Efficient data encoder for endoscopic\nimaging applications
Bibliographic record
Abstract
The invention of medical imaging technology revolved the process of diagnosing diseases and opened a new world for better studying inside of the human body.In order to capture images from different human organs, different devices have been developed.Gastro-Endoscopy is an example of a medical imaging device which captures images from human gastrointestinal.With the advancement of technology, the issues regarding such devices started to get rectified.For example, with the invention of swallow-able pill photographer which is called Wireless Capsule Endoscopy (WCE); pain, time, and bleeding risk for patients are radically decreased.The development of such technologies and devices has been increased and the demands for instruments providing better performance are grown along the time.In case of WCE, the special feature requirements such as a small size (as small as an ordinary pill) and wireless transmission of the captured images dictate restrictions in power consumption and area usage.In this research, the reduction of image encoder hardware cost for endoscopic imaging application has been focused.Several encoding algorithms have been studied and the comparative results are discussed.An efficient data encoder based on Lempel-Ziv-Welch (LZW) algorithm is presented.The encoder is a library-based one where the size of library can be modified by the user, and hence, the output data rate can be controlled according to the bandwidth requirement.The simulation is carried out with several endoscopic images and the results show that a minimum compression ratio of 92.5 % can be achieved with a minimum reconstruction quality of 30 dB.The hardware architecture and implementation This thesis is dedicated to my father, who taught me that the best kind of knowledge to have is that which is learned for its own sake.It is also dedicated to my mother, who taught me that even the largest task can be accomplished if it is done one step at a time.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.000 | 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".