A Boost Converter for Energy Harvesting Utilizing MEMS Switch
Bibliographic record
Abstract
Internet of Things (IoT) has experienced a significant growth in recent years. Billions of battery-powered wireless sensors are expected to be employed by 2020 as the (IoT) becomes an integral part of our daily lives. It is clear that battery replacement for such a significant number of sensors will soon become a formidable challenge. Ambient energy resources can be utilized to either prolong the lifetime of batteries or replace them with other elements such as supercapacitors. Light as an abundant source of energy in both indoor and outdoor environments is a natural choice for energy harvesting to power up wireless sensors. In this work, a new energy harvester using MEMS switches is presented in which a photovoltaic cell is used to extract the ambient light energy. The use of MEMS switches instead of conventional transistor-based switches reduces the leakage current and improves the overall efficiency. Simulation results indicate that the use of MEMS switches increases the efficiency of the energy harvester by 17%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".