A new intelligent systems approach to 3D animation in television

Prado, Charles B.; França, Felipe M. G.; Costa, Éder Silva; Vasconcelos, Luiz

doi:10.1145/1282280.1282300

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…This caused applications using WiSARD, performing lower than that of virtual generalising RAM (VG-RAM) WNN [ 10 , 11 ]. However, WiSARD has used to solve the problems in automatic video surveillance [ 12 ], robotics [ 13 ], 3D video animation [ 14 ] and text categorisation [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic disease diagnosis using optimised weightless neural networks for low‐power wearable devices

Cheruku

Edla

Kuppili

et al. 2017

Healthcare Technology Letters

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Low-power wearable devices for disease diagnosis are used at anytime and anywhere. These are non-invasive and pain-free for the better quality of life. However, these devices are resource constrained in terms of memory and processing capability. Memory constraint allows these devices to store a limited number of patterns and processing constraint provides delayed response. It is a challenging task to design a robust classification system under above constraints with high accuracy. In this Letter, to resolve this problem, a novel architecture for weightless neural networks (WNNs) has been proposed. It uses variable sized random access memories to optimise the memory usage and a modified binary TRIE data structure for reducing the test time. In addition, a bio-inspired-based genetic algorithm has been employed to improve the accuracy. The proposed architecture is experimented on various disease datasets using its software and hardware realisations. The experimental results prove that the proposed architecture achieves better performance in terms of accuracy, memory saving and test time as compared to standard WNNs. It also outperforms in terms of accuracy as compared to conventional neural network-based classifiers. The proposed architecture is a powerful part of most of the low-power wearable devices for the solution of memory, accuracy and time issues.

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Section: Introductionmentioning

confidence: 99%

Automatic disease diagnosis using optimised weightless neural networks for low‐power wearable devices

Cheruku

Edla

Kuppili

et al. 2017

Healthcare Technology Letters

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“…With this organization, the total amount of memory required per neuron is reduced from 2 p to q × 2 p/q . This framework, and many variants, have been used for a large variety of applications, including indoor positioning systems [8], robot localization system [9,10,11], recognition of DNA chains [12], feature tracking in images [13], data clustering [14], and audio recognition [15]. However, given the constraints imposed by dividing the neuron input vector in q segments, the representation capacity of the network is reduced [16], which hinders its generalization ability [17].…”

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confidence: 99%

Fat-Fast VG-RAM WNN: A high performance approach

et al. 2016

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This is the accepted version of the paper.This version of the publication may differ from the final published version. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Permanent repository link AbstractThe Virtual Generalizing Random Access Memory Weightless Neural Network (VG-RAM WNN) is a type of WNN that only requires storage capacity proportional to the training set. As such, it is an effective machine learning technique that offers simple implementation and fast training -it can be made in one shot. However, the VG-RAM WNN test time for applications that require many training samples can be large, since it increases with the size of the memory of each neuron. In this paper, we present Fat-Fast VG-RAM WNNs. Fat-Fast VG-RAM WNNs employ multi-index chained hashing for fast neuron memory search. Our chained hashing technique increases the VG-RAM memory consumption (fat) but reduces test time substantially (fast), while keeping most of its machine learning performance. To address the memory consumption problem, we employ a data clustering technique to reduce the overall size of the neurons' memory. This can be achieved by replacing clusters of neurons' memory by their respective centroid values. With our approach, we were able to reduce VG-RAM WNN test time and memory footprint, while maintaining a high and acceptable machine learning performance. We performed experiments with the Fat-Fast VG-RAM WNN applied to two recognition problems: (i) handwritten digit recognition, and (ii) traffic sign recognition. Our experimental results showed that, in both recognition problems, our new VG-RAM WNN approach was able to run three orders of magnitude faster and consume two orders of magnitude less memory than standard VG-RAM, while experiencing only a small reduction in recognition performance.

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