A novel approach to the design of DSP systems using minimum complexity Finite State Machines

The paper presents a new and different approach to the design and realisation of Digital Signal Processing (DSP)systems by utilising Finite State Machines (FSM). The DSP system is modelled by mapping all its potential states into an FSM, whose complexity is usually very high. The FSM mirrors the complete functionality of the system and thus describes its behaviour in full detail. Examples for FSMs of first and second order digital recursive filters are provided and the current version of the software simulating the FSM corresponding to any linear time-invariant DSP system is described. The potential of this approach including state reduction techniques as well as the inclusion of non-linear DSP systems is also outlined, and further future research intentions are briefly explored

Fuzzy image segmentation of generic shaped clusters

The segmentation performance of any clustering algorithm is very sensitive to the features in an image, which ultimately restricts their generalisation capability. This limitation was the primary motivation in our investigation into using shape information to improve the generality of these algorithms. Fuzzy shape-based clustering techniques already consider ring and elliptical profiles in segmentation, though most real objects are neither ring nor elliptically shaped. This paper addresses this issue by introducing a new shape-based algorithm called fuzzy image segmentation of generic shaped clusters (FISG) that incorporates generic shape information into the framework of the fuzzy c-means (FCM) algorithm. Both qualitative and quantitative analyses confirm the superiority of FISG compared to other shape-based fuzzy clustering methods including, Gustafson-Kessel algorithm, ring-shaped, circular shell, c-ellipsoidal shells and elliptic ring-shaped clusters. The new algorithm has also been shown to be application independent so it can be applied in areas such as video object plane segmentation in MPEG-4 based coding

FollowMe: A Bigraphical Approach

In this paper we illustrate the use of modelling techniques using bigraphs to specify and refine elementary aspects of the FollowMe framework. This framework provides the seamless migration of bi-directional user interfaces for users as they navigate between zones within an intelligent environment

Towards FollowMe User Profiles for Macro Intelligent Environments

We envision an Ambient Intelligent Environment as an environment with technology embedded within the framework of that environment to help enhance an users experience in that environment. Existing implementations , while working effectively, are themselves an expensive and time consuming investment. Applying the same expertise to an environment on a monolithic scale is very inefficient, and thus, will require a different approach. In this paper, we present this problem, propose theoretical solutions that would solve this problem, with the guise of experimentally verifying and comparing these approaches, as well as a formal method to model the entire scenario

Feature weighting methods for abstract features applicable to motion based video indexing

Content based labels, associated with image sequences in contemporary video indexing methods, can be textual, numerical as well as abstract, including colour-histograms and motion co-occurrence matrices. Abstract features or indices are not explicitly numeric entities but rather are composed of numeric entities. When multiple abstract features are involved, distance metrics between image sequences need to be weighted. Most feature weighting methods in the literature assume that the space is numeric (either discrete or continuous) and so not applicable to abstract feature weighting. This paper elaborates some feature weighting methods applicable to abstract features and both binary (feature selection) and real-valued weighting methods are discussed. The performance of different feature selection and weighting methods are provided and a comparative study based on motion classification-experiments is presented

Policy Brief No. 4 - What Determines Success in University

We examine persistence and success using a rich administrative data set that links information on individual students at four Ontario universities with information on the high school performance of individual students, the high school that the student attended, and the neighbourhood in which the student grew up. These data sets provide many relevant factors, a large number of observations, and the actual measures (not self-reports) of such academic outcomes as grade averages, credits and degrees completed

Fuzzy image segmentation combining ring and elliptic shaped clustering algorithms

Results from any existing clustering algorithm that are used for segmentation are highly sensitive to features that limit their generalization. Shape is one important attribute of an object. The detection and separation of an object using fuzzy ring-shaped clustering (FKR) and elliptic ring-shaped clustering (FKE) already exists in the literature. Not all real objects however, are ring or elliptical in shape, so to address these issues, this paper introduces a new shape-based algorithm, called fuzzy image segmentation combining ring and elliptic shaped clustering algorithms (FCRE) by merging the initial segmented results produced by FKR and FKE. The distribution of unclassified pixels is performed by connectedness and fuzzy c-means (FCM) using a combination of pixel intensity and normalized pixel location. Both qualitative and quantitative analysis of the results for different varieties of images proves the superiority of the proposed FCRE algorithm compared with both FKR and FKE

Fuzzy image segmentation using shape information

Results of any clustering algorithm are highly sensitive to features that limit their generalization and hence provide a strong motivation to integrate shape information into the algorithm. Existing fuzzy shape-based clustering algorithms consider only circular and elliptical shape information and consequently do not segment well, arbitrary shaped objects. To address this issue, this paper introduces a new shape-based algorithm, called fuzzy image segmentation using shape information (FISS) by incorporating general shape information. Both qualitative and quantitative analysis proves the superiority of the new FISS algorithm compared to other well-established shape-based fuzzy clustering algorithms, including Gustafson-Kessel, ring-shaped, circular shell, c-ellipsoidal shells and elliptic ring-shaped clusters

Chlamydia Hijacks ARF GTPases To Coordinate Microtubule Posttranslational Modifications and Golgi Complex Positioning.

The intracellular bacterium Chlamydia trachomatis develops in a parasitic compartment called the inclusion. Posttranslationally modified microtubules encase the inclusion, controlling the positioning of Golgi complex fragments around the inclusion. The molecular mechanisms by which Chlamydia coopts the host cytoskeleton and the Golgi complex to sustain its infectious compartment are unknown. Here, using a genetically modified Chlamydia strain, we discovered that both posttranslationally modified microtubules and Golgi complex positioning around the inclusion are controlled by the chlamydial inclusion protein CT813/CTL0184/InaC and host ARF GTPases. CT813 recruits ARF1 and ARF4 to the inclusion membrane, where they induce posttranslationally modified microtubules. Similarly, both ARF isoforms are required for the repositioning of Golgi complex fragments around the inclusion. We demonstrate that CT813 directly recruits ARF GTPases on the inclusion membrane and plays a pivotal role in their activation. Together, these results reveal that Chlamydia uses CT813 to hijack ARF GTPases to couple posttranslationally modified microtubules and Golgi complex repositioning at the inclusion.IMPORTANCEChlamydia trachomatis is an important cause of morbidity and a significant economic burden in the world. However, how Chlamydia develops its intracellular compartment, the so-called inclusion, is poorly understood. Using genetically engineered Chlamydia mutants, we discovered that the effector protein CT813 recruits and activates host ADP-ribosylation factor 1 (ARF1) and ARF4 to regulate microtubules. In this context, CT813 acts as a molecular platform that induces the posttranslational modification of microtubules around the inclusion. These cages are then used to reposition the Golgi complex during infection and promote the development of the inclusion. This study provides the first evidence that ARF1 and ARF4 play critical roles in controlling posttranslationally modified microtubules around the inclusion and that Chlamydia trachomatis hijacks this novel function of ARF to reposition the Golgi complex