Blood image analysis to detect malaria using filtering image edges and classification

Malaria is a most dangerous mosquito borne disease and its infection spread through the infected mosquito. It especially affects the pregnant females and Children less than 5 years age. Malarial species commonly occur in five different shapes, Therefore, to avoid this crucial disease the contemporary researchers have proposed image analysis based solutions to mitigate this death causing disease. In this work, we propose diagnosis algorithm for malaria which is implemented for testing and evaluation in Matlab. We use Filtering and classification along with median filter and SVM classifier. Our proposed method identifies the infected cells from rest of blood images. The Median filtering smoothing technique is used to remove the noise. The feature vectors have been proposed to find out the abnormalities in blood cells. Feature vectors include (Form factor, measurement of roundness, shape, count total number of red cells and parasites). Primary aim of this research is to diagnose malaria by finding out infected cells. However, many techniques and algorithm have been implemented in this field using image processing but accuracy is not up to the point. Our proposed algorithm got more efficient results along with high accuracy as compared to NCC and Fuzzy classifier used by the researchers recently

Frequency Offset Compensation for OFDM Systems Using a Combined Autocorrelation and Wiener Filtering Scheme, Journal of Telecommunications and Information Technology, 2010, nr 1

One of the orthogonal frequency division multiplexing (OFDM) system disadvantages is its sensitivity to frequency offset and phase noise, which lead to losing the orthogonality between the subcarriers and thereby degrade the system performance. In this paper a joint scheme for frequency offset and pilot-based channel estimation is introduced in which the frequency offset is first estimated using an autocorrelation method, and then is fined further by applying an iterative phase correction by means of pilot-based Wiener filtering method. In order to verify the capability of the estimation algorithm, the scheme has been implemented and tested using a real measurement system in a multipath indoor environment. The results show the algorithm capability of compensating for the frequency offset with different transmission and channel conditions

Blood image analysis to detect malaria using filtering image edges and classification

Malaria is a most dangerous mosquito borne disease and its infection spread through the infected mosquito. It especially affects the pregnant females and Children less than 5 years age. Malarial species commonly occur in five different shapes, Therefore, to avoid this crucial disease the contemporary researchers have proposed image analysis based solutions to mitigate this death causing disease. In this work, we propose diagnosis algorithm for malaria which is implemented for testing and evaluation in Matlab. We use Filtering and classification along with median filter and SVM classifier. Our proposed method identifies the infected cells from rest of blood images. The Median filtering smoothing technique is used to remove the noise. The feature vectors have been proposed to find out the abnormalities in blood cells. Feature vectors include (Form factor, measurement of roundness, shape, count total number of red cells and parasites). Primary aim of this research is to diagnose malaria by finding out infected cells. However, many techniques and algorithm have been implemented in this field using image processing but accuracy is not up to the point. Our proposed algorithm got more efficient results along with high accuracy as compared to NCC and Fuzzy classifier used by the researchers recently

Paper Frequency Offset Compensation for OFDM Systems Using a Combined Autocorrelation and Wiener Filtering Scheme

Abstract—One of the orthogonal frequency division multiplexing (OFDM) system disadvantages is its sensitivity to frequency offset and phase noise, which lead to losing the orthogonality between the subcarriers and thereby degrade the system performance. In this paper a joint scheme for frequency offset and pilot-based channel estimation is introduced in which the frequency offset is first estimated using an autocorrelation method, and then is fined further by applying an iterative phase correction by means of pilot-based Wiener filtering method. In order to verify the capability of the estimation algorithm, the scheme has been implemented and tested using a real measurement system in a multipath indoor environment. The results show the algorithm capability of compensating for the frequency offset with different transmission and channel conditions. Keywords—channel characterization, frequency offset, OFDM measurement, Wiener filtering. 1

Using Reversed MFCC and IT-EM for Automatic Speaker Verification

This paper proposes text independent automatic speaker verification system using IMFCC (Inverse/ Reverse Mel Frequency Coefficients) and IT-EM (Information Theoretic Expectation Maximization). To perform speaker verification, feature extraction using Mel scale has been widely applied and has established better results. The IMFCC is based on inverse Mel-scale. The IMFCC effectively captures information available at the high frequency formants which is ignored by the MFCC. In this paper the fusion of MFCC and IMFCC at input level is proposed. GMMs (Gaussian Mixture Models) based on EM (Expectation Maximization) have been widely used for classification of text independent verification. However EM comes across the convergence issue. In this paper we use our proposed IT-EM which has faster convergence, to train speaker models. IT-EM uses information theory principles such as PDE (Parzen Density Estimation) and KL (Kullback-Leibler) divergence measure. IT-EM acclimatizes the weights, means and covariances, like EM. However, IT-EM process is not performed on feature vector sets but on a set of centroids obtained using IT (Information Theoretic) metric. The IT-EM process at once diminishes divergence measure between PDE estimates of features distribution within a given class and the centroids distribution within the same class. The feature level fusion and IT-EM is tested for the task of speaker verification using NIST2001 and NIST2004. The experimental evaluation validates that MFCC/IMFCC has better results than the conventional delta/MFCC feature set. The MFCC/IMFCC feature vector size is also much smaller than the delta MFCC thus reducing the computational burden as well. IT-EM method also showed faster convergence, than the conventional EM method, and thus it leads to higher speaker recognition scores

Distance Measurement Error Reduction Analysis for the Indoor Positioning System

ABSTRACT INTRODUCTION Sputnikovaya In the paper the introduction is followed by the section wireless communication channel model, which describes the model, its impairments and TOA/TDOA channel profile. In Section 3, the deployment of an indoor positioning system and experimental details are discussed. Section 4 includes the results obtained and finally the paper is concluded in Section 5. WIRELESS COMMUNICATION CHANNEL MODEL We can simply model the wireless channel in frequency where β is called phase factor and represented by: where c is the free space speed of light. The received signal will have a time delay τ which depends on range r. The relation between time delay and the distance can be written as follows: The electric field now can be written as: The signal is received through multiple number of paths in a typical wireless communication system. These paths, shown in A typical wireless channel generally comprises these paths and its CIR can be represented by a tapped delay module, shown i

A Novel Method to Implement the Matrix Pencil Super Resolution Algorithm for Indoor Positioning

This article highlights the estimation of the results for the algorithms implemented in order to estimate the delays and distances for the indoor positioning system. The data sets for the transmitted and received signals are captured at a typical outdoor and indoor area. The estimation super resolution algorithms are applied. Different state of art and super resolution techniques based algorithms are applied to avail the optimal estimates of the delays and distances between the transmitted and received signals and a novel method for matrix pencil algorithm is devised. The algorithms perform variably at different scenarios of transmitted and received positions. Two scenarios are experienced, for the single antenna scenario the super resolution techniques like ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique) and theMatrix Pencil algorithms give optimal performance compared to the conventional techniques. In two antenna scenario RootMUSIC and Matrix Pencil algorithm performed better than other algorithms for the distance estimation, however, the accuracy of all the algorithms is worst than the single antenna scenario. In all cases our devised Matrix Pencil algorithm achieved the best estimation results

An Efficient Channel Model for OFDM and Time Domain Single Carrier Transmission Using Impulse Responses

The OFDM (Orthogonal Frequency Division Multiplexing) is well-known, most utilized wideband communication technique of the current era. SCT (Single Carrier Transmission) provides equivalent performance in time domain while decision equalizer is implemented in frequency domain. SCT annihilates the ICT (Inter Carrier Interference) and the PAPR (Peak to Average Power Ratio) which is inherent to OFDM and degrades its performance in time varying channels. An efficient channel model is presented in this contribution, to implement OFDM and SCT in time domain using impulse responses. Both OFDM and SCT models are derived dialectically to model the channel impulse responses. Our model enhances the performance of time domain SCT compared with OFDM and subsides the PAPR and ICI problems of OFDM. SCT is implemented at symbol level contained in blocks. Simulation results implementing Digital Radio Monadiale (DRM) assert the performance gain of SCT over OFDM

Evaluation of SVR: A Wireless Sensor Network Routing Protocol

The advancement in technology has made it possible to create small in size, low cost sensor nodes. However, the small size and low cost of such nodes comesat at price that is, reduced processing power, low memory and significantly small battery energy storage. WSNs (Wireless Sensor Networks) are inherently ad hoc in nature and are assumed to work in the toughest terrain. The network lifetime plays a pivotal role in a wireless sensor network. A long network lifetime, could be achieved by either making significant changes in these low cost devices, which is not a feasible solution or by improving the means of communication throughout the network. The communication in such networks could be improved by employing energy efficient routing protocols, to route the data throughout the network. In this paper the SVR (Spatial Vector Routing) protocol is compared against the most common WSN routing protocols, and from the results it could be inferred that the SVR protocol out performs its counterparts. The protocol provides an energy efficient means of communication in the networ