Smartlife: A Point of Intelligence for Wireless Sensor Networks in Ubiquitous Environment
Bibliographic record
Abstract
As home becomes more technologically advanced nowadays people not only need to protect their homes and families from theft or fire, but from carbon monoxide, excessive heat or low temperatures, flooding as well as monitoring their loved ones while they are away. In this research Project, we present the design case for an intelligent embedded system (Hardware and Software) called "SmartLife". We build a working prototype for SmartLife which is made up of tiny sensors, mobile devices, appliances and personal computers from diverse sources. Considering diversity in smart home environment the architecture must be open and flexible to embrace a variety of entities without any special favor towards particular participants or target domains. SmartLife will be the point of intelligence in the smart home environment (complete pervasive environment) which not only communicates with wireless sensors network (monitor & control) but also provides a secure state of mind to elderly homeowners. The work introduces the basics of uClinux kernel as well as the differences between uClinux and the general purpose operating system Linux. It also introduces FPGA based softcore CPU NIOS-II as an embedded platform for our research project. Our uClinux based architecture provides an integrated and comprehensive framework for building pervasive applications. We describe the design and implementation of our architecture as well as building SmartLife application within it.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".