Development of Ultrasonic Radar System for Object Detection Using PIC 16F877A
Bibliographic record
Abstract
Ultrasonic radar represents a promising technology, offering reduced cost while combining efficiency and versatility. This project details the design and implementation of a radar system based on the PIC 16F877A microcontroller, capable of measuring both the distance and direction of objects in their environment. To achieve this, an ultrasonic sensor is used in combination with a stepper motor, enabling the system to scan a full 360° area. The collected data is then displayed in real-time on an LCD screen, providing clear and precise visualization of the information. The system also integrates LEDs that serve as visual indicators to signal the proximity of detected objects. When objects are at critical distances, a buzzer is activated to alert the user, adding an auditory dimension to the detection. This feature is particularly useful for applications requiring rapid response, such as surveillance, automotive, or navigation. Comprehensive tests were conducted to evaluate the system's performance. These tests included both stationary and moving targets, allowing for the verification of reliability and measurement accuracy under various conditions. The results showed that the distances measured by the system had minimal deviations from the actual distances, confirming the high precision of the device.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".