, and our electrochemical transfer process allows for these expensive foils to be reused with no measurable degradation. In addition, we demonstrate monolayer h-BN as an ultrathin, 3.33 $\unicode{x212B}$ barrier protecting MoS2 from damage at high temperatures and discuss other applications that take advantage of the conformal h-BN deposition on various substrates demonstrated in this work.

cond-mat.mtrl-sci↗

arXiv2020

Direct Visualization of Irreducible Ferrielectricity in Crystals

In solids, charge polarity can one-to-one correspond to spin polarity phenomenologically, e.g. ferroelectricity/ferromagnetism, antiferroelectricity/antiferromagnetism, and even dipole-vortex/magnetic-vortex, but ferrielectricity/ferrimagnetism kept telling a disparate story in microscopic level. Since the definition of a charge dipole involves more than one ion, there may be multiple choices for a dipole unit, which makes most ferrielectric orders equivalent to ferroelectric ones, i.e. this ferrielectricity is not necessary to be a real independent branch of polarity. In this work, by using the spherical aberration-corrected scanning transmission electron microscope, we visualize a nontrivial ferrielectric structural evolution in BaFe2Se3, in which the development of two polar sub-lattices is out-of-sync, for which we term it as irreducible ferrielectricity. Such irreducible ferrielectricity leads to a non-monotonic behavior for the temperature-dependent polarization, and even a compensation point in the ordered state. Our finding unambiguously distinguishes ferrielectrics from ferroelectrics in solids.

cond-mat.mtrl-sci↗

arXiv2019

Experimental Observation of Equilibrium and Dynamical Quantum Phase Transitions via Out-of-Time-Ordered Correlators

The out-of-time-ordered correlators (OTOC) have been established as a fundamental concept for quantifying quantum information scrambling and diagnosing quantum chaotic behavior. Recently, it was theoretically proposed that the OTOC can be used as an order parameter to dynamically detect both equilibrium quantum phase transitions (EQPTs) and dynamical quantum phase transitions (DQPTs) in one-dimensional many-body systems. Here we report the first experimental observation of EQPTs and DQPTs in a quantum spin chain via quench dynamics of OTOC on a nuclear magnetic resonance quantum simulator. We observe that the quench dynamics of both the order parameter and the two-body correlation function cannot detect the DQPTs, but the OTOC can unambiguously detect the DQPTs. Moreover, we demonstrate that the long-time average value of the OTOC in quantum quench signals the equilibrium quantum critical point and ordered quantum phases, thus one can measure the EQPTs from the non-equilibrium quantum quench dynamics. Our experiment paves a way for experimentally investigating DQPTs through OTOCs and for studying the EQPTs through the non-equilibrium quantum quench dynamics with quantum simulators.

quant-ph↗

arXiv2019

Detecting scrambling via statistical correlations between randomized measurements on an NMR quantum simulator

Out-of-time-order correlator (OTOC), been suggested as a measure of quantum information scrambling in quantum many-body systems, has received enormous attention recently. The experimental measurement of OTOC is quite challenging. The existing theoretical protocols consist in implementing time-reversal operations or using ancillary quantum systems, therefore only a few experiments have been reported. Recently, a new protocol to detect OTOC using statistical correlations between randomized measurements was put forward. In this work, we detect the OTOCs of a kicked-Ising model using this new measurement method on a 4-qubit nuclear magnetic resonance quantum simulator. In experiment, we use random Hamiltonian evolutions to generate the random operations that are required by the randomized OTOC detection protocol. Our experimental results are in good agreement with the theoretical predictions, thus confirming the feasibility of the protocols. Therefore, our work represents a step in exploring realistic quantum chaotic dynamics in complicated quantum systems.

quant-ph↗