Yan Li

Macquarie University

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Co-Authors

David Huang

New York University

Bill Moran

University of Melbourne

Allison Kealy

RMIT University

Shuai Sun

Dalian Maritime University

Maolong Tang

Oregon Health & Science University

Guenther Retscher

TU Wien

Jianguo Wang

Jiangxi Copper (China)

Pascal Perez

Australian Urban Research Infrastructure Network

Johan Barthélemy

University of Wollongong

Wayne S. T. Rowe

RMIT University

Cooperative Institutions

Chinese Academy of Sciences

Peking University

Shanghai Jiao Tong University

University of Chinese Academy of Sciences

Chinese Academy of Medical Sciences & Peking Union Medical College

Central South University

Wuhan University

Zhejiang University

Sun Yat-sen University

Harbin Institute of Technology

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Iterative decoding of greater than 100-Gb/in/sup 2/ recordings

IEEE Transactions on Magnetics (2003)

Nan-Hsiung Yeh Philip Steiner Yan Li

As signal-to-noise ratio decreases from ever-shrinking bit cells, advanced signal processing has played a key role in enabling system performance. In this paper, spin-stand head/media waveforms from greater than 100-Gb/in/sup 2/ recordings were captured and processed through a software channel using a turbo product code with single parity check (TPC/SPC). Up to 200 Mb of user data were captured from each on-track or off-track location to characterize the bit-error rate (BER) performance. Almost four orders of magnitude BER improvement have been observed through TPC/SPC compared with the uncoded Viterbi algorithm. Typical iteration histogram and burst-error profiles are analyzed. Performance loss from real waveforms compared with the additive white Gaussian noise simulations are also discussed.

Viterbi decoder

Viterbi algorithm

Turbo code

10.1109/tmag.2003.816483

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Citations (1)

More General QAM Complementary Sequences

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences (2018)

Fanxin Zeng Xiping He Guojun Li Guixin Xuan Zhenyu Zhang

This paper improves the family size of quadrature amplitude modulation (QAM) complementary sequences with binary inputs. By employing new mathematical description: B-type-2 of 4q-QAM constellation (integer q ≥ 2), a new construction yielding 4q-QAM complementary sequences (CSs) with length 2m (integer m ≥ 2) is developed. The resultant sequences include the known QAM CSs with binary inputs as special cases, and the family sizes of new sequences are approximately 22·2q-4q-1(22·2q-3-1) times as many as the known. Also, both new sequences and the known have the same the peak envelope power (PEP) upper bounds, when they are used in an orthogonal frequency-division multiplexing communication system.

QAM

10.1587/transfun.e101.a.2409

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Citations (9)

Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution

arXiv (Cornell University) (2023)

Yan Li Xinjiang Lu Haoyi Xiong Jian Tang Jiantao Su

Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. Though one could lower the complexity of Transformers by inducing the sparsity in point-wise self-attentions for LTTF, the limited information utilization prohibits the model from exploring the complex dependencies comprehensively. To this end, we propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects: (i) an encoder-decoder architecture incorporating a linear complexity without sacrificing information utilization is proposed on top of sliding-window attention and Stationary and Instant Recurrent Network (SIRN); (ii) a module derived from the normalizing flow is devised to further improve the information utilization by inferring the outputs with the latent variables in SIRN directly; (iii) the inter-series correlation and temporal dynamics in time-series data are modeled explicitly to fuel the downstream self-attention mechanism. Extensive experiments on seven real-world datasets demonstrate that Conformer outperforms the state-of-the-art methods on LTTF and generates reliable prediction results with uncertainty quantification.

Sliding window protocol

Feature (linguistics)

10.48550/arxiv.2301.02068

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Smartphone User Tracking by Incorporating User Orientation Using a Double-Layer HMM

IEEE Transactions on Vehicular Technology (2022)

Shuai Sun Shaoxi Li Yan Li Bill Moran Wayne S. T. Rowe

We consider the problem of localizing a smartphone user using received signal strength (RSS) measured by a set of known network nodes in a harsh indoor environment. While the RSS of a wireless signal can be conveniently accessed, using it to estimate location is non-trivial in the presence of multipath propagation, shadowing and radio interference. Auxiliary information, such as the indoor building map and user's orientation information, potentially can help to improve localization performance. As the indoor layout is usually known as a priori, a user's moving direction or orientation in a given indoor map may contain valuable information to assist for reducing location ambiguities at estimation, typically when the radio signal channel is corrupted with noise. In this paper, we propose a double-layer hidden Markov model (DHMM) within a Bayesian learning framework for combining user orientation information and processing RSS data in the localization process to deal with RSS fluctuations induced by human body shadowing and multipath interference. Simulation and experimental results show that incorporating user orientation can potentially provide promising indoor positioning results.

RSS

Crowdsourcing

Shadow mapping

10.1109/tvt.2022.3168142

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Citations (7)

Optimizing the quantity and spatial patterns of farmland shelter forests increases cotton productivity in arid lands

Agriculture Ecosystems & Environment (2020)

Xiaoyu Li Lijuan Liu Jiangbo Xie Zhongyuan Wang Shuya Yang

10.1016/j.agee.2020.106832

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Citations (18)

Tree of Life

Yan Li

Tree (set theory)

Source

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A Comparative Study of Lstm and Tcn Models for Wave Runup Prediction

SSRN Electronic Journal (2023)

Yan Li Longfei Xiao Handi Wei Deyu Li Li Xu

The wave runup prediction is necessary for the design and early warning of offshore structures. The data-driven methods based on machine learning have inspired the reduced-order solutions for wave-structure interaction problems. In this study, two prominent deep learning models were utilized to predict the wave runups along the fore column of semi-submersible under head seas. The Long Short-Term Memory (LSTM) and the Temporal Convolutional Networks (TCN) were comprehensively compared based on datasets from the experiments. First, the structures of LSTM and TCN models were optimized to provide reasonable comparisons in prediction accuracy and computational complexity. The multi-step prediction was further conducted to demonstrate their fitting abilities to the long-term uncertainty. Moreover, the noise-extended datasets were used to evaluate their robustness and investigate the characteristics of predictions. Various simplified input tensors were fed into the LSTM and TCN models to predict the wave runup with the least data dependency. The promising results showed that the TCN model was more precise, powerful, and robust to predict the nonlinear wave runup with less time and memory costs. Besides, design and optimization of input tensors based on physical understanding also play a significant role in the prediction performance.

10.2139/ssrn.4349108

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Demand-driven train timetabling for air and intercity high-speed rail synchronization service

Transportation Letters (2022)

Yangsheng Jiang Shuiwang Chen Wenyao An Lu Hu Yan Li

Synchronizing air and intercity high-speed railway (A-IHSR) services has recently been developing vigorously. Train timetables are critical for improving A-IHSR synchronization. This paper proposes a method to optimize intercity high-speed railway (IHSR) timetables based on the dynamic passenger demand of the A-IHSR. An integer non-linear programming (INLP) model is formulated to minimize the passenger waiting time, in which rigorous train capacity, oversaturation situation, and skip-stop are also taken into consideration. We also extend the train timetabling model by considering the synchronization events of flight-train and the train circulation plan. Then, this model is reformulated as a relaxed non-linear programming (NLP) model. . A multimodal nomad algorithm (MNA) is proposed to solve it. The train circulation plan solves by the Gurobi solver. A numerical experiment based on data of the operation of the Jinnan–Binzhou HSR is implemented to demonstrate the efficiency and performance of our proposed model and algorithm.

Synchronizing

Solver

10.1080/19427867.2022.2051275

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Citations (13)

A General Framework Protocol for Indoor and Outdoor Location-Based Service

Advances in intelligent systems research/Advances in Intelligent Systems Research (2015)

Yan Li Li Zhu

To satisfy the requirement of the increasing location-based service, there have been a lot of solutions on indoor and outdoor locating.The general data protocols for transferring outdoor location data are also mature.However, there still lack general data format and transferring protocol for indoor or hybrid indoor-outdoor location.In this paper, a general framework protocol for indoor and outdoor positioning, which includes locating data format and the transfer frame format, is proposed.The data tables and frame format of the protocol are presented and examples on how to use them are given.It is a protocol aimed to be used by different mobile devices sending locating data to the locating server.The design idea of this protocol is to use various positioning methods available at present, and different kinds of data should be combined easily according to different environment identified by the mobile device.

Location-based service

10.2991/isrme-15.2015.71

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Citations (1)

Simultaneous Search and Tracking of Non-cooperative Mobile Targets Using Multiple UAVs in Uneven Environments

International Journal of Aeronautical and Space Sciences (2024)

Zhang Zhan Yan Li Jiwei Xu Zhong Wang

Tracking (education)

10.1007/s42405-024-00750-4

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