Drink-driving and over-speeding of vehicles are the major causes of injuries and deaths on the road globally and South Africa (SA) is not an exception. Different systems which are currently used in detecting high alcohol concentration in drivers’ breath and detecting vehicles that exceeds stipulated speed limit are not effective, efficient and poses health risks to traffic personnel. In an attempt to provide effective solutions to these challenges, this paper proposed a smart transportation system for real-time detection of drink-driving and over-speeding on the roads using technology of vehicular networks. The objective is to allow for early intervention by traffic personnel aim at saving lives before actual accident occurred. We designed a theoretical framework of the system and implemented an application prototype which is web-based for use by traffic personnel to monitor the detection of traffic offenders in the capacity of drink-driving and over-speeding. We presented and discussed the operation of the system as well as the functionalities it offers. Additionally, we utilized the application to simulate the actual system and based on its working, we found that the system is feasible and can accomplish the tasks of road safety more effective than the existing approaches.

Sun being a non-conventional source of energy produces solar energy which is clean and available in abundance. The power obtained from solar panels is used to charge solar batteries and utilized to get continuous power supply. Solar panels are installed in open atmosphere and when subjected to different weather conditions involve many problems such as damage to different components, loss of power generation and heating. The different deposits on solar panel surface such as cement deposits, bird droppings increase temperature of deposited area and produce heating. The heating in solar panels develop hot spots. Batteries placed in a battery room attain high temperature and produce heat. This overheating affect working and performance of solar panels and batteries. These effects of heat are not visible by naked eye but are visible in thermal images, captured using thermal imaging camera. This paper focus on implementing an efficient visualization technique to segment the desired portion of heat from thermal images of solar panels and batteries using watershed transform. 

In this paper, an Adaptive Dynamic Scheduling Algorithm (ADSA) based on Artificial Bee Colony (ABC) was developed for vehicular traffic control. The developed model optimally scheduled green light timing in accordance with traffic condition in order to minimize the Average Waiting Time (AWT) at the cross intersection. A MATLAB based Graphic User Interface (GUI) traffic control simulator was developed. In order to demonstrate the effectiveness of the developed ADSA this paper was validated with the existing work in the literature. The result obtained for the AWT of the developed ADSA had a performance of 76.67%. While for vehicular queues cleared at the intersection the developed ADSA had a performance of 53.33%. The results clearly expressed that the developed ADSA method has been successful in minimizing the Average Waiting Time and vehicular queues at the intersection. 

The quality of remote sensing satellite images are expressed in terms of ground sample distance, modular transfer function, signal to noise ratio and National Imagery Interpretability Rating Scale (NIIRS) by user community. The proposed system estimates NIIRS of an image, by incorporating a new automated method to calculate the Relative Edge Response (RER). The prominent edges which contribute the most for the estimation of RER are uniquely extracted with a combined application of certain filters and morphological operators. RER is calculated from both horizontal and vertical edges separately and the geometric mean is considered as the final result. Later applying the estimated RER along with other parameters, the system returns the NIIRS value of the input image. This work has proved the possible implementation of automated techniques to estimate the NIIRS from images and specifics in the metafile contents of imagery.

Cancer is a life threatening disease and it engulfs the lives of many women. Due to the technology advancement, the medical science is drastically improved. A statistical report claims that the diagnostic decisions of radiologists show more false positive rates, which is very dangerous. However, when the radiologists are supported by computer aided applications, the false positive results are considerably reduced. Understanding the potentiality of computer aided applications, this paper presents a double layered segmentation algorithm for cervical cell images. The entire work is subdivided into three important phases, which are cervical image pre-processing, coarse and fine level segmentation. The pre-processing phase attempts to remove the noise and enhance the image quality by means of adaptive mean filter and Contrast Limited Adaptive Histogram Equalization (CLAHE) technique respectively. The coarse level segmentation process is achieved by Generalized Hierarchical Fuzzy C Means (GHFCM) and the fine level segmentation process is carried out by Artificial Bee Colony (ABC) algorithm. The performance of the proposed segmentation algorithm is analysed in terms of accuracy, sensitivity and specificity. The experimental results show the efficacy of the proposed segmentation algorithm.

Digital watermarking is one of the ways to have Copyright protection for digital information. The digital watermarking scheme used for watermark embedding has to satisfy robustness property to ensure the security of the secret information hidden. The scheme presented here will support the above said statement significantly. We propose here the scheme as composition of both blind and non-blind digital watermarking technique in a process of serial watermarking. A secret binary image is embedded in the first cover image to get first watermarked image by using blind watermarking technique. Then this first watermarked image is again embedded into second cover image to get serial watermarked image using non-blind watermarking technique. To extract secret binary image, first non-blind watermark extraction technique and then blind watermark extraction techniques are used. From this composite approach and serial watermark embedding procedure, we achieved considerable fidelity and robustness against - Rotation, JPEG compression and for noises Salt & pepper, Gaussian, Speckle, Poisson and multiple noises.

Haze and fog lead to image degradation by various degradation processes like image contrast, image blurring and pixel distortion. It has effected the efficiency of computer and machine vision algorithms. A number of single image and multiple image restoration based image defogging algorithms have aimed to solve the problem in an efficient and fast manner. The objective of the paper is to summarize present state of the art image defogging algorithms. Firstly, an image classification algorithm has been presented and then we summarized present state of the art image restoration based image defogging algorithms. Finally, we summarized image quality assessment methods followed by their comparisons of various image defogging algorithms. Problems of image dehazing and future scope have been discussed thereafter.