Study of Software Quality Improvement through Reliability Metrics Models and Root Cause Analysis Program

Software quality is the most crucial requirement of any organization to survive in the current market scenario. The improvement of quality in software products depends on number of factors such as its architecture, quality metrics, reliability models and root cause analysis for defect handling. This paper focuses on the use of different reliability metrics to improve the reliability in software systems. Paper also emphasizes that removing defects for once is not beneficial for quality improvement, rather using a quality improvement program such as root cause analysis to get to base of defects and decrease the chances of occurrence of same defects again and again. Paper shows that effective root cause analysis program improves the learning process in any organization and helps in development of standards. This article provides an overview of software reliability metrics and root cause analysis program for the improvement of total software quality

Industrial Control Systems (ICS): Cyber-attacks & Security Optimization

Cyber-security of digital industrial control system in reality is complex and challenging research area, due to various interconnections of electro-mechanical related components driving national critical infrastructures. These networked system components performs monitoring and controlling tasks in several industries and organization through the access of Internet connectivity across the world. More recently, there are myriad of security threats and attacks by malicious elements on ICS which now presents a priority to organizations and researchers for optimal security solutions. Development of the Internet and communication systems has also exacerbated such security concerns. Activities of cyber-attacks malicious elements on ICS may result in serious disaster in industrial environments, human casualties and loss. This paper critically looks at the SCADA/industrial control systems, architecture, cyber-attacks. Other aspect of the paper examines current ICS security technologies including a computational secured algorithm for PLC

Fuzzy Image Processing Based Architecture for Contrast Enhancement in Diabetic Retinopathy Images

Diabetic retinopathy” is damage to retina denotes one of the problems of diabetes which is a significant reason for visual infirmity and blindness. A comprehensive and routine eye check is important to early detection and rapid treatment. This study proposes a hardware system that can enhance the contrast in the diabetic retinopathy eye fundus images as a first step in different eye disease diagnoses. The fuzzy histogram equalization technique is proposed to increases the local contrast of Diabetic Retinopathy Images. First, a histogram construction hardware architecture for different image processing purposes has been built then modified with fuzzy techniques to create fuzzy histogram equalization architecture, which is used to enhance the original images. Both architectures are designed using a finite-state machine (FSM) technique and programmed with VHDL programming language. The first one is implemented using two (Spartan 3E-XC3S500 and Xilinx Artix-7 XC7A100T) kits, while the second architecture is implemented using (Spartan 3E-XC3S500) kit. The system consists also of a modified video graphics array (VGA) port to display the input and resulted images with a proper resolution. All the hardware outputs are compared to that results produce from MatLab for verification and the resulted images are tested by PSNR, MSE, ENTROPY ,and AMBE

Detection of Coordinate Based Accident-Prone Areas on Road Surface using Machine Learning Methods

The growth in the road networks in India and other developing countries have influenced the growth in transport industry and other industries, which depends on the road network for operations. The industries such as postal services or mover services have influenced the similar growths in these industries as well. However, the dependency of these industries is high on the road surface conditions and any deviation on the road surface conditions can also influence the performance of the services provided by the mentioned services. Nonetheless, the conditions of the road surface are one of the prime factors for road safety and number of evidences are found, which are discussed in subsequent sections of this work, that the bad road surface conditions are increasing the road accidents. Several parallel research attempts are deployed in order to find out, the regions where the road surface conditions are not proper, and the traffic density is higher. Nevertheless, outcomes of these parallel works are highly criticised due to the lack of accuracy in detection of the road surface defects, detection of accurate location of the defects and detection of the traffic density data from various sources. Thus, this work proposes a novel framework for detection of the road defect and further mapping to the spatial data coordinates resulting into the detection of the accident-prone zones or accident affinities of the roads. This work deploys a self-adjusting parametric coefficient-based regression model for detection of the risk factors of the road defects and in the other hand, extracts the traffic density of the road regions and further maps the accident affinities. This work outcomes into 97.69% accurate detection of the road accident affinity and demonstrates less complexity compared with the other parallel research outcomes

A Mobile Healthcare Application for Chronic Disease Patient

This paper presents a personalized food advisory application that can be used by chronic disease patients to manage and control their food intake. The application was developed as an Arabic Android application for mobile devices. The preliminary result confirms the importance of having such application to assist chronic diseases patients to follow the nutrition facts. Almost all the participants are willing to recommend the proposed system to their patients

SMS Based Remote Monitoring and Control of Industrial Processes using Artificial

This paper presents artificial neural network (ANN) based intelligent control system for effective monitoring and control of industrial processes based on Global System for Mobile Communication network (GSM). This system provides ideal solution for monitoring critical oil/gas plant variables such as temperature, pressure, level and flow- rate. Historical and real time data can be accessed worldwide using the GSM network. The proposed system monitors and controls these variables from the remote location and whenever it crosses the set limit, a microcontroller will sends an SMS to concerned plant personnel(s) mobile phone via GSM network. The concerned personnel can control the system through his mobile phone by sending AT Commands to GSM MODEM and in turn to the microcontroller A Nokia N90 series GSM mobile phone was interfaced with a PC via RS 232 serial port. A PC was used as SMS gateway as well as to create a database to store different records to provide list of changes. An SMS enable was also introduced to enable reporting agent to communicate with the plant. Assembly language, C Language and Visual BASIC were used at different stages to program the necessary hardware. Data obtained from this experiment were analyzed using MATLAB. The performance evaluation and the curve generated with MATLAB shows that using ANN controllers to control industrial Process gives better result/performance than on/off controllers