Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses

Spiking neural networks (SNN) are artificial computational models that have been inspired by the brain's ability to naturally encode and process information in the time domain. The added temporal dimension is believed to render them more computationally efficient than the conventional artificial neural networks, though their full computational capabilities are yet to be explored. Recently, computational memory architectures based on non-volatile memory crossbar arrays have shown great promise to implement parallel computations in artificial and spiking neural networks. In this work, we experimentally demonstrate for the first time, the feasibility to realize high-performance event-driven in-situ supervised learning systems using nanoscale and stochastic phase-change synapses. Our SNN is trained to recognize audio signals of alphabets encoded using spikes in the time domain and to generate spike trains at precise time instances to represent the pixel intensities of their corresponding images. Moreover, with a statistical model capturing the experimental behavior of the devices, we investigate architectural and systems-level solutions for improving the training and inference performance of our computational memory-based system. Combining the computational potential of supervised SNNs with the parallel compute power of computational memory, the work paves the way for next-generation of efficient brain-inspired systems

Fish biomass estimation by calibrating the echointegrator deflection against catch data

Acoustic survey for fish resources was conducted using echosounder (EK-400) with echointegrator (QD). The echointegrator coupled with echosounder sums-up the echo signal received. The sum of the echo signal received per nautical mile covered is an index of the quantum of fish recorded and therefore a measure of the relative density of fish in surveyed area. It is converted into absolute biomass using the calibrarion constant obtained by correlating the trawl catch data against the echointegrator reading corresponding to the opening of the net. The calibration constant thus arrived at was 1327 kg/n.mile corresponding to 1 mm integrator deflection per nautical mile covered

Performance of a C4F8O Gas Radiator Ring Imaging Cherenkov Detector Using Multi-anode Photomultiplier Tubes

We report on test results of a novel ring imaging Cherenkov (RICH) detection system consisting of a 3 meter long gaseous C4F8O radiator, a focusing mirror, and a photon detector array based on Hamamatsu multi-anode photomultiplier tubes. This system was developed to identify charged particles in the momentum range from 3-70 GeV/c for the BTeV experiment.Comment: 28 pages, 23 figures, submitted to Nuclear Instruments and Method

Nonlinear Behaviour of Perforated Plate with Lining

Perforated plate with lining has a construction of plate with perforation and a lining plate welded together to form a single plate. This type of plate is used as an acoustic sonar dome. Perforated plate with lining (PPL) is prone to stress concentration and subsequently such structural system falls into the large strain category. Experimental investigation on PPL is carried out in the present study to determine the static deflection of the plate. Numerical method is also followed for geometric nonlinear analysis using finite element method as an iterative interactive procedure. The deflection obtained from the numerical method is 8 per cent less than that obtained from experimental method. From numerical analysis, von Mises stress and maximum principal stress is also estimated to understanda bout the failure mode characteristics of PPL.Defence Science Journal, 2012, 62(4), pp.248-251, DOI:http://dx.doi.org/10.14429/dsj.62.92

Salinity changes in the estuary and the coastal sea adjacent to the portmouth at Cochin

The article deals with the details of salinity changes in the Cochin estuary and its influence and interrelations with the Vembanad lake