Hardware/software 2D-3D backprojection on a SoPC platform

International audienceThe reduction of image reconstruction time is needed to spread the use of PET for research and routine clinical practice. In this purpose, this article presents a hardware/software architecture for the acceleration of 3D backprojection based upon an efficient 2D backprojection. This architecture has been designed in order to provide a high level of parallelism thanks to an efficient management of the memory accesses which would have been otherwise strongly slowed by the external memory. The reconstruction system is embedded in a SoPC platform (System on Programmable Chip), the new generation of reconfigurable circuit. The originality of this architecture comes from the design of a 2D Adaptative and Predictive Cache (2D-AP Cache) which has proved to be an efficient way to overcome the memory access bottleneck. Thanks to a hierarchical use of this cache, several backprojection operators can run in parallel, accelerating in this manner noteworthy the reconstruction process. This 2D reconstruction system will next be used to speed up 3D image reconstruction

Model-Based Calibration of Filter Imperfections in the Random Demodulator for Compressive Sensing

The random demodulator is a recent compressive sensing architecture providing efficient sub-Nyquist sampling of sparse band-limited signals. The compressive sensing paradigm requires an accurate model of the analog front-end to enable correct signal reconstruction in the digital domain. In practice, hardware devices such as filters deviate from their desired design behavior due to component variations. Existing reconstruction algorithms are sensitive to such deviations, which fall into the more general category of measurement matrix perturbations. This paper proposes a model-based technique that aims to calibrate filter model mismatches to facilitate improved signal reconstruction quality. The mismatch is considered to be an additive error in the discretized impulse response. We identify the error by sampling a known calibrating signal, enabling least-squares estimation of the impulse response error. The error estimate and the known system model are used to calibrate the measurement matrix. Numerical analysis demonstrates the effectiveness of the calibration method even for highly deviating low-pass filter responses. The proposed method performance is also compared to a state of the art method based on discrete Fourier transform trigonometric interpolation.Comment: 10 pages, 8 figures, submitted to IEEE Transactions on Signal Processin

Haptic insights: model making as historical methodology

This article explores the value of digital reconstruction practice to the theatre historian in general but in particular the historian concerned with exploring visual histories offered by the areas of theatre design and architecture. It will articulate differences between the expectations and reality of digital reconstruction as illustration (both fixed and interactive) and suggest caveats and opportunities offered by digital (and virtual) outputs as a mode of communication. While the article will explore practice and critical commentary related to reconstruction as illustration, it will focus in more detail on the model as practice as research. The intention is to explore the methodological value of reconstructive practice in the process of the historian and identify possibilities for communicating the tacit knowledge generated by these approaches in ways which move beyond the simple presentation of visualised outputs as illustration

LabelFusion: A Pipeline for Generating Ground Truth Labels for Real RGBD Data of Cluttered Scenes

Deep neural network (DNN) architectures have been shown to outperform traditional pipelines for object segmentation and pose estimation using RGBD data, but the performance of these DNN pipelines is directly tied to how representative the training data is of the true data. Hence a key requirement for employing these methods in practice is to have a large set of labeled data for your specific robotic manipulation task, a requirement that is not generally satisfied by existing datasets. In this paper we develop a pipeline to rapidly generate high quality RGBD data with pixelwise labels and object poses. We use an RGBD camera to collect video of a scene from multiple viewpoints and leverage existing reconstruction techniques to produce a 3D dense reconstruction. We label the 3D reconstruction using a human assisted ICP-fitting of object meshes. By reprojecting the results of labeling the 3D scene we can produce labels for each RGBD image of the scene. This pipeline enabled us to collect over 1,000,000 labeled object instances in just a few days. We use this dataset to answer questions related to how much training data is required, and of what quality the data must be, to achieve high performance from a DNN architecture

Practice architectures and sustainable curriculum renewal

While there are numerous pedagogical innovations and varying forms of professional learning to support change, teachers rarely move beyond the initial implementation of new ideas and policies and few innovations reach the institutionalised stage. Building on both site ontologies and situated learning in communities of practice perspectives, this paper explores the theory of practice architectures to offer a different and legitimate perspective on sustainable curriculum renewal. Specifically, a practice architecture either enables or constrains particular practice and constitutes the construction of practice from semantic (e.g. language), social (e.g. power relations), and physical (e.g. materials) spaces. Through the juxtaposition of practice architectures with an empirical illustration of longer-term pedagogical change, the paper argues that for pedagogical change to be sustained a practice architecture that relates to an innovation’s intended learning outcomes and the contexts in which an innovation can be used needs to be created. Consequently, the theory of practice architectures can guide reform programmes. Curricularists can begin programmes with a pre-planned approach to assist, a) teachers’ understanding of how to use an innovation, and b) the deconstruction and reconstruction of practice architectures to support an innovation’s survival