A tutorial on signal energy and its applications
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[1] Di Zhang,et al. Global plus local: A complete framework for feature extraction and recognition , 2014, Pattern Recognit..
[2] Sergios Theodoridis,et al. Pattern Recognition, Fourth Edition , 2008 .
[3] Yan Li,et al. Fabric Weave Pattern’s Recognition Based on Texture Orientation Features , 2013 .
[4] Yineng Zheng,et al. A novel hybrid energy fraction and entropy-based approach for systolic heart murmurs identification , 2015, Expert Syst. Appl..
[5] Jin Jiang,et al. Time-frequency feature representation using energy concentration: An overview of recent advances , 2009, Digit. Signal Process..
[6] Yang Yang,et al. Supervised feature learning via l2-norm regularized logistic regression for 3D object recognition , 2015, Neurocomputing.
[7] M. Liang,et al. Intelligent bearing fault detection by enhanced energy operator , 2014, Expert Syst. Appl..
[8] P.S. Addison,et al. Time--frequency analysis of biosignals , 2009, IEEE Engineering in Medicine and Biology Magazine.
[9] R. Amutha,et al. Energy-efficient low bit rate image compression in wavelet domain for wireless image sensor networks , 2015 .
[10] Bin Ma,et al. Text-dependent speaker verification: Classifiers, databases and RSR2015 , 2014, Speech Commun..
[11] Changxin Gao,et al. Multi-structure local binary patterns for texture classification , 2011, Pattern Analysis and Applications.
[12] M. Inés Torres,et al. Integration of complex language models in ASR and LU systems , 2014, Pattern Analysis and Applications.
[13] Marie Chavent,et al. EEG classification for the detection of mental states , 2015, Appl. Soft Comput..
[14] Simon Haykin,et al. Neural Networks and Learning Machines , 2010 .
[15] Fuchun Sun,et al. Dynamic texture video classification using extreme learning machine , 2016, Neurocomputing.
[16] Oliver Faust,et al. COMPUTER-BASED IDENTIFICATION OF NORMAL AND ALCOHOLIC EEG SIGNALS USING WAVELET PACKETS AND ENERGY MEASURES , 2013 .
[17] Nam Ik Cho,et al. Text-Line Extraction in Handwritten Chinese Documents Based on an Energy Minimization Framework , 2012, IEEE Transactions on Image Processing.
[18] Richard Weber,et al. Feature selection for Support Vector Machines via Mixed Integer Linear Programming , 2014, Inf. Sci..
[19] Pei-Wen Chen,et al. Recognition of control chart patterns using a neural network-based pattern recognizer with features extracted from correlation analysis , 2012, Pattern Analysis and Applications.
[20] GuptaDaya,et al. Hybrid bio-inspired techniques for land cover feature extraction , 2012 .
[21] Miguel Cazorla,et al. Feature selection, mutual information, and the classification of high-dimensional patterns , 2008, Pattern Analysis and Applications.
[22] David E. Losada,et al. An empirical study of sentence features for subjectivity and polarity classification , 2014, Inf. Sci..
[23] Man-Wai Mak,et al. SNR-Invariant PLDA Modeling in Nonparametric Subspace for Robust Speaker Verification , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.
[24] K. I. Ramachandran,et al. Cosine distance features for improved speaker verification , 2015 .
[25] Sorin Moga,et al. Combined pattern search optimization of feature extraction and classification parameters in facial recognition , 2011, Pattern Recognit. Lett..
[26] Matej Grasic,et al. Online Speech/Music Segmentation Based on the Variance Mean of Filter Bank Energy , 2009, EURASIP J. Adv. Signal Process..
[27] Quansen Sun,et al. Graph regularized multiset canonical correlations with applications to joint feature extraction , 2014, Pattern Recognit..
[28] Abdolreza Ohadi,et al. Application of wavelet energy and Shannon entropy for feature extraction in gearbox fault detection under varying speed conditions , 2014, Neurocomputing.
[29] Dingkun Du,et al. An energy-efficient spike encoding circuit for speech edge detection , 2013 .
[30] Bjarne Stroustrup,et al. C++ Programming Language , 1986, IEEE Softw..
[31] George Baciu,et al. Lightness biased cartoon-and-texture decomposition for textile image segmentation , 2015, Neurocomputing.
[32] Gui-Song Xia,et al. Dynamic texture recognition by aggregating spatial and temporal features via ensemble SVMs , 2016, Neurocomputing.
[33] Richard Heusdens,et al. Speech energy redistribution for intelligibility improvement in noise based on a perceptual distortion measure , 2014, Comput. Speech Lang..
[34] Hae-Chang Rim,et al. Knowledge-based question answering using the semantic embedding space , 2015, Expert Syst. Appl..
[35] Xiaoyan Xiong,et al. Feature subset selection by gravitational search algorithm optimization , 2014, Inf. Sci..
[36] Thanh Phuong Nguyen,et al. Statistical binary patterns for rotational invariant texture classification , 2016, Neurocomputing.
[37] Luiz Eduardo Soares de Oliveira,et al. Dynamic selection of classifiers - A comprehensive review , 2014, Pattern Recognit..
[38] Binu P. Chacko,et al. Handwritten character recognition using wavelet energy and extreme learning machine , 2012, Int. J. Mach. Learn. Cybern..
[39] Jinku Yang Non-member,et al. A new multi-focus image fusion algorithm based on BEMD and improved local energy , 2015 .
[40] Ying Li,et al. Adaptive energy detection for bird sound detection in complex environments , 2015, Neurocomputing.
[41] Mohammad Hossein Kahaei,et al. An Introduction to Energy-Based Blind Separating Algorithm for Speech Signals , 2014 .
[42] Juan F. Ramirez-Villegas,et al. Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions , 2012, Neurocomputing.
[43] Tarek A. Tutunji,et al. Speaker identification using vowels features through a combined method of formants, wavelets, and neural network classifiers , 2015, Appl. Soft Comput..
[44] Goutam Saha,et al. On robustness of speech based biometric systems against voice conversion attack , 2015, Appl. Soft Comput..
[45] Cheng-Yu Yeh,et al. A study on the consistency analysis of energy parameter for Mandarin speech , 2012, EURASIP J. Audio Speech Music. Process..
[46] S. Ramachandran,et al. Face recognition using transform domain feature extraction and PSO-based feature selection , 2014, Appl. Soft Comput..
[47] C. Kamath. ECG beat classification using features extracted from teager energy functions in time and frequency domains , 2011 .
[48] Pedro M. Q. Aguiar,et al. Optimized filters for efficient multi-texture discrimination , 2013, Pattern Analysis and Applications.
[49] Meng Joo Er,et al. A local binary pattern based texture descriptors for classification of tea leaves , 2015, Neurocomputing.
[50] Yu-Liang Hsu,et al. Automatic sleep stage recurrent neural classifier using energy features of EEG signals , 2013, Neurocomputing.
[51] Shan Huang,et al. An Energy-Efficient Design for ECG Recording and R-Peak Detection Based on Wavelet Transform , 2015, IEEE Transactions on Circuits and Systems II: Express Briefs.
[52] Konstantinos I. Diamantaras,et al. Efficient binary classification through energy minimisation of slack variables , 2015, Neurocomputing.
[53] Celia Shahnaz,et al. A semisoft thresholding method based on Teager energy operation on wavelet packet coefficients for enhancing noisy speech , 2013, EURASIP J. Audio Speech Music. Process..
[54] Loris Nanni,et al. Combination of projectors, standard texture descriptors and bag of features for classifying images , 2016, Neurocomputing.
[55] Sergios Theodoridis,et al. Introduction to Pattern Recognition: A Matlab Approach , 2010 .
[56] Hao Liu,et al. Dense depth image synthesis via energy minimization for three-dimensional video , 2015, Signal Process..
[57] M. L. Dewal,et al. Multiresolution local binary pattern variants based texture feature extraction techniques for efficient classification of microscopic images of hardwood species , 2015, Appl. Soft Comput..
[58] Chang-Dong Wang,et al. Energy based competitive learning , 2011, Neurocomputing.
[59] Zhenyu Wang,et al. A collaborative representation based projections method for feature extraction , 2015, Pattern Recognit..
[60] Lírio Onofre Baptista de Almeida,et al. A new technique to construct a wavelet transform matching a specified signal with applications to digital, real time, spike, and overlap pattern recognition , 2006, Digit. Signal Process..
[61] Francesco Piazza,et al. Environmental robust speech and speaker recognition through multi-channel histogram equalization , 2012, Neurocomputing.
[62] Hairong Qi,et al. Spatio-temporal feature extraction and representation for RGB-D human action recognition , 2014, Pattern Recognit. Lett..
[63] Sourjya Sarkar,et al. Stochastic feature compensation methods for speaker verification in noisy environments , 2014, Appl. Soft Comput..
[64] Andrew R. Webb,et al. Statistical Pattern Recognition , 1999 .
[65] Milan Sonka,et al. Image Processing, Analysis and Machine Vision , 1993, Springer US.
[66] Lei Guo,et al. A new multi‐focus image fusion algorithm based on BEMD and improved local energy , 2015 .
[67] V. K. Panchal,et al. Hybrid bio-inspired techniques for land cover feature extraction: A remote sensing perspective , 2012, Appl. Soft Comput..
[68] Arun K. Majumdar,et al. Detection of artifacts from high energy bursts in neonatal EEG , 2013, Comput. Biol. Medicine.
[69] Florian Schiel,et al. The influence of alcoholic intoxication on the short-time energy function of speech. , 2014, The Journal of the Acoustical Society of America.
[70] Xianyi Zeng,et al. Could linear model bridge the gap between low-level statistical features and aesthetic emotions of visual textures? , 2015, Neurocomputing.
[71] Qiang Chen,et al. Active contours driven by local likelihood image fitting energy for image segmentation , 2015, Inf. Sci..
[72] Peter A. Flach,et al. Machine Learning - The Art and Science of Algorithms that Make Sense of Data , 2012 .
[73] Ömer Faruk Ertuğrul,et al. Two novel local binary pattern descriptors for texture analysis , 2015, Appl. Soft Comput..
[74] Heitor Silvério Lopes,et al. Genetic programming for epileptic pattern recognition in electroencephalographic signals , 2007, Appl. Soft Comput..
[75] Israel Cohen,et al. Monaural speech/music source separation using discrete energy separation algorithm , 2010, Signal Process..
[76] Petros Maragos,et al. On the Effects of Filterbank Design and Energy Computation on Robust Speech Recognition , 2011, IEEE Transactions on Audio, Speech, and Language Processing.
[77] James Walker,et al. Introducing wavelets and time--frequency analysis. , 2009, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.
[78] Amir Hussain,et al. Local energy-based shape histogram feature extraction technique for breast cancer diagnosis , 2015, Expert Syst. Appl..
[79] M. Sabarimalai Manikandan,et al. Wavelet energy based diagnostic distortion measure for ECG , 2007, Biomed. Signal Process. Control..
[80] Frank Rudzicz,et al. Fast incremental LDA feature extraction , 2015, Pattern Recognit..
[81] Pooja Jain,et al. Marginal energy density over the low frequency range as a feature for voiced/non-voiced detection in noisy speech signals , 2013, J. Frankl. Inst..
[82] Verónica Bolón-Canedo,et al. A review of microarray datasets and applied feature selection methods , 2014, Inf. Sci..
[83] Shuping He,et al. Energy-to-peak filtering for T-S fuzzy systems with Markovian jumping: The finite-time case , 2015, Neurocomputing.
[84] Eli Brenner,et al. Structure learning and the Occam's razor principle: a new view of human function acquisition , 2014, Front. Comput. Neurosci..
[85] Manuela M. Veloso,et al. Prioritized Multihypothesis Tracking by a Robot with Limited Sensing , 2009, EURASIP J. Adv. Signal Process..
[86] Ahmad Akbari,et al. Energy-based speech enhancement technique for hands-free communication , 2009 .
[87] Radu Ioanitescu,et al. Handwritten Documents Text Line Segmentation based on Information Energy , 2014, Int. J. Comput. Commun. Control.
[88] Manuel Graña,et al. Energy demands of diverse spiking cells from the neocortex, hippocampus, and thalamus , 2014, Front. Comput. Neurosci..