The Vase’s Representation (Cantharus, Crater) on the Roman Mosaic in Portugal: A Significant Formal and Iconographic Path from Classic Antiquity to Late Antiquity

11TH INTERNATIONAL COLLOQUIUM ON ANCIENT MOSAICS OCTOBER 16TH  20TH, 2009, BURSA TURKEY Mosaics of Turkey and Parallel Developments in the Rest of the Ancient and Medieval World: Questions of Iconography, Style and Technique from the Beginnings of Mosaic until the Late Byzantine Era

About using serious games to teach (Portuguese) sign language

Sign language is the form of communication used by Deaf people, which, in most cases have been learned since childhood. The problem arises when a non-Deaf tries to contact with a Deaf. For example, when non-Deaf parents try to communicate with their Deaf child. In most cases, this situation tends to happen when the parents did not have time to properly learn sign language. This dissertation proposes the teaching of sign language through the usage of serious games. Currently, similar solutions to this proposal do exist, however, those solutions are scarce and limited. For this reason, the proposed solution is composed of a natural user interface that is intended to create a new concept on this field. The validation of this work, consisted on the implementation of a serious game prototype, which can be used as a source for learning (Portuguese) sign language. On this validation, it was first implemented a module responsible for recognizing sign language. This first stage, allowed the increase of interaction and the construction of an algorithm capable of accurately recognizing sign language. On a second stage of the validation, the proposal was studied so that the pros and cons can be determined and considered on future works.

[Portrait de la reine Olga de Grèce (même type que Wc 218) non signé, 2 phot. de types grecs par Rubellin, phot. à Smyrne, le tout donné par P.P. Potagos en 1887]

Donateur : Potagos, Panagiotes (1839-1903)

CT angiography helps to differentiate acute from chronic carotid occlusion: "carotid ring sign".

INTRODUCTION: Currently, there is no reliable method to differentiate acute from chronic carotid occlusion. We propose a novel CTA-based method to differentiate acute from chronic carotid occlusions that could potentially aid clinical management of patients. METHODS: We examined 72 patients with 89 spontaneously occluded extracranial internal carotids with CT angiography (CTA). All occlusions were confirmed by another imaging modality and classified as acute (imaging <1 week of presumed occlusion) orchronic (imaging >4 weeks), based on circumstantial clinical and radiological evidence. A neuroradiologist and a neurologist blinded to clinical information determined the site of occlusion on axial sections of CTA. They also looked for (a) hypodensity in the carotid artery (thrombus), (b) contrast within the carotid wall (vasa vasorum), (c) the site of the occluded carotid, and (d) the "carotid ring sign" (defined as presence of a and/or b). RESULTS: Of 89 occluded carotids, 24 were excluded because of insufficient circumstantial evidence to determine timing of occlusion, 4 because of insufficient image quality, and 3 because of subacute timing of occlusion. Among the remaining 45 acute and 13 chronic occlusions, inter-rater agreement (kappa) for the site of proximal occlusion was 0.88, 0.45 for distal occlusion, 0.78 for luminal hypodensity, 0.82 for wall contrast, and 0.90 for carotid ring sign. The carotid ring sign had 88.9% sensitivity, 69.2% specificity, and 84.5% accuracy to diagnose acute occlusion. CONCLUSION: The carotid ring sign helps to differentiate acute from chronic carotid occlusion. If further confirmed, this information may be helpful in studying ischemic symptoms and selecting treatment strategies in patients with carotid occlusions.

Brock Sign

View of the site of the Glenridge Campus on Lockhart Drive and possibly the first sign to announce Brock University's future presense there. This is photo is ca. 1963, and it appears that it may have been taken in the fall of 1963, shortly after the University acquired the land.

Greenhouse and Sign

View of the Greenhose from the northwest.

Mackenzie Chown Complex Sign

The Mackenzie Chown Complex in the background and its sign in the foreground.

Sign, Chapman College Chapel, Orange, California

Four people posing by the sign board for Chapman College Chapel, Orange, California. A Catholic mass is scheduled. The wooden-shingled church, constructed in 1909 for the congregation of Trinity Episcopal Church, is located on the northeast corner of East Maple Avenue and North Grand Street. Chapman College (now Chapman University) purchased the church for their chapel when the congregation moved to a new church on Canal Street.

Sheet Metal Department Sign

This is a black and white photograph of a sign for the Sheet Metal Department of the New York Trade School likely created by the department. It contains ornate metal work and displays the year 1938, probably the beginning year of the Sheet Metal Department.

Sign Painting Department

In this section of the Sign Painting Department several students are shown working on projects. Decorating the walls are numerous signs, likely painted by students in the department. Black and white photograph.

Sign Painting Students at Work

A classroom of students in the Sign Painting Department at the New York Students is shown working on a variety of signs. Black and white photograph.

Sign Painting Student Working Outside

A Sign Painting student from the New York Trade School is pictured working outside on scaffolding on an AMOCO sign. Black and white photograph.

Section of Sign Painting Department

A section of the Sign Painting Department is shown with students working at tables. A variety of signs can be seen hanging around the room including one for a Holiday Inn, an American Meat Market, and one for a Laundrette. Black and white photograph with slight damage from a tear.

Road Sign Recognition based onInvariant Features using SupportVector Machine

Since last two decades researches have been working on developing systems that can assistsdrivers in the best way possible and make driving safe. Computer vision has played a crucialpart in design of these systems. With the introduction of vision techniques variousautonomous and robust real-time traffic automation systems have been designed such asTraffic monitoring, Traffic related parameter estimation and intelligent vehicles. Among theseautomatic detection and recognition of road signs has became an interesting research topic.The system can assist drivers about signs they don’t recognize before passing them.Aim of this research project is to present an Intelligent Road Sign Recognition System basedon state-of-the-art technique, the Support Vector Machine. The project is an extension to thework done at ITS research Platform at Dalarna University [25]. Focus of this research work ison the recognition of road signs under analysis. When classifying an image its location, sizeand orientation in the image plane are its irrelevant features and one way to get rid of thisambiguity is to extract those features which are invariant under the above mentionedtransformation. These invariant features are then used in Support Vector Machine forclassification. Support Vector Machine is a supervised learning machine that solves problemin higher dimension with the help of Kernel functions and is best know for classificationproblems.

Sign Painting Students

A professor instructs two students working on a large sign for the "Carousel Shoppe." Black and white photograph.