Reverberation reduction in a room for multiple positions

Reverberation in a room occurs when the direct path sound from a sound source undergoes multiple reflections from the walls of the room before reaching the listener. An impulse response of the room can be measured called the room impulse response (RIR) which captures the effects of the room. This can be represented digitally on a computer. A filter is designed to cancel the effects of the room using the information in the room impulse response. This filter is called an equalization filter and is usually placed between the source signal and loudspeaker to perform the equalization. The RIR changes for varying source and listener locations, hence an equalization filter designed for one RIR will not perform equalization for multiple positions. This thesis explores methods to perform equalization for multiple positions. One of the simplest methods is spatial averaging equalization, which was used to perform the equalization for multiple positions. Equalizing RIR is only concerned about trying to flatten the frequency spectrum and stabilizing the inverse RIR by looking at its minimum-phase component. Other methods are explored which consider the masking effects of the human auditory system which relates to the perception of sound by the human ear. One such method is impulse response shortening/reshaping which emphasizes the direct path component in the RIR relative to the rest of the components using p-norm and infinity-norm optimization which is an iterative algorithm. This concept is extended for performing reshaping on RIR for multiple positions using the idea in spatial averaging equalization by using RIR\u27s measure for different positions --Abstract, page iii

SNAPPING SCAPULAR SYNDROME DUE TO RECALCITRANT SCAPULA- THORACIC BURSITIS WITH SCAPULAR DYSKINESIA

The Snapping Scapula Syndrome(SSS) characterized by palpable/audible crackling sensation over scapula during scapulothoracic movements. Etiology can be osseous lesions in and around scapula or scapulothoracic bursitis. Conservative treatment has to be tried initially in cases of symptomatic scapulothoracic bursitis for 3-6 months and surgical treatment is a viable option in cases of refractory and recalcitrant bursitis either open or arthroscopically. This case report highlights on arthroscopic bursectomy of scapulothoracic bursitis after failed conservative treatment

Customized mask region based convolutional neural networks for un-uniformed shape text detection and text recognition

In image scene, text contains high-level of important information that helps to analyze and consider the particular environment. In this paper, we adapt image mask and original identification of the mask region based convolutional neural networks (R-CNN) to allow recognition at 3 levels such as sequence, holistic and pixel-level semantics. Particularly, pixel and holistic level semantics can be utilized to recognize the texts and define the text shapes, respectively. Precisely, in mask and detection, we segment and recognize both character and word instances. Furthermore, we implement text detection through the outcome of instance segmentation on 2-D feature-space. Also, to tackle and identify the text issues of smaller and blurry texts, we consider text recognition by attention-based of optical character recognition (OCR) model with the mask R-CNN at sequential level. The OCR module is used to estimate character sequence through feature maps of the word instances in sequence to sequence. Finally, we proposed a fine-grained learning technique that trains a more accurate and robust model by learning models from the annotated datasets at the word level. Our proposed approach is evaluated on popular benchmark dataset ICDAR 2013 and ICDAR 2015

Analysis and prediction of seed quality using machine learning

The mainstay of the economy has always been agriculture, and the majority of tasks are still carried out without the use of modern technology. Currently, the ability of human intelligence to forecast seed quality is used. Because it lacks a validation method, the existing seed prediction analysis is ineffective. Here, we have tried to create a prediction model that uses machine learning algorithms to forecast seed quality, leading to high crop yield and high-quality harvests. For precise seed categorization, this model was created using convolutional neural networks and trained using the seed dataset. Using data that can be used to forecast the future, this model is used to learn about whether the seeds are of premium quality, standard quality, or regular quality. While testing data are employed in the algorithm’s predictive analytics, training data and validation data are used for categorization reasons. Thus, by examining the training accuracy of the convolution neural network (CNN) model and the prediction accuracy of the algorithm, the project’s primary goal is to develop the best method for the more accurate prediction of seed quality

Molecular Characterization of Macrophomina phaseolina, the Incitant of Coleus forskohlii Revealed by RAPD Markers

Coleus forskohlii belong to family lamiaceae is one of the commercial plants grown extensively in the country, the chemical found in the Coleus which has both medicinal application and gives great economy to the industrial organizations. Unfortunately, these plants are being highly succumbed to serious diseases like wilt and root rot caused by a fungus, hence the growers and industrialists are facing severe problem in safeguarding this crop in the field irrespective of the agro climatic conditions. Root rot disease, is one of the major diseases of Coleus forskohlii which, is caused by Macrophomina phaseolina , Pathogen variability was studied at both morphological and molecular level using cultural characteristics and Rapid Amplification of Polymorphic DNA (RAPD) analysis respectively. Totally thirty two isolates were isolated from roots of Coleus forskohlii . In RAPD 165 bands were obtained out of them 121 bands (73.3%) were polymorphic with a similarity coefficient of 0.48-0.66. Clusters analysis of RAPD data when Unweighted Pair Group Method with Arithmetic Mean (UPGMA) Tree constructed using NTSYS, it showed 6 groups. Among them two were major clusters and 4 were minor clusters with similarity coefficient 0.48-0.66. The pathogenicity of the isolates was tested on Coleus forskohlii plants. Analysis of the pathogenicity tests results revealed that the isolates grouped under two major clusters which were different from the one obtained using RAPD data. The results indicate that the data from RAPD analysis and Pathogenicity tests do not correlate with each other

Improved mechanical and microstructural performance of high-density polyethylene-chitosan-hydroxyapatite composites as potential bone implant materials

High-density polyethylene (HDPE)-chitosan-hydroxyapatite hybrid composite series with varying concentration of hydroxyapatite were prepared and compared with its corresponding HDPE-chitosan binary composite. The microstructural and mechanical characterizations of the prepared composites were studied. A 12% increase for the composite system with 8 wt% hydroxyapatite (HA4) has been noted when compared with its corresponding binary system and has been optimized for further applications. The structural characterization and miscibility of the components in the composite system were studied by using Fourier transform infrared spectroscopy and X-ray diffractometry. Positron annihilation lifetime spectroscopy studies showed that the free holes are formed in the range of similar to 115.8 angstrom(3). Contact angle studies and sorption studies were further correlated with the biocompatibility analysis to study cell adhesion and protein absorption on the surface of the composites. MC3T3 E1 cell lines showed good cell proliferation on the optimized systems. The presence of micropores along with chitosan and hydroxyapatite promoted cell growth in the prepared composites. The current research study presents the development of an improved hybrid biocomposite material that has potential in biomedical implants. (C) 2022 Elsevier Ltd. All rights reserved

Reinforcement learning strategies using Monte-Carlo to solve the blackjack problem

Blackjack is a classic casino game in which the player attempts to outsmart the dealer by drawing a combination of cards with face values that add up to just under or equal to 21 but are more incredible than the hand of the dealer he manages to come up with. This study considers a simplified variation of blackjack, which has a dealer and plays no active role after the first two draws. A different game regime will be modeled for everyone to ten multiples of the conventional 52-card deck. Irrespective of the number of standard decks utilized, the game is played as a randomized discrete-time process. For determining the optimum course of action in terms of policy, we teach an agent-a decision maker-to optimize across the decision space of the game, considering the procedure as a finite Markov decision chain. To choose the most effective course of action, we mainly research Monte Carlo-based reinforcement learning approaches and compare them with q-learning, dynamic programming, and temporal difference. The performance of the distinct model-free policy iteration techniques is presented in this study, framing the game as a reinforcement learning problem

Customized mask region based convolutional neural networks for un-uniformed shape text detection and text recognition

In image scene, text contains high-level of important information that helps to analyze and consider the particular environment. In this paper, we adapt image mask and original identification of the mask region based convolutional neural networks (R-CNN) to allow recognition at 3 levels such as sequence, holistic and pixel-level semantics. Particularly, pixel and holistic level semantics can be utilized to recognize the texts and define the text shapes, respectively. Precisely, in mask and detection, we segment and recognize both character and word instances. Furthermore, we implement text detection through the outcome of instance segmentation on 2-D feature-space. Also, to tackle and identify the text issues of smaller and blurry texts, we consider text recognition by attention-based of optical character recognition (OCR) model with the mask R-CNN at sequential level. The OCR module is used to estimate character sequence through feature maps of the word instances in sequence to sequence. Finally, we proposed a fine-grained learning technique that trains a more accurate and robust model by learning models from the annotated datasets at the word level. Our proposed approach is evaluated on popular benchmark dataset ICDAR 2013 and ICDAR 2015