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Behavior modeling and verification of movement authority scenario of Chinese Train Control System using AADL

Science China(information Sciences) 2015年第11期58卷 125-144页

作者： AHMAD Ehsan DONG YunWei LARSON Brian Lü JiDong TANG Tao ZHAN NaiJun School of Computer Science Northwestern Polytechnical University State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Computing and Information Systems Kansas State University State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University

Train control systems like most digital controllers are, by definition, hybrid systems as they interact with or try to control some aspects of the physical world. Detailed behavior modeling with constraints specification and formal verification, required for reliability prediction, is a great challenge for hybrid system *** control systems further intensify this challenge with extensive interaction between computing units and their physical environment and their mutual dependence on each other. In this paper, we investigate behavior modeling and formal verification of Chinese Train Control System Level 3(CTCS-3) using Architectural Analysis & Design Language(AADL) to cope with this challenge. AADL is an architecture description language for embedded systems and is based on model-based engineering paradigm. Along with structural modeling of embedded systems using the core language constructs, AADL also provides support for language extension through annex sublanguages. In system requirements specification document, the behavior of the CTCS-3 is specified as a set of basic operation scenarios that cooperate with each other to achieve safe and secure functionality of *** Authority(MA) scenario, explored in this paper, is considered as a basic and most crucial scenario to prevent trains from colliding with each other. The detailed discrete behavior of control system is modeled and verified using the Behavior Language for Embedded systems with Software(BLESS) annex sublanguage of AADL, and the continuous behavior of train with the cyber–physical interaction(communication between train and control system) is modeled using the Hybrid annex sublanguage. The behavior of the MA scenario at system level is verified using the Hybrid Hoare Logic theorem prover. Behavior constraints are specified as assertions using first-order logic formulas augmented with a simple temporal operator.

关键词： AADL behavior modeling BLESS annex CTCS hybrid annex train control system

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Efficient 3D Hilbert Curve Encoding and Decoding Algorithms

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Chinese Journal of Electronics 2022年第2期31卷 277-284页

作者： JIA Lianyin LIANG Binbin LI Mengjuan LIU Yong CHEN Yinong DING Jiaman Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Library Yunnan Normal University School of Information Science and Engineering Guangxi University for Nationalities School of Computing Informatics and Decision Systems Arizona State University

Hilbert curve describes a one-to-one mapping between multidimensional space and 1 D *** traditional 3D Hilbert encoding and decoding algorithms work on order-wise manner and are not aware of the difference between different input data and spend equivalent computing costs on them, thus resulting in a low efficiency. To solve this problem, in this paper we design efficient 3D state views for fast encoding and decoding. Based on the state views designed, a new encoding algorithm(JFK-3HE) and a new decoding algorithm(JFK-3HD) are proposed. JFK-3HE and JFK-3HD can avoid executing iteratively encoding or decoding each order by skipping the first 0 s in input data, thus decreasing the complexity and improving the efficiency. Experimental results show that JFK-3HE and JFK-3HD outperform the state-of-the-arts algorithms for both uniform and skew-distributed data.

关键词： order-wise manner multidimensional space encoding state-of-the-arts algorithms encoding algorithm parallel algorithms JFK-3HE statistical distributions decoding solid modelling design efficient 3D JFK-3HD traditional 3D Hilbert encoding equivalent computing costs different input data decoding algorithm Hilbert curve state views iterative methods

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Adaptive Wavelet Filters as Practical Texture Feature Amplifiers for Parkinson’s Disease Screening in OCT

arXiv

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arXiv 2025年

作者： Zhang, Xiaoqing Shi, Hanfeng Li, Xiangyu Ye, Haili Xu, Tao Li, Na Hu, Yan Lv, Fan Chen, Jiangfan Liu, Jiang Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Ophthalmology Optometry and Vision Science Wenzhou Medical University Wenzhou China Centre for Computational Science and Mathematical Modelling Coventry University Coventry United Kingdom School of Computer Science University of Nottingham Ningbo China Ningbo315100 China School of Ophthalmology and Optometry Wenzhou Medical University Wenzhou325035 China Department of Electronic and Information Engineering Changchun University Changchun130022 China

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder globally. The eye’s retina is an extension of the brain and has great potential in PD screening. Recent studies have suggested that texture features extracted from retinal layers can be adopted as biomarkers for PD diagnosis under optical coherence tomography (OCT) images. Frequency domain learning techniques can enhance the feature representations of deep neural networks (DNNs) by decomposing frequency components involving rich texture features. Additionally, previous works have not exploited texture features for automated PD screening in OCT. Motivated by the above analysis, we propose a novel Adaptive Wavelet Filter (AWF) that serves as the Practical Texture Feature Amplifier to fully leverage the merits of texture features to boost the PD screening performance of DNNs with the aid of frequency domain learning. Specifically, AWF first enhances texture feature representation diversities via channel mixer, then emphasizes informative texture feature representations with the well-designed adaptive wavelet filtering token mixer. By combining the AWFs with the DNN stem, AWFNet is constructed for automated PD screening. Additionally, we introduce a novel Balanced Confidence (BC) Loss by mining the potential of sample-wise predicted probabilities of all classes and class frequency prior, to further boost the PD screening performance and trustworthiness of AWFNet. The extensive experiments manifest the superiority of our AWFNet and BC over state-of-the-art methods in terms of PD screening performance and trustworthiness. © 2025, CC BY-NC-SA.

关键词： Neurodegenerative diseases

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A data-centric framework for development and deployment of Internet of Things applications in clouds

A data-centric framework for development and deployment of I...

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Intelligent Sensors, Sensor Networks and information Processing Conference (ISSNIP)

作者： Farzad Khodadadi Rodrigo N. Calheiros Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory The University of Melbourne Australia

As technology develops, more human-made devices are able to make use of Internet to communicate with each other, thus enabling the Internet of Things (IoT) era to emerge. The amount of data IoT entities generate can overwhelm computer infrastructures not prepared for such data deluge and consequent need for more CPU power. cloud computing offers a solution at infrastructure level that alleviates such a problem by offering highly scalable computing platforms that can be configured on demand to meet constant changes of applications requirements in a pay-per-use mode. Current approaches enabling IoT applications are domain-specific or focus only on interaction with sensors, thus they cannot be easily ported to other domains nor provide means of interactions with data sources other than sensors. To address this issue, in this paper we introduce a data-centric framework for development of IoT applications executed in clouds. The framework handles connection to data sources, data filtering, and utilization of cloud resources including provisioning, load balancing, and scheduling, enabling developers to focus on the application logic and therefore facilitating the development of IoT applications. We present a prototype application executing on AWS, built on top of the Aneka cloud Application Platform and present experiments demonstrating the use of our framework for building an application capable of processing input from different sources and analysing them using easy-to-use APIs provided by Aneka.

关键词： Monitoring Prototypes Quality of service Facebook Twitter Global Positioning System

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Heuristic Fuzzy Control of Traffic Junctions in Urban Areas 7

Heuristic Fuzzy Control of Traffic Junctions in Urban Areas

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7th International Conference on Big Data, Knowledge and Control systems Engineering, BdKCSE 2021

作者： Vatchova, Boriana Boneva, Yordanka Gegov, Alexander Institute of Information and Communication Technologies Bulgarian Academy of Sciences Department of Distributed Information and Control Systems Sofia Bulgaria University of Portsmouth School of Computing Portsmouth United Kingdom

ISBN: (纸本)9781665410427

The paper describes the design and implementation of a heuristic fuzzy controller for traffic management in urban areas. The controller uses expert knowledge for the derivation of its fuzzy rules. The proposed controller is simulated and compared to a conventional proportional controller by means of the software platform for traffic management AIMSUN. © 2021 IEEE.

关键词： Controllers

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Gauze:enabling communication-friendly block synchronization with cuckoo filter

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Frontiers of Computer Science 2023年第3期17卷 113-123页

作者： Xiaoqiang DING Liushun ZHAO Lailong LUO Junjie XIE Deke GUO Jinxi LI College of Intelligence and Computing Tianjin UniversityTianjin 300350China School of Computer Science and Technology Xidian UniversityXi’an 710071China Science and Technology on Information Systems Engineering Laboratory National University of Defense TechnologyChangsha 410073China Institute of Systems Engineering AMSPLABeijing 100141China

Block synchronization is an essential component of blockchain ***,blockchain systems tend to send all the transactions from one node to another for ***,such a method may lead to an extremely high network bandwidth overhead and significant transmission *** is crucial to speed up such a block synchronization process and save bandwidth consumption.A feasible solution is to reduce the amount of data transmission in the block synchronization process between any pair of ***,existing methods based on the Bloom filter or its variants still suffer from multiple roundtrips of communications and significant synchronization *** this paper,we propose a novel protocol named Gauze for fast block *** utilizes the Cuckoo filter(CF)to discern the transactions in the receiver’s mempool and the block to verify,providing an efficient solution to the problem of set reconciliation in the P2P(Peer-to-Peer Network)*** up to two rounds of exchanging and querying the CFs,the sending node can acknowledge whether the transactions in a block are contained by the receiver’s mempool or *** on this message,the sender only needs to transfer the missed transactions to the receiver,which speeds up the block synchronization and saves precious bandwidth *** evaluation results show that Gauze outperforms existing methods in terms of the average processing latency(about lower than Graphene)and the total synchronization space cost(about lower than Compact Blocks)in different scenarios.

关键词： block synchronization cuckoo filter probabilistic data structure

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Financial Option Market Model for Federated cloud Environments

Financial Option Market Model for Federated Cloud Environmen...

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IEEE International Conference on Utility and cloud computing (UCC)

作者： Adel Nadjaran Toosi Ruppa K. Thulasiram Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory Department of Computing and Information Systems University of Melbourne Australia Department of Computer Science University of Manitoba Canada

Pay-per-use service by cloud service providers has attracted customers in the recent past and is still evolving. Since the resources being dealt within clouds are non-storable and the physical resources need to be replaced very often, pricing the service in a way that would return profit on the initial capital investments to the service providers has been a major issue. Moreover, to maintain Quality of Service (QoS) to customers who reserve the resources in advance and may or may not be using the resources at a future date makes the resources wasted, if not allocated to other on-demand users. Therefore, a need for a mechanism to guarantee the resources to reserved users whenever they need them, while keeping the resources busy all the time is in very high demand. The concept of federation of cloud service providers has been proposed in the past wherein resources are traded between the providers whenever need arises. We propose a financial option based cloud resources pricing model to address the above situation. This model allows a provider to hedge the critical and risky situation of reserved users requesting the resources while all the resources have been allocated to other users, by trading (buying or outsourcing) resources from other service providers in the cloud federation. We show that using financial option based contracts between cloud providers in a cloud federation, providers are able to enhance profit and acquire the needed resources at any given time. It would also help creating a trust and goodwill from the clients on the cloud service providers by less number of QoS violation.

关键词： Contracts Pricing Quality of service cloud computing Availability Computational modeling Load modeling

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Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision

arXiv

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arXiv 2025年

作者： Han, Xiaofeng Chen, Shunpeng Fu, Zenghuang Feng, Zhe Fan, Lue An, Dong Wang, Changwei Guo, Li Meng, Weiliang Zhang, Xiaopeng Xu, Rongtao Xu, Shibiao The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center China

Robot vision has greatly benefited from advancements in multimodal fusion techniques and vision-language models (VLMs). We systematically review the applications of multimodal fusion in key robotic vision tasks, including semantic scene understanding, simultaneous localization and mapping (SLAM), 3D object detection, navigation and localization, and robot manipulation. We compare VLMs based on large language models (LLMs) with traditional multimodal fusion methods, analyzing their advantages, limitations, and synergies. Additionally, we conduct an in-depth analysis of commonly used datasets, evaluating their applicability and challenges in real-world robotic scenarios. Furthermore, we identify critical research challenges such as cross-modal alignment, efficient fusion strategies, real-time deployment, and domain adaptation, and propose future research directions, including self-supervised learning for robust multimodal representations, transformer-based fusion architectures, and scalable multimodal frameworks. Through a comprehensive review, comparative analysis, and forward-looking discussion, we provide a valuable reference for advancing multimodal perception and interaction in robotic vision. A comprehensive list of studies in this survey is available at https://***/Xiaofeng-Han-Res/MF-RV. Copyright © 2025, The Authors. All rights reserved.

关键词： Supervised learning

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The packing problem of uncertain multicasts

The packing problem of uncertain multicasts

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作者： Ren, Bangbang Guo, Deke Xie, Junjie Li, Wenxin Yuan, Bo Liu, Yunfei Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China School of Computer Science and Technology Dalian University of Technology Dalian China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology Guilin China

Multicast performs better than unicast in delivering the same content from a fixed single source to a set of destinations. Many efforts have been made to optimize such kind of deterministic multicast, such as minimizing the transmission cost of each multicast session. In practice, it is not necessary that the source of each multicast session has to be in a specific location, as long as certain constraints are satisfied. Accordingly, applications usually meet a novel multicast with uncertain sources, ie, uncertain multicast. That is, multiple nodes have the responsibility to act as the root node of a multicast session. Prior proposals have addressed an uncertain multicast by constructing the minimum cost forest. However, it is still unknown how to efficiently share the network resources, when a set of uncertain multicast occupies the network simultaneously. To tackle such a challenging issue, we present the packing problem of uncertain multicasts (MPU) to minimize the total transmission cost, under the constraint of link capacity. We prove that the MPU problem is NP-hard. An intrinsic solution is constructing the minimum cost forest for each uncertain multicast individually. This method, however, is inefficient and may be infeasible because of the constraint of link capacity. Thus, we design 2 dedicated greedy methods, named priority-based and adjusting congested link, to approximate the optimal solution. The comprehensive results indicate that both of our 2 methods can find a feasible solution for the MPU problem. Moreover, given a set of uncertain multicasts, the adjusting congested link method can generate a desired transmission structure for each uncertain multicast and achieve the least total cost when packing them. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Routing algorithms

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Symbiotic evolution of photonics and artificial intelligence: a comprehensive review

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Advanced Photonics 2025年第02期 8-85页

作者： Fu Feng Dewang Huo Ziyang Zhang Yijie Lou Shengyao Wang Zhijuan Gu Dong-Sheng Liu Xinhui Duan Daqian Wang Xiaowei Liu Ji Qi Shaoliang Yu Qingyang Du Guangyong Chen Cuicui Lu Yu Yu Xifeng Ren Xiaocong Yuan Zhejiang Lab Research Center for Frontier Fundamental Studies Westlake Institute for Optoelectronics Zhejiang Key Laboratory of 3D Micro/Nano Fabrication and Characterization Beijing Institute of Technology School of Physics Center for Interdisciplinary Science of Optical Quantum and NEMS Integration Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurements of Ministry of Education Beijing Key Laboratory of Nanophotonics and Ultrafine Optoelectronic Systems Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics University of Science and Technology of China CAS Key Laboratory of Quantum Information University of Science and Technology of China CAS Center for Excellence in Quantum Information and Quantum Physics Zhejiang Lab Research Center for Life Sciences Computing

The rapid advancement of artificial intelligence(AI) has significantly impacted photonics, creating a symbiotic relationship that accelerates the development and applications of both fields. From the perspective of AI aiding photonics, deep-learning methods and various intelligent algorithms have been developed for designing complex photonic structures, where traditional design approaches fall short. AI's capability to process and analyze large data sets has enabled the discovery of novel materials, such as for photovoltaics,leading to enhanced light absorption and efficiency. AI is also significantly transforming the field of optical imaging with improved performance. In addition, AI-driven techniques have revolutionized optical communication systems by optimizing signal processing and enhancing the bandwidth and reliability of data transmission. Conversely, the contribution of photonics to AI is equally profound. Photonic technologies offer unparalleled advantages in the development of AI hardware, providing solutions to overcome the bottlenecks of electronic systems. The implementation of photonic neural networks, leveraging the high speed and parallelism of optical computing, demonstrates significant improvements in the processing speed and energy efficiency of AI computations. Furthermore, advancements in optical sensors and imaging technologies not only enrich AI applications with high-quality data but also expand the capabilities of AI in fields such as autonomous vehicles and medical imaging. We provide comprehensive knowledge and a detailed analysis of the current state of the art, addressing both challenges and opportunities at the intersection of AI and photonics. The multifaceted interactions between AI and photonics will be explored, illustrating how AI has become an indispensable tool in the development of photonics and how photonics, in turn,facilitates advancements in AI. Through a collection of case studies and examples, we underscore the potential

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