Image denoising involves processing of the image data to produce a visually high quality image. The denoising algorithms may be classified into two categories, spatial filtering algorithms and transform domain based algorithms. In this paper a comparative study of different denoising filters for speckle noise reduction in ultrasonic b-mode images based on calculating the Peak Signal to Noise Ratio (PSNR) value as a metric is presented. The quantitative results of comparison are tabulated by calculating the PSNR of the output image.

Classifying an object captured in an image is useful for understanding the contents of the image and annotating it exactly with corresponding tags automatically is the problem faced recently. As the real world data set is highly imbalanced it degrades the performance of automatic image annotation and object detection. To prevail over this drawback we have proposed a new system for pattern matching and annotation which is based on the fusion of principles obtained from Fractal Transform and gentle AdaBoost algorithm. This paper, also tries to overcome deterioration in the performance occurring through imbalance dataset, different orientation, scaling in image annotation by choosing an over sampling method for learning the classifier. The proposed IFSMOTE classifier is initially trained up by setting a threshold value which helps to identify the objects correctly and an over-sampling technique based on fractal is used to classify the imbalanced dataset. Experimental results on the Flicker image dataset have shown superior performance results in terms of precision, recall and F-measure. This paper also presents the comparative results of our proposed system with other traditional image annotation algorithm like SVM, SMOTE and FSMOTE.

Premature ventricular contractions (PVC) are premature heartbeats originating from the ventricles of the heart. These heartbeats occur before the regular heartbeat. The Fractal analysis is most mathematical models produce intractable solutions. Some studies tried to apply the fractal dimension (FD) to calculate of cardiac abnormality. Based on FD change, we can identify different abnormalities present in Electrocardiogram (ECG). Present of the uses of Poincaré plot indexes and the sample entropy (SE) analyses of heart rate variability (HRV) from short term ECG recordings as a screening tool for PVC. Poincaré plot indexes and the SE measure used for analyzing variability and complexity of HRV. A clear reduction of standard deviation (SD) projections in Poincaré plot pattern observed a significant difference of SD between healthy Person and PVC subjects. Finally, a comparison shows for FD, SE and Poincaré plot parameters.

In modern days, digital watermarking has become an admired technique for hitting data in digital images to help guard against copyright infringement. The proposed Permutation-based Homogeneous Block Content authentication (PHBC) methods develop a secure and excellence strong watermarking algorithm that combines the reward of permutation-based Homogeneous block (PHB) with that of significant and insignificant bit values with X0R encryption function using Max coefficient of least coordinate value for embedding the watermark. In the projected system uses the relationship between the permutation blocks to embed many data into Homogeneous blocks without causing solemn distortion to the watermarked image. The experimental results show that the projected system is very efficient in achieving perceptual invisibility with an increase in the Peak Signal to Noise Ratio (PSNR). Moreover, the projected system is robust to a variety of signal processing operations, such as image Cropping, Rotation, Resizing, Adding noise, Filtering , Blurring and Motion blurring.

In wireless communication fading of channels is the serious cause of the received degraded signals. The effect of fading can be minimized by using various time and space domain techniques. However, space domain techniques are preferred over the others due to its advantages. In this paper, comparison of the wireless MIMO system under Almouti's and maximum ratio combining schemes is presented. Basic idea in these schemes is to transmit and receive more than one copy of the original signals. Using two transmitter antennas and one receiver antenna, the scheme provides the nearly same diversity order as the maximal-ratio receiver combining (MRRC) with one transmitter antenna, and two receiver antennas. Results for one transmitter and four receivers under MRRC is also presented and compared. Finally, results are presented while varying the average transmitted power.

The purpose of the proposed research work is to develop a computer system that can recognize a person by comparing the characteristics of face to those of known individuals. The main focus is on frontal two dimensional images that are taken in a controlled environment i.e. the illumination and the background will be constant. All the other methods of person's identification and verification like iris scan or finger print scan require high quality and costly equipment's but in face recognition we only require a normal camera giving us a 2-D frontal image of the person that will be used for the process of the person's recognition. Principal Component Analysis technique has been used in the proposed system of face recognition. The purpose is to compare the results of the technique under the different conditions and to find the most efficient approach for developing a facial recognition system

The conventional method for medical resonance brain images classification and tumors detection is by human inspection. Operator-assisted classification methods are impractical for large amounts of data and are also non-reproducible. Hence, this paper presents Neural Network techniques for the classification of the magnetic resonance human brain images. The proposed Neural Network technique consists of the following stages namely, feature extraction, dimensionality reduction, and classification. The features extracted from the magnetic resonance images (MRI) have been reduced using principles component analysis (PCA) to the more essential features such as mean, median, variance, correlation, values of maximum and minimum intensity. In the classification stage, classifier based on Back-Propagation Neural Network has been developed. This classifier has been used to classify subjects as normal, benign and malignant brain tumor images. The results show that BPN classifier gives fast and accurate classification than the other neural networks and can be effectively used for classifying brain tumor with high level of accuracy.

The twins are classified into two categories namely fraternal and identical twins. Fraternal twins differ in face structures and DNA sequences but, identical twins have the same face structure and share same DNA sequence. Therefore, it is difficult to identify identical twins on the basis of their faces and DNA sequences. In this research paper, we have introduced a new approach for identifying identical twins on the basis of minutiae coordinates, orientation angles, and minutiae distances of their thumbprint images. 
We tested the proposed method on thumbprint images of an identical twin pair generated by using Incept H3 T&A Terminal and fifty pairs of identical twins of FVC04, and FVC06 datasets. We have found that the proposed approach is superior, and robust in comparison to existing techniques in terms of accuracy, efficiency, and storage space.