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arXiv 2022年

作者： Wu, Chong Feng, Zhenan Zhang, Houwang Yan, Hong Department of Electrical Engineering Centre for Intelligent Multidimensional Data Analysis City University of Hong Kong Kowloon Hong Kong

Convolutional neural networks (CNNs) are usually used as a backbone to design methods in biomedical image segmentation. However, the limitation of receptive field and large number of parameters limit the performance of these methods. In this paper, we propose a graph neural network (GNN) based method named GNN-SEG for the segmentation of brain tissues. Different to conventional CNN based methods, GNN-SEG takes superpixels as basic processing units and uses GNNs to learn the structure of brain tissues. Besides, inspired by the interaction mechanism in biological vision systems, we propose two kinds of interaction modules for feature enhancement and integration. In the experiments, we compared GNN-SEG with state-of-the-art CNN based methods on four datasets of brain magnetic resonance images. The experimental results show the superiority of GNN-SEG. Copyright © 2022, The Authors. All rights reserved.

关键词： Superpixels

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Undersea Cable Path Planning with Curvature Constraints

Undersea Cable Path Planning with Curvature Constraints

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International Conference on Transparent Optical Networks

作者： Xinyu Wang Fahan Chen Zengfu Wang Tianjiao Wang Elias Tahchi Bill Moran Moshe Zukerman Department of Electrical Engineering City University of Hong Kong Hong Kong SAR China School of Automation Northwestern Polytechnical University Xi’an China Center for Intelligent Multidimensional Data Analysis Hong Kong SAR China Fugro Germany Marine GmbH Bremen Germany Department of Electrical and Electronic Engineering University of Melbourne Victoria Australia

ISBN: (数字)9798350377323

ISBN: (纸本)9798350377330

Undersea optical fiber cables that span vast distances are integral to the Internet’s infrastructure. Manual path planning of such cables is an arduous task. The Fast Marching Method (FMM), a precise numerical approach capable of solving the Eikonal equation, offers a viable, rigorous, and efficient alternative by optimizing the path between source and destination, taking account of a summary objective function of costs and risk factors. However, its implementation often neglects the crucial curvature constraints arising from the flexural rigidity of cables and maneuvering capabilities of the laying vessels in real-world scenarios. In an attempt to address this issue in real-world cable path planning, our work incorporates these constraints. We propose modifying our previous FMM-based work to generate paths by systematically expanding forbidden regions and replacing sharp angular changes with circular arcs of a predefined radius to ensure compliance with the curvature constraints. By factoring curvature into cable system design, we ensure robust, real-world applicability of cable path design methods. While this modification may result in a departure from the FMM’s optimally calculated path, potentially compromising its cost-effectiveness or risk minimization, the adjusted route adheres to essential curvature constraints and the structural integrity of cables. A more rigorously optimal approach to curvature is possible, but its implementation in our context appears to be difficult and does not appear to yield significant improvements.

关键词： Costs Risk minimization Optical fiber cables Optical fiber networks Path planning Mathematical models Rigidity

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data Resolution and Future Challenges in Automated Undersea Cable System Design

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IEEE Network 2025年

作者： Wang, Tianjiao Wang, Xinyu Moran, Bill Wang, Zengfu Zukerman, Moshe Hong Kong Science Park Center for Intelligent Multidimensional Data Analysis Hong Kong Hong Kong The University of Melbourne Department of Electrical and Electronic Engineering MelbourneVIC3010 Australia Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China Northwestern Polytechnical University School of Automation Xi’an710072 China City University of Hong Kong Department of Electrical Engineering Kowloon Hong Kong

Undersea optical cables form the backbone of global Internet infrastructure, carrying nearly 99% of international Internet data. As demand for high-speed and reliable connectivity continues to grow, optimizing undersea cable path planning has become essential for reducing costs, mitigating risks, and ensuring high-quality service delivery. While traditional manual planning methods remain widely used by cable system designers, surveyors, and planners, the increasing complexity of cable deployment necessitates the integration of automated methods that leverage advanced computational techniques to enhance efficiency and decision-making. This paper reviews the state-of-the-art in cable path planning and system design, emphasizing the important role of data density in achieving optimal cable paths. In particular, we present realistic path-planning scenarios on the Earth’s surface and describe the Fast Marching Method (FMM), a computational approach that efficiently determines optimal routing based on available data. Simulation results show that increasing map resolution, along with accompanying data at the higher resolution, significantly enhances path planning accuracy, reduces deployment costs, and improves risk assessment, underscoring the importance of acquiring and utilizing high-density data. This understanding leads to a discussion of the challenges of data acquisition and computational scalability in processing large-scale datasets, and the importance of collaboration between cable system engineers, geospatial data scientists, and software developers. This paper proposes a collaborative framework in which automated algorithms complement traditional planning methods, enabling cost-effective solutions that continually adapt and become more effective through expert feedback. This research contributes to a more resilient, efficient, and adaptive global undersea communication network, paving the way for the next generation of intelligent, optimized cable infrastructur

关键词： Risk assessment

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Directional And Topological Transformer With Topology Priors For 4D Cellular Image Segmentation

Directional And Topological Transformer With Topology Priors...

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IEEE International Conference on Image Processing

作者： Zelin Li Zhaoke Huang Zhen Zhu Sicheng You Zhongying Zhao Hong Yan Centre for Intelligent Multidimensional Data Analysis Hong Kong Science Park Hong Kong SAR Department of Electrical Engineering City University of Hong Kong Hong Kong SAR Department of Biology Hong Kong Baptist University Hong Kong SAR

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Cellular segmentation is a crucial step in creating cell shape maps and morphological graphs for living embryos from time-lapse 3D fluorescence images (laser confocal). One reliable method for segmenting cell shapes through deep learning networks is to incorporate voxel distance and topology priors to model shapes in topological structures. However, automated and CNN-based segmentation methods often suffer from low signal-to-noise ratios and insufficient training data. Previous works on semantic segmentation have ignored directional distance and topological information. In this paper, we propose a 3D directional and topological transformer named DTTR (Directional distance mapping and Topological learning TRansformer), which uses topology priors to binarization, and demonstrates an effective directional latent space. We use attention calculation on directional distance maps and utilize topological loss and priors, along with an optimized Delaunay-clustering algorithm, to measure voxel predictions in higher dimensional topology space. DTTR outperforms other existing deep learning models and provides a reliable segmented cell instance dataset (22 new living C. elegans embryos) for establishing 4D cellular morphology map.

关键词： Image segmentation Three-dimensional displays Embryo Shape Manuals Fluorescence Transformers

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Domain Generalization with Small data

arXiv

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arXiv 2024年

作者： Chen, Kecheng Gal, Elena Yan, Hong Li, Haoliang Department of Electrical Engineering Center for Intelligent Multidimensional Data Analysis City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong Department of Engineering University of Oxford Old Road Campus Research Building Roosevelt Drive OxfordOX3 7DQ United Kingdom

In this work, we propose to tackle the problem of domain generalization in the context of insufficient samples. Instead of extracting latent feature embeddings based on deterministic models, we propose to learn a domain-invariant representation based on the probabilistic framework by mapping each data point into probabilistic embeddings. Specifically, we first extend empirical maximum mean discrepancy (MMD) to a novel probabilistic MMD that can measure the discrepancy between mixture distributions (i.e., source domains) consisting of a series of latent distributions rather than latent points. Moreover, instead of imposing the contrastive semantic alignment (CSA) loss based on pairs of latent points, a novel probabilistic CSA loss encourages positive probabilistic embedding pairs to be closer while pulling other negative ones apart. Benefiting from the learned representation captured by probabilistic models, our proposed method can marriage the measurement on the distribution over distributions (i.e., the global perspective alignment) and the distribution-based contrastive semantic alignment (i.e., the local perspective alignment). Extensive experimental results on three challenging medical datasets show the effectiveness of our proposed method in the context of insufficient data compared with state-of-the-art methods. © 2024, CC BY.

关键词： Alignment

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VARIABLE T-PRODUCT AND ZERO-PADDING TENSOR COMPLETION WITH APPLICATIONS

arXiv

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arXiv 2023年

作者： Qi, Liqun Yan, Rui Luo, Ziyan Yan, Hong Yu, Gaohang Department of Mathematics Hangzhou Dianzi University Hangzhou310018 China School of Mathematics and Statistics Beijing Jiaotong University Beijing100044 China Department of Electrical Engineering Center for Intelligent Multidimensional Data Analysis City University of Hong Kong Kowloon Hong Kong

The T-product method based upon Discrete Fourier Transformation (DFT) has found wide applications in engineering, in particular, in image processing. In this paper, we propose variable T-product, and apply the Zero-Padding Discrete Fourier Transformation (ZDFT), to convert third order tensor problems to the variable Fourier domain. An additional positive integer parameter is introduced, which is greater than the tubal dimension of the third order tensor. When the additional parameter is equal to the tubal dimension, the ZDFT reduces to DFT. Then, we propose a new tensor completion method based on ZDFT and TV regularization, called VTCTF-TV. Extensive numerical experiment results on visual data demonstrate that the superior performance of the proposed method. In addition, the zero-padding is near the size of the original tubal dimension, the resulting tensor completion can be performed much better. © 2023, CC BY.

关键词： Discrete Fourier transforms

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Facial Micro-Expression Recognition Using Double-Stream 3d Convolutional Neural Network with Domain Adaptation

SSRN

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SSRN 2022年

作者： Li, Zhengdao Zhang, Yupei Xing, Hanwen Chan, Kwok-Leung Tjahjadi, Tardi Department of Electrical Engineering City University of Hong Kong Hong Kong Centre for Intelligent Multidimensional Data Analysis Limited Hong Kong School of Engineering University of Warwick United Kingdom

Humans show micro-expressions (MEs) under some circumstances. MEs are display of emotions that a human wants to conceal. Recognition of MEs has been applied in various fields. However, automatic ME recognition remains a challenging problem due to two major obstacles. As MEs are typically of short duration and low intensity, it is hard to extract discriminative features from ME videos. Moreover, it is tedious to collect ME data. Existing ME datasets usually contain insufficient video samples. In this paper, we propose a deep learning model, Double-Stream 3-dimensional Convolutional Neural Network (DS-3DCNN), for recognizing the ME captured in video. The recognition framework contains two streams of 3D-CNN. The first extracts spatiotemporal features from the raw ME videos. The second extracts variations of the facial motion within the spatiotemporal domain. To facilitate feature extraction, the subtle motion embedded in a ME is amplified. To address the insufficient ME data, a macro-expression dataset is employed to expand the training sample size. Supervised domain adaptation is adopted in model training in order to bridge the difference between ME and macro-expression datasets. The DS-3DCNN model is evaluated on two publicly available ME datasets. The results show that the model outperforms various state-of-the-art models, in particular the best model presented in MEGC2019 by more than 6%. © 2022, The Authors. All rights reserved.

关键词： Optical flows

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Joint Learning of Deep Texture and High-Frequency Features for Computer-Generated Image Detection

arXiv

引用

arXiv 2022年

作者： Xu, Qiang Jia, Shan Jiang, Xinghao Sun, Tanfeng Wang, Zhe Yan, Hong Department of Electrical Engineering Centre for Intelligent Multidimensional Data Analysis City University of Hong Kong Kowloon Hong Kong State University of New York NY United States School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Advanced Design and Systems Engineering Centre for Intelligent Multidimensional Data Analysis City University of Hong Kong Hong Kong

Distinguishing between computer-generated (CG) and natural photographic (PG) images is of great importance to verify the authenticity and originality of digital images. However, the recent cutting-edge generation methods enable high qualities of synthesis in CG images, which makes this challenging task even trickier. To address this issue, a joint learning strategy with deep texture and high-frequency features for CG image detection is proposed. We first formulate and deeply analyze the different acquisition processes of CG and PG images. Based on the finding that multiple different modules in image acquisition will lead to different sensitivity inconsistencies to the convolutional neural network (CNN)-based rendering in images, we propose a deep texture rendering module for texture difference enhancement and discriminative texture representation. Specifically, the semantic segmentation map is generated to guide the affine transformation operation, which is used to recover the texture in different regions of the input image. Then, the combination of the original image and the high-frequency components of the original and rendered images are fed into a multi-branch neural network equipped with attention mechanisms, which refines intermediate features and facilitates trace exploration in spatial and channel dimensions respectively. Extensive experiments on two public datasets and a newly constructed dataset1 with more realistic and diverse images show that the proposed approach outperforms existing methods in the field by a clear margin. Besides, results also demonstrate the detection robustness and generalization ability of the proposed approach to postprocessing operations and generative adversarial network (GAN) generated images. Copyright © 2022, The Authors. All rights reserved.

关键词： Textures

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An effective method for quantification, visualization, and analysis of 3D cell shape during early embryogenesis

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Quantitative Biology 2025年第1期 134-147页

作者： Zelin Li Zhaoke Huang Jianfeng Cao Guoye Guan Zhongying Zhao Hong Yan Department of Electrical Engineering City University of Hong Kong Centre for Intelligent Multidimensional Data Analysis Limited Center for Quantitative Biology Peking University Department of Systems Biology Harvard Medical School Department of Data Science Dana-Farber Cancer Institute Department of Biology Hong Kong Baptist University State Key Laboratory of Environmental and Biological Analysis Hong Kong Baptist University

Embryogenesis is the most basic process in developmental *** and simply quantifying cell shape is challenging for the complex and dynamic 3D embryonic cells. Traditional descriptors such as volume,surface area, and mean curvature often fall short, providing only a global view and lacking in local detail and reconstruction capability. Addressing this, we introduce an effective integrated method, 3D Cell Shape Quantification(3DCSQ), for transforming digitized 3D cell shapes into analytical feature vectors, named eigengrid(proposed grid descriptor like eigen value), eigenharmonic, and eigenspectrum. We uniquely combine spherical grids, spherical harmonics, and principal component analysis for cell shape quantification. We demonstrate 3DCSQ's effectiveness in recognizing cellular morphological phenotypes and clustering cells. Applied to Caenorhabditis elegans embryos of 29 living embryos from 4-to 350-cell stages,3DCSQ identifies and quantifies biologically reproducible cellular patterns including distinct skin cell deformations. We also provide automatically cell shape lineaging analysis program. This method not only systematizes cell shape description and evaluation but also monitors cell differentiation through shape changes, presenting an advancement in biological imaging and analysis.

关键词：

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WiLoc: Encoding-based WiFi Indoor Localization

WiLoc: Encoding-based WiFi Indoor Localization

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Zhao Huang Mikko Valkama Juan Zhang Meng Xu Cunyi Yin Minglei Guan Computer and Information Science Northumbria University Newcastle UK Department of Electrical Engineering Tampere University Tampere Finland School of Information Technology & Management University of International Business and Economics Beijing China Centre for Intelligent Multidimensional Data Analysis Limited Hongkong China School of Artificial Intelligence Shenzhen Polytechnic University Shenzhen China

ISBN: (数字)9798350366402

ISBN: (纸本)9798350366419

WiFi Indoor localization plays a crucial role in an emerging application domain for tracking indoor people, however, the serious issue is that the WiFi signals from access points (APs) vary greatly over time and the deployment structure of APs may be changed, for example, some APs are replaced or removed over time, which cause localization accuracy reduced. To solve this problem, this paper presents WiLoc, a Long-term WiFi localization with Lightweight Siamese Neural Network. This method introduces a Siamese neural encoder-based framework to learn the similarity between three inputs, where the Siamese network only consists of three linear layers without any convolutional layer or transformer. The triplet loss function is utilized to supervise the training of the feature encoder. Then, the encodings from this encoder are input to K-Nearest Neighbors (KNN) to predict the user’s positions. Extensive experiments on the UJI dataset, show the proposed WiLoc can effectively relieve the degradation of localization accuracy over time compared to the state-of-the-art algorithms, the degradation is reduced from 51% to 12.1%, and the average localization error is 2.06 m.

关键词： Location awareness Degradation Training Accuracy Convolution Neural networks Nearest neighbor methods Transformers Real-time systems Wireless fidelity

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