Multiple Luminaire Identification in Airborne Images of Airport’s Approach Lighting Using Mathematical Morphology With Variable Length Structuring Element

The increasing demand for fast air transportation around the clock
has increased the number of night flights in civil aviation over
the past few decades. In night aviation, to land an aircraft, a
pilot needs to be able to identify an airport. The approach
lighting system (ALS) at an airport is used to provide
identification and guidance to pilots from a distance. ALS
consists of more than $100$ luminaires which are installed in a
defined pattern following strict guidelines by the International
Civil Aviation Organization (ICAO). ICAO also has strict
regulations for maintaining the performance level of the
luminaires. However, once installed, to date there is no automated
technique by which to monitor the performance of the lighting. We
suggest using images of the lighting pattern captured using a camera
placed inside an aircraft. Based on the information contained
within these images, the performance of the luminaires has to be
evaluated which requires identification of over $100$ luminaires
within the pattern of ALS image. This research proposes analysis
of the pattern using morphology filters which use a variable
length structuring element (VLSE). The dimension of the VLSE changes
continuously within an image and varies for different images.
A novel
technique for automatic determination of the VLSE is proposed and
it allows successful identification of the luminaires from the
image data as verified through the use of simulated and real data.

Confidence factor determination for performance evaluation of lighting pattern

In this paper the authors propose a new technique for determining a confidence factor applied to the performance prediction of individual luminaires within an overall pattern of luminaires. This work has relevance to any application where it is necessary to determine the performance of a lighting pattern e.g. street lighting, signal lighting etc. In this paper we apply our technique to a transportation application, namely, an airport landing lighting pattern. In the aviation industry it is imperative that the landing lighting pattern at individual airports performs according to standards. We have developed an automated technique which can be used to access the performance of luminaires within this pattern. We extend this work to also derive a confidence factor related to this prediction based on the quality of the data being utilised. ©2010 IEEE.

Monitoring the performance of aerodrome ground lighting

This article provides an overview of a novel prototype device that can be used to aid airports in monitoring their landing lighting. Known as Aerodrome Ground Lighting (AGL), the device is comprised of a camera that is capable of capturing images of landing lighting as aircraft approach the airport. AGL is designed to automatically examine landing lighting to assess if it is operating under uniform brightness standards (i.e., luminous intensity of luminares) that aviation governing bodies require. A detailed discussion of the hardware and software requirements of AGL -- currently under joint development by researchers at Queens University Belfast and Cobham Flight Inspection Limited -- is presented. Results from the research indicate that assessing the performance of both ground-based runway luminaries and elevated approach luminaries is possible, though further testing is needed for full validation.

Using Graphs to Visualise and Detect Anomalies in Access Control System Transactions

Physical Access Control Systems are commonly used to secure doors in buildings such as airports, hospitals, government buildings and offices. These systems are designed primarily to provide an authentication mechanism, but they also log each door access as a transaction in a database. Unsupervised learning techniques can be used to detect inconsistencies or anomalies in the mobility data, such as a cloned or forged Access Badge, or unusual behaviour by staff members. In this paper, we present an overview of our method of inferring directed graphs to represent a physical building network and the flows of mobility within it. We demonstrate how the graphs can be used for Visual Data Exploration, and outline how to apply algorithms based on Information Theory to the graph data in order to detect inconsistent or abnormal behaviour.

Infra-Éireann: Making Ireland Modern - Irish Pavilion for the 14th Architectural biennale, Venice 2014.

After an open competition, we were selected to commission, curate and design the Irish pavilion for the Venice biennale 2014. Our proposal engage with the role of infrastructure and architecture in the cultural development of the new Irish state 1914-2014. This curatorial programme was realised in a demountable, open matrix pavilion measuring 12 x 5 x 6 metres.

How modernity is absorbed into national cultures usually presupposes an attachment to previous conditions and a desire to reconcile the two. In an Irish context, due to the processes of de-colonisation and political independence, this relationship is more complicated.

In 1914, Ireland was largely agricultural and lacked any significant industrial complex. The construction of new infrastructures after independence in 1921 became central to the cultural imagining of the new nation. The adoption of modernist architecture was perceived as a way to escape the colonial past. As the desire to reconcile cultural and technological aims developed, these infrastructures became both the physical manifestation and concrete identity of the new nation with architecture an essential element in this construct.

Technology and infrastructure are inherently cosmopolitan. Beginning with the Shannon hydro-electric facility at Ardnacrusha (1929) involving the German firm of Siemens-Schuckert, Ireland became a point of various intersections between imported international expertise and local need. By the turn of the last century, it had become one of the most globalised countries in the world, site of the European headquarters of multinationals such as Google and Microsoft. Climatically and economically expedient to the storing and harvesting of data, Ireland has subsequently become an important repository of digital information farmed in large, single-storey sheds absorbed into dispersed suburbs. In 2013, it became the preferred site for Intel to design and develop its new microprocessor board, the Galileo, a building block for the internet of things.

The story of the decades in between, of shifts made manifest in architecture and infrastructure, from the policies of economic protectionism to the embracing of the EU is one of the influx of technologies and cultural references into a small country on the edges of Europe: Ireland as both a launch-pad and testing ground for a series of aspects of designed modernity.

Video Event Recognition by Dempster-Shafer Theory

This paper presents an event recognition framework, based on Dempster-Shafer theory, that combines evidence of events from low-level computer vision analytics. The proposed method employing evidential network modelling of composite events, is able to represent uncertainty of event output from low level video analysis and infer high level events with semantic meaning along with degrees of belief. The method has been evaluated on videos taken of subjects entering and leaving a seated area. This has relevance to a number of transport scenarios, such as onboard buses and trains, and also in train stations and airports. Recognition results of 78% and 100% for four composite events are encouraging.

A techno-economic feasibility study of high altitude wind power in Northern Ireland

Globally the amount of installed terrestrial wind power both onshore and offshore has grown rapidly over the last twenty years. Most large onshore and offshore wind turbines are designed to harvest winds within the atmospheric boundary layer, which can be vary variable due to terrain and weather effects. The height of the neutral atmospheric boundary layer is estimated at above 1300m. A relatively new concept is to harvest more consistent wind conditions above the atmospheric boundary layer using high altitude wind harvesting devices such as tethered kites, air foils and dirigible rotors. This paper presents a techno-economic feasibility study of high altitude wind power in Northern Ireland. First this research involved a state of the art review of the resource and the technologies proposed for high altitude wind power. Next the techno-economic analysis involving four steps is presented. In step one, the potential of high altitude wind power in Northern Ireland using online datasets (e.g. Earth System Research Laboratory) is estimated. In step two a map for easier visualisation of geographical limitations (e.g. airports, areas of scenic beauty, flight paths, military training areas, settlements etc.) that could impact on high altitude wind power is developed. In step three the actual feasible resource available is recalculated using the visualisation map to determine the ‘optimal’ high altitude wind power locations in Northern Ireland. In the last step four the list of equipment, resources and budget needed to build a demonstrator is provided in the form of a concise techno-economic appraisal using the findings of the previous three steps.

Semantic feature-based visual attention model for pedestrian detection

Objective
Pedestrian detection under video surveillance systems has always been a hot topic in computer vision research. These systems are widely used in train stations, airports, large commercial plazas, and other public places. However, pedestrian detection remains difficult because of complex backgrounds. Given its development in recent years, the visual attention mechanism has attracted increasing attention in object detection and tracking research, and previous studies have achieved substantial progress and breakthroughs. We propose a novel pedestrian detection method based on the semantic features under the visual attention mechanism.
Method
The proposed semantic feature-based visual attention model is a spatial-temporal model that consists of two parts: the static visual attention model and the motion visual attention model. The static visual attention model in the spatial domain is constructed by combining bottom-up with top-down attention guidance. Based on the characteristics of pedestrians, the bottom-up visual attention model of Itti is improved by intensifying the orientation vectors of elementary visual features to make the visual saliency map suitable for pedestrian detection. In terms of pedestrian attributes, skin color is selected as a semantic feature for pedestrian detection. The regional and Gaussian models are adopted to construct the skin color model. Skin feature-based visual attention guidance is then proposed to complete the top-down process. The bottom-up and top-down visual attentions are linearly combined using the proper weights obtained from experiments to construct the static visual attention model in the spatial domain. The spatial-temporal visual attention model is then constructed via the motion features in the temporal domain. Based on the static visual attention model in the spatial domain, the frame difference method is combined with optical flowing to detect motion vectors. Filtering is applied to process the field of motion vectors. The saliency of motion vectors can be evaluated via motion entropy to make the selected motion feature more suitable for the spatial-temporal visual attention model.
Result
Standard datasets and practical videos are selected for the experiments. The experiments are performed on a MATLAB R2012a platform. The experimental results show that our spatial-temporal visual attention model demonstrates favorable robustness under various scenes, including indoor train station surveillance videos and outdoor scenes with swaying leaves. Our proposed model outperforms the visual attention model of Itti, the graph-based visual saliency model, the phase spectrum of quaternion Fourier transform model, and the motion channel model of Liu in terms of pedestrian detection. The proposed model achieves a 93% accuracy rate on the test video.
Conclusion
This paper proposes a novel pedestrian method based on the visual attention mechanism. A spatial-temporal visual attention model that uses low-level and semantic features is proposed to calculate the saliency map. Based on this model, the pedestrian targets can be detected through focus of attention shifts. The experimental results verify the effectiveness of the proposed attention model for detecting pedestrians.