Abstract

For ages, human body temperature has been used as an indicator for judging the health status. Over the years, the science of Medical Thermography has evolved to measure body surface temperatures that help in making relevant judgments about diseases. Unusual temperature patterns or the values above permissible limits indicate abnormality. In most of the cases, such temperature differences indicate a high chance of inflammation, infection or malignancy. Localized increase in temperature is termed as Hot spot and is indicative of such abnormalities. This paper develops an algorithm to detect the highest temperature region in breast thermogram to predict the breast disease. Thermal image is captured using an infrared camera. The temperature data are processed to find the hotspot. The location and shape of the hotspot is detected. The thermograms are further segmented in left and right part manually. Statistical parameters are calculated from temperature data of segmented region. Significant differences in these parameters is observed for healthy and sick cases showing asymmetry. The presence of asymmetric hotspot suggests the further follow up. Results are validated by a radiologist confirming the performance of the algorithm.

Abstract

In biomedical signal analysis, classification plays an important role and gives the promising solution to the electroencephalogram (EEG) analysis. An automatic EEG signal classification is proposed in this system and contribution of the diagnostician is replaced by using the soft computing techniques, since the manual classifications carried out in the clinical analysis is a time consuming task. In the proposed methodology, the EEG features are extracted from the raw EEG signals which are then fed to these ANFIS classifiers. It is a sophisticated framework for the classification of the different brain states in the human brain by representing their experts based knowledge as an Adaptive Neuro Fuzzy Inference System (ANFIS). This algorithm has a capability to detect the two types of brain states, including dementia and encephalopathy. Finally, the tentative outcome of the results is expressed in terms of classification accuracy and improves execution. The analyses are demonstrated with the ANFIS algorithm to improve and enhance the performances in the MATLAB.

Abstract

In this paper, a novel method Galois Field Fourier Transform has been proposed for extracting the primitive Bose-Chaudhuri- Hocquenghem (BCH) Code which is intercepted with the noise. Firstly, the intercepted bit stream is divided into sequences of the same length and GFFT is performed on all the sequences, from which spectral components are recorded. Then, based on the locations of common null spectral components, the code length and the roots of respective generator polynomial are found. Finally, the performance of proposed technique is measured by computing the false alarm and miss detection probabilities for both roots and non-roots of the Generator Polynomial. The code recognition of the proposed method is plotted for different code lengths and also compared with previous techniques.

Abstract

The listeners outperform Automatic speech recognition structures in each and every speech reputation task. Modern excessive-tech automated speech recognition systems carry out very well in environments, wherein the speech indicators are reasonably easy. In maximum of the instances, popularity with the aid of machines degrades dramatically with mild adjustment in speech signals or talking environment, for this reason complicated algorithms are used to symbolize this unpredictability. So, the speech can be easily identified. Speech generation gives many possibilities for private identity, which is practical and non-intrusive. Besides that, speech era offers the capability to verify the identity of a person remotely over long distance by using an ordinary phone. In this paper, the authors have proposed a technique to apprehend any words or speech thru the spectrogram analysis. This technique is used to look at the ideas of speaker reputation in multiple languages and apprehend its uses in identification and verification systems and to assess the recognition capability of various voice functions and parameters. To find out the technique, this is appropriate for Automatic Speaker Recognition systems in phrases of reliability and computational efficiency.

Abstract

Visual data transmitted in the form of a digital image is becoming a major method for visual communication in today's era, but the image obtained after the transmission is often corrupted with many types of noise. Noise is an important factor which, when get added to an image, reduces the quality and appearance. So in order to enhance the image quality, it must be removed with preserving the textural information and structural features of image. There are different types of noises exist which corrupts the images. Selection of the de-noising algorithm is application oriented [17]. Here in this paper, two filters were used; one is trim median filter used for estimation and removal of noise from image corrupted with Salt and Pepper and the other filter is Gaussian filter which is used for Gaussian noise estimation and reduction [16]. This is clearly a better algorithm because it is based on a modified decision based system. In this paper, the authors propose a modified decision based modified trim median filter algorithm for the restoration and effective suppression of gray scale and the color images that are highly corrupted by salt and pepper noise. The authors also calculate the presence of Gaussian noise in any noisy digital images. The authors implement a GUI for estimating the density of saltpepper noise in degraded images using joint entropy value and mutual information. The joint entropy value between the noisy image and the original image or other typical images was introduced in this paper to depict the inter-correlation.

The system is tested against the different color and grayscale images and its gives the better [2] Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF).