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Toughness and hardness are critical properties of aggregate materials used in concrete, particularly on pavement surfaces and industrial flooring subjected to heavy loads. Toughness is defined as the aggregate's resistance to failure by impact and is measured by the aggregate impact value (AIV). For this, the aggregate impact value test is performed, wherein the impact is delivered by a standard hammer, which falls freely under its own weight onto the aggregates. The aggregates fragment in...
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Related Experiment Video

Updated: Feb 14, 2026

Author Spotlight: Separation of Coral Host Tissues and Algal Symbionts and Analyzing Their Metabolites
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A Novel Hard Decision Based Simultaneous Target Tracking and Classification Approach.

Wen Cao1, Meng Hui2, Qisheng Wu3

  • 1School of Electronics and Control Engineering, Chang'an University, Xi'an 710064, China. caowen@chd.edu.cn.

Sensors (Basel, Switzerland)
|February 22, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a Hard decision-based Simultaneous Tracking and Classification (HSTC) method for joint tracking and classification problems. HSTC provides a necessary hard decision for class labels, improving upon existing methods by considering decision error, timeliness, and estimation accuracy.

Keywords:
hard decisionjoint target tracking and classificationsequential probability ratio testsimultaneous tracking and classification

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Signal Processing

Background:

  • Simultaneous Tracking and Classification (STC) methods offer modular solutions for joint tracking and classification (JTC).
  • Existing STC methods provide only soft decisions (class probabilities), which are insufficient for applications requiring explicit class labels.

Purpose of the Study:

  • To propose a novel Hard decision-based STC (HSTC) method that addresses the need for hard class label decisions in JTC.
  • To develop a method that integrates decision error rate, timeliness, and estimation error into the tracking and classification process.

Main Methods:

  • The proposed HSTC method utilizes the sequential probability ratio test (SPRT) for decision-making due to its favorable properties and adaptability.
  • Flexible tracking schemes are introduced, leveraging two-way information exchange between the tracker and classifier.
  • HSTC categorizes tracking results based on the timing of the hard decision.

Main Results:

  • The HSTC method effectively integrates the strengths of SPRT and STC.
  • Evaluations on two JTC examples demonstrate the HSTC method's capability to meet practical demands for hard decisions.
  • The proposed method exhibits superior performance in both decision accuracy and estimation quality compared to existing approaches.

Conclusions:

  • The HSTC method provides a viable solution for JTC problems requiring hard class label decisions.
  • The integration of SPRT and STC, along with flexible tracking schemes, enhances overall performance.
  • HSTC offers a significant advancement for applications needing timely and accurate joint tracking and classification.