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Synthesized Acoustic Vortex-Frequency Comb via Rotational Doppler Effect

Physical Review Letters 2025年第13期134卷 137001-137001页

作者： Yurou Jia Xuan Zhang Suying Zhang Guoqiang Xu Taoyu Chen Hongtao Zhou Yechao Bai Ying Cheng Dajian Wu Department of Physics MOE Key Laboratory of Modern Acoustics Collaborative Innovation Center of Advanced Microstructures Jiangsu Physical Science Research Center Nanjing University Nanjing 210093 China Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Mechanics School of Mechanical Engineering Tianjin University Tianjin 300350 China School of Electronic Science and Engineering Nanjing University Nanjing 210023 China Jiangsu Key Lab on Opto-Electronic Technology School of Physics and Technology Nanjing Normal University Nanjing 210023 China

The synergistic control of light’s frequency and orbital angular momentum (OAM) via integrated nonlinear ring microresonators is crucial for creating spatiotemporal optical waveforms and advancing optical metrology. A direct analog of this capability in acoustics is challenging due to the nonlinearity-based mechanisms’ dependence on external driving and limited frequency conversion efficiency. Here, we present an acoustic synthesized comb structure derived from spatiotemporal metasurfaces, concurrently controlling frequency and OAM characteristics with high conversion efficiency and removing the constraints of critical driving frequency and power level in nonlinear mechanisms. By optimizing comb structures, energy distributions among OAM modes are adjusted to different frequency lines. We finally propose spatiotemporal metalens devices based on vortex-frequency combs, enabling synchronously multiple-order spatial differential operations for parallel data processing. This Letter is groundbreaking in integrating spectral- and spatial-domain acoustic waves, promising multifunctional imaging and advantages in spatiotemporal beams.

关键词： Vortex flow

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Sensing With Random Communication Signals

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IEEE Network 2025年

作者： Lu, Shihang Liu, Fan Xiong, Yifeng Du, Zhen Cui, Yuanhao Li, Shuangyang Yuan, Weijie Yang, Jie Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing210044 China Technical University of Berlin Faculty of Electrical Engineering and Computer Science Berlin10587 Germany Shenzhen518055 China Frontiers Science Center for Mobile Information Communication and Security China Southeast University MOE Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing211189 China

Communication-centric Integrated Sensing and Communication (ISAC) has been recognized as a promising methodology to implement wireless sensing functionality over existing network architectures, due to its cost-effectiveness and backward compatibility to legacy cellular systems. However, the inherent randomness of the communication signal may incur huge fluctuations in sensing capabilities, leading to unfavorable detection and estimation performance. To address this issue, we elaborate on random ISAC signal processing methods in this article, aiming at improving the sensing performance without unduly deteriorating the communication functionality. Specifically, we commence by discussing the fundamentals of sensing with random communication signals, including the performance metrics and optimal ranging waveforms. Building on these concepts, we then present a general framework for random ISAC signal transmission, followed by an in-depth exploration of time-domain pulse shaping, frequency-domain constellation shaping, and spatial-domain precoding methods. We provide a comprehensive overview of each of these topics, including models, results, and design guidelines. Finally, we conclude this article by identifying several promising research directions for random ISAC signal transmission. © 2025 IEEE.

关键词： Mobile telecommunication systems

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OptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filtering

arXiv

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arXiv 2025年

作者： Ding, Guanhua Xia, Yuxuan Guan, Runwei Wu, Qinchen Huang, Tao Ding, Weiping Sun, Jinping Mao, Guoqiang School of Electronic Information Engineering Beihang University Beijing100191 China Department of Automation Shanghai Jiaotong University Shanghai200240 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Guangzhou511455 China College of Science and Engineering James Cook University SmithfieldQLD4878 Australia School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China Faculty of Data Science City University of Macau 999078 China Research Laboratory of Smart Driving and Intelligent Transportation Systems Southeast University Nanjing210096 China

Accurate 3D multi-object tracking (MOT) is crucial for autonomous driving, as it enables robust perception, navigation, and planning in complex environments. While deep learning-based solutions have demonstrated impressive 3D MOT performance, model-based approaches remain appealing for their simplicity, interpretability, and data efficiency. Conventional model-based trackers typically rely on random vector-based Bayesian filters within the tracking-by-detection (TBD) framework but face limitations due to heuristic data association and track management schemes. In contrast, random finite set (RFS)-based Bayesian filtering handles object birth, survival, and death in a theoretically sound manner, facilitating interpretability and parameter tuning. In this paper, we present OptiPMB, a novel RFS-based 3D MOT method that employs an optimized Poisson multi-Bernoulli (PMB) filter while incorporating several key innovative designs within the TBD framework. Specifically, we propose a measurement-driven hybrid adaptive birth model for improved track initialization, employ adaptive detection probability parameters to effectively maintain tracks for occluded objects, and optimize density pruning and track extraction modules to further enhance overall tracking performance. Extensive evaluations on nuScenes and KITTI datasets show that OptiPMB achieves superior tracking accuracy compared with state-of-the-art methods, thereby establishing a new benchmark for model-based 3D MOT and offering valuable insights for future research on RFS-based trackers in autonomous driving. Copyright © 2025, The Authors. All rights reserved.

关键词： Wiener filtering

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Adam-family methods for nonsmooth optimization with convergence guarantees

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 2398-2450页

作者： Nachuan Xiao Xiaoyin Hu Xin Liu Kim-Chuan Toh Institute of Operations Research and Analytics National University of Singapore Singapore School of Computer and Computing Science Hangzhou City University Hangzhou China State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Department of Mathematics and Institute of Operations Research and Analytics National University of Singapore Singapore

In this paper, we present a comprehensive study on the convergence properties of Adam-family methods for nonsmooth optimization, especially in the training of nonsmooth neural networks. We introduce a novel two-timescale framework that adopts a two-timescale updating scheme, and prove its convergence properties under mild assumptions. Our proposed framework encompasses various popular Adam-family methods, providing convergence guarantees for these methods in training nonsmooth neural networks. Furthermore, we develop stochastic subgradient methods that incorporate gradient clipping techniques for training nonsmooth neural networks with heavy-tailed noise. Through our framework, we show that our proposed methods converge even when the evaluation noises are only assumed to be integrable. Extensive numerical experiments demonstrate the high efficiency and robustness of our proposed methods.

关键词： nonsmooth optimization stochastic subgradient methods Adam nonconvex optimization gradient clipping

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U-Shaped Transformer-Based 360-Degree No Reference Image Quality Assessment

U-Shaped Transformer-Based 360-Degree No Reference Image Qua...

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IEEE International Conference on Cognitive Informatics

作者： Xuekai Wei Mingliang Zhou Sam Kwong Department of Electrical and Computer Engineering State Key Laboratory of Internet of Things for Smart City University of Macau Macao China School of Computer Science Chongqing University Chongqing China Department of Computer Science City University of Hong Kong Kowloon Hong Kong China

Thanks to creative rendering and display techniques, 360-degree images can provide a more immersive and interactive experience for streaming users. However, such features make the perceptual characteristics of 360-degree images more complex than those of fixed-view images, making it impossible to achieve a comprehensive and accurate image quality assessment (IQA) task using a simple stack of pre-processing, post-processing, compression, and rendering tasks. In order to thoroughly learn global and local features in 360-degree images, reduce the complexity of multichannel neural network models and simplify the training process, this paper proposes a user-aware joint architecture and an efficient converter dedicated to 360-degree no-reference (NR) IQA. The input of the proposed method is a 360-degree cubic mapping projection (CMP) image. In addition, the proposed 360-degree NRIQA method includes a non-overlapping self-attentive selection module based on a dominant map and a feature extraction module based on a U-shaped transformer (U-former) to address perceptual region significance and projection distortion. The transformer-based architecture and the weighted averaging technique are jointly used to predict local perceptual quality. Experimental results obtained on widely used databases show that the proposed model outperforms other state-of-the-art methods in the case of NR 360-degree image quality assessment. In addition, cross-database evaluation and ablation studies demonstrate the intrinsic robustness and generalization of the proposed model.

关键词： Training Image quality Image recognition Neural networks computer architecture Transformers Rendering (computer graphics)

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Layout-Bridging Text-to-Image Synthesis

arXiv

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arXiv 2022年

作者： Liang, Jiadong Pei, Wenjie Lu, Feng The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China The Department of Computer Science Harbin Institute of Technology at Shenzhen Shenzhen518057 China

The crux of text-to-image synthesis stems from the difficulty of preserving the cross-modality semantic consistency between the input text and the synthesized image. Typical methods, which seek to model the text-to-image mapping directly, could only capture keywords in the text that indicates common objects or actions but fail to learn their spatial distribution patterns. An effective way to circumvent this limitation is to generate an image layout as guidance, which is attempted by a few methods. Nevertheless, these methods fail to generate practically effective layouts due to the diversity of input text and object location. In this paper we push for effective modeling in both text-to-layout generation and layout-to-image synthesis. Specifically, we formulate the text-to-layout generation as a sequence-to-sequence modeling task, and build our model upon Transformer to learn the spatial relationships between objects by modeling the sequential dependencies between them. In the stage of layout-to-image synthesis, we focus on learning the textual-visual semantic alignment per object in the layout to precisely incorporate the input text into the layout-to-image synthesizing process. To evaluate the quality of generated layout, we design a new metric specifically, dubbed Layout Quality Score, which considers both the absolute distribution errors of bounding boxes in the layout and the mutual spatial relationships between them. Extensive experiments on three datasets demonstrate the superior performance of our method over state-of-the-art methods on both predicting the layout and synthesizing the image from the given text. © 2022, CC BY.

关键词： Image processing

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Channel Estimation for Massive MIMO-OFDM: Simplified Information Geometry Approach

Channel Estimation for Massive MIMO-OFDM: Simplified Informa...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Jiyuan Yang Yan Chen An-An Lu Wen Zhong Xiqi Gao Xiaohu You Xiang-Gen Xia Dirk Slock National Mobile Communications Research Laboratory Southeast University Nanjing China Purple Mountain Laboratories Nanjing China Department of Electrical and Computer Engineering University of Delaware Newark DE USA Department of Communication Systems EURECOM Biot France

In this paper, we investigate the channel estimation for massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. We revisit the information geometry approach (IGA) for massive MIMO-OFDM channel estimation. By using the constant magnitude property of the entries of the measurement matrix and the asymptotic analysis, we find that the second-order natural parameters (SONPs) of the distributions on all the auxiliary manifolds (AMs) are equivalent to each other at each iteration of IGA, and the first-order natural parameters (FONPs) of the distributions on all the AMs are asymptotically equivalent to each other at the fixed point. Motivated by these results, we simplify the iterative process of IGA and propose a simplified IGA for massive MIMO-OFDM channel estimation. It is proved that at the fixed point, the a posteriori mean obtained by the simplified IGA is asymptotically optimal. The simplified IGA allows efficient implementation with fast Fourier transformation (FFT). Simulations confirm that the simplified IGA can achieve near the optimal performance with low complexity in a limited number of iterations.

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Electrochemical Detection of Lactate and Glucose with Enzyme-Free and Dual-Sensing Using Nickel-Oxide Nanoparticles

SSRN

引用

SSRN 2025年

作者： Nasiri, Rohollah Guagliano, Giuseppe Van Gastel, Dirkje Sanei, Reihaneh Madadelahi, Masoud Tanriverdi, Selim Jain, Saumey Fayazbaksh, Farzaneh Lee, Sang Won Zhu, Yangzhi Joensson, Haakan N. Russom, Aman Herland, Anna Division of Nanobiotechnology Department of Protein Science Science for Life Laboratory KTH Royal Institute of Technology Solna Sweden AIMES Center for the Advancement of Integrated Medical and Engineering Sciences Department of Neuroscience Karolinska Institute Solna Sweden Department of Chemistry Materials and Chemical Engineering "G. Natta" Politecnico di Milano Milan Italy Division of Micro and Nanosystems Department of Intelligent Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Terasaki Institute for Biomedical Innovation Los AngelesCA90064 United States

Detection and monitoring of lactate and glucose levels in biological fluids and cell cultures are critical for understanding metabolic disorders. While electrochemical biosensors are commonly used, traditional enzymatic sensors face stability, reproducibility, and cost challenges. To address these issues, we developed non-enzymatic sensors using nanostructured electrodes, specifically nickel oxide (NiO)-based, for lactate and glucose detection. The sensors were fabricated by drop-casting a NiO/Nafion/ethanol dispersion onto the working electrode, with performance evaluated through cyclic voltammetry and amperometry. Optimal sensitivity and linearity were achieved at a working voltage of ~0.5 V. To address glucose interference, sensors were modified with Prussian Blue, creating a dual sensor for lactate and glucose. The sensors respectively exhibited linear responses for lactate (0.1 – 5 mM) and glucose (0.1 – 5 mM) with detection limits of 0.60 mM and 0.35 mM, and it showed sensitivities of 0.43 ± 0.05 µA/mM (lactate) and 0.82 ± 0.02 µA/mM (glucose) in phosphate buffer solution. Validation against the lactate and glucose assay kits in a cell culture medium showed good agreement, highlighting the potential of these enzyme-free electrochemical sensors for metabolic analysis. Finally, the sensor was integrated with a microfluidic device, demonstrating its potential as a flow-through, enzyme-free metabolic sensor for future organ-on-chip applications. © 2025, The Authors. All rights reserved.

关键词： Cyclic voltammetry

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Scalar optical hopfions

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eLight 2022年第1期2卷 284-290页

作者： Chenhao Wan Yijie Shen Andy Chong Qiwen Zhan School of Optical‑Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Optical and Electronic Information and Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074HubeiChina Optoelectronics Research Centre University of SouthamptonSouthampton SO171BJUK Department of Physics Pusan National UniversityGeumjeong‑GuBusan 46241South Korea Zhangjiang Laboratory 100 Haike RoadShanghai 201204China Shanghai Key Lab of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China

Hopfions are three-dimensional(3D)topological states discovered in field theory,magnetics,and hydrodynamics that resemble particle-like objects in physical *** inherit the topological features of the Hopf fibration,a homotopic mapping from unit sphere in 4D space to unit sphere in 3D *** we design and demonstrate dynamic scalar optical hopfions in the shape of a toroidal vortex and expressed as an approximate solution to Maxwell’s *** lines correspond to disjoint and interlinked loops forming complete ring tori in 3D *** Hopf invariant,product of two winding numbers,is determined by the topological charge of the poloidal spatiotemporal vortices and toroidal spatial vortices in toroidal *** hopfions provide a photonic testbed for studying topological states and may be utilized as high-dimensional information carriers.

关键词： Hopfion Toroidal vortex Spatiotemporal vortex Wave packet Topology Topological charge

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Graph partitioning and sparse matrix ordering using reinforcement learning and graph neural networks

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 13675-13702页

作者： Alice Gatti Zhixiong Hu Tess Smidt Esmond G. Ng Pieter Ghysels Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA LinkedIn Corporation Sunnyvale CA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA

We present a novel method for graph partitioning, based on reinforcement learning and graph convolutional neural networks. Our approach is to recursively partition coarser representations of a given graph. The neural network is implemented using SAGE graph convolution layers, and trained using an advantage actor critic (A2C) agent. We present two variants, one for finding an edge separator that minimizes the normalized cut or quotient cut, and one that finds a small vertex separator. The vertex separators are then used to construct a nested dissection ordering to permute a sparse matrix so that its triangular factorization will incur less fill-in. The partitioning quality is compared with partitions obtained using METIS and SCOTCH, and the nested dissection ordering is evaluated in the sparse solver SuperLU. Our results show that the proposed method achieves similar partitioning quality as METIS, SCOTCH and spectral partitioning. Furthermore, the method generalizes across different classes of graphs, and works well on a variety of graphs from the SuiteSparse sparse matrix collection.

关键词： graph partitioning sparse matrix ordering graph neural networks reinforcement learning machine learning for scientific computing

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