Jānis Judvaitis has been working at the Institute of Electronics and Computer Science (EDI) since 2013. He is currently a leading researcher and head of the Cyber-Physical Systems Laboratory. Jānis graduated from the Faculty of Computer Science of the University of Latvia, which resulted in the defense of his dissertation “Enabling a Wireless Sensor Network Test Environment for High TRL Research” in 2023. Research interests includes Cyber-Physical Systems, Wireless Sensor Networks, Internet of Things, edge computing and embedded systems. Jānis is involved in various international projects related to wireless sensor networks, embedded systems, the Internet of Things, and peripheral artificial intelligence. Competence in the design, development, implementation and testing of information and communication technology systems. A total of 14 successful international projects. Author of more than 20 scientific articles. He has also lectured at the University of Latvia.
Recent projects
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One Step Open DBL solution (openDBL) #Horizon Europe
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Smart non-contact phenotyping of raspberries and quinces using machine learning methods, hyperspectral and 3D images (AKFen) #Horizon Europe
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Edge AI Technologies for Optimised Performance Embedded Processing (EdgeAI) #ChipsJU
- Investigation of possible LTE-M and NB-IoT communication system operation and operating modes based on modules (SoM) in the case of using different energy sources #ChipsJU
- LPWAN technology review study and research of electronic components available on the market #ChipsJU
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Reliable Powerdown for Industrial Drives (R-PODID) #ChipsJU
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Smart Materials, Photonics, Technologies, and Engineering Ecosystem (MOTE) #VPP
- Improve the efficiency, reliability and adaptability of computing equipment to various operating conditions (LMT BOX) #Contract research (Līgumpētījumi)
Recent publications
- Arents, Janis, Valters Abolins, Janis Judvaitis, Oskars Vismanis, Aly Oraby, and Kaspars Ozols. 2021. "Human–Robot Collaboration Trends and Safety Aspects: A Systematic Review" Journal of Sensor and Actuator Networks 10, no. 3: 48. https://doi.org/10.3390/jsan10030048
- Miha Deniša, Aleš Ude, Mihael Simoniˇc, Tero Kaarlela, Tomi Pitkäaho, Sakari Pieskä, Janis Arents, Janis Judvaitis, Kaspars Ozols, Levente Raj, András Czmerk, Morteza Dianatfar, Jyrki Latokartano, Patrick Alexander Schmidt, Anton Mauersberger, Adrian Singer, Halldor Arnarson, Beibei Shu, Dimosthenis Dimosthenopoulos, Panagiotis Karagiannis, Teemu-Pekka Ahonen, Veikko Valjus and Minna Lanz. 2023. "Technology Modules Providing Solutions for Agile Manufacturing" Machines 23, 11 (9): pp. 877, https://doi.org/10.3390/machines11090877
- Ovidiu Vermesan, Kai vorm Walde, Roy Bahr, Cordula Conrady, Jānis Judvaitis, Gatis Gaigals, Tore Karlsen, Marcello Coppola, Hans-Erik Sand (2023). Recenzēta nodaļa "Edge AI LoRa Mesh Technologies" grāmatā "Advancing Edge Artificial Intelligence System Contexts", River Publishers
- Jānis Judvaitis, Krišjānis Nesenbergs (2023). Poster: IoT-Edge-Cloud Continuum data flow validation tool. Proceedings of Embedded Wireless Systems and Networks 2023, Association for Computing Machinery
- Ingrida Lavrinovica, Janis Judvaitis, Dans Laksis, Marija Skromule, Kaspars Ozols "A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques" Applied Sciences, 12(21) https://www.scopus.com/record/display.uri?eid=2-s2.0-85208554778&origin=resultslist
- Rihards Balass, Vladislavs Medvedevs, Andris Ivars Mackus, Juris Ormanis, Armands Ancans, Janis Judvaitis "Precise realtime current consumption measurement in IoT TestBed" 3(7) https://www.scopus.com/record/display.uri?eid=2-s2.0-85197849889&origin=resultslist
- Francisco Parrilla, Sergio Jiménez Gómez, Modris Greitans, Janis Judvaitis. Intelligent Transport System: The Indra Use Case
- Janis Judvaitis, Rihards Balass, Modris Greitans. Mobile IoT-Edge-Cloud Continuum Based and DevOps Enabled Software Framework
- Edīte Kaufmane, Kaspars Sudars, Ivars Namatēvs, Ieva Kalniņa, Jānis Judvaitis, Rihards Balašs, Sarmīte Strautiņa. QuinceSet: Dataset of annotated Japanese quince images for object detection
- S. Strautiņa, I. Kalniņa, E. Kaufmane, K. Sudars, I. Namatēvs, J. Judvaitis, R. Balašs, A. Ņikuļins, "Initial results of the development of intelligent non-invasive phenotyping of raspberries using machine learning and 3D imaging", Acta Horticulturae, :pp. 14
- Kaspars Sudars, Ivars Namatēvs, Jānis Judvaitis, Rihards Balašs, Artūrs Ņikuļins, Astile Peter, Sarmīte Strautiņa, Edīte Kaufmane, Ieva Kalniņa. YOLOv5 Deep Neural Network for Quince and Raspberry Detection on RGB Images
- Janis Judvaitis, Valters Abolins, Artis Mednis, Rihards Balass, Krisjanis Nesenbergs. The Definitive Guide to Actual Sensor Network Deployments in Research Studies from 2013–2017: A Systematic Review
- Janis Judvaitis, Valters Abolins, Amr Elkenawy, Rihards Balass, Leo Selavo, Kaspars Ozols, 2023. "Testbed Facilities for IoT and Wireless Sensor Networks: A Systematic Review" ,J. Sens. Actuator Netw 2023, 12(3), 48. https://www.mdpi.com/2224-2708/12/3/48
- Janis Judvaitis, Valters Abolins, Amr Elkenawy, Kaspars Ozols. Available Wireless Sensor Network and Internet of Things testbed facilities: dataset
- Arzovs, A.; Judvaitis, J.; Nesenbergs, K.; Selavo, L. Distributed Learning in the IoT–Edge–Cloud Continuum. MDPI Machine Learning and Knowledge Extraction 6, no. 1: 283-315.
- Tomass Zutis, Peteris Racinskis, Anzelika Bureka, Janis Judvaitis, Janis Arents, and Modris Greitans. Multi-Step Object Re-Identification on Edge Devices: A Pipeline for Vehicle Re-Identification.
- Dmitrijs Orlovs, Artis Rušiņš, Valters Skrastiņš, Janis Judvaitis. 2025. LPWAN Technologies for IoT: Real-World Deployment Performance and Practical Comparison.
- Judvaitis J, Blumbergs E, Arzovs A, Mackus AI, Balass R, Selavo L. 2024. A Set of Tools and Data Management Framework for the IoT–Edge–Cloud Continuum. https://www.mdpi.com/2571-5577/7/6/130
- Valters Skrastiņš, Vladislavs Medvedevs, Dmitrijs Orlovs, Juris Ormanis, Janis Judvaitis. 2025. Experimental Evaluation of NB-IoT Power Consumption and Energy Source Feasibility for Long-Term IoT Deployments