Laia spektriga Airy valgusimpulsid ja nende eksperimentaalne registreerimine

http://tartu.ester.ee/record=b2652835~S1*es

Action recognition using single-pixel time-of-flight detection

Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject's privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene. Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average 96.47 % accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrent neural network

Graphologues: image sociale et statut professionnel. La graphologie

The social image and occupational status of the graphologist are investigated by examining published data. Three occupational groups are cited to show that image building is complex. These groups are detectives, psychologists and graphoanalysts. Such examples illustrate how status can be modified by unexpected people (e.g. writers of fiction and politicians). They also show that the occupation itself can help to shape reputation. There are obstacles to measuring image in graphology, but an attempt is made to identify countries where graphology is likely to be most well known. The calculation is based on the assumption that the level of awareness is related to the number of practitioners operating in a given country. Important countries are identified as Switzerland, France, Israel, Italy and the Netherlands. It is concluded that the image of the graphologist is fragmented and inconsistent. Part of the population is unaware of its nature or its existence. When it is known it is associated with such diverse groups as psychologists, questioned document examiners and "occult" practitioners. These facts do not imply that graphology has a good image. This implication should be a major concern to the graphological community. Without a good image, clients will be hard to find and new students will also think carefully before committing themselves to a training programme. In this situation a major image-building exercise is necessary

Media 2: Realization of laterally nondispersing ultrabroadband Airy pulses

Originally published in Optics Letters on 01 April 2014 (ol-39-7-1877

Media 1: Realization of laterally nondispersing ultrabroadband Airy pulses

Originally published in Optics Letters on 01 April 2014 (ol-39-7-1877

Media 1: Spatiotemporal characterization of ultrabroadband Airy pulses

Originally published in Optics Letters on 01 April 2013 (ol-38-7-1143

Action Recognition Using Single-Pixel Time-of-Flight Detection

Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject’s privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene. Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average 96.47 % accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrent neural network