Lumina: a soft kinetic material for morphing architectural skins and organic user interfaces

The pervasive computing era has seen sensor and actuator technologies integrated into the design of kinetic building skins. This paper presents an investigation of a new soft kinetic material that has potential applications for morphing architectural building skins and organic user interfaces. The material capacities of Lumina to sense the ambient environment, morph and change forms, and emit light are demonstrated in the two prototypes presented in the paper. The first prototype is Blind, a form-changing organic user interface with multiple eye-like apertures that can be programmed to accept data input for visual communication. The second prototype is Blanket, a responsive morphing architectural skin with minimal mechanical and discrete components that sense real-time space occupancy data, manipulate light effects, perform active illumination, and act as an ambient display

Designing elastic transformable structures: towards soft responsive architecture

This paper discusses the issues of designing and building environment involving spatial conditions that can be physically reconfigured to meet changing needs. To achieve this architectural vision, most current research focuses on the kinetic, mechanical systems and physical control mechanisms for actuation and structural transformation. Instead of the 'hard' mechanical joints and components, there is an unexplored 'soft' approach using lightweight elastic composite materials for designing responsive architectural skins and structures. This paper investigates the new possibilities for the manipulation of various architectural enclosures using 'soft' and elastic transformable structures, in response to environmental, communication and adapting to various contexts. This approach intends to minimise the mechanistic actuations and reduce weight for such operations. Therefore, this research introduces two modules (a tetrahedron and a cube) as responsive spatial models to test the potentials and limitations for the implementation of elastic materials with responsive capability towards reconfigurable architectural enclosure. Despite their individual differences, these experiments identify a trajectory for new possibilities for elastic architectural components that are more appropriate for 'soft' responsive architecture. We argue that this approach can provide an early hypothesis for design responsive architecture with a mix of passive and active design strategies

A responsive morphing media skin

Existing media façades do not function as fenestration devices. They have been used mainly for visual communication and aesthetic purposes. This paper introduces a responsive morphing skin that can act as an active fenestration device as well as a media skin. We investigate new possibilities of using form-changing materials in designing responsive morphing skins that respond to environmental conditions and act as a communicative display. The design experiment that embodied this investigation, namely Blind, serves as a new layer of analogue media brise-soleil for existing space. It communicates the relationships between interior and exterior spaces visually and projects mutable imageries to the surrounding environment through sunlight. The design process of Blind simulates the responsive behaviour of the intended architectural skin by integrating physical computing and parametric design tools. This process includes the integration of soft apertures and architectural morphing skin to introduce a novel design method that enables an architectural skin to be a means of communication and fenestration. It responds to changing stimuli and intends to improve the spatial quality of existing environments through two types of transformations: morphological and patterned

Predicting imbalanced taxi and passenger queue contexts in airport

The taxi and passenger queue contexts indicate the various states of queues related to taxis and passengers (i.e. taxis are waiting for passengers, passengers are waiting for taxis, both are waiting for each other, none is waiting). Predicting these queue contexts in a future time is very important for better airport ground transport operations. However, queue context prediction at the airport is a challenging problem due to the presence of different contextual factors i.e., time, weather, taxi trips, flight arrivals and many more. Also these taxi and passenger queue contexts at the airport are imbalanced since some of the contexts are very infrequently occurring compared to others. In this paper, we address the problem of predicting imbalanced taxi and passenger queue contexts at the airport. First, we investigate different contextual factors, including time, taxi trips, passengers and weather for queue context prediction. Then we propose a detailed step by step solution to address this problem. To support the effectiveness of our detailed approach, we generate a queue context dataset by fusing three real world datasets including taxi trip, passenger wait time and weather condition that represent the taxi and passenger queue contexts at a major international airport in the New York City. The experimental results demonstrate that our developed queue context prediction framework provides detailed solutions to deliver higher accuracy in queue context prediction

CoAcT: A framework for context-aware trip planning using active transport

Policy makers and urban planners around the world are encouraging people to use active transport by providing more easily accessible facilities for active transport users. However, trip planning using active transport is not straight forward and requires consideration of various trip contexts such as congestion, accessibility, attractiveness, safety as well as the physical ability of the traveller. The existing approaches do not provide a unified solution to integrate and represent these diverse set of contexts in active transport trip planning. In this paper, we propose a new framework called CoAcT which is able to integrate and represent various trip contexts for context aware trip planning using active transport. We also present two real world deployments of our proposed framework

CoAcT: A framework for context-aware trip planning using active transport

Policy makers and urban planners around the world are encouraging people to use active transport by providing more easily accessible facilities for active transport users. However, trip planning using active transport is not straight forward and requires consideration of various trip contexts such as congestion, accessibility, attractiveness, safety as well as the physical ability of the traveller. The existing approaches do not provide a unified solution to integrate and represent these diverse set of contexts in active transport trip planning. In this paper, we propose a new framework called CoAcT which is able to integrate and represent various trip contexts for context aware trip planning using active transport. We also present two real world deployments of our proposed framework

Queue context prediction using taxi driver knowledge

This paper addresses the problem of taxi-passenger queue context prediction using neighborhood based methods. We capture the taxi drivers' knowledge based on how they move in terms of temporal driver-knowledge deviation (TDKD). Then a TDKD-aided feature importance scheme is introduced for neighborhood based queue context prediction. We apply our proposed scheme to predict different queue contexts at a busy international airport in New York. We argue that the incorporation of taxi drivers' knowledge for calculating feature importance significantly improves the quality of selected neighborhood, thus boosting the prediction accuracy. The experimental results demonstrate the effectiveness of our proposed TDKD-aided feature importance scheme for neighborhood based taxi-passenger queue context prediction

SECC: simultaneous extraction of context and community from pervasive signals

Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain the human dynamics or behaviors and then use them as the way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture high-order and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows nested structure to be built to explain data at multiple levels. We demonstrate our framework on three public datasets where the advantages of the proposed approach are validated

CAPRA: A contour-based accessible path routing algorithm

Existing journey planners and route recommenders mainly focus on calculating the shortest path with minimum distance or travel time. However, elderly people and those with special needs (i.e. those in wheelchairs or walking with sticks) often prefer a safer and more gentle journey. Given that their route options are affected by accessibility issues such as climbing a steep slope, it is important to design a journey planner that takes in to account the accessibility of the route, as well as the standard metrics, such as travel time and distance. Accessibility has not been explored widely in path finding problems. There are two key challenges for computing accessibility. First, the accessibility of a route is not well-defined. Second, the accessibility of a route varies from user to user. In this paper, a new algorithm is designed to tackle the above two challenges. Two metrics are defined to reflect the accessibility of a route, in terms of the total vertical distance and the maximum slope. Then, a multi-objective A* search algorithm is designed to obtain a set of Pareto-optimal routes in terms of the total distance covered and the two accessibility metrics. The user can then choose from the routes provided by the new algorithm, the most suitable one according to their own preferences. The experimental results show that the proposed algorithm is able to provide a diverse set of routes with different accessibility options, including the shortest path which does not consider any accessibility metrics. In other words, the new journey planner can satisfy the preferences of a wide range of users including both the healthy and those with special needs

U&I aware: a framework using data mining and collision detection to increase awareness for intersection users

An intersection safety system should adapt to the particular characteristics that identify an intersection, by mining traffic and collision data. Given the large amount of sensor data that are obtained for intersections and from sensor-equipped cars, analysis and learning of such data is essential. This paper presents a new method to improve safety at intersections using a combination of a mathematical based collision detection algorithm and data mining. A number of scenarios at a simulated intersection are explored with encouraging results from our data mining implementation. The results suggest that our approach can help improve situation awareness and automate understanding of intersections, which, in turn, can be used to increase safety at intersections