Implementation of Artificial Neural Network on Raspberry Pi for Signal Processing Applications

被引：0

作者：

Bharadwaja, Vishwanath ^{[1
]}

Ananmy, R. ^{[1
]}

Nikhil, Sarraf ^{[1
]}

Vineetha, K. V. ^{[1
]}

Shah, Jalpa ^{[2
]}

Kurup, Dhanesh G. ^{[2
]}

机构：

[1] Amrita Vishwa Vidyapeetham, Amrita Sch Engn, Dept Comp Sci & Engn, Bengaluru, India

[2] Amrita Vishwa Vidyapeetham, Amrita Sch Engn, Dept Elect & Commun Engn, Bengaluru, India

来源：

2018 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI) | 2018年

关键词：

Logistic Regression; Demodulation; Analog to Digital Converter; Artificial Neural Network;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Signal demodulation is one of the key signal processing application that enables modern communication systems. Signal demodulation normally requires complex hardware and prior information about the frequencies of the carrier. In this paper, we propose a method for demodulating signals with digital modulating signals based on Artificial Neural Network (ANN). The implementation of the proposed method has been carried in Raspberry Pi computer which will enable the deployment of the system in real time with the additional advantage of reconfigurability.

引用

页码：1488 / 1491

页数：4

共 50 条

[31] Analog/digital implementation of neural networks for pattern discovery and optimization in signal processing applications
AbdelAtyZohdy, HS
Zohdy, MA
38TH MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS, PROCEEDINGS, VOLS 1 AND 2, 1996, : 277 - 277
[32] Conversion of Artificial Neural Network to Spiking Neural Network for Hardware Implementation
Chen, Yi-Lun
Lu, Chih-Cheng
Juang, Kai-Cheung
Tang, Kea-Tiong
2019 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN (ICCE-TW), 2019,
[33] The architecture and circuital implementation scheme of a new cell neural network for analog signal processing
Wang, Youren
Zhang, Zhiqiang
Cui, Jiang
JOURNAL OF UNIVERSAL COMPUTER SCIENCE, 2007, 13 (09) : 1344 - 1353
[34] Artificial neural network for rolling applications
Leven, J.
Jonsson, N.G.
Wiklund, O.
Steel Times, 1995, 223 (04): : 137 - 138
[35] Herbal Leaf Authentication Using Convolutional Neural Network on Raspberry Pi 3
Haryono
Anam, Khairul
Sujanarko, Bambang
CLIMATE CHANGE AND SUSTAINABILITY ENGINEERING IN ASEAN 2019, 2020, 2278
[36] A review on the artificial neural network applications for small-signal modeling of microwave FETs
Marinkovic, Zlatica d
Crupi, Giovanni
Caddemi, Alina
Markovic, Vera
Schreurs, Dominique M. M. -P.
INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS, 2020, 33 (03)
[37] PARALLEL IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK TRAINING
Scanzio, Stefano
Cumani, Sandro
Gemello, Roberto
Mana, Franco
Laface, P.
2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2010, : 4902 - 4905
[38] ARTIFICIAL NEURAL NETWORK IMPLEMENTATION OF THE HOUGH TRANSFORM
MCVEY, ES
DEMPSEY, GL
GARCIA, J
INIGO, RM
TWENTY-THIRD ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS, VOLS 1 AND 2: CONFERENCE RECORD, 1989, : 128 - 132
[39] A comprehensive review on applications of Raspberry Pi
Mathe, Sudha Ellison
Kondaveeti, Hari Kishan
Vappangi, Suseela
Vanambathina, Sunny Dayal
Kumaravelu, Nandeesh Kumar
COMPUTER SCIENCE REVIEW, 2024, 52
[40] Smart Mirror Applications with Raspberry Pi
Ok, Fatma
Can, Murat
Ucgun, Hakan
Yuzgec, Ugur
2017 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ENGINEERING (UBMK), 2017, : 94 - 98

← 1 2 3 4 5 →