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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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Evaluation of Spatial and Temporal Variations of Surface Water Quality in the Nile River Damietta Branch

(Ruda River, Poland). Ecol Chem Eng S. 2017;24(2):285-298. DOI: 10.1515/eces-2017-0020. [4] Karbassi A, Nouri J, Mehrdadi N, Ayaz G. Flocculation of heavy metals during mixing of freshwater with Caspian Sea water. Environ Geol. 2008;53(8):1811-1816. DOI: 10.1007/s00254-007-0786-7. [5] Najafpour S, Alkarkhi A, Kadir M, Najafpour GD. Evaluation of spatial and temporal variation in river water quality. Int J Environ Res. 2009;2(4):349-358. DOI: 10.1016/j.jenvman.2009.11.001. [6] Wahaab RA, Badawy MI. Water quality assessment of the River Nile system: an

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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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Temporal variability of sewage sludge heavy metal content from Greek wastewater treatment plants

Abstract

The aim of this study was to evaluate the temporal variations of selected heavy metals level in anaerobic fermented and dewatered sewage sludge. Sewage sludge samples were collected in different seasons and years from three municipal wastewater treatment plants (WWTPs) located in Northern Greece, in Kavala (Kavala and Palio localities) and Drama (Drama locality) Prefectures. An investigation of the potential of sludge utilization in agriculture was performed, based on the comparison of average total heavy metal concentrations and of chromium species (hexavalent, trivalent) concentrations with the allowed values according to the Council Directive 86/278/EEC and Greek national legislation (Joint Cabinet Decision 80568/4225/91) guidelines. In this regard, all the investigated heavy metals (Cd, Cr, Cu, Ni, Pb, Zn, Hg) and chromium species Cr(VI) and Cr(III) have average concentrations (dry matter weight) well below the legislated thresholds for soil application, as following: 2.12 mg kg−1 Cd; 103.7 mg kg−1 Cr; 136.4 mg kg−1 Cu; < 0.2 mg kg−1 Hg; 29.1 mg kg−1 Ni; 62.0 mg kg−1 Pb; 1253.2 mg kg−1 Zn; 1.56 mg kg−1 Cr(VI) and 115.7 mg kg−1 Cr(III). Values of relative standard deviation (RSD) indicate a low or moderate temporal variability for domestic-related metals Zn (10.3-14.7%), Pb (27.9-44.5%) and Cu (33.5-34.2%), and high variability for the metals of mixed origin or predominantly resulted from commercial activities, such as Ni (42.4-50.7%), Cd (44.3-85.5%) and Cr (58.2-102.0%). For some elements the seasonal occurrence pattern is the same for Kavala and Palio sludge, as following: a) Cd and Cr: spring>summer>winter; b) Cu, Ni and Pb: winter>spring>summer. On average, in summer months (dry season) metal concentrations are lower than in spring and winter (wet seasons), with the exception of Zn. For Kavala and Palio the results demonstrate that the increased number of inhabitants (almost doubled) in summer time due to tourism does not influence the metal levels in sludge. Comparing the results obtained for similar spring-summer-winter sequences in 2007 and 2010/11 and for the spring season in 2007, 2008 and 2010, it can be noticed that, in general, the average heavy metal contents show an increasing tendency towards the last year. In all the measurement periods, the Palio sludge had the highest metal contents and Kavala sludge the lowest, leading to the conclusion that the WWTP operating process rather than population has a significant effect upon the heavy metal content of sludge. Cr(VI)/Cr(total) concentration ratios are higher for Kavala sludge in the majority of sampling campaigns, followed by Drama and Palio sludge. The metals which present moderate to strong positive correlation have common origin, which could be a domestic-commercial mixed source.

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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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