scholarly journals FPGA Based Optimized Reconfigurable Base-2 Constant Coefficient Multiplier Architecture for Image Filtering

2019 ◽  
Vol 8 (6S2) ◽  
pp. 822-825
Keyword(s):  
Computational Complexity ◽  
Reconfigurable Computing ◽  
Constant Coefficient ◽  
Noise Removal ◽  
Practical Implementation ◽  
Image Processing Algorithm ◽  
Embedded Computer Vision ◽  
Embedded Computer ◽  
Coefficient Multiplier ◽  
Computational Requirement

Image convolution using FPGA has been comprehensively used for noise removal of Reconfigurable computing based image Processing Algorithm. Particularly these filters are widely used in embedded computer vision applications like edge detection and Feature extraction analysis. Practical implementation of filter requires enormous computational requirement. The multiplier plays very important role in the image convolution. The existed multiplier design requires more computational complexity for the 3x3 test image. For this the proposed reconfigurable constant coefficient multiplier uses base-2 Common sub expression algorithm. which reduces the computational complexity in a better way. The proposed 2D-convolution in image application is the value of resultant output is multiplication of image pixel with corresponding kernel value. In this work the realization of 2D convolution to be done using proposed constant coefficient multiplier and analyzed using Xilinx Virtex-5 FPGA platform

Tailoring design for embedded computer vision applications

Computer ◽  
2015 ◽  
Vol 48 (5) ◽  
pp. 58-62 ◽  
Author(s):  
Jason Schlessman ◽  
Marilyn Wolf
Keyword(s):  
Computer Vision ◽  
Embedded Computer Vision ◽  
Embedded Computer ◽  
Computer Vision Applications

scholarly journals Image Noise Removal Method Based on Improved Nonlocal Mean Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Keya Huang ◽  
Hairong Zhu
Keyword(s):  
Signal To Noise Ratio ◽  
Median Filter ◽  
Visual Quality ◽  
Noise Removal ◽  
Interactive Systems ◽  
Image Processing Algorithm ◽  
Signal To Noise ◽  
Adaptive Median Filter ◽  
Mixed Noise ◽  
Filtering Effect

Aiming at the problem of unclear images acquired in interactive systems, an improved image processing algorithm for nonlocal mean denoising is proposed. This algorithm combines the adaptive median filter algorithm with the traditional nonlocal mean algorithm, first adjusts the image window adaptively, selects the corresponding pixel weight, and then denoises the image, which can have a good filtering effect on the mixed noise. The experimental results show that, compared with the traditional nonlocal mean algorithm, the algorithm proposed in this paper has better results in the visual quality and peak signal-to-noise ratio (PSNR) of complex noise images.

scholarly journals Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8200
Author(s):  
Jonathan Aguiar Soares ◽  
Kayol Soares Mayer ◽  
Fernando César Comparsi de Castro ◽  
Dalton Soares Arantes
Keyword(s):  
Computational Complexity ◽  
Maximum Likelihood ◽  
Cost Effective ◽  
Performance Comparison ◽  
Practical Implementation ◽  
Limiting Factor ◽  
Rbf Neural Networks ◽  
Maximum Likelihood Decoding ◽  
Mimo Channels ◽  
Mimo Ofdm

Multi-input multi-output (MIMO) transmission schemes have become the techniques of choice for increasing spectral efficiency in bandwidth-congested areas. However, the design of cost-effective receivers for MIMO channels remains a challenging task. The maximum likelihood detector can achieve excellent performance—usually, the best performance—but its computational complexity is a limiting factor in practical implementation. In the present work, a novel MIMO scheme using a practically feasible decoding algorithm based on the phase transmittance radial basis function (PTRBF) neural network is proposed. For some practical scenarios, the proposed scheme achieves improved receiver performance with lower computational complexity relative to the maximum likelihood decoding, thus substantially increasing the applicability of the algorithm. Simulation results are presented for MIMO-OFDM under 5G wireless Rayleigh channels so that a fair performance comparison with other reference techniques can be established.

Reducing Computational Complexity and Enhancing Performance of IKSD Algorithm for Encoded MIMO Systems

2016 ◽  
Vol 2 (3) ◽  
pp. 636 ◽  
Author(s):  
Mohammed Qasim Sulttan
Keyword(s):  
Computational Complexity ◽  
Maximum Likelihood ◽  
High Performance ◽  
Mimo Systems ◽  
Practical Implementation ◽  
Mimo Detection ◽  
Detection Algorithms ◽  
Sphere Detection ◽  
Manhattan Metric ◽  
Main Challenge

<p>The main challenge in MIMO systems is how to design the MIMO detection algorithms with lowest computational complexity and high performance that capable of accurately detecting the transmitted signals. In last valuable research results, it had been proved the Maximum Likelihood Detection (MLD) as the optimum one, but this algorithm has an exponential complexity especially with increasing of a number of transmit antennas and constellation size making it an impractical for implementation. However, there are alternative algorithms such as the K-best sphere detection (KSD) and Improved K-best sphere detection (IKSD) which can achieve a close to Maximum Likelihood (ML) performance and less computational complexity. In this paper, we have proposed an enhancing IKSD algorithm by adding the combining of column norm ordering (channel ordering) with Manhattan metric to enhance the performance and reduce the computational complexity. The simulation results show us that the channel ordering approach enhances the performance and reduces the complexity, and Manhattan metric alone can reduce the complexity. Therefore, the combined channel ordering approach with Manhattan metric enhances the performance and much reduces the complexity more than if we used the channel ordering approach alone. So our proposed algorithm can be considered a feasible complexity reduction scheme and suitable for practical implementation.</p>

scholarly journals Smart Doll: Emotion Recognition Using Embedded Deep Learning

Symmetry ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 387 ◽  
Author(s):  
Jose Espinosa-Aranda ◽  
Noelia Vallez ◽  
Jose Rico-Saavedra ◽  
Javier Parra-Patino ◽  
Gloria Bueno ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Ethical Issues ◽  
Real Life ◽  
Graphic Processing Units ◽  
Cloud Processing ◽  
Embedded Computer Vision ◽  
Embedded Computer ◽  
Learning Techniques ◽  
Learning Capabilities

Computer vision and deep learning are clearly demonstrating a capability to create engaging cognitive applications and services. However, these applications have been mostly confined to powerful Graphic Processing Units (GPUs) or the cloud due to their demanding computational requirements. Cloud processing has obvious bandwidth, energy consumption and privacy issues. The Eyes of Things (EoT) is a powerful and versatile embedded computer vision platform which allows the user to develop artificial vision and deep learning applications that analyse images locally. In this article, we use the deep learning capabilities of an EoT device for a real-life facial informatics application: a doll capable of recognizing emotions, using deep learning techniques, and acting accordingly. The main impact and significance of the presented application is in showing that a toy can now do advanced processing locally, without the need of further computation in the cloud, thus reducing latency and removing most of the ethical issues involved. Finally, the performance of the convolutional neural network developed for that purpose is studied and a pilot was conducted on a panel of 12 children aged between four and ten years old to test the doll.

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