Daragh Byrne

Constructing a SenseCam visual diary as a media process

The SenseCam is a small wearable personal device which automatically captures up to 2,500 images per day. This yields a very large personal collection of images, or in a sense a large visual diary of a person’s day. Intelligent techniques are necessary for effective structuring, searching and browsing of this image collection for locating important or significant events in a person’s life. In this paper we identify three stages in the process of capturing and structuring SenseCam images and then displaying them to an end user to review. These stages are expressed in terms of the Canonical process stages to which they correlate.

Everyday concept detection in visual lifelogs: validation, relationships and trends

The Microsoft SenseCam is a small lightweight wearable camera used to passively capture photos and other sensor readings from a user’s day-to-day activities. It captures on average 3,000 images in a typical day, equating to almost 1 million images per year. It can be used to aid memory by creating a personal multimedia lifelog, or visual recording of the wearer’s life. However the sheer volume of image data captured within a visual lifelog creates a number of challenges, particularly for locating relevant content. Within this work, we explore the applicability of semantic concept detection, a method often used within video retrieval, on the domain of visual lifelogs. Our concept detector models the correspondence between low-level visual features and high-level semantic concepts (such as indoors, outdoors, people, buildings, etc.) using supervised machine learning. By doing so it determines the probability of a concept’s presence. We apply detection of 27 everyday semantic concepts on a lifelog collection composed of 257,518 SenseCam images from 5 users. The results were evaluated on a subset of 95,907 images, to determine the accuracy for detection of each semantic concept. We conducted further analysis on the temporal consistency, co-occurance and relationships within the detected concepts to more extensively investigate the robustness of the detectors within this domain.

Towards computational autobiographical narratives through human digital memories

Lifelogging is an emergent technology which offers the ability to capture an individuals life experiences through digital means. While emphasis has been placed on taming such voluminous multimodal collections, it additionally opens new opportunities for retelling and sharing our personal life experiences. We explore these opportunities within the domain of lifelogging and present the research questions, challenges and considerations to be addressed to achieve meaningful autobiographical digital narratives within such collections.

An examination of a large visual lifelog

Lifelogging is the act of recording some aspect of your life in digital format. A basic and common form of lifelogging is the creation and maintenance of blogs, which are typically textual in nature, though often with multi-media elements. In this paper we are concerned with visual lifelogging, a new form of lifelogging based on the passive capture of photos of a persons experiences. We examine the nature of visual lifelogs, and the differences between visual lifelog photos and explicitly captured digital photos. This is done by examining a million lifelog photos encompassing a year of a visual lifelog from the life of one individual.

Life editing: third-party perspectives on lifelog content

Lifelog collections digitally capture and preserve personal experiences and can be mined to reveal insights and understandings of individual significance. These rich data sources also offer opportunities for learning and discovery by motivated third parties. We employ a custom-designed storytelling application in constructing meaningful lifelog summaries from third-party perspectives. This storytelling initiative was implemented as a core component in a university media-editing course. We present promising results from a preliminary study conducted to evaluate the utility and potential of our approach in creatively interpreting a unique experiential dataset.

A catalyst-free and facile route to periodically ordered and c-axis aligned ZnO nanorod arrays on diverse substrates

In this work we present a method for the deposition of periodically ordered, c-axis aligned ZnO nanorod arrays. By using chemical bath deposited films in conjunction with silica templating through nanosphere monolayers, masks suitable for high temperature deposition are created. A vapour phase transport technique is then used to deposit ordered arrays, quickly and inexpensively in a manner ideal for low cost, scalable and reproducible growth on a diverse range of substrates.

Multiphoton-absorption induced ultraviolet luminescence of ZnO nanorods using low-energy femtosecond pulses

Multiphoton-absorption (MPA) induced ultraviolet (UV) luminescence of ZnO nanorods grown by vapor phase transport was demonstrated using ultrafast excitation at pulse energies in the few nanojoules range, directly generated by a Ti:sapphire laser oscillator at wavelengths around 800 nm. The dependence of the UV luminescence on the excitation density reveals a two-photon absorption process as the responsible excitation mechanism. The broad spectral bandwidth of the excitation pulses obviously promotes the feasibility of the observed two-photon channel. Theoretical estimates concerning the contribution of nonlinear absorbance strongly support the experimental findings. The essential conditions for proper utilization of this process are discussed.

Validating the Detection of Everyday Concepts in Visual Lifelogs

The Microsoft SenseCam is a small lightweight wearable camera used to passively capture photos and other sensor readings from a users day-to-day activities. It can capture up to 3,000 images per day, equating to almost 1 million images per year. It is used to aid memory by creating a personal multimedia lifelog, or visual recording of the wearers life. However the sheer volume of image data captured within a visual lifelog creates a number of challenges, particularly for locating relevant content. Within this work, we explore the applicability of semantic concept detection, a method often used within video retrieval, on the novel domain of visual lifelogs. A concept detector models the correspondence between low-level visual features and high-level semantic concepts (such as indoors, outdoors, people, buildings, etc.) using supervised machine learning. By doing so it determines the probability of a concepts presence. We apply detection of 27 everyday semantic concepts on a lifelog collection composed of 257,518 SenseCam images from 5 users. The results were then evaluated on a subset of 95,907 images, to determine the precision for detection of each semantic concept and to draw some interesting inferences on the lifestyles of those 5 users. We additionally present future applications of concept detection within the domain of lifelogging.

Unambiguous identification of the role of a single Cu atom in the ZnO structured green band

High quality and purity single crystal ZnO samples doped with single isotopes of (63)Cu and (65)Cu, with equal concentrations of both these isotopes, and with natural Cu using a wet chemical atomic substitution reaction and anneal were studied using low temperature optical spectroscopy. Our data on the zero phonon line of the structured green band in ZnO confirm unambiguously the involvement of a single Cu atom in this defect emission. These data allow us to confirm the main features of the assignment proposed by Dingle in 1969 and to comment further on the defect structure.

Effects of Cu diffusion-doping on structural, optical, and magnetic properties of ZnO nanorod arrays grown by vapor phase transport method

Well-aligned ZnO nanorods were prepared by the vapor phase transport method on Si covered with a ZnO buffer layer. After the nanostructure growth, Cu was doped into the ZnO nanorods by diffusion at three different temperatures and for different times. Undoped and Cu diffusion-doped ZnO samples are highly textured, with the c axis of the wurtzite structure along the growth direction. The incorporation of Cu caused some slight changes in the nanorod alignment, although the wurtzite crystal structure was maintained. X-ray photoelectron spectroscopy measurements revealed that Cu ions were in a divalent state and substituted for the Zn2+ ions of the ZnO matrix. Photoluminescence results at 10 K indicate that the incorporation of copper leads to a relative increase of Cu-related structured green band deep level intensity. Magnetic measurements revealed that both undoped and Cu diffusion-doped ZnO samples exhibited room temperature ferromagnetism. It was also found that bound magnetic polarons play an important ...