Shailendra Tiwari

Variation of pressure from cervical to distal end of oesophagus during swallowing: Study of a mathematical model

The investigation is an attempt to explore the cause that generates high pressure in the distal oesophagus compared to that in the proximal part. We observe through computer simulation that peristaltic waves of even slightly but progressively increasing amplitude can generate high pressure near the distal end. This is illustrated through exponential growth in the wave amplitude, which represents the dependence of the rate of growth of amplitude on its current magnitude. This may be physically interpreted that the generation of high pressure in the lower oesophagus ensures complete bolus delivery to the stomach through the cardiac sphincter. This finding may prove to be a very prominent result towards creating a prosthetic oesophagus. Some more conclusions with regard to progressive exponential increase in amplitude are also drawn. The pressure falls to zero invariably in the proximal half of every bolus, whereas for constant amplitude, zero pressure is located exactly at the midpoints of the boluses for Newtonian flows. Backward flow of fluid takes place in a smaller region if amplitude increases. Circular muscles contract more in the lower oesophagus to generate higher pressure in the distal oesophagus. In a sharp contrast to the case of constant-amplitude, pressure is neither uniformly distributed in a wave, nor is of identical shape for all boluses in the case of train wave propagation. Pressure distribution along the axis of the oesophagus differs in shape and magnitude both when a single wave propagates.

An Efficient Approach for the Prediction of G-Protein Coupled Receptors and Their Subfamilies

G-protein coupled receptors are responsible for many physiochemical processes such as neurotransmission, metabolism, cellular growth and immune response. So it necessary to design a robust and efficient approach for the prediction of G-protein coupled receptors their subfamilies. To address the issue of efficient classification G-protein coupled receptors and their subfamilies, here in this paper we propose to use a weighted k-nearest neighbor classifier with UNION of best 50 features selected by Fisher score based feature selection, ReliefF, fast correlation based filter, minimum redundancy maximum relevancy and support vector machine based recursive feature elimination feature selection methods. The proposed method achieved an overall accuracy of 99.9, 98.3 % MCC values of 1.00, 0.98 ROC area values of 1.00, 0.998 and precision of 99.9 and 98.3 % using 10-fold cross validation to predict the G-protein coupled receptors and their subfamilies respectively.

An efficient and robust approach for biomedical image retrieval using Zernike moments

Abstract Success of any image retrieval system depends heavily on the feature extraction capability of its feature descriptor. In this paper, we present a biomedical image retrieval system which uses Zernike moments (ZMs) for extracting features from CT and MRI medical images. ZMs belong to the class of orthogonal rotation invariant moments (ORIMs) and possess very useful characteristics such as superior information representation capability with minimum redundancy, insensitivity to image noise etc. Existence of these properties as well as the ability of lower order ZMs to discriminate between different image shapes and textures motivated us to explore ZMs for biomedical retrieval application. To prove the effectiveness of our system, experiments have been carried out on both noise-free and noisy versions of two different medical databases i.e. Emphysema-CT database for CT image retrieval and OASIS-MRI database for MRI image retrieval. The proposed ZMs-based approach has been compared with the existing and recently published approaches based on local binary pattern (LBP), local ternary patterns (LTP), local diagonal extrema pattern (LDEP), etc., in terms of various evaluation measures like ARR , ARP , F  _  score , and mAP . The results after being investigated have shown a significant improvement (10–14% and 15–17% in case of noise-free and noisy images, respectively) in comparison to the state-of-the-art techniques on the respective databases.

Swallowing of Casson fluid in oesophagus under the influence of peristaltic waves of varying amplitude

The experimentally verified fact that there is a high pressure zone in the lower part of the oesophagus has established that the earlier models fall short of representing the realistic swallowing process in the oesophagus. Since the high pressure is created by gradually increasing amplitudes of peristaltic waves, swallowing of Casson fluid in oesophagus is mathematically remodeled. It is revealed that in the case of exponentially increasing amplitude, pressure is non-uniformly distributed for different cycles. Pressure increases along the entire length of the oesophagus; and finally toward the end of the oesophageal flow, it increases quite significantly, probably to ensure delivery into the stomach. This is a similar observation for Newtonian as well as non-Newtonian fluids but Casson fluids need more pressure; and hence more efforts are required by the oesophagus to transport the fluid forward. When wave amplitude is small, flow rates are small. In such a case, Casson fluid requires higher flow rates fo...

Simulation of intelligent target hitting in obstructed path using physical body animation and genetic algorithm

Nowadays there are many real-time applications such as robotic motion, driver-less vehicle, intelligent target shooter(bullets and missiles), traffic routing in which human intervention is avoided. This paper proposes an exciting and generalized approach for intelligent target hitting in an obstructed path using physical body animation and genetic algorithm. This approach uses the concepts of the genetic algorithm to train the object for finding the right path to target and concepts of physical body animation to provide the motion and to react as per the collision with obstacles. Physical body animation provides a very natural feel of a real-time environment as we deal with all the external natural forces such as gravity, wind resistance the object and so on. Proposed approach deals not only with the static target but also deals with the dynamic target during the simulation.

Providing security and privacy to huge and vulnerable songs repository using visual cryptography

In the today’s scenario, the number of online song repositories such as iTunes, Hungama.com, etc. is increasing day-by-day. The reason for this can be attributed to the exponential growth in the Internet users in the past few years. These song repositories store huge number of songs (mostly in millions) and charge their users for listening and downloading them. With increased number of users requires more enhanced security measures to protect such vulnerable songs repository. Any breach in security of such song repositories would not only cause huge financial loss but also copyright infringement for the owners. Therefore, in this paper we have presented a novel and efficient approach for providing security and privacy to huge and vulnerable songs repository using visual cryptography. Presented approach not only provides confidentiality to the songs but also provides integrity verification with access control to the songs repository. We have also removed various basic security constraints of (2, 2) visual cryptography existed in most of the state of art approaches like meaningless pattern of the shares, explicit codebook requirement, contrast loss, lossy recovery etc which are eliminated in the proposed approach.

Feature selection for image steganalysis using levy flight-based grey wolf optimization

Image steganalysis is the process of detecting the availability of hidden messages in the cover images. Therefore, it may be considered as a classification problem which categorizes an image either into a cover images or a stego image. Feature selection is one of the important phases of image steganalysis which can increase its computational efficiency and performance. In this paper, a novel levy flight-based grey wolf optimization has been introduced which is used to select the prominent features for steganalysis algorithm from a set of original features. For the same, SPAM and AlexNet have been used to generate the high dimensional features. Furthermore, the random forest classifier is used to classify the images over selected features into cover images and stego images. The experimental results show that the proposed levy flight-based grey wolf optimization shows preferable convergence precision and effectively reduces the irrelevant and redundant features while maintaining the high classification accuracy as compared to other feature selection methods.

A variational framework for low-dose sinogram restoration

Pre-processing the noisy sinogram before reconstruction is an effective and efficient way to solve the low-dose X-ray Computed Tomography (CT) problem. The objective of this paper is to develop a low-dose CT image reconstruction method based on statistical sonogram smoothing approach. The proposed method is casted into a variational framework and the solution of the method is based on minimisation of energy functional. The solution of the method consists of two terms viz. data fidelity term and a regularisation term. The data fidelity term is obtained by minimising the negative log likelihood of the signal dependent Gaussian probability distribution which depicts the noise distribution in low dose X-ray CT. The second term i.e. regularisation term is a non-linear CONvolutional Virtual Electric Field Anisotropic Diffusion (CONVEF-AD) based filter which is an extension of Perona-Malik (P-M) anisotropic diffusion filter. The main task of regularisation function is to address the issue of ill-posedness of the solution for the first term. The proposed method is capable of dealing with both signal dependent and signal independent Gaussian noise i.e. mixed noise. For experimentation purpose, two different sinograms, generated from test phantom images are used. The performance of the proposed method is compared with that of existing methods. The obtained results show that the proposed method outperforms many recent approaches and is capable of removing the mixed noise in low dose X-ray CT.

A Nonlinear Modified CONVEF-AD Based Approach for Low-Dose Sinogram Restoration

Preprocessing the noisy sinogram before reconstruction is an effective and efficient way to solve the low-dose X-ray computed tomography (CT) problem. The objective of this paper is to develop a low-dose CT image reconstruction method based on statistical sonogram smoothing approach. The proposed method is casted into a variational framework and the solution of the method is based on minimization of energy functional. The solution of the method consists of two terms, viz., data fidelity term and a regularization term. The data fidelity term is obtained by minimizing the negative log likelihood of the signal-dependent Gaussian probability distribution which depicts the noise distribution in low-dose X-ray CT. The second term, i.e., regularization term is a nonlinear CONvolutional Virtual Electric Field Anisotropic Diffusion (CONVEF-AD) based filter which is an extension of Perona–Malik (P–M) anisotropic diffusion filter. The main task of regularization function is to address the issue of ill-posedness of the solution of the first term. The proposed method is capable of dealing with both signal-dependent and signal-independent Gaussian noise, i.e., mixed noise. For experimentation purpose, two different sinograms generated from test phantom images are used. The performance of the proposed method is compared with that of existing methods. The obtained results show that the proposed method outperforms many recent approaches and is capable of removing the mixed noise in low-dose X-ray CT.

Score level fusion of Iris and Fingerprint using wavelet features

Unimodal biometric systems have been serving the security demands of real world applications to a great level but these systems show vulnerabilities to certain aspects like noisy inputs, non-universality, intra-class variability and spoofing. To overcome these limitations multimodal biometric systems were developed which use more than one biometric trait for recognition. Iris and Fingerprint were considered as biometric modalities in this work because of their high compatibility in real world applications. A combination of 2-level Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are used to obtain features of iris. Similarly, a combination of 2-level DWT and Fast Fourier Transform (FFT) are used to obtain features of Fingerprint. Feature matching was performed using Euclidean distance algorithm. Fusion is done using linear summation of scores obtained from individual modalities. Verification and identification tests were conducted on proposed multimodal biometric systems of iris and fingerprint. The proposed system has shown better performance than the existing system.