862 resultados para machine tools and accessories
Suite of tools for statistical N-gram language modeling for pattern mining in whole genome sequences
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Genome sequences contain a number of patterns that have biomedical significance. Repetitive sequences of various kinds are a primary component of most of the genomic sequence patterns. We extended the suffix-array based Biological Language Modeling Toolkit to compute n-gram frequencies as well as n-gram language-model based perplexity in windows over the whole genome sequence to find biologically relevant patterns. We present the suite of tools and their application for analysis on whole human genome sequence.
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In this paper, we present a machine learning approach for subject independent human action recognition using depth camera, emphasizing the importance of depth in recognition of actions. The proposed approach uses the flow information of all 3 dimensions to classify an action. In our approach, we have obtained the 2-D optical flow and used it along with the depth image to obtain the depth flow (Z motion vectors). The obtained flow captures the dynamics of the actions in space time. Feature vectors are obtained by averaging the 3-D motion over a grid laid over the silhouette in a hierarchical fashion. These hierarchical fine to coarse windows capture the motion dynamics of the object at various scales. The extracted features are used to train a Meta-cognitive Radial Basis Function Network (McRBFN) that uses a Projection Based Learning (PBL) algorithm, referred to as PBL-McRBFN, henceforth. PBL-McRBFN begins with zero hidden neurons and builds the network based on the best human learning strategy, namely, self-regulated learning in a meta-cognitive environment. When a sample is used for learning, PBLMcRBFN uses the sample overlapping conditions, and a projection based learning algorithm to estimate the parameters of the network. The performance of PBL-McRBFN is compared to that of a Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers with representation of every person and action in the training and testing datasets. Performance study shows that PBL-McRBFN outperforms these classifiers in recognizing actions in 3-D. Further, a subject-independent study is conducted by leave-one-subject-out strategy and its generalization performance is tested. It is observed from the subject-independent study that McRBFN is capable of generalizing actions accurately. The performance of the proposed approach is benchmarked with Video Analytics Lab (VAL) dataset and Berkeley Multimodal Human Action Database (MHAD). (C) 2013 Elsevier Ltd. All rights reserved.
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Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification.
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The aim of this work is to enable seamless transformation of product concepts to CAD models. This necessitates availability of 3D product sketches. The present work concerns intuitive generation of 3D strokes and intrinsic support for space sharing and articulation for the components of the product being sketched. Direct creation of 3D strokes in air lacks in precision, stability and control. The inadequacy of proprioceptive feedback for the task is complimented in this work with stereo vision and haptics. Three novel methods based on pencil-paper interaction analogy for haptic rendering of strokes have been investigated. The pen-tilt based rendering is simpler and found to be more effective. For the spatial conformity, two modes of constraints for the stylus movements, corresponding to the motions on a control surface and in a control volume have been studied using novel reactive and field based haptic rendering schemes. The field based haptics, which in effect creates an attractive force field near a surface, though non-realistic, provided highly effective support for the control-surface constraints. The efficacy of the reactive haptic rendering scheme for the constrained environments has been demonstrated using scribble strokes. This can enable distributed collaborative 3D concept development. The notion of motion constraints, defined through sketch strokes enables intuitive generation of articulated 3D sketches and direct exploration of motion annotations found in most product concepts. The work, thus, establishes that modeling of the constraints is a central issue in 3D sketching.
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We propose to develop a 3-D optical flow features based human action recognition system. Optical flow based features are employed here since they can capture the apparent movement in object, by design. Moreover, they can represent information hierarchically from local pixel level to global object level. In this work, 3-D optical flow based features a re extracted by combining the 2-1) optical flow based features with the depth flow features obtained from depth camera. In order to develop an action recognition system, we employ a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). The m of McFIS is to find the decision boundary separating different classes based on their respective optical flow based features. McFIS consists of a neuro-fuzzy inference system (cognitive component) and a self-regulatory learning mechanism (meta-cognitive component). During the supervised learning, self-regulatory learning mechanism monitors the knowledge of the current sample with respect to the existing knowledge in the network and controls the learning by deciding on sample deletion, sample learning or sample reserve strategies. The performance of the proposed action recognition system was evaluated on a proprietary data set consisting of eight subjects. The performance evaluation with standard support vector machine classifier and extreme learning machine indicates improved performance of McFIS is recognizing actions based of 3-D optical flow based features.
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Query suggestion is an important feature of the search engine with the explosive and diverse growth of web contents. Different kind of suggestions like query, image, movies, music and book etc. are used every day. Various types of data sources are used for the suggestions. If we model the data into various kinds of graphs then we can build a general method for any suggestions. In this paper, we have proposed a general method for query suggestion by combining two graphs: (1) query click graph which captures the relationship between queries frequently clicked on common URLs and (2) query text similarity graph which finds the similarity between two queries using Jaccard similarity. The proposed method provides literally as well as semantically relevant queries for users' need. Simulation results show that the proposed algorithm outperforms heat diffusion method by providing more number of relevant queries. It can be used for recommendation tasks like query, image, and product suggestion.
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Since the dawn of civilization, natural resources have remained the mainstay of various remedial approaches of humans vis-a-vis a large number of illnesses. Saraca asoca (Roxb.) de Wilde (Saraca indica L.) belonging to the family Caesalpiniaceae has been regarded as a universal panacea in old Indian Ayurvedic texts and has especially been used to manage gynaecological complications and infections besides treating haemmorhagic dysentery, uterine pain, bacterial infections, skin problems, tumours, worm infestations, cardiac and circulatory problems. Almost all parts of the plant are considered pharmacologically valuable. Extensive folkloric practices and ethnobotanical applications of this plant have even lead to the availability of several commercial S. asoca formulations recommended for different indications though adulteration of these remains a pressing concern. Though a wealth of knowledge on this plant is available in both the classical and modern literature, extensive research on its phytomedicinal worth using state-of-the-art tools and methodologies is lacking. Recent reports on bioprospecting of S. asoca endophytic fungi for industrial bioproducts and useful pharmacologically relevant metabolites provide a silver lining to uncover single molecular bio-effectors from its endophytes. Here, we describe socio-ethnobotanical usage, present the current pharmacological status and discuss potential bottlenecks in harnessing the proclaimed phytomedicinal worth of this prescribed Ayurvedic medicinal plant. Finally, we also look into the possible future of the drug discovery and pharmaceutical R&D efforts directed at exploring its pharma legacy.
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A low-order harmonic pulsating torque is a major concern in high-power drives, high-speed drives, and motor drives operating in an overmodulation region. This paper attempts to minimize the low-order harmonic torques in induction motor drives, operated at a low pulse number (i.e., a low ratio of switching frequency to fundamental frequency), through a frequency domain (FD) approach as well as a synchronous reference frame (SRF) based approach. This paper first investigates FD-based approximate elimination of harmonic torque as suggested by classical works. This is then extended into a procedure for minimization of low-order pulsating torque components in the FD, which is independent of machine parameters and mechanical load. Furthermore, an SRF-based optimal pulse width modulation (PWM) method is proposed to minimize the low-order harmonic torques, considering the motor parameters and load torque. The two optimal methods are evaluated and compared with sine-triangle (ST) PWM and selective harmonic elimination (SHE) PWM through simulations and experimental studies on a 3.7-kW induction motor drive. The SRF-based optimal PWM results in marginally better performance than the FD-based one. However, the selection of optimal switching angle for any modulation index (M) takes much longer in case of SRF than in case of the FD-based approach. The FD-based optimal solutions can be used as good starting solutions and/or to reasonably restrict the search space for optimal solutions in the SRF-based approach. Both of the FD-based and SRF-based optimal PWM methods reduce the low-order pulsating torque significantly, compared to ST PWM and SHE PWM, as shown by the simulation and experimental results.
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Traffic classification using machine learning continues to be an active research area. The majority of work in this area uses off-the-shelf machine learning tools and treats them as black-box classifiers. This approach turns all the modelling complexity into a feature selection problem. In this paper, we build a problem-specific solution to the traffic classification problem by designing a custom probabilistic graphical model. Graphical models are a modular framework to design classifiers which incorporate domain-specific knowledge. More specifically, our solution introduces semi-supervised learning which means we learn from both labelled and unlabelled traffic flows. We show that our solution performs competitively compared to previous approaches while using less data and simpler features. Copyright © 2010 ACM.
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This monograph is a result of a 3-year project to produce a decision-support toolkit with supporting databases and case studies to help researchers, planners and extension agents working on freshwater pond aquaculture. The purpose of the work was to provide tools and information to help practitioners identify places and conditions where pond aquaculture can benefit the poor, both as producers and as consumers of fish. This monograph is the case study for Malawi. Written in three parts, it describes the historical background, practices, stakeholder profiles, production levels, economic and institutional environment, policy issues, and prospects for aquaculture in the country. First, it documents the history and current status of the aquaculture in the country. Second, it assesses the technologies and approaches that either succeeded or failed to foster aquaculture development and discusses why. Third, it identifies the key reasons for aquaculture adoption.
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A study was conducted, in association with the Sapelo Island and North Carolina National Estuarine Research Reserves (NERRs), to evaluate the impacts of coastal development on sentinel habitats (e.g., tidal creek ecosystems), including potential impacts to human health and well-being. Uplands associated with southeastern tidal creeks and the salt marshes they drain are popular locations for building homes, resorts, and recreational facilities because of the high quality of life and mild climate associated with these environments. Tidal creeks form part of the estuarine ecosystem characterized by high biological productivity, great ecological value, complex environmental gradients, and numerous interconnected processes. This research combined a watershed-level study integrating ecological, public health and human dimension attributes with watershed-level land use data. The approach used for this research was based upon a comparative watershed and ecosystem approach that sampled tidal creek networks draining developed watersheds (e.g., suburban, urban, and industrial) as well as undeveloped sites. The primary objective of this work was to clearly define the relationships between coastal development with its concomitant land use changes and non-point source pollution loading and the ecological and human health and well-being status of tidal creek ecosystems. Nineteen tidal creek systems, located along the southeastern United States coast from southern North Carolina to southern Georgia, were sampled during summer (June-August), 2005 and 2006. Within each system, creeks were divided into two primary segments based upon tidal zoning: intertidal (i.e., shallow, narrow headwater sections) and subtidal (i.e., deeper and wider sections), and watersheds were delineated for each segment. In total, we report findings on 24 intertidal and 19 subtidal creeks. Indicators sampled throughout each creek included water quality (e.g., dissolved oxygen concentration, salinity, nutrients, chlorophyll-a levels), sediment quality (e.g., characteristics, contaminants levels including emerging contaminants), pathogen and viral indicators, and abundance and genetic responses of biological resources (e.g., macrobenthic and nektonic communities, shellfish tissue contaminants, oyster microarray responses). For many indicators, the intertidally-dominated or headwater portions of tidal creeks were found to respond differently than the subtidally-dominated or larger and deeper portions of tidal creeks. Study results indicate that the integrity and productivity of headwater tidal creeks were impaired by land use changes and associated non-point source pollution, suggesting these habitats are valuable early warning sentinels of ensuing ecological impacts and potential public health threats. For these headwater creeks, this research has assisted the validation of a previously developed conceptual model for the southeastern US region. This conceptual model identified adverse changes that generally occurred in the physical and chemical environment (e.g., water quality indicators such as indicator bacteria for sewage pollution or sediment chemical contamination) when impervious cover levels in the watershed reach 10-20%. Ecological characteristics responded and were generally impaired when impervious cover levels exceed 20-30%. Estimates of impervious cover levels defining where human uses are impaired are currently being determined, but it appears that shellfish bed closures and the flooding vulnerability of headwater regions become a concern when impervious cover values exceed 10-30%. This information can be used to forecast the impacts of changing land use patterns on tidal creek environmental quality as well as associated human health and well-being. In addition, this study applied tools and technologies that are adaptable, transferable, and repeatable among the high quality NERRS sites as comparable reference entities to other nearby developed coastal watersheds. The findings herein will be of value in addressing local, regional and national needs for understanding multiple stressor (anthropogenic and human impacts) effects upon estuarine ecosystems and response trends in ecosystem condition with changing coastal impacts (i.e., development, climate change). (PDF contaions 88 pages)
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Harmful Algal Research and Response: A Human Dimensions Strategy (HARR-HD) justifies and guides a coordinated national commitment to human dimensions research critical to prevent and respond to impacts of harmful algal blooms (HABs). Beyond HABs, it serves as a framework for developing hu-man dimensions research as a cross-cutting priority of ecosystem science supporting coastal and ocean management, including hazard research and mitigation planning. Measuring and promoting commu-nity resilience to hazards require human dimensions research outcomes such as effective risk commu-nication strategies; assessment of community vulnerability; identification of susceptible populations; comprehensive assessment of environmental, sociocultural, and economic impacts; development of effective decision support tools; and improved coordination among agencies and stakeholders. HARR-HD charts a course for human dimensions research to achieve these and other priorities through co-ordinated implementation by the Joint Subcommittee on Ocean Science and Technology (JSOST) In-teragency Working Group on HABs, Hypoxia and Human Health (IWG-4H); national HAB funding programs; national research and response programs; and state research and monitoring programs. (PDF contains 72 pages)