A5036582948- Microwave Engineering and Waveguides
- Antenna Design and Analysis
- Advanced Antenna and Metasurface Technologies
- Electromagnetic Compatibility and Noise Suppression
- Bluetooth and Wireless Communication Technologies
- Satellite Communication Systems
- Smart Agriculture and AI
- Non-Invasive Vital Sign Monitoring
- IoT and Edge/Fog Computing
- Water Quality Monitoring Technologies
- Handwritten Text Recognition Techniques
- IoT-based Smart Home Systems
- Antenna Design and Optimization
- Speech Recognition and Synthesis
- Natural Language Processing Techniques
- Radio Frequency Integrated Circuit Design
- Energy Harvesting in Wireless Networks
- Optical Wireless Communication Technologies
- Context-Aware Activity Recognition Systems
- Food Supply Chain Traceability
Netaji Subhas University of Technology
2024
Guru Gobind Singh Indraprastha University
2022
Maharaja Engineering College
2021
IFTM University
2018
A novel slotted fractal microstrip antenna having high directivity is proposed in this paper. The miniaturized size and exhibits a very broad bandwidth (equal to 1.5 GHz) large value of gain (having mean equal 4.9 dB) with respect the isotropic despite its small size. multi-band has also been analyzed terms return loss graph radiation pattern graphs. simulated on HFSS 17.0 help parametric variations methodology. much significant useful for C-band, X-band Ku-band applications.
In this paper a frequency reconfigurable antenna is proposed. The uses electrical switching by means of PIN diodes to achieve properties. are located such that increase the total effective surface area and simultaneously current. Three slots cut from alter current improve resonant antenna. location based on optimization in response. Diode 1 placed excite another patch when it will be ON state whereas diode 2 used make an interconnect. Antenna resonates at 0.915 GHz, 1.575 GHz 2.4GHz...
Good feature representation is the chief requirement for improving Language Identification (LID) system recognition performance. In this work LID Indian languages proposed based on unsupervised learning utilizing Deep Belief Network (DBN). The methodology implemented in two parts. first phase of extracting MFCC features combined with SDC hybrid features. resultant are further stacked to second investigating performance various Feed forward back propagation neural network models...