Gunawath Mudiyanselage Roshan Indika Godaliyadda

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This paper presents a subspace signature based approach for the identification of turned on appliances at a given observation time using one single-function smart meter. The novelty of the proposed approach compared to existing method is its capability for proper identification while relying on a significantly lower amount of measurement data. Unlike(More)
This paper addresses the specific problem of human event detection from a video sequence in both indoor and outdoor environments. Foreground image pixels are identified through the principle of background subtraction by defining a reference background model using a mixture of time varying Gaussian distributions. Color filtering in the RGB space is then used(More)
We propose a new Non-Intrusive Load Monitoring (NILM) approach for appliances power profile/signal estimation at low sampling rate (1 s or greater). The proposed method relay on two main phases: identification of turned on appliance combination in a given time period and estimation of the active power consumption signal of each individual appliances in that(More)
A real-time event tracking method is proposed that is immune to background variances. The proposed method models each pixel as a collection of Gaussian distributions to handle background variations and uses manipulations in the RGB space to mitigate the effects of foreground shadows. A two stepped connected component analysis method is also introduced in(More)
Extraction of unknown independent source signals from a noisy mixture is a fundamental problem in most signal processing applications. The existing independent component analysis (ICA) algorithms have tackled this problem for complex and real valued mixtures for both super and sub Gaussian sources. However in reality super and sub Gaussian sources exist(More)
The challenging conditions prevalent in indoor environments have rendered many traditional positioning methods inept to yield satisfactory results. Our work tackles the challenging problem of accurate indoor positioning in hazardous multipath environments through three versatile super resolution techniques: time domain Multiple Signal Classification(More)
This paper tackles the challenging problem of accurate indoor geolocation for UWB systems in hazardous multipath environments through three versatile super resolution techniques: time domain MUSIC (Multiple Signal Classification), frequency domain MUSIC algorithms and frequency domain EV (Eigen Value) method. The resultant pseudo-spectrums generated by(More)
In this paper design and implementation of an accurate positioning system in indoor environments for an Omni directional Robot and a stereo vision based depth estimation system is discussed. The positioning system which is robust for wheel slip and jerky motion was implemented fusing odometry, Inertial Navigation System (INS) and magnetic compass based(More)