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Vohra, Anil
- Software Defined Radio (SDR) 4-bit QAM Modem Using Lab-VIEW for Gaussian Channel
Abstract Views :172 |
PDF Views:1
Authors
Affiliations
1 Communication Engineering Department,University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, IN
2 Communication Engineering Department, NIT, Kurukshetra, IN
3 Electronics and Communication Engineering Department, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, IN
4 Electronics Science Department, Kurukshetra University, Kurukshetra, IN
1 Communication Engineering Department,University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, IN
2 Communication Engineering Department, NIT, Kurukshetra, IN
3 Electronics and Communication Engineering Department, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, IN
4 Electronics Science Department, Kurukshetra University, Kurukshetra, IN
Source
Wireless Communication, Vol 3, No 4 (2011), Pagination: 237-245Abstract
A Software Defined Radio (SDR) is a reconfigurable radio, in which channel modulation waveforms are defined in software. In this paper Software Defined Radio (SDR) 4-bit QAM Modem for Gaussian Channel is implemented using abVIEW software. LabVIEW is a graphical development environment with built-in functionality for simulation, data acquisition, instrument control, measurement analysis, and data presentation. The aim of this paper is to simulate SDR for next generation wireless communication having Gaussian channel and Adaptive Filter is used to remove Gaussian noise present in received signal and minimize the effect of Intersymbol Interference. It is obvious from the Simulation results that SDR is suitable for Gaussian channel giving Optimum Performance.
Keywords
Software Defined Radio, QAM Modem, Gaussian Channel, Adaptive Filter, ISI, LabVIEW Graphical Programming.
- A Review on Channel Equalization for Software Defined Radio
Abstract Views :153 |
PDF Views:3
Authors
Affiliations
1 Electronics and Communication Engineering Department, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, IN
2 Electronics and Communication Engineering Department, NIT, Kurukshetra, IN
3 Electronics and Communication Engineering Department, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, IN
4 Electronics Science Department, Kurukshetra University, Kurukshetra, IN
1 Electronics and Communication Engineering Department, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, IN
2 Electronics and Communication Engineering Department, NIT, Kurukshetra, IN
3 Electronics and Communication Engineering Department, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, IN
4 Electronics Science Department, Kurukshetra University, Kurukshetra, IN
Source
Wireless Communication, Vol 3, No 3 (2011), Pagination: 206-213Abstract
We provide a brief overview over the development of software-defined radio system and channel equalization techniques. Software Defined Radio (SDR) is an all new technology being developed in the 21st century. The primary goal of Software Defined Radio is to replace as many analog components and hardwired digital VLSI devices of the transceiver as possible with programmable devices. One of the major practical problems in digital communication systems is channel distortion which causes errors due to Intersymbol Interference (ISI). In order to restore the transmitted sequence and given the observed sequence at the channel output which is accomplished by equalizers. In this paper Nonlinear equalizers, Adaptive equalizers, Fuzzy equalizers, Neural equalizers are underlined. We discuss that which equalizer is best out of these and the reasons for this also proposed.Keywords
Adaptive Equalizers, Fuzzy Equalizers, ISI, Neural Equalizers, Nonlinear Equalizers, SDR.- Efficientnet for Human Fer Using Transfer Learning
Abstract Views :78 |
PDF Views:2
Authors
Affiliations
1 Department of Electronic Science, Kurukshetra University, IN
2 CSIR-Central Electronics Engineering Research Institute, Pilani, IN
1 Department of Electronic Science, Kurukshetra University, IN
2 CSIR-Central Electronics Engineering Research Institute, Pilani, IN
Source
ICTACT Journal on Soft Computing, Vol 13, No 1 (2023), Pagination: 2792-2797Abstract
Automatic facial expression recognition (FER) remained a challenging problem in computer vision. Recognition of human facial expression is difficult for machine learning techniques since there is a variation in emotional expression from person to person. With the advancement in deep learning and the easy availability of digital data, this process has become more accessible. We proposed an efficient facial expression recognition model based EfficientNet as backbone architecture and trained the proposed model using the transfer learning technique. In this work, we have trained the network on publicly available emotion datasets (RAF-DB, FER-2013, CK+). We also used two ways to compare our trained model: inner and cross-data comparisons. In an internal comparison, the model achieved an accuracy of 81.68 % on DFEW and 71.02 % on FER-2013. In a cross-data comparison, the model trained on RAF-DB and tested on CK+ achieved 78.59%, while the model trained on RAF-DB and tested on FER-2013 achieved 56.10% accuracy. Finally, we generated an t-SEN distribution of our model on both datasets to demonstrate the model's inter-class discriminatory power.Keywords
FER, Deep Convolution Neural Network, EfficientNet, Transfer LearningReferences
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