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A chemometric study on the analgesic activity of cannabinoid compounds using SDA, KNN and SIMCA methods

ARROIO, A.; LIMA, E. F.; HONORIO, K. M.; SILVA, A. B. F. da
Fonte: SPRINGER/PLENUM PUBLISHERS Publicador: SPRINGER/PLENUM PUBLISHERS
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
27.53883%
The supervised pattern recognition methods K-Nearest Neighbors (KNN), stepwise discriminant analysis (SDA), and soft independent modelling of class analogy (SIMCA) were employed in this work with the aim to investigate the relationship between the molecular structure of 27 cannabinoid compounds and their analgesic activity. Previous analyses using two unsupervised pattern recognition methods (PCA-principal component analysis and HCA-hierarchical cluster analysis) were performed and five descriptors were selected as the most relevants for the analgesic activity of the compounds studied: R (3) (charge density on substituent at position C(3)), Q (1) (charge on atom C(1)), A (surface area), log P (logarithm of the partition coefficient) and MR (molecular refractivity). The supervised pattern recognition methods (SDA, KNN, and SIMCA) were employed in order to construct a reliable model that can be able to predict the analgesic activity of new cannabinoid compounds and to validate our previous study. The results obtained using the SDA, KNN, and SIMCA methods agree perfectly with our previous model. Comparing the SDA, KNN, and SIMCA results with the PCA and HCA ones we could notice that all multivariate statistical methods classified the cannabinoid compounds studied in three groups exactly in the same way: active...

Algoritmo kNN para previsão de dados temporais: funções de previsão e critérios de seleção de vizinhos próximos aplicados a variáveis ambientais em limnologia; Time series prediction using a KNN-based algorithm prediction functions and nearest neighbor selection criteria applied to limnological data

Ferrero, Carlos Andres
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 04/03/2009 PT
Relevância na Pesquisa
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A análise de dados contendo informações sequenciais é um problema de crescente interesse devido à grande quantidade de informação que é gerada, entre outros, em processos de monitoramento. As séries temporais são um dos tipos mais comuns de dados sequenciais e consistem em observações ao longo do tempo. O algoritmo k-Nearest Neighbor - Time Series Prediction kNN-TSP é um método de previsão de dados temporais. A principal vantagem do algoritmo é a sua simplicidade, e a sua aplicabilidade na análise de séries temporais não-lineares e na previsão de comportamentos sazonais. Entretanto, ainda que ele frequentemente encontre as melhores previsões para séries temporais parcialmente periódicas, várias questões relacionadas com a determinação de seus parâmetros continuam em aberto. Este trabalho, foca-se em dois desses parâmetros, relacionados com a seleção de vizinhos mais próximos e a função de previsão. Para isso, é proposta uma abordagem simples para selecionar vizinhos mais próximos que considera a similaridade e a distância temporal de modo a selecionar os padrões mais similares e mais recentes. Também é proposta uma função de previsão que tem a propriedade de manter bom desempenho na presença de padrões em níveis diferentes da série temporal. Esses parâmetros foram avaliados empiricamente utilizando várias séries temporais...

Aplicação da Lógica Fuzzy kNN e análises estatísticas para seleção de características e classificação de abelhas.; Application of Fuzzy kNN and statistical analysis for features selection and classification of bees.

Buani, Bruna Elisa Zanchetta
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 07/10/2010 PT
Relevância na Pesquisa
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Este trabalho propõe uma alternativa para o problema de classificação de espécies de abelhas a partir da implementação de um algoritmo com base na Morfométria Geométrica e estudo das Formas dos marcos anatômicos das imagens obtidas pelas asas das abelhas. O algoritmo implementado para este propósito se baseia no algoritmo dos k-Vizinho mais Próximos (do inglês, kNN) e na Lógica Fuzzy kNN (Fuzzy k-Nearest Neighbor) aplicados a dados analisados e selecionados de pontos bidimensionais referentes as características geradas por marcos anatômicos. O estudo apresentado envolve métodos de seleção e ordenação de marcos anatômicos para a utilização no algoritmo por meio da implementação de um método matemático que utiliza o calculo dos marcos anatômicos mais significativos (que são representados por marcos matemáticos) e a formulação da Ordem de Significância onde cada elemento representa variáveis de entrada para a Fuzzy kNN. O conhecimento envolvido neste trabalho inclui uma perspectiva sobre a seleção de características não supervisionada como agrupamentos e mineração de dados, analise de pré-processamento dos dados, abordagens estatísticas para estimação e predição, estudo da Forma, Analise de Procrustes e Morfométria Geométrica sobre os dados e o tópico principal que envolve uma modificação do algoritmo dos k- Vizinhos mais Próximos e a aplicação da Fuzzy kNN para o problema. Os resultados mostram que a classificação entre amostras de abelhas no seu próprio grupo apresentam acuracia de 90%...

Electromechanical properties of textured K0.5Na0.5NbO3 ceramics; Propriedades electromecânicas de cerâmicos de K0.5Na0.5NbO3 texturizado

Pinho, Rui Manuel de Oliveira
Fonte: Universidade de Aveiro Publicador: Universidade de Aveiro
Tipo: Dissertação de Mestrado
ENG
Relevância na Pesquisa
27.961133%
This work is about lead-free ceramic materials intended for electromechanical applications and candidates to replace lead-based electroceramics. One of the most widely used piezoelectric ceramics is lead zirconate titanate (PZT). However, it contains more than 60% of lead and it is toxic for humans and environment. In 2003, a directive from European Union has prohibited the use of potentially hazardous elements as lead. Due to the lack of competitive materials for PZT replacement an exception was created until a competitive alternative be found. Potassium and sodium niobate due to its high Curie temperature and moderate piezoelectric properties is currently one of the most promising lead-free materials for PZT substitution. However, its effective industrial adoption requires, among others, optimization of its properties. In this context, in this work we initially studied the effect of dopants, texturing and sintering temperature of KNN ceramics. For this purpose KNN ceramics doped with i) 1.5 mol% CuO + 2.0 mol% Li2O, ii) 1.5 mol% CuO + 4.0 mol% Li2O and iii) 1.5 mol% CuO + 0.5 mol% MnO using different sintering temperatures (1050, 1065 and 1080 °C) were prepared. In addition in order to maximize the preferential crystallographic orientation of the ceramic KNN (texturing)...

Improvements on the KNN classifier

Mestre, Ricardo Jorge Palheira
Fonte: Faculdade de Ciências e Tecnologia Publicador: Faculdade de Ciências e Tecnologia
Tipo: Dissertação de Mestrado
Publicado em //2013 ENG
Relevância na Pesquisa
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Dissertação para obtenção do Grau de Mestre em Engenharia Informática; The object classification is an important area within the artificial intelligence and its application extends to various areas, whether or not in the branch of science. Among the other classifiers, the K-nearest neighbor (KNN) is among the most simple and accurate especially in environments where the data distribution is unknown or apparently not parameterizable. This algorithm assigns the classifying element the major class in the K nearest neighbors. According to the original algorithm, this classification implies the calculation of the distances between the classifying instance and each one of the training objects. If on the one hand, having an extensive training set is an element of importance in order to obtain a high accuracy, on the other hand, it makes the classification of each object slower due to its lazy-learning algorithm nature. Indeed, this algorithm does not provide any means of storing information about the previous calculated classifications,making the calculation of the classification of two equal instances mandatory. In a way, it may be said that this classifier does not learn. This dissertation focuses on the lazy-learning fragility and intends to propose a solution that transforms the KNNinto an eager-learning classifier. In other words...

Distributed Computation of the knn Graph for Large High-Dimensional Point Sets

Plaku, Erion; Kavraki, Lydia E.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 01/03/2007 EN
Relevância na Pesquisa
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High-dimensional problems arising from robot motion planning, biology, data mining, and geographic information systems often require the computation of k nearest neighbor (knn) graphs. The knn graph of a data set is obtained by connecting each point to its k closest points. As the research in the above-mentioned fields progressively addresses problems of unprecedented complexity, the demand for computing knn graphs based on arbitrary distance metrics and large high-dimensional data sets increases, exceeding resources available to a single machine. In this work we efficiently distribute the computation of knn graphs for clusters of processors with message passing. Extensions to our distributed framework include the computation of graphs based on other proximity queries, such as approximate knn or range queries. Our experiments show nearly linear speedup with over one hundred processors and indicate that similar speedup can be obtained with several hundred processors.

Fabrication of transparent lead-free KNN glass ceramics by incorporation method

Yongsiri, Ploypailin; Eitssayeam, Sukum; Rujijanagul, Gobwut; Sirisoonthorn, Somnuk; Tunkasiri, Tawee; Pengpat, Kamonpan
Fonte: Springer Publicador: Springer
Tipo: Artigo de Revista Científica
Publicado em 16/02/2012 EN
Relevância na Pesquisa
27.687446%
The incorporation method was employed to produce potassium sodium niobate [KNN] (K0.5Na0.5NbO3) glass ceramics from the KNN-SiO2 system. This incorporation method combines a simple mixed-oxide technique for producing KNN powder and a conventional melt-quenching technique to form the resulting glass. KNN was calcined at 800°C and subsequently mixed with SiO2 in the KNN:SiO2 ratio of 75:25 (mol%). The successfully produced optically transparent glass was then subjected to a heat treatment schedule at temperatures ranging from 525°C -575°C for crystallization. All glass ceramics of more than 40% transmittance crystallized into KNN nanocrystals that were rectangular in shape and dispersed well throughout the glass matrix. The crystal size and crystallinity were found to increase with increasing heat treatment temperature, which in turn plays an important role in controlling the properties of the glass ceramics, including physical, optical, and dielectric properties. The transparency of the glass samples decreased with increasing crystal size. The maximum room temperature dielectric constant (εr) was as high as 474 at 10 kHz with an acceptable low loss (tanδ) around 0.02 at 10 kHz.

QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases

Saini, Indu; Singh, Dilbag; Khosla, Arun
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
27.53883%
The performance of computer aided ECG analysis depends on the precise and accurate delineation of QRS-complexes. This paper presents an application of K-Nearest Neighbor (KNN) algorithm as a classifier for detection of QRS-complex in ECG. The proposed algorithm is evaluated on two manually annotated standard databases such as CSE and MIT-BIH Arrhythmia database. In this work, a digital band-pass filter is used to reduce false detection caused by interference present in ECG signal and further gradient of the signal is used as a feature for QRS-detection. In addition the accuracy of KNN based classifier is largely dependent on the value of K and type of distance metric. The value of K = 3 and Euclidean distance metric has been proposed for the KNN classifier, using fivefold cross-validation. The detection rates of 99.89% and 99.81% are achieved for CSE and MIT-BIH databases respectively. The QRS detector obtained a sensitivity Se = 99.86% and specificity Sp = 99.86% for CSE database, and Se = 99.81% and Sp = 99.86% for MIT-BIH Arrhythmia database. A comparison is also made between proposed algorithm and other published work using CSE and MIT-BIH Arrhythmia databases. These results clearly establishes KNN algorithm for reliable and accurate QRS-detection.

Exploring Bit-Difference for Approximate KNN Search in High-dimensional Databases

Cui, Bin; Shen, Heng Tao; Shen, Jialie; Tan, Kian Lee
Fonte: MIT - Massachusetts Institute of Technology Publicador: MIT - Massachusetts Institute of Technology
Tipo: Artigo de Revista Científica Formato: 150433 bytes; application/pdf
EN
Relevância na Pesquisa
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In this paper, we develop a novel index structure to support efficient approximate k-nearest neighbor (KNN) query in high-dimensional databases. In high-dimensional spaces, the computational cost of the distance (e.g., Euclidean distance) between two points contributes a dominant portion of the overall query response time for memory processing. To reduce the distance computation, we first propose a structure (BID) using BIt-Difference to answer approximate KNN query. The BID employs one bit to represent each feature vector of point and the number of bit-difference is used to prune the further points. To facilitate real dataset which is typically skewed, we enhance the BID mechanism with clustering, cluster adapted bitcoder and dimensional weight, named the BID⁺. Extensive experiments are conducted to show that our proposed method yields significant performance advantages over the existing index structures on both real life and synthetic high-dimensional datasets.; Singapore-MIT Alliance (SMA)

Classificação de mangas Tommy Atkins y-irradiadas: Um modelo metabolômico

Santos, Maria de Jesus Lessa; Navarro, Daniela Ferraz (Orientadora); Silva, Ricardo Oliveira da (Orientador); Silva, Josenilda Maria da (Coorientadora)
Fonte: Universidade Federal de Pernambuco Publicador: Universidade Federal de Pernambuco
Tipo: Dissertação
BR
Relevância na Pesquisa
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Neste trabalho foram investigadas as composições dos voláteis a partir de mangas da cultivar Tommy Atkins expostas à radiação gama na dose de 0,5 kGy quando comparadas à composição de voláteis obtidos a partir de mangas que não passaram por este tratamento fitossanitário. O objetivo foi construir um modelo metabolômico para classificar as mangas através de modelo não invasivo. Foram analisadas 80 amostras classificadas com grau de maturação entre 4 e 5, segundo classificação da Embrapa. Os voláteis foram coletados após 18 dias de armazenamento sob temperatura de 12°C, usando um sistema Headspace Dinâmico (HD) e submetidos à corrida cromatográfica em fase gasosa seguida de detecção por espectrometria de massas (GC/MS). Os compostos foram identificados a partir da determinação do Índice de Retenção Van den Dool and Kratz e do espectro de massas, que foram comparados aos descritos na biblioteca de espectros do ADAMS. Foram identificados 16 compostos já mencionados na literatura e classificados como terpenos (mono e sesquiterpenos) e ésteres. Entre os terpenos, o α-Pineno e o 3-Careno foram os majoritários tanto para as mangas irradiadas, como para as não irradiadas. Após a identificação dos mesmos...

Variações do método kNN e suas aplicações na classificação automática de textos; kNN Method Variations and its applications in Text Classification

SANTOS, Fernando Chagas
Fonte: Universidade Federal de Goiás; BR; UFG; Mestrado em Ciência da Computação; Ciências Exatas e da Terra - Ciências da Computação Publicador: Universidade Federal de Goiás; BR; UFG; Mestrado em Ciência da Computação; Ciências Exatas e da Terra - Ciências da Computação
Tipo: Dissertação Formato: application/pdf
POR
Relevância na Pesquisa
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Most research on Automatic Text Categorization (ATC) seeks to improve the classifier performance (effective or efficient) responsible for automatically classifying a document d not yet rated. The k nearest neighbors (kNN) is simpler and it s one of automatic classification methods more effective as proposed. In this paper we proposed two kNN variations, Inverse kNN (kINN) and Symmetric kNN (kSNN) with the aim of improving the effectiveness of ACT. The kNN, kINN and kSNN methods were applied in Reuters, 20ng and Ohsumed collections and the results showed that kINN and kSNN methods were more effective than kNN method in Reuters and Ohsumed collections. kINN and kSNN methods were as effective as kNN method in 20NG collection. In addition, the performance achieved by kNN method is more stable than kINN and kSNN methods when the value k change. A parallel study was conducted to generate new features in documents from the similarity matrices resulting from the selection criteria for the best results obtained in kNN, kINN and kSNN methods. The SVM (considered a state of the art method) was applied in Reuters, 20NG and Ohsumed collections - before and after applying this approach to generate features in these documents and the results showed statistically significant gains for the original collection.; Grande parte das pesquisas relacionadas com a classificação automática de textos (CAT) tem procurado melhorar o desempenho (eficácia ou eficiência) do classificador responsável por classificar automaticamente um documento d...

Hyperspectral data processing algorithm combining principal component analysis and K nearest neighbours

García Allende, Pilar Beatriz; Conde Portilla, Olga María; Amado González, Marta; Quintela Incera, Antonio; López Higuera, José Miguel
Fonte: SPIE Society of Photo-Optical Instrumentation Engineers Publicador: SPIE Society of Photo-Optical Instrumentation Engineers
Tipo: info:eu-repo/semantics/conferenceObject; publishedVersion
ENG
Relevância na Pesquisa
27.53883%
A processing algorithm to classify hyperspectral images from an imaging spectroscopic sensor is investigated in this paper. In this research two approaches are followed. First, the feasibility of an analysis scheme consisting of spectral feature extraction and classification is demonstrated. Principal component analysis (PCA) is used to perform data dimensionality reduction while the spectral interpretation algorithm for classification is the K nearest neighbour (KNN). The performance of the KNN method, in terms of accuracy and classification time, is determined as a function of the compression rate achieved in the PCA pre-processing stage. Potential applications of these hyperspectral sensors for foreign object detection in industrial scenarios are enormous, for example in raw material quality control. KNN classifier provides an enormous improvement in this particular case, since as no training is required, new products can be added in any time. To reduce the high computational load of the KNN classifier, a generalization of the binary tree employed in sorting and searching, kd-tree , has been implemented in a second approach. Finally, the performance of both strategies, with or without the inclusion of the kd-tree, has been successfully tested and their properties compared in the raw material quality control of the tobacco industry.

DNIDS: A dependable network intrusion detection system using the CSI-KNN algorithm

Kuang, Liwei
Fonte: Quens University Publicador: Quens University
Tipo: Tese de Doutorado Formato: 1291983 bytes; 1291983 bytes; application/pdf; application/pdf
EN; EN
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The dependability of an Intrusion Detection System (IDS) relies on two factors: ability to detect intrusions and survivability in hostile environments. Machine learning-based anomaly detection approaches are gaining increasing attention in the network intrusion detection community because of their intrinsic ability to discover novel attacks. This ability has become critical since the number of new attacks has kept growing in recent years. However, most of today’s anomaly-based IDSs generate high false positive rates and miss many attacks because of a deficiency in their ability to discriminate attacks from legitimate behaviors. These unreliable results damage the dependability of IDSs. In addition, even if the detection method is sound and effective, the IDS might still be unable to deliver detection service when under attack. With the increasing importance of the IDS, some attackers attempt to disable the IDS before they launch a thorough attack. In this thesis, we propose a Dependable Network Intrusion Detection System (DNIDS) based on the Combined Strangeness and Isolation measure K-Nearest Neighbor (CSI-KNN) algorithm. The DNIDS can effectively detect network intrusions while providing continued service even under attacks. The intrusion detection algorithm analyzes different characteristics of network data by employing two measures: strangeness and isolation. Based on these measures...

Electromechanical properties of engineered lead free potassium sodium niobate based materials; Propriedades electromecânicas de materiais à base de niobato de potássio e sódio

Rafiq, Muhammad Asif
Fonte: Universidade de Aveiro Publicador: Universidade de Aveiro
Tipo: Tese de Doutorado
ENG
Relevância na Pesquisa
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K0.5Na0.5NbO3 (KNN), is the most promising lead free material for substituting lead zirconate titanate (PZT) which is still the market leader used for sensors and actuators. To make KNN a real competitor, it is necessary to understand and to improve its properties. This goal is pursued in the present work via different approaches aiming to study KNN intrinsic properties and then to identify appropriate strategies like doping and texturing for designing better KNN materials for an intended application. Hence, polycrystalline KNN ceramics (undoped, non-stoichiometric; NST and doped), high-quality KNN single crystals and textured KNN based ceramics were successfully synthesized and characterized in this work. Polycrystalline undoped, non-stoichiometric (NST) and Mn doped KNN ceramics were prepared by conventional ceramic processing. Structure, microstructure and electrical properties were measured. It was observed that the window for mono-phasic compositions was very narrow for both NST ceramics and Mn doped ceramics. For NST ceramics the variation of A/B ratio influenced the polarization (P-E) hysteresis loop and better piezoelectric and dielectric responses could be found for small stoichiometry deviations (A/B = 0.97). Regarding Mn doping...

Comparing K-Nearest Neighbors and Potential Energy Method in classification problem. A case study using KNN applet by E.M. Mirkes and real life benchmark data sets

Shi, Yanshan
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 05/11/2012
Relevância na Pesquisa
27.623457%
K-nearest neighbors (KNN) method is used in many supervised learning classification problems. Potential Energy (PE) method is also developed for classification problems based on its physical metaphor. The energy potential used in the experiments are Yukawa potential and Gaussian Potential. In this paper, I use both applet and MATLAB program with real life benchmark data to analyze the performances of KNN and PE method in classification problems. The results show that in general, KNN and PE methods have similar performance. In particular, PE with Yukawa potential has worse performance than KNN when the density of the data is higher in the distribution of the database. When the Gaussian potential is applied, the results from PE and KNN have similar behavior. The indicators used are correlation coefficients and information gain.; Comment: 23 pages, 27 figures

Large-Margin kNN Classification Using a Deep Encoder Network

Min, Martin Renqiang; Stanley, David A.; Yuan, Zineng; Bonner, Anthony; Zhang, Zhaolei
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 09/06/2009
Relevância na Pesquisa
27.777778%
KNN is one of the most popular classification methods, but it often fails to work well with inappropriate choice of distance metric or due to the presence of numerous class-irrelevant features. Linear feature transformation methods have been widely applied to extract class-relevant information to improve kNN classification, which is very limited in many applications. Kernels have been used to learn powerful non-linear feature transformations, but these methods fail to scale to large datasets. In this paper, we present a scalable non-linear feature mapping method based on a deep neural network pretrained with restricted boltzmann machines for improving kNN classification in a large-margin framework, which we call DNet-kNN. DNet-kNN can be used for both classification and for supervised dimensionality reduction. The experimental results on two benchmark handwritten digit datasets show that DNet-kNN has much better performance than large-margin kNN using a linear mapping and kNN based on a deep autoencoder pretrained with retricted boltzmann machines.; Comment: 13 pages (preliminary version)

Molecule model for kaonic nuclear cluster anti-KNN

Faber, M.; Ivanov, A. N.; Kienle, P.; Marton, J.; Pitschmann, M.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
27.838496%
We analyse the properties of the kaonic nuclear cluster (KNC) anti-KNN with the structure Nx(anti-KN)_(I = 0), having the quantum numbers I(J^P) = 1/2(0^-), and treated as a quasi-bound hadronic molecule state. We describe the properties of the hadronic molecule, or the KNC Nx(anti-KN)_(I = 0), in terms of vibrational degrees of freedom with oscillator wave functions and chiral dynamics. These wave functions, having the meaning of trial wave functions of variational calculations, are parameterised by the frequency of oscillations of the (anti-KN)_(I = 0) pair, which is fixed in terms of the binding energy of the strange baryon resonance Lambda(1405), treated as a quasi-bound (anti-KN)_(I = 0) state. The binding energies B_X and widths Gamma_X of the states X = (anti-KN)_(I = 0) and X = anti-KNN, respectively, are calculated in the heavy-baryon approximation by using chiral Lagrangians with meson-baryon derivative couplings invariant under chiral SU(3)xSU(3) symmetry at the tree-level approximation. The results are B_(anti-KNN) = 40.2 MeV and Gamma_(anti-KNN) = Gamma^(non-pionic)_(anti-KNN) + Gamma^(pionic)_(anti-KNN) ~ (85 - 106) MeV and, where Gamma^(non-pionic)_(anti-KNN) ~ 21 MeV and Gamma^(pionic)_(anti-KNN) ~ (64 - 86) MeV are the widths of non-pionic anti-KNN -> N Lambda^0...

Matching-CNN Meets KNN: Quasi-Parametric Human Parsing

Liu, Si; Liang, Xiaodan; Liu, Luoqi; Shen, Xiaohui; Yang, Jianchao; Xu, Changsheng; Lin, Liang; Cao, Xiaochun; Yan, Shuicheng
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 06/04/2015
Relevância na Pesquisa
27.687446%
Both parametric and non-parametric approaches have demonstrated encouraging performances in the human parsing task, namely segmenting a human image into several semantic regions (e.g., hat, bag, left arm, face). In this work, we aim to develop a new solution with the advantages of both methodologies, namely supervision from annotated data and the flexibility to use newly annotated (possibly uncommon) images, and present a quasi-parametric human parsing model. Under the classic K Nearest Neighbor (KNN)-based nonparametric framework, the parametric Matching Convolutional Neural Network (M-CNN) is proposed to predict the matching confidence and displacements of the best matched region in the testing image for a particular semantic region in one KNN image. Given a testing image, we first retrieve its KNN images from the annotated/manually-parsed human image corpus. Then each semantic region in each KNN image is matched with confidence to the testing image using M-CNN, and the matched regions from all KNN images are further fused, followed by a superpixel smoothing procedure to obtain the ultimate human parsing result. The M-CNN differs from the classic CNN in that the tailored cross image matching filters are introduced to characterize the matching between the testing image and the semantic region of a KNN image. The cross image matching filters are defined at different convolutional layers...

Spatial Queries with Two kNN Predicates

Aly, Ahmed M.; Aref, Walid G.; Ouzzani, Mourad
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 31/07/2012
Relevância na Pesquisa
27.737522%
The widespread use of location-aware devices has led to countless location-based services in which a user query can be arbitrarily complex, i.e., one that embeds multiple spatial selection and join predicates. Amongst these predicates, the k-Nearest-Neighbor (kNN) predicate stands as one of the most important and widely used predicates. Unlike related research, this paper goes beyond the optimization of queries with single kNN predicates, and shows how queries with two kNN predicates can be optimized. In particular, the paper addresses the optimization of queries with: (i) two kNN-select predicates, (ii) two kNN-join predicates, and (iii) one kNN-join predicate and one kNN-select predicate. For each type of queries, conceptually correct query evaluation plans (QEPs) and new algorithms that optimize the query execution time are presented. Experimental results demonstrate that the proposed algorithms outperform the conceptually correct QEPs by orders of magnitude.; Comment: VLDB2012

Filtros Robustos RM-KNN con Diferentes Funciones de Influencia para Supresión de Ruido Impulsivo en Imágenes Digitales

Gallegos,Francisco; Ponomaryov,Volodymyr; Pogrebnyak,Oleksiy; Niño de Rivera,Luis
Fonte: Centro de Investigación en computación, IPN Publicador: Centro de Investigación en computación, IPN
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/03/2003 ES
Relevância na Pesquisa
37.24868%
Presentamos la implementación de filtros robustos para imágenes con supresión de ruido impulsivo y preservación de detalles. Los esquemas de filtrado usan una técnica similar al filtro KNN para proveer la preservación de detalles finos y la combinación de estimadores-M con el estimador de la mediana o Wilcoxon proveen la supresión de ruido impulsivo. Usamos diferentes tipos de funciones de influencia en el estimador-Mpara proveer una mejor supresión de ruido impulsivo. El filtrado de secuencias de vídeo corrompidas con ruido impulsivo demuestra que los métodos propuestos potencialmente proveen una solución para mejorar la calidad de las transmisiones de TV/Vídeo. La eficiencia de los filtros propuestos fue evaluada por numerosas simulaciones. La implementación de los filtros propuestos fue realizada en tiempo real mediante el uso del DSP TMS320C6701.