J. Padmapriya

Bio: J. Padmapriya is an academic researcher. The author has contributed to research in topics: Feature extraction & Filter bank. The author has an hindex of 1, co-authored 7 publications receiving 2 citations.

Voice Extraction from Background Noise using Filter Bank Analysis for Voice Communication Applications

Abstract: Automatic Speech Recognition plays an evident role in extracting the voice signal in the noisy background. The reduction of noise in the signal is susceptible to the information which is to be transmitted since not all the information is emphasized. This leads to the deterioration in the transmitted information and paved furtherance for automatic speech recognition. Prevalent feature extraction techniques are applied to extract the speech signal with the trade off of complexity, compression ratio. For the application of voice communication, filter bank analysis is applied to extract the voice signals in the noisy environment. This work emphasized on the attributes of the perceptual quality of Loudness, Pitch Intensity, Timing. Band pass filtering provides reliable extraction of the voice signal features in the noisy environment. The power distribution of the extracted signals for the selected audio signal with the length of more than 20 seconds wave file with a sampling rate of 16 Khz along with the background noises has been plotted and its respective spectrogram also been plotted.

Investigation of Hybrid Power System for Marine Applications

Abstract: Today’s Marine industries are undergoing transformation because of rapid growth of advancement in the field of automation. Shipping industries use hybrid propulsion systems to de-carbonize and orient the path towards zero emission. The renewable energy supply (RES) is utilized by reducing the dependence on imported conventional fossil fuels; greenhouse gas emissions produced by the usage of fossil fuels are reduced. Renewable green energy is used to generate power at the distribution level. Energy sources are distributed around the world. The utility's hybrid (wind/solar) power system has proven to be a reliable source of energy. In this article, PV and wind (hybrid) power used for marine applications with the reduction of fuel consumption is proposed. The hybrid buck boost converter used for regulating DC output voltage. A multi-level H bridge inverter between DC-DC converter and load provides the load's ac voltage requirement in hybrid systems. For a given output waveform quality, MLI topology provide lower THD and EMI output, higher efficiency and better output waveform. In order to design a multilevel inverter, a cascaded H-Bridge structure was adopted. PWM (Pulse Width Modulation) techniques enable the operation of Cascaded H Bridges to generate an approximate sine wave output from a multilayer inverter. To improve the hybrid system's performance, output of converter is supplied to the thirteen level H bridge inverter. This combination can maintain the appropriate voltage to load ratio. Voltage profile is improved by using H-bridge multilevel inverter. The proposed framework is re-enacted utilizing MATLAB/Simulink.

Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management

Abstract: Plant stress is one of the most significant factors affecting plant fitness and, consequently, food production. However, plant stress may also be profitable since it behaves hormetically; at low doses, it stimulates positive traits in crops, such as the synthesis of specialized metabolites and additional stress tolerance. The controlled exposure of crops to low doses of stressors is therefore called hormesis management, and it is a promising method to increase crop productivity and quality. Nevertheless, hormesis management has severe limitations derived from the complexity of plant physiological responses to stress. Many technological advances assist plant stress science in overcoming such limitations, which results in extensive datasets originating from the multiple layers of the plant defensive response. For that reason, artificial intelligence tools, particularly Machine Learning (ML) and Deep Learning (DL), have become crucial for processing and interpreting data to accurately model plant stress responses such as genomic variation, gene and protein expression, and metabolite biosynthesis. In this review, we discuss the most recent ML and DL applications in plant stress science, focusing on their potential for improving the development of hormesis management protocols.

Automation of Crop Disease Detection through Conventional Machine Learning and Deep Transfer Learning Approaches

Abstract: With the rapid population growth, increasing agricultural productivity is an extreme requirement to meet demands. Early identification of crop diseases is essential to prevent yield loss. Nevertheless, it is a tedious task to manually monitor leaf diseases, as it demands in-depth knowledge of plant pathogens as well as a lot of work, and excessive processing time. For these purposes, various methods based on image processing, deep learning, and machine learning are developed and examined by researchers for crop leaf disease identification and often have obtained significant results. Motivated by this existing work, we conducted an extensive comparative study between traditional machine learning (SVM, LDA, KNN, CART, RF, and NB) and deep transfer learning (VGG16, VGG19, InceptionV3, ResNet50, and CNN) models in terms of precision, accuracy, f1-score, and recall on a dataset taken from the PlantVillage Dataset composed of diseased and healthy crop leaves for binary classification. Moreover, we applied several activation functions and deep learning optimizers to further enhance these CNN architectures’ performance. The classification accuracy (CA) of leaf diseases that we obtained by experimentation is quite impressive for all models. Our findings reveal that NB gives the least CA at 60.09%, while the InceptionV3 model yields the best CA, reaching an accuracy of 98.01%.

A Double Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on Slope Entropy and Fuzzy Entropy

Abstract: Rolling bearings are the key components for the safe operation of mechanical equipment. It plays an irreplaceable role in the normal operation of mechanical equipment. Higher load makes higher failure rate of rolling bearing. Accurate identification of the fault location is an important step in the diagnosis of the rolling bearing fault. In recent years, the entropy features of rolling bearing vibration signals are usually extracted to identify fault. In this paper, a double feature extraction method based on slope entropy (SlE) and fuzzy entropy (FE) is proposed to recognize the fault state of rolling bearing through rolling bearing signals. In the single feature extraction experiment, the recognition rate of these two kinds of entropy is not high. Through the improvement of the single feature extraction experiment, SlE and FE are selected as two feature combinations. After combining approximate entropy (AE), SlE, FE, permutation entropy (PE), and sample entropy (SE). The identification rate of these combinations was calculated using k nearest neighbor (KNN). The result shows that the recognition rate of this combination is 98% and 3.3% higher than other combinations.