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Praveen Kumar, M.
- Shriek-Public Address System
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Authors
Affiliations
1 Department of Electronics and Communication Engineering, Tamilnadu College of Engineering, Coimbatore - 641659., IN
1 Department of Electronics and Communication Engineering, Tamilnadu College of Engineering, Coimbatore - 641659., IN
Source
Networking and Communication Engineering, Vol 11, No 3 (2019), Pagination: 44-46Abstract
A system that transmits the information in public places like railway stations through a wireless medium using high frequency signals. A proposed system evolves an information signal is broadcast by the transmitter device with the help of high frequency signals and antennas at certain frequency. The signal covers a region that depends upon the station’s area availability and it can be varied according to the need of the range of railway junctions.
Keywords
Wireless, High Frequency Signal, Frequency, Transmitter, Range.References
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- N.A.molapo, GP Hancke B.J Silva,”Design of a WSN Public Address system”,2016
- Muhamed Omer Farooq and Thomas Kunz, ”Operating systems for wireless sensor networks: A survey”, Department of systems and Computer Engineering, Carleton University Ottawa,Canada,May 2011
- Wu Shijin, Fan Xiang, Design of FM wireless transmitter circuit based on BH1417.
- An Improved Segmentation Method for Brain Cancer Using Capsule Neural Networks
Abstract Views :184 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Chettinad College of Engineering and Technology, IN
2 Faculty of Engineering and Technology, Botho University, BW
3 Department of Control and Automation, Vellore Institute of Technology, Vellore, IN
4 Department of Electronics and Communication Engineering, CMR Institute of Technology, IN
1 Department of Electronics and Communication Engineering, Chettinad College of Engineering and Technology, IN
2 Faculty of Engineering and Technology, Botho University, BW
3 Department of Control and Automation, Vellore Institute of Technology, Vellore, IN
4 Department of Electronics and Communication Engineering, CMR Institute of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 4 (2023), Pagination: 2987-2994Abstract
Brain cancer is a life-threatening disease that requires accurate and efficient segmentation methods for effective diagnosis and treatment planning. In this study, we propose an improved segmentation method for brain cancer using Capsule Neural Networks (CapsNets). CapsNets are a promising alternative to traditional convolutional neural networks (CNNs) as they capture spatial relationships between features more effectively. However, existing CapsNet-based segmentation methods suffer from limitations such as low segmentation accuracy and high computational complexity. To address these limitations, we introduce an improved CapsNet architecture that incorporates dynamic routing and attention mechanisms. The dynamic routing algorithm enhances the routing process between capsules, allowing for better feature representation and improved segmentation accuracy. Additionally, the attention mechanism focuses the network’s attention on important regions, reducing the computational complexity without sacrificing segmentation quality. We evaluate the proposed method on a publicly available brain cancer dataset and compare its performance against state-of-the-art segmentation approaches. The experimental results demonstrate that our method achieves superior segmentation accuracy and outperforms existing methods in terms of Dice coefficient and Hausdorff distance. Furthermore, our method demonstrates faster convergence and reduced computational complexity compared to previous CapsNet-based approaches. In conclusion, this study presents an improved segmentation method for brain cancer using Capsule Neural Networks. The proposed method addresses the limitations of existing CapsNet-based approaches by incorporating dynamic routing and attention mechanisms. The experimental results validate the effectiveness of our method, showcasing superior segmentation accuracy and reduced computational complexity. The improved segmentation method has the potential to enhance the diagnosis and treatment planning of brain cancer, ultimately contributing to improved patient outcomes.Keywords
Brain, Segmentation, Capsule Network, Capsules.References
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