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Inductive Lottery Ticket Learning for Graph Neural Networks

Journal of computer science & technology 2024年第6期39卷 1223-1237页

作者： Yong-Duo Sui Xiang Wang Tianlong Chen Meng Wang Xiang-Nan He Tat-Seng Chua School of Data Science University of Science and Technology of ChinaHefei 230027China Department of Electrical and Computer Engineering The University of Texas at AustinAustin 78712U.S.A School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Computing National University of SingaporeSingapore

Graph neural networks (GNNs) have gained increasing popularity, while usually suffering from unaffordable computations for real-world large-scale applications. Hence, pruning GNNs is of great need but largely unexplored. The recent work Unified GNN Sparsification (UGS) studies lottery ticket learning for GNNs, aiming to find a subset of model parameters and graph structures that can best maintain the GNN performance. However, it is tailed for the transductive setting, failing to generalize to unseen graphs, which are common in inductive tasks like graph classification. In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity. To prune the input graphs, we design a predictive model to generate importance scores for each edge based on the input. To prune the model parameters, it views the weight’s magnitude as their importance scores. Then we design an iterative co-pruning strategy to trim the graph edges and GNN weights based on their importance scores. Although it might be strikingly simple, ICPG surpasses the existing pruning method and can be universally applicable in both inductive and transductive learning settings. On 10 graph-classification and two node-classification benchmarks, ICPG achieves the same performance level with 14.26%–43.12% sparsity for graphs and 48.80%–91.41% sparsity for the GNN model.

关键词： lottery ticket hypothesis graph neural networks neural network pruning

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A color image encryption scheme based on a 2D coupled chaotic system and diagonal scrambling algorithm

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Chinese Physics B 2024年第7期33卷 233-243页

作者：苏静明方士辉洪炎温言 Anhui Mining Machinery and Electrical Equipment Coordination Innovation Center Anhui University of Science and TechnologyHuainan 232001China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China

A novel color image encryption scheme is developed to enhance the security of encryption without increasing the complexity. Firstly, the plain color image is decomposed into three grayscale plain images, which are converted into the frequency domain coefficient matrices(FDCM) with discrete cosine transform(DCT) operation. After that, a twodimensional(2D) coupled chaotic system is developed and used to generate one group of embedded matrices and another group of encryption matrices, respectively. The embedded matrices are integrated with the FDCM to fulfill the frequency domain encryption, and then the inverse DCT processing is implemented to recover the spatial domain signal. Eventually,under the function of the encryption matrices and the proposed diagonal scrambling algorithm, the final color ciphertext is obtained. The experimental results show that the proposed method can not only ensure efficient encryption but also satisfy various sizes of image encryption. Besides, it has better performance than other similar techniques in statistical feature analysis, such as key space, key sensitivity, anti-differential attack, information entropy, noise attack, etc.

关键词： color image encryption discrete cosine transform two-dimensional(2D)coupled chaotic system diagonal scrambling

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Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding

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Light(science & Applications) 2024年第3期13卷 466-477页

作者： Xinyuan Fang Xiaonan Hu Baoli Li Hang Su Ke Cheng Haitao Luan Min Gu Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Machine learning with optical neural networks has featured unique advantages of the information processing including high speed,ultrawide bandwidths and low energy consumption because the optical dimensions(time,space,wavelength,and polarization)could be utilized to increase the degree of ***,due to the lack of the capability to extract the information features in the orbital angular momentum(OAM)domain,the theoretically unlimited OAM states have never been exploited to represent the signal of the input/output nodes in the neural network ***,we demonstrate OAM-mediated machine learning with an all-optical convolutional neural network(CNN)based on Laguerre-Gaussian(LG)beam modes with diverse diffraction *** proposed CNN architecture is composed of a trainable OAM mode-dispersion impulse as a convolutional kernel for feature extraction,and deep-learning diffractive layers as a *** resultant OAM mode-dispersion selectivity can be applied in information mode-feature encoding,leading to an accuracy as high as 97.2%for MNIST database through detecting the energy weighting coefficients of the encoded OAM modes,as well as a resistance to eavesdropping in point-to-point free-space ***,through extending the target encoded modes into multiplexed OAM states,we realize all-optical dimension reduction for anomaly detection with an accuracy of 85%.Our work provides a deep insight to the mechanism of machine learning with spatial modes basis,which can be further utilized to improve the performances of various machine-vision tasks by constructing the unsupervised learning-based auto-encoder.

关键词： polarization mode momentum

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Halo reduction multi-exposure image fusion technique

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Multimedia Tools and Applications 2025年第13期84卷 12347-12370页

作者： Sharif, Rizwan Amin, Benish Sukhia, Komal Nain Department of Electrical Engineering Institute of Space Technology Islamabad Pakistan Department of Computer Science Institute of Space Technology Islamabad Pakistan

Multi-exposure image fusion (MEF) involves combining images captured at different exposure levels to create a single, well-exposed fused image. MEF has a wide range of applications, including low light, low contrast, night photography, medical imaging, and remote sensing. However, MEF methods often face issues like artifacts, halos around edges, color inconsistencies, noise amplification, and difficulty in preserving fine details. Moreover, assessing the quality of fused images objectively is complex due to the subjective nature of human perception. Solving the challenges is essential to developing efficient MEF techniques that produce high-quality results across various scenarios. The proposed technique introduces an approach to handling halo artifacts and implementing MEF. The Dense Scale-Invariant Feature Transform (DSIFT) is used to capture vital information about image brightness, texture, and edges from source images. Three weight maps are computed from the local mean, signal strength, and the global gradient for initial weight estimation. The local mean represents the brightness of specific image areas, signal strength preserves essential details like textures and edges while reducing image noise, and global gradient helps identify regions with significant pixel value shifts across multiple exposure images. The weight maps are then combined using a weighted average and a Gaussian smoothing filter is applied to reduce inherent noise and discontinuities in the original weights. Subsequently, pyramid decomposition is performed to generate a fused image. The efficiency of the proposed method is extensively tested on challenging multi-exposure image sequences. The results of the proposed approach demonstrate its superiority in both subjective evaluation and objective metrics like the MEF-Structural Similarity Index (MEF-SSIM), Natural Image Quality Evaluator (NIQE) and Gradient based performance measure (QAB/f). © The Author(s), under exclusive licence to Springer

关键词： Image fusion

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Reconfigurable MIMO-based self-powered battery-less light communication system

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Light(science & Applications) 2024年第10期13卷 2300-2311页

作者： Jose Ilton De Oliveira Filho Abderrahmen Trichili Omar Alkhazragi Mohamed-Slim Alouini Boon S.Ooi Khaled Nabil Salama Computer Electrical and Mathematical Sciences&EngineeringKing Abdullah University of Science and TechnologyThuwalSaudi Arabia NEOM Institute for Ocean Science and Solutions NeomThuwalSaudi Arabia

Simultaneous lightwave information and power transfer (SLIPT), co-existing with optical wireless communication,holds an enormous potential to provide continuous charging to remote Internet of Things (IoT) devices while ensuringconnectivity. Combining SLIPT with an omnidirectional receiver, we can leverage a higher power budget whilemaintaining a stable connection, a major challenge for optical wireless communication systems. Here, we design amultiplexed SLIPT-based system comprising an array of photodetectors (PDs) arranged in a 3 × 3 configuration. Thesystem enables decoding information from multiple light beams while simultaneously harvesting energy. The PDs canswiftly switch between photoconductive and photovoltaic modes to maximize information transfer rates and provideon-demand energy harvesting. Additionally, we investigated the ability to decode information and harvest energywith a particular quadrant set of PDs from the array, allowing beam tracking and spatial diversity. The design wasexplored in a smaller version for higher data rates and a bigger one for higher power harvesting. We report a selfpoweringdevice that can achieve a gross data rate of 25.7 Mbps from a single-input single-output (SISO) and an 85.2Mbps net data rate in a multiple-input multiple-output (MIMO) configuration. Under a standard AMT1.5 illumination,the device can harvest up to 87.33 mW, around twice the power needed to maintain the entire system. Our workpaves the way for deploying autonomous IoT devices in harsh environments and their potential use in spaceapplications.

关键词： communication MIMO system

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Sawtooth-enhanced bend sensor for gesture recognition

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science China(Technological sciences) 2024年第6期67卷 1727-1736页

作者： BAI YanRu ZHANG ZiHang WANG HaoYu GUO Rui LI XiSheng School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China School of Advanced Engineering University of Science and Technology BeijingBeijing100083China School of Precision Instrument and Opto-Electronics Engineering Tianjin UniversityTianjin300072China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China

Gesture recognition has diverse application prospects in the field of human-computer ***,gesture recognition devices based on strain sensors have achieved remarkable results,among which liquid metal materials have considerable advantages due to their high tensile strength and *** improve the detection sensitivity of liquid metal strain sensors,a sawtooth-enhanced bending sensor is proposed in this *** with the results from previous studies,the bending sensor shows enhanced resistance *** addition,combined with machine learning algorithms,a gesture recognition glove based on the sawtooth-enhanced bending sensor is also fabricated in this study,and various gestures are accurately *** the fields of human-computer interaction,wearable sensing,and medical health,the sawtooth-enhanced bending sensor shows great potential and can have wide application prospects.

关键词： liquid metal bending sensor gesture recognition machine learning

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Alkali Atomic Density Detection with Arbitrarily Polarized Light

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IEEE Sensors Journal 2025年第11期25卷 20466-20473页

作者： Liu, Xuejing Wang, Menglu Li, Yuhan Wang, Yangtao Zhang, Xuedian Li, Yang University of Shanghai for Science and Technology Key Laboratory of Optical Technology and Instrument for Medicine Ministry of Education School of Optical-Electrical and Computer Engineering Shanghai200093 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Tongji University Shanghai Institute of Intelligent Science and Technology Shanghai200093 China

The atomic density of an alkali vapor cell is a critical factor in determining the performance of atomic magnetometers. This paper presents a novel method to detect the density of an alkali atomic ensemble under arbitrary polarization states. By establishing a comprehensive model that correlates the absorption spectrum with the incident light’s polarization, our approach enables real-time and flexible measurements across varying temperatures and polarization angles. Experimental validations are conducted by systematically rotating a quarter-wave plate and controlling the vapor cell temperature, thereby collecting transmission spectra at multiple polarizations. The results demonstrate that the proposed method simplifies density measurements while preserving high accuracy, laying a solid foundation for further research on Spin-Exchange Relaxation-Free (SERF) atomic magnetometers. This technique serves as a versatile and effective tool for in-situ monitoring of alkali metal vapor density in various sensor applications. © 2001-2012 IEEE.

关键词： Absorption spectroscopy

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Road Surface Analysis through Machine Learning Techniques

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IEIE Transactions on Smart Processing and Computing 2024年第4期13卷 344-353页

作者： Singh, Prabhat Sharma, Shilpi Kamal, Ahmed E. Kumar, Sunil Department of Computer Science and Engineering Amity School of Engineering & Technology Uttar Pradesh Noida India Department of Electrical and Computer Engineering Iowa State University Ames United States Computer Science and Engineering Amity School of Engineering & Technology Uttar Pradesh Noida India

Roads are an important part of transporting goods and products from one place to another. In developing countries, the main challenge is to maintain road conditions regularly. Roads can deteriorate from time to time. Monitoring the conditions of the roads, which may degrade with time, is very difficult, resulting in a delay in transportation and damage to the vehicles moving on the roads. Poor road conditions cause road accidents. A model is being proposed to monitor the conditions of the road surface by smartphone sensors. Accelerometer, gyroscope, and GPS sensors are deployed in the mobile phones, which will help to collect data on the road conditions. After collecting the data about the road conditions, various machine learning approaches, such as supervised, multi-layered, and multiclass, are applied to data filtration. Road conditions are divided into three categories to achieve this methodology: potholes, deep traverse cracks, and smooth roads. This categorization helped in analyzing the road surface condition through smartphone sensors over all three axes instead of taking it over a single axis. Neural networks helped analyze data or road conditions more accurately than Decision Tree and SVM. Copyrights © 2024 The Institute of Electronics and Information Engineers.

关键词： Decision trees

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High-Efficiency Asymmetrically Designed Three-Phase Virtual 48-pulse Power Supply for Electrolytic Hydrogen

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IEEE Transactions on Power Electronics 2025年第8期40卷 10335-10342页

作者： Tian, Hanlei Cui, Haoran Han, Wei Sathik M, Jagabar Liang, Guozhuang Chen, Maolin Mekhilef, Saad Sustainable Energy and Environment Thrust The Hong Kong University of Science and Technology (GZ) Guangzhou China Electrical Engineering School of Hebei University of Science and Technology Shijiazhuang China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India College of Electrical Engineering Sichuan University Chengdu China School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne VIC Australia

To address the low-voltage, high-current requirements in hydrogen production applications, a virtual 48-pulse three-phase rectifier is proposed, and achieves the equivalent performance of four parallel 12-pulse rectifiers (12-PR). The proposed approach builds upon the conventional 12-PR design and incorporates novel Asymmetric Current Injection Units (ACIU). The ACIU is installed on the DC side of the AC-DC converter, generating a specific current at the DC link to modulate and enhance the operating modes of the 12-PR. Consequently, the 12-pulse is extended to 48, resulting in an almost sinusoidal input current with a Total Harmonic Distortion (THD) of 3.2% and an output current with a ripple rate (RR) of 2.3%, thus significantly improving power quality. Furthermore, the asymmetric design of the current injection units eliminates the need for a phase-shifting transformer. The efficacy of the proposed rectifier for electrolytic hydrogen production is validated using a 2.2 kW experimental prototype. © 1986-2012 IEEE.

关键词： Voltage Hydrogen Rectifiers Production Windings Circuits Inductors Transformers Harmonic analysis Electrochemical processes

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Real-time holographic camera for obtaining real 3D scene hologram

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Light(science & Applications) 2025年第3期14卷 789-799页

作者： Zhao-Song Li Chao Liu Xiao-Wei Li Yi Zheng Qian Huang Yi-Wei Zheng Ye-Hao Hou Chen-Liang Chang Da-Wei Zhang Song-Lin Zhuang Di Wang Qiong-Hua Wang School of Instrumentation and Optoelectronic Engineering Beihang UniversityBeijing100191China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China

As a frontier technology,holography has important research values in fields such as bio-micrographic imaging,light feld modulation and data ***,the real-time acquisition of 3D scenes and high-fidelity reconstruction technology has not yet made a breakthrough,which has seriously hindered the development of ***,a novel holographic camera is proposed to solve the above inherent problems *** proposed holographic camera consists of the acquisition end and the calculation *** the acquisition end of the holographic camera,specially configured liquid materials and liquid lens structure based on voice-coil motor-driving are used to produce the liquid camera,so that the liquid camera can quickly capture the focus stack of the real 3D scene within 15 *** the calculation end,a new structured focus stack network(FS-Net)is designed for hologram *** training the FS-Net with the focus stack renderer and learnable Zernike phase,it enables hologram calculation within 13 *** the first device to achieve real-time incoherent acquisition and high-fidelity holographic reconstruction of a real 3D scene,our proposed holographic camera breaks technical bottlenecks of difficulty in acquiring the real 3D scene,low quality of the holographic reconstructed image,and incorrect defocus *** experimental results demonstrate the effectiveness of our holographic camera in the acquisition of focal plane information and hologram calculation of the real 3D *** proposed holographic camera opens up a new way for the application of holography in fields such as 3D display,light field modulation,and 3D measurement.

关键词： holographic camera feld modulation hologram calculation liquid lens structured focus stack network D scene reconstruction d scenes real time acquisition

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