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School of computer Science,
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Just a quick recommendation for PGRs in Computer Science: Check out the Doctoral School’s offered courses and sign up to anything you deem interested. This is perfect content for your Training-Needs-Analysis updates!
The monthly PGR meeting was held on Wednesday 13th July, 14:00-16:00, Room MC3108.
We had 2 speakers for this month seminar:
* Claudio Coppola
We investigate how incremental learning of long-term human activity patterns improves the accuracy of activity classification over time.Rather than trying to improve the classification methods themselves, we assume that they can take into account prior probabilities of activities occurring at a particular time and location.We use the classification results to build spatial and temporal models that can provide these priors to the classifiers.As our system gradually learns about typical patterns of human activities, the accuracy of activity classification improves, which results in even more accurate priors. Two datasets collected over several months containing hand-annotated activity in residential and office environments were chosen to evaluate the approach.Several types of spatial and temporal models were evaluated for each of these datasets.The results indicate that incremental learning of daily routines leads to a significant improvement in activity classification.
* Evangelia Kotsiliti
Diabetic retinopathy is a complication of diabetes affecting the eye and a leading cause of blindness worldwide. In many countries around the world, systematic screening for diabetic retinopathy is provided to the patients diagnosed with diabetes in order to reduce the burden of blindness. However, considering the rising numbers of people who are diagnosed with diabetes every year, it is plausible to think that the provision of screening services to an increased demand may no longer be affordable. This projects aims at the utilisation of simple, commonly available patient characteristics and biochemical measures to identify patients at high risk of having retinopathy at the time of screening. A subsequent step aims at the development of a cost-effectiveness model to compare the cost and consequences of the risk model against the current manual grading. The ultimate outcome would be the development of reliable clinical model that can reduce the overall screening cost and retain effectiveness.
There will be no seminar in August. The date and venue for the next meeting in September 2016 will be announced.
The monthly PGR meeting was held on Wednesday 8th June, 14:00-16:00, Room MC3108.
This month speaker was Dr Saddam Bekhet who gave a talk on his latest research findings. The title of his talk was ‘Signature-based Videos’ Visual Similarity Detection and Measurement’.
Dr Massoud Zolgharni and Dr Marc Hanheide also discussed different issues including:
The date and venue for the next meeting will be announced.
The monthly PGRs Research Presentations was held on Thursday 12th May, 2pm, Room MC3108.
This session we had the following presentations:
Title: A ROS framework for single antenna RFID tag localisation with mobile robots.
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By: George Broughton |
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Over the last few years, RFID technology has evolved to give mobile robots an extra dimension to sense their surroundings. By determining whether certain tags, often fixed to interesting objects, can be read or not, the robot can effectively sense the objects presence. This is not just useful for finding lost objects, but has also been used for activity recognition. Additionally, tags have been used as landmarks within environments to aid with navigation. This presentation looks at the development of a ROS framework for localising RFID tags from a mobile robot. The framework combines several different approaches to make use of the information provided by the tag and from the reader, to estimate possible locations of the tag. This is done by taking the output of the different algorithms, and then combining and feeding them into a densely populated occupancy grid using a bayesian update system to calculate the most probable tag location. Rather than rely on multiple antennas for trilateration, the framework exploits a robot’s ability to move within its environment to seek optimal positions to hone in on a tag. This also has the additional benefit of providing resistance to multipath signal errors. This will lead to a framework that is future-proof, robust, works with multiple models of readers, and can be moulded to suit many needs. |
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