HHC Networks delivers optical communication equipment, carrier switches, OTN routers, industrial PoE switches, and smart city infrastructure across Africa and Europe.
HOME / Audio Extraction Using Fiber Optic Communication Technology - HHC Networks & Smart City Solutions
Abstract: Current fiber-optic distributed acoustic sensor (DAS) target recognition technologies continue to prioritize updating the feature learning tools while neglecting varying contributions of information
In this paper, we propose a fiber optic audio feature extraction method based on wavelet transform, which decomposes the signal at multiple scales by wavelet transform and extracts energy
RF over fiber can be thought of, in the simplest of terms, as a media converter, much like that for taking Ethernet over copper CAT5/6/7 twisted pair cable and converting it to light for transport over fiber
To establish stable communication networks in harsh environments where power supply is difficult, such as coal mines and underwater, we propose an effective scheme for co-transmission of
Based on Power-over-Fiber (PWoF) and passive optical fiber sensing technology, this paper designs and implements a passive bi-directional audio over fiber (PB-AOF) transmission
This study presents a dataset comprising acoustic vibration patterns recorded by a commercial DAS system, providing valuable insights into the acoustic sensitivity of optical fibers.
In modern fiber optic AV systems, analog video and audio are converted to digital signals, avoiding nonlinear effects that plague direct optical conversion of analog signals. Digital transmission ensures
l fiber transmission system for audio signal was constructed. The purpose of the system will be used for high quality audio transmitter in place of the coaxial or copper wire data transmitting system.
Our goal is to create a short range one-way audio communication system as a proof of concept that such a audio communication system is both feasible and practical to us.
In this paper, we propose a hybrid deep learning architecture that combines a complex-valued convolutional neural network and a long short-term memory (CV-CNN-LSTM) for speech