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检索条件"任意字段=Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition"

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proceedings - 2015 IEEE 17th international conference on High Performance computing and Communications, 2015 IEEE 7th international Symposium on Cyberspace Safety and Security and 2015 IEEE 12th international conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015

Proceedings - 2015 IEEE 17th International Conference on Hig...

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17th IEEE international conference on High Performance computing and Communications, IEEE 7th international Symposium on Cyberspace Safety and Security and IEEE 12th international conference on Embedded Software and Systems, HPCC-ICESS-CSS 2015

ISBN: (纸本)9781479989362

the proceedings contain 243 papers. the topics discussed include: availability-aware virtual network embedding for multi-tier applications in cloud networks;a cluster-based hybrid access strategy using non-cooperative game theory for ultra-dense HetNet;a cost and contention conscious scheduling for recovery in cloud environment;customer churn aware resource allocation and virtual machine placement in cloud;a head record cache structure to improve the operations on big files in cloud storage servers;predicting scheduling failures in the cloud: a case study with Google clusters and Hadoop on Amazon EMR;parallel BP neural network on single-chip cloud computer;different implementations of AES cryptographic algorithm;HETS: heterogeneous edge and task scheduling algorithm for heterogeneous computing systems;constructing a mobility and acceleration computing platform with NVIDIA Jetson TK1;evaluation of hybrid parallel cell list algorithms for Monte Carlo simulation;and efficient parallel multi-pattern matching using GPGPU acceleration for packet filtering.

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proceedings of the 12th international conference in Sound and Music computing, SMC 2015

Proceedings of the 12th International Conference in Sound an...

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12th international conference on Sound and Music computing, SMC 2015

ISBN: (纸本)9780992746629

the proceedings contain 75 papers. the topics discussed include: investigating the effects of introducing nonlinear dynamical processes into digital musical interfaces;Songrium: browsing and listening environment for music content creation community;archaeology and virtual acoustics. a pan flute from ancient Egypt;developing mixer-style controllers based on Arduino/teensy micro controllers;crosssong puzzle: generating and unscrambling music mashups with real-time interactivity;voice quality transformation using an extended source-filter speech model;an augmented guitar with active acoustics;synchronizing spatially distributed musical ensembles;a music performance assistance system based on vocal, harmonic, and percussive source separation and content visualization for music audio signals;a score-informed piano tutoring system with mistake detection and score simplification;target-based rhythmic pattern generation and variation with genetic algorithms;a computational model of tonality cognition based on prime factor representation of frequency ratios and its application;and rendering and subjective evaluation of real vs. synthetic vibrotactile cues on a digital piano keyboard.

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Analyzing the influence of pitch quantization and note segmentation on singing voice alignment in the context of audio-based Query-by-Humming 12

Analyzing the influence of pitch quantization and note segme...

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12th international conference on Sound and Music computing, SMC 2015

作者： Valero-Mas, Jose J. Salamon, Justin Gómez, Emilia Pattern Recognition and Artificial Intelligence Group University of Alicante Spain Music and Audio Research Laboratory New York University United States Music Technology Group Universitat Pompeu Fabra Spain

ISBN: (纸本)9780992746629

Query-by-Humming (QBH) systems base their operation on aligning the melody sung/hummed by a user with a set of candidate melodies retrieved from polyphonic songs. While MIDI-based QBH builds on the premise of existing annotated transcriptions for any candidate song, audiobased research makes use of melody estimation algorithms for the songs. In both cases, a melody abstraction process is required for solving issues commonly found in queries such as key transpositions or tempo deviations. Full automatic music processes are commonly used for this, but due to the reported limitations in state-of-the-art methods for real-world queries, other possibilities should be considered. In this work we explore three different melody representations, ranging from a general time-series one to more musical abstractions, which avoid full automatic transcription, in the context of an audio-based QBH system. Results show that this abstraction process plays a key role in the overall accuracy of the system, obtaining the best scores when temporal segmentation is dynamically performed in terms of pitch change events in the melodic contour. © 2015 Jose J. Valero-Mas et al.

关键词： Transcription

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Limited-memory warping LCSS for real-time low-power pattern recognition in wireless nodes 12

Limited-memory warping LCSS for real-time low-power pattern ...

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12th European conference on Wireless Sensor Networks, EWSN 2015

作者： Roggen, Daniel Cuspinera, Luis Ponce Pombo, Guilherme Ali, Falah Nguyen-Dinh, Long-Van Sensor Technology Research Centre University of Sussex United Kingdom Wearable Computing Laboratory ETH Zurich Switzerland

ISBN: (纸本)9783319155814

We present and evaluate a microcontroller-optimized limitedmemory implementation of aWarping Longest Common Subsequence algorithm (WarpingLCSS). It permits to spot patterns within noisy sensor data in real-time in resource constrained sensor nodes. It allows variability in the sensed system dynamics through warping;it uses only integer operations;it can be applied to various sensor modalities;and it is suitable for embedded training to recognize new *** illustrate themethod on 3 applications from wearable sensing and activity recognition using 3 sensor modalities: spotting the QRS complex in ECG, recognizing gestures in everyday life, and analyzing beach volleyball. We implemented the system on a low-power 8-bit AVR wireless node and a 32-bit ARMCortex M4 microcontroller. Up to 67 or 140 10-second gestures can be recognized simultaneously in real-time from a 10Hz motion sensor on the AVR and M4 using 8mW and 10mW respectively. A single gesture spotter uses as few as 135μW on the AVR. the method allows low data rate distributed innetwork recognition and we show a 100 fold data rate reduction in a complex activity recognition scenario. the versatility and low complexity of the method makes it well suited as a generic pattern recognition method and could be implemented as part of sensor front-ends. © Springer international Publishing Switzerland 2015.

关键词： Microcontrollers

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Parallelizing abnormal event detection in crowded scenes with GPU

Parallelizing abnormal event detection in crowded scenes wit...

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international conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Mohammadreza Yavari Maozhen Li Siguang Li Man Qi Department of Electronic and Computer Engineering Brunel University London Uxbridge UB8 3PH UK The Key Laboratory of Embedded Systems and Service Computing Tongji University Shanghai China

Analyzing human activities in surveillance videos a challenging task due to the high volume of data that needs to be processed in a timely manner. this paper presents a GPU based Gaussian Mixture Model (GMM) to detect abnormal activities in crowded scenes. GMM is a fully unsupervised method that predicts abnormal crowd behaviors based on the processing of normal crowd behaviors. Specifically, we use crowd distribution and GMM to estimate the speed and to predict the behaviors of the crowd. the performance of the parallel GMM is evaluated from the aspects of computation efficiency and accuracy in terms of area under the curve.

关键词： Videos Graphics processing units Computational modeling Computer vision Surveillance Image motion analysis pattern recognition

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Volumetric Image Reconstruction by 3D Hahn Moments

Volumetric Image Reconstruction by 3D Hahn Moments

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IEEE/ACS international conference on Computer Systems and Applications

作者： Mostafa El Mallahi Abderrahim Mesbah Hassan Qjidaa Aissam Berrahou Khalid Zenkouar Hakim El Fadili CED-ST Center of Doctoral Studies in Sciences and Technologies LESSI laboratory Faculty of Sciences Dhar el Mehraz Sidi Mohamed Ben Abdellah University Fez Morocco Mohammadia School of Engineers Mohamed V University Rabat LSIA laboratory Faculty of Science Technique Sidi Mohamed Ben Abdellah University Fez ENSA National school of engineering Sidi Mohamed Ben Abdellah University Fez

ISBN: (纸本)9781509004799

three-Dimensional Hahn moments are performant tool in the domain of image processing applications and pattern classification. In this work, we propose a new method for computing the three-Dimensional Hahn moments. this method is based on matrix multiplication and symmetry property to decrease the complexity and computational time for volumetric image reconstruction. Experimental results showed that the proposed method is very efficient in terms of computation time, but also in terms of volumetric image reconstruction capability.

关键词： three-Dimensional Hahn moments Volumetric Image symmetry property matrix multiplication Matrix multiplication image reconstruction computational time Moment of force pattern recognition new methods

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Radius-margin based support vector machine with LogDet regularizaron 14

Radius-margin based support vector machine with LogDet regul...

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14th international conference on Machine Learning and Cybernetics, ICMLC 2015

作者： Zhu, Yuan-Yuan Wu, Xiao-He Xu, Jun Zhang, David Zuo, Wang-Meng School of Computer Science and Technology Harbin Institute of Technology Harbin China Department of Computing Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

ISBN: (纸本)9781467372213

theoretically, Support Vector Machine (SVM) has the generalization error bound of radius-margin ratio, while the standard SVM only maximizes the margin. Several SVM variants based on the radius-margin ratio error bound have been proposed. However, most of them either require the form of the transformation matrix to be diagonal, or the optimization is computationally expensive. In this paper, we propose a novel convex radius-margin based SVM model with-LogDet regularization, ie., L-S VM Our model not only takes radius into consideration, but also increases the stability by combing the individual inequality constraints into one integrated inequality constraint. In L-SVM, we introduce a-LogDet regularization term to make the model more effective and get a dosed-form solution of the transformation matrix. Furthermore, we extend the L-SVM model to kernel space for nonlinear cases with the advantages of kernel principal component analysis. the experimental results show that L-SVM achieves significantly better performance both in accuracy and efficiency, compared to the standard SVM and the state-of-the-art radius-margin based SVM methods, e.g., RMM, R-SVM+ and R-SVM+ μ. © 2015 IEEE.

关键词： Support vector machines

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Audio Recorder Forensic Identification in 21 Audio Recorders

Audio Recorder Forensic Identification in 21 Audio Recorders

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2015 IEEE international conference on Progress in Informatics and computing(PIC 2015)

作者： Jinhua Zeng Shaopei Shi Xu Yang Yan Li Qimeng Lu Xiulian Qiu Huaping Zhu Institute of Forensic Science Ministry of Justice College of Civil Engineering Tongji University

Audio recorder forensic identification is to determine originating devices of questioned audio recordings. In this paper, statistical features both in time and frequency domains were studied and were used to represent and encode device-related information. Statistical techniques in fields of machine learning and pattern recognition were utilized to classify computed features into each originating audio recorder. More specifically, features of sampling histogram and spectral mean were extracted from near-silence segments of digital recordings which were recorded by using 21 audio recorders. A support vector machine classifier was used to learn device-related feature patterns upon one half of the complete set of audio samples, and then was evaluated by using the rest of data. the experimental results show a high accuracy of up to 96.72% in the correct classification of the 21 audio recorders. Our paper was the first study which was to study and evaluate the performances of different features for device-related information encoding in a relatively big audio recorder database. the proposed method would offer a practical guide for forensic identification of audio recorders.

关键词： Audio recorder forensic identification pattern classification Sampling histogram Spectral mean Statistical feature

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A Wireless Body Sensor Network For Activity Monitoring With Low Transmission Overhead 12

A Wireless Body Sensor Network For Activity Monitoring With ...

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12th IEEE/IFIP international conference on Embedded and Ubiquitous computing (EUC)

作者： Braojos, Ruben Beretta, Ivan Constantin, Jeremy Burg, Andreas Atienza, David Ecole Polytech Fed Lausanne Embedded Syst Lab CH-1015 Lausanne Switzerland Ecole Polytech Fed Lausanne Telecommun Circuits Lab CH-1015 Lausanne Switzerland

ISBN: (纸本)9780769552491

Activity recognition has been a research field of high interest over the last years, and it finds application in the medical domain, as well as personal healthcare monitoring during daily home-and sports-activities. With the aim of producing minimum discomfort while performing supervision of subjects, miniaturized networks of low-power wireless nodes are typically deployed on the body to gather and transmit physiological data, thus forming a Wireless Body Sensor Network (WBSN). In this work, we propose a WBSN for online activity monitoring, which combines the sensing capabilities of wearable nodes and the high computational resources of modern smartphones. the proposed solution provides different tradeoffs between classification accuracy and energy consumption, thanks to different workloads assigned to the nodes and to the mobile phone in different network configurations. In particular, our WBSN is able to achieve very high activity recognition accuracies (up to 97.2%) on multiple subjects, while significantly reducing the sampling frequency and the volume of transmitted data with respect to other state-of-the-art solutions.

关键词： body sensor networks computerised instrumentation energy consumption fuzzy neural nets mobile computing mobile handsets pattern classification WBSN activity monitoring activity recognition accuracies classification accuracy mobile phone neuro-fuzzy classifier sampling frequency smartphones transmission overhead wearable nodes wireless body sensor network Accuracy Biomedical monitoring Feature extraction Monitoring Smart phones Wireless communication Wireless sensor networks Body sensor networks Smartphone Mobile phones Fuzzy neural networks energy consumption computerised instrumentation Biomedical monitoring CARRIER SYSTEM pattern recognition Mobile computing Feature extraction Wireless sensor networks SAMPLING FREQUENCY

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Fixed-lag particle filter for continuous context discovery using Indian Buffet Process

Fixed-lag particle filter for continuous context discovery u...

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2014 12th IEEE international conference on Pervasive computing and Communications, PerCom 2014

作者： Nguyen, thuong Gupta, Sunil Venkatesh, Svetha Phung, Dinh Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781479934454

Exploiting context from stream data in pervasive environments remains a challenge. We aim to extract proximal context from Bluetooth stream data, using an incremental, Bayesian nonparametric framework that estimates the number of contexts automatically. Unlike current approaches that can only provide final proximal grouping, our method provides proximal grouping and membership of users over time. Additionally, it provides an efficient online inference. We construct co-location matrix over time using Bluetooth data. A Poisson-exponential model is used to factorize this matrix into a factor matrix, interpreted as proximal groups, and a coefficient matrix that indicates factor usage. the coefficient matrix follows the Indian Buffet Process prior, which estimates the number of factors automatically. the non-negativity and sparsity of factors are enforced by using the exponential distribution to generate the factors. We propose a fixed-lag particle filter algorithm to process data incrementally. We compare the incremental inference (particle filter) with full batch inference (Gibbs sampling) in terms of normalized factorization error and execution time. the normalized error obtained through our incremental inference is comparable to that of full batch inference, whilst the execution time is more than 100 times faster. the discovered factors have similar meaning to the results of the popular Louvain method for community detection. © 2014 IEEE.

关键词： Bluetooth

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