Nitin Sinha ¹, John Sahaya Rani Alex ¹

Affiliations
1 School of Electronics Engineering, VIT University, Chennai – 600127, Tamil Nadu, IN

Abstract

Background: An Internet of Things (IoT) based iPower Saver Meter which steps toward monitoring the total amount of power consumed by the domestic appliances. There by notifying the beneficiary a detail wise power consumption of appliances. Additionally provides, a configurable interface for user to limit the usage of high power consuming devices, such as Washing Machine, Air conditioner, etc. This facilitates user to set the timer on those appliances as per their affordability to check bulky increase in electricity bill. Methods: The sensing environment of the smart system is built upon BeagleBone Black board. The hardware environment is integrated with an IoT Platform called Xively and a secure web templates are designed using Flask interface. Findings: This platform yields a calibrated measurement of the power usage hence enabling the user to easily track down, by simply logging into Xively, the power consumption of every appliance and the current bill drawn from the usage. Conclusion/Improvements: The proposed intelligent power savor meter not only rectifies the inclining problem by providing a specified domain of power to be put under utility but also keeps a cautious vigilance over the area by turning off the devices which is running needlessly thereby saving surplus amount of wastage power.

Keywords

Beaglebone Black, Flask, IoT, Smart Power Meter, Xively

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Shreyas Patel ¹, John Sahaya Rani Alex ¹, Nithya Venkatesan ¹

Affiliations
1 School of Electronics Engineering, VIT University, Chennai - 600127, Tamil Nadu, IN

Abstract

Background/Objectives: Recent research focuses on low-power design techniques. This has been mainly inspired by the demand of hand-held electronic devices which have to consume less power. Neural network based classifiers are widely used in speech recognition which needs higher power. Methods/Statistical Analysis: A hybrid approach for designing architecture of Multi-Layer Perceptron (MLP) based Neural Network (NN) for speech recognition is proposed using bipartite tabular method and banking organization method. This approach is prototyped in Xilinx xc3s1200. The Back propagation neural network is trained in MATLAB using TIDIGITS corpus. Using the optimized weights from MATLAB, the proposed prototyped low-power architecture is evaluated in Xilinx. Findings: The outcome shows a considerable reduction both in area and power. Conclusion/Improvements: The reduction in switching power is by 33% and the average power is by 25% are noted. There was 2% reduction in the resources used by the proposed architecture.

Keywords

Banking Organization Method, Bipartite Tabular Method, Low Power Architecture, Multi-Layer Perceptron Neural Network, Speech Recognition

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