275,965 research outputs found

    Evolving spiking neural networks for temporal pattern recognition in the presence of noise

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    Creative Commons - Attribution-NonCommercial-NoDerivs 3.0 United StatesNervous systems of biological organisms use temporal patterns of spikes to encode sensory input, but the mechanisms that underlie the recognition of such patterns are unclear. In the present work, we explore how networks of spiking neurons can be evolved to recognize temporal input patterns without being able to adjust signal conduction delays. We evolve the networks with GReaNs, an artificial life platform that encodes the topology of the network (and the weights of connections) in a fashion inspired by the encoding of gene regulatory networks in biological genomes. The number of computational nodes or connections is not limited in GReaNs, but here we limit the size of the networks to analyze the functioning of the networks and the effect of network size on the evolvability of robustness to noise. Our results show that even very small networks of spiking neurons can perform temporal pattern recognition in the presence of input noiseFinal Published versio

    An Orientation Selective Neural Network and its Application to Cosmic Muon Identification

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    We propose a novel method for identification of a linear pattern of pixels on a two-dimensional grid. Following principles employed by the visual cortex, we employ orientation selective neurons in a neural network which performs this task. The method is then applied to a sample of data collected with the ZEUS detector at HERA in order to identify cosmic muons which leave a linear pattern of signals in the segmented uranium-scintillator calorimeter. A two dimensional representation of the relevant part of the detector is used. The results compared with a visual scan point to a very satisfactory cosmic muon identification. The algorithm performs well in the presence of noise and pixels with limited efficiency. Given its architecture, this system becomes a good candidate for fast pattern recognition in parallel processing devices.Comment: 19 pages, 10 Postrcipt figure

    Optical Correlation

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    Pattern recognition may supplement or replace certain navigational aids on spacecraft in docking or landing activities. The need to correctly identify terrain features remains critical in preparation of autonomous planetary landing. One technique that may solve this problem is optical correlation. Correlation has been successfully demonstrated under ideal conditions; however, noise significantly affects the ability of the correlator to accurately identify input signals. Optical correlation in the presence of noise must be successfully demonstrated before this technology can be incorporated into system design. An optical correlator is designed and constructed using a modified 2f configuration. Liquid crystal televisions (LCTV) are used as the spatial light modulators (SLM) for both the input and filter devices. The filter LCTV is characterized and an operating curve is developed. Determination of this operating curve is critical for reduction of input noise. Correlation of live input with a programmable filter is demonstrated

    Towards the text compression based feature extraction in high impedance fault detection

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    High impedance faults of medium voltage overhead lines with covered conductors can be identified by the presence of partial discharges. Despite it is a subject of research for more than 60 years, online partial discharges detection is always a challenge, especially in environment with heavy background noise. In this paper, a new approach for partial discharge pattern recognition is presented. All results were obtained on data, acquired from real 22 kV medium voltage overhead power line with covered conductors. The proposed method is based on a text compression algorithm and it serves as a signal similarity estimation, applied for the first time on partial discharge pattern. Its relevancy is examined by three different variations of classification model. The improvement gained on an already deployed model proves its quality.Web of Science1211art. no. 214

    Impact of stimulus-related factors and hearing impairment on listening effort as indicated by pupil dilation

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    Previous research has reported effects of masker type and signal-to-noise ratio (SNR) on listening effort, as indicated by the peak pupil dilation (PPD) relative to baseline during speech recognition. At about 50% correct sentence recognition performance, increasing SNRs generally results in declining PPDs, indicating reduced effort. However, the decline in PPD over SNRs has been observed to be less pronounced for hearing-impaired (HI) compared to normal-hearing (NH) listeners. The presence of a competing talker during speech recognition generally resulted in larger PPDs as compared to the presence of a fluctuating or stationary background noise. The aim of the present study was to examine the interplay between hearing-status, a broad range of SNRs corresponding to sentence recognition performance varying from 0 to 100% correct, and different masker types (stationary noise and single-talker masker) on the PPD during speech perception. Twenty-five HI and 32 age-matched NH participants listened to sentences across a broad range of SNRs, masked with speech from a single talker (−25 dB to +15 dB SNR) or with stationary noise (−12 dB to +16 dB). Correct sentence recognition scores and pupil responses were recorded during stimulus presentation. With a stationary masker, NH listeners show maximum PPD across a relatively narrow range of low SNRs, while HI listeners show relatively large PPD across a wide range of ecological SNRs. With the single-talker masker, maximum PPD was observed in the mid-range of SNRs around 50% correct sentence recognition performance, while smaller PPDs were observed at lower and higher SNRs. Mixed-model ANOVAs revealed significant interactions between hearing-status and SNR on the PPD for both masker types. Our data show a different pattern of PPDs across SNRs between groups, which indicates that listening and the allocation of effort during listening in daily life environments may be different for NH and HI listeners

    Pattern recognition with a magnon-scattering reservoir

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    Magnons are elementary excitations in magnetic materials and undergo nonlinear multimode scattering processes at large input powers. In experiments and simulations, we show that the interaction between magnon modes of a confined magnetic vortex can be harnessed for pattern recognition. We study the magnetic response to signals comprising sine wave pulses with frequencies corresponding to radial mode excitations. Three-magnon scattering results in the excitation of different azimuthal modes, whose amplitudes depend strongly on the input sequences. We show that recognition rates above 95\% can be attained for four-symbol sequences using the scattered modes, with strong performance maintained with the presence of amplitude noise in the inputs

    Improved Periodicity Mining in Time Series Databases

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    Time series data represents information about real world phenomena and periodicity mining explores the interesting periodic behavior that is inherent in the data. Periodicity mining has numerous applications such as in weather forecasting, stock market prediction and analysis, pattern recognition, etc. Recently, the suffix tree, a powerful data structure that efficiently solves many strings related problems has been used to gather information about repeated substrings in the text and then perform periodicity mining. However, periodicity mining deals with large amounts of data which makes it difficult to perform mining in main memory due to the space constraints of the suffix tree. Thus, we first propose the use of the Compressed Suffix Tree (CST) for space efficient periodicity mining in very large datasets. Given the time-space trade-off that comes with any practical usage of the CST, we provide a comprehensive empirical analysis on the practical usage of CSTs and traditional suffix trees for periodicity mining.;Noise is an inherent part of practical time series data, and it is important to mine periods in spite of the noise. This leads to the problem of approximate periodicity mining. Existing algorithms have dealt with the noise introduced between the occurrences of the periodic pattern, but not the noise introduced in the structure of the pattern itself. We present a taxonomy for approximate periodicity and then propose an algorithm that performs periodicity mining in the presence of noise introduced simultaneously in both the structure of the pattern and between the periodic occurrences of the pattern

    Design of coupled mace filters for optical pattern recognition using practical spatial light modulators

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    Spatial light modulators (SLMs) are being used in correlation-based optical pattern recognition systems to implement the Fourier domain filters. Currently available SLMs have certain limitations with respect to the realizability of these filters. Therefore, it is necessary to incorporate the SLM constraints in the design of the filters. The design of a SLM-constrained minimum average correlation energy (SLM-MACE) filter using the simulated annealing-based optimization technique was investigated. The SLM-MACE filter was synthesized for three different types of constraints. The performance of the filter was evaluated in terms of its recognition (discrimination) capabilities using computer simulations. The correlation plane characteristics of the SLM-MACE filter were found to be reasonably good. The SLM-MACE filter yielded far better results than the analytical MACE filter implemented on practical SLMs using the constrained magnitude technique. Further, the filter performance was evaluated in the presence of noise in the input test images. This work demonstrated the need to include the SLM constraints in the filter design. Finally, a method is suggested to reduce the computation time required for the synthesis of the SLM-MACE filter
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