Human Rights as tools for political progress

La pratique des droits de l'homme est souvent décrite comme une entreprise qui vise à établir des standards minimaux pour guider l'action des États et des individus. Dans cet article, je tente de remettre en question la position minimaliste défendue par deux auteurs, James Nickel et James Griffin, en défendant une thèse selon laquelle la philosophie et la morale ne devraient pas être limitées par la pratique et les circonstances du monde. Sans apporter une réponse précise à la question de la faisabilité des droits de l'homme tels qu'on les connaît, je soutiens que le minimalisme défendu par Griffin et Nickel n'est pas nécessaire, ni souhaitable, dans la lutte pour la défense des droits de l'homme, lutte dans laquelle la philosophie a un rôle de premier ordre à jouer.

Polyaniline as an Automatic Beam Steering Material

This 'study' deals with a preliminary study of automatic beam steering properly in conducting polyaniline . Polyaniline in its undoped and doped .state was prepared from aniline by the chemical oxidative polymerization method. Dielectric properties of the samples were studied at S-band microwave frequencies using cavity perturbation technique. It is found that undoped po/vanihne is having greater dielectric loss and conductivity contpared with the doped samples. The beam steering property is studied using a perspex rod antenna and HP 85/OC vector network analyzer. The shift in the radiated beam is studied for different do voltages. The results show that polyaniline is a good nutterial far beam steering applications.

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.

ICT Tools for Teaching & Learning

Page 1. ICT Tools for Teaching & Learning G Santhosh Kumar Cochin University Page 2. Agenda • What is ICT? • Why integrate ICT in Education? • What are the Challenges? • What are the good resources? Page 3. Questions # “Despite the increasing use of ICT, the need for teachers is as great as ever” # “Placing ICT in schools will automatically improve the quality of education that children receive” #”The Internet is unsafe for children to use because there is so much dangerous material available on it” Page 4. What is ICT? • ICT is short for ..

Automatic Image Annotation Using SURF Descriptors

In recent years there is an apparent shift in research from content based image retrieval (CBIR) to automatic image annotation in order to bridge the gap between low level features and high level semantics of images. Automatic Image Annotation (AIA) techniques facilitate extraction of high level semantic concepts from images by machine learning techniques. Many AIA techniques use feature analysis as the first step to identify the objects in the image. However, the high dimensional image features make the performance of the system worse. This paper describes and evaluates an automatic image annotation framework which uses SURF descriptors to select right number of features and right features for annotation. The proposed framework uses a hybrid approach in which k-means clustering is used in the training phase and fuzzy K-NN classification in the annotation phase. The performance of the system is evaluated using standard metrics.

The Effect of SIFT Features as Content Descriptors in the Context of Automatic Writer Identification in Malayalam Language

The span of writer identification extends to broad domes like digital rights administration, forensic expert decisionmaking systems, and document analysis systems and so on. As the success rate of a writer identification scheme is highly dependent on the features extracted from the documents, the phase of feature extraction and therefore selection is highly significant for writer identification schemes. In this paper, the writer identification in Malayalam language is sought for by utilizing feature extraction technique such as Scale Invariant Features Transform (SIFT).The schemes are tested on a test bed of 280 writers and performance evaluated

Automatic Segmentation Framework for Primary Tumors From Brain MRIs Using Morphological Filtering Techniques

This paper describes a novel framework for automatic segmentation of primary tumors and its boundary from brain MRIs using morphological filtering techniques. This method uses T2 weighted and T1 FLAIR images. This approach is very simple, more accurate and less time consuming than existing methods. This method is tested by fifty patients of different tumor types, shapes, image intensities, sizes and produced better results. The results were validated with ground truth images by the radiologist. Segmentation of the tumor and boundary detection is important because it can be used for surgical planning, treatment planning, textural analysis, 3-Dimensional modeling and volumetric analysis

A semi-automatic method for segmentation and 3D modeling of glioma tumors from brain MRI

This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the pre- operative tumor volume is essential. Tumor portions were automatically segmented from 2D MR images using morphological filtering techniques. These seg- mented tumor slices were propagated and modeled with the software package. The 3D modeled tumor consists of gray level values of the original image with exact tumor boundary. Axial slices of FLAIR and T2 weighted images were used for extracting tumors. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming proc- ess and is prone to error. These defects are overcome in this method. Authors verified the performance of our method on several sets of MRI scans. The 3D modeling was also done using segmented 2D slices with the help of a medical software package called 3D DOCTOR for verification purposes. The results were validated with the ground truth models by the Radi- ologist.

Automatic Detection and Classification of Glioma Tumors using Statistical Features

The characterization and grading of glioma tumors, via image derived features, for diagnosis, prognosis, and treatment response has been an active research area in medical image computing. This paper presents a novel method for automatic detection and classification of glioma from conventional T2 weighted MR images. Automatic detection of the tumor was established using newly developed method called Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA).Statistical Features were extracted from the detected tumor texture using first order statistics and gray level co-occurrence matrix (GLCM) based second order statistical methods. Statistical significance of the features was determined by t-test and its corresponding p-value. A decision system was developed for the grade detection of glioma using these selected features and its p-value. The detection performance of the decision system was validated using the receiver operating characteristic (ROC) curve. The diagnosis and grading of glioma using this non-invasive method can contribute promising results in medical image computing

Automatic Optic Disc Boundary Extraction from Color Fundus Images

Efficient optic disc segmentation is an important task in automated retinal screening. For the same reason optic disc detection is fundamental for medical references and is important for the retinal image analysis application. The most difficult problem of optic disc extraction is to locate the region of interest. Moreover it is a time consuming task. This paper tries to overcome this barrier by presenting an automated method for optic disc boundary extraction using Fuzzy C Means combined with thresholding. The discs determined by the new method agree relatively well with those determined by the experts. The present method has been validated on a data set of 110 colour fundus images from DRION database, and has obtained promising results. The performance of the system is evaluated using the difference in horizontal and vertical diameters of the obtained disc boundary and that of the ground truth obtained from two expert ophthalmologists. For the 25 test images selected from the 110 colour fundus images, the Pearson correlation of the ground truth diameters with the detected diameters by the new method are 0.946 and 0.958 and, 0.94 and 0.974 respectively. From the scatter plot, it is shown that the ground truth and detected diameters have a high positive correlation. This computerized analysis of optic disc is very useful for the diagnosis of retinal diseases

Modellbasiertes Service Level Management verteilter Anwendungssysteme

Zur Senkung von Kosten werden in vielen Unternehmen Dienstleistungen, die nicht zur Kernkompetenz gehören, an externe Dienstleister ausgelagert. Dieser Prozess wird auch als Outsourcing bezeichnet. Die dadurch entstehenden Abhängigkeiten zu den externen Dienstleistern werden mit Hilfe von Service Level Agreements (SLAs) vertraglich geregelt. Die Aufgabe des Service Level Managements (SLM) ist es, die Einhaltung der vertraglich fixierten Dienstgüteparameter zu überwachen bzw. sicherzustellen. Für eine automatische Bearbeitung ist daher eine formale Spezifikation von SLAs notwendig. Da der Markt eine Vielzahl von unterschiedlichen SLM-Werkzeugen hervorgebracht hat, entstehen in der Praxis Probleme durch proprietäre SLA-Formate und fehlende Spezifikationsmethoden. Daraus resultiert eine Werkzeugabhängigkeit und eine limitierte Wiederverwendbarkeit bereits spezifizierter SLAs. In der vorliegenden Arbeit wird ein Ansatz für ein plattformunabhängiges Service Level Management entwickelt. Ziel ist eine Vereinheitlichung der Modellierung, so dass unterschiedliche Managementansätze integriert und eine Trennung zwischen Problem- und Technologiedomäne erreicht wird. Zudem wird durch die Plattformunabhängigkeit eine hohe zeitliche Stabilität erstellter Modelle erreicht. Weiteres Ziel der Arbeit ist, die Wiederverwendbarkeit modellierter SLAs zu gewährleisten und eine prozessorientierte Modellierungsmethodik bereitzustellen. Eine automatisierte Etablierung modellierter SLAs ist für eine praktische Nutzung von entscheidender Relevanz. Zur Erreichung dieser Ziele werden die Prinzipien der Model Driven Architecture (MDA) auf die Problemdomäne des Service Level Managements angewandt. Zentrale Idee der Arbeit ist die Definition von SLA-Mustern, die konfigurationsunabhängige Abstraktionen von Service Level Agreements darstellen. Diese SLA-Muster entsprechen dem Plattformunabhängigen Modell (PIM) der MDA. Durch eine geeignete Modelltransformation wird aus einem SLA-Muster eine SLA-Instanz generiert, die alle notwendigen Konfigurationsinformationen beinhaltet und bereits im Format der Zielplattform vorliegt. Eine SLA-Instanz entspricht damit dem Plattformspezifischen Modell (PSM) der MDA. Die Etablierung der SLA-Instanzen und die daraus resultierende Konfiguration des Managementsystems entspricht dem Plattformspezifischen Code (PSC) der MDA. Nach diesem Schritt ist das Managementsystem in der Lage, die im SLA vereinbarten Dienstgüteparameter eigenständig zu überwachen. Im Rahmen der Arbeit wurde eine UML-Erweiterung definiert, die eine Modellierung von SLA-Mustern mit Hilfe eines UML-Werkzeugs ermöglicht. Hierbei kann die Modellierung rein graphisch als auch unter Einbeziehung der Object Constraint Language (OCL) erfolgen. Für die praktische Realisierung des Ansatzes wurde eine Managementarchitektur entwickelt, die im Rahmen eines Prototypen realisiert wurde. Der Gesamtansatz wurde anhand einer Fallstudie evaluiert.

Virtual Radio Engine

Software Defined Radio (SDR) hardware platforms use parallel architectures. Current concepts of developing applications (such as WLAN) for these platforms are complex, because developers describe an application with hardware-specifics that are relevant to parallelism such as mapping and scheduling. To reduce this complexity, we have developed a new programming approach for SDR applications, called Virtual Radio Engine (VRE). VRE defines a language for describing applications, and a tool chain that consists of a compiler kernel and other tools (such as a code generator) to generate executables. The thesis presents this concept, as well as describes the language and the compiler kernel that have been developed by the author. The language is hardware-independent, i.e., developers describe tasks and dependencies between them. The compiler kernel performs automatic parallelization, i.e., it is capable of transforming a hardware-independent program into a hardware-specific program by solving hardware-specifics, in particular mapping, scheduling and synchronizations. Thus, VRE simplifies programming tasks as developers do not solve hardware-specifics manually.

Exploration tools in formal concept analysis

The development of conceptual knowledge systems specifically requests knowledge acquisition tools within the framework of formal concept analysis. In this paper, the existing tools are presented, and furhter developments are discussed.

KAM: Automatic Planning and Interpretation of Numerical Experiments Using Geometrical Methods

KAM is a computer program that can automatically plan, monitor, and interpret numerical experiments with Hamiltonian systems with two degrees of freedom. The program has recently helped solve an open problem in hydrodynamics. Unlike other approaches to qualitative reasoning about physical system dynamics, KAM embodies a significant amount of knowledge about nonlinear dynamics. KAM's ability to control numerical experiments arises from the fact that it not only produces pictures for us to see, but also looks at (sic---in its mind's eye) the pictures it draws to guide its own actions. KAM is organized in three semantic levels: orbit recognition, phase space searching, and parameter space searching. Within each level spatial properties and relationships that are not explicitly represented in the initial representation are extracted by applying three operations ---(1) aggregation, (2) partition, and (3) classification--- iteratively.