Shao Ying Zhu

A Review of Routing Protocols for Mobile Ad-Hoc NETworks (MANET)

 Abstract—The increase in availability and popularity of mobile wireless devices has lead researchers to develop a wide variety of Mobile Ad-hoc NETworking (MANET) protocols to exploit the unique communication opportunities presented by these devices. Devices are able to communicate directly using the wireless spectrum in a peer-to-peer fashion, and route messages through intermediate nodes, however the nature of wireless shared communication and mobile devices result in many routing and security challenges which must be addressed before deploying a MANET. In this paper we investigate the range of MANET routing protocols available and discuss the functionalities of several ranging from early protocols such as DSDV to more advanced such as MAODV, our protocol study focuses upon works by Perkins in developing and improving MANET routing. A range of literature relating to the field of MANET routing was identified and reviewed, we also reviewed literature on the topic of securing AODV based MANETs as this may be the most popular MANET protocol. The literature review identified a number of trends within research papers such as exclusive use of the random waypoint mobility model, excluding key metrics from simulation results and not comparing protocol performance against available alternatives.

An evaluation of lossless compression algorithms for medical infrared images

Several popular lossless image compression algorithms were evaluated for the application of compressing medical infrared images. Lossless JPEG, JPEG-LS, JPEG2000, PNG, and CALIC were tested on an image dataset of 380+ thermal images. The results show that JPEG-LS is the algorithm with the best performance, both in terms of compression ratio and compression speed

CT Liver Segmentation Using Artificial Bee Colony Optimisation

Abstract The automated segmentation of the liver area is an essential phase in liver diagnosis from medical images. In this paper, we propose an artificial bee colony (ABC) optimisation algorithm that is used as a clustering technique to segment the liver in CT images. In our algorithm, ABC calculates the centroids of clusters in the image together with the region corresponding to each cluster. Using mathematical morphological operations, we then remove small and thin regions, which may represents flesh regions around the liver area, sharp edges of organs or small lesions inside the liver. The extracted regions are integrated to give an initial estimate of the liver area. In a final step, this is further enhanced using a region growing approach. In our experiments, we employed a set of 38 images, taken in pre-contrast phase, and the similarity index calculated to judge the performance of our proposed approach. This experimental evaluation confirmed our approach to afford a very good segmentation accuracy of 93.73% on the test dataset.

A survey on top security threats in cloud computing

Cloud computing enables the sharing of resources such as storage, network, applications and software through internet. Cloud users can lease multiple resources according to their requirements, and pay only for the services they use. However, despite all cloud benefits there are many security concerns related to hardware, virtualization, network, data and service providers that act as a significant barrier in the adoption of cloud in the IT industry. In this paper, we survey the top security concerns related to cloud computing. For each of these security threats we describe, i) how it can be used to exploit cloud components and its effect on cloud entities such as providers and users, and ii) the security solutions that must be taken to prevent these threats. These solutions include the security techniques from existing literature as well as the best security practices that must be followed by cloud administrators.

Overlay of thermal and visual medical images using skin detection and image registration.

Thermography captures the temperature distribution of the human skin and is employed in various medical applications. Often it is useful to cross-reference the resulting thermograms with visual images of the patient, either to see which part of the anatomy is affected by a certain disease or to judge the efficacy of the treatment. An attractive approach to provide this information is to overlay the two image types and show a composite image to the clinician. Producing such an overlay however is a non-trivial task due to differences in image capturing conditions of the two modalities. In this paper we introduce an approach that produces accurate overlays of thermal and visual medical images. First unnecessary background information of the visual part are removed by an image segmentation step based on skin detection. The thermal image is then aligned through an intensity based image registration technique. Experimental results based on an set of visual-thermal image pairs demonstrate the effectiveness of the proposed approach

Content-based image retrieval for medical infrared images

Past efforts on the automated processing on medical infrared images has typically focused on specialized applications like the detection of breast cancer. We propose the application of content-based image retrieval (CBIR) to medical thermal images. CBIR allows the retrieval of similar images based on features directly extracted from the image data. Hence, image retrieval for a thermal image that shows symptoms of a certain disease will provide visually similar cases which will usually also represent similarities in medical terms. The image features we propose for this purpose are a set of moment invariants of the grayscale thermal images.

Adopting the DICOM standard for medical infrared images

In recent years there has been a resurgence of interest in the application of infrared thermal imaging in medicine. Yet fairly little effort has been spent towards standardisation of the field and a common communication and exchange format for thermal images. Most other medical areas where digital imaging is employed have subscribed to DICOM (Digital Imaging and Communication in Medicine) as a common standard for storing and retrieving medical imagery. In this paper we investigate how the DICOM standard in its current form can be adopted to store and communicate medical infrared images

Thermal Medical Image Retrieval by Moment Invariants

Thermal medical imaging provides a valuable method for detecting various diseases such as breast cancer or Raynaud’s syndrome. While previous efforts on the automated processing on thermal infrared images were designed for and hence constrained to a certain type of disease we apply the concept of content-based image retrieval (CBIR) as a more generic approach to the problem. CBIR allows the retrieval of similar images based on features extracted directly from image data. Image retrieval for a thermal image that shows symptoms of a certain disease will provide visually similar cases which usually also represent similarities in medical terms. The image features we investigate in this study are a set of combinations of geometric image moments which are invariant to translation, scale, rotation and contrast.

An Innovative Approach for Attribute Reduction Using Rough Sets and Flower Pollination Optimisation

Optimal search is a major challenge for wrapper-based attribute reduction. Rough sets have been used with much success, but current hill-climbing rough set approaches to attribute reduction are insufficient for finding optimal solutions. In this paper, we propose an innovative use of an intelligent optimisation method, namely the flower search algorithm (FSA), with rough sets for attribute reduction. FSA is a relatively recent computational intelligence algorithm, which is inspired by the pollination process of flowers. For many applications, the attribute space, besides being very large, is also rough with many different local minima which makes it difficult to converge towards an optimal solution. FSA can adaptively search the attribute space for optimal attribute combinations that maximise a given fitness function, with the fitness function used in our work being rough set-based classification. Experimental results on various benchmark datasets from the UCI repository confirm our technique to perform well in comparison with competing methods.

Big-Data Analytics and Cloud Computing

This book reviews the theoretical concepts, leading-edge techniques and practical tools involved in the latest multi-disciplinary approaches addressing the challenges of big data. Illuminating perspectives from both academia and industry are presented by an international selection of experts in big data science. Topics and features: describes the innovative advances in theoretical aspects of big data, predictive analytics and cloud-based architectures; examines the applications and implementations that utilize big data in cloud architectures; surveys the state of the art in architectural approaches to the provision of cloud-based big data analytics functions; identifies potential research directions and technologies to facilitate the realization of emerging business models through big data approaches; provides relevant theoretical frameworks, empirical research findings, and numerous case studies; discusses real-world applications of algorithms and techniques to address the challenges of big datasets.