scholarly journals Optimal Stochastic Computing Randomization

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2985
Author(s):  
Christiam F. Frasser ◽  
Miquel Roca ◽  
Josep L. Rosselló

Stochastic computing (SC) is a probabilistic-based processing methodology that has emerged as an energy-efficient solution for implementing image processing and deep learning in hardware. The core of these systems relies on the selection of appropriate Random Number Generators (RNGs) to guarantee an acceptable accuracy. In this work, we demonstrate that classical Linear Feedback Shift Registers (LFSR) can be efficiently used for correlation-sensitive circuits if an appropriate seed selection is followed. For this purpose, we implement some basic SC operations along with a real image processing application, an edge detection circuit. Compared with the literature, the results show that the use of a single LFSR architecture with an appropriate seeding has the best accuracy. Compared to the second best method (Sobol) for 8-bit precision, our work performs 7.3 times better for the quadratic function; a 1.5 improvement factor is observed for the scaled addition; a 1.1 improvement for the multiplication; and a 1.3 factor for edge detection. Finally, we supply the polynomials and seeds that must be employed for different use cases, allowing the SC circuit designer to have a solid base for generating reliable bit-streams.

Author(s):  
John Bosco P ◽  
S Janakiraman

Background: In the present digital world, Content Based Image Retrieval (CBIR) has gained significant importance. In this context, the image processing technology has become the most sought one, as a result its demand has increased to a large extend. The complex growth concerning computer technology offers a platform to apply the image processing application. Well-known image retrieval techniques suitable for application zone are 1.Text Based Image Retrieval (TBIR) 2. Content Based Image Retrieval (CBIR) and 3.Semantic Based Image Retrieval (SBIR) etc. In recent past, many researchers have conducted extensive research in the field of content-based image retrieval (CBIR). However, many related research studies on image retrieval and characterization have exemplified to be an immense issue and it should be progressively developed in its techniques. Hence, by putting altogether the research conducted in the recent years, this survey study makes a comprehensive attempt to review the state-of –the art in the field. Aims: This paper aims to retrieve similar images according to visual properties, which defined as Shape, color, Texture and edge detection. Objective: To investigate the CBIR to achieve the task because of the essential and fundamentals problems. The present and future trends are addressed to show come contributions and directions and it can inspire more research in the CBIR methods. Result: we present a deep analysis of the state of the art on CBIR methods; we explain the methods based on Color, Texture, and shape, and edge detection with performance evaluation metrics. In addition, we have discussed some significant future research directions reviewed. Methods: This paper has quickly anticipated the noteworthiness of CBIR and its related improvement, which incorporates Edge Detection Techniques, Various sorts of Distance Metric (DM), Performance measurements and various kinds of Datasets. This paper shows the conceivable outcomes to overcome the difficulties concerning re-positioning strategies with an exceptional spotlight on the improvement of accuracy and execution. Discussion: At last, we have proposed another technique for consolidating different highlights in a CBIR framework that can give preferred outcomes over the current strategies.


Author(s):  
Y.A. Hamad ◽  
K.V. Simonov ◽  
A.S. Kents

The paper considers general approaches to image processing, analysis of visual data and computer vision. The main methods for detecting features and edges associated with these approaches are presented. A brief description of modern edge detection and classification algorithms suitable for isolating and characterizing the type of pathology in the lungs in medical images is also given.


Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 457
Author(s):  
Manuel Henriques ◽  
Duarte Valério ◽  
Paulo Gordo ◽  
Rui Melicio

Many image processing algorithms make use of derivatives. In such cases, fractional derivatives allow an extra degree of freedom, which can be used to obtain better results in applications such as edge detection. Published literature concentrates on grey-scale images; in this paper, algorithms of six fractional detectors for colour images are implemented, and their performance is illustrated. The algorithms are: Canny, Sobel, Roberts, Laplacian of Gaussian, CRONE, and fractional derivative.


Author(s):  
Indiketiya I.H.O.H ◽  
Kulasekara K.M.R.A ◽  
J.M. Thomas ◽  
Ishara Gamage ◽  
Thusithanjana Thilakarathna

2005 ◽  
Vol 15 (12) ◽  
pp. 3999-4006 ◽  
Author(s):  
FENG-JUAN CHEN ◽  
FANG-YUE CHEN ◽  
GUO-LONG HE

Some image processing research are restudied via CNN genes with five variables, and this include edge detection, corner detection, center point extraction and horizontal-vertical line detection. Although they were implemented with nine variables, the results of computer simulation show that the effect with five variables is identical to or better than that with nine variables.


2016 ◽  
Vol 12 (04) ◽  
pp. 23
Author(s):  
Jorge Lobo

This short paper introduces the basic concepts of Stochastic Computing (SC), and presents additions to a remote lab with reconfigurable logic to allow testing SC circuits. Recently, SC has been revisited and evaluated as a possible way of performing approximate probabilistic computations for artificial perception systems. New modules allow the generation of pseudo-random numbers, given a seed key and using linear feedback shift registers, but also having true random number generation using ring oscillators and embedded PLLs. Stochastic computing allows a tradeoff between resource usage and precision, allowing very simple circuits to perform computations, at the expense of a longer integration time to have reasonable results. We provide the basic stochastic computing modules, so that any user can use them to build a stochastic computing circuit and go beyond software simulations, providing a remote hardware device to test real circuits at high clock speeds.


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