Peter Planinsic

Using RSSI value for distance estimation in wireless sensor networks based on ZigBee

In todaypsilas modern wireless ZigBee-based modules, there are two well-known values for link quality estimation: RSSI (received signal strength indicator) and LQI (link quality indicator). In respect to wireless channel models, received power should be a function of distance. From this aspect, we believed that RSSI can be used for evaluating distances between nodes. The experiment described in this paper indicates that RSSI is, in fact, a poor distance estimator when using wireless sensor networks in buildings. Reflection, scattering and other physical properties have an extreme impact on RSSI measurement and so we can conclude: RSSI is a bad distance estimator.

Angle of Arrival Estimation Using RSSI and Omnidirectional Rotatable Antennas

This paper proposes a simple novel method for angle of arrival estimation using multiple rotating omnidirectional antennas on a receiver device. The used omnidirectional microstrip antenna has a symmetrical radiation pattern with sharp minimum along the x antenna axis. An algorithm based on the fact that an angle of arrival is obtained along a direction where the measured received strength signal indicator is minimal. Our experimental results for the outdoor measurements reached a mean error of less than 4°, and an indoor of less than 6°.

The Accuracy of Propagation Models for Distance Measurement between WSN Nodes

The accuracy of RF propagation models for distance measurement between WSN nodes using received signal strength indicator (RSSI) was studied. We implemented two simple propagation models, the free space log-normal model and 2-ray ground reflection model. The results show, that first model is not very precise in closed space (rooms). The second model gives quite well agreement with experimental RSSI measurements for greater distances between transmitting and receiving nodes.

Entropy-threshold method for best basis selection

The methods which were adapted for the best basis selection optimise the decomposition by minimising the criterion cost function. This usually does not produce optimal rate–distortion (R–D) behaviour of the complete transform coder. This paper presents an extension of the well-known entropy-based best basis selection method titled entropy-threshold best basis selection. Using the threshold method, the criterion used in the original entropy-based method is strengthened and eliminates the portions of decomposition that are not cost effective. As the experiments illustrate, this new method exhibits great improvement in both R–D performance and simplified decomposition complexity.

Classification of Power Disturbances using Fuzzy Logic

With an increasing usage of sensitive electronic equipment power quality has become a major concern now. It has been estimated that after 2006 static power converters will be process 65% of the total electric power. Impulse and sags going to be risk for electronic equipment. Power electronic loads resulting from the solid-state equipment are constantly increasing in distribution systems. The non-linear nature of switching devices give rise to harmonic current flows in power transmission lines, thus causing considerable losses and voltage distortion, electronic equipment failure, and inefficient use of electric energy. One critical aspect of power quality studies is the ability to perform automatic power quality data analysis and categorization. Inherent features are extracted from recorded waveforms using Fourier and wavelet analyses and fed into a fuzzy expert system. In this work we present a technique based on fuzzy logic to categorize power quality events. The use of OPC (Object Linking and Embedding for Process Control) opens up the exchange of data across multiple historians. The categorization technique has been implemented using the Fourier, Wavelet, Fuzzy Logic Toolboxes in Matlab. We view XML as a roadmap to the future, which is why we use XML data format for communicating with other systems at WEB level.

SAR Image Categorization Using Parametric and Nonparametric Approaches Within a Dual Tree CWT

This letter presents synthetic aperture radar (SAR) image classification based on feature descriptors within the discrete wavelet transform (DWT) domain using parametric and nonparametric features. The DWT enables an efficient multiresolution description of SAR images due to its geometric and stochastic features. A 2-D DWT, a real 2-D oriented dual tree wavelet transform (2-D RODTWT) and an oriented dual tree complex wavelet transform (2-D ODTCWT) were used for the estimation of subband features. First and second moments, entropy, coding gain, and fractal dimension were used for the nonparametric approach. A parametric approach considers a Gauss Markov Random Field model for feature extraction. A database with 2000 images representing 20 different classes with 100 images per class was used for classification efficiency assessment. Several SAR scenes were divided into small patches with dimension of 200 × 200 pixels. 10% and 20% of the test images per class were used during the learning stage. Supervised learning using a support vector machine was used for all experiments. The experimental results showed that the proposed methods had superior performances compared with (GLCM) and log comulants of Fourier transform. Amongst the proposed methods, the nonparametric features within oriented dual tree complex wavelet transform gave the best results for classes when categorizing SAR images.

Progressive space frequency quantization for SAR data compression

The authors propose a new wavelet image coding technique for synthetic aperture radar (SAR) data compression called a progressive space-frequency quantization (PSFQ). PSFQ performs spatial quantization via rate distortion-optimized zerotree pruning of wavelet coefficients that are coded using a progressive subband coding technique. They compared the performances of zerotree-based methods: EZW, SPIHT, SFQ, and PSFQ with the classical wavelet-based method (CWM), which uses uniform scalar quantization of subbands followed by recency rank coding. The performances of the methods based on zerotree quantization were better than the CWM in the rate distortion sense. The embedded coding techniques perform better SNR results than the methods using scalar quantization. However, the probability density function (PDF) of the reconstructed amplitude SAR data compressed using CWM, better corresponded to the PDF of the original data than the PDF of the reconstructed data compressed using the zerotree based methods. The amplitude PDF of the reconstructed data obtained using PSFQ compression algorithm better corresponded to the original PDF than the amplitude PDF of the data obtained using the multilook method.

Mammographic Lesions Discrimination Based on Fractal Dimension as an Indicator

In this paper it is investigated how fractal properties can be used to characterize a mammographic lesion. The idea is suggested by the similarity between the breast tissue and a synthetically generated fractal image. Fractals are pertinent tools to describe the complexity of a shape; meanwhile, radiologists use the complexity of the lesions contour to classify the abnormality. Tests on 30 cases mammographic lesions shows that fractal dimension of the lesions contour is higher in cancer cases and lower in benign cases. This could be an important observation in order to classify BI-RADS 4 lesions, with no need of further examination (biopsy).

Angle of Arrival Measurement Using Multiple Static Monopole Antennas

The angle of arrival (AoA) is an important factor in the localization of a wireless sensor network. This paper deals with AoA measurement using omnidirectional antennas. In our case, microstrip monopole antennas are used, which have radiation patterns with two sharp minimums. Therefore, an algorithm based on an approach where an AoA is obtained along a direction where the measured received strength signal indicator is minimal. Multiple stationary microstrip antennas are placed on a printed circuit board and no moving parts are needed for measurement. In comparison with rotating antennas this simple method has lower resolution. We show that the resolution can be improved using interpolations and approximations. When using the proposed algorithm, the experimental results for the outdoor measurements reached a root-mean-square error of <;10°, and an indoor environment of <;25°.

Wavelet Based Multiscale Edge Preserving Segmentation Algorithm for Object Recognition and Object Tracking

Although edge detection is basic task in computer vision, its results are very important for further procedures. In order to make task of finding edges of objects more efficient, a novel approach is proposed. It uses wavelet transform of the image to detect edges and in the same time also to segment edge points. Partial segmentation of the image is performed only around edge points, which in return enables us to preserve found edges, group edges of the same object into clusters and defines shapes of objects, where edges are missing. Because we do not perform segmentation of the whole image, but use only information from segmentation of point around edges, the procedure of finding object shapes is faster and in turn the whole step of object recognition, defined by the shape, is faster.