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The Monthly Newsletter of IEEE Vehicular Technology Society—December 2024

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Automotive Electronics
CE Models for Automotive Electronics
Binh-Minh Nguyen and João P. Trovão

Motivated by these basic benefits, the scale and concept of CE can be extended beyond the boundaries of a company, school, country, or continent to tackle the new challenges in automotive electronics. To demonstrate this idea, this article examines three CE models, namely, PPBL, hierarchically decentralized CE (HDCE), and university social CE (USCE).

Since the last decade, PPBL has been implemented by many universities worldwide. For instance, the University of Sherbrooke developed a PPBL (in French: Apprentissage par problemes et par projets en ingénierie) [4]. This PPBL model is based on two pillars: “knowledge via competencies” and “project-based learning (PBL) units.”

Knowledge via Competencies

A competency is not merely the ability to execute a given procedure; rather, it requires the ability to adapt to various situations to solve a category of problems to realize nontrivial tasks. In the PPBL framework, competencies can be classified in four categories: engineering and scientific, design, interpersonal, and intrapersonal competencies. Knowledge acquisition becomes a means for reaching the objective of developing competencies. Three types of knowledge can be acquired:

  • Declarative knowledge: the ability to know factual information, such as a definition, a hypothesis, or an algorithm
  • Procedural knowledge: knowing how to use factual information, for instance, the ability to execute repetitive tasks
  • Conditional knowledge: knowing when and where to use factual information, for instance, the ability to select appropriate resolution methods.

Full Article: IEEE Vehicular Technology Magazine, Volume 19, Number 3, September 2024

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In This Issue
Message from the EiC
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Mobile Radio
Generative AI on Snapdragon
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Automotive Electronics
CE Models for Automotive Electronics Education
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Transportation Systems
New Light Rail East of the Puget Sound
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From the IEEE Open Journal of Vehicular Technology
Recent Articles in the IEEE OJVT
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From the IEEE Transactions on Vehicular Technology
Vehicular Edge Computing Through Deep Reinforcement Learning
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F. Richard Yu

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