In this paper, the inverse problems of cardiac sources using analytical and probabilistic methods are solved and discussed. The standard Tikhonov regularization technique is solved initially to estimate the under-determined heart surface potentials from Magnetocardiographic (MCG) signals. The results of the deterministic method subjected to noise in the measurements are discussed and compared with the probabilistic models. Hierarchical Bayesian modeling with fixed Gaussian prior is employed to quantify the uncertainties in source reconstructions. A novel application of Variational Bayesian inference approach has been presented to estimate the heart sources. The reconstruction results of Variational Bayesian model with non-stationary priors are compared with solutions of simplistic Bayesian approach; and the performances are evaluated using Root Mean Square Error (RMSE) and correlation co-efficient metrics. The Bayesian solutions in the study are also extended to localize the MCG sources for two types of Myocardial infarction cases.

In this paper we have implemented Kannada isolated digit recognition system using Mel frequency cepstral coefficients (MFCC) as feature vector. The system is designed to recognize isolated utterances of Kannada numbers. MFCC are used as the features and Hidden Markov Model (HMM) as pattern recognizer. K-means procedure is performed on the feature vectors to obtain the observation sequence. Discrete HMM is used in the system. The system is developed by considering the requirement of a voice controlled machine in Kannada language. Performance of the system is evaluated and compared based on the MFCC along with its first and second order derivatives.

This comprehensive study examines the pivotal role of technology in seed quality inspection and computer-aided seed germination monitoring. Focusing on cutting-edge automated methods, the review explores how image processing and machine learning techniques are employed for seed quality assessment. It provides a comprehensive overview of the methodologies, image modalities, evaluation approaches, and metrics employed in these advancements, drawing from recent literature sources. The study underscores the importance of real-time monitoring and identifies the requirements for developing automated seed testing systems that minimize human intervention while maximizing productivity in the agricultural sector. By synthesizing supporting literature, the review offers valuable guidance and future directions for researchers seeking to enhance seed quality inspection and implement computer-aided monitoring systems, ultimately improving accuracy and productivity in seed germination processes.

We build an automatic phoneme recognition system based on Hidden Markov Modeling (HMM) which is a Dynamic modeling scheme. Models were built by using Stochastic pattern recognition and Acoustic phonetic schemes to recognise phonemes. Since our native language is Kannada, a rich South Indian Language, we have used 15 Kannada phonemes to train and test these models. Since Mel - Frequency Cepstral Coefficients (MFCC) are well known Acoustic features of speech [1,2], we have used the same in speech feature extraction. Finally performance analysis of models in terms of Phoneme Error Rate (PER) justifies the fact that Dynamic modeling yields good results and can be used in developing Automatic Speech Recognition systems.

Spine radiographs in the standing position are the recommended standard for diagnosing idiopathic scoliosis. Though the deformity exists in 3D, its diagnosis is currently carried out with the help of 2D radiographs due to the unavailability of an efficient, low-cost 3D alternative. Computed tomography (CT) and magnetic resonance imaging (MRI) are not suitable in this case, as they are obtained in the supine position. Research on 3D modelling of scoliotic spine began with multiplanar radiographs and later moved on to biplanar radiographs and finally a single radiograph. Nonetheless, modern advances in diagnostic imaging have the potential to preserve image quality and decrease radiation exposure. They include the DIERS formetric scanner system, the EOS imaging system, and ultrasonography. This review article briefly explains the technology behind each of these methods. They are compared with the standard imaging techniques. The DIERS system and ultrasonography are radiation free but have limitations with respect to the quality of the 3D model obtained. There is a need for 3D imaging technology with less or zero radiation exposure and that can produce a quality 3D model for diseases like adolescent idiopathic scoliosis. Accurate 3D models are crucial in clinical practice for diagnosis, planning surgery, patient follow-up examinations, biomechanical applications, and computer-assisted surgery.