Yashwant Vasantrao Joshi
@sggs.ac.in
Director
Shri Guru Gobind Singhji Institute of Engineering and Technology, Vishnupuri, Nanded 431606 Maharashtra India
EDUCATION
B. E. Electronics Engineering,(1986) M E Electronics Engineering (1991) Ph D Electrical Engineering IIT Delhi (1998)
RESEARCH INTERESTS
Digital SiIgnal and Image Processing and VLSI design
Scopus Publications
Scopus Publications
S. C. Dutta Roy and Y. V. Joshi
Springer Science and Business Media LLC
S. C. Dutta Roy and Y. V. Joshi
Springer Science and Business Media LLC
S. C. Dutta Roy, Yashwant V. Joshi, and R. K. Patney
Informa UK Limited
Jayashri V. Shinde, Yashwant V. Joshi, Ramchandra R. Manthalkar, and Joshi
Walter de Gruyter GmbH
Abstract Objectives Intervertebral disc segmentation is one of the methods to diagnose spinal disease through the degeneration in asymptomatic and symptomatic patients. Even though numerous intervertebral disc segmentation techniques are available, classifying the grades in the intervertebral disc is a hectic challenge in the existing disc segmentation methods. Thus, an effective Whale Spine-Generative Adversarial Network (WSpine-GAN) method is proposed to segment the intervertebral disc for effective grade classification. Methods The proposed WSpine-GAN method effectively performs the disc segmentation, wherein the weights of Spine-GAN are optimally tuned using Whale Optimization Algorithm (WOA). Then, the refined disc features, such as pixel-based features and the connectivity features are extracted. Finally, the K-Nearest Neighbor (KNN) classifier based on the pfirrmann’s grading system performs the grade classification. Results The implementation of the grade classification strategy based on the proposed WSpine-GAN and KNN is performed using the real-time database, and the performance based on the metrics yielded the accuracy, true positive rate (TPR), and false positive rate (FPR) values of 97.778, 97.83, and 0.586% for the training percentage and 92.382, 90.580, and 1.972% for the K-fold value. Conclusions The proposed WSpine-GAN method effectively performs the disc segmentation by integrating the Spine-GANmethod and WOA. Here, the spinal cord images are segmented using the proposed WSpine-GAN method by tuning the weights optimally to enhance the performance of the disc segmentation.
Vinay Kulkarni, Yashwant Joshi, Ramachandra Manthalkar, and Irraivan Elamvazuthi
Springer Science and Business Media LLC
Vinay Kulkarni, Yashwant Joshi, and Ramchandra Manthalkar
Springer Nature Singapore
J. V. Shinde, Y. V. Joshi, and R. R. Manthalkar
Informa UK Limited
Grading of discs is essential for the assessment of degeneration progression which subsequently plays a vital role in decision making in the removal of a disc. In particular, Pfirrmann’s five-scale...
Shankar S. Gupta, Trupti J. Taori, Mahesh Y. Ladekar, Ramchandra R. Manthalkar, Suhas S. Gajre, and Yashwant V. Joshi
Elsevier BV
Mahesh Y. Ladekar, Yashwant V. Joshi, and Ramchandra R. Manthalkar
Springer Science and Business Media LLC
Mahesh Y. Ladekar, Shankar S. Gupta, Yashwant V. Joshi, and Ramchandra R. Manthalkar
Elsevier BV
Satish S. Salunkhe, Yashwant Joshi, and Ashok Deshpande
IEEE
Several factors, either numeric or nonnumeric can have varying degrees of influence on academic performance of students. It is believed that ranking of these factors will, hopefully, assist management to initiate corrective action, with a view to improve students’ academic performance. In an academic environment, teachers are frequently in close contact with students and therefore their belief about degree of influence of factors on students’ academic performance could be of vital significance. The case study reported in this paper analyzes and proposes a possible and practicable ranking strategy of only selected factors via Dempster Shafer theory of evidence and fuzzy relational calculus.
Jyoti Sadalage, Arnab Das, and Yashwant Joshi
Wiley
J. V. Shinde, Y. V. Joshi, and R. R. Manthalkar
Springer International Publishing
This paper describes the semiautomatic method for diagnosis of intervertebral disc degeneration according to Pfirrmann’s five scale (1–5) grading system, which is used in the assessment of disc degeneration severity. Total 1123 discs are obtained after augmentation from 120 subject’s T2-weighted lumbar scans. Manual classification into five grades is done by experts. Our method is extracting 59 features using Local Binary Pattern for texture analysis and 4096 features using pretrained CNN. 1 × 59 and 1 × 4096 feature vectors are fused to form 1 × 4155 feature vector to train our multiclass Support Vector Machine classifier. This feature level fusion method is able to achieve 80.40% accuracy. A Quantitative analysis is done using parameters, viz.,—Accuracy, Sensitivity, Specificity, Precision, Recall, F1 score, etc.
Satish S. Salunkhe, Ashok Deshpande, and Yashwant Joshi
Walter de Gruyter GmbH
Abstract The academic performance assessment of students helps teachers, administrators, and policymakers to initiate corrective measures on academically poor students. This paper revisits the Zadeh-Deshpande formalism for evaluating students’ answer scripts using the concept of the reliability of information (degree of confidence) via the “degree of match” and fuzzy inference system in students’ performance evaluation. The case study infers that the overall performance of all the students is “average”. Furthermore, 206 of 237 students (87%) are declared as “average” with “high degree of certainty” by the evaluators (teachers). The aim of the proposed method is not to replace the traditional method of evaluation. Instead, the proposed technique is a step forward to enrich the present system of students’ performance assessment. Policymakers can use this method as it provides reliable information. A comparison between the results obtained from the academic performance of students using multiexpert and single-expert systems is also discussed in the paper.
Shruti Phutke, Narendra Jadhav, Ramchandra Manthalkar, and Yashwant Joshi
Springer Singapore
People are experiencing difficulties in adapting to the rapid changes in work and social fabric due to the evolution of advanced technologies in everyday life. Health and well-being of an individual in the existing world is important for proper living. Meditation improves the adaptability of an individual to live a healthy and social life. To verify this, an experiment is designed with the simple meditation practice called Focused Attention for 8 weeks. The brain activity is recorded of 11 subjects using EMOTIV EPOC+ EEG device before (pre-meditation) and after (post-meditation) meditation. Features called Higher Order Crossings and Functional Connectivity are used to analyze the effect of meditation. The results indicated a decrease in HOC values for frontal, parietal, and occipital lobes and increase in HOC of temporal lobe. The interhemispheric connectivity increased after meditation practice.
Mahesh Ladekar, Yashwant Joshi, and Ramchandra Manthalkar
Springer Singapore
This paper investigates the new approach to FIR filter design based on nonuniform frequency sampling. This method generates the nonuniform samples in passband and stopband separately using Gaussian function. For the generated nonuniform sample, the desired frequency response values are generated using ideal filter characteristics. Then, taking its nonuniform IDFT gives the required filter coefficients. The proposed method is compared with existing methods like uniform frequency sampling and optimal filter design method and results show that the investigated approach has a better advantage over uniform frequency sampling and Parks–McClellan method with regard to the frequency response of designed filter.
Jyoti A. Sadalage, Arnab Das, and Yashwant Joshi
Springer Singapore
Underwater acoustics has made significant strides over the last century, which finds applications over a wide range from basic bathymetry study to high-end research extensions. The acoustic propagation in underwater is typically governed by physical properties of the underwater channel, such as temperature, pressure, and salinity. The seasonal fluctuations in the physical properties of the tropical region manifest as thermal stratification. The random thermal stratification has a significant impact on the Sound Speed Profile (SSP), thereby distorting the received echoes from the surface and the bottom. The site-specific behavior in the tropical region makes it an interesting research problem to investigate the correlation of the surface parameters like temperature with the surface and bottom reflection due to variations in the SSP. In this work, we attempt to present underwater channel characteristics of the tropical freshwater lake system at Khadakwasla (18.43° N, 73.76° E), located in the municipal limits of Pune city in India. The temperature gradient along the water column is computed using the one-dimensional Freshwater Lake Model (FLake) to derive the SSP using Medwin relation. The statistical analysis of the sound speed fluctuations resulted due to seasonal variation in the water temperature is presented using the Kolmogorov–Smirnov (KS) Goodness-of-Fit test is used to find a close Probability Density Function (pdf) match for the surface and the bottom path impulse response. The results indicate a good match of the surface and bottom path impulse response with Weibull distribution with a high confidence level. Such characterization can facilitate the design of adaptive algorithms to minimize the underwater channel impact based on a precise estimate of the channel impulse response.
Satish S. Salunkhe, Yashwant Joshi, and Ashok Deshpande
Springer International Publishing
Traditionally, academic ranking of students’ performance is based on test score which can be interpreted in linguistic terms such as ‘very good’, ‘good’, ‘poor’, ‘very poor’ with varying degree of certainty attached to each description. There could be several students in a school having ‘very poor’ performance with varying degree of certainty. The authorities would certainly like to improve students’ academic performance based on their ranking. The case study relates to the combination of Zadeh-Deshpande formalism with Bellman-Zadeh method to arrive at an optimal ranking of especially ‘very poor’ students based on well-defined performance shaping factors.
Madhav V. Vaidya and Yashwant V. Joshi
Springer International Publishing
In this paper, pixel level features of the character are used for Devanagari numeral Recognition. The pixel distribution features for each numeral can be calculated after preprocessing the document image and converting it to binary. Based on these features the numerals are classified into appropriate groups. Histogram feature matching method gives erroneous results for the numbers like one and nine as they are having nearly similar histogram. In the proposed approach pixel distribution features are extracted in four directions. The overall performance of classification can be improved if more number of features is compared. The proposed approach gives improved results as compared to simple histogram matching criteria.
Narendra Jadhav, Ramchandra Manthalkar, and Yashwant Joshi
Elsevier BV
Abstract Meditation is one of the accepted practices to flourish in life. Emotional harmony is essential for personal well-being. In this paper, the effect of meditation on emotional response using EEG is investigated. The simple meditative technique such as focused attention on breathing is taught to the subjects. EEG is recorded at the beginning of meditation experiment and after eight weeks of regular meditation (every day 20 min). EEG signals are recorded using 14 electrodes EMOTIV EPOC+ device. The asymmetry of band power (theta, alpha and beta band) and Hjorth features are used as emotion-specific EEG features. The average effect of these features is more on frontal asymmetry. EEG functional connectivity of selected brain regions during four emotions (Happy, Angry, Sad, and Relax) in pre and post-meditation state is examined. The results revealed that more coherence in the post-meditation is found for all emotions. The K-Nearest Neighbors (K-NN) classifier is used and emotion classification accuracy after 8 weeks of meditation is decreased. This indicates the divergence of four emotions (happy, angry, sad, and relax) are less as a result of increased awareness and reduced aversion in the 11 subjects after 8 weeks of meditation.
Madhav Vaidya, , Yashwant Joshi, Milind Bhalerao, , and
The Intelligent Networks and Systems Society
Recognition of handwritten Marathi character/digits is comparatively a tough task as compared to English. It has several types of applications including the postal code reading and sorting, banks check amount processing. In this paper a novel feature extraction and selection method is proposed for the recognition of isolated handwritten Marathi numbers which is based on one dimensional Discrete Cosine Transform (DCT) algorithm. The scanned document is pre-processed and segmented to create isolated numerals. Features for each numeral can be calculated after normalizing the numeral image to 32 × 32 size. Based on these reduced features, the numerals are classified into appropriate groups. Neural network is used for classification of numerals based on the extracted features. The results of proposed method are compared with the results obtained using Discrete Wavelet Transform and zonal discrete cosine transform (ZDCT). The proposed approach gives improved results as compared to zonal DCT and DWT method. Experimental results shows accuracy observed for the method is 90.30% with normalized numeral image of size 32 × 32.
Narendra Jadhav, Ramchandra Manthalkar, and Yashwant Joshi
SPIE
Recent research suggests that meditation affects the structure and function of the brain. Cognitive load can be handled in effective way by the meditators. EEG signals are used to quantify cognitive load. The research of investigating effect of meditation on cognitive workload using EEG signals in pre and post-meditation is an open problem. The subjects for this study are young healthy 11 engineering students from our institute. The focused attention meditation practice is used for this study. EEG signals are recorded at the beginning of meditation and after four weeks of regular meditation using EMOTIV device. The subjects practiced meditation daily 20 minutes for 4 weeks. The 7 level arithmetic additions of single digit (low level) to three digits with carry (high level) are presented as cognitive load. The cognitive load indices such as arousal index, performance enhancement, neural activity, load index, engagement, and alertness are evaluated in pre and post meditation. The cognitive indices are improved in post meditation data. Power Spectral Density (PSD) feature is compared between pre and post-meditation across all subjects. The result hints that the subjects were handling cognitive load without stress (ease of cognitive functioning increased for the same load) after 4 weeks of meditation.
Piyush Asolkar, Arnab Das, Suhas Gajre, and Yashwant Joshi
Elsevier BV
Abstract Ambient noise variability is a critical aspect for the sub-optimal performance of sonar systems. The sea surface parameters (SSP) like wind speed, sea surface temperature and wave height are known to be dominant factors responsible for ambient noise levels and their variability is a major challenge in designing noise mitigation algorithms. Variability across three regions namely tropical, temperate and polar region is significant and quantification of these random fluctuations could potentially facilitate signal processing algorithms to enhance signal to noise ratio. This work presents comprehensive statistical analysis of the spatio-temporal variations of these SSP across three regions and correlates their impact on ambient noise in the specific region. Fluctuations in the tropical region are significant and do justify the challenges for effective sonar design in the region. Analysis has been done on SSP data available in open source collected by ocean observatories deployed at designated sites. Real experimental ambient noise data recordings in tropical littoral waters of the west coast of India and open source acoustic data from other regions have been used to validate the proposed analysis. Understanding of the statistical variability of SSP and its correlation with ambient noise may improve modeling efforts and design generalized mitigation strategy.
Narendra Jadhav, Ramchandra Manthalkar, and Yashwant Joshi
Springer Singapore
Emotions are very essential for our day-to-day activities such as communication, decision-making and learning. Electroencephalography (EEG) is a non-invasive method to record electrical activity of the brain. To make Human–Machine Interaction (HMI) more natural, human emotion recognition is important. Over the past decade, various signal processing methods are used for analysing EEG-based emotion recognition (ER). This paper proposes a novel technique for ER using Gray-Level Co-occurrence Matrix (GLCM)-based features. The features are validated on benchmark DEAP database upto four emotions and classified using K-nearest neighbor (K-NN) classifier.
Piyush Asolkar, Arnab Das, Suhas Gajre, and Yashwant Joshi
Springer Singapore
Ambient noise variability is a critical challenge encountered by multiple stakeholders, including sonar designers and operators. Among the sources of ambient noise in the ocean, wind related noise has significant impact on sonar performance. The tropical waters in the Indian Ocean Region (IOR), present random fluctuations in the surface parameters, namely the wind speed, surface temperature, wave height, etc. resulting in variations in the ambient noise characteristics. The site-specific surface fluctuations in the tropical regions restrict the possibility of generalized algorithm design to mitigate the ambient noise impact. The work attempts to study the variations in the ambient noise levels corresponding to the fluctuations in the surface parameters. The site-specific behavior of the tropical IOR is demonstrated using surface data available from moored buoy at three distinct locations of the IOR. The analysis methodology can be used to characterize, predict and improve sonar performance, particularly in severe conditions of the tropical IOR.