scholarly journals Data-Intensive Sound Acquisition System with Large-scale Microphone Array

2011 ◽  
Vol 19 ◽  
pp. 129-140 ◽  
Author(s):  
Hiroki Noguchi ◽  
Tomoya Takagi ◽  
Koji Kugata ◽  
Shintaro Izumi ◽  
Masahiko Yoshimoto ◽  
...  
Keyword(s):  
Large Scale ◽  
Microphone Array ◽  
Acquisition System ◽  
Data Intensive

scholarly journals A Proximal Algorithm for Fork-Choice in Distributed Ledger Technology for Context-Based Clustering on Edge Computing

2020 ◽  
Vol 2 (1) ◽  
pp. 92
Author(s):  
Rahim Rahmani ◽  
Ramin Firouzi ◽  
Sachiko Lim ◽  
Mahbub Alam
Keyword(s):  
Large Scale ◽  
Cluster Head ◽  
Edge Computing ◽  
Computing Technology ◽  
Proximal Algorithm ◽  
Data Intensive ◽  
Distributed Ledger ◽  
Smart Contract ◽  
Distributed Ledger Technology ◽  
Iot Devices

The major challenges of operating data-intensive of Distributed Ledger Technology (DLT) are (1) to reach consensus on the main chain as a set of validators cast public votes to decide on which blocks to finalize and (2) scalability on how to increase the number of chains which will be running in parallel. In this paper, we introduce a new proximal algorithm that scales DLT in a large-scale Internet of Things (IoT) devices network. We discuss how the algorithm benefits the integrating DLT in IoT by using edge computing technology, taking the scalability and heterogeneous capability of IoT devices into consideration. IoT devices are clustered dynamically into groups based on proximity context information. A cluster head is used to bridge the IoT devices with the DLT network where a smart contract is deployed. In this way, the security of the IoT is improved and the scalability and latency are solved. We elaborate on our mechanism and discuss issues that should be considered and implemented when using the proposed algorithm, we even show how it behaves with varying parameters like latency or when clustering.

scholarly journals Instability wave models for the near-field fluctuations of turbulent jets

2011 ◽  
Vol 689 ◽  
pp. 97-128 ◽  
Author(s):  
K. Gudmundsson ◽  
Tim Colonius
Keyword(s):  
Flow Field ◽  
Large Scale ◽  
Microphone Array ◽  
Near Field ◽  
Turbulent Jets ◽  
Instability Wave ◽  
Mean Flow ◽  
Velocity Fluctuations ◽  
The Mean ◽  
Scale Structures

AbstractPrevious work has shown that aspects of the evolution of large-scale structures, particularly in forced and transitional mixing layers and jets, can be described by linear and nonlinear stability theories. However, questions persist as to the choice of the basic (steady) flow field to perturb, and the extent to which disturbances in natural (unforced), initially turbulent jets may be modelled with the theory. For unforced jets, identification is made difficult by the lack of a phase reference that would permit a portion of the signal associated with the instability wave to be isolated from other, uncorrelated fluctuations. In this paper, we investigate the extent to which pressure and velocity fluctuations in subsonic, turbulent round jets can be described aslinearperturbations to the mean flow field. The disturbances are expanded about the experimentally measured jet mean flow field, and evolved using linear parabolized stability equations (PSE) that account, in an approximate way, for the weakly non-parallel jet mean flow field. We utilize data from an extensive microphone array that measures pressure fluctuations just outside the jet shear layer to show that, up to an unknown initial disturbance spectrum, the phase, wavelength, and amplitude envelope of convecting wavepackets agree well with PSE solutions at frequencies and azimuthal wavenumbers that can be accurately measured with the array. We next apply the proper orthogonal decomposition to near-field velocity fluctuations measured with particle image velocimetry, and show that the structure of the most energetic modes is also similar to eigenfunctions from the linear theory. Importantly, the amplitudes of the modes inferred from the velocity fluctuations are in reasonable agreement with those identified from the microphone array. The results therefore suggest that, to predict, with reasonable accuracy, the evolution of the largest-scale structures that comprise the most energetic portion of the turbulent spectrum of natural jets, nonlinear effects need only be indirectly accounted for by considering perturbations to the mean turbulent flow field, while neglecting any non-zero frequency disturbance interactions.

scholarly journals GATECloud.net: a platform for large-scale, open-source text processing on the cloud

2013 ◽  
Vol 371 (1983) ◽  
pp. 20120071 ◽  
Author(s):  
Valentin Tablan ◽  
Ian Roberts ◽  
Hamish Cunningham ◽  
Kalina Bontcheva
Keyword(s):  
Cloud Computing ◽  
Language Processing ◽  
Large Scale ◽  
Virtual Machines ◽  
Cost Benefit Analysis ◽  
Text Processing ◽  
Cost Benefit ◽  
Data Intensive ◽  
On Demand ◽  
Usage Evaluation

Cloud computing is increasingly being regarded as a key enabler of the ‘democratization of science’, because on-demand, highly scalable cloud computing facilities enable researchers anywhere to carry out data-intensive experiments. In the context of natural language processing (NLP), algorithms tend to be complex, which makes their parallelization and deployment on cloud platforms a non-trivial task. This study presents a new, unique, cloud-based platform for large-scale NLP research—GATECloud. net. It enables researchers to carry out data-intensive NLP experiments by harnessing the vast, on-demand compute power of the Amazon cloud. Important infrastructural issues are dealt with by the platform, completely transparently for the researcher: load balancing, efficient data upload and storage, deployment on the virtual machines, security and fault tolerance. We also include a cost–benefit analysis and usage evaluation.

scholarly journals Performance Comparison of OpenMP, MPI, and MapReduce in Practical Problems

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Sol Ji Kang ◽  
Sang Yeon Lee ◽  
Keon Myung Lee
Keyword(s):  
Parallel Programming ◽  
Large Scale ◽  
Memory Systems ◽  
Performance Comparison ◽  
Benchmark Problems ◽  
Distributed Programming ◽  
Problem Size ◽  
Good Picture ◽  
Data Intensive ◽  
The Right

With problem size and complexity increasing, several parallel and distributed programming models and frameworks have been developed to efficiently handle such problems. This paper briefly reviews the parallel computing models and describes three widely recognized parallel programming frameworks: OpenMP, MPI, and MapReduce. OpenMP is the de facto standard for parallel programming on shared memory systems. MPI is the de facto industry standard for distributed memory systems. MapReduce framework has become the de facto standard for large scale data-intensive applications. Qualitative pros and cons of each framework are known, but quantitative performance indexes help get a good picture of which framework to use for the applications. As benchmark problems to compare those frameworks, two problems are chosen: all-pairs-shortest-path problem and data join problem. This paper presents the parallel programs for the problems implemented on the three frameworks, respectively. It shows the experiment results on a cluster of computers. It also discusses which is the right tool for the jobs by analyzing the characteristics and performance of the paradigms.

scholarly journals Using the Grid to Test the ATLAS Trigger and Data Acquisition System at Large Scale

2007 ◽  
Author(s):  
Alessandra Forti ◽  
Hegoi Garitaonandia ◽  
Jiri Masik ◽  
Sarah Wheeler ◽  
Thorsten Wengler
Keyword(s):  
Data Acquisition ◽  
Large Scale ◽  
Data Acquisition System ◽  
Acquisition System

scholarly journals Simultaneous EEG Acquisition System for Multiple Users: Development and Related Issues

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4592
Author(s):  
Sunghan Lee ◽  
Hohyun Cho ◽  
Kiseong Kim ◽  
Sung Chan Jun
Keyword(s):  
Large Scale ◽  
Acquisition System ◽  
Signal Acquisition ◽  
Human Interactions ◽  
Multiple Users ◽  
Simultaneous Acquisition ◽  
Eeg Data ◽  
Reasonable Cost ◽  
Average Loss ◽  
Crowd Psychology

Social interaction is one of humans’ most important activities and many efforts have been made to understand the phenomenon. Recently, some investigators have attempted to apply advanced brain signal acquisition systems that allow dynamic brain activities to be measured simultaneously during social interactions. Most studies to date have investigated dyadic interactions, although multilateral interactions are more common in reality. However, it is believed that most studies have focused on such interactions because of methodological limitations, in that it is very difficult to design a well-controlled experiment for multiple users at a reasonable cost. Accordingly, there are few simultaneous acquisition systems for multiple users. In this study, we propose a design framework for an acquisition system that measures EEG data simultaneously in an environment with 10 or more people. Our proposed framework allowed us to acquire EEG data at up to 1 kHz frequency from up to 20 people simultaneously. Details of our acquisition system are described from hardware and software perspectives. In addition, various related issues that arose in the system’s development—such as synchronization techniques, system loads, electrodes, and applications—are discussed. In addition, simultaneous visual ERP experiments were conducted with a group of nine people to validate the EEG acquisition framework proposed. We found that our framework worked reasonably well with respect to less than 4 ms delay and average loss rates of 1%. It is expected that this system can be used in various hyperscanning studies, such as those on crowd psychology, large-scale human interactions, and collaborative brain–computer interface, among others.

scholarly journals Time and Cost Efficient Cloud Resource Allocation for Real-Time Data-Intensive Smart Systems

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5706
Author(s):  
Muhammad Shuaib Qureshi ◽  
Muhammad Bilal Qureshi ◽  
Muhammad Fayaz ◽  
Muhammad Zakarya ◽  
Sheraz Aslam ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Real Time ◽  
Large Scale ◽  
Smart Devices ◽  
Budget Constraints ◽  
Smart Systems ◽  
Data Intensive ◽  
Cost Efficient ◽  
And Storage

Cloud computing is the de facto platform for deploying resource- and data-intensive real-time applications due to the collaboration of large scale resources operating in cross-administrative domains. For example, real-time systems are generated by smart devices (e.g., sensors in smart homes that monitor surroundings in real-time, security cameras that produce video streams in real-time, cloud gaming, social media streams, etc.). Such low-end devices form a microgrid which has low computational and storage capacity and hence offload data unto the cloud for processing. Cloud computing still lacks mature time-oriented scheduling and resource allocation strategies which thoroughly deliberate stringent QoS. Traditional approaches are sufficient only when applications have real-time and data constraints, and cloud storage resources are located with computational resources where the data are locally available for task execution. Such approaches mainly focus on resource provision and latency, and are prone to missing deadlines during tasks execution due to the urgency of the tasks and limited user budget constraints. The timing and data requirements exacerbate the efficient task scheduling and resource allocation problems. To cope with the aforementioned gaps, we propose a time- and cost-efficient resource allocation strategy for smart systems that periodically offload computational and data-intensive load to the cloud. The proposed strategy minimizes the data files transfer overhead to computing resources by selecting appropriate pairs of computing and storage resources. The celebrated results show the effectiveness of the proposed technique in terms of resource selection and tasks processing within time and budget constraints when compared with the other counterparts.

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