Solar‐powered ZigBee‐based wireless motion surveillance: a prototype development and experimental results
Bibliographic record
Abstract
Abstract This paper describes the design and implementation of a solar‐powered wireless motion sensor surveillance network. Commercially available systems with similar functionality which exist today have several disadvantages including single points of failure and requires (semi) constant personnel attention as well as an elaborate power system. These systems require a lot of time to set up, they cannot be used in remote areas where a main power supply is unavailable, and are quite costly. Therefore, there is a need to develop a system which is portable, easy to set up, and is energy efficient. The wireless motion surveillance network described in this paper is designed to be portable, economically inexpensive, and energy efficient. The network is created using the IEEE 802.15.4 ZigBee wireless standard by implementing multiple Microchip PICDEM Z nodes. Each node in the network is equipped with a Direction Sensing Infrared Motion Detector (DSIMD) and a solar power unit (SPU). The DSIMD allows for detection of humans and animals alike moving into or out of the network. The system is powered by solar energy that makes it quite adaptable for remote applications. The network is able to cover an area of radius 30 m. By developing a low‐cost system, which is portable, easy to set up, and has an unlimited power supply, this technology is made accessible to a wider range of applications. The implementation of a CMOS camera is discussed at the end which can be used to take a snapshot of the detected object. Copyright © 2007 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".