Mr. Rifa Asyari | Signal Processing Awards | Best Researcher Award

Mr. Rifa Asyari | Signal Processing Awards | Best Researcher Award

Mr. Rifa Asyari, University of Southern Denmark, Denmark.

Rifa Atul Izza Asyari is a highly skilled RF Engineer with over four years of hands-on experience in designing, analyzing, and optimizing advanced RF systems such as radar, RF front-end modules, metasurfaces, and antennas. He is currently pursuing a Ph.D. in Biomedical Engineering at the University of Southern Denmark, with his research focused on radar technology for vital sign monitoring, and is set to graduate in December 2024. He holds an M.Sc. in Telecommunication Engineering from National Sun Yat-Sen University, Taiwan, where he developed high-gain array antennas with frequency-selective surfaces, and a B.Sc. in Electrical Engineering from Universitas Islam Indonesia, Indonesia, with a thesis on optical network design for 4G LTE systems.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award

Rifa Atul Izza Asyari demonstrates an exemplary profile as a highly skilled RF Engineer and researcher with significant contributions in academia and industry. Here’s why Rifa is an excellent candidate for the Best Researcher Award

πŸŽ“ Education:

  • PhD in Biomedical Engineering 🏫
    University of Southern Denmark (Odense, Denmark)
    πŸ“… Expected Completion: Dec 2024
    πŸ“‘ Thesis: Radar for Vital Sign Monitoring
  • MSc in Telecommunication Engineering πŸ“‘
    National Sun Yat-Sen University (Kaohsiung, Taiwan)
    πŸ“… June 2019
    πŸ“‘ Thesis: High Gain Array Antenna with Frequency Selective Surface for Vital Sign Monitoring
  • BSc in Electrical Engineering ⚑
    Universitas Islam Indonesia (Yogyakarta, Indonesia)
    πŸ“… Feb 2016
    πŸ“‘ Thesis: Optical Network Design for 4G Long Term Evolution Sleman

πŸ’Ό Work Experience:

  • Senior Hardware R&D Engineer βš™οΈ at Pegatron (Batam, Indonesia)
    πŸ“… Sept 2019 – Jan 2022

    • Designed and validated RF front-end WiFi modules and IP cameras.
    • Improved broadband performance by 40% and optimized key metrics like EVM and BER.
    • Conducted DVT, OTA measurements, and troubleshooting for WiFi and 5G communication standards.
  • Fibre Optic Engineer 🌐 at Biznet Networks (Yogyakarta, Indonesia)
    πŸ“… May 2016 – Jun 2017

    • Planned and designed fiber-optic network architectures.
    • Conducted fusion splicing and troubleshooting using OTDR.
  • Heavy Dump Truck Operator πŸš› at Pamapersada Nusantara (Tabalong, Indonesia)
    πŸ“… Jan 2010 – Sept 2012

    • Operated Komatsu trucks for open-cast mining operations.

πŸ† Achievements:

  • πŸ… Best Student Paper Award at Taiwan Telecommunication Annual Symposium (2020)
  • 🌟 Top 50 Online Global Startup Weekend Unite to Fight COVID-19 (2020)
  • πŸ“œ IMPTE Scholarship Award at National Sun Yat-Sen University (2017)

πŸ”§ Technical Skills:

  • Programming: πŸ–₯️ MATLAB, Python, C/C++, R
  • RF Tools: πŸ“‘ CST, Ansys, ADS, Spectrum Analyzer
  • Networking: 🌐 LAN/WAN, TCP/IP, VPN
  • Soft Skills: πŸ—£οΈ Leadership, Problem-solving, Presentation

Publication Top Notes:

High Gain Array Antenna With FSS for Vital Sign Monitoring Through the Wall

Mrs. Ainhoa Osa Sanchez | Signal processing | Best Researcher Award

Mrs. Ainhoa Osa Sanchez | Signal processing | Best Researcher AwardΒ 

Mrs. Ainhoa Osa Sanchez, EVIDA Research Group, University of Deusto, Spain

Ainhoa Osa SΓ‘nchez is a dedicated researcher specializing in sensors for biological applications. Born on April 11, 1999, she completed her degree in Industrial Electronics and Automation Engineering from the University of Deusto in 2021 and earned a Master’s degree in Industry 4.0 from the International University of La Rioja in 2022. Since 2022, she has been an active member of the eVIDA group, where she initially joined as a researcher in 2020.During her academic and professional journey, Ainhoa has collaborated in various capacities, including internships and research positions, focusing on advanced technological solutions. Her master’s thesis revolved around telemonitoring vital signs at home for the elderly, utilizing IoT and 3D design with a serverless architecture for data storage and visualization via Amazon Web Services.Currently, Ainhoa is pursuing her doctorate at the University of Deusto. Her research is centered on using portable EEG and NIR sensor signals for pain detection case studies, incorporating artificial intelligence models. This work has significant implications for elderly care, chronic pain, and fibromyalgia. She is also engaged in a collaborative project with the University of Louisville and Alamein International University to develop a neural network aimed at identifying the degree of macular degeneration through image analysis.

Professional Profile:

GOOGLE SCHOLAR

Education

Degree: Master’s Degree in Industry 4.0
University / Country: International University of La Rioja
Year: 2022

Degree: Degree in Industrial Electronics and Automatic Engineering
University / Country: University of Deusto
Year: 2021

Skills and Abilities:

  • Advanced understanding of computing, IoT, and artificial intelligence
  • Ability to use data structures to improve programming results
  • Excellent knowledge of several programming languages, including Java, C, and Python
  • Very good knowledge of big data and cybersecurity
  • Experience using and analyzing data from wearable biomedical devices such as EEG, fNIRS, and EMG

Β Relevant Accomplishments

C.1. Most Important Publications in National or International Peer-Reviewed Journals, Books, and Conferences

Scientific Papers:

  1. Ainhoa Osa-Sanchez, Oscar Jossa-Bastidas, Amaia Mendez-Zorrilla, Ibon Oleagordia-Ruiz, Begonya Garcia-Zapirain. 2023. “Design of intelligent monitoring of loneliness in the elderly using a serverless architecture with real-time communication API.” Technology and Health Care, IOS Press. 31-6, pp. 2401-2409.
  2. Jossa-Bastidas, Oscar, Osa Sanchez, Ainhoa, Bravo-Lamas, Leire, Garcia-Zapirain, Begonya. 2023. “IoT System for Gluten Prediction in Flour Samples Using NIRS Technology, Deep and Machine Learning Techniques.” Electronics, 12-8. ISSN 2079-9292.

Publication top Notes:

IoT system for gluten prediction in flour samples using nirs technology, Deep and Machine Learning Techniques

CITED : 1

Design and implementation of food quality system using a Serverless Architecture: case study of gluten intolerance

CITED : 1

Gluten Analysis Composition Using Nir Spectroscopy and Artificial Intelligence Techniques

CITED : 1

Design of intelligent monitoring of loneliness in the elderly using a serverless architecture with real-time communication API