Author: IDI

Welcome Assoc. Prof. Indrarini Dyah Irawati was invited as a speaker at the ICECC 2021 International conference. She presented a paper entitled The Application of Compressive Sensing. She is very proud to be able to share his current research experiences. Research on Compressive Sensing is currently Telkom University’s flagship research supported by researchers from the School of Applied Science and School of Electrical Engineering, in collaboration with the Telkom Purwokerto Institute of Technology and the Bandung Institute of Technology. Invited Speakers Assoc. Prof. Indrarini D. IrawatiTelkom University, Indonesia She is currently a member of the Association for Computing Machinery (ACM) and the International Association of Engineers (IAENG). She received Certificate of Merit from 2018 IAENG International Conference on Internet Computing and Web Services and the 2020 Best Presenter Award from International Conference on Electronics, Computer, and Communication Engineering (ICECC) Speech Title: The Application of Compressive Sensing Abstract: Compressive sensing / sampling (CS) is a new paradigm in the field of signal processing which has been widely applied in various applications. This theorem takes advantage of the sparse signal in the transformation region to reduce the sample size below the Shannon-Nyquist sampling rate. The main idea is that the number of information signals shows some structure or redundancy so that they can be used for signal acquisition and reconstruction simultaneously. The compressive sensing process aims to reduce the number of samples so that the data size becomes smaller, while the reconstruction process aims to restore the original data. Applying the right acquisition system will produce reconstruction results with good accuracy. Compressive sensing is widely used in several applications because it can improve system performance. In video processing and compression applications, it can significantly reduce the sampling rate. In monitoring internet network traffic, it can be used to detect traffic anomalies and reconstruct missing traffic. In a sensor network, it can co-reconstruct the intra-sensor and inter-sensor signals that come from separate measurements. Compressive sensing proves that a signal can be reconstructed spatially from information previously thought incomplete. This presentation discussed the use of CS in several applications, including monitoring internet traffic networks, telemedicine and audio watermarking.