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Integrated Sensing and Communications With Affine Frequency Division Multiplexing

IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第5期13卷 1255-1259页

作者： Bemani, Ali Ksairi, Nassar Kountouris, Marios Huawei France R&D Math & Algorithm Sci Lab F-92100 Paris France EURECOM Commun Syst Dept F-06904 Sophia Antipolis France

Integrated sensing and communications (ISAC) is regarded as a key technology in next-generation (6G) mobile communication systems. Affine frequency division multiplexing (AFDM) is a recently proposed waveform that achieves optimal diversity gain in high mobility scenarios and has appealing properties in high-frequency communication. In this letter, we present an AFDM-based ISAC system. We first show that in order to identify all delay and Doppler components associated with the propagation medium, either the full AFDM signal or only its pilot part consisting of one discrete affine Fourier transform (DAFT) domain symbol and its guard interval can be used. Our results show that using one pilot symbol achieves almost the same sensing performance as using the entire AFDM frame. Furthermore, due to the chirp nature of AFDM, sensing with one pilot provides a unique feature allowing for simple self-interference cancellation, thus avoiding the need for expensive full duplex methods.

关键词： AFDM ISAC affine Fourier transform chirps doubly dispersive channels

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Low hysteresis, water retention, anti-freeze multifunctional hydrogel strain sensor for human-machine interfacing and real-time sign language translation

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MATERIALS HORIZONS 2024年第16期11卷 3856-3866页

作者： Zhou, Lijuan Zhao, Bin Liang, Jingye Lu, Fangying Yang, Weiping Xu, Jishuai Zheng, Jingxuan Liu, Yong Wang, Run Liu, Zunfeng Tiangong Univ Sch Text Sci & Engn 399 West Binshui Rd Tianjin 300387 Peoples R China Guangxi Coll Univ Key Lab AI Algorithm Engn Guangxi Coll & Univ Key Lab AI Algorithm Engn Sch Artificial Intelligence Guilin 541004 Guangxi Peoples R China Nankai Univ Frontiers Sci Ctr New Organ Matter State Key Lab Med Chem Biol Key Lab Funct Polymer MatColl Chem Tianjin 300071 Peoples R China

Hydrogel strain sensors have received increasing attention due to their potential applications in human-machine interfaces and flexible electronics. However, they usually suffer from both mechanical and electrical hysteresis and poor water retention, which limit their practical applications. To address this challenge, a poly(acrylic acid-co-acrylamide) hydrogel crosslinked by silica nanoparticles is fabricated via photo polymerization and salting-out of hydrophilic ions for the strain sensor. The resulting hydrogel strain sensor possessed low electrical hysteresis (1.6%), low mechanical hysteresis (<7%), high cycle stability (>10 000 cycles), high durability, water retention and anti-freezing ability. Moreover, this strain sensor can be used as a wearable sensor for real-time control of robotic hands and hand gesture recognition. Finally, a sign language translation system has been demonstrated with the aid of machine learning, achieving recognition rates of over 98% for 15 different sign languages. This work offers a promising prospect for human-machine interfaces, smart wearable devices, and the Internet of Things.

关键词： Hydrogels

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Foggy Drone Teacher: Domain Adaptive Drone Detection Under Foggy Conditions

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DRONES 2025年第2期9卷 146-146页

作者： Zheng, Guida Tan, Benying Wu, Jingxin Qin, Xiao Li, Yujie Ding, Shuxue Guilin Univ Elect Technol Guangxi Coll & Univ Key Lab AI Algorithm Engn Sch Artificial Intelligence Guilin 541004 Peoples R China Guangxi Key Lab Human Machine Interact & Intellige Nanning 530007 Peoples R China

With the growing use of drones, efficient detection algorithms are crucial, especially under adverse weather conditions. Most existing drone detection algorithms perform well only in clear weather, resulting in significant performance drops in foggy conditions. This study focuses on improving drone detection in foggy environments using the Mean Teacher framework for domain adaptation. The Mean Teacher framework's performance relies on the quality of the teacher model's pseudo-labels. To enhance the quality of the pseudo-labels from the teacher model, we introduce Foggy Drone Teacher (FDT), which includes three key components: (1) Adaptive Style and Context Augmentation to reduce domain shift and improve pseudo-label quality;(2) Simplified Domain Alignment with a novel adversarial strategy to boost domain adaptation;and (3) Progressive Domain Adaptation Training, a two-stage process that helps the teacher model produce more stable and accurate pseudo-labels. In addition, owing to the lack of publicly available data, we created Foggy Drone Dataset (FDD) to support this research. Extensive experiments show that our model achieves a 21.1-point increase in AP0.5 compared to the baseline and outperforms state-of-the-art models. This method significantly improves drone detection accuracy in foggy conditions.

关键词： drone detection object detection domain adaptation foggy condition

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An intelligent retrievable object-tracking system with real-time edge inference capability

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IET IMAGE PROCESSING 2025年第1期19卷

作者： Li, Yujie Wang, Yifu Ma, Zihang Wang, Xinghe Tan, Benying Ding, Shuxue Guilin Univ Elect Technol Guangxi Coll Sch Artificial Intelligence Guilin Peoples R China Univ Key Lab AI Algorithm Engn Guilin Univ Elect Technol Guilin Peoples R China

An intelligent retrievable object-tracking system assists users in quickly and accurately locating lost objects. However, challenges such as real-time processing on edge devices, low image resolution, and small-object detection significantly impact the accuracy and efficiency of video-stream-based systems, especially in indoor home environments. To overcome these limitations, a novel real-time intelligent retrievable object-tracking system is designed. The system incorporates a retrievable object-tracking algorithm that combines DeepSORT and sliding window techniques to enhance tracking capabilities. Additionally, the YOLOv7-small-scale model is proposed for small-object detection, integrating a specialized detection layer and the convolutional batch normalization LeakyReLU spatial-depth convolution module to enhance feature capture for small objects. TensorRT and INT8 quantization are used for inference acceleration on edge devices, doubling the frames per second. Experiments on a Jetson Nano (4 GB) using YOLOv7-small-scale show an 8.9% improvement in recognition accuracy over YOLOv7-tiny in video stream processing. This advancement significantly boosts the system's performance in efficiently and accurately locating lost objects in indoor home settings.

关键词： computer vision image processing object detection object tracking

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Controllability of piecewise linear state-delay systems

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APPLIED MATHEMATICS AND COMPUTATION 2025年 494卷

作者： Luo, Huiping Wang, Jinrong Guizhou Univ Sch Math & Stat Guiyang 550025 Guizhou Peoples R China Guizhou Univ Supercomp Algorithm & Applicat Lab Guian Sci Innovat Co Guiyang 550025 Guizhou Peoples R China

In this paper, we study the controllability of piecewise linear state-delay systems (PLSDSs). To do this, we introduce a series of new functions and give the explicit representation of the solution. Then, the Gramian and the rank criteria for the controllability of PLSDSs are established by the piecewise delayed Gramian matrix. Further, all control functions driving the solution from an initial function to a desired final state are characterized by virtue of shifted Legendre polynomials. In addition, the controllability of PLSDSs constrained in an invariant subspace and weakly nonlinear piecewise systems are discussed as well, respectively. Numerical examples are provided to verify the effectiveness of theoretical results.

关键词： Controllability Piecewise system State-delay Control function Shifted Legendre polynomial

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A 2D-Programmable and Scalable Reconfigurable Intelligent Surface Remotely Controlled via Digital Infrared Code

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2023年第1期71卷 570-580页

作者： Sayanskiy, Andrey Belov, Andrei Yafasov, Ruslan Lyulyakin, Andrey Sherstobitov, Alexander Glybovski, Stanislav Lyashev, Vladimir ITMO Univ Sch Phys & Engn St Petersburg 197101 Russia Huawei Technol Co Ltd RTT Algorithm Lab Moscow Res Ctr Moscow 127106 Russia

Reconfigurable intelligent surfaces (RISs) are promising and relatively low-cost tools for improving signal propagation in wireless communications. A RIS assists a base station (BS) in optimizing the channel and maximizing its capacity by dynamically manipulating the reflected field. Typically, RISs are based on dynamically reconfigurable reflectarrays (RAs), i.e., 2-D arrays of passive patch antennas, individually switchable between two or more reflection phases. The spatial resolution of provided reflected field patterns is governed by the aperture dimensions and the number of patches to meet the requirements of different communication scenarios and environments. Here, we demonstrate a 1 bit RIS for 5 GHz Wi-Fi band made by assembling together multiple independently operating and structurally detached building blocks all powered by the same DC source. Each block contains four separately phase-switchable patch antennas with varactor diodes and a common microcontroller extracting digital control commands from modulated infrared light illuminating the entire RIS. Such distributed light-sensitive controllers grant the possibility of scaling the aperture by adding or removing blocks without redesigning any control circuitry. Moreover, in the proposed RIS a full 2-D phase encoding capability is achieved along with a robust remote infrared control.

关键词： Varactors Reflection Light emitting diodes Apertures PIN photodiodes Switches Remote control Control with light patch array reconfigurable intelligent surface (RIS) reflectarray (RA) varactor diode

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TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods 50th

TFB: Towards Comprehensive and Fair Benchmarking of Time Ser...

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50th International Conference on Very Large Data Bases, VLDB 2024

作者： Qiu, Xiangfei Hu, Jilin Zhou, Lekui Wu, Xingjian Du, Junyang Zhang, Buang Guo, Chenjuan Zhou, Aoying Jensen, Christian S. Sheng, Zhenli Yang, Bin East China Normal University China Huawei Cloud Algorithm Innovation Lab China Aalborg University Denmark

Time series are generated in diverse domains such as economic, traffic, health, and energy, where forecasting of future values has numerous important applications. Not surprisingly, many forecasting methods are being proposed. To ensure progress, it is essential to be able to study and compare such methods empirically in a comprehensive and reliable manner. To achieve this, we propose TFB, an automated benchmark for Time Series Forecasting (TSF) methods. TFB advances the state-of-the-art by addressing shortcomings related to datasets, comparison methods, and evaluation pipelines: 1) insufficient coverage of data domains, 2) stereotype bias against traditional methods, and 3) inconsistent and inflexible pipelines. To achieve better domain coverage, we include datasets from 10 different domains: traffic, electricity, energy, the environment, nature, economic, stock markets, banking, health, and the web. We also provide a time series characterization to ensure that the selected datasets are comprehensive. To remove biases against some methods, we include a diverse range of methods, including statistical learning, machine learning, and deep learning methods, and we also support a variety of evaluation strategies and metrics to ensure a more comprehensive evaluations of different methods. To support the integration of different methods into the benchmark and enable fair comparisons, TFB features a flexible and scalable pipeline that eliminates biases. Next, we employ TFB to perform a thorough evaluation of 21 Univariate Time Series Forecasting (UTSF) methods on 8,068 univariate time series and 14 Multivariate Time Series Forecasting (MTSF) methods on 25 datasets. The results offer a deeper understanding of the forecasting methods, allowing us to better select the ones that are most suitable for particular datasets and settings. Overall, TFB and this evaluation provide researchers with improved means of designing new TSF methods. © 2024, VLDB Endowment. All rights reserve

关键词： Pipelines

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The weakness of the Erdos-Moser theorem under arithmetic reductions

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JOURNAL OF MATHEMATICAL LOGIC 2025年第0期

作者： Patey, Ludovic Levy Mimouni, Ahmed Univ Paris Cite CNRS IMJ PRG Paris France Univ Paris Est Creteil Lab Algorithm Complex & Log LACL F-94010 Creteil Val De Marne France

The Erdos-Moser (EM) theorem says that every infinite tournament admits an infinite transitive subtournament. We study the computational behavior of the EM theorem with respect to the arithmetic hierarchy, and prove that Delta(0)(n) instances of EM admit low(n+1) solutions for every n >= 1, and that if a set B is not arithmetical, then every instance of EM admits a solution relative to which B is still not arithmetical. We also provide a level-wise refinement of this theorem. These results are part of a larger program of computational study of combinatorial theorems in Reverse Mathematics.

关键词： Erdos-Moser Reverse Mathematics computability theory Ramsey theory arithmetic hierarchy

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Affine Frequency Division Multiplexing for Compressed Sensing of Time-Varying Channels 25

Affine Frequency Division Multiplexing for Compressed Sensin...

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25th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Benzine, Wissal Bemanil, Ali Ksairi, Nassar Slock, Dirk Huawei France R&D Math & Algorithm Sci Lab Paris France EURECOM Commun Syst Dept Sophia Antipolis France

ISBN: (纸本)9798350393194;9798350393187

This paper addresses compressed sensing of linear time-varying (LTV) wireless propagation links under the assumption of double sparsity i.e., sparsity in both the delay and Doppler domains, using Affine Frequency Division Multiplexing (AFDM) measurements. By rigorously linking the double sparsity model to the hierarchical sparsity paradigm, a compressed sensing algorithm with recovery guarantees is proposed for extracting delay-Doppler profiles of LTV channels using AFDM. Through mathematical analysis and numerical results, the superiority of AFDM over other waveforms in terms of channel estimation overhead and minimal sampling rate requirements in sub-Nyquist radar applications is demonstrated.

关键词： compressed sensing channel estimation timevarying channels AFDM chirps sparsity

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Automatic clustering of single-molecule break junction data through task-oriented representation learning

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RARE METALS 2025年第5期44卷 3244-3257页

作者： Zhao, Yi-Heng Pang, Shen-Wen Huang, Heng-Zhi Wu, Shao-Wen Sun, Shao-Hua Liu, Zhen-Bing Pan, Zhi-Chao Guilin Univ Elect Technol Sch Artificial Intelligence Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Coll & Univ Key Lab AI Algorithm Engn Guilin 541004 Peoples R China

Clustering is a pivotal data analysis method for deciphering the charge transport properties of single molecules in break junction experiments. However, given the high dimensionality and variability of the data, feature extraction remains a bottleneck in the development of efficient clustering methods. In this regard, extensive research over the past two decades has focused on feature engineering and dimensionality reduction in break junction conductance. However, extracting highly relevant features without expert knowledge remains an unresolved challenge. To address this issue, we propose a deep clustering method driven by task-oriented representation learning (CTRL) in which the clustering module serves as a guide for the representation learning (RepL) module. First, we determine an optimal autoencoder (AE) structure through a neural architecture search (NAS) to ensure efficient RepL;second, the RepL process is guided by a joint training strategy that combines AE reconstruction loss with the clustering objective. The results demonstrate that CTRL achieves excellent performance on both the generated and experimental data. Further inspection of the RepL step reveals that joint training robustly learns more compact features than the unconstrained AE or traditional dimensionality reduction methods, significantly reducing misclustering possibilities. Our method provides a general end-to-end automatic clustering solution for analyzing single-molecule break junction data. (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)

关键词： Single-molecule conductance Break junction Deep clustering Representation learning Neural architecture search

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