An Architecture for Enhancing Capability and Energy Efficiency of Wireless Handheld Devices
Bibliographic record
Abstract
With the tremendous growth in the hardware miniaturization technology, there are many types of digital electronic gadgets being used in daily life for different purposes. These devices are built to work alone and they typically cannot access or share each other’s hardware or software resources. But the fact is that such devices are constrained in battery-energy and resources. In the presence of a generic resource sharing infrastructure, these are able to share and access each other’s hardware, software resources, and data. Thus these devices become more efficient in terms of energy expense, and more enhanced in functionality, and usability. In this paper, we propose the concept of Universal Computing and Communication Interface (UCCI) that facilitates such sharing of resources between two wireless portable devices. This model comprises two basic components: Device and Connection Management (DCM) protocol and Framework for Information Exchange (FIX). DCM devises a unique way to save energy by allowing a server to stay in sleep state while its service is not needed. On the other hand, FIX enables software applications on a small device to use resources such as CPU, Internet bandwidth and storage available on a larger computer. We have used state-of-the-art smartphones HTC Nexus One, BlackBerry 9700 and a laptop to develop prototypes of the proposed idea. We have conducted extensive experiments on the devices and measured the real-time energy consumptions. This papers explains situations under which such resource sharing can lead to energy saving. We also assessed the latency in accomplishing a task performed through sharing of resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".