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Search Results: 1 - 10 of 9211 matches for " pattern recognition "
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Identification of Patterns of Consumption through the Daily Mean Outdoor Temperature  [PDF]
Aparicio-Ruiz Pablo, Guadix-Martín José, Onieva-Giménez Luis, Cortés-Achedad Pablo, Mu?uzuri-Sanz Jesús
World Journal of Engineering and Technology (WJET) , 2017, DOI: 10.4236/wjet.2017.53B003
The identification and recognition of patterns in the context of building is a necessary feedback to create intelligent buildings. In this context, the key is empowering the systems with learning elements to make decisions. The challenge is detected element to predict the human behavior in the building. Daily mean outdoor temperature is one of the variables with incidence in the human comfort due to the weather adaptation of the users. In this paper it analyzed the consumption in an office respect to the internal temperature and the daily mean temperature through cluster techniques. The cluster can be used as a forecasting of consumption.
A Feasible Approach for Automatic Detection and Recognition of the Bengalese Finch Songnotes and Their Sequences  [PDF]
Khan Md. Mahfuzus Salam, Tetsuro Nishino, Kazutoshi Sasahara, Miki Takahasi, Kazuo Okanoya
Journal of Intelligent Learning Systems and Applications (JILSA) , 2010, DOI: 10.4236/jilsa.2010.24025
Abstract: The Bengalese finch song has been widely studied for its unique features and similarity to human language. For com-putational analysis the songs must be represented in songnote sequences. An automated approach for this purpose is highly desired since manual processing makes human annotation cumbersome, and human annotation is very heu-ristic and easily lacks objectivity. In this paper, we propose a new approach for automatic detection and recognition of the songnote sequences via image processing. The proposed method is based on human recognition process to visually identify the patterns in a sonogram image. The songnotes of the Bengalese finch are dependent on the birds and similar pattern does not exist in two different birds. Considering this constraint, our experiments on real birdsong data of different Bengalese finch show high accuracy rates for automatic detection and recognition of the songnotes. These results indicate that the proposed approach is feasible and generalized for any Bengalese finch songs.
Using Least Squares Support Vector Machines for Frequency Estimation  [PDF]
Xiaoyun Teng, Xiaoyi Zhang, Hongyi Yu
Int'l J. of Communications, Network and System Sciences (IJCNS) , 2010, DOI: 10.4236/ijcns.2010.310111
Abstract: Frequency estimation is transformed to a pattern recognition problem, and a least squares support vector machine (LS-SVM) estimator is derived. The estimator can work efficiently without the need of statistics knowledge of the observations, and the estimation performance is insensitive to the carrier phase. Simulation results are presented showing that proposed estimators offer better performance than traditional Maximum Likelihood (ML) estimator at low SNR, since classification-based method does not have the threshold effect of nonlinear estimation.
Raman spectroscopy for human cancer tissue diagnosis: A pattern recognition approach  [PDF]
Maher Rizkalla, Parvin Ghane, Mangilal Agarwal, Sudhir Shrestha, Kody Varahramyan
Journal of Biomedical Science and Engineering (JBiSE) , 2012, DOI: 10.4236/jbise.2012.512A113
Abstract: In this work, optical scattering using Raman spectroscopy has been analyzed for various cancer tissues. The Raman shifts obtained at the Indiana University Bloomington (IUB) and Indiana University-Purdue University Indianapolis (IUPUI) laboratories have been processed for diagnosing various types of cancer tissues. The objective of this research is to distinguish between cancerous and non-cancerous tissues. Small size tissue samples have been processed, seeking the minimum size tissue that can be diagnosed via Raman spectroscopy. The tests have been conducted on nearly 20 human tissues. A Matlab program has been written following Parzen-Window classifier to recognize the Raman shift pattern for various types of cancer tissues, including breast cancer, kidney, and Gyn-Uterus. A software visual model has been used for data processing. Unique signals for breast and kidney tumors have been obtained. The approach followed in this paper shows promise for early cancer detection in humans.
Development and implementation of an automated system to aid laboratory diagnosis using image processing  [PDF]
álvaro Manoel de Souza Soares, Marco Rogério da Silva Richetto, Jo?o Bosco Gon?alves, Pedro Paulo Leite do Prado
Journal of Biomedical Science and Engineering (JBiSE) , 2013, DOI: 10.4236/jbise.2013.65073

The objective of this work is to provide an automatic system to count white blood cells in a blood smear. To do so an experiment was assembled, composed by a standard microscope with two step motors coupled to its knobs in order to move the microscope in x and y directions and a web cam which was mounted in the top of the microscope responsible for to acquire images from the smear. The step motors and the web cam are controlled by a microcomputer PC standard via software developed inDelphi. The motors use the parallel port to communicate with the PC and the camera use the USB port. The main idea is to set an initial point into the smear and the automated system will carry over the smear acquiring images (frames with 640 × 480 pixels) and counting the white blood cells encountered. The double histogram threshold technique is implemented to initially exclude the red cells from the image leaving only the white ones. Preliminaries results are obtained and show that the system is quite fast and has a good capacity of selection, even when different kinds of smear are used.

K-Means Graph Database Clustering and Matching for Fingerprint Recognition  [PDF]
Vaishali Pawar, Mukesh Zaveri
Intelligent Information Management (IIM) , 2015, DOI: 10.4236/iim.2015.74019
Abstract: The graph can contain huge amount of data. It is heavily used for pattern recognition and matching tasks like symbol recognition, information retrieval, data mining etc. In all these applications, the objects or underlying data are represented in the form of graph and graph based matching is performed. The conventional algorithms of graph matching have higher complexity. This is because the most of the applications have large number of sub graphs and the matching of these sub graphs becomes computationally expensive. In this paper, we propose a graph based novel algorithm for fingerprint recognition. In our work we perform graph based clustering which reduces the computational complexity heavily. In our algorithm, we exploit structural features of the fingerprint for K-means clustering of the database. The proposed algorithm is evaluated using realtime fingerprint database and the simulation results show that our algorithm outperforms the existing algorithm for the same task.
High-Capacity Quantum Associative Memories  [PDF]
M. Cristina Diamantini, Carlo A. Trugenberger
Journal of Applied Mathematics and Physics (JAMP) , 2016, DOI: 10.4236/jamp.2016.411207
Abstract: We review our models of quantum associative memories that represent the “quantization” of fully coupled neural networks like the Hopfield model. The idea is to replace the classical irreversible attractor dynamics driven by an Ising model with pattern-dependent weights by the reversible rotation of an input quantum state onto an output quantum state consisting of a linear superposition with probability amplitudes peaked on the stored pattern closest to the input in Hamming distance, resulting in a high probability of measuring a memory pattern very similar to the input. The unitary operator implementing this transformation can be formulated as a sequence of one-qubit and two-qubit elementary quantum gates and is thus the exponential of an ordered quantum Ising model with sequential operations and with pattern-dependent interactions, exactly as in the classical case. Probabilistic quantum memories, that make use of postselection of the measurement result of control qubits, overcome the famed linear storage limitation of their classical counterparts because they permit to completely eliminate crosstalk and spurious memories. The number of control qubits plays the role of an inverse fictitious temperature. The accuracy of pattern retrieval can be tuned by lowering the fictitious temperature under a critical value for quantum content association while the complexity of the retrieval algorithm remains polynomial for any number of patterns polynomial in the number of qubits. These models thus solve the capacity shortage problem of classical associative memories, providing a polynomial improvement in capacity. The price to pay is the probabilistic nature of information retrieval.
Electronic Nose Technology Based on Quantum Dot Filters  [PDF]
Zhenan Li, Jie Bao
Optics and Photonics Journal (OPJ) , 2017, DOI: 10.4236/opj.2017.78B005
The electronic nose with chemical dyes as sensor can react with target gas and have specific color changes. In general, RGB camera collects a group of images to record these changes used for pattern recognition. RGB filters are not sensitive to the slight color changes, which limits the performance of this kind of electronic nose. This paper demonstrates using quantum dot spec-troscopy technology to solve this problem. Multiple quantum dot filters are placed on the surface of image sensor. When capturing images, there are more response channels of the same incident light than RGB filters. Simulation and experiment both prove that quantum dot filters with appropriate processing are more sensitive to color changes than RGB filters.
Reconocimiento de formas con un correlador óptico aplicado a imágenes desenfocadas: invariancia por medio de la selección y fusión de bandas
Vargas, A.;Figueroa, R.;Campos, J.;San Martín, C.;Marileo, J.;
Revista mexicana de física , 2006,
Abstract: in this work, experimental result measuring the performance of a composite filter designed to solve the optical pattern recognition problem of defocus images is presented. we apply a methodology that considers optical decomposition of the input scene in frequency bands. in each band a pattern recognition filter is applied. finally this information is fused by means of addition or geometric mean. we analyze both the individual information, and the fusion scheme. we also present results when total spectral information of the optical pattern recognition process is taken in to account.
Journal of the Chilean Chemical Society , 2008, DOI: 10.4067/S0717-97072008000400016
Abstract: three supervised pattern recognition methods (sprm) were evaluated to discriminate between eucalyptus globulus and eucalyptus nitens species applying near infrared (nir) spectroscopy on leaves. the methods used were k-nearest neighbor (knn), soft modeling class analogy (simca) and discriminant partial least squares (pls-da). first and second derivatives were used as transform techniques and mean-center (mc) and autoscaling (as) as preprocessing techniques. the training set was constitued by 288 samples and 20 samples were used as validation set. a significant difference between the assayed methods was not observed, however best results for separation of classes and prediction rate were obtained when first derivative and mc were used for all the recognition pattern methods. use of leaves and nir spectroscopy avoids the destructive usual wood analysis in forest industries and facilities the fast classification of these species for forest applications.
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