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Peng Qin

Peng Qin appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
Networking and Internet ArchitecturePerformanceArtificial IntelligenceComputer Visioncond-mat.mtrl-sciDistributed, Parallel, and Cluster Computingeess.SPeess.SYMachine Learningphysics.ins-detphysics.optics

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Published work

9 published item(s)

preprint2026arXiv

Hybrid RIS-Aided Digital Over-the-Air Computing for Edge AI Inference: Joint Feature Quantization and Active-Passive Beamforming Design

The vision of 6G networks aims to enable edge inference by leveraging ubiquitously deployed artificial intelligence (AI) models, facilitating intelligent environmental perception for a wide range of applications. A critical operation in edge inference is for an edge node (EN) to aggregate multi-view sensory features extracted by distributed agents, thereby boosting perception accuracy. Over-the-air computing (AirComp) emerges as a promising technique for rapid feature aggregation by exploiting the waveform superposition property of analog-modulated signals, which is, however, incompatible with existing digital communication systems. Meanwhile, hybrid reconfigurable intelligent surface (RIS), a novel RIS architecture capable of simultaneous signal amplification and reflection, exhibits potential for enhancing AirComp. Therefore, this paper proposes a Hybrid RIS-aided Digital AirComp (HRD-AirComp) scheme, which employs vector quantization to map high-dimensional features into discrete codewords that are digitally modulated into symbols for wireless transmission. By judiciously adjusting the AirComp transceivers and hybrid RIS reflection to control signal superposition across agents, the EN can estimate the aggregated features from the received signals. To endow HRD-AirComp with a task-oriented design principle, we derive a surrogate function for inference accuracy that characterizes the impact of feature quantization and over-the-air aggregation. Based on this surrogate, we formulate an optimization problem targeting inference accuracy maximization, and develop an efficient algorithm to jointly optimize the quantization bit allocation, agent transmission coefficients, EN receiving beamforming, and hybrid RIS reflection beamforming. Experimental results demonstrate that the proposed HRD-AirComp outperforms baselines in terms of both inference accuracy and uncertainty.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Joint Energy Management and Coordinated AIGC Workload Scheduling for Distributed Data Centers: A Diffusion-Aided Reward Shaping Approach

Artificial intelligence-generated content (AIGC) has emerged as a transformative paradigm for automating the creation of diverse and customized content, giving rise to rapidly growing computational workloads in cloud data centers. It is imperative for AIGC service providers (ASPs) to strategically schedule AIGC workloads to reduce data center energy costs while guaranteeing high-quality content generation. However, the distinctive characteristics of AIGC services pose critical challenges, including model heterogeneity across ASPs, implicit service quality evaluation, and complex inference process control. To tackle these challenges, we propose a joint energy management and coordinated AIGC workload scheduling framework, which introduces an explicit mathematical characterization of service quality to promote both job transfer among ASPs and fine-grained inference process configuration. Moreover, various energy resources within data centers are jointly considered to enhance power usage flexibility. Subsequently, a system utility maximization problem is formulated to balance AIGC service revenue with operational penalties and costs. Nevertheless, the strong coupling among job scheduling decisions induces severe reward sparsity, which limits the effectiveness of existing deep reinforcement learning (DRL) algorithms. To address this issue, we develop a diffusion model-aided reward shaping approach to synthesize complementary reward signals through a multi-step denoising process. This approach is seamlessly integrated with DRL to enable efficient learning of scheduling policies under sparse environmental feedback. Experiments based on real-world models and datasets demonstrate that our scheme effectively accommodates electricity price fluctuations and AIGC model heterogeneity, while achieving superior learning convergence and system utility compared with benchmark methods.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Wetland mapping from sparse annotations with satellite image time series and temporal-aware segment anything model

Accurate wetland mapping is essential for ecosystem monitoring, yet dense pixel-level annotation is prohibitively expensive and practical applications usually rely on sparse point labels, under which existing deep learning models perform poorly, while strong seasonal and inter-annual wetland dynamics further render single-date imagery inadequate and lead to significant mapping errors; although foundation models such as SAM show promising generalization from point prompts, they are inherently designed for static images and fail to model temporal information, resulting in fragmented masks in heterogeneous wetlands. To overcome these limitations, we propose WetSAM, a SAM-based framework that integrates satellite image time series for wetland mapping from sparse point supervision through a dual-branch design, where a temporally prompted branch extends SAM with hierarchical adapters and dynamic temporal aggregation to disentangle wetland characteristics from phenological variability, and a spatial branch employs a temporally constrained region-growing strategy to generate reliable dense pseudo-labels, while a bidirectional consistency regularization jointly optimizes both branches. Extensive experiments across eight global regions of approximately 5,000 km2 each demonstrate that WetSAM substantially outperforms state-of-the-art methods, achieving an average F1-score of 85.58%, and delivering accurate and structurally consistent wetland segmentation with minimal labeling effort, highlighting its strong generalization capability and potential for scalable, low-cost, high-resolution wetland mapping.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Solid-state Janus nanoprecipitation enables amorphous-like heat conduction in crystalline Mg3Sb2-based thermoelectric materials

Solid-state precipitation can be used to tailor materials properties, ranging from ferromagnets and catalysts to mechanical strengthening and energy storage. Thermoelectric properties can be modified by precipitation to enhance phonon scattering while retaining charge-carrier transmission. Here, we uncover unconventional dual Janus-type nanoprecipitates in Mg3Sb1.5Bi0.5 formed by side-by-side Bi- and Ge-rich appendages, in contrast to separate nanoprecipitate formation. These Janus nanoprecipitates result from local co-melting of Bi and Ge during sintering, enabling an amorphous-like lattice thermal conductivity. A precipitate size effect on phonon scattering is observed due to the balance between alloy-disorder and nanoprecipitate scattering. The thermoelectric figure-of-merit ZT reaches 0.6 near room temperature and 1.6 at 773 K. The Janus nanoprecipitation can be introduced into other materials and may act as a general property-tailoring mechanism.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

A Survey on Network Tomography with Network Coding

The overhead of internal network monitoring motivates techniques of network tomography. Network coding (NC) presents a new opportunity for network tomography as NC introduces topology-dependent correlation that can be further exploited in topology estimation. Compared with traditional methods, network tomography with NC has many advantages such as the improvement of tomography accuracy and the reduction of complexity in choosing monitoring paths. In this paper we first introduce the problem of tomography with NC and then propose the taxonomy criteria to classify various methods. We also present existing solutions and future trend. We expect that our comprehensive review on network tomography with NC can serve as a good reference for researchers and practitioners working in the area.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Bandwidth-Aware Scheduling with SDN in Hadoop: A New Trend for Big Data

Software Defined Networking (SDN) is a revolutionary network architecture that separates out network control functions from the underlying equipment and is an increasingly trend to help enterprises build more manageable data centers where big data processing emerges as an important part of applications. To concurrently process large-scale data, MapReduce with an open source implementation named Hadoop is proposed. In practical Hadoop systems one kind of issue that vitally impacts the overall performance is know as the NP-complete minimum make span problem. One main solution is to assign tasks on data local nodes to avoid link occupation since network bandwidth is a scarce resource. Many methodologies for enhancing data locality are proposed such as the HDS and state-of-the-art scheduler BAR. However, all of them either ignore allocating tasks in a global view or disregard available bandwidth as the basis for scheduling. In this paper we propose a heuristic bandwidth-aware task scheduler BASS to combine Hadoop with SDN. It is not only able to guarantee data locality in a global view but also can efficiently assign tasks in an optimized way. Both examples and experiments demonstrate that BASS has the best performance in terms of job completion time. To our knowledge, BASS is the first to exploit talent of SDN for big data processing and we believe it points out a new trend for large-scale data processing.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Sub-20-Attosecond Timing Jitter Mode-Locked Fiber Lasers

We demonstrate 14.3-attosecond timing jitter [integrated from 10 kHz to 94 MHz offset frequency] optical pulse trains from 188-MHz repetition-rate mode-locked Yb-fiber lasers. In order to minimize the timing jitter, we shorten the non-gain fiber length to shorten the pulsewidth and reduce excessive higher-order nonlinearity and nonlinear chirp in the fiber laser. The measured jitter spectrum is limited by the amplified spontaneous emission limited quantum noise in the 100 kHz - 1 MHz offset frequency range, while it was limited by the relative intensity noise-converted jitter in the lower offset frequency range. This intrinsically low timing jitter enables sub-100-attosecond synchronization between the two mode-locked Yb-fiber lasers over the full Nyquist frequency with a modest 10-kHz locking bandwidth. The demonstrated performance is the lowest timing jitter measured from any free-running mode-locked fiber lasers, comparable to the performance of the lowest-jitter Ti:sapphire solid-state lasers.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Taking A Free Ride for Routing Topology Inference in Peer-to-Peer Networks

A Peer-to-Peer (P2P) network can boost its performance if peers are provided with underlying network-layer routing topology. The task of inferring the network-layer routing topology and link performance from an end host to a set of other hosts is termed as network tomography, and it normally requires host computers to send probing messages. We design a passive network tomography method that does not requires any probing messages and takes a free ride over data flows in P2P networks. It infers routing topology based on end-to-end delay correlation estimation (DCE) without requiring any synchronization or cooperation from the intermediate routers. We implement and test our method in the real world Internet environment and achieved the accuracy of 92% in topology recovery. We also perform extensive simulation in OMNet++ to evaluate its performance over large scale networks, showing that its topology recovery accuracy is about 95% for large networks.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

DCE: A Novel Delay Correlation Measurement for Tomography with Passive Realization

Tomography is important for network design and routing optimization. Prior approaches require either precise time synchronization or complex cooperation. Furthermore, active tomography consumes explicit probeing resulting in limited scalability. To address the first issue we propose a novel Delay Correlation Estimation methodology named DCE with no need of synchronization and special cooperation. For the second issue we develop a passive realization mechanism merely using regular data flow without explicit bandwidth consumption. Extensive simulations in OMNeT++ are made to evaluate its accuracy where we show that DCE measured delay correlation is highly identical with the true value. Also from test result we find that mechanism of passive realization is able to achieve both regular data transmission and purpose of tomography with excellent robustness versus different background traffic and package size.

0 citations0 reviewsNo code linkedOpen access