MEASURING SOFTWARE COMPLEXITY USING NEURAL NETWORKS

Measuring the software complexity is an important task in the management of software projects. In the recent years, many researchers have paid much attention to this challenging task due to the commercial importance of software projects. In the literature, there are some software metrics and estimation models to measure the complexity of software. However, we still need to introduce novel models of software metrics to obtain more accurate results regarding software complexity. In this paper, we will show that neural networks can be used as an alternative method for estimation of software complexity metrics. We use a neural network of three layers with a single hidden layer and train this network by using distinct training algorithms to determine the accuracy of software complexity. We compare our results of software complexity obtained by using neural networks with those calculated by Halstead model. This comparison shows that the difference between our estimated results obtained by Bayesian Regularization Algorithm with 10 hidden neurons and Halstead calculated results of software complexity is less than 2%, implying the effectiveness of our proposed method of neural networks in estimating software complexity

On-chip template training system and image processing applications using iterative annealing on ACE16k chip

Cellular neural networks proved to be a useful parallel computing system for image processing applications. Cellular neural networks (CNNs) constitute a class of recurrent and locally coupled arrays of identical cells. The connectivity among the cells is determined by a set of parameters called templates. CNN templates are the key parameters to perform a desired task. One of the challenging problems in designing templates is to find the optimal template that functions appropriately for the solution of the intended problem. In this paper, we have implemented the Iterative Annealing Optimization Method on the analog CNN chip to find an optimum template by training a randomly selected initial template. We have been able to show that the proposed system is efficient to find the suitable template for some specific image processing applications. (C) 2011 Elsevier Ltd. All rights reserved

Real-Time People Counting Application by Using GPU Programming

This study focuses on people counting in a video stream captured from a fixed camera. Aforementioned counting process is implemented by graphical processing unit (GPU) programming real-timely. For this reason, two video streams with different resolution and two different NVIDIA graphic cards are used. For all combinations of these video streams and graphic cards, the number of people are obtained in the video streams and they are compared with regard to performances. Consequently, it is examined that real-time people counting process can be successfully implemented by compute unified device architecture (CUDA) programming on NVIDIA graphic cards

Exudates Detection in Diabetic Retinopathy by Two Different Image Processing Techniques

Different techniques developed in the previous decades are used for blood vessel detection. Different kinds of image processing approaches in the detection and analysis of blood vessels can be applied to diagnose many human diseases and help in various medical and health diagnoses. Image processing for blood vessels could be used in areas such as disease diagnosis, severity measurement of specific diseases, and in biometric security. This study compares two different techniques to accurately diagnose a specific disease according to some selective features. Diabetic retinopathy is used for this comparative study as it is one of the most severe eye disorders and chronic diseases to cause blindness. Classifications and accurate measurements for blood vessel abnormalities (exudates, hemorrhages, and micro-aneurysms) enabled the correct and accurate diagnosis in retina and diabetic retinopathy.To avoid blindness, it is essential to utilize fundus image processing application to facilitate the early discovery of a diseased retinal. Throughout the fundus automated image process, the retinal features are extracted. The techniques applied in this study are a morphological-based image processing technique and an edge detection technique using Kirsch's template. First, the application of these image processing techniques are described and explained in detail. Subsequently, a classification process is proposed to assess and evaluate the performance of each technique

Detecting and counting people using real-time directional algorithms implemented by compute unified device architecture

This paper implements a real-time and directional counting algorithm using the Graphic Processing Unit (GPU) Programming for the purpose of detecting and counting people. We use the Compute Unified Device Architecture (CUDA) as the environment of the GPU programming. The proposed algorithm is implemented for detecting and counting people employing the single virtual line and two virtual lines, respectively, using three video streams and two GPU graphic cards GeForce GT 630 and GeForce GTX 550Ti. We first test the video streams on the algorithm by using GeForce GT 630 together with applying the single virtual line and two virtual lines, respectively. Then, we repeat the same procedures for the GPU graphic card GeForce GTX 550Ti. The obtained experimental results show that our proposed algorithm running on GPU can be successfully programmed and implemented for people detecting and counting problems. (C) 2017 Elsevier B.V. All rights reserved

Cellular Neural Networks Template Training System Using Iterative Annealing Optimization Technique on ACE16k Chip

Cellular neural networks proved to be a useful parallel computing system for image processing applications. Cellular neural networks (CNNs) constitute a class of recurrent and locally coupled arrays of identical cells. The connectivity among the cells is determined by a set of parameters called templates. CNN templates are the key parameters to perform a desired task. One of the challenging problems in designing templates is to find the optimal template that functions appropriately for the solution of the intended problem. In this paper, we have implemented the Iterative Annealing Optimization Method on the analog CNN chip to find an optimum template by training a randomly selected initial template. We have been able to show that the proposed system is efficient to find the suitable template for some specific image processing applications

SOLVING SUDOKU PUZZLE with NUMBERS RECOGNIZED by USING ARTIFICIAL NEURAL NETWORKS

This paper proposed a method to solve 9x9 SUDOKU puzzles automatically. To this end, a captured puzzle image is used, the numbers in this image are recognized by using Artificial Neural Networks (ANN) and a 9x9 number array with these numbers is constituted, respectively. Then, the proposed method is applied to the prepared numerical array for solving the puzzle. The validity of the proposed method is demonstrated with results from an example 9 x 9 SUDOKU puzzle image

Number Recognition of Sudoku Grid Image with Artificial Neural Networks

In this study it is aimed to capture a SUDOKU grid image, to process this image, to recognize the numbers in the grid image with Artificial Neural Networks and finally to constitute a 9 x 9 number array with these numbers. The reason of choosing SUDOKU game as the input material is the thought of SUDOKU game as a prototype of real world fitting problems. After this number recognition is completed successfully, a robot software who finds the right solution of a SUDOKU game automatically will be developed. The next aim of this robot software is solving real world fitting problems

A general approach for porosity estimation using artificial neural network method: a case study from Kansas gas field

This study aims to design a back-propagation artificial neural network (BP-ANN) to estimate the reliable porosity values from the well log data taken from Kansas gas field in the USA. In order to estimate the porosity, a neural network approach is applied, which uses as input sonic, density and resistivity log data, which are known to affect the porosity. This network easily sets up a relationship between the input data and the output parameters without having prior knowledge of petrophysical properties, such as pore-fluid type or matrix material type. The results obtained from the empirical relationship are compared with those from the neural network and a good correlation is observed. Thus, the ANN technique could be used to predict the porosity from other well log data

Implementation of a CNN based Object Counting Algorithm on Bi-i Cellular Vision System

Abstract Object counting has been used in many areas such as medical and industrial applications. It is a challenging problem to count the target objects in high speed. It is useful to implement image processing applications using the high capability computational power offered by Cellular Neural Network type analog processor named as ACE16k. In this paper, we implement an efficient object counting algorithm working on ACE16k chip. Our results have proved that the proposed algorithm can count objects on a given image rapidly and accurately