An efficacious implementation of Internet of Things(IoTs) based omnipresent sensing and monitoring system for domestic as well as non-domestic environments. The structure of the sensing-monitoring system is established on the combination of ubiquitous sensing units, controlling system for data acquisition, manipulation and aggregation and internet based platform for setting an efficient monitoring. The proposed model consists of sensing units which perceives the environmental value (such as Humidity, temperature, heat index, gas, etc), voltage and current parameters of the various household appliances for monitoring the amount of power consumed. Which is further calibrated by the controlling system to yields the aggregated data and finally collected on the Internet based platform. The framework of connecting the smart sensor to the internet is achieved by an IoT platform called Xively, which provides channel utility to deploy the prototype into an integrated product.
This paper presents robust feature extraction techniques for isolated word recognition under noisy conditions. The proposed hybrid feature extraction techniques are Bark Frequency Cepstral Coefficients (BFCC) and Weighted Average Mel-Frequency Cepstral Coefficient (WMFCC). Both methods are tested in various noisy environments using a single Gaussian Hidden Markov Model (HMM) based isolated digit recognition system. The results clearly indicates that WMFCC performed well compared to Mel-Frequency Cepstral Coefficient (MFCC) in noisy environment using NOISEX-92 database.
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