Scalable real-time sound source localization method based on TDOA

Heydari, Zahra; Mahabadi, Aminollah

doi:10.1007/s11042-022-14256-2

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…It is possible to reduce computational complexity at a reasonable cost using software techniques in multicore hardware, grid structures, fog environments, and cloud computing to precisely control large search spaces, correct resource consumption, network load balancing, timely real-time reactions, and direct generation of consequence transactions. The possible speed and correct computing of big data, careful control of data propagation, and proper monitoring of information diffusion in various large complex networks for modern text, visible image, and acoustic data types naturally increase the time complexity and memory usage for big text data [78], big image data [6], big acoustic data [69], and the possible combination of exclusive video and audio for multimedia applications in uncertain and high-risk environments [14], different topology streaming [15], and various channel utilization [16] for automatic decision-making. This potential problem routinely requires modern definitions and economic modeling for the subsequent definition of big image data generation in a reliable form suitable for various online interactions of intelligent multimedia applications, from camera imaging to knowledge extraction of sequential images [17,5].…”

Section: Big Data Oceansmentioning

confidence: 99%

“…Intelligent virtualization is a real-time process that aims to overcome the physical challenges of the environment by generating virtual data that involves conducting real or simulated operations in diverse domains, including realworld military battlegrounds or gaming operations [54]. Intelligent virtualization represents a real-time process that focuses on creating virtual big data to eliminate physical environmental problems.…”

Section: Data Intelligencementioning

confidence: 99%

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Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things

Mahabadi,

Abdolazimi

2024

Preprint

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The classification of large image data using traditional methods has been based on changes in image gray features, extraction of edge and contour feature information, or conversion between image coordinate sets. However, due to the growth of new image size and big image data (BID) in real-time multimedia communication and future online big data (BD) applications, these methods have become increasingly complex and resulting in poor real-time performance. These complex methods suffer from complex algorithms, massive data communication, slow processing speed, unintelligent predictive modeling, weak data classification, limited accuracy, uncleaned usage data, combined destructive artificial and natural noise, exchanging data values over time, and exhausting updating data for accurate operation of media storage. In the era of the Internet of Multimedia Things (IoMT), most modern devices can accurately capture vast amounts of observational data in the form of valuable images, text, and acoustic recordings. This reliable data must be kept valid for local data processing at different times and for globally extracting knowledge from information at various data levels to address forthcoming challenges related to big image data. The challenges of secure communication require accurate, reliable, fast, and effective information gathering for local decision-making and global knowledge mining of BID to support cyber-physical systems for speedy data virtualization of smart city devices. Complex and uncontrollable problems persist on the outskirts of grids, fogs, and cloud networks regarding data cleansing and privacy protection for intelligent data ocean management. This study developed a new 15Vs model empirically to examine distributed big image data processing based on a modern BID connectivity approach. The model accurately extracts textured features from visible data images and overcomes a unique set of key challenges. The model introduces a strategic analyzer, uses intelligent local agents, and recruits clever global bots to provide a suitable platform for generous support of bot-oriented BD processing. This instantly forms a hierarchical data level for better data acquisition and cleansing, safe privacy protection, suitable information diffusion, and speedy knowledge extraction. Our study presents a novel definition of BID to address the ultimate challenges of data management in a high-risk environment for further big image data modeling research needs.

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