Hybrid ASR for Resource-Constrained Robots: HMM - Deep Learning Fusion

This paper presents a novel hybrid Automatic Speech Recognition (ASR) system designed specifically for resource-constrained robots. The proposed approach combines Hidden Markov Models (HMMs) with deep learning models and leverages socket programming to distribute processing tasks effectively. In this architecture, the HMM-based processing takes place within the robot, while a separate PC handles the deep learning model. This synergy between HMMs and deep learning enhances speech recognition accuracy significantly. We conducted experiments across various robotic platforms, demonstrating real-time and precise speech recognition capabilities. Notably, the system exhibits adaptability to changing acoustic conditions and compatibility with low-power hardware, making it highly effective in environments with limited computational resources. This hybrid ASR paradigm opens up promising possibilities for seamless human-robot interaction. In conclusion, our research introduces a pioneering dimension to ASR techniques tailored for robotics. By employing socket programming to distribute processing tasks across distinct devices and strategically combining HMMs with deep learning models, our hybrid ASR system showcases its potential to enable robots to comprehend and respond to spoken language adeptly, even in environments with restricted computational resources. This paradigm sets a innovative course for enhancing human-robot interaction across a wide range of real-world scenarios.Comment: To be published in IEEE Access, 9 pages, 14 figures, Received valuable support from CCBD PESU, for associated code, see https://github.com/AnshulRanjan2004/PyHM

Belly depth studies for shrimp trawls - Part III

Earlier investigations with 13.69 m (45') four seam shrimp trawl indicated the optimum depth of belly to be 70 meshes. Present communication details further experiments on similar lines with a bigger shrimp trawl of size 17.07 m (56') without overhang. The results obtained have not only given corroborating evidence in support of the earlier findings but also helped in arriving at a relationship that for a given stretched width of belly ‘L’ the stretched depth of belly could be either 2L/5 or 40% of ‘L’ with an allowance of ± 2 meshes

Structure-based Generalized Models for Selected Pure-fluid Saturation Properties

This study focused on developing generalized structure-based models for predicting pure-fluid surface tensions and saturation viscosities. Reliable experimental data for a wide range of molecular species were assembled from the DIPPR physical property database. The Scaled-Variable-Reduced-Coordinate (SVRC) framework was used to correlate the available data for the saturation properties under consideration. Quantitative Structure-Property Relationships (QSPR) was used to generalize the SVRC model parameters. Non-linear QSPR models involving a hybrid of Genetic Algorithms (GA) and Artificial Neural Networks (ANN) were developed for the model parameters. Specifically, the SVRC-QSPR models, in general, were found to be capable of providing generalized a priori predictions for surface tension and saturation viscosities with an absolute average deviation (AAD) of approximately 2% using end-point input data.School of Chemical Engineerin

WIRELESS POWER TRANSFER TO BIOMEDICAL IMPLANTS

Ph.DDOCTOR OF PHILOSOPH

INFLUENCE OF TRICHODERMA HARZIANUM T22 TO ENRICH THE MINERALS REQUIRES FOR PLANT GROWTH IN VERMICAST

In several developing countries bagasse is one of the major source of bio fertilizer and animal feed production. About 25 million tonnes of bagasse are available every year but 60 –75 % of dry matter of bagasse acquires in the form cellulose and hemicelluloses and its digestibility is very poor because of the presence of lignin. It is regarded as a cheap substrate, collected at the site of processing and constant supply generated within the sugarcane industry. Trichoderma harzianum T22 produce ligno cellulolytic enzyme which is used to break the indigestive compounds and help earthworm to easily digest food matters in the bagasse. In the current research three different groups each group had three different combinations of the composting beds with the size 3L×2B×1H were prepared. The first group had three different combinations of composting beds with bagasse and cow dung; then second group had three different combinations of composting beds with bagasse, cow dung and earthworm; finally third group had three different combinations of composting beds with bagasse, cow dung, earthworm and Trichoderma harzianum T22 were constructed and the beds were maintained at optimum temperature and pH. The presences of the minerals were analysed using standard method and heavy metals were analysed using the Atomic absorption spectroscopy at regular interval 30th, 60th, and 90th day of composting respectively. Finally, the results revels that the vermicast from Trichoderma harzianum T22 inoculated composed beds especially T3c have higher quantity of essential minerals with lesser heavy metals when compare to others. We concluded that the Trichoderma harzianum T22 plays a major role to enrich the essential minerals required for various plants growth in the vermicast