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Optical encryption of hyperspectral images using improved binary tree structure and phase-truncated discrete multiple-parameter fractional Fourier transform

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JOURNAL OF OPTICS 2020年第5期22卷 055701-055701页

作者： Li, Honglin Bai, Xiuying Shan, Mingguang Zhong, Zhi Liu, Lei Liu, Bin Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Heilongjiang Peoples R China

We present an optical encryption scheme for hyperspectral images using an improved binary tree structure (IBTS) and phase-truncated discrete multiple-parameter fractional Fourier transform (PTdmpFrFT). In the IBTS, the encryption modules based on the phase-truncated fractional Fourier transform represented the branch nodes, while the hyperspectral bands of the plain images were considered as leaf nodes. All pairs of bands were encoded into intermediate data using the IBTS, with subsequent encryption into asymmetric ciphertexts using the PTdmpFrFT. The proposed approach generated different pairs of bands with different secret keys, and encryption and decryption paths. Compared with state-of-the-art optical hyperspectral encryption methods, our approach not only achieved superior encryption security but also reduced the computation and storage requirements of the encrypted data. The original hyperspectral image information could be successfully decrypted only when using all the correct keys. Numerical simulations were performed to verify the performance of the proposed method.

关键词： hyperspectral image encryption binary tree structure discrete multiple-parameter fractional fourier transform

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Model Based on the L-System's binary tree structure and Its Application

Model Based on the L-System's Binary Tree Structure and Its ...

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International Conference on Advanced in Control Engineering and Information Science (CEIS)

作者： Song, Jing Kui, Fang Yan, Chen Hunan Agr Univ Changsha 410128 Hunan Peoples R China

Embarked from the definition of the L system, this paper explained the design principle and proposed relation model as well as the corresponding algorithm, taking the binary tree structure as its foundation. Then using the plant heredity between organs as the background, this paper simply introduced the above model applied to prevent and control plant disease, with aim to provide new theory and the method for crops heredity and the prevention of plant diseases. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS 2011]

关键词： binary tree structure L-system fractal Plant model Prevention and control of plant disease

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Model Based on the L-System's binary tree structure and Its ApplicationN

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Procedia Engineering 2011年 15卷 4446-4450页

作者： Jing Song Fang Kui Chen Yan Hunan Agricultural Universitys ChangSha 410128 China ChangSha Universitys ChangSha 410003 Chuna

关键词： binary tree structure L-system fractal Plant model Prevention and control of plant disease

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Partial binary tree fair train scheduling based on supervisory control of discrete event systems

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INTERNATIONAL JOURNAL OF CONTROL 2025年

作者： Wang, Xi Li, Cengkai Wang, Deguang Li, Zhiwu Xidian Univ Sch Electromech Engn Xian Peoples R China Guizhou Univ Sch Elect Engn Guiyang Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa Peoples R China

As railway platforms expand in size, complexity, and traffic volume, ensuring efficiency and safety of train entrance and exit a station becomes increasingly critical. This study presents a scheduling approach for monorail one-way stations that minimises reliance on the train timetable while validating scheduling decisions against it, thereby enhancing the feasibility and safety of the scheduling process. For multi-platform stations, where switches are organised into partial binary trees, this paper introduces a modular discrete event system (DES) modelling approach to design and implement train entrance and exit schedules. The fundamental modelling unit of a multi-platform station consists of a single module, comprising an entrance/exit switch and its associated sensors. The complete DES model of the train station is then constructed as the synchronous product of multiple such modular units. In addressing train scheduling, the proposed approach incorporates fair train entrance/exit specifications, platform capacity constraints, and crossover capacity limitations. Through the supervisory control of DES, an optimal supervisor is synthesised to prevent collision incidents. This innovative methodology and accompanying algorithm facilitate seamless and equitable scheduling for train entrances and exits, without distinguishing between individual trains traversing the station.

关键词： Railway station binary tree structure fair train scheduling safety-critical systems supervisory control theory

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DISTRIBUTED FAULT-TOLERANCE OF tree-structureS

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IEEE TRANSACTIONS ON COMPUTERS 1987年第11期36卷 1378-1382页

作者： HOSSEINI, SH KUHL, JG REDDY, SM UNIV IOWA DEPT ELECT & COMP ENGNIOWA CITYIA 52242

tree structures, as the interconnection structure in networks of many processing elements, have interesting features such as regularity ease of expansion, simple routing, simple addressing, suitability for VLSI/WSI implementation, etc. Distributed fault tolerance of these networks is considered. It is assumed that in these structures, there does not exist any central failure-free entity for providing services such as diagnosis of faulty components, system reconfiguration after failure, control, or coordination among the processing elements. Every processing element is able to diagnose the condition of every other node or internode communication paths via a truly distributed scheme.

关键词： binary tree structure VLSI/WSI implementation distributed fault-diagnosis faulttolerant structure

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binary tree-like network with two-path Fusion Attention Feature for cervical cell nucleus segmentation

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COMPUTERS IN BIOLOGY AND MEDICINE 2019年 108卷 223-233页

作者： Zhang, Jianwei Liu, Zhenmei Du, Bohai He, Junting Li, Guanzhao Chen, Danni South China Univ Technol Sch Comp Sci & Engn Guangzhou Guangdong Peoples R China Guangzhou Univ Tradit Chinese Med Dept Pathol Affiliated Hosp 1 Guangzhou Guangdong Peoples R China

Early detection of cervical lesion is of great significance in reducing mortality from cervical cancer, and segmentation of cervical cell nuclei plays an important role in screening for cervical lesion. Compared with traditional algorithms, several deep learning methods can improve the segmentation;however, due to blurred boundaries and complex gradients of medical images, the segmentation remains unsatisfactory. In addition, the existing datasets used by cervical cell nucleus segmentation research are lacking in terms of both quantity and diversity, so methods based on those datasets cannot effectively cope with challenging cases. This paper releases a new cervical cell dataset and proposes a network named binary tree-like Network with Two-path Fusion Attention Feature (BTTFA). The simplified diagram of BTTFA is similar to a binary tree structure and combines ResNeXt's last four layers of information by integrating adjacent pairs of layers each time;therefore, the information of multilayers can be fully integrated, and the information lost by the pooling layers can be recovered. BTTFA also applies a two-path fusion attention to selectively utilize information close to the root nodes, thereby taking full advantage of low-level detail and high-level semantic information and selectively emphasizing important features while suppressing less useful ones. Meanwhile, at some nodes of the binary tree-like network, focal loss is imposed to calculate the loss between the ground truth and the feature map during the training process. Experiments demonstrate that BTTFA performs better than the existing technology on our dataset and another public dataset.

关键词： Cervical cell nucleus segmentation Convolutional neural network binary tree structure Dataset

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Authentication and Key Agreement Based binary tree for D2D Group Communication 28

Authentication and Key Agreement Based Binary Tree for D2D G...

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28th Iranian Conference on Electrical Engineering (ICEE)

作者： Madanchi, Mehdi Abolhassani, Bahman Iran Univ Sci & Technol IUSTs Sch Elect Engn Tehran Iran

ISBN: (纸本)9781728172965

Emerging device-to-device (D2D) communication in 5th generation (5G) mobile communication networks and internet of things (IoTs) provides many benefits in improving network capabilities such as energy consumption, communication delay and spectrum efficiency. D2D group communication has the potential for improving group-based services including group games and group discussions. Providing security in D2D group communication is the main challenge to make their wide usage possible. Nevertheless, the issue of security and privacy of D2D group communication has been less addressed in recent research work. In this paper, we propose an authentication and key agreement tree group-based (AKATGB) protocol to realize a secure and anonymous D2D group communication. In our protocol, a group of D2D users are first organized in a tree structure, authenticating each other without disclosing their identities and without any privacy violation. Then, D2D users negotiate to set a common group key for establishing a secure communication among themselves. Security analysis and performance evaluation of the proposed protocol show that it is effective and secure.

关键词： Device-to-Device group communication anonymous authentication and key agreement binary tree structure

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A method based on tree-structured Markov random field for forest area classification

A method based on tree-structured Markov random field for fo...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Cuozzo, G D'Elia, C Puzzolo, V Univ Cassino DAEIMI I-03043 Cassino Italy

ISBN: (纸本)0780387422

The forest cover classification is extremely important for land use planning and management. In this framework, the application of pixel based classifications of middle resolution images is well assessed while the usefulness of segmentation processes and object classification is still improving. In this paper, a method based on tree-structured Markov random field (TS-MRF) is applied to Landsat TM images in order to assess the capability of the TS-MRF segmentation algorithm for discriminating forest-non forest covers in a test area located in the Eastern Italian Alps of Trentino. In particular, the regions of interest are selected from the image using a two step process based on a segmentation algorithm and an analysis process. The segmentation is achieved applying a MRF a-prior model, which takes into account the spatial dependencies in the image, and the TS-MRF optimisation algorithm which segments recursively the image in smaller regions using a binary tree structure. The analysis process links to each object identified by the segmentation a set of features related to the geometry (like shape, smoothness, etc.), to the spectral signature and to the neighbour regions (contextual features). These features were used in this study for classifying each object as forest or non-forest thought a simple supervised classification algorithm based on a thresholds built on the feature values obtained from a set of training objects. This method already allowed the detection of the Forest area within the study area with an accuracy of 90%, while better performances could be achieved using more sophisticated classification algorithm, like Neural Networks and Support Vector Machine.

关键词： forestry vegetation mapping terrain mapping geophysical signal processing land use planning image classification image segmentation tree data structures Markov processes feature extraction tree-structured Markov random field forest area classification forest cover classification land use management pixel based classifications middle resolution images object classification Landsat TM images TS-MRF segmentation algorithm forest-nonforest cover discrimination Eastern Italian Alps Trentino Italy image spatial dependencies TS-MRF optimisation algorithm binary tree structure object identification spectral signature contextual features supervised classification algorithm forest area detection neural networks support vector machine Land use planning image segmentation Spectrum signatures wooded area Trentino tree data structures Sorting algorithm object recognition geophysical signal processing Land use management Support Vector Network terrain mapping vegetation mapping Image classification Neural network

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Informative visual words construction to improve bag of words image representation

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IET IMAGE PROCESSING 2014年第5期8卷 310-318页

作者： Farhangi, Mohammad Mehdi Soryani, Mohsen Fathy, Mahmood Iran Univ Sci & Technol Sch Comp Engn Tehran *** Iran

Bag of visual words model has recently attracted much attention from computer vision society because of its notable success in analysing images and exploring their content. This study improves this model by utilising the adjacency information between words. To explore this information, a binary tree structure is constructed from the visual words in order to model the is a relationships in the vocabulary. Informative nodes of this tree are extracted by using the. 2 criterion and are used to capture the adjacency information of visual words. This approach is a simple and computationally effective way for modelling the spatial relations of visual words, which improves the image classification performance. The authors evaluated our method for visual classification of three known datasets: 15 natural scenes, Caltech-101 and Graz-01.

关键词： computer vision image classification image representation informative visual words construction computer vision image representation adjacency information binary tree structure image classification

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An algorithm for designing a pattern classifier by using MDL criterion

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 1996年第6期E79A卷 910-920页

作者： Tsuchiya, H Itoh, S Hashimoto, T Univ of Electro-Communications Chofushi Japan

A algorithm for designing a pattern classifier, which uses MDL criterion and a binary data structure, is proposed. The algorithm gives a partitioning of the range of the multi-dimensional attribute and gives an estimated probability model for this partitioning. The volume of bins in this partitioning is upper bounded by O((log N/N)(K/(K+2))) almost surely, where N is the length of training sequence and K is the dimension of the attribute. The convergence rates of the code length and the divergence of the estimated model are asymptotically upper bounded by O((log N/N)(2/(K+2))). The classification error is asymptotically upper bounded by O((log N/N)(1/(K+2))). Simulation results for 1-dimensional and 2-dimensional attribute cases show that the algorithm is practically efficient.

关键词： classifier noiseless coding MDL criterion binary tree structure context gathering

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