Improve Computer Visualization of Architecture Based on the Bayesian Network

Shen, Tao; Nagai, Yukari; Gao, Chan

doi:10.32604/cmc.2019.04876

Cited by 7 publications

(2 citation statements)

References 5 publications

(5 reference statements)

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“…In which encryption and compression are performed as two different stages, the adversary will fissure the encryption system regardless of the compression. On observing the NPCR values of this system, it is weaker against differential attacks also, the compression performance was inimical [8].Some more research work can be addressed in the references [40]- [48]…”

Section: A Compression Before Encryption Systemsmentioning

confidence: 87%

Low Energy Interleaved Chaotic Secure Image Coding Scheme for Visual Sensor Networks Using Pascal’s Triangle Transform

et al. 2021

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The resource-constrained camera integrated Visual Sensor Networks (VSN) have conquered numerous visual aided services from visual surveillance to habitat monitoring. VSN is capable of sensing, processing and communicating visual data wirelessly. These networks are built with inexpensive low power sensor motes with a lightweight processor, limited storage, and bandwidth. The huge amount of redundancy present in the images makes the processing and communication consume more energy than expected. The number of bits must be reduced using energy-efficient compression techniques for efficient transmission. Low computational energy and communication energy are always favored for an increased lifetime of the wireless sensor network. The highly sensitive and self-descriptive nature of images makes security in VSN even more critical. In this work, we propose an energy-efficient low bitrate secured image coder for resource-constrained VSN. Light weight design protocols are highly required in secured image transmission over VSN. Through this communication, we also propose a novel chaotic map using Pascal's triangle. The system follows a unique interleaved fashion of compression and encryption process to consume less computational resources. A series of tests were carried out to validate the secured image coder's ruggedness and its suitability in VSN. The performance and the strength of the low bitrate secured image coder are tested with compression efficiency and cryptanalysis tests. Simulations were carried out in Atmel's ATmega128 processor for energy consumption analysis. The energy consumed by the proposed system for compression, encryption and transmission of an image of size 512 x 512 is 109.364mJ (milli Joules), which is only 4.57 % of the energy consumed by raw image transmission. In addition, the system is implemented in real time image sensor platform based on Arduino Due board integrated with OV7670 camera module for real time verification and the experimental results validated the suitability of the indicated scheme in real time VSN. INDEX TERMS VSN, low bitrate, secured image coding, chaotic map, image compression, encryption I. INTRODUCTION T HE modern advancements in MEMS, CMOS hardware technologies embraced with communication technologies empowered image communication over low power Wireless Sensor Networks (WSN) and evolved as Visual Sensor Networks (VSN). Due to the significance of VSN, image communication has reached potential applications, including but not limited to habitat monitoring, critical infrastructure monitoring, surveillance, industrial control systems, smart home systems, traffic monitoring [1]-[3]. Besides, VSN is a resource-constrained system in terms of both hardware and power. Due to the volume of image data, low-power sensor nodes are more energy-constrained in processing and transmitting the image data. Thus, to increase the network's

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Section: A Compression Before Encryption Systemsmentioning

confidence: 87%

Low Energy Interleaved Chaotic Secure Image Coding Scheme for Visual Sensor Networks Using Pascal’s Triangle Transform

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Traditional features extraction methods rely on eigenvalue function to screen eigenvalues as features [3][4][5]. Traditional classification models include Decision Tree [6][7], Bayesian classifier [8][9], Support Vector Machine [10][11], etc. However, the traditional numerical representation method has two major problems: semantic gap and dimension explosion; the traditional feature extraction method has poor ability to identify typical features; and the traditional classification models relies heavily on specific tasks, and the text association relationship processing is rough [12].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Mapping Method of Intelligent Recorder Configuration Datasets Based on Chinese Semantic Deep Learning

Ren

Gen

et al. 2020

IEEE Access

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Mapping the Intelligent Electronic Devices (IEDs) output interface address description datasets to the intelligent recorder is the groundwork for the recorder to accurately collect IEDs' operation information. These datasets, which are also intelligent recorder configuration datasets, are included in the Substation Configuration Description (SCD) file of an intelligent substation. The mainstream mapping method is manually mapping these datasets based on output interface Chinese description texts. When the number of IEDs is extremely large, the manual operation often takes a huge amount of time, together with the higher labor cost. And since the Chinese description texts have a certain degree of irregularity, it also poses a problem for the automatic mapping of the datasets. Aiming at this problem, this paper proposes an automatic mapping method of IEDs configuration datasets based on a deep learning framework-Dynamic Convolutional Neural Network (DCNN). Firstly, it uses the word representation model Word2vec to vectorize words in Chinese description texts as well as their semantics relationships. Then word vectors will be imported in the DCNN, which, based on its multilayer abstract learning characteristics of typical sample features, can perform semantic law mining and automatic mapping. The configuration datasets of intelligent recorder will be automatically mapped based on the Chinese descriptions mapping result. The Practical example shows that the Chinese description texts classification method based on the Dynamic Convolutional Neural Network model has strong semantic analysis ability and high classification accuracy, which effectively improves the accuracy of automatic mapping of intelligent recorder configuration data.

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Design and Implementation of Heterogeneous Identity Alliance Risk Assessment System

Gan

Sheng

Zhang

et al. 2020

Lecture Notes in Computer Science

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Improve Computer Visualization of Architecture Based on the Bayesian Network

Cited by 7 publications

References 5 publications

Low Energy Interleaved Chaotic Secure Image Coding Scheme for Visual Sensor Networks Using Pascal’s Triangle Transform

Low Energy Interleaved Chaotic Secure Image Coding Scheme for Visual Sensor Networks Using Pascal’s Triangle Transform

An Automatic Mapping Method of Intelligent Recorder Configuration Datasets Based on Chinese Semantic Deep Learning

Design and Implementation of Heterogeneous Identity Alliance Risk Assessment System

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