arXiv subjects

Zhixin Chen

Publications and source records attributed to Zhixin Chen.

13 recordsLinked to original sources

arXiv2026

RIS-Aided Cooperative ISAC Network for Imaging-Based Low-Altitude Surveillance

The low-altitude economy is integral to the advancement of numerous sectors, necessitating the development of advanced low-altitude surveillance techniques. Nevertheless, conventional methods encounter limitations of high deployment costs and low signal strength. This study proposes a reconfigurable intelligent surface (RIS)-aided cooperative integrated sensing and communication (ISAC) network for low-altitude surveillance. This network employs RISs to reflect ISAC signals into low-altitude space for sensing. To enhance signal strength, we employ active RIS (ARIS) to amplify the signals. Moreover, in order to avoid error propagation and data association in traditional sensing methods, we model low-altitude surveillance as an imaging problem based on compressed sensing theory, which can be solved through the subspace pursuit algorithm. We derive the Cramer-Rao lower bound (CRLB) of the proposed RIS-aided low-altitude imaging system and analyze the impacts of various system parameters on sensing performance, providing guidance for ISAC system configuration. Numerical results show that ARIS outperforms passive RIS under identical power constraints, achieving effective imaging and target detection at altitudes up to 300 meters.

cs.IT↗

arXiv2025

Raman Microspectroscopy for Real-Time Structure Indicator in Ultrafast Laser Writing

Femtosecond laser fabrication enables the creation of a wide range of devices, but its scalability and yield can be limited by the lack of real-time, in-situ monitoring tools. In particular, there is a strong need for metrics that directly correlate with device performance. Raman microspectroscopy provides a non-destructive route for in-situ characterization. Here, we demonstrate its potential to assess the electrical performance of laser-written graphitic electrodes in diamond. By combining hyperspectral mapping with electrical testing, we show that depletion of the 1332 cm$^{-1}$ sp3 Raman line serves as a monotonic and robust predictor of resistance, offering clear advantages over commonly used spectral features. We further introduce hyperspectral unmixing as a label-free approach to identify relevant spectral signatures in fabrication processes where Raman markers are less defined. Importantly, the methodology we present is not restricted to diamond but can be adapted to other host materials and functionalities, offering a practical path toward specification-driven fs-laser microfabrication.

physics.app-ph↗

arXiv2025

Difference Imaging-Based Parking Lot Surveillance in Multi-RIS-Aided Collaborative ISAC System

Parking lot surveillance with integrated sensing and communication (ISAC) system is one of the potential application scenarios defined by 3rd Generation Partnership Project (3GPP). Traditional surveillance systems using cameras or magnetic sensors face limitations such as light dependence, high costs, and constrained scalability. Wireless sensing with reconfigurable intelligent surfaces (RISs) has the ability to address the above limitations due to its light independence and lower deployment overhead. In this study, we propose a difference imaging-based multi-RIS-aided collaborative ISAC system to achieve parking lot surveillance. In a parking lot, the presence of vehicles induces impacts on wireless environments due to scattering characteristic variation. By delineating the parking lot into a two-dimensional image with several grid units, the proposed system can capture the variation of their scattering coefficients in free and occupied states. The variation between these two states is sparse, which can be captured through compressed sensing (CS)-based imaging algorithms. Additionally, we collaboratively employ multiple RISs to enable higher surveillance performance. Experimental results demonstrate that our method can achieve high-accuracy parking occupancy detection, and the employment of collaborative RISs further enhances the detection rate.

cs.IT↗

arXiv2025

DifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-Resolution

Infrared imaging is essential for autonomous driving and robotic operations as a supportive modality due to its reliable performance in challenging environments. Despite its popularity, the limitations of infrared cameras, such as low spatial resolution and complex degradations, consistently challenge imaging quality and subsequent visual tasks. Hence, infrared image super-resolution (IISR) has been developed to address this challenge. While recent developments in diffusion models have greatly advanced this field, current methods to solve it either ignore the unique modal characteristics of infrared imaging or overlook the machine perception requirements. To bridge these gaps, we propose DifIISR, an infrared image super-resolution diffusion model optimized for visual quality and perceptual performance. Our approach achieves task-based guidance for diffusion by injecting gradients derived from visual and perceptual priors into the noise during the reverse process. Specifically, we introduce an infrared thermal spectrum distribution regulation to preserve visual fidelity, ensuring that the reconstructed infrared images closely align with high-resolution images by matching their frequency components. Subsequently, we incorporate various visual foundational models as the perceptual guidance for downstream visual tasks, infusing generalizable perceptual features beneficial for detection and segmentation. As a result, our approach gains superior visual results while attaining State-Of-The-Art downstream task performance. Code is available at https://github.com/zirui0625/DifIISR

cs.CV↗

arXiv2024

Contourlet Refinement Gate Framework for Thermal Spectrum Distribution Regularized Infrared Image Super-Resolution

Image super-resolution (SR) is a classical yet still active low-level vision problem that aims to reconstruct high-resolution (HR) images from their low-resolution (LR) counterparts, serving as a key technique for image enhancement. Current approaches to address SR tasks, such as transformer-based and diffusion-based methods, are either dedicated to extracting RGB image features or assuming similar degradation patterns, neglecting the inherent modal disparities between infrared and visible images. When directly applied to infrared image SR tasks, these methods inevitably distort the infrared spectral distribution, compromising the machine perception in downstream tasks. In this work, we emphasize the infrared spectral distribution fidelity and propose a Contourlet refinement gate framework to restore infrared modal-specific features while preserving spectral distribution fidelity. Our approach captures high-pass subbands from multi-scale and multi-directional infrared spectral decomposition to recover infrared-degraded information through a gate architecture. The proposed Spectral Fidelity Loss regularizes the spectral frequency distribution during reconstruction, which ensures the preservation of both high- and low-frequency components and maintains the fidelity of infrared-specific features. We propose a two-stage prompt-learning optimization to guide the model in learning infrared HR characteristics from LR degradation. Extensive experiments demonstrate that our approach outperforms existing image SR models in both visual and perceptual tasks while notably enhancing machine perception in downstream tasks. Our code is available at https://github.com/hey-it-s-me/CoRPLE.

cs.CV↗

arXiv2024

Electron Phase Detection in Single Molecules by Interferometry

Interferometry has underpinned a century of discoveries, ranging from the disproval of the ether theory to the detection of gravitational waves, offering insights into wave dynamics with unrivalled precision through the measurement of phase relationships. In electronics, phase-sensitive measurements can probe the nature of transmissive topological and quantum states, but are only possible using complex device structures in magnetic fields. Here we demonstrate electronic interferometry in a single-molecule device through the study of non-equilibrium Fano resonances. We show the phase difference between an electronic orbital and a coupled Fabry-Perot resonance are tuneable through electric fields, and consequently it is possible to read out quantum information in the smallest devices, offering new avenues for the coherent manipulation down to single molecules.

physics.chem-ph↗

arXiv2024

Deep learning for cosmological parameter inference from a dark matter halo density field

We propose a lightweight deep convolutional neural network (lCNN) to estimate cosmological parameters from simulated three-dimensional dark matter (DM) halo distributions and associated statistics. The training dataset comprises 2000 realizations of a cubic box with a side length of 1000 $h^{-1}{\rm Mpc}$, and interpolated over a cubic grid of $300^3$ voxels, with each simulation produced using $512^3$ DM particles and $512^3$ neutrinos. Under the flat $Λ$CDM model, simulations vary standard six cosmological parameters including $Ω_m$, $Ω_b$, $h$, $n_s$, $σ_8$, $w$, along with the neutrino mass sum, $M_ν$. We find that: 1) within the framework of lCNN, extracting large-scale structure information is more efficient from the halo density field compared to relying on the statistical quantities including the power spectrum, the two-point correlation function, and the coefficients from wavelet scattering transform; 2) combining the halo density field with its Fourier transformed counterpart enhances predictions, while augmenting the training dataset with measured statistics further improves performance; 3) achieving high accuracy in inferring $Ω_m$, $h$, and $σ_8$ by the neural network model, while being inefficient in predicting $Ω_b$, { $n_s$}, $M_ν$ and $w$; 4) { compared to the simple fully connected network trained with three statistical quantities, our CNN yields statistically reduced errors, showing improvements of approximately 23\% for $Ω_m$, 11\% for $h$, 8\% for $n_s$, and 21\% for $σ_8$. Additionally, in comparison with the likelihood-based analysis on $P(k)$ data, our CNN provides much tighter constraints on parameters, especially on $Ω_m$ and $σ_8$.} Our study emphasizes this lCNN-based novel approach in extracting large-scale structure information and estimating cosmological parameters.

astro-ph.CO↗

arXiv2023

Quantum Interference Enhances the Performance of Single-Molecule Transistors

An unresolved challenge facing electronics at a few-nm scale is that resistive channels start leaking due to quantum tunneling. This affects the performance of nanoscale transistors, with single-molecule devices displaying particularly low switching ratios and operating frequencies, combined with large subthreshold swings.1 The usual strategy to mitigate quantum effects has been to increase device complexity, but theory shows that if quantum effects are exploited correctly, they can simultaneously lower energy consumption and boost device performance.2-6 Here, we demonstrate experimentally how the performance of molecular transistors can be improved when the resistive channel contains two destructively-interfering waves. We use a zinc-porphyrin coupled to graphene electrodes in a three-terminal transistor device to demonstrate a >104 conductance-switching ratio, a subthreshold swing at the thermionic limit, a > 7 kHz operating frequency, and stability over >105 cycles. This performance is competitive with the best nanoelectronic transistors. We fully map the antiresonance interference features in conductance, reproduce the behaviour by density functional theory calculations, and trace back this high performance to the coupling between molecular orbitals and graphene edge states. These results demonstrate how the quantum nature of electron transmission at the nanoscale can enhance, rather than degrade, device performance, and highlight directions for future development of miniaturised electronics.

physics.chem-ph↗

arXiv2022

Phase-Coherent Charge Transport through a Porphyrin Nanoribbon

Quantum interference in nano-electronic devices could lead to reduced-energy computing and efficient thermoelectric energy harvesting. When devices are shrunk down to the molecular level it is still unclear to what extent electron transmission is phase coherent, as molecules usually act as scattering centres, without the possibility of showing particle-wave duality. Here we show electron transmission remains phase coherent in molecular porphyrin nanoribbons, synthesized with perfectly defined geometry, connected to graphene electrodes. The device acts as a graphene Fabry-Pérot interferometer, allowing direct probing of the transport mechanisms throughout several regimes, including the Kondo one. Electrostatic gating allows measurement of the molecular conductance in multiple molecular oxidation states, demonstrating a thousand-fold increase of the current by interference, and unravelling molecular and graphene transport pathways. These results demonstrate a platform for the use of interferometric effects in single-molecule junctions, opening up new avenues for studying quantum coherence in molecular electronic and spintronic devices.

cond-mat.mes-hall↗

arXiv2022

Charge-state dependent vibrational relaxation in a single-molecule junction

The interplay between nuclear and electronic degrees of freedom strongly influences molecular charge transport. Herein, we report on transport through a porphyrin dimer molecule, weakly coupled to graphene electrodes, that displays sequential tunneling within the Coulomb-blockade regime. The sequential transport is initiated by current-induced phonon absorption and proceeds by rapid sequential transport via a non-equilibrium vibrational distribution. We demonstrate this is possible only when the vibrational dissipation is slow relative to sequential tunneling rates, and obtain a lower bound for the vibrational relaxation time of 8 ns, a value that is dependent on the molecular charge state.

cond-mat.mes-hall↗

arXiv2021

Self-Imitation Learning for Robot Tasks with Sparse and Delayed Rewards

The application of reinforcement learning (RL) in robotic control is still limited in the environments with sparse and delayed rewards. In this paper, we propose a practical self-imitation learning method named Self-Imitation Learning with Constant Reward (SILCR). Instead of requiring hand-defined immediate rewards from environments, our method assigns the immediate rewards at each timestep with constant values according to their final episodic rewards. In this way, even if the dense rewards from environments are unavailable, every action taken by the agents would be guided properly. We demonstrate the effectiveness of our method in some challenging continuous robotics control tasks in MuJoCo simulation and the results show that our method significantly outperforms the alternative methods in tasks with sparse and delayed rewards. Even compared with alternatives with dense rewards available, our method achieves competitive performance. The ablation experiments also show the stability and reproducibility of our method.

cs.LG↗

arXiv2020

Towards Generalization and Data Efficient Learning of Deep Robotic Grasping

Deep reinforcement learning (DRL) has been proven to be a powerful paradigm for learning complex control policy autonomously. Numerous recent applications of DRL in robotic grasping have successfully trained DRL robotic agents end-to-end, mapping visual inputs into control instructions directly, but the amount of training data required may hinder these applications in practice. In this paper, we propose a DRL based robotic visual grasping framework, in which visual perception and control policy are trained separately rather than end-to-end. The visual perception produces physical descriptions of grasped objects and the policy takes use of them to decide optimal actions based on DRL. Benefiting from the explicit representation of objects, the policy is expected to be endowed with more generalization power over new objects and environments. In addition, the policy can be trained in simulation and transferred in real robotic system without any further training. We evaluate our framework in a real world robotic system on a number of robotic grasping tasks, such as semantic grasping, clustered object grasping, moving object grasping. The results show impressive robustness and generalization of our system.

cs.RO↗

arXiv2020

Vision-based Robot Manipulation Learning via Human Demonstrations

Vision-based learning methods provide promise for robots to learn complex manipulation tasks. However, how to generalize the learned manipulation skills to real-world interactions remains an open question. In this work, we study robotic manipulation skill learning from a single third-person view demonstration by using activity recognition and object detection in computer vision. To facilitate generalization across objects and environments, we propose to use a prior knowledge base in the form of a text corpus to infer the object to be interacted with in the context of a robot. We evaluate our approach in a real-world robot, using several simple and complex manipulation tasks commonly performed in daily life. The experimental results show that our approach achieves good generalization performance even from small amounts of training data.

cs.RO↗