Research on Anti-interference Algorithm for Cab Signal Demodulation Based on Deep Learning

In this letter, we present the first attempt of active light-emitting diode (LED) indexes estimating for the generalized LED index modulation optical orthogonal frequency-division multiplexing (GLIM-OFDM) in visible light communication (VLC) system by using deep learning (DL). Instead of directly estimating the transmitted binary bit sequence with DL, the active LEDs at the transmitter are estimated to maintain acceptable complexity and improve the performance gain compared with those of previously proposed receivers. Particularly, a novel DL-based estimator termed index estimator-based deep neural network (IE-DNN) is proposed, which can employ three different DNN structures with fully connected layers (FCL) or convolution layers (CL) to recover the indexes of active LEDs in a GLIM-OFDM system. By using the received signal dataset generated in simulations, the IE-DNN is first trained offline to minimize the index error rate (IER); subsequently, the trained model is deployed for the active LED index estimation and signal demodulation of the GLIM-OFDM system. The simulation results show that the IE-DNN significantly improves the IER and bit error rate (BER) compared with those of conventional detectors with acceptable run time.

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Deep learning detection method for signal demodulation in short range multipath channel

2017 IEEE 2nd International Conference on Opto-Electronic Information Processing (ICOIP) ◽

10.1109/optip.2017.8030690 ◽

2017 ◽

Cited By ~ 11

Author(s):

Lanting Fang ◽

Lenan Wu

Keyword(s):

Deep Learning ◽

Short Range ◽

Detection Method ◽

Multipath Channel ◽

Signal Demodulation

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An Empirical Study of Deep Learning Models for LED Signal Demodulation in Optical Camera Communication

Network ◽

10.3390/network1030016 ◽

2021 ◽

Vol 1 (3) ◽

pp. 261-278

Author(s):

AbdulHaseeb Ahmed ◽

Sethuraman Trichy Viswanathan ◽

MD Rashed Rahman ◽

Ashwin Ashok

Keyword(s):

Deep Learning ◽

Empirical Studies ◽

Multiple Input Multiple Output ◽

Empirical Evaluation ◽

Learning Models ◽

Led Array ◽

Input Multiple Output ◽

Signal Demodulation ◽

Light Beams ◽

Optical Camera Communication

Optical camera communication is an emerging technology that enables communication using light beams, where information is modulated through optical transmissions from light-emitting diodes (LEDs). This work conducts empirical studies to identify the feasibility and effectiveness of using deep learning models to improve signal reception in camera communication. The key contributions of this work include the investigation of transfer learning and customization of existing models to demodulate the signals transmitted using a single LED by applying the classification models on the camera frames at the receiver. In addition to investigating deep learning methods for demodulating a single VLC transmission, this work evaluates two real-world use-cases for the integration of deep learning in visual multiple-input multiple-output (MIMO), where transmissions from a LED array are decoded on a camera receiver. This paper presents the empirical evaluation of state-of-the-art deep neural network (DNN) architectures that are traditionally used for computer vision applications for camera communication.

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LoRa Signal Demodulation Using Deep Learning, a Time-Domain Approach

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) ◽

10.1109/vtc2021-spring51267.2021.9448711 ◽

2021 ◽

Author(s):

Kosta Dakic ◽

Bassel Al Homssi ◽

Akram Al-Hourani ◽

Margaret Lech

Keyword(s):

Deep Learning ◽

Time Domain ◽

Signal Demodulation

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Deep Learning

10.1017/cbo9780511780295 ◽

2009 ◽

Cited By ~ 94

Author(s):

Stellan Ohlsson

Keyword(s):

Deep Learning

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Intelligence artificielle : le futur de l’Orthodontie ?

Revue d Orthopédie Dento-Faciale ◽

10.1051/odf/2019026 ◽

2019 ◽

Vol 53 (3) ◽

pp. 281-294

Author(s):

Jean-Michel Foucart ◽

Augustin Chavanne ◽

Jérôme Bourriau

Keyword(s):

Big Data ◽

Deep Learning ◽

Intelligence Artificielle ◽

Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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