scholarly journals PAPR Reduction at Large Multi-User-MIMO-OFDM using Adaptive Data Detection Algorithm

2018 ◽  
Vol 12 (3) ◽  
pp. 1071
Author(s):  
N Praba ◽  
K. M. Ravikumar
Keyword(s):  
Network Architecture ◽  
Large Scale ◽  
Impulse Noise ◽  
Error Correcting Code ◽  
Wireless Systems ◽  
Noise Removal ◽  
Detection Algorithm ◽  
Substantial Improvement ◽  
Papr Reduction ◽  
Data Detection

<span>Wireless communication in present era contains large-scale MIMO network architecture that need to deliver an optimize-QoS to multi-user (MU). <br /> The optimize data rate transmission in massive MU-MIMO wireless systems is one of the most difficult task due to the extremely high implementation complexity. The practical wireless system channels generally exhibits the PAPR and frequency selective fading, it is also necessary to have a precoding solution in PAPR for the selected desirable channels. A solution for the designed problem of a noble error-correcting code for OFDM process with a low PAPR, in the case of impulse noise should be considered. In this paper, Adaptive-Data-detection (ADD) algorithm is proposed to obtain lower-complexity data-detection that corresponds to high throughput design and impulse noise removal for large MUI-MIMO wireless systems by the OFDM modulation technique. That contains some steps such as; initialization, <br /> pre-processing and equalization steps in order to get no performance loss and to minimalize the recurrent amount at each iterations during operation. In order to use simplify model, here we assume suitably perfect synchronization, large cyclic prefix and perfect-CSI (channel-state-information) which has been developed through the pilot depended training. Simulation results analysis show the proposed method substantial improvement over the existing algorithm in terms of both ‘Error-rate’ minimization and PAPR reduction.</span>

A Mixed Denoising Method Based on Median Filter and Lifting Wavelet Technology for Sewage Sensing Signal Treatment

2013 ◽  
Vol 330 ◽  
pp. 967-972 ◽  
Author(s):  
Ai Ai Fan ◽  
Guang Long Wang
Keyword(s):  
Gaussian Noise ◽  
Impulse Noise ◽  
Median Filter ◽  
Noise Removal ◽  
Detection Algorithm ◽  
Signal Acquisition ◽  
Denoising Method ◽  
Lifting Wavelet ◽  
Aeration Volume ◽  
Signal Treatment

Digital signals are often contaminated by noise during signal acquisition and transmission for sewage sensing signal treatment such as aeration volume, oxygen content and water transparency etc. Sometimes, noise is a mixed one of gaussian noise and impulse noise. Unfortunately, existing denoising algorithms are often designed for removing single gaussian noise or impulse noise. In this paper, an efficient algorithm for mixed noise removal in signal is proposed, including space impulse noise removal and wavelet Gaussian noise removal. An impulse noise detection algorithm based on median filter is given to filter impulse signal, also a lifting wavelet was constructed by lifting original wavelet. The threshold based on lifting wavelet transform for signal was applied to denoising gaussian noise. Simulations are conducted on the presented algorithm, and the simulation result shows that this algorithm can remove mixed Gaussian and impulse noise in signal efficiently.

A Novel Edge Detection Algorithm of Color Image

2013 ◽  
Vol 446-447 ◽  
pp. 976-980
Author(s):  
De Rui Song ◽  
Dao Yan Xu ◽  
Li Li
Keyword(s):  
Edge Detection ◽  
Impulse Noise ◽  
Color Image ◽  
Peer Group ◽  
Color Space ◽  
Noise Removal ◽  
Detection Algorithm ◽  
Image Edge Detection ◽  
Image Edge ◽  
Image Edges

This paper proposes a novel algorithm of edge detection using LUV color space. Firstly, according to peer group filtering (PGF), a nonlinear algorithm for image smoothing and impulse noise removal in color image is used. Secondly, color image edges in an image are obtained automatically by combining an improved isotropic edge detector and a fast entropy threshold technique. Thirdly, according to color distance between the pixel and its eight neighbor-pixels, color image edges can further be detected. Finally, the experiment demonstrates the outcome of proposed algorithm in color image edge detection.

scholarly journals Random Valued Impulse Noise Removal Using Region Based Detection Approach

2017 ◽  
Vol 7 (6) ◽  
pp. 2288-2292 ◽  
Author(s):  
S. Banerjee ◽  
A. Bandyopadhyay ◽  
A. Mukherjee ◽  
A. Das ◽  
R. Bag
Keyword(s):  
Impulse Noise ◽  
Noise Removal ◽  
Detection Algorithm ◽  
Density Level ◽  
Detection Scheme ◽  
Impulse Noise Removal ◽  
Detection Approach ◽  
Noisy Pixel ◽  
Noise Density

Removal of random valued noisy pixel is extremely challenging when the noise density is above 50%. The existing filters are generally not capable of eliminating such noise when density is above 70%. In this paper a region wise density based detection algorithm for random valued impulse noise has been proposed. On the basis of the intensity values, the pixels of a particular window are sorted and then stored into four regions. The higher density based region is considered for stepwise detection of noisy pixels. As a result of this detection scheme a maximum of 75% of noisy pixels can be detected. For this purpose this paper proposes a unique noise removal algorithm. It was experimentally proved that the proposed algorithm not only performs exceptionally when it comes to visual qualitative judgment of standard images but also this filter combination outsmarts the existing algorithm in terms of MSE, PSNR and SSIM comparison even up to 70% noise density level.

A Low Cost Image De-noising Implementation Using Low Area CSLA for Impulse Noise Removal

2017 ◽  
Vol 27 (04) ◽  
pp. 1850060
Author(s):  
B. Bommy ◽  
A. Albert Raj
Keyword(s):  
Integrated Circuit ◽  
Medical Image ◽  
Large Scale ◽  
Impulse Noise ◽  
Low Cost ◽  
Noise Removal ◽  
Low Area ◽  
Edge Features ◽  
Reduction Of Area ◽  
Salt And Pepper

In the process of image acquisition and transmission, data can be corrupted by impulse noise. This paper presented the removal of impulse noise in medical image by using Very Large Scale Integrated circuit (VLSI) implementation. The Low Cost Reduced Simple Edge Preserved De-noising (LCRSEPD) technique is introduced using Low Area Carry Select Adder (CSLA) to remove the salt and pepper noise instead of normal adder. Thus, LCRSEPD technique preserves visual performance and edge features in terms of quality and quantitative evaluation. By optimizing the architecture, low cost RSEPD can achieve low computational complexity that will reflect in area, power and delay. Compared to the previous VLSI implementations, the LCRSEPD implementation with CSLA adder has achieved good medical image quality and less hardware cost due to the reduction of area, power and delay.

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

scholarly journals Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

2021 ◽  
Vol 13 (9) ◽  
pp. 5108
Author(s):  
Navin Ranjan ◽  
Sovit Bhandari ◽  
Pervez Khan ◽  
Youn-Sik Hong ◽  
Hoon Kim
Keyword(s):  
Traffic Congestion ◽  
Network Architecture ◽  
Road Network ◽  
Large Scale ◽  
Pattern Analysis ◽  
Image Data ◽  
Urban Life ◽  
Prediction Algorithm ◽  
Rapid Urbanization ◽  
Convolutional Autoencoder

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

scholarly journals SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

2021 ◽  
Vol 40 (3) ◽  
pp. 1-13
Author(s):  
Lumin Yang ◽  
Jiajie Zhuang ◽  
Hongbo Fu ◽  
Xiangzhi Wei ◽  
Kun Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Large Scale ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Structure Information ◽  
Graph Neural Networks ◽  
Node Labels ◽  
Point Level

We introduce SketchGNN , a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph with nodes representing the sampled points along input strokes and edges encoding the stroke structure information. To predict the per-node labels, our SketchGNN uses graph convolution and a static-dynamic branching network architecture to extract the features at three levels, i.e., point-level, stroke-level, and sketch-level. SketchGNN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.2% in the pixel-based metric and 18.2% in the component-based metric over a large-scale challenging SPG dataset) and has magnitudes fewer parameters than both image-based and sequence-based methods.

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