1-bit compressed sensing with edge detection for compressed radio wave data transfer

2012 ◽  
Author(s):  
Takayuki Yamada ◽  
Doohwan Lee ◽  
Hideki Toshinaga ◽  
Kazunori Akabane ◽  
Yo Yamaguchi ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Edge Detection ◽  
Radio Wave ◽  
Data Transfer ◽  
Wave Data

Li-Fi Synchronicity towards 5 G: concepts, challenges and opportunities

2020 ◽  
Vol 1 (4) ◽  
pp. 1-8
Author(s):  
Shilpi Sharma
Keyword(s):  
Radio Wave ◽  
Data Transfer ◽  
Fibre Optics ◽  
Information Rates ◽  
Led Light ◽  
Challenges And Opportunities ◽  
Wireless Transmissions ◽  
At Home

It is quite frustrating when the moderate speed of system prompts restricts availability and long handling hours while utilizing remote web either at home system or coffeehouse or airplane terminal or going after data transfer capacity at a meeting. As more clients are tapped in with their gadgets, the stopped up wireless transmissions make it hard to hook on a dependable sign. Radio wave is by all accounts completely abused and other range should also be investigated. For this reason, fibre optics have been utilized to send information through LED light. D-Light can deliver information rates speedier than 10 super bits for each second, which is far quicker than normal broadband association.

Compressed sensing based CT reconstruction algorithm combined with modified Canny edge detection

2018 ◽  
Vol 63 (15) ◽  
pp. 155011 ◽  
Author(s):  
Chia-Jui Hsieh ◽  
Ta-Ko Huang ◽  
Tung-Han Hsieh ◽  
Guo-Huei Chen ◽  
Kun-Long Shih ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Edge Detection ◽  
Reconstruction Algorithm ◽  
Ct Reconstruction ◽  
Canny Edge Detection ◽  
Canny Edge

The Study of Image Reconstruction Based on Compressed Sensing Theory

2011 ◽  
Vol 127 ◽  
pp. 32-35
Author(s):  
Min Fang ◽  
Yi Min Liu ◽  
Wan Liu ◽  
Hui Chen
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Data Acquisition ◽  
Data Transfer ◽  
Original Signal ◽  
Paper Briefly ◽  
Linear Projection ◽  
Key Technology ◽  
Nyquist Rate ◽  
Simulation Results

Compressed Sensing (Compressed Sensing, referred to as CS) is a new theory of data acquisition technology. On sparse or compressible signals, it can capture and represent the compressible signal at a rate significantly below Nyquist rate and adopt non-adaptive linear projection to keep the information and structure of original signal, and then reconstructs the original signal accurately by solving the optimizational problem. Compressed sensing breaks the bottleneck of the Shannon Theorem because it cuts down the costs of saving and transmission in data transfer. This paper briefly describes theoretical framework and the key technology of the CS theory, focuses on introducing the application in reconstructing image information of CS theory and then makes a simulation using matlab. As expected, the simulation results show that CS can reconstruct the original signal accurately under certain conditions.

scholarly journals A novel framework for compressed sensing brain MRI edge detection and de-noising

2021 ◽  
Author(s):  
Rouzbeh Zamyadi
Keyword(s):  
Compressed Sensing ◽  
Edge Detection ◽  
Noise Reduction ◽  
Wavelet Transforms ◽  
Brain Mri ◽  
Qualitative Evaluation ◽  
Visual Quality ◽  
Edge Preservation ◽  
Image Reconstruction Techniques ◽  
Detection Techniques

In this thesis a novel edge detection technique is developed that employs compressed sensing image reconstruction techniques. The ability of compressed sensing noise reduction is combined with wavelet transforms, acting both as a sparsifying transform as well as an edge detection media. The proposed design was implemented and simulated on a brain phantom. The simulation results were provided for a variety of different sets of variables, and the differences were explained. The results obtained are compared with other edge detection techniques already in use. One important comparison criteria is the visual quality of images; according to which the proposed technique presents improved noise reduction and edge preservation. In addition to qualitative evaluation a method of quantitative measurement based on structural content is also utilized. It is found that the values for such a measure of the proposed method is 1.0755, 1.0174 and 0.5590 for Gaussian, Speckle, and Salt & Pepper noise types respectively. These results indicate that this novel method also improves edge preservation, while the visual quality inspection indicates how much noise has been suppressed.

scholarly journals A novel framework for compressed sensing brain MRI edge detection and de-noising

2021 ◽  
Author(s):  
Rouzbeh Zamyadi
Keyword(s):  
Compressed Sensing ◽  
Edge Detection ◽  
Noise Reduction ◽  
Wavelet Transforms ◽  
Brain Mri ◽  
Qualitative Evaluation ◽  
Visual Quality ◽  
Edge Preservation ◽  
Image Reconstruction Techniques ◽  
Detection Techniques

In this thesis a novel edge detection technique is developed that employs compressed sensing image reconstruction techniques. The ability of compressed sensing noise reduction is combined with wavelet transforms, acting both as a sparsifying transform as well as an edge detection media. The proposed design was implemented and simulated on a brain phantom. The simulation results were provided for a variety of different sets of variables, and the differences were explained. The results obtained are compared with other edge detection techniques already in use. One important comparison criteria is the visual quality of images; according to which the proposed technique presents improved noise reduction and edge preservation. In addition to qualitative evaluation a method of quantitative measurement based on structural content is also utilized. It is found that the values for such a measure of the proposed method is 1.0755, 1.0174 and 0.5590 for Gaussian, Speckle, and Salt & Pepper noise types respectively. These results indicate that this novel method also improves edge preservation, while the visual quality inspection indicates how much noise has been suppressed.

scholarly journals A Descriptive Algorithm for Sobel Image Edge Detection

10.28945/3351 ◽  
2009 ◽  
Author(s):  
Olufunke Vincent ◽  
Olusegun Folorunso
Keyword(s):  
Edge Detection ◽  
Data Transfer ◽  
Data Communication ◽  
Spatial Gradient ◽  
Data Set ◽  
Redundant Data ◽  
Sobel Operator ◽  
Image Edge Detection ◽  
Gradient Magnitude ◽  
Image Edge

Image edge detection is a process of locating the edge of an image which is important in finding the approximate absolute gradient magnitude at each point I of an input grayscale image. The problem of getting an appropriate absolute gradient magnitude for edges lies in the method used. The Sobel operator performs a 2-D spatial gradient measurement on images. Transferring a 2-D pixel array into statistically uncorrelated data set enhances the removal of redundant data, as a result, reduction of the amount of data is required to represent a digital image. The Sobel edge detector uses a pair of 3 x 3 convolution masks, one estimating gradient in the x-direction and the other estimating gradient in y-direction. The Sobel detector is incredibly sensitive to noise in pictures, it effectively highlight them as edges. Hence, Sobel operator is recommended in massive data communication found in data transfer.

scholarly journals Sparse Signal Acquisition via Compressed Sensing and Principal Component Analysis

2018 ◽  
Vol 18 (5) ◽  
pp. 175-182 ◽  
Author(s):  
Imrich Andráš ◽  
Pavol Dolinský ◽  
Linus Michaeli ◽  
Ján Šaliga
Keyword(s):  
Compressed Sensing ◽  
Data Transfer ◽  
Principal Component ◽  
Sparse Signal ◽  
Signal Acquisition ◽  
Embedded Devices ◽  
Reconstructed Signal ◽  
One Step ◽  
Computational Simplicity ◽  
Reduced Data

Abstract This paper presents a way of acquiring a sparse signal by taking only a limited number of samples; sampling and compression are performed in one step by the analog to information conversion. The signal is recovered with minimal information loss from the reduced data record via compressed sensing reconstruction. Several methods of analog to information conversion are described with focus on numerical complexity and implementation in existing embedded devices. Two novel analog to information conversion methods are proposed, distinctive by their computational simplicity - direct subsampling and subsampling with integration. Proposed sensing methods are intended for and evaluated with real water parameter signals measured by a wireless sensor network. Compressed sensing proves to reduce the data transfer rate by >80 % with very little signal processing performed at the sensing side and no appreciable distortion of the reconstructed signal.

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