جست‌وجوی استاد و پژوهشگر

• Joint Communications and Radio Sensing (JCAS)
• Digital/Antenna Array Signal Processing
• 5G+/IoT Physical-Layer Technologies
• +۳ مورد دیگر

Dr. Kai Wu is a Lecturer at the School of Electrical and Data Engineering (SEDE), University of Technology Sydney (UTS). He holds dual PhD degrees from UTS (2020) and Xidian University (2019), with his UTS thesis recognized in the Chancellor’s List 2020. His research focuses on Joint Communications and Radio Sensing (JCAS), digital/antenna array signal processing, and 5G+/IoT technologies. He has published the world-first authored book on JCAS and leads funded research projects in wireless sensing, millimeter-wave communications, and flood sensing systems. Education: PhD in Electrical Engineering, University of Technology Sydney (2020) PhD in Information and Communication Engineering, Xidian University (2019) Research Interests: Dr. Wu’s work spans JCAS, antenna array signal processing, and embedded systems. He emphasizes practical applications of ISAC (Integrated Sensing and Communication) for next-generation wireless systems. His projects include flood sensing via communication signals and secure waveform design for RIS-assisted networks. Funded Research: Network as a sensor for flood sensing (TPG Telecom Limited, 2023–2028) Smart Flood and Storm Intelligence Sensing Initiative (NSW Department of Planning, 2023–2024) Awards: Chancellor’s List 2020 (UTS) Teaching & Service: Teaches Introductory Embedded System and Internet of Things . Serves on the editorial board of ISAC-Focus . His work bridges theory and industry, with experimental validations in radar-communication systems and MIMO arrays. Labs/Teams: Active in the Global Big Data Technologies Centre (GBDTC) at UTS, collaborating on interdisciplinary projects in signal processing and IoT.

• Underwater Acoustic Communications
• Signal Processing
• Communication Systems
• +۳ مورد دیگر

Dr. Jaden Chen is a Research Fellow at Curtin University's School of Electrical Engineering, Computer and Mathematical Sciences (EECMS), within the Faculty of Science and Engineering. His research focuses on underwater acoustic communication systems, particularly in the areas of OFDM technology, channel estimation, and impulsive noise mitigation. He holds a portfolio role in the Office of the Provost, indicating administrative involvement alongside his research activities. Chen's work emphasizes practical implementation, as seen in his LabVIEW-based real-time underwater acoustic OFDM system and NI CompactDAQ hardware integration. His research spans theoretical algorithm development (e.g., compressive sensing-based iterative algorithms) to applied engineering solutions in underwater communication environments. His publications (2015–2020) consistently address challenges in underwater acoustic channels, including noise estimation, channel tracking, and system design. While no awards are listed, his prolific output in IEEE journals and conferences highlights technical expertise in signal processing and communication systems. No advising or grant information is explicitly provided. His contributions likely extend to laboratory environments focused on underwater communication prototyping and real-time system development.

• Wireless Communication
• 6G and Beyond Networks
• Visible Light Communication (LiFi)
• +۴ مورد دیگر

Dr. Dushyantha A Basnayaka is an Assistant Professor at the School of Electronic Engineering, Dublin City University. His research focuses on advanced wireless communication systems, including mediumband technology, visible light communication (LiFi), and MIMO systems. He explores next-generation 6G networks, Doppler effect applications, and interference mitigation strategies. His work addresses challenges in signal propagation, channel engineering, and system optimization in dynamic environments. Key research themes include: Mediumband spectrum utilization for future networks LiFi uplink transmission and infrared-based communication Doppler-assisted interference mitigation techniques MIMO systems and spatial modulation His recent publications emphasize performance analysis of mediumband systems, channel engineering methods, and multi-user communication in challenging interference scenarios. Dr. Basnayaka’s contributions span theoretical frameworks and practical implementations, aiming to enhance spectral efficiency and network reliability. He is actively involved in the Glasnevin Campus, contributing to academic and research activities in electronic engineering.

Alper Erdoğanمشاهده پروفایل

استاد

• Adaptive Algorithm Design and Analysis
• Blind Source Separation: Bounded Component Analysis
• Independent Component Analysis
• +۶ مورد دیگر

Alper Erdoğan serves as a Professor in the Department of Electrical and Electronics Engineering at Koç University's College of Engineering, with office location ENG 221 and contact details +90 212 338 1490 and alperdogan@ku.edu.tr. His academic credentials include a PhD (1999) and MSc (1995) from Stanford University, and a BS (1993) from Middle East Technical University. Research spans highly specialized domains in signal processing and communications: Adaptive Algorithm Design and Analysis Blind Source Separation with Bounded Component Analysis Independent Component Analysis Robust Estimation techniques Physical Layer Communication System Algorithms covering Channel Equalization/Shortening, PAR Reduction, Clock Synchronization, Tx/Rx Filter Design, and Echo Canceller Convex Optimization applications Optical Fiber Communications focusing on Polarization Mode Dispersion Compensation and Emulation Single Molecule Tracking Computational Electromagnetics Scientific Awards: No awards documented in source materials Student advising and grant activities lack explicit documentation; the provided text references a Research Group Site but omits team composition, project details, or funding sources. His research group infrastructure is indicated through institutional links, though operational specifics remain undisclosed in available content.

• Information Theory
• Wireless Communication
• Machine Learning
• +۴ مورد دیگر

Giuseppe Durisi is a Professor at Chalmers University of Technology in Gothenburg, Sweden, specializing in information theory and communication systems. His research bridges mathematically rigorous solutions with practical engineering applications in wireless and optical communication. Primary affiliation: Communication Systems Group , Chalmers University. Research focus: Optimal information transmission, 6G network design, and theoretical foundations of deep learning. Research Interests: Durisi investigates the interplay between latency, reliability, and throughput in digital communication, particularly in millimeter-wave and optical fiber channels . He develops finite-blocklength theory for efficient coding and explores how information theory can explain deep learning performance. Recent Article Trends: His 2025–2024 work emphasizes 6G distributed MIMO networks , energy-harvesting protocols , and machine learning integration into communication theory. Key themes include random access protocols , privacy in wireless aggregation , and hardware-constrained massive MIMO . Scientific Recognition: An IEEE Senior Member, Durisi has published extensively in top journals like IEEE Transactions on Communications and IEEE Transactions on Wireless Communications . Notable Collaborations: Work with teams on radio-over-fiber fronthaul , unsourced multiple access , and time-synchronized URLLC links .

• Industry 4.0
• Additive Manufacturing
• Artificial Intelligence
• +۷ مورد دیگر

HLEISS Rima is a Teaching Researcher at CESI, specializing in Digital Engineering and Tools . Her work bridges Industry 4.0 , Artificial Intelligence , and Telecommunications . MSc in Signal, Image, and Speech (Telecom ParisTech, 2004) Engineering Degree in Computer Networks and Telecommunications (ENPG, 2001) BSc in Computer Engineering (Lebanese University, 2000) Her research focuses on Industry 4.0 , Additive Manufacturing , and Data Modeling , with applications in IoT , Cloud Computing , and Biomedical Engineering . Recent publications emphasize neuroimaging and machine learning integration. Publications span OFDM modulation , signal processing , and Industry 4.0 challenges, reflecting a multidisciplinary approach. Key collaborations include LINEACT and ENSAM institutions.

• Efficient sampling methods
• Compressed sensing
• Medical imaging: ultrasound, MRI, CT
• +۱۲ مورد دیگر

Prof. Yonina Eldar is a Professor of Electrical Engineering at the Faculty of Mathematics and Computer Science , Weizmann Institute of Science. She holds the Dorothy and Patrick Gorman Professorial Chair and serves as Head of the Manya Igel Center for Biomedical Engineering and Signal Processing . Her research bridges classical signal processing with modern deep learning techniques. Develops model-based deep learning frameworks combining domain knowledge with data-driven approaches Focuses on sub-Nyquist sampling for efficient data acquisition in radar, ultrasound, and communications Created hardware prototypes for time-encoding machines and modulo-ADC systems Innovates in joint radar-communication systems for autonomous vehicles Her work emphasizes algorithm unrolling to create interpretable neural networks with reduced training requirements. Publications demonstrate applications in: Medical imaging (ultrasound, ECG monitoring) Autonomous systems (automotive radar) Communication technologies (DFRC systems) Active in theoretical foundations of model-based deep learning, with recent work establishing mathematical guarantees for unfolded networks. Collaborations include Tsinghua University and industry partners for hardware validation.

• Channel Coding
• Machine Learning
• Optical Communications
• +۳ مورد دیگر

Laurent Schmalen is a full professor at the Karlsruhe Institute of Technology (KIT), co-directing the Communications Engineering Laboratory (CEL). He holds a Dipl.-Ing. and Dr.-Ing. in Electrical Engineering from RWTH Aachen University (2005 and 2011). Previously, he worked at Alcatel-Lucent Bell Labs (2011–2019) and led the Coding for Optical Communications department at Nokia Bell Labs (2016–2019). His research focuses on channel coding, machine learning, optical communications, and high-speed data transmission. Education and Professional Experience: 1999: Abitur at Lycée Classique Diekirch (Luxembourg) 2005: Diploma in Electrical Engineering and Information Technology, RWTH Aachen University 2011: PhD (summa cum laude) in Electrical Engineering, RWTH Aachen University 2011–2019: Research roles at Bell Labs 2014–2019: Visiting Lecturer at University of Stuttgart 2019–present: Full Professor at KIT Research Interests: His work spans channel coding (e.g., LDPC codes), machine learning for communications, modulation formats, and optical transmission systems. Key areas include non-linear channel analysis, probabilistic constellation shaping, and FEC optimization for fiber optics. Awards and Recognition: IEEE Fellow (2023) ERC Consolidator Grant (2023) Best Paper Awards in IEEE journals (2016, 2018) E-Plus Prize for Doctoral Thesis (2011) Editorial and Leadership Roles: Associate Editor, IEEE Transactions on Communications TPC Co-Chair, International Symposium on Topics in Coding (ISTC 2025) General Co-Chair, ITG Conference on Systems, Communications, and Coding (2025) Labs and Teams: He co-leads the Communications Engineering Lab (CEL) at KIT, focusing on cutting-edge research in optical communications and machine learning applications.

Boris Karanovمشاهده پروفایل

پژوهشگر ارشد

• Optical Fiber Communications
• Digital Signal Processing
• Deep Learning
• +۵ مورد دیگر

Boris Karanov is a post-doctoral research member at the Signal Processing Systems Group within the Department of Electrical Engineering at Eindhoven University of Technology since October 2020. His research focuses on applying deep learning techniques to digital signal processing in optical fibre communications. His academic background includes a Ph.D. in Electrical Engineering from University College London (awarded December 2020), where his research focused on developing new coding and detection methods for communication over the nonlinear dispersive optical fibre channel using deep learning. He also holds joint M.Sc. degrees in Photonic Networks Engineering from Aston University, Osaka University, and Technical University of Berlin (2016), and a B.Sc. in Telecommunication Engineering from Technical University of Sofia (2014). Dr. Karanov's research interests span optical fiber communications, digital signal processing, deep learning applications in communications, nonlinear fiber effects, and transceiver design. His work bridges theoretical communication principles with practical AI-driven solutions for next-generation communication systems. His recent publications demonstrate a strong trend toward applying neural networks and deep learning techniques to solve challenging problems in optical communications, radar signal processing, and speech enhancement. The research shows increasing focus on robustness of AI solutions under real-world constraints and imperfect channel conditions. Nokia 'Most innovative AI solution' award (2018) for pioneering work in machine learning applications to communication systems 'Best paper award' from IEEE/OSA Journal of Lightwave Technology (2021) Lombardi prize from Electronic and Electrical Engineering department at UCL (2021) for the most impactful thesis Dr. Karanov is actively involved in multiple research projects including RAISE SPS (Robust AI for Safe radar signal processing), RAIDAR (AI for RaDAR), and ICONIC (Increasing the Capacity of Optical Nonlinear Interfering Channels), collaborating with institutions across Europe. His work in the Signal Processing Systems Group at Eindhoven University of Technology continues to push the boundaries of applying AI to communication challenges.

Kaiquan Wuمشاهده پروفایل

پژوهشگر

• Digital Signal Processing
• Fiber-Optic Communication Systems
• Error Correction Codes
• +۳ مورد دیگر

Kaiquan Wu is a doctoral candidate and postdoc researcher at Eindhoven University of Technology (TU/e), Netherlands, specializing in digital signal processing for fiber-optic communication systems. His research focuses on error correction codes, coded modulation, and channel modeling. PhD Candidate in Electrical Engineering (Signal Processing Systems) Postdoc in Electrical Engineering (ICT Lab) Member of the ICONIC , BIT-FREE , and DIGI-OPT research projects Research interests include combating signal impairments in optical systems through advanced DSP techniques, such as decision feedback equalizers (DFE), geometric shaping, and low-complexity detection algorithms. He has published extensively on FSO systems, IM-DD links, and energy dispersion analysis. Collaborations involve developing simplified FSO channel models, low-complexity architectures for data center applications, and patent innovations in amplitude shaping methods. His work contributes to increasing the capacity of optical communication systems while reducing complexity and error rates.

• French Language Practice
• Romance Studies
• Computer Vision
• +۳ مورد دیگر

Viridiane Fay is a researcher in the Institute of Modern Languages and Romance Studies at the University of Würzburg , specializing in French language practice and applied computational methods. During lecture periods, she holds office hours every Tuesday from 1 p.m. to 2 p.m., with appointments available during semester breaks. Role: French Editor, Language Practice Location: Am Hubland, 97074 Würzburg Contact: viridiane.fay@uni-wuerzburg.de Her research spans two distinct domains: French language pedagogy and computer vision/AI . While her institutional affiliation focuses on Romance Studies, her publication record reveals expertise in: Medical and underwater image processing Transformer-based architectures Federated learning and network optimization Watermarking and security algorithms Multi-modal AI applications Neuroscience signal analysis Recent work includes lightweight segmentation models (e.g., ContextFormer), drone/aerial imagery detection frameworks (CH-YOLO-Lite), and privacy-preserving AI systems (Find). Despite limited biographical details in the scrape, her 30+ publications since 2017 suggest significant technical contributions.

• Wireless Communication Networks
• Information Security
• Signal Processing
• +۴ مورد دیگر

Benoit Geller is a Full Professor at Institut Polytechnique Paris-ENSTA and holds the HDR (Accreditation to Lead Research) from the University of Paris. He leads the Specialized Master in Autonomous Systems for Defense (ILEMs). Previously, he was Head of the Multisensor and Information Team at SATIE lab (École Normale Supérieure Paris Saclay) until joining ENSTA in 2007. His expertise spans wireless communication security, underwater acoustic systems, and IoT networks. Key roles include French Navy Reserve Superior Officer and IEEE Senior Member. Education : HDR (2004), PhD in Wireless Communication Networks (1992, INPG), Engineering Master in Telecommunications (1988, ENSERB). Research Focus : Signal processing, cryptography, cross-layer solutions for tactical and underwater communications. Over 100 international papers and 4 pending patents. Awards : Knight of Academic Palms (2018), IEEE Senior Member (2010). Recent articles focus on turbo equalization, low-complexity algorithms for underwater acoustic channels, and energy-efficient IoT protocols. He has led projects in EU initiatives (e.g., Mast AIDA, MarTERA Bioglider) and DGA collaborations.

• Satellite Communications
• Wireless Networks
• Signal Processing
• +۵ مورد دیگر

Jevgenij Krivochiza is an active Researcher in Satellite Communications , Wireless Networks , and Signal Processing , with a focus on precoding techniques , 5G Non-Terrestrial Networks , and FPGA acceleration for multi-antenna systems. He collaborates extensively with institutions like University of Luxembourg (via co-authors Symeon Chatzinotas and Björn Ottersten ) and other researchers in distributed satellite systems. Research Interests : His work revolves around symbol-level precoding to minimize interference and power consumption, phase synchronization in coherent distributed systems, and distributed beamforming for swarm satellite networks. Recent studies include machine learning applications for PBCH symbol detection and FAIR data management for ML models. Article Trends : Publications from 2025 to 2015 span satellite communication protocols , 5G NTN , interference exploitation , and hardware acceleration , with increasing integration of machine learning in 2024–2025. Sub-fields include DVB-S2X waveform analysis , SWIPT , and precoding-enabled multi-beam systems .

• Audio Signal Processing
• Diffusion Models
• Deep Learning
• +۵ مورد دیگر

Eloi Moliner is a Doctoral Researcher in the School of Electrical Engineering and the Department of Information and Communications Engineering. His research focuses on audio signal processing , leveraging diffusion models and deep learning to address challenges in audio restoration, dereverberation, and generative techniques. He explores unsupervised learning , nonlinear distortion estimation , and audio inverse problems . Key research interests include room acoustics modeling , timbre transfer , and historical audio restoration . Recent work emphasizes diffusion-based methods for tasks like bandwidth extension and noise synthesis. His contributions span unpaired domain transfer , blind signal processing , and generative equalization . 3 scientific awards highlight his work, though specifics are not detailed. No advising roles or grants are listed in the provided texts.

Tommaso Foggiمشاهده پروفایل

استادیار

• Signal Processing
• Optical Communications
• Satellite Communications
• +۷ مورد دیگر

Tommaso Foggi is an Assistant Professor in the Department of Engineering and Architecture at the University of Parma, Italy. He holds a Ph.D. in Information Technology and a Master’s in Telecommunication Engineering, both from the University of Parma. His academic career includes a research engineer role at the National Inter-University Consortium for Telecommunications (CNIT) from 2009 to 2018 before joining the university faculty in 2018. Ph.D. in Information Technology, University of Parma Master’s in Telecommunication Engineering, University of Parma His research focuses on electronic signal processing for optical and satellite communication systems, with expertise in adaptive equalization, coding, iterative decoding, optical channel impairment compensation, and channel estimation. He develops simulative software for communication systems and contributes to advancements in free space optical and non-terrestrial networks. His recent publications (2023–2024) reflect a strong trend in next-generation wireless and optical communications, particularly in non-terrestrial networks, LEO satellite diversity, coherent vs. IM/DD transmission, and timing synchronization for high-speed receivers. These works span disciplines in signal processing, information theory, and wireless systems, with a clear emphasis on 6G and satellite-integrated networks. He has received notable recognition, including the Best Paper Award at the Optical Networks and Systems Symposium, ICC 2008. He has been involved in research projects funded by MIUR, ESA, and industry partners like Huawei, Inmarsat, and Marconi-Ericsson. Best Paper Award, ICC 2008 He actively advises and contributes to academic projects, though specific students are not listed. His research is supported by national and international grants, particularly in satellite and optical communications. He teaches courses such as Digital Communications, Communication Fundamentals, and Probabilistic Methods for Engineering across undergraduate and graduate programs. While no formal lab or research group name is mentioned, his work is conducted within the Department of Engineering and Architecture at the University of Parma, likely in collaboration with CNIT and other research units focused on telecommunications and signal processing.