Spherical harmonics descriptor for 2D-image retrieval

In this paper, spherical harmonics are proposed as shape descriptors for 2D images. We introduce the concept of connectivity; 2D images are decomposed using connectivity, which is followed by 3D model construction. Spherical harmonics are obtained for 3D models and used as descriptors for the underlying 2D shapes. Difference between two images is computed as the Euclidean distance between their spherical harmonics descriptors. Experiments are performed to test the effectiveness of spherical harmonics for retrieval of 2D images. Item S8 within the MPEG-7 still images content set is used for performing experiments; this dataset consists of 3621 still images. Experimental results show that the proposed descriptors for 2D images are effective

Spherical harmonics and distance transform for image representation and retrieval

In this paper, we have proposed a method for 2D image retrieval based on object shapes. The method relies on transforming the 2D images into 3D space based on distance transform. Spherical harmonics are obtained for the 3D data and used as descriptors for the underlying 2D images. The proposed method is compared against two existing methods which use spherical harmonics for shape based retrieval of images. MPEG-7 Still Images Content Set is used for performing experiments; this dataset consists of 3621 still images. Experimental results show that the performance of the proposed descriptors is significantly better than other methods in the same category.

Spectral shape descriptor using spherical harmonics

In this paper, we propose a spectral descriptor for shapes of objects. The method relies on transforming the 2D objects into 3D space; distance transform and scale space theory is used to transform objects into 3D space. Spherical harmonics of the voxel grid are used to obtain shape descriptors. The proposed methods are compared against two existing methods which use spherical harmonics for shape based retrieval of images. Comparison is done based on ranking of images which is articulated in recall-precision curves. MPEG-7 Still Images Content Set is used for performing experiments. Experimental results show that the performance of the proposed descriptor is significantly better than other methods in the same category.

Detection of anomalous crowd behaviour using hyperspherical clustering

Analysis of crowd behaviour in public places is an indispensable tool for video surveillance. Automated detection of anomalous crowd behaviour is a critical problem with the increase in human population. Anomalous events may include a person loitering about a place for unusual amounts of time; people running and causing panic; the size of a group of people growing over time etc. In this work, to detect anomalous events and objects, two types of feature coding has been proposed: spatial features and spatio-temporal features. Spatial features comprises of contrast, correlation, energy and homogeneity, which are derived from Gray Level Co-occurrence Matrix (GLCM). Spatio-temporal feature includes the time spent by an object at different locations in the scene. Hyperspherical clustering has been employed to detect the anomalies. Spatial features revealed the anomalous frames by using contrast and homogeneity measures. Loitering behaviour of the people were detected as anomalous objects using the spatio-temporal coding.

Hyperspherical cluster based distributed anomaly detection in wireless sensor networks

This article describes a distributed hyperspherical cluster based algorithm for identifying anomalies in measurements from a wireless sensor network, and an implementation on a real wireless sensor network testbed. The communication overhead incurred in the network is minimised by clustering sensor measurements and merging clusters before sending a compact description of the clusters to other nodes. An evaluation on several real and synthetic datasets demonstrates that the distributed hyperspherical cluster-based scheme achieves comparable detection accuracy with a significant reduction in communication overhead compared to a centralised scheme, where all the sensor node measurements are communicated to a central node for processing. .

Acoustic features involved in the neighbour-stranger vocal recognition process in male Australian fur seals

Many territorial species have the ability to recognise neighbours from stranger individuals. If the neighbouring individual is assumed to pose less of a threat, the territorial individual responds less and avoids unnecessary confrontations with familiar individuals at established boundaries, thus avoiding the costly energy expenditure associated with fighting. Territorial male Australian fur seals respond more to strangers than to neighbouring males. The present study evaluated which acoustic features were important in the neighbour–stranger recognition process in male Australian fur seals. The results reveal that there was an increase in response strength or intensity from males when they heard more bark units, indicating the importance of repetition to detect a caller. However, lengthening and shortening the inter-unit spaces, (i.e. changing the rhythm of the call) did not appear to significantly affect an animal's response. In addition, the whole frequency spectrum was considered important to recognition with results suggesting that they may vary in their importance. A call containing the dominant and surrounding harmonics was considered important to a male's ability to recognise its neighbour. Furthermore, recognition occurs even with a partial bark, but males need to hear between 25 and 75% of each bark unit from neighbouring seals. Our study highlights which acoustic features induce stronger or weaker responses from territorial males, decoding the important features in neighbour–stranger recognition.

Novel spectral descriptor for object shape

In this paper, we propose a novel descriptor for shapes. The proposed descriptor is obtained from 3D spherical harmonics. The inadequacy of 2D spherical harmonics is addressed and the method to obtain 3D spherical harmonics is described. 3D spherical harmonics requires construction of a 3D model which implicitly represents rich features of objects. Spherical harmonics are used to obtain descriptors from the 3D models. The performance of the proposed method is compared against the CSS approach which is the MPEG-7 descriptor for shape contour. MPEG-7 dataset of shape contours, namely, CE-1 is used to perform the experiments. It is shown that the proposed method is effective.

Connectivity-based shape descriptors

In this paper, we propose a method for indexing and retrieval of images based on shapes of objects. The concept of connectivity is introduced. 3D models are used to represent 2D images. 2D images are decomposed a priori using connectivity which is followed by 3D model construction. 3D model descriptors are obtained for 3D models and used to represent the underlying 2D shapes. We have used spherical harmonics descriptors as the 3D model descriptors. Difference between two images is computed as the Euclidean distance between their descriptors. Experiments are performed to test the effectiveness of spherical harmonics for retrieval of 2D images. The proposed method is compared with methods based on principal components analysis (PCA) and generic Fourier descriptors (GFD). It is found that the proposed method is effective. Item S8 within the MPEG-7 still images content set is used for performing experiments.

Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model

In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

Application of the fuzzy min-max neural network to fault detection and diagnosis of induction motors

In this paper, an application of the motor current signature analysis (MCSA) method and the fuzzy min–max (FMM) neural network to detection and classification of induction motor faults is described. The finite element method is employed to generate simulated data pertaining to changes in the stator current signatures under different motor conditions. The MCSA method is then used to process the stator current signatures. Specifically, the power spectral density is employed to extract harmonics features for fault detection and classification with the FMM network. Various types of induction motor faults, which include stator winding faults and eccentricity problems, under different load conditions are experimented. The results are analyzed and compared with those from other methods. The outcomes indicate that the proposed technique is effective for fault detection and diagnosis of induction motors under different conditions.

Modelling and power quality analysis of a grid-connected solar PV system

Increased concern about global warming coupled with the escalating demand of energy has driven the conventional power system to be more reliable one by integrating Renewable Energies (RE) in to grid. Over the recent years, integration of solar PV forming a gridconnected PV is considered as one of the most promisingtechnologies to the developed countries like Australia to meet the growing demand of energy. This rapid increase in grid connected photovoltaic (PV) systems has made the supply utilities concerned about the drastic effects that have to be considered on the distribution network in particular voltage fluctuations, harmonic distortions and the Power factor for sustainable power generation. However, irrespective of thefact that the utility grid can accommodate the variability of load or irregular solar irradiance, it is essential to study the impact of grid connected PV systems during higher penetration levels as the intermittent nature of solar PV adversely effects the grid characteristics in meeting the load demand. Hence, keeping this in track, this paper examines the grid-connected PV system considering a residential network of Geelong region (38◦.09' S and 144◦.21’ E) and explores the level of impacts considering summer load profile with a change in the level of integrations. Initially, a PV power system network model is developed in Matlab-Simulink environment and the simulations are carried out to explore the impacts of solar PV penetration at low voltage distribution network considering power quality (PQ) issues such as voltage fluctuations, harmonics distortion at different load conditions.

A low overhead scaled equalized harmonic-based voice authentication system

With the increasing trends of mobile interactions, voice authentication applications are in a higher demand, giving rise to new rounds of research activities. Authentication is an important security mechanism that requires the intended communication parties to present valid credentials to the communication network. In a stronger sense, all the involved parties are required to be authenticated to one another (mutual authentication). In the voice authentication technique described in this paper, the voice characteristics of an intended individual wishing to take part in a communication channel will be classified and processed. This involves a low overhead voice authentication scheme, which features equalization and scaling of the voice frequency harmonics. The performance of this system is discussed in a Labview 8.5 visual development environment, following a complete security analysis. © 2013 Elsevier Ltd. All rights reserved.

Development of a compact rectenna for wireless powering of a head-mountable deep brain stimulation device

Design of a rectangular spiral planar inverted-F antenna (PIFA) at 915 MHz for wireless power transmission applications is proposed. The antenna and rectifying circuitry form a rectenna, which can produce dc power from a distant radio frequency energy transmitter. The generated dc power is used to operate a low-power deep brain stimulation pulse generator. The proposed antenna has the dimensions of 10 mm × 12.5 mm × 1.5 mm and resonance frequency of 915 MHz with a measured bandwidth of 15 MHz at return loss of -10 dB. A dielectric substrate of FR-4 of εr = 4.8 and δ = 0.015 with thickness of 1.5 mm is used for both antenna and rectifier circuit simulation and fabrication because of its availability and low cost. An L-section impedance matching circuit is used between the PIFA and voltage doubler rectifier. The impedance matching circuit also works as a low-pass filter for elimination of higher order harmonics. Maximum dc voltage at the rectenna output is 7.5 V in free space and this rectenna can drive a deep brain stimulation pulse generator at a distance of 30 cm from a radio frequency energy transmitter, which transmits power of 26.77 dBm.