924 research outputs found

    A survey of visual preprocessing and shape representation techniques

    Get PDF
    Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)

    Neuromorphic deep convolutional neural network learning systems for FPGA in real time

    Get PDF
    Deep Learning algorithms have become one of the best approaches for pattern recognition in several fields, including computer vision, speech recognition, natural language processing, and audio recognition, among others. In image vision, convolutional neural networks stand out, due to their relatively simple supervised training and their efficiency extracting features from a scene. Nowadays, there exist several implementations of convolutional neural networks accelerators that manage to perform these networks in real time. However, the number of operations and power consumption of these implementations can be reduced using a different processing paradigm as neuromorphic engineering. Neuromorphic engineering field studies the behavior of biological and inner systems of the human neural processing with the purpose of design analog, digital or mixed-signal systems to solve problems inspired in how human brain performs complex tasks, replicating the behavior and properties of biological neurons. Neuromorphic engineering tries to give an answer to how our brain is capable to learn and perform complex tasks with high efficiency under the paradigm of spike-based computation. This thesis explores both frame-based and spike-based processing paradigms for the development of hardware architectures for visual pattern recognition based on convolutional neural networks. In this work, two FPGA implementations of convolutional neural networks accelerator architectures for frame-based using OpenCL and SoC technologies are presented. Followed by a novel neuromorphic convolution processor for spike-based processing paradigm, which implements the same behaviour of leaky integrate-and-fire neuron model. Furthermore, it reads the data in rows being able to perform multiple layers in the same chip. Finally, a novel FPGA implementation of Hierarchy of Time Surfaces algorithm and a new memory model for spike-based systems are proposed

    Conceptual Transformation and Cognitive Processes in Origami Paper Folding

    Get PDF
    Research on problem solving typically does not address tasks that involve following detailed and/or illustrated step-by-step instructions. Such tasks are not seen as cognitively challenging problems to be solved. In this paper, we challenge this assumption by analyzing verbal protocols collected during an Origami folding task. Participants verbalised thoughts well beyond reading or reformulating task instructions, or commenting on actions. In particular, they compared the task status to pictures in the instruction, evaluated the progress so far, referred to previous experience, expressed problems and confusions, and—crucially—added complex thoughts and ideas about the current instructional step. The last two categories highlight the fact that participants conceptualised this spatial task as a problem to be solved, and used creativity to achieve this aim. Procedurally, the verbalisations reflect a typical order of steps: reading—reformulating—reconceptualising—evaluating. During reconceptualisation, the creative range of spatial concepts represented in language highlights the complex mental operations involved when transferring the two-dimensional representation into the real world. We discuss the implications of our findings in terms of problem solving as a multilayered process involving diverse types of cognitive effort, consider parallels to known conceptual challenges involved in interpreting spatial descriptions, and reflect on the benefit of reconceptualisation for cognitive processes

    Belle II Technical Design Report

    Full text link
    The Belle detector at the KEKB electron-positron collider has collected almost 1 billion Y(4S) events in its decade of operation. Super-KEKB, an upgrade of KEKB is under construction, to increase the luminosity by two orders of magnitude during a three-year shutdown, with an ultimate goal of 8E35 /cm^2 /s luminosity. To exploit the increased luminosity, an upgrade of the Belle detector has been proposed. A new international collaboration Belle-II, is being formed. The Technical Design Report presents physics motivation, basic methods of the accelerator upgrade, as well as key improvements of the detector.Comment: Edited by: Z. Dole\v{z}al and S. Un

    Cbinfer: Change-based inference for convolutional neural networks on video data

    Get PDF
    Extracting per-frame features using convolutional neural networks for real-time processing of video data is currently mainly performed on powerful GPU-accelerated workstations and compute clusters. However, there are many applications such as smart surveillance cameras that require or would benefit from on-site processing. To this end, we propose and evaluate a novel algorithm for changebased evaluation of CNNs for video data recorded with a static camera setting, exploiting the spatio-temporal sparsity of pixel changes. We achieve an average speed-up of 8.6 \uc3\u97 over a cuDNN baseline on a realistic benchmark with a negligible accuracy loss of less than 0.1% and no retraining of the network. The resulting energy efficiency is 10 \uc3\u97 higher than that of per-frame evaluation and reaches an equivalent of 328 GOp/s/W on the Tegra X1 platform

    Diversity in Computer Science

    Get PDF
    This is an open access book that covers the complete set of experiences and results of the FemTech.dk research which we have had conducted between 2016-2021 – from initiate idea to societal communication. Diversity in Computer Science: Design Artefacts for Equity and Inclusion presents and documents the principles, results, and learnings behind the research initiative FemTech.dk, which was created in 2016 and continues today as an important part of the Department of Computer Science at the University of Copenhagen’s strategic development for years to come. FemTech.dk was created in 2016 to engage with research within gender and diversity and to explore the role of gender equity as part of digital technology design and development. FemTech.dk considers how and why computer science as a field and profession in Denmark has such a distinct unbalanced gender representation in the 21st century. This book is also the story of how we (the authors) as computer science researchers embarked on a journey to engage with a new research field – equity and gender in computing – about which we had only sporadic knowledge when we began. We refer here to equity and gender in computing as a research field – but in reality, this research field is a multiplicity of entangled paths, concepts, and directions that forms important and critical insights about society, gender, politics, and infrastructures which are published in different venues and often have very different sets of criteria, values, and assumptions. Thus, part of our journey is also to learn and engage with all these different streams of research, concepts, and theoretical approaches and, through these engagements, to identify and develop our own theoretical platform, which has a foundation in our research backgrounds in Human–Computer Interaction broadly – and Interaction Design & Computer Supported Cooperative Work specifically
    • 

    corecore