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High-Dimensional Computing as a Nanoscalable Paradigm
We outline a model of computing with high-dimensional (HD) vectors—where the dimensionality is in the thousands. It is built on ideas from traditional (symbolic) computing and artificial neuralExpand
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Classification and Recall With Binary Hyperdimensional Computing: Tradeoffs in Choice of Density and Mapping Characteristics
Hyperdimensional (HD) computing is a promising paradigm for future intelligent electronic appliances operating at low power. This paper discusses tradeoffs of selecting parameters of binary HDExpand
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Holographic Graph Neuron: A Bioinspired Architecture for Pattern Processing
In this paper, we propose a new approach to implementing hierarchical graph neuron (HGN), an architecture for memorizing patterns of generic sensor stimuli, through the use of vector symbolicExpand
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Comparison of Machine Learning Techniques for Vehicle Classification Using Road Side Sensors
The main contribution of this paper is a comparison of different machine learning algorithms for vehicle classification according to the "Nordic system for intelligent classification of vehicles"Expand
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Exploiting bacterial properties for multi-hop nanonetworks
Molecular communication is a relatively new communication paradigm for nanomachines where the communication is realized by utilizing existing biological components found in nature. In recent yearsExpand
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Detection of Atrial Fibrillation from Short ECGs: Minimalistic Complexity Analysis for Feature-Based Classifiers
In order to facilitate data-driven solutions for early detection of atrial fibrillation (AF), the 2017 CinC conference challenge was devoted to automatic AF classification based on short ECGExpand
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A Theory of Sequence Indexing and Working Memory in Recurrent Neural Networks
To accommodate structured approaches of neural computation, we propose a class of recurrent neural networks for indexing and storing sequences of symbols or analog data vectors. These networks withExpand
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Fault detection in the hyperspace: Towards intelligent automation systems
This article presents a methodology for intelligent, biologically inspired fault detection system for generic complex systems of systems. The proposed methodology utilizes the concepts of associativeExpand
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On Bidirectional Transitions between Localist and Distributed Representations: The Case of Common Substrings Search Using Vector Symbolic Architecture
The contribution of this article is twofold. First, it presents an encoding approach for seamless bidirectional transitions between localist and distributed representation domains. Second, the apprExpand
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Modality classification of medical images with distributed representations based on cellular automata reservoir computing
Modality corresponding to medical images is a vital filter in medical image retrieval systems. This article presents the classification of modalities of medical images based on the usage ofExpand
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