A general description of Loran-C: Present and potential applications

Loran-C is a low frequency (100 kHz) pulse navigation system. The pulse format and phase stability of the system are of paramount importance for both navigation and time synchronization using this system. Present Loran-C installations operated by the U. S. Coast Guard cover much of the northern hemisphere. A recent government-wide decision has declared that Loran-C will be the U. S. Coastal confluence navigation system for the immediate future. Therefore, Loran-C stations are presently being installed or planned to cover the entire U. S. coastline

Ultraviolet dayglow and stellar brightness measurement from the X-15 aircraft

Airborne stellar photometry and spectrometric determination of day sky brightnes

Combining face detection and novelty to identify important events in a visual lifelog

The SenseCam is a passively capturing wearable camera, worn around the neck and takes an average of almost 2,000 images per day, which equates to over 650,000 images per year. It is used to create a personal lifelog or visual recording of the wearer’s life and generates information which can be helpful as a human memory aid. For such a large amount of visual information to be of any use, it is accepted that it should be structured into “events”, of which there are about 8,000 in a wearer’s average year. In automatically segmenting SenseCam images into events, it is desirable to automatically emphasise more important events and decrease the emphasis on mundane/routine events. This paper introduces the concept of novelty to help determine the importance of events in a lifelog. By combining novelty with face-to-face conversation detection, our system improves on previous approaches. In our experiments we use a large set of lifelog images, a total of 288,479 images collected by 6 users over a time period of one month each

Structuring and augmenting a visual personal diary

This paper refers to research in the domain of visual lifelogging, whereby individuals capture much of their lives using digital cameras. The potential benefits of lifelogging include: applications to review tourist trips, memory aid applications, learning assistants, etc. The SenseCam, developed by Microsoft Research in Cambridge, UK, is a small wearable device which incorporates a digital camera and onboard sensors (motion, ambient temperature, light level, and passive infrared to detect presence of people). There exists a number of challenges in managing the vast quantities of data generated by lifelogging devices such as the SenseCam. Our work concentrates on the following areas withing visual lifelogging: Segmenting sequences of images into events (e.g. breakfast, at meeting); retrieving similar events (what other times was I at the park?); determining most important events (meeting an old friend is more important than breakfast); selection of ideal keyframe to provide an event summary; and augmenting lifeLog events with images taken by millions of users from "Web 2.0" websites (show me other pictures of the Statue of Liberty to augment my own lifelog images)

Intelligent image processing techniques for structuring a visual diary

The SenseCam is a small wearable personal device which automatically captures up to 3,500 images per day. This yields a very large personal collection of images or in a sense, a diary of a person's day. Over one million images will need to be stored each year, therefore intelligent techniques are necessary for the effective searching and browsing of this image collection for important or significant events in a person's life, and one of the issues is how to detect and then relate similar events in a lifetime. This is necessary in order to detect unusual or once-off events, as well as determining routine activities. This poster will present the various sources of data that can be collected with a SenseCam device, and also other sources that can be collected to compliment the SenseCam data sources. Different forms of image processing that can be carried out on this large set of images will be detailed, specifically how to detect what images belong to individual events, and also how similar various events are to each other. There will be hundreds of thousands of images of everyday routines; as a result more memorable events are quite likely to be significantly different to other normal reoccurring events

Organising a large quantity of lifelog images

Preliminary research indicates that a visual recording of one’s activities may be beneficial for sufferers of neurodegenerative diseases. However there exists a number of challenges in managing the vast quantities of data generated by lifelogging devices such as the SenseCam. Our work concentrates on the following areas within visual lifelogging: Segmenting sequences of images into events (e.g. breakfast, at meeting); retrieving similar events (“what other times was I at the park?”); determining most important events (meeting an old friend is more important than breakfast); selection of ideal keyframe to provide an event summary; and augmenting lifeLog events with images taken by millions of users from ‘Web 2.0’ websites (“show me other pictures of the Statue of Liberty to augment my own lifelog images”)

Video shot boundary detection: seven years of TRECVid activity

Shot boundary detection (SBD) is the process of automatically detecting the boundaries between shots in video. It is a problem which has attracted much attention since video became available in digital form as it is an essential pre-processing step to almost all video analysis, indexing, summarisation, search, and other content-based operations. Automatic SBD was one of the tracks of activity within the annual TRECVid benchmarking exercise, each year from 2001 to 2007 inclusive. Over those seven years we have seen 57 different research groups from across the world work to determine the best approaches to SBD while using a common dataset and common scoring metrics. In this paper we present an overview of the TRECVid shot boundary detection task, a high-level overview of the most significant of the approaches taken, and a comparison of performances, focussing on one year (2005) as an example

Network Synthesis of Linear Dynamical Quantum Stochastic Systems

The purpose of this paper is to develop a synthesis theory for linear dynamical quantum stochastic systems that are encountered in linear quantum optics and in phenomenological models of linear quantum circuits. In particular, such a theory will enable the systematic realization of coherent/fully quantum linear stochastic controllers for quantum control, amongst other potential applications. We show how general linear dynamical quantum stochastic systems can be constructed by assembling an appropriate interconnection of one degree of freedom open quantum harmonic oscillators and, in the quantum optics setting, discuss how such a network of oscillators can be approximately synthesized or implemented in a systematic way from some linear and non-linear quantum optical elements. An example is also provided to illustrate the theory.Comment: Revised and corrected version, published in SIAM Journal on Control and Optimization, 200