scholarly journals A Watermark Approach for Image Transmission: Implementation of Channel Coding Technique with Security

2021 ◽  
Vol 12 (3) ◽  
pp. 3976-3984
Author(s):  
G.Aparna Et.al
Keyword(s):  
Channel Coding ◽  
Wireless Channels ◽  
Hardware Implementation ◽  
Image Data ◽  
Image Transmission ◽  
Timing Constraints ◽  
Computer Engineering ◽  
And Performance ◽  
The One ◽  
Turbo Encoder

In this paper an approach for secured digital image transmission with watermark is being proposed.  The tremendous growth in technology for various applications demand secured communications across the wireless channels. Secured image transmission is the one of the prominent process in digital communication applications. A watermark is embedded in to the image data that is to be protected from unauthorized users.  The cryptographic algorithms chosen for secured transmission led to the need for hardware implementation.  In the process of secured image transmission turbo encoder is proposed for error correction.  The proposed approach is realized in terms of hardware for the digital logic size, area and power consumption using Xilinx 14.2 software. Synthesizing and implementation of verilog code on the target device xc6slx150-2fgg484 for timing constraints, device utilization and performance details. © 2020 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Mechanical, Electronics and Computer Engineering

Data Transmission Oriented on the Object, Communication Media, Application, and State of Communication Systems

2013 ◽  
pp. 117-132
Author(s):  
Mike Sabelkin ◽  
François Gagnon
Keyword(s):  
Data Transmission ◽  
Channel Coding ◽  
Communication Systems ◽  
Reference Model ◽  
Image Transmission ◽  
Wireless Channel ◽  
Communication Media ◽  
Fast Analysis ◽  
Open Systems Interconnection ◽  
The One

The proposed communication system architecture is called TOMAS, which stands for data Transmission oriented on the Object, communication Media, Application, and state of communication Systems. TOMAS could be considered a Cross-Layer Interface (CLI) proposal, since it refers to multiple layers of the Open Systems Interconnection Basic Reference Model (OSI). Given particular scenarios of image transmission over a wireless LOS channel, the wireless TOMAS system demonstrates superior performance compared to a JPEG2000+OFDM system in restored image quality parameters over a wide range of wireless channel parameters. A wireless TOMAS system provides progressive lossless image transmission under influence of moderate fading without any kind of channel coding and estimation. The TOMAS system employs a patent pending fast analysis/synthesis algorithm, which does not use any multiplications, and it uses three times less real additions than the one of JPEG2000+OFDM.

scholarly journals Secure image transmission over wireless network

2018 ◽  
Vol 7 (4) ◽  
pp. 2758
Author(s):  
Salah A. Aliesawi ◽  
Dena S. Alani ◽  
Abdullah M. Awad
Keyword(s):  
Turbo Codes ◽  
Channel Coding ◽  
Communication Systems ◽  
Wireless Channels ◽  
Image Transmission ◽  
Signal Propagation ◽  
Data Encryption ◽  
Error Rates ◽  
Delay Spread ◽  
High Level

The advances recently seen in data compression, and communication systems, have made it viable to design wireless image transmission systems. For many applications such as confidential transmission, military and medical applications, data encryption techniques should be also used to protect the confidential data from intruders. For these applications, both encryption and compression need to be performed to transmit a message in a fast and secure way. Further, the wireless channels have fluctuating channel qualities and high bit error rates. In this paper, a new scheme based on encryption and channel coding has been proposed for secure image transmission over wireless channels. In the proposed scheme, the encryption process is based on keys generator and Chaotic Henon map. Turbo codes are utilized as channel coding to deal effectively with the channel errors, multipath signal propagation and delay spread. Simulation results show that the proposed system achieves a high level of robustness against wide different of attacks and channel impairments. Further, it improves image quality with acceptable data rates. 

scholarly journals Simplified on-The-Fly IAG QPP Interleaver for Turbo Channel Coding Scheme

2019 ◽  
Vol 8 (3) ◽  
pp. 1443-1448
Keyword(s):  
Computational Complexity ◽  
Channel Coding ◽  
Cmos Technology ◽  
Timing Constraints ◽  
Efficient Design ◽  
Turbo Decoder ◽  
Design And Implementation ◽  
Coding Scheme ◽  
Turbo Encoder ◽  
Block Sizes

Interleaver is an indispensable component in the design of Turbo encoder and Turbo Decoder. QPP interleaver is a 3GPP specified conflict free interleaver for turbo channel coding scheme for all code block sizes of 40 to 6144. Thus the efficient design of a conflict free reconfigurable QPP interleaver for turbo encoder and turbo decoder is a pre-eminent task in turbo channel coding scheme. In this article, Design of a simplified reconfigurable (40 to 6144 block sizes) Recursive QPP interleaver for computation of address locations to minimize the computational complexity and to avoid storage of interleaver tables has been presented. The proposed interleaver will be further integrated in the design and implementation of high throughput parallel turbo decoder. The proposed design is synthesized and implemented using 28nm CMOS technology Zynq Zed FPGA and achieved low processing timing constraints, utilization and power constraints compared with other conventional designs.

Fast and Robust Dictionary-based Classification for Image Data

2021 ◽  
Vol 15 (6) ◽  
pp. 1-22
Author(s):  
Shaoning Zeng ◽  
Bob Zhang ◽  
Jianping Gou ◽  
Yong Xu ◽  
Wei Huang
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Image Data ◽  
Robust Classification ◽  
Theoretical System ◽  
Learned Dictionary ◽  
And Performance ◽  
The One ◽  
New Formulation ◽  
Two Stages

Dictionary-based classification has been promising in knowledge discovery from image data, due to its good performance and interpretable theoretical system. Dictionary learning effectively supports both small- and large-scale datasets, while its robustness and performance depends on the atoms of the dictionary most of the time. Empirically, using a large number of atoms is helpful to obtain a robust classification, while robustness cannot be ensured when setting a small number of atoms. However, learning a huge dictionary dramatically slows down the speed of classification, which is especially worse on the large-scale datasets. To address the problem, we propose a Fast and Robust Dictionary-based Classification (FRDC) framework, which fully utilizes the learned dictionary for classification by staging - and -norms to obtain a robust sparse representation. The new objective function, on the one hand, introduces an additional -norm term upon the conventional -norm optimization, which generates a more robust classification. On the other hand, the optimization based on both - and -norms is solved in two stages, which is much easier and faster than current solutions. In this way, even when using a limited size of dictionary, which makes sure the classification runs very fast, it still can gain higher robustness for multiple types of image data. The optimization is then theoretically analyzed in a new formulation, close but distinct to elastic-net, to prove it is crucial to improve the performance under the premise of robustness. According to our extensive experiments conducted on four image datasets for face and object classification, FRDC keeps generating a robust classification no matter whether using a small or large number of atoms. This guarantees a fast and robust dictionary-based image classification. Furthermore, when simply using deep features extracted via some popular pre-trained neural networks, it outperforms many state-of-the-art methods on the specific datasets.

Sea Urchins and Circuses: The Modernist Natural Histories of Jean Painlevé and Alexander Calder

2018 ◽  
Vol 13 (2) ◽  
pp. 187-211
Author(s):  
Patricia E. Chu
Keyword(s):  
New Media ◽  
Sea Urchins ◽  
Life Sciences ◽  
Three Dimensional ◽  
Machine Age ◽  
Avant Garde ◽  
History Of ◽  
And Performance ◽  
The One ◽  
Natural Historian

The Paris avant-garde milieu from which both Cirque Calder/Calder's Circus and Painlevé’s early films emerged was a cultural intersection of art and the twentieth-century life sciences. In turning to the style of current scientific journals, the Paris surrealists can be understood as engaging the (life) sciences not simply as a provider of normative categories of materiality to be dismissed, but as a companion in apprehending the “reality” of a world beneath the surface just as real as the one visible to the naked eye. I will focus in this essay on two modernist practices in new media in the context of the history of the life sciences: Jean Painlevé’s (1902–1989) science films and Alexander Calder's (1898–1976) work in three-dimensional moving art and performance—the Circus. In analyzing Painlevé’s work, I discuss it as exemplary of a moment when life sciences and avant-garde technical methods and philosophies created each other rather than being classified as separate categories of epistemological work. In moving from Painlevé’s films to Alexander Calder's Circus, Painlevé’s cinematography remains at the forefront; I use his film of one of Calder's performances of the Circus, a collaboration the men had taken two decades to complete. Painlevé’s depiction allows us to see the elements of Calder's work that mark it as akin to Painlevé’s own interest in a modern experimental organicism as central to the so-called machine-age. Calder's work can be understood as similarly developing an avant-garde practice along the line between the bestiary of the natural historian and the bestiary of the modern life scientist.

scholarly journals Deep Learning Approach for Vibration Signals Applications

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3929
Author(s):  
Han-Yun Chen ◽  
Ching-Hung Lee
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Parameters Optimization ◽  
Frequency Spectra ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Time Frequency ◽  
And Performance ◽  
The One ◽  
Short Time

This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.

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