Reinforcement Learning Based Sensor Optimization for Bio-markers
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866911096538923008 |
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| author | Khandelwal, Sajal Kumar, Pawan Azeemuddin, Syed |
| author_facet | Khandelwal, Sajal Kumar, Pawan Azeemuddin, Syed |
| contents | Radio frequency (RF) biosensors, in particular those based on inter-digitated capacitors (IDCs), are pivotal in areas like biomedical diagnosis, remote sensing, and wireless communication. Despite their advantages of low cost and easy fabrication, their sensitivity can be hindered by design imperfections, environmental factors, and circuit noise. This paper investigates enhancing the sensitivity of IDC-based RF sensors using novel reinforcement learning based Binary Particle Swarm Optimization (RLBPSO), and it is compared to Ant Colony Optimization (ACO), and other state-of-the-art methods. By focusing on optimizing design parameters like electrode design and finger width, the proposed study found notable improvements in sensor sensitivity. The proposed RLBPSO method shows best optimized design for various frequency ranges when compared to current state-of-the-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_10649 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning Based Sensor Optimization for Bio-markers Khandelwal, Sajal Kumar, Pawan Azeemuddin, Syed Machine Learning Neural and Evolutionary Computing Signal Processing Radio frequency (RF) biosensors, in particular those based on inter-digitated capacitors (IDCs), are pivotal in areas like biomedical diagnosis, remote sensing, and wireless communication. Despite their advantages of low cost and easy fabrication, their sensitivity can be hindered by design imperfections, environmental factors, and circuit noise. This paper investigates enhancing the sensitivity of IDC-based RF sensors using novel reinforcement learning based Binary Particle Swarm Optimization (RLBPSO), and it is compared to Ant Colony Optimization (ACO), and other state-of-the-art methods. By focusing on optimizing design parameters like electrode design and finger width, the proposed study found notable improvements in sensor sensitivity. The proposed RLBPSO method shows best optimized design for various frequency ranges when compared to current state-of-the-art methods. |
| title | Reinforcement Learning Based Sensor Optimization for Bio-markers |
| topic | Machine Learning Neural and Evolutionary Computing Signal Processing |
| url | https://arxiv.org/abs/2308.10649 |