MONO, DI and TRI SSRs data extraction & storage from 1403 virus genomes with next generation retrieval mechanism

Now a day׳s SSRs occupy the dominant role in different areas of bio-informatics like new virus identification, DNA finger printing, paternity & maternity identification, disease identification, future disease expectations and possibilities etc., Due to their wide applications in various fields and their significance, SSRs have been the area of interest for many researchers. In the SSRs extraction, retrieval algorithms are used; if retrieval algorithms quality is improved then automatically SSRs extraction system will achieve the most relevant results. For this retrieval purpose in this paper a new retrieval mechanism is proposed which will extracted the MONO, DI and TRI patterns. To extract the MONO, DI and TRI patterns using proposed retrieval mechanism in this paper, DNA sequence of 1403 virus genome data sets are considered and different MONO, DI and TRI patterns are searched in the data genome sequence file. The proposed Next Generation Sequencing (NGS) retrieval mechanism extracted the MONO, DI and TRI patterns without missing anything. It is observed that the retrieval mechanism reduces the unnecessary comparisons. Finally the extracted SSRs provide the useful, single view and useful resource to researchers

Automatic music composition based on HMM and identified wavelets in musical instruments

Automatic Music Composition plays a crucial role in the musical research and can become a tool for the incorporation of artificial intelligence in computer musicology. This paper finds an efficient method for identifying the wavelets and filter bank coefficients in musical instruments using NLMS algorithm and the usage of these wavelets for Automatic music Composition using Hidden Markov Model. In this paper, a technique to identify the scaling function and the wavelet functions of the wavelets present in musical instruments, violin and flute, is presented. NLMS algorithm is used to identify the filter bank coefficients of wavelet-like elements, found repeating in musical notes of the instruments. Pre-trained hidden markov models for each raga of South Indian Music is used for the composition. The HMM selected has twelve states which represent the twelve notes in South Indian music. Fundamental frequency tracking algorithm, followed by quantization is done. The resulting sequence of frequency jumps of different musical clips of same musical pattern (Raga) is presented to Hidden Markov Model of a particular Raga for training. The HMM model of that Raga along with the filter coefficient is used to regenerate a piece of music in that particular raga. The methodology is tested in the context of South Indian Classical Music, using the wavelet of classical music instruments, Flute and Violin.by M.S. Sinith and K.V.V. Murth

SSM wavelets for analysis of music signals using Particle Swarm Optimization

The waveform of a single note played by musical instruments has a repeating element, as it contains fundamental frequency and its harmonics. This waveform can be used as the scaling function for analysing the signals produced by that particular musical instrument, provided it satisfies the necessary and sufficient condition for a scaling function. In this paper, the filter coefficients corresponding to this scaling function is obtained using Particle Swarm Optimization(PSO) technique. For known wavelets, like Daubechies, the scaling function can be iteratively found from the filter coefficients. However, it is difficult to generate the filter coefficients from the wavelets without the knowledge of some characteristics of the scaling function or the wavelet. In this context, the PSO model which has been developed here gives very accurate values of the filter coefficients for any given scaling function. Further, ordinary PSO is modified for better optimization resulting in a new wavelet for music signals called as Sinith-Shikha-Murthy (SSM) wavelet. The working of the proposed models are verified using Daubechies wavelet. The filter coefficients corresponding to the signal generated by musical instruments flute and violin are also found. The regeneration of the scaling function iteratively using the obtained filter coefficients confirmed the results.by M.S. Sinith, Shikha Tripathi and K.V.V. Murth

Trigonometry-based motion blur parameter estimation algorithm

Restoration of blurred images requires information about the blurring function, which is generally unknown in practical applications. Identification of blur parameters is essential for yielding blurring function. This paper proposes a technique for estimation of motion blur parameters by formulating trigonometric relationship between the spectral lines of the motion blurred image and the blur parameters. In majority of the existing motion blur parameter estimation approaches, length of motion blur is estimated by rotating the Fourier spectrum to estimated motion angle. This requires angle estimation to be done forehand. The proposed method estimates both, length and angle simultaneously by exploring the trigonometric relation between spectral lines, thereby eliminating the need of spectrum rotation for length estimation. The proposed technique is applied on Berkeley segmentation dataset, Pascal VOC 2007 and USC-SIPI image database. The simulation results prove that the proposed method exhibit better parameter estimation performance as compared to existing state-of-the-art techniques.by Ruchi Gajjar, Tanish Zaveri, Asim Banerjee and K.V.V. Murth

Neuro-Fuzzy-Based Control for Parallel Cascade Control

This paper discusses the application of adaptive neuro-fuzzy inference system (ANFIS) control for a parallel cascade control system. Parallel cascade controllers have two controllers, primary and secondary controllers in cascade. In this paper the primary controller is designed based on neuro-fuzzy approach. The main idea of fuzzy controller is to imitate human reasoning process to control ill-defined and hard to model plants. But there is a lack of systematic methodology in designing fuzzy controllers. The neural network has powerful abilities for learning, optimization and adaptation. A combination of neural networks and fuzzy logic offers the possibility of solving tuning problems and design difficulties of fuzzy logic. Due to their complementary advantages, these two models are integrated together to form more robust learning systems, referred to as adaptive neuro-fuzzy inference system (ANFIS). The secondary controller is designed using the internal model control approach. The performance of the proposed ANFIS-based control is evaluated using different case studies and the simulated results reveal that the ANFIS control approach gives improved servo and regulatory control performances compared to the conventional proportional integral derivative controller.by K.V.V. Murthy et al.

Image Super Resolution Using Sparse Image and Singular Values as Priors

In this paper single image superresolution problem using sparse data representation is described. Image super-resolution is ill -posed inverse problem. Several methods have been proposed in the literature starting from simple interpolation techniques to learning based approach and under various regularization frame work. Recently many researchers have shown interest to super-resolve the image using sparse image representation. We slightly modified the procedure described by a similar work proposed recently. The modification suggested in the proposed approach is the method of dictionary training, feature extraction from the trained data base images and regularization. We have used singular values as prior for regularizing the ill-posed nature of the single image superresolution problem. Method of Optimal Directions algorithm (MOD) has been used in the proposed algorithm for obtaining high resolution and low resolution dictionaries from training image patches. Using the two dictionaries the given low resolution input image is super-resolved. The results of the proposed algorithm showed improvements in visual, PSNR, RMSE and SSIM metrics over other similar methods.K. V. V. Murthy et al.

Enhanced MRAC Based Parallel Cascade Control Strategy for Unstable Process with Application to a Continuous Bioreactor

In this paper, Enhanced Model Reference Adaptive Control (E-MRAC) based Parallel Cascade Control strategy (PCC) is proposed for the control of unstable continuous bioreactor. This control system consists of secondary and primary loop. The secondary loop comprises of PID controller, which is designed based on the direct synthesis method. In order to achieve stable responses for unstable processes like continuous bioreactor, non linear control strategy in the primary loop would gain edge over linear control. Hence, the Enhanced MRAC (includes smith predictor) is introduced in the primary loop. The presence of Smith predictor has minimized the discrepancies due to dead times. This seems to be an added advantage over existing ones. From the simulation studies it is observed that Enhanced MRAC based PCC has shown better tracking performance when compared to the PID based PCC control strategy.by Murthy et al.