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Journal articles on the topic 'Bhattacharyya coefficients'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Peng, Xiao Bin, and Zhi Jun Li. "Target Scale Adaptive Control Based on Comparing Bhattacharyya Coefficient." Advanced Materials Research 971-973 (June 2014): 1772–77. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1772.

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Aiming at the limitations of the traditional mean shift, such as invariable kernel bandwidth, an improved tracking algorithm with the following strategies is proposed. The target model and the candidate are described by the similarity between them is evaluated by Bhattacharyya coefficient. This algorithm firstly calculates the Bhattacharyya coefficient of the template target histogram and template background histogram and calculates the Bhattacharyya coefficient of the candidate target histogram of the current frame and template background histogram when tracking. Then judge the change tendenc

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2

Peng, Ning Song, Jie Yang, and D. K. Zhou. "Study on Bhattacharyya Coefficients within Mean-Shift Framework and its Application." Soft Computing 10, no. 12 (2006): 1127–34. http://dx.doi.org/10.1007/s00500-005-0035-5.

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Dr., Aziz Makandar, Rashmi Somshekhar Mrs., and Smitha M. Ms. "Face Recognition by using wavelet based frame work." International Journal of Trend in Scientific Research and Development 2, no. 5 (2018): 1980–87. https://doi.org/10.31142/ijtsrd17067.

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Content based indexing methods are of great interest for image and video retrieval in audio visual archives, such as in the DiVAN project that we are currently developing. Detecting and recognizing human faces automatically in video data provide users with powerful tools for performing queries. The work is done for recognition of the face by using wavelet packet decomposition. Each face is described by a subset of band filtered images containing wavelet coefficients. These coefficients characterize the face texture and a set of simple statistical measures allow us to form compact and meaningfu

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He, Liang, Yuming Bo, and Gaopeng Zhao. "Multifeatures Based Compressive Sensing Tracking." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/439614.

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To benefit from the development of compressive sensing, we cast tracking as a sparse approximation problem in a particle filter framework based on multifeatures. In this framework, the target template is composed of multiple features extracted from visible and infrared frames; in addition, occlusion, interruption, and noises are addressed through a set of trivial templates. With this model, the sparsity is achieved via a compressive sensing approach without nonnegative constraints; then the residual between sparsity representation and the compressed sensing observation is used to measure the l

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Maza-Quiroga, Rosa, Karl Thurnhofer-Hemsi, Domingo López-Rodríguez, and Ezequiel López-Rubio. "Regression of the Rician Noise Level in 3D Magnetic Resonance Images from the Distribution of the First Significant Digit." Axioms 12, no. 12 (2023): 1117. http://dx.doi.org/10.3390/axioms12121117.

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This paper investigates the distribution characteristics of Fourier, discrete cosine, and discrete sine transform coefficients in T1 MRI images. This paper reveals their adherence to Benford’s law, characterized by a logarithmic distribution of first digits. The impact of Rician noise on the first digit distribution is examined, which causes deviations from the ideal distribution. A novel methodology is proposed for noise level estimation, employing metrics such as the Bhattacharyya distance, Kullback–Leibler divergence, total variation distance, Hellinger distance, and Jensen–Shannon divergen

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6

Al Mahmud, Nahyan, and Shahfida Amjad Munni. "Qualitative Analysis of PLP in LSTM for Bangla Speech Recognition." International journal of Multimedia & Its Applications 12, no. 5 (2020): 1–8. http://dx.doi.org/10.5121/ijma.2020.12501.

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The performance of various acoustic feature extraction methods has been compared in this work using Long Short-Term Memory (LSTM) neural network in a Bangla speech recognition system. The acoustic features are a series of vectors that represents the speech signals. They can be classified in either words or sub word units such as phonemes. In this work, at first linear predictive coding (LPC) is used as acoustic vector extraction technique. LPC has been chosen due to its widespread popularity. Then other vector extraction techniques like Mel frequency cepstral coefficients (MFCC) and perceptual

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7

Nahyan, Al Mahmud, and Amjad Munni Shahfida. "Qualitative Analysis of PLP in LSTM for Bangla Speech Recognition." International Journal of Multimedia & Its Applications (IJMA) 12, no. 5 (2022): 1–8. https://doi.org/10.5281/zenodo.7229505.

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The performance of various acoustic feature extraction methods has been compared in this work using Long Short-Term Memory (LSTM) neural network in a Bangla speech recognition system. The acoustic features are a series of vectors that represents the speech signals. They can be classified in either words or sub word units such as phonemes. In this work, at first linear predictive coding (LPC) is used as acoustic vector extraction technique. LPC has been chosen due to its widespread popularity. Then other vector extraction techniques like Mel frequency cepstral coefficients (MFCC) and perceptual

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Sahu, Sima, Harsh Vikram Singh, Basant Kumar, and Amit Kumar Singh. "A Bayesian Multiresolution Approach for Noise Removal in Medical Magnetic Resonance Images." Journal of Intelligent Systems 29, no. 1 (2018): 189–201. http://dx.doi.org/10.1515/jisys-2017-0402.

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Abstract A Bayesian approach using wavelet coefficient modeling is proposed for de-noising additive white Gaussian noise in medical magnetic resonance imaging (MRI). In a parallel acquisition process, the magnetic resonance image is affected by white Gaussian noise, which is additive in nature. A normal inverse Gaussian probability distribution function is taken for modeling the wavelet coefficients. A Bayesian approach is implemented for filtering the noisy wavelet coefficients. The maximum likelihood estimator and median absolute deviation estimator are used to find the signal parameters, si

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9

Trabelsi, Imen, and Med Salim Bouhlel. "Feature Selection for GUMI Kernel-Based SVM in Speech Emotion Recognition." International Journal of Synthetic Emotions 6, no. 2 (2015): 57–68. http://dx.doi.org/10.4018/ijse.2015070104.

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Speech emotion recognition is the indispensable requirement for efficient human machine interaction. Most modern automatic speech emotion recognition systems use Gaussian mixture models (GMM) and Support Vector Machines (SVM). GMM are known for their performance and scalability in the spectral modeling while SVM are known for their discriminatory power. A GMM-supervector characterizes an emotional style by the GMM parameters (mean vectors, covariance matrices, and mixture weights). GMM-supervector SVM benefits from both GMM and SVM frameworks. In this paper, the GMM-UBM mean interval (GUMI) ke

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10

Poloczek, Łukasz, Roman Kuziak, Valeriy Pidvysots’kyy, Danuta Szeliga, Jan Kusiak, and Maciej Pietrzyk. "Physical and Numerical Simulations for Predicting Distribution of Microstructural Features during Thermomechanical Processing of Steels." Materials 15, no. 5 (2022): 1660. http://dx.doi.org/10.3390/ma15051660.

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The design of modern construction materials with heterogeneous microstructures requires a numerical model that can predict the distribution of microstructural features instead of average values. The accuracy and reliability of such models depend on the proper identification of the coefficients for a particular material. This work was motivated by the need for advanced experimental data to identify stochastic material models. Extensive experiments were performed to supply data to identify a model of austenite microstructure evolution in steels during hot deformation and during the interpass tim

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11

Levada, Alexandre L. M. "Entropic Semi-Supervised Dimensionality Reduction for Distance Metric Learning." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 33, no. 02 (2025): 219–34. https://doi.org/10.1142/s0218488525500096.

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Distance metric learning and nonlinear dimensionality reduction are intrinsically related, since they are both different perspectives of the same fundamental problem: to learn compact and meaningful data representations for classification and visualization. In this paper, we propose a graph-based generalization of Semi-Supervised Dimensionality Reduction (SSDR) algorithm that uses stochastic distances (Kullback-Leibler, Bhattacharyya and Cauchy-Schwarz divergences) to compute the similarity between local multivariate Gaussian distributions along the K Nearest Neighbors (KNN) graph build from t

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Shi, Manhong, Hongjie Yu, and Hongjie Wang. "Automated Detection of Sudden Cardiac Death by Discrete Wavelet Transform of Electrocardiogram Signal." Symmetry 14, no. 3 (2022): 571. http://dx.doi.org/10.3390/sym14030571.

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Sudden cardiac death (SCD) results in millions of deaths annually; as it is a fatal heart abnormality, early prediction of SCD could save peoples’ lives to the greatest extent. Symmetry and asymmetry play an important role in many fields. Electrocardiograms (ECG) as a noninvasive process for acquiring the electrical activity of the heart, has both asymmetric and non-stationary characteristics; it is frequently employed to diagnose and evaluate the heart’s condition. In this work, we have detected SCD 14 min (separately for each one-minute interval) prior to its occurrence by analyzing ECG sign

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13

Subašić, Senad, Nicola Piana Agostinetti, and Christopher J. Bean. "Estimating lateral and vertical resolution in receiver function data for shallow crust exploration." Geophysical Journal International 218, no. 3 (2019): 2045–53. http://dx.doi.org/10.1093/gji/ggz262.

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SUMMARY In order to test the horizontal and vertical resolution of teleseismic receiver functions, we perform a complete receiver function analysis and inversion using data from the La Barge array. The La Barge Passive Seismic Experiment was a seismic deployment in western Wyoming, recording continuously between November 2008 and June 2009, with 55 instruments deployed 250 m apart—up to two orders of magnitude closer than in typical receiver function studies. We analyse each station separately. We calculate receiver functions and invert them using a Bayesian algorithm. The inversion results ar

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14

Himes, Michael D., Joseph Harrington, Adam D. Cobb, et al. "Accurate Machine-learning Atmospheric Retrieval via a Neural-network Surrogate Model for Radiative Transfer." Planetary Science Journal 3, no. 4 (2022): 91. http://dx.doi.org/10.3847/psj/abe3fd.

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Abstract Atmospheric retrieval determines the properties of an atmosphere based on its measured spectrum. The low signal-to-noise ratios of exoplanet observations require a Bayesian approach to determine posterior probability distributions of each model parameter, given observed spectra. This inference is computationally expensive, as it requires many executions of a costly radiative transfer (RT) simulation for each set of sampled model parameters. Machine learning (ML) has recently been shown to provide a significant reduction in runtime for retrievals, mainly by training inverse ML models t

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15

Wang, Yong, Li Wang, Ling Zhao, Xun Ran, and Siyuan Deng. "Privacy Recommendation Based on Bhattacharyya Coefficient." Procedia Computer Science 188 (2021): 61–68. http://dx.doi.org/10.1016/j.procs.2021.05.053.

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16

姜, 少鑫. "Collaborative Filtering Algorithm Based Bhattacharyya Coefficient." Computer Science and Application 07, no. 05 (2017): 473–80. http://dx.doi.org/10.12677/csa.2017.75058.

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17

Zheng, Pingping, Adrianne E. Vasey, Jeanette Baker, et al. "Increased Activity in the T Cell Effector Phase and Enhanced T Cell Repertoire Target-Tissue Stability Distinguish Alloreactivity Across Major Versus Minor Histocompatibility Barriers." Blood 126, no. 23 (2015): 4284. http://dx.doi.org/10.1182/blood.v126.23.4284.4284.

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Abstract Graft-versus-host disease (GVHD) occurs when transplanted donors' T cells recognized the recipients' antigens and damaged host tissues and cells, particularly the skin, gut and liver in the acute setting. Although it is well known GVHD is more aggressive and manifests more quickly across major versus minor histocompatibility barrier, little is known comparatively about the donor T cell activation and T cell repertoire changes. To investigate temporal and spatial events of GHVD development, side-by-side transplants were conducted into major and minor-mismatched murine recipients (Balb.

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18

we Soe, Nwe N. "Image Matching Scheme by using Bhattacharyya Coefficient Algorithm." International Journal of Innovative Research in Computer and Communication Engineering 03, no. 07 (2015): 6364–70. http://dx.doi.org/10.15680/ijircce.2015.0307002.

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19

Lu, Na, and Zuren Feng. "Mathematical model of blob matching and modified Bhattacharyya coefficient." Image and Vision Computing 26, no. 10 (2008): 1421–34. http://dx.doi.org/10.1016/j.imavis.2008.01.004.

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20

WEN, Zhi-Qiang, and Zi-Xing CAI. "Errors of Bhattacharyya Coefficient and Its Reduction in Object Tracking." Chinese Journal of Computers 31, no. 7 (2009): 1165–74. http://dx.doi.org/10.3724/sp.j.1016.2008.01165.

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21

Djouadi, A., O. Snorrason, and F. D. Garber. "The quality of training sample estimates of the Bhattacharyya coefficient." IEEE Transactions on Pattern Analysis and Machine Intelligence 12, no. 1 (1990): 92–97. http://dx.doi.org/10.1109/34.41388.

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22

Van Molle, Pieter, Tim Verbelen, Bert Vankeirsbilck, et al. "Leveraging the Bhattacharyya coefficient for uncertainty quantification in deep neural networks." Neural Computing and Applications 33, no. 16 (2021): 10259–75. http://dx.doi.org/10.1007/s00521-021-05789-y.

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AbstractModern deep learning models achieve state-of-the-art results for many tasks in computer vision, such as image classification and segmentation. However, its adoption into high-risk applications, e.g. automated medical diagnosis systems, happens at a slow pace. One of the main reasons for this is that regular neural networks do not capture uncertainty. To assess uncertainty in classification, several techniques have been proposed casting neural network approaches in a Bayesian setting. Amongst these techniques, Monte Carlo dropout is by far the most popular. This particular technique est

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23

Dixit, Veer Sain, and Parul Jain. "Proposed similarity measure using Bhattacharyya coefficient for context aware recommender system." Journal of Intelligent & Fuzzy Systems 36, no. 4 (2019): 3105–17. http://dx.doi.org/10.3233/jifs-18341.

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24

OH, SHU LIH, YUKI HAGIWARA, MUHAMMAD ADAM, et al. "SHOCKABLE VERSUS NONSHOCKABLE LIFE-THREATENING VENTRICULAR ARRHYTHMIAS USING DWT AND NONLINEAR FEATURES OF ECG SIGNALS." Journal of Mechanics in Medicine and Biology 17, no. 07 (2017): 1740004. http://dx.doi.org/10.1142/s0219519417400048.

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Shockable ventricular arrhythmias (VAs) such as ventricular tachycardia (VT) and ventricular fibrillation (VFib) are the life-threatening conditions requiring immediate attention. Cardiopulmonary resuscitation (CPR) and defibrillation are the significant immediate recommended treatments for these shockable arrhythmias to obtain the return of spontaneous circulation. However, accurate classification of these shockable VAs from nonshockable ones is the key step during defibrillation by automated external defibrillator (AED). Therefore, in this work, we have proposed a novel algorithm for an auto

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25

An-qi, Huang, Hou Zhi-qiang, Yu Wang-sheng, and Liu Xiang. "Visual tracking algorithm based on improved Bhattacharyya coefficient and model update strategy." Journal of Applied Optics 36, no. 1 (2015): 52–57. http://dx.doi.org/10.5768/jao201536.0102001.

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Zhou, Qingzhao, and Bangchun Wen. "High Cycle Fatigue Damage Monitoring Based on Bhattacharyya Coefficient of Acoustic Emission." Russian Journal of Nondestructive Testing 61, no. 3 (2025): 331–42. https://doi.org/10.1134/s1061830925600303.

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27

Patra, Bidyut Kr, Raimo Launonen, Ville Ollikainen, and Sukumar Nandi. "A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data." Knowledge-Based Systems 82 (July 2015): 163–77. http://dx.doi.org/10.1016/j.knosys.2015.03.001.

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28

Ray, S. "On a theoretical property of the bhattacharyya coefficient as a feature evaluation criterion." Pattern Recognition Letters 9, no. 5 (1989): 315–19. http://dx.doi.org/10.1016/0167-8655(89)90059-7.

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29

Peng, Weishi, Yangwang Fang, and Renjun Zhan. "A variable step learning algorithm for Gaussian mixture models based on the Bhattacharyya coefficient and correlation coefficient criterion." Neurocomputing 239 (May 2017): 28–38. http://dx.doi.org/10.1016/j.neucom.2017.01.074.

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30

Rao, K. Aruna, and A. R. S. Bhatta. "A Note on Test for Coefficient of Variation." Calcutta Statistical Association Bulletin 38, no. 3-4 (1989): 225–30. http://dx.doi.org/10.1177/0008068319890311.

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In this paper we deal with large sample test for coefficient of variation when the observations come from a normal population. Such large sample tests are based on normal approximation for sample coefficient of variation. When the sa mple size is small or moderate, the normal approximation may not be accurate in the sense that the attained level of the test Is not close to the nominal level. Following Bhattacharya and Ghosh (1978), we obtain valid Edgeworth expansion for sample coefficient of variation to O(n-1) under simple null hypo thesis and contiguous alternative hypothesis. This helps on

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Chen, Ken, and Chul Gyu Jhun. "Multi-target visual tracking and occlusion detection by combining Bhattacharyya Coefficient and Kalman filter innovation." Journal of Electronics (China) 30, no. 3 (2013): 275–82. http://dx.doi.org/10.1007/s11767-013-2152-0.

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32

Singh, Pradeep Kumar, Madhabendra Sinha, Suvrojit Das, and Prasenjit Choudhury. "Enhancing recommendation accuracy of item-based collaborative filtering using Bhattacharyya coefficient and most similar item." Applied Intelligence 50, no. 12 (2020): 4708–31. http://dx.doi.org/10.1007/s10489-020-01775-4.

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33

Weng, Hanli, Hao Chen, Lei Wu, Jingguang Huang, and Zhenxing Li. "A novel pilot protection scheme for transmission lines based on current distribution histograms and their Bhattacharyya coefficient." Electric Power Systems Research 194 (May 2021): 107056. http://dx.doi.org/10.1016/j.epsr.2021.107056.

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34

Singh, Pradeep Kumar, Shreyashee Sinha, and Prasenjit Choudhury. "An improved item-based collaborative filtering using a modified Bhattacharyya coefficient and user–user similarity as weight." Knowledge and Information Systems 64, no. 3 (2022): 665–701. http://dx.doi.org/10.1007/s10115-021-01651-8.

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35

Xu, Yonggang, Yongzhi Xue, Gang Hua, and Jianwei Cheng. "An Adaptive Distributed Compressed Video Sensing Algorithm Based on Normalized Bhattacharyya Coefficient for Coal Mine Monitoring Video." IEEE Access 8 (2020): 158369–79. http://dx.doi.org/10.1109/access.2020.3020140.

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36

Leduc, Guillaume, and Kenneth Palmer. "The Convergence Rate of Option Prices in Trinomial Trees." Risks 11, no. 3 (2023): 52. http://dx.doi.org/10.3390/risks11030052.

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We study the convergence of the binomial, trinomial, and more generally m-nomial tree schemes when evaluating certain European path-independent options in the Black–Scholes setting. To our knowledge, the results here are the first for trinomial trees. Our main result provides formulae for the coefficients of 1/n and 1/n in the expansion of the error for digital and standard put and call options. This result is obtained from an Edgeworth series in the form of Kolassa–McCullagh, which we derive from a recently established Edgeworth series in the form of Esseen/Bhattacharya and Rao for triangular

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Yin, Zhu, Xiaojian Ma, and Hang Wang. "A New Divergence Based on the Belief Bhattacharyya Coefficient with an Application in Risk Evaluation of Aircraft Turbine Rotor Blades." International Journal of Intelligent Systems 2024 (January 10, 2024): 1–24. http://dx.doi.org/10.1155/2024/2140919.

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Belief divergence is a significant measure to quantify the discrepancy between evidence, which is beneficial for conflict information management in Dempster–Shafer evidence theory. In this article, three new concepts are given, namely, the belief Bhattacharyya coefficient, adjustment function, and enhancement factor. And based on them, a novel enhanced belief divergence, called EBD, is proposed, which can assess the correlation of subsets and fully reflect the uncertainty of multielement sets. The important properties of the EBD have been studied. In particular, a new EBD-based multisource inf

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Ragumadhavan, R., K. R. Aravind Britto, and R. Vimala. "Melanoma Skin Cancer Detection Using Wavelet Transform and Local Ternary Pattern." Journal of Medical Imaging and Health Informatics 12, no. 1 (2022): 15–19. http://dx.doi.org/10.1166/jmihi.2022.3856.

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Melanoma is the most serious form of skin cancer that affects millions of people globally. Through image analytics, early identification of skin cancer is enabled, resulting in more effective treatment and a lower mortality rate. The ph2 and human against machine datasets were used to collect images. After preprocessing the image with a weighted median filter, segmentation is investigated using a number of common techniques, with the best result generated by combining watershed transform and maximum similarity region merging. U-net architecture is explored for segmentation. Segmentation effici

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Anand, Divya, Babita Pandey, and Devendra K. Pandey. "A Novel Hybrid Feature Selection Model for Classification of Neuromuscular Dystrophies Using Bhattacharyya Coefficient, Genetic Algorithm and Radial Basis Function Based Support Vector Machine." Interdisciplinary Sciences: Computational Life Sciences 10, no. 2 (2016): 244–50. http://dx.doi.org/10.1007/s12539-016-0183-6.

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El merabet, Y., Y. Ruichek, S. Ghaffarian, et al. "Maximal similarity based region classification method through local image region descriptors and Bhattacharyya coefficient-based distance: Application to horizon line detection using wide-angle camera." Neurocomputing 265 (November 2017): 28–41. http://dx.doi.org/10.1016/j.neucom.2017.03.084.

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Zulqarnain, Rana Muhammad, Imran Siddique, Aiyared Iampan, and Ebenezer Bonyah. "Algorithms for Multipolar Interval-Valued Neutrosophic Soft Set with Information Measures to Solve Multicriteria Decision-Making Problem." Computational Intelligence and Neuroscience 2021 (November 10, 2021): 1–29. http://dx.doi.org/10.1155/2021/7211399.

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Similarity measures (SM) and correlation coefficients (CC) are used to solve many problems. These problems include vague and imprecise information, excluding the inability to deal with general vagueness and numerous information problems. The main purpose of this research is to propose an m-polar interval-valued neutrosophic soft set (mPIVNSS) by merging the m-polar fuzzy set and interval-valued neutrosophic soft set and then study various operations based on the proposed notion, such as AND operator, OR operator, truth-favorite, and false-favorite operators with their properties. This research

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Singh, Siddharth, Michael Durand, Edward Kim, and Ana P. Barros. "Bayesian physical–statistical retrieval of snow water equivalent and snow depth from X- and Ku-band synthetic aperture radar – demonstration using airborne SnowSAr in SnowEx'17." Cryosphere 18, no. 2 (2024): 747–73. http://dx.doi.org/10.5194/tc-18-747-2024.

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Abstract. A physical–statistical framework to estimate snow water equivalent (SWE) and snow depth from synthetic aperture radar (SAR) measurements is presented and applied to four SnowSAR flight-line data sets collected during the SnowEx'2017 field campaign in Grand Mesa, Colorado, USA. The physical (radar) model is used to describe the relationship between snowpack conditions and volume backscatter. The statistical model is a Bayesian inference model that seeks to estimate the joint probability distribution of volume backscatter measurements, snow density and snow depth, and physical model pa

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Yan, Sujuan, and Hong Jin. "An improved localization method for lesion area in gynecological ultrasound image." EURASIP Journal on Image and Video Processing 2020, no. 1 (2020). http://dx.doi.org/10.1186/s13640-020-00530-6.

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Abstract The false positive and false negative rates of current image localization methods in gynecological lesion area are high because the effectiveness is affected by random noise. Therefore, by using Bhattacharyya coefficient-based scale-invariant feature transform (B-SIFT), a novel localization method of lesion area in gynecological ultrasound image is proposed in this paper. Firstly, Rayleigh mean filtering is used to suppress the noise in the ultrasound image based on Rayleigh distribution characteristics of the noise. Then, the segmentation method of the lesion region is designed by us

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44

Li, Ou. "SAR edge detection using weighted directional Bhattacharyya coefficients." IEEE Geoscience and Remote Sensing Letters, 2022, 1. http://dx.doi.org/10.1109/lgrs.2022.3228239.

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Bi, Sifeng, Michael Beer, Jingrui Zhang, Lechang Yang, and Kui He. "Optimization or Bayesian Strategy? Performance of the Bhattacharyya Distance in Different Algorithms of Stochastic Model Updating." ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg 7, no. 2 (2021). http://dx.doi.org/10.1115/1.4050168.

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Abstract The Bhattacharyya distance has been developed as a comprehensive uncertainty quantification metric by capturing multiple uncertainty sources from both numerical predictions and experimental measurements. This work pursues a further investigation of the performance of the Bhattacharyya distance in different methodologies for stochastic model updating, and thus to prove the universality of the Bhattacharyya distance in various currently popular updating procedures. The first procedure is the Bayesian model updating where the Bhattacharyya distance is utilized to define an approximate li

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Nahyan, Al Mahmud and Shahfida Amjad Munni. "QUALITATIVE ANALYSIS OF PLP IN LSTM FOR BANGLA SPEECH RECOGNITION." November 10, 2020. https://doi.org/10.5281/zenodo.4265800.

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ABSTRACT The performance of various acoustic feature extraction methods has been compared in this work using Long Short-Term Memory (LSTM) neural network in a Bangla speech recognition system. The acoustic features are a series of vectors that represents the speech signals. They can be classified in either words or sub word units such as phonemes. In this work, at first linear predictive coding (LPC) is used as acoustic vector extraction technique. LPC has been chosen due to its widespread popularity. Then other vector extraction techniques like Mel frequency cepstral coefficients (MFCC) and p

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47

S, Jayashree, Maya V. Karki, Indira K, and Dinesh P A. "Estimation of spectral similarities utilizing segmented regions' probability distribution in the block‐optimized pan‐sharpened image for material classification." Luminescence 39, no. 2 (2024). http://dx.doi.org/10.1002/bio.4670.

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AbstractPan‐sharpening is an image fusion approach that combines the spectral information in multispectral (MS) images with the spatial properties of PAN (Panchromatic) images. This vital technique is used in categorization, detection, and other remote sensing applications. In the first step, the article focuses on increasing the finer spatial details in the MS image with PAN images using two levels of fusion without causing spectral deterioration. The suggested fusion method efficiently utilizes image transformation techniques and spatial domain image fusion methods. The luminance component o

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Nahyan, Al Mahmud. "QUALITATIVE ANALYSIS OF PLP IN LSTM FOR BANGLA SPEECH RECOGNITION." March 3, 2021. https://doi.org/10.5281/zenodo.7805623.

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The performance of various acoustic feature extraction methods has been compared in this work using Long Short-Term Memory (LSTM) neural network in a Bangla speech recognition system. The acoustic features are a series of vectors that represents the speech signals. They can be classified in either words or sub word units such as phonemes. In this work, at first linear predictive coding (LPC) is used as acoustic vector extraction technique. LPC has been chosen due to its widespread popularity. Then other vector extraction techniques like Mel frequency cepstral coefficients (MFCC) and perceptual

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S., A. Mohammadi, and R. Mahzoun M. "Introducing New Parameters to Compare the Accuracy and Reliability of Mean-Shift Based Tracking Algorithms." September 30, 2011. https://doi.org/10.5281/zenodo.6865434.

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Mean shift algorithms are among the most functional tracking methods which are accurate and have almost simple computation. Different versions of this algorithm are developed which are differ in template updating and their window sizes. To measure the reliability and accuracy of these methods one should normally rely on visual results or number of iteration. In this paper we introduce two new parameters which can be used to compare the algorithms especially when their results are close to each other.

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Rajeev, Anupoju, Lavish Pamwani, Shivam Ojha, and Amit Shelke. "Adaptive Autoregressive Modelling Based Structural Health Monitoring of RC Beam-Column Joint Subjected to Shock Loading." Structural Health Monitoring, May 30, 2022, 147592172211013. http://dx.doi.org/10.1177/14759217221101325.

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In the present work, a novel technique based on the combination of singular spectral analysis (SSA) and recursive estimate of coefficients of adaptive autoregressive (AR) modelling is employed to identify the damage in the reinforced (RC) beam-column joints. The damage is induced by imparting shock load at the tip of the beam-column joints. The damage is identified with the help of the acceleration response of the healthy and damaged specimens excited by high intensity white noise. The proposed approach has two major components, first, filtering and removing the noise from the dynamic response

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