Active glass-polymer superlattice structure for photonic integration.

We propose an all-laser processing approach allowing controlled growth of organic-inorganic superlattice structures of rare-earth ion doped tellurium-oxide-based glass and optically transparent polydimethyl siloxane (PDMS) polymer; the purpose of which is to illustrate the structural and thermal compatibility of chemically dissimilar materials at the nanometer scale. Superlattice films with interlayer thicknesses as low as 2 nm were grown using pulsed laser deposition (PLD) at low temperatures (100 °C). Planar waveguides were successfully patterned by femtosecond-laser micro-machining for light propagation and efficient Er(3+)-ion amplified spontaneous emission (ASE). The proposed approach to achieve polymer-glass integration will allow the fabrication of efficient and durable polymer optical amplifiers and lossless photonic devices. The all-laser processing approach, discussed further in this paper, permits the growth of films of a multitude of chemically complex and dissimilar materials for a range of optical, thermal, mechanical and biological functions, which otherwise are impossible to integrate via conventional materials processing techniques.

Paradoxical evidence integration in rapid decision processes

Decisions about noisy stimuli require evidence integration over time. Traditionally, evidence integration and decision making are described as a one-stage process: a decision is made when evidence for the presence of a stimulus crosses a threshold. Here, we show that one-stage models cannot explain psychophysical experiments on feature fusion, where two visual stimuli are presented in rapid succession. Paradoxically, the second stimulus biases decisions more strongly than the first one, contrary to predictions of one-stage models and intuition. We present a two-stage model where sensory information is integrated and buffered before it is fed into a drift diffusion process. The model is tested in a series of psychophysical experiments and explains both accuracy and reaction time distributions. © 2012 Rüter et al.

Content Based Integration of Construction Site Images in Architecture, Engineering, Construction, and Facilities Management (AEC/FM) Model based Systems

This book explores the processes for retrieval, classification, and integration of construction images in AEC/FM model based systems. The author describes a combination of techniques from the areas of image and video processing, computer vision, information retrieval, statistics and content-based image and video retrieval that have been integrated into a novel method for the retrieval of related construction site image data from components of a project model. This method has been tested on available construction site images from a variety of sources like past and current building construction and transportation projects and is able to automatically classify, store, integrate and retrieve image data files in inter-organizational systems so as to allow their usage in project management related tasks. objects. Therefore, automated methods for the integration of construction images are important for construction information management. During this research, processes for retrieval, classification, and integration of construction images in AEC/FM model based systems have been explored. Specifically, a combination of techniques from the areas of image and video processing, computer vision, information retrieval, statistics and content-based image and video retrieval have been deployed in order to develop a methodology for the retrieval of related construction site image data from components of a project model. This method has been tested on available construction site images from a variety of sources like past and current building construction and transportation projects and is able to automatically classify, store, integrate and retrieve image data files in inter-organizational systems so as to allow their usage in project management related tasks.

Content Based Integration of Construction Site Images in AEC/FM Model Based Systems

The Architecture, Engineering, Construction and Facilities Management (AEC/FM) industry is rapidly becoming a multidisciplinary, multinational and multi-billion dollar economy, involving large numbers of actors working concurrently at different locations and using heterogeneous software and hardware technologies. Since the beginning of the last decade, a great deal of effort has been spent within the field of construction IT in order to integrate data and information from most computer tools used to carry out engineering projects. For this purpose, a number of integration models have been developed, like web-centric systems and construction project modeling, a useful approach in representing construction projects and integrating data from various civil engineering applications. In the modern, distributed and dynamic construction environment it is important to retrieve and exchange information from different sources and in different data formats in order to improve the processes supported by these systems. Previous research demonstrated that a major hurdle in AEC/FM data integration in such systems is caused by its variety of data types and that a significant part of the data is stored in semi-structured or unstructured formats. Therefore, new integrative approaches are needed to handle non-structured data types like images and text files. This research is focused on the integration of construction site images. These images are a significant part of the construction documentation with thousands stored in site photographs logs of large scale projects. However, locating and identifying such data needed for the important decision making processes is a very hard and time-consuming task, while so far, there are no automated methods for associating them with other related objects. Therefore, automated methods for the integration of construction images are important for construction information management. During this research, processes for retrieval, classification, and integration of construction images in AEC/FM model based systems have been explored. Specifically, a combination of techniques from the areas of image and video processing, computer vision, information retrieval, statistics and content-based image and video retrieval have been deployed in order to develop a methodology for the retrieval of related construction site image data from components of a project model. This method has been tested on available construction site images from a variety of sources like past and current building construction and transportation projects and is able to automatically classify, store, integrate and retrieve image data files in inter-organizational systems so as to allow their usage in project management related tasks.

A neurophysiologically plausible population code model for feature integration explains visual crowding.

An object in the peripheral visual field is more difficult to recognize when surrounded by other objects. This phenomenon is called "crowding". Crowding places a fundamental constraint on human vision that limits performance on numerous tasks. It has been suggested that crowding results from spatial feature integration necessary for object recognition. However, in the absence of convincing models, this theory has remained controversial. Here, we present a quantitative and physiologically plausible model for spatial integration of orientation signals, based on the principles of population coding. Using simulations, we demonstrate that this model coherently accounts for fundamental properties of crowding, including critical spacing, "compulsory averaging", and a foveal-peripheral anisotropy. Moreover, we show that the model predicts increased responses to correlated visual stimuli. Altogether, these results suggest that crowding has little immediate bearing on object recognition but is a by-product of a general, elementary integration mechanism in early vision aimed at improving signal quality.

The Silent Period of Evidence Integration in Fast Decision Making

In a typical experiment on decision making, one out of two possible stimuli is displayed and observers decide which one was presented. Recently, Stanford and colleagues (2010) introduced a new variant of this classical one-stimulus presentation paradigm to investigate the speed of decision making. They found evidence for "perceptual decision making in less than 30 ms". Here, we extended this one-stimulus compelled-response paradigm to a two-stimulus compelled-response paradigm in which a vernier was followed immediately by a second vernier with opposite offset direction. The two verniers and their offsets fuse. Only one vernier is perceived. When observers are asked to indicate the offset direction of the fused vernier, the offset of the second vernier dominates perception. Even for long vernier durations, the second vernier dominates decisions indicating that decision making can take substantial time. In accordance with previous studies, we suggest that our results are best explained with a two-stage model of decision making where a leaky evidence integration stage precedes a race-to-threshold process. © 2013 Rüter et al.