Sakuntala Mahapatra

A Seamless Vertical Handoff Algorithm in 4G Networks

The rapid improvement of the mobile generations was for the purpose of supporting as many mobile devices as possible that could benefit the users at anytime and anywhere in terms of common practical applications such as internet access, video-on-demand, video conferencing system and many more applications. The emergence of 4G wireless network technologies is intended to complement and replace the current generations. The keys features of 4G technologies include accessing information anywhere, anytime, with a seamless connection to a wide range of information and services, and receiving a large volume of information, data, pictures, video, and so on. Based on the developing trends of mobile communication, 4G will have broader bandwidth, higher data rate, and smoother and quicker handoff to provide seamless service across a multitude of wireless systems and networks. One of the major issues of seamless mobility is handoff management. To achieve seamless handoff between different wireless technologies, known as vertical handoff (VHO), it is a major challenge to design intelligent handoff management schemes for 4G-systems. In this paper we have presented the design of an adaptive multi-attribute vertical handoff decision algorithm based on genetic algorithm which is both cost effective and useful.

A Proposed Multithreading Fuzzy C-Mean Algorithm for Detecting Underwater Fishes

Recently, Scientists needs to know the behavior of fish populations in underwater. Previously many algorithms are used but they are suffered in complex textures and low detection rate. This paper proposed a multi threading fuzzy c-mean (MFC mean) approach to detect multi-moving fishes in a noisy and dense condition. In this approach, we combines the multi threaded parallel (MTP) approach and kernel based approach for optical flow. A fuzzy c-mean concept provided as a supporting factor. The simulation results show that the proposed method can able to track and detect underwater fishes with high detection rate.

A Comparative Framework of Probabilistic Atlas Segmentation Method for Human Organ’s MRI

Recently, different image analysis methods are used for human body parts. But the internal pectoral muscle segmentation of important body parts in a automatic way is widely used. This is also vital for multi modal image registration. Previously, breast MRI image analysis by automatic pectoral muscle segmentation is studied. In this paper, we introduce a comparative framework of probabilistic atlas segmentation method for breast with brain, chest, heart and liver MRI. For breast, brain, heart and liver and chest segmentation, the obtained DSC values are 0.76 ± 0.12, 0.71 ± 0.15, 0.66 ± 0.08, 0.77 ± 0.12 and 0.72 ± 0.13 respectively. The total overlap values for each case are 0.76 ± 0.12, 0.76 ± 0.15, 0.71 ± 0.08, 0.70 ± 0.12 and 0.70 ± 0.13 respectively.

A Novel Approach for Tracking Sperm from Human Semen Particles to Avoid Infertility

Now a days, the infertility is a big problem for human being, especially for men. The mobility of the sperm does not depend on the number of sperm present in the semen. To avoid infertility, the detection rate of the multi moving sperms is to measured. There are different algorithms are utilized for detection of sperms in the human semen, but their detection rate is not up to the mark. This article proposed a method to track and detect the human sperm with high detection rate as compared to existing approaches. The sperm candidates are tracked using Kalman filters and proposed algorithms.

Classification of EMG Signals Using ANFIS for the Detection of Neuromuscular Disorders

Electromyography is used as a diagnostic tool for detecting different neuromuscular diseases and it is also a research tool for studying kinesiology which is the study of human- and animal-body muscular movements. Electromyography techniques can be employed with the diagnosis of muscular nerve compression and expansion abnormalities and other problems of muscles and nervous systems. An electromyogram (EMG) signal detects the electrical potential activities generated by muscle cells. These cells are activated by electrochemical signals and neurological signals. It is so difficult for the neurophysiologist to distinguish the individual waveforms generated from the muscle. Thus, the classification and feature extraction of the EMG signal becomes highly necessary. The principle of independent component analysis (ICA), fast Fourier transform (FFT) and other methods is used as dimensionality reduction methods of different critical signals extracted from human body. These different existing techniques for analysis of EMG signals have several limitations such as lower recognition rate waveforms, sensitive to continuous training and poor accuracy. In this chapter, the EMG signals are trained using soft computing techniques like adaptive neuro-fuzzy inference system (ANFIS). ANFIS is the hybrid network where fuzzy logic principle is used in neural network. This proposed technique has different advantages for better training of the EMG signals using ANFIS network with a higher reliability and better accuracy. Discrete wavelet transformation (DWT) method is used for feature extraction of the signal.

Alternate machine validation of early brain tumor detection

In this age of modernization, there exists a trend of computerized completely automated trend of living. In such an age, automated diagnostic systems play an essential role, especially for cancer detection. For the detection of cancer, it is quite difficult to detect cancer in an early stage as symptoms appear in the advanced stage. If cancer detection is made possible at early stages, the mortality rate can be greatly reduced. There are various existing techniques for the detection of cancer, but most of the techniques detect cancer at the advanced stage, thereby bringing down the chance of survival of a patient. The early detection of cancer is a challenge because of the overlapped structure of the cancer cells. In this project digital image processing techniques are used for preprocessing of the images and feature extraction process and support vector machine (SVM) and neural network classifier to checking early stage.

Two anchor RF switch with variable step size

The emerging trend in wireless communication devices have shaped the requirement of micro-devices having the inimitable capability of operating in the radio frequency (RF) range. RF MEMS switches have brought about a revolution in the volatile area of nanotechnology, due to its exceptional and extensive switching capability. The focus of this research is the analysis and redesign of contact type radio frequency micro-electro-mechanical system (RF MEMS) that has improved sustainability, durability and switching speed. The work includes analysis of the discrepancy in the conventional switch (pull-in voltage optimized switch). Then the switch has been redesigned geometrically and the stationary properties of the switch have been observed. Further, to improve the results, the properties have been compared for various materials and parameters. It has been observed from the simulation results that reduction in displacement is achieved and the use of metals improves the result.

An ideal ANFIS-based approach for classification of biomedical signals employing wavelet sub-band energy-based characteristics feature

There is a critical linkage of the error detection and classification of various biomedical signals with the diagnosis of different abnormalities. In this paper, basing on the characteristic features of wavelet sub-band energy coefficient, an ideal approach has been implemented for ECG and EEG classification. The ECG and EEG signals are pre-processed using adaptive filter and further are decomposed into time-frequency representation by the use of wavelet transformation. These extracted wavelet coefficients are helpful in calculating certain statistical parameters. For the differentiation between normal and abnormal beats, the types of EEG and ECG beats are taken into consideration. For this classification, Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. The real time signals are obtained from medical and diagnostic centers for the analysis purposes.

7μm Core diameter fiber sensor: A solution for efficient strain measurement

In this paper the monitoring capabilities like static and dynamic nature of micro meter diameter fiber transmission lines are experimentally setup. This small diameter fiber optical lines have very less impact on mechanical performance. A 7micrometer fiber is used as strain sensor. The experimental results shows that the central axial shift linearly, without creating any displacement with strain and in different temperature environment.

Multimoving Human Sperm Tracking Using CGM-CS Approach and Comparative Analysis for Proper Diagnosis in Infertility

In this paper, we introduced an algorithm for tracking and detecting multi moving human sperm using Course Grained Multi Threading Cam Shift (CGM-CS) approach in a microscopic human sperm moving video. This method is fully based on adaptive Cam Shift algorithm using color model. This algorithm is design to track and detect the sperms by using multi threading concept. The multi threading concept is compared continuously to its mean value in the successive frame in the appropriate video. The result obtained by the proposed method is also compared with the Maximum Intensity Region (MIR) algorithm, Lukas-Kanade (LK) algorithm and Kernel Based (KB) algorithm. Experimental results demonstrate that the CGM-CS algorithm is capable of tracking the sperm with high detection rate with minimum time taken as compared to existing approaches.