Audio signal processing is used in acoustic IoT sensor nodes which have limitations in data storage, computation speed, hardware size and power. In most audio signal processing systems, the recovered data constitutes far less fraction of the sampled data providing scope for compressive sensing (CS) as an efficient way for sampling and signal recovery. Compressive sensing is a signal processing technique in which a sparse approximated signal is reconstructed at the receiving node by a signal recovery algorithm, using fewer samples compared to traditional sampling methods. It has two main stages: sparse approximation to convert the signal into a sparse domain and reconstruction through sparse signal recovery algorithms. Recovery algorithms involve complex matrix multiplication and linear equations in sampling and reconstruction, increasing the computational complexity and leading to highly resourceful hardware implementations. This work reconstructs the sparse audio signal using LASSO and orthogonal matching pursuit (OMP) algorithm. OMP is an iterative greedy algorithm involving least square method that takes a compressed signal as input and recovers it from the sparse approximation, while LASSO is L1 norm based with a controlled L2 penalty. The paper reviews the reconstruction and study of sparsity and error obtained for reconstructing an audio signal by OMP and LASSO.
Microprocessors and microcontrollers is a course that demands concurrent delivery of pragmatic and dogmatic approaches to the students in order to ensure effective learning. Online sessions and active learning for such courses pose challenges to both teachers and students, especially during this pandemic period. In order to combat these challenges, and to kindle the interest among the students in learning the course, a new methodology which supports blended learning through virtual experience is presented in this paper. The proposed methodology was implemented for the third year engineering students of circuit branches. This paper highlights the effectiveness of using open source emulating environments like Edsim51 and EMU8086 for providing a virtual laboratory experience to the students. These virtual environment provides a complete visualization of the internal functionality of the microprocessor/microcontroller architecture, and also enhances practical exposure of the students. In order to further augment the students' affinity towards the course, the teacher adopts different pedagogical approaches, which is best suited for ICT based blended learning in the online teaching environment. The assessment of this virtual experience is carried out using different online assessment components such as quizzes, assignments as concept maps/videos shared by the students, mini projects etc. Based on the assessment results, an analysis is carried out and it shows significant improvement in student engagement, in depth understanding of the course and improvement in programming skills of the students. Thus, this experience not only acts as a countermeasure to the lack of real time laboratory sessions but also promotes 'understanding by doing', which seems almost impossible during this pandemic situation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.