J. V. Wijayakulasooriya

Automatic recognition of elephant infrasound calls using formant analysis and Hidden Markov Model

A method is proposed for automatic recognition of elephant infrasound calls. It uses Linear Predictive Coding (LPC) for extracting the formants of the elephant rumble. A novel feature extraction technique is proposed to capture the unique features of elephant rumbles. The features are then processed using a two state Hidden Markov Model (HMM) to automatically detect the presence of elephants. The method is tested on pure elephant rumbles as well as field recorded signals containing environmental noise. The results show that the proposed method can be used to successfully detect the presence of elephants even under noisy conditions.

Residential Appliance Identification Based on Spectral Information of Low Frequency Smart Meter Measurements

A nonintrusive load monitoring (NILM) method for residential appliances based on uncorrelated spectral components of an active power consumption signal is presented. This method utilizes the Karhunen Loéve expansion to breakdown the active power signal into subspace components (SCs) so as to construct a unique information rich appliance signature. Unlike existing NILM techniques that rely on multiple measurements at high sampling rates, this method works effectively with a single active power measurement taken at a low sampling rate. After constructing the signature data base, SC level power conditions were introduced to reduce the number of possible appliance combinations prior to applying the maximum a posteriori estimation. Then, an appliances matching algorithm was presented to identify the turned-on appliance combination in a given time window. After identifying the turned-on appliance combination, an energy estimation algorithm was introduced to disaggregate the energy contribution of each individual appliance in that combination. The proposed NILM method was validated by using two public databases: 1) tracebase; and 2) reference energy disaggregation data set. The presented results demonstrate the ability of the proposed method to accurately identify and disaggregate individual energy contributions of turned-on appliance combinations in real households.

Power Quality: Overview and monitoring

ldquoPower qualityrdquo (PQ) is a generic term often used in relation with unwanted disturbances of the electricity supply. In recent years, there has been an increased number of PQ related problems. This is mainly due to the rapid growth in the use of equipment that generate PQ disturbances and also increase of equipment that are sensitive to these disturbances. This increased concern about PQ issues from both suppliers and consumers of electricity has increased the demand for advanced PQ monitoring systems. Each PQ disturbance has a unique wave shape resembling its characteristics. Therefore PQ disturbances could be identified by monitoring the voltage/current signal waveform and analysing its features. PQ monitoring instruments can vary from a simple true r.m.s. meter to advanced techniques that are capable of automatically capturing and classifying PQ events. This paper presents an overview of the characteristics, effects and causes of PQ events and addresses recent trends in PQ monitoring.

Remotely Accessible Single Phase Energy Measuring System

This paper presents a model developed in order to measure the electricity consumption of the domestic electricity users. The model has a special capability of transmitting the measured values to a central database via power line or a ISM band wire-less channel. Two modules were developed for power line communication and wireless communication where users can select the channel on their choice. The central location and all the energy meters in the network were connected in bus topology. Each energy meter was given a unique identification number by which the central data acquisition server identified each of it. A communication protocol was developed in such a way that it would communicate one meter at a time. Time division multiplexing was used to eliminate the collisions and to obtain a reliable transmission. A microcontroller functions as the main processing element of this product. It was programmed in such a way that it would measure energy and transmit the measured values through a specified channel. The designed meter was not only capable of measuring electricity, but also capable of monitoring parameters like voltage, current and power factor. As a final product a comprehensive system was devised to read each energy meter in the network and store the values in a database

A subspace signature based approach for residential appliances identification using less informative and low resolution smart meter data

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 existing techniques which rely on multiple measurements at high sampling rate, the proposed method uses only the active power consumption of a grid sampled at second level sampling intervals. This is enabled by constructing signatures for each appliance based on a subspace analysis which incorporates amplitude and frequency information based on active power data. The identification is performed using a Maximum a Posteriori (MAP) criteria using the subspace database that was constructed. This accurately predicts which appliances are turned at any given observation time window. A performance validation is performed for various scenarios to highlight the methods superior accuracy while operating on minimal data at large sampling interval.

Human motion tracking under dynamic background conditions

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 to remove image distortions due to camera effects and shadowing. A novel approach to tackle the issue of sudden foreground bursts that appear as a result of impulsive environmental changes is also embedded in to the foreground segmentation algorithm. Objects are tracked throughout its presence in the video using an assignment problem based tracker which is capable of handling multiple object interactions such as merges, splits, re-appearances and disappearances. A feature space for each object is constructed and is refined using a Kaiman filter. A fusion of multiple features is used to obtain feature trajectories that closely represent real feature variations of objects. An important aspect of the proposed method is its ability to operate and produce satisfactory results in a scene where there are dynamic background changes and complex inter-human interactions.

Spectrum sensing in cognitive sensor networks

In cognitive sensor networks, the first cognitive task preceding any form of dynamic spectrum management is the sensing and identification of spectrum holes in wireless environments. Research in the physical and medium access control layers for DSA (Dynamic Spectrum Access) and DSS (Dynamic Spectrum Sharing) is currently at its early development. Aspects such as spectrum sensing and spectrum management greatly support the future improvement of DSA and DSS in cognitive sensor networks. This paper expresses the necessities of spectrum sensing and the spectrum sensing framework for cognitive sensor networks and it describes several methods used for evaluating specific spectrum sensing techniques. The sensing techniques describe in this paper provide an effective and efficient spectrum sensing architecture to identify and locate spectrum holes in the signal spectrum.

Object identification, enhancement and tracking under dynamic background conditions

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 refining the estimated foreground and clustering pixels into silhouettes based on objects. Pixel clusters are formed by filling inter-cluster pixels on the basis of neighborhood solidity of individual pixels. Clustered pixels are defined as object fragments and objects are formed by combining object fragments considering their size and mutual distances. The proposed tracker employs an algorithm to boil down the multiple object interaction problems (objects merging, objects splitting, new object appearance and lost objects) into a simple matrix interpretation problem to construct a consistent feature space. Specifically, splitting of merged objects and temporary disappearances of objects due to occlusions with background objects are handled by the means of a feature correspondence matching. A novel object identification method is proposed for this purpose.

Material based acoustic signal classification - A subspace-based approach

In this paper, approaches to develop subspace-based classifiers for material based signal classification are presented. Material based signal classification is essential in many applications for the identification of the cause of the acoustic signal. Four approaches are presented in this paper for classification. They are namely, classification via eigen-filter characteristics, eigenvector projection method, classification through eigen-filter and eigen-filter bank outputs. For these methods, the optimum order of the eigen-filters is selected based on the maximum true positive counts. The optimal eigenvectors that are most suitable for classification are selected by minimizing the cross-correlation among different classes. Finally, the robustness and the superior performance of the proposed techniques are highlighted by comparing them with time and frequency domain techniques such as AR modeling, spectrogram based classification.

Ultra Wideband (UWB) antenna design for cognitive radios in the UHF TV band

Presently, Ultra Wide Band (UWB) radio technology has attracted much interest in academics, industrial and standardization (IEEE) activities. UWB characterizes transmission systems with instantaneous spectral occupancy of higher bandwidth or higher fractional bandwidth. The antenna is probably the most overlooked part of a RF (Radio Frequency) design. The range, performance, and legality of a RF link are critically dependent upon the antenna. During this work, the UHF (Ultra High Frequency) TV Band is specifically addressed which is a target band of IEEE802.22 standardization. The UHF TV band is 336MHz wider according to CCIR (Consultative Committee on International Radio) standards. One major challenge in designing a UWB antenna for UHF band is limiting the physical size of the antenna. The paper illustrates the successful design, implementation and testing of a UWB antenna for cognitive radios in the UHF TV Band. The antenna is simple, low cost and compact enough to be used in fixed transceivers.