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Zibo Zheng

Publications and source records attributed to Zibo Zheng.

9 recordsLinked to original sources
arXiv2026

Nonlinear Reshaping of Gaussian Water Wave Packets

The propagation of narrowband wave packets is a classical problem with broad relevance across nonlinear wave physics. Within the framework of the nonlinear Schrödinger equation, solitons are known to propagate without changing shape, whereas Gaussian envelopes may undergo substantial reshaping depending on the balance between dispersion and nonlinearity. Here, we combine laboratory experiments with numerical simulations to systematically investigate the evolution of hydrodynamic Gaussian wave packets over a wide range of amplitudes and spectral bandwidths. We show that when the envelope amplitude and width match those of the stationary NLSE soliton solution, the wave packet propagates steadily in the wave flume. More generally, by varying the amplitude of the Gaussian envelope, the wave packet may undergo either dispersive broadening or nonlinear focusing, resembling the dynamics of Satsuma-Yajima breathers. These findings may facilitate the controlled generation and manipulation of localized wave packets in a variety of nonlinear dispersive systems, including optics, plasmas, and Bose-Einstein condensates.

nlin.PS↗
arXiv2025

Comb-Driven Coherent Optical Transmitter for Scalable DWDM Interconnects

Driven by the growing demand for large-scale artificial intelligence applications, disaggregated compute nodes and high-radix switches in next-generation computing clusters are set to surpass the capacity of current optical interconnect technologies. Such a surge turns several aspects of transmitters into critical bottlenecks: shoreline bandwidth density and energy efficiency are effectively limiting the scalability. We present a comb-driven coherent optical transmitter architecture on a Si/SiN platform that provides the bandwidth density, energy efficiency, and compact footprint required for such co-packaged-enabled optical interconnects. We evaluate scalability through critical building blocks, including ultra-compact microring-assisted Mach--Zehnder modulators (MRA-MZMs) and dense wavelength-division multiplexing (DWDM) interleavers. Single-tone experiments demonstrate a net line rate of 400 Gbps per polarization (16-QAM, 120 GBd) in silicon within the O-band, achieving a record shoreline density of 4 Tbps/mm while consuming only 10 fJ/bit for modulation. We also demonstrate transmission rates of up to 160 GBd QPSK in back-to-back and 100 GBd over 7 km of fiber without dispersion compensation. Using a quantum-dot frequency comb, six 100 GHz-spaced WDM channels transmit 1.08 Tbps over 5 km. System-level analyses show that by leveraging advanced modulation formats through the integration of wavelength and polarization multiplexing, our proposed architecture can realistically support combined transmission rates exceeding 10 Tbps per fiber within practical limits of power consumption and packaging, outlining a clear path toward future petabit-scale interconnects.

physics.optics↗
arXiv2025

OpenIN: Open-Vocabulary Instance-Oriented Navigation in Dynamic Domestic Environments

In daily domestic settings, frequently used objects like cups often have unfixed positions and multiple instances within the same category, and their carriers frequently change as well. As a result, it becomes challenging for a robot to efficiently navigate to a specific instance. To tackle this challenge, the robot must capture and update scene changes and plans continuously. However, current object navigation approaches primarily focus on the semantic level and lack the ability to dynamically update scene representation. In contrast, this paper captures the relationships between frequently used objects and their static carriers. It constructs an open-vocabulary Carrier-Relationship Scene Graph (CRSG) and updates the carrying status during robot navigation to reflect the dynamic changes of the scene. Based on the CRSG, we further propose an instance navigation strategy that models the navigation process as a Markov Decision Process. At each step, decisions are informed by the Large Language Model's commonsense knowledge and visual-language feature similarity. We designed a series of long-sequence navigation tasks for frequently used everyday items in the Habitat simulator. The results demonstrate that by updating the CRSG, the robot can efficiently navigate to moved targets. Additionally, we deployed our algorithm on a real robot and validated its practical effectiveness. The project page can be found here: https://OpenIN-nav.github.io.

cs.RO↗
arXiv2024

Ultrafast Coherent Dynamics of Microring Modulators

Next-generation computing clusters require ultra-high-bandwidth optical interconnects to support large-scale artificial-intelligence applications. In this context, microring modulators (MRMs) emerge as a promising solution. Nevertheless, their potential is curtailed by inherent challenges, such as pronounced frequency chirp and dynamic non-linearity. Moreover, a comprehensive understanding of their coherent dynamics is still lacking, which further constrains their applicability and efficiency. Consequently, these constraints have confined their use to spectrally inefficient intensity-modulation direct-detection links. In this work, we present a thorough study of MRM coherent dynamics, unlocking phase as a new dimension for MRM-based high-speed data transmission in advanced modulation formats. We demonstrate that the phase and intensity modulations of MRMs exhibit distinct yet coupled dynamics, limiting their direct application in higher-order modulation formats. This challenge can be addressed by embedding a pair of MRMs within a Mach-Zehnder interferometer in a push-pull configuration, enabling a bistable phase response and unchirped amplitude modulation. Furthermore, we show that its amplitude frequency response exhibits a distinct dependency on frequency detuning compared to phase and intensity modulations of MRMs, without strong peaking near resonance. Harnessing the ultra-fast coherent dynamics, we designed and experimentally demonstrated an ultra-compact, ultra-wide-bandwidth in-phase/quadrature (I/Q) modulator on a silicon chip fabricated using a CMOS-compatible photonic process. Achieving a record on-chip shoreline bandwidth density exceeding 5Tb/s/mm, our device enabled coherent transmission for symbol rates up to 180Gbaud and a net bit rate surpassing 1Tb/s over an 80km span, with modulation energy consumption as low as 10.4fJ/bit.

physics.optics↗
arXiv2024

120 GOPS Photonic Tensor Core in Thin-film Lithium Niobate for Inference and in-situ Training

Photonics offers a transformative approach to artificial intelligence (AI) and neuromorphic computing by enabling low-latency, high-speed, and energy-efficient computations. However, conventional photonic tensor cores face significant challenges in constructing large-scale photonic neuromorphic networks. Here, we propose a fully integrated photonic tensor core, consisting of only two thin-film lithium niobate (TFLN) modulators, a III-V laser, and a charge-integration photoreceiver. Despite its simple architecture, it is capable of implementing an entire layer of a neural network with a computational speed of 120 GOPS, while also allowing flexible adjustment of the number of inputs (fan-in) and outputs (fan-out). Our tensor core supports rapid in-situ training with a weight update speed of 60 GHz. Furthermore, it successfully classifies (supervised learning) and clusters (unsupervised learning) 112 * 112-pixel images through in-situ training. To enable in-situ training for clustering AI tasks, we offer a solution for performing multiplications between two negative numbers.

physics.optics↗
arXiv2024

OpenObject-NAV: Open-Vocabulary Object-Oriented Navigation Based on Dynamic Carrier-Relationship Scene Graph

In everyday life, frequently used objects like cups often have unfixed positions and multiple instances within the same category, and their carriers frequently change as well. As a result, it becomes challenging for a robot to efficiently navigate to a specific instance. To tackle this challenge, the robot must capture and update scene changes and plans continuously. However, current object navigation approaches primarily focus on semantic-level and lack the ability to dynamically update scene representation. This paper captures the relationships between frequently used objects and their static carriers. It constructs an open-vocabulary Carrier-Relationship Scene Graph (CRSG) and updates the carrying status during robot navigation to reflect the dynamic changes of the scene. Based on the CRSG, we further propose an instance navigation strategy that models the navigation process as a Markov Decision Process. At each step, decisions are informed by Large Language Model's commonsense knowledge and visual-language feature similarity. We designed a series of long-sequence navigation tasks for frequently used everyday items in the Habitat simulator. The results demonstrate that by updating the CRSG, the robot can efficiently navigate to moved targets. Additionally, we deployed our algorithm on a real robot and validated its practical effectiveness.

cs.RO↗
arXiv2024

Auto-Encoder Optimized PAM IM/DD Transceivers for Amplified Fiber Links

We examine pulse amplitude modulation (PAM) for intensity modulation and direct detection systems. Using a straight-forward, mixed noise model, we optimize the constellations with an autoencoder-based neural network (NN), an improve required signal-to-noise ratio of 4 dB for amplified spontaneous emission (ASE)-limited PAM4 and PAM8, without increasing system complexity. Performance can also be improved in O-band wavelength division multiplexing system with semiconductor optical amplifier amplification and chromatic dispersion. We show via simulation that for such a system operating at 53 Gbaud, we can extend the reach of PAM4 by 10-25 km with an optimized constellation and a NN decoder. We present an experimental validation of 4 dB improvement of an ASE-limited PAM4 at 60 Gbaud using an optimized constellation and a NN decoder.

eess.SP↗
arXiv2023

Statistics of weakly nonlinear waves on currents with strong vertical shear

We investigate how the presence of a vertically sheared current affects wave statistics, including the probability of rogue waves, and apply it to a real-world case using measured spectral and shear current data from the Mouth of the Columbia River. A theory for weakly nonlinear waves valid to second order in wave steepness is derived, and used to analyze statistical properties of surface waves; the theory extends the classic theory by Longuet-Higgins [J. Fluid Mech. 12, 3 (1962)] to allow for an arbitrary depth-dependent background flow, $U(z)$, with $U$ the horizontal velocity along the main direction of wave propagation and $z$ the vertical axis. Numerical statistics are collected from a large number of realisations of random, irregular sea-states following a JONSWAP spectrum, on linear and exponential model currents of varying strengths. A number of statistical quantities are presented and compared to a range of theoretical expressions from the literature; in particular the distribution of wave surface elevation, surface maxima, and crest height; the exceedance probability including the probability of rogue waves; the maximum crest height among $N_s$ waves, and the skewness of the surface elevation distribution. We find that compared to no-shear conditions, opposing vertical shear ($U'(z)>0$) leads to increased wave height and increased skewness of the nonlinear-wave elevation distribution, while a following shear ($U'(z)<0$) has opposite effects. With the wave spectrum and velocity profile measured in the Columbia River estuary by Zippel & Thomson [J. Geophys. Res: Oceans 122, 3311 (2017)] our second--order theory predicts that the probability of rogue waves is significantly reduced and enhanced during ebb and flood, respectively, adding support to the notion that shear currents need to be accounted for in wave modelling and prediction.

physics.flu-dyn↗