检索结果-内蒙古大学图书馆

A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Zhang, Jingang Su, Runmu Fu, Qiang Ren, Wenqi Heide, Felix Nie, Yunfeng School of Future Technology University of Chinese Academy of Sciences Beijing100039 China School of Computer Science and Technology Xidian University Xi’an710071 China King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Computational Imaging Lab Princeton University PrincetonNJ08544 United States Department of Applied Physics and Photonics Vrije Universiteit Brussel Brussels1050 Belgium

Hyperspectral imaging enables many versatile applications for its competence in capturing abundant spatial and spectral information, which is crucial for identifying substances. However, the devices for acquiring hyperspectral images are typically expensive and very complicated, hindering the promotion of their application in consumer electronics, such as daily food inspection and point-of-care medical screening, etc. Recently, many computational spectral imaging methods have been proposed by directly reconstructing the hyperspectral information from widely available RGB images. These reconstruction methods can exclude the usage of burdensome spectral camera hardware while keeping a high spectral resolution and imaging performance. We present a thorough investigation of more than 25 state-of-the-art spectral reconstruction methods which are categorized as prior-based and data-driven methods. Simulations on open-source datasets show that prior-based methods are more suitable for rare data situations, while data-driven methods can unleash the full potential of deep learning in big data cases. We have identified current challenges faced by those methods (e.g., loss function, spectral accuracy, data generalization) and summarized a few trends for future work. With the rapid expansion in datasets and the advent of more advanced neural networks, learnable methods with fine feature representation abilities are very promising. This comprehensive review can serve as a fruitful reference source for peer researchers, thus paving the way for the development of computational hyperspectral imaging. Copyright © 2021, The Authors. All rights reserved.

关键词： Hyperspectral imaging

Integrating privacy enhancing techniques into blockchains using sidechains

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Parizi, Reza M. Homayoun, Sajad Yazdinejad, Abbas Dehghantanha, Ali Choo, Kim-Kwang Raymond Department of Software Engineering and Game Development Kennesaw State University GA United States Department of Computer Engineering and Information Technology Shiraz University of Technology Shiraz Iran Faculty of Computer Engineering University of Isfahan School of Computer Science University of Guelph ON Canada Department of Information Systems and Cyber Security University of Texas San AntonioTX United States

Blockchains are turning into decentralized computing platforms and are getting worldwide recognition for their unique advantages. There is an emerging trend beyond payments that blockchains could enable a new breed of decentralized applications, and serve as the foundation for Internet's security infrastructure. The immutable nature of the blockchain makes it a winner on security and transparency;it is nearly inconceivable for ledgers to be altered in a way not instantly clear to every single user involved. However, most blockchains fall short in privacy aspects, particularly in data protection. Garlic Routing and Onion Routing are two of major Privacy Enhancing Techniques (PETs) which are popular for anonymization and security. Garlic Routing is a methodology using by I2P Anonymous Network to hide the identity of sender and receiver of data packets by bundling multiple messages into a layered encryption structure. The Onion Routing attempts to provide lowlatency Internet-based connections that resist traffic analysis, deanonymization attack, eavesdropping, and other attacks both by outsiders (e.g. Internet routers) and insiders (Onion Routing servers themselves). As there are a few controversies over the rate of resistance of these two techniques to privacy attacks, we propose a PET-Enabled Sidechain (PETES) as a new privacy enhancing technique by integrating Garlic Routing and Onion Routing into a Garlic Onion Routing (GOR) framework suitable to the structure of blockchains. The preliminary proposed GOR aims to improve the privacy of transactions in blockchains via PETES structure. Copyright © 2019, The Authors. All rights reserved.

关键词： Blockchain

The impact of AI on engineering design procedures for dynamical systems

学校读者我要写书评

暂无评论

arXiv 2024年

作者： de Payrebrune, Kristin M. Flaßkamp, Kathrin Ströhla, Tom Sattel, Thomas Bestle, Dieter Röder, Benedict Eberhard, Peter Peitz, Sebastian Stoffel, Marcus Rutwik, Gulakala Aditya, Borse Wohlleben, Meike Sextro, Walter Raff, Maximilian Remy, C. David Yadav, Manish Stender, Merten van Delden, Jan Lüddecke, Timo Langer, Sabine C. Schultz, Julius Blech, Christopher RPTU Kaiserslautern-Landau Institute for Computational Physics in Engneering Gottlieb-Daimler-Straße 74 Kaiserslautern67663 Germany Saarland University Systems Engineering Systems Modeling and Simulation Campus Saarbrücken66123 Germany TU Ilmenau Mechatronics group Max-Planck-Ring 12 Ilmenau98693 Germany Brandenburg University of Technology Cottbus-Senftenberg Chair of Engineering Mechanics and Vehicle Dynamics Siemens-Halske-Ring 14 Cottbus03046 Germany University of Stuttgart Institute of Engineering and Computational Mechanics Pfaffenwaldring 9 Stuttgart70569 Germany TU Dortmund Department of Computer Science & Lamarr Institute Joseph-von-Fraunhofer-Str. 25 Dortmund44227 Germany RWTH Aachen University Institute of General Mechanics Eilfschornsteinstraße 18 Aachen52062 Germany Paderborn University Faculty of Mechanical Engineering Dynamics and Mechatronics Warburger Str. 100 Paderborn33098 Germany University of Stuttgart Institute for Nonlinear Mechanics Pfaffenwaldring 9 Stuttgart70569 Germany Technische Universität Berlin Chair of Cyber-Physical Systems in Mechanical Engineering Straße des 17. Juni 135 Berlin10623 Germany University of Göttingen Institute of Informatics Goldschmidtstr. 7 Göttingen37077 Germany Technische Universität Braunschweig Institute for Acoustics and Dynamics Langer Kamp 19 Braunschweig38106 Germany

Artificial intelligence (AI) is driving transformative changes across numerous fields, revolutionizing conventional processes and creating new opportunities for innovation. The development of mechatronic systems is undergoing a similar transformation. Over the past decade, modeling, simulation, and optimization techniques have become integral to the design process, paving the way for the adoption of AI-based methods. In this paper, we examine the potential for integrating AI into the engineering design process, using the V-model from the VDI guideline 2206, considered the state-of-the-art in product design, as a foundation. We identify and classify AI methods based on their suitability for specific stages within the engineering product design workflow. Furthermore, we present a series of application examples where AI-assisted design has been successfully implemented by the authors. These examples, drawn from research projects within the DFG Priority Program SPP 2353: Daring More Intelligence - Design Assistants in Mechanics and Dynamics, showcase a diverse range of applications across mechanics and mechatronics, including areas such as acoustics and robotics. © 2024, CC BY.

关键词： Mechatronics

Privacy-Preserving Location-Based Query Over Encrypted Data in Outsourced Environment

学校读者我要写书评

暂无评论

Privacy-Preserving Location-Based Query Over Encrypted Data ...

IEEE International Conference on Data Science in cyberspace (DSC)

作者： Huijuan Lian Weidong Qiu Di Yan Zheng Huang Peng Tang School of Cyber Security Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

The location-based services in the outsourced environment has become a research hotspot with the rise of cloud computing. Meanwhile, various privacy issues have been increasingly prominent. We propose an efficient spatial query protocol and another novel secure spatial query protocol using fully homomorphic encryption to accomplish privacy-preserving query processing on outsourced data. We adopt the Moore curve to transform the spatial data into one-dimensional sequence and utilize the random permutation to protect the location privacy. Furthermore, the proposed efficient scheme provides the considerable performance with data privacy preservation. Meanwhile, the secure spatial query protocol is robust against various attacks. Experiments show that the proposed schemes achieve high accuracy while improving the security when compared with the existing related approaches.

关键词： Encryption Data privacy Protocols Privacy Gold

Hierarchical bi-directional self-attention networks for paper review rating recommendation

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Deng, Zhongfen Peng, Hao Xia, Congying Li, Jianxin He, Lifang Yu, Philip S. Department of Computer Science University of Illinois at Chicago ChicagoIL United States Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Cyber Science and Technology Beihang University Beijing China Department of Computer Science and Engineering Lehigh University BethlehemPA United States

Review rating prediction of text reviews is a rapidly growing technology with a wide range of applications in natural language processing. However, most existing methods either use handcrafted features or learn features using deep learning with simple text corpus as input for review rating prediction, ignoring the hierarchies among data. In this paper, we propose a Hierarchical bi-directional self-attention Network framework (HabNet) for paper review rating prediction and recommendation, which can serve as an effective decision-making tool for the academic paper review process. Specifically, we leverage the hierarchical structure of the paper reviews with three levels of encoders: sentence encoder (level one), intra-review encoder (level two) and inter-review encoder (level three). Each encoder first derives contextual representation of each level, then generates a higher-level representation, and after the learning process, we are able to identify useful predictors to make the final acceptance decision, as well as to help discover the inconsistency between numerical review ratings and text sentiment conveyed by reviewers. Furthermore, we introduce two new metrics to evaluate models in data imbalance situations. Extensive experiments on a publicly available dataset (PeerRead) and our own collected dataset (OpenReview) demonstrate the superiority of the proposed approach compared with state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal encoding

An improvement of PAA on trend-based approximation for time series

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Zhang, Chunkai Chen, Yingyang Yin, Ao Qin, Zhen Zhang, Xing Zhang, Keli Jiang, Zoe L. Department of Computer Science and Technology Harbin Institute of Technology Shenzhen China Engineering Laboratory for Big Data Collaborative Security Technology

Piecewise Aggregate Approximation (PAA) is a competitive basic dimension reduction method for high-dimensional time series mining. When deployed, however, the limitations are obvious that some important information will be missed, especially the trend. In this paper, we propose two new approaches for time series that utilize approximate trend feature information. Our first method is based on relative mean value of each segment to record the trend, which divide each segment into two parts and use the numerical average respectively to represent the trend. We proved that this method satisfies lower bound which guarantee no false dismissals. Our second method uses a binary string to record the trend which is also relative to mean in each segment. Our methods are applied on similarity measurement in classification and anomaly detection, the experimental results show the improvement of accuracy and effectiveness by extracting the trend feature suitably. Copyright © 2019, The Authors. All rights reserved.

关键词： Time series

Dynamic data fusion using multi-input models for malware classification

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Zenkov, Viktor Laska, Jason Department of Computer Science and Electrical Engineering University of Tennessee Knoxville United States Cyber and Information Security Research Group Oak Ridge National Laboratory

Floor Based Sensors Walk Identification System Using Dynamic Time Warping with Cloudlet Support

学校读者我要写书评

暂无评论

Floor Based Sensors Walk Identification System Using Dynamic...

International Conference on Semantic Computing

作者： Robert Hughes Fadi Muheidat Mason Lee Lo'ai A. Tawalbeh Electrical and Computer Engineering Department Univeristy of the Pacific Stockton CA USA Department of Computing and Cyber Security Texas A&M University San Antonio TX USA

Current advances in sensor design technologies and computing power (computational and artificial intelligence) have made it possible to build smart assistive living systems that can improve the lives of people. Because older adults want to age in place at home, there is a need to monitor their health status, detect emergency situations, and notify health care providers. We have improved a floor based monitoring system, which we call the smart carpet, originally to detect falls, but we can take advantage of the continual 24/7 monitoring capability to get important information on gait, fall detection, counting the number of people traversing the carpet and studied the waveform for useful information. Recently, we studied the characteristics of the waveform of the scavenged signal from the sensors and used computational intelligence and feature extractions and classifications to separate people. In this paper, we used Dynamic Time Warping (DTW) to help improve on walk identification, compared with the MFCC feature extraction methods. Results showed that our system identifies walks using a dynamic time warping algorithm and KNN classifier with 86% precision, 76% recall, 81% accuracy. We also present a cooperative cloudlet mobile computing model for eldercare and medical applications where the decisions are very time sensitive. The sensors data will be sent to the nearest cloudlet for analysis and extracting real-time decisions in minimal delay. Users can obtain these results and make decisions by accessing the cloud through their mobile devices and in a real time manner.

关键词： Cloud computing Heuristic algorithms Intelligent sensors Time series analysis Sensor systems Task analysis

Detecting Bogus Information Attack in Vehicular Ad Hoc Network: A Context-Aware Approach 16

学校读者我要写书评

暂无评论

Detecting Bogus Information Attack in Vehicular Ad Hoc Netwo...

16th International Learning and technology Conference, L and T 2019

作者： Ghaleb, Fuad A. Zainal, Anazida Maroof, Mohd Aizaini Rassam, Murad A. Saeed, Faisal Cybersecurity X Lap Information Assurance and Security Research Group School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor81310 Malaysia Department of Computer and Electronic Engineering Sana'a Community College Sana'a5695 Yemen Faculty of Engineering and Information Technology Taiz University Taiz6803 Yemen College of Computer Science and Engineering University of Taibah Medina Saudi Arabia

Most of Vehicular Ad Hoc Network's (VANET) and Fly Ad Hoc Network (FANET) applications, protocols, and service rely on cooperativeness among network participants. Vehicles, could be unmanned aerial vehicles (e.g. drones), share their sensors' information to improve network performance and provide safety, traffic efficiency, and entertainments. The accuracy and reliability of this information are vital for VANET performance. However, misbehaving (or faulty) vehicles, which exploit the cooperativeness feature by sharing bogus information, can disrupt the fundamental operations of any potential application and cause loss of people lives and properties. Unfortunately, existing detection approaches cannot effectively thwart such attacks. The highly dynamic vehicular context has been vastly neglected as well as they rely on predefined and static security perimeters to differentiate between false and correct information. In this paper, a context-aware detection approach is proposed to improve the detection accuracy and reduce the false alarm rate. Firstly, the features that well represent the vehicular context has been extracted. Then, an online unsupervised learning method namely the hierarchical clustering algorithm is used to classify the received messages into either genuine or spurious. Finally, Bayesian-based hypothesis testing has been used to confirm the validity of the classification. Results show that the proposed detection approach is promising in effectively detect bogus information attack and improve applications performance. © 2019 The Authors. Published by Elsevier B.V.

关键词： Vehicular ad hoc networks