arXiv subjects

Chenbo Zhang

Publications and source records attributed to Chenbo Zhang.

13 recordsLinked to original sources

arXiv2026

Conjugate phase-noise cancellation enables submicrometre dual-comb ranging with free-running megahertz-linewidth lasers

Frequency-domain dual-comb ranging combines rapid acquisition with interferometric sensitivity, but high-performance implementations often rely on mutually coherent or actively stabilised comb sources. Free-running sources can reduce this hardware burden, but their phase noise and drift of the optical frequency offset can blur radio-frequency (RF) comb teeth and weaken probe-reference phase correlation. Previous phase-slope implementations have therefore relied on sufficiently resolved RF teeth, within-coherence-length probe-reference paths or explicit digital tracking of these fluctuations. Here we demonstrate a low-cost frequency-domain dual-comb ranging architecture that combines independent free-running distributed-feedback (DFB) lasers with conjugate phase-noise cancellation (CPNC). By forming a self-conjugate signal before the phases of individual RF-comb teeth are extracted, CPNC cancels the common laser phase factor and drifting optical-frequency-offset term while retaining the distance-dependent phase slope. Using electro-optic combs seeded by DFB lasers with linewidths of 12 MHz and 9 MHz, we achieve an Allan deviation of $219~\mathrm{nm}$ at $246~\mu\mathrm{s}$ and reduce the single-frame distance standard deviation from $558~\mu\mathrm{m}$ to $9.31~\mu\mathrm{m}$ with CPNC. Across the tested megahertz-linewidth configurations, CPNC delivered minimum Allan deviations below $250~\mathrm{nm}$. These results show that CPNC enables submicrometre ranging in a low-cost frequency-domain dual-comb architecture with reduced source-stabilisation and phase-management complexity.

physics.optics↗

arXiv2026

Compositionally tuned phase transformations enhance pyroelectric energy harvesting from low-grade heat

Phase-transforming pyroelectric materials have emerged as promising candidates for low-grade thermal energy harvesting. However, whether first-order transformations with large pyroelectric coefficient or second-order transformations with better reversibility are preferable remains unclear. Here we report compositionally tunable phase transformations in Ba$_{1-x}$Sr$_x$TiO$_3$ ($x \in [0, 0.3]$), revealing evolution from first-order to second-order character. We identify a transitional regime between Sr$_{0.15}$ and Sr$_{0.22}$ where transformation mechanism fundamentally changes. Within this regime, Sr$_{0.19}$ achieves optimal lattice compatibility, exhibiting electrical leakage suppressed by over two orders of magnitude while retaining substantial polarization response. Energy conversion demonstrations show the multilayer Sr$_{0.19}$ device delivers pyroelectric current of $\sim$1.6 $\mu$A at 64$~^\circ$C with an energy density of 1.6 mJ/cm$^3$ per cycle and 5.5\% conversion efficiency. Remarkably, this composition operates stably over 10,000 full energy conversion cycles without external bias field or recharging, demonstrating that transitional regime compositions provide the optimal balance between energy density and operational durability for practical low-grade heat harvesting.

cond-mat.mtrl-sci↗

arXiv2025

Fine-Grained Zero-Shot Object Detection

Zero-shot object detection (ZSD) aims to leverage semantic descriptions to localize and recognize objects of both seen and unseen classes. Existing ZSD works are mainly coarse-grained object detection, where the classes are visually quite different, thus are relatively easy to distinguish. However, in real life we often have to face fine-grained object detection scenarios, where the classes are too similar to be easily distinguished. For example, detecting different kinds of birds, fishes, and flowers. In this paper, we propose and solve a new problem called Fine-Grained Zero-Shot Object Detection (FG-ZSD for short), which aims to detect objects of different classes with minute differences in details under the ZSD paradigm. We develop an effective method called MSHC for the FG-ZSD task, which is based on an improved two-stage detector and employs a multi-level semantics-aware embedding alignment loss, ensuring tight coupling between the visual and semantic spaces. Considering that existing ZSD datasets are not suitable for the new FG-ZSD task, we build the first FG-ZSD benchmark dataset FGZSD-Birds, which contains 148,820 images falling into 36 orders, 140 families, 579 genera and 1432 species. Extensive experiments on FGZSD-Birds show that our method outperforms existing ZSD models.

cs.CV↗

arXiv2025

FerroAI: A Deep Learning Model for Predicting Phase Diagrams of Ferroelectric Materials

Composition-temperature phase diagrams are crucial for designing ferroelectric materials, however predicting them accurately remains challenging due to limited phase transformation data and the constraints of conventional methods. Here, we utilize natural language processing (NLP) to text-mine 41,597 research articles, compiling a dataset of 2,838 phase transformations across 846 ferroelectric materials. Leveraging this dataset, we develop FerroAI, a deep learning model for phase diagram prediction. FerroAI successfully predicts phase boundaries and transformations among different crystal symmetries in Ce/Zr co-doped BaTiO$3$ (BT)-$x$Ba${0.7}$Ca$_{0.3}$TiO$_3$ (BCT). It also identifies a morphotropic phase boundary in Zr/Hf co-doped BT-$x$BCT at $x = 0.3$, guiding the discovery of a new ferroelectric material with an experimentally measured dielectric constant of 9535. These results establish FerroAI as a powerful tool for phase diagram construction, guiding the design of high-performance ferroelectric materials.

cond-mat.mtrl-sci↗

arXiv2024

Highly coherent two-color laser with stability below 3E-17 at 1 second

Two-color lasers with high coherence are paramount in precision measurement, accurate light-matter interaction, and low-noise photonic microwave generation. However, conventional two-color lasers often suffer from low coherence, particularly when these two colors face large frequency spacings. Here, harnessing the Pound-Drever-Hall technique, we synchronize two lasers to a shared ultra-stable optical reference cavity to break through the thermal noise constraint, achieving a highly coherent two-color laser. With conquering these non-common mode noises, we demonstrate an exceptional fractional frequency instability of 2.7E-17 at 1 second when normalized to the optical frequency. Characterizing coherence across large frequency spacings poses a significant challenge. To tackle this, we employ electro-optical frequency division to transfer the relative stability of a 0.5 THz spacing two-color laser to a 25 GHz microwave signal. As its performance surpasses the sensitivity of the current apparatus, we establish two independent systems for comparative analyses. The resulting 25 GHz signals exhibit exceptional phase noise of -74 dBc/Hz at 1 Hz and -120 dBc/Hz at 100 Hz, demonstrating the two-color laser's performance approaching the quantum noise limit of its synchronization system. It also sets a new record for the two-point frequency division method in photonic microwave generation. Our achievement in highly coherent two-color lasers and low-noise microwave signals will usher in a new era for precision measurements and refine the accuracy of light-matter and microwave-matter interactions to their next decimal place.

physics.optics↗

arXiv2024

Electric leakage suppression of phase-transforming ferroelectrics with donor impurities

Phase-transforming ferroelectric materials are widely used in energy harvesting and conversion devices. However, the functionality of these devices is significantly impeded by electrical leakage at high temperatures. In this study, we fundamentally study the mechanism of electrical leakage suppression due to phase transformation in a series of donor-doped ferroelectric oxides,Ba0.955Eu0.03Ti(1-x)ZrxO3 with 0<= x<= 0.15. Our experiments clearly demonstrate that the symmetry-breaking phase transformations result in the reduction in electrical conductivity of the donor-doped ferroelectric oxides. The DFT calculation suggests that the donor energy level undergoes a shallow-to-deep transition at the phase transformation temperature. By analyzing the constitutive model of the leakage current density function, we propose a leakage suppression coefficient that rationalizes the development of ferroelectrics with low electrical leakage at elevated temperatures.

cond-mat.mtrl-sci↗

arXiv2023

Meta-ZSDETR: Zero-shot DETR with Meta-learning

Zero-shot object detection aims to localize and recognize objects of unseen classes. Most of existing works face two problems: the low recall of RPN in unseen classes and the confusion of unseen classes with background. In this paper, we present the first method that combines DETR and meta-learning to perform zero-shot object detection, named Meta-ZSDETR, where model training is formalized as an individual episode based meta-learning task. Different from Faster R-CNN based methods that firstly generate class-agnostic proposals, and then classify them with visual-semantic alignment module, Meta-ZSDETR directly predict class-specific boxes with class-specific queries and further filter them with the predicted accuracy from classification head. The model is optimized with meta-contrastive learning, which contains a regression head to generate the coordinates of class-specific boxes, a classification head to predict the accuracy of generated boxes, and a contrastive head that utilizes the proposed contrastive-reconstruction loss to further separate different classes in visual space. We conduct extensive experiments on two benchmark datasets MS COCO and PASCAL VOC. Experimental results show that our method outperforms the existing ZSD methods by a large margin.

cs.CV↗

arXiv2023

Enhanced functional reversibility in lead-free ferroelectric material over long cycle pyroelectric energy conversion

The ferroelectric material usually exhibits temperature dependent spontaneous polarization, known as pyroelectricity, which can be used to directly convert thermal energy to electricity from ambient low-grade waste heat. When utilizing the structural phase transformations of the material, the conversion capability can be magnified, consequently the device performance can be strongly boosted by orders of magnitude. However, common ferroelectric oxides suffer the mechanical fatigue and functional degradation over cyclic phase transformations, hindering widespread applications of the energy conversion device. In this paper, we investigate the mechanical and functional reversibility of the material by lattice tuning and grain coarsening. We discover the lead-free compound Ba(Ce$_{0.005}$Zr$_{0.005}$)Ti$_{0.99}$O3-0.10(Ba$_{0.7}$Ca$_{0.3}$)TiO$_3$ (BCZT-0.10BCT) satisfying the compatibility condition among all present phases by its lattice parameters, making the phase transformations highly reversible. We demonstrated that the energy conversion device with the equiaxial coarse grains exhibits exceptional fatigue-resistance, with stable pyroelectric current output at 4$μ$A/cm$^2$ over 3,000 energy conversion cycles. Our work opens a new way to fabricate high-performance material that advances the pyroelectric energy conversion for practical application in engineering.

cond-mat.mtrl-sci↗

arXiv2022

Hierarchical Few-Shot Object Detection: Problem, Benchmark and Method

Few-shot object detection (FSOD) is to detect objects with a few examples. However, existing FSOD methods do not consider hierarchical fine-grained category structures of objects that exist widely in real life. For example, animals are taxonomically classified into orders, families, genera and species etc. In this paper, we propose and solve a new problem called hierarchical few-shot object detection (Hi-FSOD), which aims to detect objects with hierarchical categories in the FSOD paradigm. To this end, on the one hand, we build the first large-scale and high-quality Hi-FSOD benchmark dataset HiFSOD-Bird, which contains 176,350 wild-bird images falling to 1,432 categories. All the categories are organized into a 4-level taxonomy, consisting of 32 orders, 132 families, 572 genera and 1,432 species. On the other hand, we propose the first Hi-FSOD method HiCLPL, where a hierarchical contrastive learning approach is developed to constrain the feature space so that the feature distribution of objects is consistent with the hierarchical taxonomy and the model's generalization power is strengthened. Meanwhile, a probabilistic loss is designed to enable the child nodes to correct the classification errors of their parent nodes in the taxonomy. Extensive experiments on the benchmark dataset HiFSOD-Bird show that our method HiCLPL outperforms the existing FSOD methods.

cs.CV↗

arXiv2021

Energy conversion from heat to electricity by highly reversible phase-transforming ferroelectrics

Searching for performant multiferroic materials attracts general research interests in energy science as they have been increasingly exploited as the conversion media among thermal, electric, magnetic and mechanical energies by using their temperature-dependent ferroic properties. Here we report a material development strategy that guides us to discover a reversible phase-transforming ferroelectric material exhibiting enduring energy harvesting from small temperature differences. The material satisfies the crystallographic compatibility condition between polar and nonpolar phases, which shows only 2.5C thermal hysteresis and high figure of merit. It stably generates 15uA electricity in consecutive thermodynamic cycles in absence of any bias fields. We demonstrate our device to consistently generate 6uA/cm2 current density near 100C over 540 complete phase transformation cycles without any electric and functional degradation. The energy conversion device can light up a LED directly without attaching an external power source. This promising material candidate brings the low-grade waste heat harvesting closer to a practical realization, e.g. small temperature fluctuations around the water boiling point can be considered as a clean energy source.

cond-mat.mtrl-sci↗

arXiv2020

Impact of Leakage for Electricity Generation by Pyroelectric Converter

Pyroelectric energy converter is a functional capacitor using pyroelectric material as the dielectric layer. Utilizing the first-order phase transformation of the material, the pyroelectric device can generate adequate electricity within small temperature fluctuations. However, most pyroelectric capacitors are leaking during energy conversion. In this paper, we analyze the thermodynamics of pyroelectric energy conversion with consideration of the electric leakage. Our thermodynamic model is verified by experiments using three phase-transforming ferroelectric materials with different pyroelectric properties and leakage behaviors. We demonstrate that the impact of leakage for electric generation is prominent, and sometimes may be confused with the actual power generation by pyroelectricity. We discover an ideal material candidate, (Ba,Ca)(Ti,Zr,Ce)O$_3$, which exhibits large pyroelectric current and extremely low leakage current. The pyroelectric converter made of this material generates 1.95 $μ$A/cm$^2$ pyroelectric current density and 0.2 J/cm$^3$ pyroelectric work density even after 1389 thermodynamic conversion cycles.

physics.app-ph↗

arXiv2019

Data-driven approach for synchrotron X-ray Laue microdiffraction scan analysis

We propose a novel data-driven approach for analyzing synchrotron Laue X-ray microdiffraction scans based on machine learning algorithms. The basic architecture and major components of the method are formulated mathematically. We demonstrate it through typical examples including polycrystalline BaTiO$_3$, multiphase transforming alloys and finely twinned martensite. The computational pipeline is implemented for beamline 12.3.2 at the Advanced Light Source, Lawrence Berkeley National Lab. The conventional analytical pathway for X-ray diffraction scans is based on a slow pattern by pattern crystal indexing process. This work provides a new way for analyzing X-ray diffraction 2D patterns, independent of the indexing process, and motivates further studies of X-ray diffraction patterns from the machine learning prospective for the development of suitable feature extraction, clustering and labeling algorithms.

cond-mat.mtrl-sci↗

arXiv2019

Battery Detached Energy Conversion by Pyroelectric Effect

We propose a pyroelectric energy conversion device that converts heat directly to electricity. In contrast to conventional pyroelectric energy conversion designs, this energy harvesting system is detached from any external power sources, operating only under periodically varying temperature. Such detachment unambiguously attributes the converted electricity to heat that drives the change of polarization in the pyroelectric material, not to the electric field alternation caused by the external battery. Using pure and Zr doped BaTiO$_3$, we demonstrate the electricity generation in consecutive temperature cycles. We further develop a thermodynamic model for the energy conversion system. Our model suggests that the work output is rate dependent: the work output per cycle is linearly dependent on the heat/cooling frequency below the predicted threshold. The linearity is confirmed by experiments, and the threshold frequency is derived by theory. Finally we propose a figure of merit that separates the materials intrinsic properties from the system design parameters. The figure of merit guides the future material development and device improvement. Our work clears out confusions and reforms the foundation for pyroelectric materials' resurgence as a competitor for green electricity.

physics.app-ph↗