arXiv subjects

Ting Zeng

Publications and source records attributed to Ting Zeng.

14 recordsLinked to original sources

arXiv2026

Multi-Periodogram Velocity Estimation with Irregular Reference Signals for Robot-Aided ISAC

This paper addresses velocity estimation within robot-aided integrated sensing and communications (ISAC), where mobile robots act as sensing nodes but can only opportunistically reuse irregular 5G/6G reference signals (RSs). We show that the velocity profile induced by such irregular time-domain patterns can be decomposed into a periodic-peak component and an amplitude-shaping (weighting) component. Leveraging this structure, we propose a multi-periodogram velocity estimation algorithm that is standard-compliant and does not require new sensing-dedicated RSs or 3GPP modifications. Simulation results demonstrate that, compared with conventional periodogram processing, the proposed method improves low-SNR robustness by achieving a 3 dB SNR gain at the 10% missed-detection rate and reducing false alarms by 51%.

cs.RO↗

arXiv2026

PhysSFI-Net: Physics-informed Geometric Learning of Skeletal and Facial Interactions for Orthognathic Surgical Outcome Prediction

Orthognathic surgery repositions jaw bones to restore occlusion and enhance facial aesthetics. Accurate simulation of postoperative facial morphology is essential for preoperative planning. This study aims to develop and validate a physics-informed geometric deep learning framework named PhysSFI-Net for precise prediction of soft tissue deformation following orthognathic surgery. The model integrates a hierarchical feature extraction module with attention mechanisms to capture skeletal-facial interactions, an LSTM-based sequential predictor for incremental deformation, and a biomechanics-inspired reconstruction module for high-resolution facial modeling. The model was trained on 135 patients and externally validated on an independent cohort of 33 patients. Model performance was assessed using point cloud shape error, surface deviation error and landmark error between predicted facial shapes with corresponding ground truths. Quantitative analysis demonstrated that PhysSFI-Net achieved a global shape error of 1.070 +/- 0.088 mm, a surface deviation error of 1.296 +/- 0.349 mm and a landmark error of 2.445 +/- 1.326 mm. Comparative experiments indicated that PhysSFI-Net outperformed the state-of-the-art method ACMT-Net and baseline models. External validation further confirmed its robustness with a global HD of 1.431 +/- 0.087 mm and consistently lower subregional and mesh-based errors. In conclusion, PhysSFI-Net enables interpretable, high-resolution prediction of postoperative facial morphology, showing strong potential for clinical application in orthognathic surgical planning.

cs.CV↗

arXiv2025

Free-Space Twin-Field Quantum Key Distribution

Twin-field quantum key distribution (TF-QKD) elevates the secure key rate from a linear to a square-root dependence on channel loss while preserving measurement-device-independent security. This protocol is uniquely positioned to enable global-scale quantum networks, even under extreme channel loss. While fiber-based TF-QKD implementations have advanced rapidly since its proposal, free-space realizations have remained elusive due to atmospheric turbulence-induced phase distortions. Here, we report the first experimental demonstration of free-space TF-QKD over 14.2 km urban atmospheric channels, surpassing the effective atmospheric thickness -- a critical threshold for satellite compatibility. We achieve a secret key rate exceeding the repeaterless capacity bound, a milestone for practical quantum communication. Our approach eliminates the need for an auxiliary channel to stabilize a closed interferometer, instead leveraging open-channel time and phase control of optical pulses. This work represents a pivotal advance toward satellite-based global quantum networks, combining high-speed key distribution with inherent resistance to real-world channel fluctuations.

quant-ph↗

arXiv2024

113 km absolute ranging with nanometer precision

Accurate long-distance ranging is crucial for diverse applications, including satellite formation flying, very-long-baseline interferometry, gravitational-wave observatory, geographical research, etc. The integration of the time-of-flight mesurement with phase interference in dual-comb method enables high-precision ranging with a rapid update rate and an extended ambiguity range. Pioneering experiments have demonstrated unprecedented precision in ranging, achieving 5 nm @ 60 ms for 1.1 m and 200 nm @ 0.5 s for 25 m. However, long-distance ranging remains technically challenging due to high transmission loss and noise. In this letter, we propose a two-way dual-comb ranging (TWDCR) approach that enables successful ranging over a distance of 113 kilometers. We employ air dispersion analysis and synthetic repetition rate technique to extend the ambiguity range of the inherently noisy channel beyond 100 km. The achieved ranging precision is 11.5 $\mu$m @ 1.3 ms, 681 nm @ 1 s, and 82 nm @ 21 s, as confirmed through a comparative analysis of two independent systems. The advanced long-distance ranging technology is expected to have immediate implications for space research initiatives, such as the space telescope array and the satellite gravimetry.

physics.optics↗

arXiv2023

Dual-comb spectroscopy over 100km open-air path

Satellite-based greenhouse gases (GHG) sensing technologies play a critical role in the study of global carbon emissions and climate change. However, none of the existing satellite-based GHG sensing technologies can achieve the measurement of broad bandwidth, high temporal-spatial resolution, and high sensitivity at the same time. Recently, dual-comb spectroscopy (DCS) has been proposed as a superior candidate technology for GHG sensing because it can measure broadband spectra with high temporal-spatial resolution and high sensitivity. The main barrier to DCS's display on satellites is its short measurement distance in open air achieved thus far. Prior research has not been able to implement DCS over 20 km of open-air path. Here, by developing a bistatic setup using time-frequency dissemination and high-power optical frequency combs, we have implemented DCS over a 113 km turbulent horizontal open-air path. Our experiment successfully measured GHG with 7 nm spectral bandwidth and a 10 kHz frequency and achieved a CO2 sensing precision of <2 ppm in 5 minutes and <0.6 ppm in 36 minutes. Our results represent a significant step towards advancing the implementation of DCS as a satellite-based technology and improving technologies for GHG monitoring

physics.optics↗

arXiv2023

A new solution and concrete implementation steps for Artificial General Intelligence

In this paper, we propose a new approach to building a artificial general intelligence with self awareness, which includes: (1) a new method to implement attention mechanisms; (2) a way to give machines self-demands; (3) how to form a value evaluation system compatible with the network; (4) a way to create the world models; (5) how to realize a top-down, hierarchical thinking decision-making chain; (6) a way to achieve general decision-making and response capabilities; (7) a way for a machine to directly obtain human experience through language. In the paper, we first analyze some of the shortcomings of current LLMs (Large Language Model) and propose ideas for improvement. Then we analyze why our scheme can solve the above problems and provide detailed steps for implementing our scheme. In chapter 4, we have presented a step-by-step mplementation roadmap. And in chapter 5, we have presented a specific implementation demonstration. In chapter 6, we analyze the advantages and disadvantages of our scheme and propose further research directions. In this article, we have put forward how to create genuine artificial general intelligence step by step. It can handle data of all modalities in a unified form and can directly understand the experience that humans already possess through language, thus avoiding the problem that reinforcement learning is required for every decision-making process.

cs.LG↗

arXiv2022

113 km Free-Space Time-Frequency Dissemination at the 19th Decimal Instability

Optical clock networks play important roles in various fields, such as precise navigation, redefinition of "second" unit, and gravitational tests. To establish a global-scale optical clock network, it is essential to disseminate time and frequency with a stability of $10^{-19}$ over a long-distance free-space link. However, such attempts were limited to dozens of kilometers in mirror-folded configuration. Here, we take a crucial step toward future satellite-based time-frequency disseminations. By developing the key technologies, including high-power frequency combs, high-stability and high-efficiency optical transceiver systems, and efficient linear optical sampling, we demonstrate free-space time-frequency dissemination over two independent links with femtosecond time deviation, $3\times10^{-19}$ at 10,000 s residual instability and $1.6\times10^{-20}\pm 4.3\times10^{-19}$ offset. This level of the stability retains for an increased channel loss up to 89 dB. Our work can not only be directly used in ground-based application, but also firmly laid the groundwork for future satellite time-frequency dissemination.

physics.ins-det↗

arXiv2021

Class Knowledge Overlay to Visual Feature Learning for Zero-Shot Image Classification

New categories can be discovered by transforming semantic features into synthesized visual features without corresponding training samples in zero-shot image classification. Although significant progress has been made in generating high-quality synthesized visual features using generative adversarial networks, guaranteeing semantic consistency between the semantic features and visual features remains very challenging. In this paper, we propose a novel zero-shot learning approach, GAN-CST, based on class knowledge to visual feature learning to tackle the problem. The approach consists of three parts, class knowledge overlay, semi-supervised learning and triplet loss. It applies class knowledge overlay (CKO) to obtain knowledge not only from the corresponding class but also from other classes that have the knowledge overlay. It ensures that the knowledge-to-visual learning process has adequate information to generate synthesized visual features. The approach also applies a semi-supervised learning process to re-train knowledge-to-visual model. It contributes to reinforcing synthesized visual features generation as well as new category prediction. We tabulate results on a number of benchmark datasets demonstrating that the proposed model delivers superior performance over state-of-the-art approaches.

cs.CV↗

arXiv2021

Multi-Knowledge Fusion for New Feature Generation in Generalized Zero-Shot Learning

Suffering from the semantic insufficiency and domain-shift problems, most of existing state-of-the-art methods fail to achieve satisfactory results for Zero-Shot Learning (ZSL). In order to alleviate these problems, we propose a novel generative ZSL method to learn more generalized features from multi-knowledge with continuously generated new semantics in semantic-to-visual embedding. In our approach, the proposed Multi-Knowledge Fusion Network (MKFNet) takes different semantic features from multi-knowledge as input, which enables more relevant semantic features to be trained for semantic-to-visual embedding, and finally generates more generalized visual features by adaptively fusing visual features from different knowledge domain. The proposed New Feature Generator (NFG) with adaptive genetic strategy is used to enrich semantic information on the one hand, and on the other hand it greatly improves the intersection of visual feature generated by MKFNet and unseen visual faetures. Empirically, we show that our approach can achieve significantly better performance compared to existing state-of-the-art methods on a large number of benchmarks for several ZSL tasks, including traditional ZSL, generalized ZSL and zero-shot retrieval.

cs.CV↗

arXiv2021

Cross Knowledge-based Generative Zero-Shot Learning Approach with Taxonomy Regularization

Although zero-shot learning (ZSL) has an inferential capability of recognizing new classes that have never been seen before, it always faces two fundamental challenges of the cross modality and crossdomain challenges. In order to alleviate these problems, we develop a generative network-based ZSL approach equipped with the proposed Cross Knowledge Learning (CKL) scheme and Taxonomy Regularization (TR). In our approach, the semantic features are taken as inputs, and the output is the synthesized visual features generated from the corresponding semantic features. CKL enables more relevant semantic features to be trained for semantic-to-visual feature embedding in ZSL, while Taxonomy Regularization (TR) significantly improves the intersections with unseen images with more generalized visual features generated from generative network. Extensive experiments on several benchmark datasets (i.e., AwA1, AwA2, CUB, NAB and aPY) show that our approach is superior to these state-of-the-art methods in terms of ZSL image classification and retrieval.

cs.CV↗

arXiv2020

Time-Frequency Transfer through a 70 dB Free Space Channel: Towards Satellite-Ground Time Dissemination

Time and frequency transfer lies at the heart of the field of metrology. Compared to current microwave dissemination such as GPS, optical domain dissemination can provide more than one order of magnitude in terms of higher accuracy, which allows for many applications such as the redefinition of the second, tests of general relativity and fundamental quantum physics, precision navigation and quantum communication. Although optical frequency transfer has been demonstrated over thousand kilometers fiber lines, intercontinental time comparison and synchronization still requires satellite free space optical time and frequency transfer. Quite a few pioneering free space optical time and frequency experiments have been implemented at the distance of tens kilometers at ground level. However, there exists no detailed analysis or ground test to prove the feasibility of satellite-based optical time-frequency transfer. Here, we analyze the possibility of this system and then provide the first-step ground test with high channel loss. We demonstrate the optical frequency transfer with an instability of $10^{-18}$ level in 8,000 seconds across a 16-km free space channel with a loss of up to 70~dB, which is comparable with the loss of a satellite-ground link at medium earth orbit (MEO) and geostationary earth orbit (GEO).

physics.optics↗

arXiv2007

Thermal Impact on Spiking Properties in Hodgkin-Huxley Neuron with Synaptic Stimulus

The effect of environmental temperature on neuronal spiking behaviors is investigated by numerically simulating the temperature dependence of spiking threshold of the Hodgkin-Huxley neuron subject to synaptic stimulus. We find that the spiking threshold exhibits a global minimum in a "comfortable temperature" range where spike initiation needs weakest synaptic strength, indicating the occurrence of optimal use of synaptic transmission in neural system. We further explore the biophysical origin of this phenomenon in ion channel gating kinetics and also discuss its possible biological relevance in information processing in neural systems.

q-bio.NC↗

arXiv2007

Theoretical Analysis of Subthreshold Oscillatory Behaviors in Nonlinear Autonomous Systems

We have developed a linearization method to investigate the subthreshold oscillatory behaviors in nonlinear autonomous systems. By considering firstly the neuronal system as an example, we show that this theoretical approach can predict quantitatively the subthreshold oscillatory activities, including the damping coefficients and the oscillatory frequencies which are in good agreement with those observed in experiments. Then we generalize the linearization method to an arbitrary autonomous nonlinear system. The detailed extension of this theoretical approach is also presented and further discussed.

q-bio.QM↗

arXiv2007

Influence of Temperature on Neuronal Excitability in Cochlear Nucleus

The influence of temperature on neuronal excitability is studied by numerical simulations on the spiking threshold characteristics of bushy cells in cochlear nucleus periodically stimulated by synaptic currents. The results reveal that there is a cut-off frequency for the spiking of bushy cell in a specific temperature environment, corresponding to the existence of a critical temperature for the neuron to respond with real spikes to the synaptic stimulus of a given frequency, due to the finiteness of spike width. An optimal temperature range for neuronal spiking is also found for a specific stimulus frequency, and the temperature range span decreases with increasing stimulus frequency. These findings imply that there is a physiological temperature range which is beneficial for the information processing in auditory system.

q-bio.NC↗