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COMPUTING THE ASPECT GRAPH FOR LINE DRAWINGS OF POLYHEDRAL OBJECTS

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1990年第2期12卷 113-122页

作者： GIGUS, Z MALIK, J Dept. of Electr. Eng. & Comput. Sci. California Univ. Berkeley CA USA

An algorithm for computing the aspect graph for polyhedral objects is described. The aspects graph is a representation of three-dimensional objects by a set of two-dimensional views. The set of viewpoints on the Gaussian sphere is partitioned into regions such that in each region the qualitative structure of the line drawing remains the same. At the boundaries between adjacent regions are the accidental viewpoints where the structure for the line drawing changes. It is shown that for polyhedral objects there are two fundamental visual events: (1) the projections of an edge and a vertex coincide; and (2) the projections of three nonadjacent edges intersect at a point. The geometry of the object is reflected in the locus of the accidental viewpoints. The algorithm computes the partition together with a representative view for each region of the partition.

关键词： partitioning algorithms Object recognition Geometry Shape Computer science Algorithm design and analysis INSPEC: Controlled Indexing picture processing computational geometry graph theory pattern recognition INSPEC: Non-Controlled Indexing geometry 3D object representation picture processing edge projection pattern recognition aspect graph line drawings polyhedral objects Gaussian sphere accidental viewpoints vertex

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Fast Approximated Multiple Kernel K-Means

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第11期36卷 6171-6180页

作者： Wang, Jun Tang, Chang Zheng, Xiao Liu, Xinwang Zhang, Wei Zhu, En Zhu, Xinzhong Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Qilu Univ Technol Shandong Comp Sci Ctr Natl Supercomp Ctr Jinan Shandong Prov Key Lab Comp NetworksShandong Acad Jinan 250000 Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Sch Artificial Intelligence Jinhua 321004 Peoples R China

Multiple Kernel Clustering (MKC) has emerged as a prominent research domain in recent decades due to its capacity to exploit diverse information from multiple views by learning an optimal kernel. Despite the successes achieved by various MKC methods, a significant challenge lies in the computational complexity associated with generating a consensus partition from the optimal kernel matrix, typically of size n x n , where n represents the number of samples. This computational bottleneck restricts the practical applicability of these methods when confronted with large-scale datasets. Furthermore, certain existing MKC algorithms derive the consensus partition matrix by fusing all base partitions. However, this fusion process may inadvertently overlook critical information embedded in individual base kernels, potentially leading to inferior clustering performance. In light of these challenges, we introduce an innovative and efficient multiple kernel k -means approach, denoted as FAMKKM. Notably, FAMKKM incorporates two approximated partition matrices instead of the original individual partition matric for each base kernel. This strategic substitution significantly reduces computational complexity. Additionally, FAMKKM leverages the original kernel information to guide the fusion of all base partitions, thereby enhancing the quality of the resulting consensus partition matrix. Finally, we substantiate the efficacy and efficiency of the proposed FAMKKM through extensive experiments conducted on six benchmark datasets. Our results demonstrate its superiority over state-of-the-art methods.

关键词： Kernel Clustering algorithms partitioning algorithms Computational complexity Optimization Linear programming Matrix decomposition Multi-view clustering partition learning multiple kernel k -means data fusion

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Advances in Wearable Brain-Computer Interfaces From an Algorithm-Hardware Co-Design Perspective

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第7期69卷 3071-3077页

作者： Byun, Wooseok Je, Minkyu Kim, Ji-Hoon SAPEON Korea Architecture Team Seongnam 13486 South Korea Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 34141 South Korea Ewha Womans Univ Dept Elect & Elect Engn Smart Factory Multidisciplinary Program Seoul 03760 South Korea

Brain-computer interface (BCI), a communication technology between brain and computer developed for a long time since the 1970s, can be incorporated into wearable devices by developing powerful signal processing algorithms and semiconductor technologies. For a satisfactory user experience based on BCI, high information transfer rate and low power consumption should be considered together without losing accuracy. Although many existing BCI algorithms have been mainly focused solely on the accuracy, their deployment on wearable devices is not straightforward due to the limited hardware resources and computational capabilities. This tutorial summarizes recent advances in wearable BCI algorithms and hardware implementations from an algorithm-hardware co-design perspective and discusses future directions.

关键词： Signal processing algorithms partitioning algorithms Hardware Wearable computers Recording Visualization Signal processing Brain-computer interface (BCI) algorithm-hardware co-design domain-specific architecture deep learning accelerator linear algebra accelerator

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A Multi-Label Classification With Hybrid Label-Based Meta-Learning Method in Internet of Things

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IEEE ACCESS 2020年 8卷 42261-42269页

作者： Lin, Sung-Chiang Chen, Chih-Jou Lee, Tsung-Ju Natl Taipei Univ Educ Dept Math & Informat Educ Taipei 10671 Taiwan Natl Penghu Univ Sci & Technol Dept Mkt & Logist Management Magong 88046 Penghu Taiwan Feng Chia Univ Dept Finance Taichung 40724 Taiwan

With the widespread adoption of Internet connected devices and the application of Internet of Things (IoT), more and more research efforts focusing on using machine learning techniques in recognizing activities from IoT sensors, especially in solving multi-label classification problems. Without considering the associations among labels, traditional approaches aim to transform the original multi-label classification problem into several single-label classification problems. The loss of information among labels will damage the classification performance. In this paper, we proposed a novel hybrid label-based meta-learning algorithm for multi-label classification based on an ensemble of a cluster algorithm and generalized linear mixed model (GLMM). In this algorithm, the clustering phase is performed to catch the association among labels and to reduce the computational complexity from vast label subsets simultaneously, and the GLMM phase is performed to solve dependence of a subject with multi-labels in training data. The numerical results show that the proposed algorithm outperforms others, especially for cases with relatively large number of labels.

关键词： Clustering algorithms Classification algorithms partitioning algorithms Internet of Things Machine learning Sensors Decision trees Clustering generalized linear mixed model (GLMM) meta-learning multi-label classification

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Detection of Community Structures in Dynamic Social Networks Based on Message Distribution and Structural/Attribute Similarities

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IEEE ACCESS 2021年 9卷 67028-67041页

作者： Zardi, Hedia Alharbi, Bushra Karamti, Walid Karamti, Hanen Alabdulkreem, Eatedal Qassim Univ Dept Comp Sci Coll Comp Buraydah 51452 Saudi Arabia Qassim Univ Coll Comp BIND Res Grp Buraydah 51452 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Comp Sci Dept Coll Comp & Informat Sci Riyadh 11671 Saudi Arabia

Community detection is a crucial challenge in social network analysis. This task is important because it gives leads to study emerging phenomena. Indeed, it makes it possible to identify the different communities representing individuals with common interests and/or strong connections between them. In addition, it allows tracking the transformation of these communities over time. In this work, we propose a dynamic community detection approach called Attributes, Structure, and Messages distribution-based approach (ASMsg). In addition to the node attributes and the topological structure of the network, we use the rate of transferred messages as the key concept of this approach. Therefore, we obtain communities with similar members that are strongly connected and also frequently interacting. Furthermore, the proposed approach is able to detect all possible communities' transformations even if the communities are overlapped. To demonstrate its efficiency, we widely test ASMsg on artificial and real-world dynamic networks and compare it with representative methods. The results show the superiority of our approach in terms of detected communities.

关键词： Image edge detection Social networking (online) Heuristic algorithms partitioning algorithms Licenses Support vector machines Detection algorithms Community detection dynamic social networks attributed networks community evolution overlapping communities

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Privacy-Preserving Scheme With Account-Mapping and Noise-Adding for Energy Trading Based on Consortium Blockchain

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2022年第1期19卷 569-581页

作者： Zhang, Xiaoyan Jiang, Shunrong Liu, Yiliang Jiang, Tao Zhou, Yong China Univ Min & Technol Dept Informat Sci Xuzhou 221116 Jiangsu Peoples R China Xi An Jiao Tong Univ Sch Cyber Sci & Engn Xian 710049 Peoples R China Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China

The maturity in information technology and new energy technologies enables participants to generate, buy, and sell energy in energy trading systems. Although applying blockchain technology to energy trading has solved some drawbacks in traditional centralized energy systems, the openness and transparency characteristics make the trading records stored on the blockchain vulnerable to data-mining attacks that may cause indispensable privacy leakage. Due to high efficiency and low overhead, noise-addition is an appropriate solution for privacy preservation. Nonetheless, recent research on noise-addition needs to generate massive accounts, which brings a certain amount of waste and inconvenience for later regulation and management. To avoid the aforementioned issues, this paper proposes a consortium blockchain-enabled scheme to ensure the privacy of data stored on the blockchain and resist linking attacks initiated by data mining algorithms. Our scheme utilizes a dynamic partition algorithm to leverage an account mapping algorithm and a virtual token algorithm. Specifically, the account mapping algorithm utilizes a dynamic account allocation method to hide the trading distribution of active users. Furthermore, the virtual token algorithm applies Laplace noise to hide the actual energy consumption of inactive users and curb excessive accounts generation. Finally, we formally demonstrate the privacy and effectiveness of our proposed scheme in security analysis and experiment evaluations.

关键词： Blockchains Privacy Heuristic algorithms Batteries partitioning algorithms Encryption Differential privacy Consortium blockchain neighboring energy trading privacy-preserving account mapping Laplace noise

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A two-dimensional region growing least squares phase unwrapping algorithm for interferometric SAR processing

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1999年第5期37卷 2215-2226页

作者： Fornaro, G Sansosti, E CNR Ist Ric Elettromagnetismo & Componenti Elettron I-80124 Naples Italy

This paper presents a new two-dimensional (2-D) phase unwrapping (PhU) algorithm based on a least squares (LS) region growing strategy: the wrapped phase image is partitioned in different regions that are sequentially unwrapped via a LS algorithm. Reliable regions are dealt with at the beginning of the procedure, while critical areas are unwrapped in the final steps, thus avoiding error propagation from critical to reliable areas. ri conditioned least squares formulation of the phase unwrapping problem is the core of the proposed procedure: this allows the solution to be tied to "some" known boundary phase values, thus guaranteeing the correct joining of the reconstructed phase in between the different regions and preventing them from being independently unwrapped. The application of the finite element method allows a straightforward implementation of the algorithm in the discrete domain case. Experimental results, carried out on simulated and real interferometric SAR data, show the effectiveness of the proposed algorithm and the improved performances with respect to existing unwrapping procedures.

关键词： Least squares methods partitioning algorithms Image reconstruction Phase measurement Synthetic aperture radar interferometry Area measurement Two dimensional displays Finite element methods Instruction sets Synthetic aperture radar

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Latency Minimization of Reverse Offloading in Vehicular Edge Computing

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第5期71卷 5343-5357页

作者： Feng, Weiyang Zhang, Ning Li, Shichao Lin, Siyu Ning, Ruirui Yang, Shuzhong Gao, Yuan Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Peoples R China China Acad Railway Sci Beijing 100081 Peoples R China

Cooperative Vehicle-Infrastructure System (CVIS) can provide innovative services for traffic management and enable trips to be safer, more coordinated, and smarter. In the CVIS, the vehicles upload crowd sensing data to the Vehicular Edge Computing (VEC) server for quick data fusion and informed decision-making. However, with the ever-increasing number of vehicles, the VEC server cannot undertake massive computation-intensive tasks due to the limited edge computing capabilities. In this paper, we propose a reverse offloading framework that can fully utilize the vehicular computation resource to relieve the burden of the VEC server and further reduce the system latency. Under the proposed offloading framework, the binary reverse offloading (BRO) and partial reverse offloading (PRO) strategies are designed for two types of tasks, i.e., non-partitioned tasks and partitioned tasks. We formulate the system latency minimization problem by optimizing reverse offloading decisions, and the communication and computation resources allocation. Due to the non-convex and existing variables coupling, the original problem is transformed into the equivalent weighted-sum optimization problem. Based on the alternative optimization, we decouple the weighted-sum optimization problems into the two subproblems, and the closed-form expressions of transmission power and computation frequency of vehicles and RSU are derived. Low complexity greedy based efficient searching (GES) algorithm and joint alternative optimization based bi-section searching (JAOBSS) algorithm are proposed for BRO and PRO strategies, respectively. The algorithm complexity and performance bounds are analyzed. Simulation results show that the proposed GES algorithm can achieve optimal performance with low complexity. Besides, the proposed GES and JAOBSS algorithms can significantly improve the performance compared with other baseline schemes by 6.14% and 13.46% at least.

关键词： Task analysis Servers Resource management Sensors Edge computing Optimization partitioning algorithms Binary partial reverse offloading latency minimization resource allocation vehicular edge computing

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Block-Sparse Signal Recovery via General Total Variation Regularized Sparse Bayesian Learning

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 1056-1071页

作者： Sant, Aditya Leinonen, Markus Rao, Bhaskar D. Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA Univ Oulu Ctr Wireless Commun Radio Technol Oulu 90570 Finland

One of the main challenges in block-sparse signal recovery, as encountered in, e.g., multi-antenna mmWave channel models, is block-patterned estimation without knowledge of block sizes and boundaries. We propose a novel Sparse Bayesian Learning (SBL) method for block-sparse signal recovery under unknown block patterns. Contrary to conventional approaches that impose block-promoting regularization on the signal components, we apply two classes of hyperparameter regularizers for the SBL cost function, inspired by total variation (TV) denoising. The first class relies on a conventional TV difference unit and allows performing the SBL inference iteratively through a set of convex optimization problems, enabling a flexible choice of numerical solvers. The second class incorporates a region-aware TV penalty to penalize the signal and zero blocks in a dissimilar manner, enhancing the performance. We derive an alternating optimization algorithm based on expectation-maximization to perform the SBL inference through computationally efficient parallel updates for both the regularizer classes. The numerical results show that the proposed TV-regularized SBL algorithm is robust to the nature of the block structure and is capable of recovering signals with both block-patterned and isolated components, proving effective for various signal recovery systems.

关键词： Signal processing algorithms TV Estimation Inference algorithms Bayes methods partitioning algorithms Cost function Compressed sensing block-sparsity sparse Bayesian learning total variation expectation-maximization majorization-minimization cyclic optimization

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CONTEXT-BASED VISION - RECOGNIZING OBJECTS USING INFORMATION FROM BOTH 2-D AND 3-D IMAGERY

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1991年第10期13卷 1050-1065页

作者： STRAT, TM FISCHLER, MA SRI Int. Menlo Park CA USA

This paper describes results from an ongoing project concerned with recognizing objects in complex scene domains, especially in the domain that includes the natural outdoor world. Traditional machine recognition paradigms assume either 1) that all objects of interest are definable by a relatively small number of explicit shape models or 2) that all objects of interest have characteristic, locally measurable features. The failure of both assumptions in a complex domain such as the natural outdoor world has a dramatic impact on the form of an acceptable architecture for an object recognition system. In our work, we make the use of contextual information a central issue, and explicitly design a system to identify and use context as an integral part of recognition. In so doing, we provide a new paradigm for visual recognition that eliminates the traditional dependence on stored geometric models and universal image partitioning algorithms. This paradigm combines the results of many simple procedures that analyze monochrome, color, stereo, or 3-D range images. By interpreting their results along with relevant contextual knowledge, a reliable recognition result is achieved, even in the face of imperfect visual procedures. Initial experimentation with the system on ground-level outdoor imagery has already demonstrated competence beyond what we believe is attainable with other existing vision systems.

关键词： Shape measurement Image color analysis Layout Character recognition Object recognition Algorithm design and analysis Image recognition Solid modeling Context modeling partitioning algorithms INSPEC: Controlled Indexing picture processing pattern recognition INSPEC: Non-Controlled Indexing natural outdoor world context-based vision object recognition 2D imagery complex scene domains

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