A Content-Sensitive Approach to Search in Shared File Storages

2011 ◽  
pp. 755-761
Author(s):  
Gábor Richly ◽  
Gábor Hosszú ◽  
Ferenc Kovács
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Measurement Data ◽  
Stream Media ◽  
Recognition Algorithms ◽  
Novel Approach ◽  
Experimental Media ◽  
Shared File ◽  
Audio Data ◽  
Searching Method

The article presents a novel approach to search in shared audio file storages such as P2P-based systems. The proposed method enables the recognition of specific patterns in the audio contents, in such a way it extends the searching possibility from the description-based model to the content- based model. The targeted shared file storages seam to change contents rather unexpectedly. This volatile nature led our development to use real-time capable methods for the search process. The importance of the real-time pattern recognition algorithms that are used on audio data for content-sensitive searching in stream media has been growing over a decade (Liu, Wang, & Chen, 1998). The main problem of many algorithms is the optimal selection of the reference patterns (soundprints in our approach) used in the recognition procedure. This proposed method is based on distance maximization and is able to choose the pattern that later will be used as reference by the pattern recognition algorithms quickly (Richly, Kozma, Kovács & Hosszú, 2001). The presented method called EMESE (Experimental MEdia-Stream rEcognizer) is an important part of a lightweight content-searching method, which is suitable for the investigation of the network-wide shared file storages. This method was initially applied for real-time monitoring of the occurrence of known sound materials in broadcast audio. The experimental measurement data showed in the article demonstrate the efficiency of the procedure that was the reason for using it in shared audio database environment.

Pattern-Based Identification in P2P Systems

2009 ◽  
pp. 676-682
Author(s):  
Gábor Richly ◽  
Gábor Hosszú ◽  
Ferenc Kovács
Keyword(s):  
Pattern Recognition ◽  
Measurement Data ◽  
P2p Systems ◽  
Recognition Algorithms ◽  
Novel Approach ◽  
Experimental Media ◽  
Shared File ◽  
Audio Data ◽  
Searching Method ◽  
Media Stream

This article presents a novel approach to search in shared audio file storages such as P2P based systems. The proposed method is based on the recognition of specific patterns in the audio contents in such a way extending the searching possibility from the description based model to the content based model. The importance of the real-time pattern recognition algorithms that are used on audio data for content-based searching in streaming media is rapidly growing (Liu, Wang, & Chen, 1998). The main problem of such algorithms is the optimal selection of the reference patterns (soundprints) used in the recognition procedure. The proposed method is based on distance maximization and is able to quickly choose the pattern that later will be used as reference by the pattern recognition algorithms (Richly, Kozma, Kovács, & Hosszú, 2001). The presented method called EMESE (experimental media-stream recognizer) is an important part of a lightweight content-searching method, which is suitable for the investigation of the networkwide shared file storages. The experimental measurement data shown in the article demonstrate the efficiency of the proposed procedure.

Research on Real-Time Measuring Method of Dynamic Deformation Based on Machine Vision and its Application

2011 ◽  
Vol 130-134 ◽  
pp. 3572-3576
Author(s):  
Li Zong Lin ◽  
Xiao Peng Ni ◽  
Luo Shan Zhou ◽  
Zhi Qin Qian
Keyword(s):  
Fatigue Strength ◽  
Machine Vision ◽  
Real Time ◽  
Dynamic Deformation ◽  
Measurement Data ◽  
Measuring Method ◽  
Strength Test ◽  
Time Deformation ◽  
Vision Measurement ◽  
Searching Method

Dynamic deformation measurement of machine parts in fatigue strength test is studied by using machine vision technique. Considering the uncertainty of parts surface, we adopt circular mark to locate the object profile in order to obtain high quality images. Through some image pre-processing with linear filtering, continuous contour searching method and circular detection based on random Hough transform (RHT), the real-time deformation can be measured with image characteristic parameters. In the practical application, the deformation of the loaded bicycle handle-bar is calculated. The test results show that the machine vision measurement is very effective; measurement resolution attains 0.1mm/pixel; the discrete degree of measurement data is low and the system meets the requirement of real-time measurement. The study proves that the measurement method of dynamic deformation based on machine vision is feasible, which can give some help for fatigue strength test of machine part and other structure deformation.

Proposal of real-time Brillouin fiber sensing based on compressing sensing and pattern recognition algorithms

2021 ◽  
Author(s):  
Benzhang Wang ◽  
Yupeng Zhang ◽  
Fan Zhou ◽  
Xianlei Ye ◽  
Dongliang Quan
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Fiber Sensing ◽  
Recognition Algorithms

scholarly journals Real-time and offline evaluation of myoelectric pattern recognition for the decoding of hand movements

2020 ◽  
Author(s):  
Sara Abbaspour ◽  
Autumn Naber ◽  
Max Ortiz-Catalan ◽  
Hamid Gholamhosseini ◽  
Maria Lindén
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Classification Accuracy ◽  
Likelihood Estimation ◽  
Linear Discriminant ◽  
Time Performance ◽  
Performance Metric ◽  
Recognition Algorithms ◽  
Upper Limb Prostheses ◽  
Electromyographic Signals

<p><a></a><a>Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. Recent literature has underscored differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Since the majority of investigations on pattern recognition algorithms have been conducted offline by performing the analysis on pre-recorded datasets, less knowledge has been gained with respect to real-time performance (i.e., classification when new data becomes available with limits on latency under 200-300 milliseconds). </a>Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from the dominant forearm of fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that Linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p<0.05) outperformed other classifiers with an average classification accuracy of above 97%. The real-time investigation revealed that in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms in classification accuracy (above 68%) and completion rate (above 69%).</p>

scholarly journals Real-time and offline evaluation of myoelectric pattern recognition for the decoding of hand movements

2020 ◽  
Author(s):  
Sara Abbaspour ◽  
Autumn Naber ◽  
Max Ortiz-Catalan ◽  
Hamid Gholamhosseini ◽  
Maria Lindén
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Classification Accuracy ◽  
Likelihood Estimation ◽  
Linear Discriminant ◽  
Time Performance ◽  
Performance Metric ◽  
Recognition Algorithms ◽  
Upper Limb Prostheses ◽  
Electromyographic Signals

<p><a></a><a>Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. Recent literature has underscored differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Since the majority of investigations on pattern recognition algorithms have been conducted offline by performing the analysis on pre-recorded datasets, less knowledge has been gained with respect to real-time performance (i.e., classification when new data becomes available with limits on latency under 200-300 milliseconds). </a>Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from the dominant forearm of fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that Linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p<0.05) outperformed other classifiers with an average classification accuracy of above 97%. The real-time investigation revealed that in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms in classification accuracy (above 68%) and completion rate (above 69%).</p>

A novel approach to the real-time modelling of large urban drainage systems

1997 ◽  
Vol 36 (8-9) ◽  
pp. 19-24 ◽  
Author(s):  
Richard Norreys ◽  
Ian Cluckie
Keyword(s):  
Real Time ◽  
Dynamic Models ◽  
Drainage System ◽  
Radar Data ◽  
Urban Drainage ◽  
Real Time Control ◽  
Empirical Modelling ◽  
Time Control ◽  
Novel Approach ◽  
Non Linear Systems

Conventional UDS models are mechanistic which though appropriate for design purposes are less well suited to real-time control because they are slow running, difficult to calibrate, difficult to re-calibrate in real time and have trouble handling noisy data. At Salford University a novel hybrid of dynamic and empirical modelling has been developed, to combine the speed of the empirical model with the ability to simulate complex and non-linear systems of the mechanistic/dynamic models. This paper details the ‘knowledge acquisition module’ software and how it has been applied to construct a model of a large urban drainage system. The paper goes on to detail how the model has been linked with real-time radar data inputs from the MARS c-band radar.

Real-time Approximation of Photometric Polygonal Lights

2020 ◽  
Vol 3 (1) ◽  
pp. 1-18
Author(s):  
Christian Luksch ◽  
Lukas Prost ◽  
Michael Wimmer
Keyword(s):  
Real Time ◽  
Specular Reflection ◽  
Near Field ◽  
Measurement Data ◽  
Data Sets ◽  
Photometric Measurement ◽  
Integration Technique ◽  
Time Approximation ◽  
Light Emitter ◽  
Selection Of

We present a real-time rendering technique for photometric polygonal lights. Our method uses a numerical integration technique based on a triangulation to calculate noise-free diffuse shading. We include a dynamic point in the triangulation that provides a continuous near-field illumination resembling the shape of the light emitter and its characteristics. We evaluate the accuracy of our approach with a diverse selection of photometric measurement data sets in a comprehensive benchmark framework. Furthermore, we provide an extension for specular reflection on surfaces with arbitrary roughness that facilitates the use of existing real-time shading techniques. Our technique is easy to integrate into real-time rendering systems and extends the range of possible applications with photometric area lights.

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