Solomon A. Tesfamicael

By Solomon A. Tesfamicael and Faraz Barzideh

This chapter provides the use of Bayesian inference in compressive sensing (CS), a method in signal processing. Among the recovery methods used in CS literature, the convex relaxation methods are reformulated again using the Bayesian framework and this method is applied in different CS applications such as magnetic resonance imaging (MRI), remote sensing, and wireless communication systems, specifically on multiple-input multiple-output (MIMO) systems. The robustness of Bayesian method in incorporating prior information like sparse and structure among the sparse entries is shown in this chapter.

Part of the book: Bayesian Inference

By Solomon A. Tesfamicael and Danyal Farsani

This chapter presents an example of creating a culturally responsive mathematics education in Ethiopia using a locally available game called Gebeta. The study is framed using two theoretical frameworks: funds of knowledge and cultural commognition. To this end, an ethnographic study has been employed, which helps to describe, analyze, and investigate a particular group, culture, or community. The findings show that the game is well situated as a developed body of knowledge and skills in the culture. It has remarkable potential to foster mathematical thinking and communication at different levels of the school context. Specifically, in terms of mathematical thinking, the game has affordances to foster early numerical and algorithmic thinking. Educators and stakeholders involved in designing tasks and activities for the curriculum and syllabus should consider incorporating the Gebeta game and other culturally available activities to embed them as part of formal school mathematics in a meaningful way.

Part of the book: STEM Education