Array microphones and signal processing within an ethernet-based AVB network

Cohrs, Thaden; Treybig, Lukas

doi:10.1109/icce-berlin.2016.7684741

Cited by 3 publications

(1 citation statement)

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“…The software is designed for use in conferencing devices. To stretch multiple speakers' recording areas, array microphones with beam steering and a sound source localization algorithm are used [24]. The research deployed an IEEE 802.1 AVB Local Area Network infrastructure for time-sensitive traffic and have also suggested frame forwarding [25].…”

Section: Preliminary Researchmentioning

confidence: 99%

Image Link Through Adaptive Encoding Data Base and Optimized GPU Algorithm for Real-time Image Processing of Artificial Intelligence

Kim

2022

JWE

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This paper presents the latest Ethernet standardization of in-vehicle network and the future trends of automotive ethernet technology. The proposed system provides a design and optimization algorithm of in-vehicle networking technologies related Ethernet Audio Video Bridge (AVB) technology. We present a design of in-vehicle network system as well as the optimization of AVB for automotive. A proposal of Reduced Latency of Machin to Machine (RLMM) plays a significant role in reducing the latency between devices. The approach of RLMM on realistic test cases indicated that there was a latency reduction about 30.41% It is expected that the optimized settings for the actual automotive network environment can greatly shorten the time period in the development and design process. The results achieved from the experiments on the latency present in each function are trustworthy since average values are obtained via repeated tests for several months. It would considerably benefit the industry because analyzing the delay between each function in a short period of time is tremendously significant. In addition, through the proposed real-time camera and video streaming via optimized settings of AVB system, it is expected that AI (Artificial Intelligence) algorithms in autonomous driving will be of great help in understanding and analyzing images in real time.

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Section: Preliminary Researchmentioning

confidence: 99%