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Some compounds produce hydroxide ions when dissolved by chemically reacting with water molecules. In all cases, these compounds react only partially and so are classified as weak bases. These types of compounds are also abundant in nature and important commodities in various technologies. For example, global production of the weak base ammonia is typically well over 100 metric tons annually, being widely used as an agricultural fertilizer, a raw material for chemical synthesis of other...

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Related Experiment Video

Updated: Feb 13, 2026

Retrieval of Mouse Oocytes

Retrieval of Mouse Oocytes

Published on: April 28, 2007

Lightweight Active Object Retrieval with Weak Classifiers.

László Czúni¹, Metwally Rashad^2,3

¹Department of Electrical Engineering and Information Systems, University of Pannonia, Veszprém 8200, Hungary. czuni@almos.uni-pannon.hu.

Sensors (Basel, Switzerland)

|March 10, 2018

Summary

This summary is machine-generated.

This study introduces lightweight methods for autonomous robots to retrieve objects using multi-modal sensors. The approach enhances retrieval accuracy in noisy environments with limited resources.

Keywords:

Hough transformation active vision object retrieval sensor fusion

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Growing interest in autonomous vehicles and robotic systems.
Need for resource-efficient interaction capabilities in robotic agents.
Challenges in object retrieval for robots with limited memory and processing power.

Purpose of the Study:

To present a lightweight approach for object retrieval in autonomous systems.
To utilize multi-modal sensory information for enhanced object recognition.
To improve retrieval efficiency through active vision strategies.

Main Methods:

Employing lightweight sensory and processing techniques for object retrieval.
Fusing optical and orientation data using the Hough framework.
Implementing a spatio-temporal perception technique with active vision for view selection.

Main Results:

Successful application of lightweight methods to object retrieval tasks.
Demonstrated effectiveness of Hough framework for sensor fusion.
Improved retrieval hit-rate through active vision guidance.
Validation on datasets with significant noise.

Conclusions:

Lightweight sensory and processing techniques are viable for resource-constrained robotic systems.
Multi-modal sensor fusion and active vision enhance object retrieval performance.
The proposed approach is robust in noisy environments, suitable for real-world applications.