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Precipitation Titration: Endpoint Detection Methods

Precipitation Titration: Endpoint Detection Methods

In argentometric precipitation titrations, endpoints can be detected visually by the Mohr, Volhard, and Fajans methods. In the Mohr method, adding a soluble chromate indicator gives an initial yellow color to the analyte solution. As the titrant is added, the first excess of silver ions forms a red silver chromate precipitate, marking the endpoint. The solution pH should be maintained at about 8 by adding solid CaCO3.
In the Volhard method, a standard excess of AgNO3 is first added to the...

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Related Experiment Video

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

A Novel Algorithm for Detecting Pedestrians on Rainy Image.

Yuhang Liu¹, Jianxiao Ma¹, Yuchen Wang¹

¹College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China.

Sensors (Basel, Switzerland)

|December 30, 2020

Summary

This summary is machine-generated.

This study introduces a new pedestrian detection algorithm that effectively removes rain streaks, significantly improving accuracy in various rainy conditions. The enhanced method boosts average precision by up to 60.9% in heavy rain scenarios.

Keywords:

de-raining processing deep learning intelligent traffic system pedestrian detection

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Area of Science:

Computer Vision
Artificial Intelligence
Transportation Engineering

Background:

Pedestrian detection is crucial for cooperative vehicle infrastructure systems, but traditional methods fail in rainy conditions.
Reduced accuracy in adverse weather, like rain, compromises traffic data reliability and safety.

Purpose of the Study:

To develop an advanced pedestrian detection algorithm that overcomes limitations in rainy scenarios.
To enhance the robustness and accuracy of pedestrian detection systems under various rainfall densities.

Main Methods:

A novel de-raining module was integrated to remove rain streaks based on estimated rain density.
Pedestrians are detected using a keypoint-based approach to handle occlusion effectively.
A new dataset with rain density labels was created for algorithm training and validation.

Main Results:

The algorithm demonstrated significant improvements in average precision (AP) across different rain densities: 21.1% (light), 48.1% (medium), and 60.9% (heavy).
Experiments on synthetic and real-world datasets confirmed superior performance compared to existing state-of-the-art methods.
The de-raining module effectively mitigated the impact of rain on detection accuracy.

Conclusions:

The proposed algorithm substantially enhances pedestrian detection accuracy in rainy weather.
This advancement is vital for improving the reliability of cooperative vehicle infrastructure systems in all weather conditions.
The keypoint-based detection and de-raining module offer a robust solution for challenging environmental conditions.