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On the Optimum Architecture of the Biologically Inspired Hierarchical Temporal Memory Model Applied to the Hand-Written Digit Recognition

hierarchical Bayesian network. In: Proc. 19th Australian Joint Conf. on Artificial Intelligence, Hobart, Australia. pp. 1259-1264. Thornton, J. R., Faichney, J., Blumenstein, M., Hine, T. (2008). Character recognition using hierarchical vector quantization and temporal pooling. In: Wobcke, W., Zhang, M. (eds.) Proc 21st Australasian Joint Conf. Artificial Intelligence , Vol. Lecture Notes in Computer Science. pp. 562-572. Bobier, B. (2007). Hand-written digit recognition using Hierarchical Temporal Memory. http

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Temporal Analysis Of Adaptive Face Recognition

Abstract

Aging has profound effects on facial biometrics as it causes change in shape and texture. However, aging remains an under-studied problem in comparison to facial variations due to pose, illumination and expression changes. A commonly adopted solution in the state-of-the-art is the virtual template synthesis for aging and de-aging transformations involving complex 3D modelling techniques. These methods are also prone to estimation errors in the synthesis. Another viable solution is to continuously adapt the template to the temporal variation (ageing) of the query data. Though efficacy of template update procedures has been proven for expression, lightning and pose variations, the use of template update for facial aging has not received much attention so far. Therefore, this paper first analyzes the performance of existing baseline facial representations, based on local features, under ageing effect then investigates the use of template update procedures for temporal variance due to the facial age progression process. Experimental results on FGNET and MORPH aging database using commercial VeriLook face recognition engine demonstrate that continuous template updating is an effective and simple way to adapt to variations due to the aging process.

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Performance Evaluation of Deep Neural Networks Applied to Speech Recognition: RNN, LSTM and GRU

Association, San Francisco, CA, USA, September 2016. [4] E. Busseti, I. Osband, S. Wong, Deep Learning for Time Series Modeling, Seminar on Collaborative Intelligence in the TU Kaiserslautern, Germany, June 2012. [5] Deep Learning for Sequential Data - Part V: Handling Long Term Temporal Dependencies, https://prateekvjoshi.com/2016/05/31/deep-learning-for-sequential-data-part-v-handling-long-term-temporal-dependencies/ , last retrieved July 2017. [6] Understanding LSTM Networks, http://colah.github.io/posts/2015-08-Understanding-LSTMs/ , last retrieved

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Pattern Classification by Spiking Neural Networks Combining Self-Organized and Reward-Related Spike-Timing-Dependent Plasticity

inferior olive neurons with the llinás approach neuron model, Neural computation, vol. 28, no. 11, pp. 2505–2532, 2016. [8] S. Nobukawa, H. Nishimura, and T. Yamanishi, Chaotic resonance in typical routes to chaos in the Izhikevich neuron model, Scientific reports, vol. 7, no. 1, 1331, 2017. [9] N. K. Kasabov, Neucube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data, Neural Networks, vol. 52, pp. 62–76, 2014. [10] J. H. Lee, T. Delbruck, and M. Pfeiffer, Training deep spiking neural networks

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Identification of Microrecording Artifacts with Wavelet Analysis and Convolutional Neural Network: An Image Recognition Approach

] Gadhoumi, K., Lina, J.-M., Gotman, J. (2012). Discriminating preictal and interictal states in patients with temporal lobe epilepsy using wavelet analysis of intracerebral EEG. Clinical Neurophysiology , 123 (10), 1906-1916. [34] Gadhoumi, K., Lina, J.-M., Gotman, J. (2013). Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity. Clinical Neurophysiology , 124 (9), 1745-1754. [35] Sharma, N., Jain, V., Mishra, A. (2018). An analysis of convolutional neural networks for image classification. Procedia

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An Integrative Approach to Analyze Eeg Signals and Human Brain Dynamics in Different Cognitive States

network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes, Information Sciences, vol. 294, pp. 565-575, 2015 [11] R. Kozma, J. J. J. Davis, C.-T. Lin, and W. J. Freeman. Spatio-Temporal EEG Pattern Extraction Using High-Density Scalp Arrays, IEEE World Congr. on Comp. Intel., 2016 [12] K. Pribram, The Form Within - My Point of View. Westport, CT: Prospecta Press, 2013 [13] Mitsar Brain Diagnostics Solutions, EEG Accessories: Available: http

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Biologically Inspired Feature Detection Using Cascaded Correlations of off and on Channels

of the locust optic lobe - How self-inhibition, spatial-pooling, and signal rectification contribute to the properties of transient cells”. Visual Neurosci. vol. 7, pp. 345-3, 1991. [20] N. Jansonius, and J. Hateren, “Fast temporal adaptation of on-off units in the first optic chiasm of the blowfly,” J. Comp. Physiol. A vol. 168, pp. 631-637, 1991. [21] D.M. Bolzon, K. Nordstrom, D.C. O’Carroll “Local and large-range inhibition in feature detection” J Neurosci vol. 29 pp 14143-14150 2009 [22] S.D. Wiederman SD and D

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A Class of Neuro-Computational Methods for Assamese Fricative Classification

Abstract

In this work, a class of neuro-computational classifiers are used for classification of fricative phonemes of Assamese language. Initially, a Recurrent Neural Network (RNN) based classifier is used for classification. Later, another neuro fuzzy classifier is used for classification. We have used two different feature sets for the work, one using the specific acoustic-phonetic characteristics and another temporal attributes using linear prediction cepstral coefficients (LPCC) and a Self Organizing Map (SOM). Here, we present the experimental details and performance difference obtained by replacing the RNN based classifier with an adaptive neuro fuzzy inference system (ANFIS) based block for both the feature sets to recognize Assamese fricative sounds.

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Measurement of nanopatterned surfaces by real and reciprocal space techniques

Measurement of nanopatterned surfaces by real and reciprocal space techniques

A newly developed laboratory grazing-incidence small-angle X-ray scattering GISAXS system capable of statistical measurements of surface morphology at the nanometer scale was developed. The potential of the GISAXS system is compared to the AFM technique for a nanopatterned silicon surface produced by ion-beam erosion. The characteristic period of the ion-beam induced ripples and their lateral correlation length were estimated from AFM. Using GISAXS the reciprocal space map of surface morphology was measured and analyzed. The two microfocus X-ray sources emitting radiation at the Cu-Kα and Cr-Kα were used. The lateral periods of ripples obtained by the reciprocal space mapping techniques match the results of real space techniques. The setup has the potential to monitor and control the deposition process and formation of nanostructures with sufficient temporal and spatial resolution.

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Influence of Mobile Phones on the Quality of ECG Signal Acquired by Medical Devices

Abstract

Health aspects of the use of radiating devices, like mobile phones, are still a public concern. Stand-alone electrocardiographic systems and those built-in, more sophisticated, medical devices have become a standard tool used in everyday medical practice. GSM mobile phones might be a potential source of electromagnetic interference (EMI) which may affect reliability of medical appliances. Risk of such event is particularly high in places remote from GSM base stations in which the signal received by GSM mobile phone is weak. In such locations an increase in power of transmitted radio signal is necessary to enhance quality of the communication. In consequence, the risk of interference of electronic devices increases because of the high level of EMI.

In the present paper the spatial, temporal, and spectral characteristics of the interference have been examined. The influence of GSM mobile phone on multilead ECG recordings was studied. It was observed that the electrocardiographic system was vulnerable to the interference generated by the GSM mobile phone working with maximum transmit power and in DTX mode when the device was placed in a distance shorter than 7.5 cm from the ECG electrode located on the surface of the chest. Negligible EMI was encountered at any longer distance.

Open access