EAP-CRA for WiMAX, WLAN and 4G LTE Interoperability

Today we are moving into a “post-PC” world! Not many people sit in front of custom built PCs to do their businesses any more. Hand held devices such as iPod Touch, iPhone, Galaxy S3, iPad, Galaxy Tab, Airbook, Notepad etc. are bringing in a new paradigm as to how people use and communicate information. These devices can be thought as a theoretical “black-box”. They are for people who want to use it without wanting to know how they work. Such devices have third generation user interfaces – multi touch, physics and gestures (MPG). They need updates, but the user is not worried of how and where the files are stored. When a new application is installed, the user sees the icon and starts using it. The user is not interested in, what files were installed or where it was installed – there is no file management. The post-PC approach to dealing with software is that it’s discovered on an app store, downloaded with a single touch and deleted with another touch. Updates all come at once from the app store and it all happens behind the scene with minimal user involvement. All this is happening and adopted rapidly because people are able to do a number of things without being restricted to one place. They can download apps, watch movies, listen to news, browse the web etc. while on the move.Griffith Sciences, School of Information and Communication TechnologyFull Tex

Data assisted equalisation for high speed digital transmission systems

SIGLEAvailable from British Library Document Supply Centre- DSC:D61137 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Unsupervised clustering of texture features using SOM and Fourier Transform

Verma, B ORCiD: 0000-0002-4618-0479Texture analysis has a wide range of real-world applications. This paper presents a novel technique for texture feature extraction and compares its performance with a number of other existing techniques using a benchmark image database. The proposed feature extraction technique uses 2D-DR transform and self-organizing map (SOM). A combination of 2D-DFT and SOM with optimal parameter settings produced very promising results. The results from large sets of experiments and detailed analysis are included in this paper

IEEE 802.11i WLAN Security Protocol – A Software Engineer’s Model

Wireless local area networks (WLANs) based on the IEEE 802.11 standards are one of today’s fastest growing technologies in businesses, schools, and homes, for good reasons. As WLAN deployments increase, so does the challenge to provide these networks with security. Security risks can originate either due to technical lapse in the security mechanisms or due to defects in software implementations. Standard Bodies and researchers have mainly used UML state machines to address the implementation issues. In this paper we propose the use of GSE methodology to analyse the incompleteness and uncertainties in specifications. The IEEE 802.11i security protocol is used as an example to compare the effectiveness of the GSE and UML models. The GSE methodology was found to be more effective in identifying ambiguities in specifications and inconsistencies between the specification and the state machines. Resolving all issues, we represent the robust security network (RSN) proposed in the IEEE 802.11i standard using different GSE models

A texture feature extraction technique using 2D-DFT and hamming distance

Verma, B ORCiD: 0000-0002-4618-0479Texture analysis plays an increasingly important role in computer vision. Since the textural properties of images appear to carry useful information for discrimination purposes, it is important to develop significant features for texture. This paper presents a novel technique for texture extraction and classification. The proposed feature extraction technique uses 2D–DFT transformation. A combination of this technique and a Hamming Distance based neural network for classification of extracted features is investigated. The experimental results on a benchmark database and detailed analysis are presented

Unsupervised clustering of texture features using SOM and Fourier Transform

Texture analysis has a wide range of real-world applications. This paper presents a novel technique for texture feature extraction and compares its performance with a number of other existing techniques using a benchmark image database. The proposed feature extraction technique uses 2D-DR transform and self-organizing map (SOM). A combination of 2D-DFT and SOM with optimal parameter settings produced very promising results. The results from large sets of experiments and detailed analysis are included in this paper

A texture feature extraction technique using 2D-DFT and hamming distance

Texture analysis plays an increasingly important role in computer vision. Since the textural properties of images appear to carry useful information for discrimination purposes, it is important to develop significant features for texture. This paper presents a novel technique for texture extraction and classification. The proposed feature extraction technique uses 2D–DFT transformation. A combination of this technique and a Hamming Distance based neural network for classification of extracted features is investigated. The experimental results on a benchmark database and detailed analysis are presented

From feedback loop transitions to biomarkers in the psycho-immune-neuroendocrine network: Detecting the critical transition from health to major depression

© 2018 Elsevier Ltd Background: Biological pathways underlying major depressive disorder (MDD) can be viewed as systems biology networks. The psycho-immune-neuroendocrine (PINE) network comprises central nervous, immune, endocrine and autonomic systems, integrating biological mechanisms of MDD. Such networks exhibit recurrent motifs with specific functions, including positive and negative feedback loops, and are subject to critical transitions, influenced by feedback loop transitions (FLTs). Aims: We aim to identify critical feedback loops and their FLTs, as well sentinel network nodes (SNNs), key network nodes that drive FLTs, within the PINE network. Examples of biomarkers are provided which may reflect early warning signs of impending critical transition to MDD. Results: Disruption of homeostatic feedback loops reflects the physiological transition to MDD. Putative FLTs are identified within hypothalamic-pituitary-adrenal (HPA) and sympathetic-parasympathetic axes, the kynurenine pathway, gut function and dysbiosis. Conclusions: Progression from health to disease is driven by FLTs in the PINE network, which is likely to undergo changes characteristic of system instability. Biomarkers of system instability may effectively predict the critical transition to MDD