جست‌وجوی استاد و پژوهشگر

Alexandra Duel-Hallenمشاهده پروفایل

استاد

• Wireless Communications
• Optimization
• Game Theory Applications
• +۳ مورد دیگر

Dr. Alexandra Duel-Hallen is a Professor at North Carolina State University's Department of Electrical and Computer Engineering within the College of Engineering. Her research focuses on wireless communications, optimization, power systems, and machine learning applications. She has held significant grants, including a $600,000 NSF award for power system controller design. She is an IEEE Fellow (2011) and AAIA Fellow (2022), with notable accolades such as the 2006 ISI Highly Cited Researcher distinction. Education: PhD (Electrical Engineering, Cornell University, 1987), MS (CICE, University of Michigan, 1983), BS (Math & Computer Science, Case Western Reserve University, 1982). Research interests include game theory for cyber-physical systems, machine learning-driven wireless channel prediction, and voltage stability in power grids. Recent work emphasizes security investments in networked control systems and mmWave communication challenges. Her articles span 1980s to present, reflecting deep engagement with adaptive modulation, multiuser detection, and MIMO systems. Awards highlight her contributions to equalization, channel prediction, and 6G technologies. She leads interdisciplinary projects, including 6GNC's multi-technology initiatives and resilient microgrid energy cooperation frameworks. Her work bridges theoretical advancements with practical implementations in power systems and communications.

• Data Fusion
• Sensor Validation
• Statistical Signal Processing
• +۹ مورد دیگر

Apoorva Chawla is a Postdoctoral Researcher at the Department of Electronic Systems, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. She holds a B.Tech. in Electronics and Communication Engineering from Gautam Buddh Technical University (2012) and an M.Tech.-Ph.D. Dual Degree in Electrical Engineering from Indian Institute of Technology Kanpur (2022), where she was supported by the TCS Research Fellowship. She was a finalist for the Qualcomm Innovation Fellowship (2019). Research Interests : Data Fusion & Sensor Validation Statistical Signal Processing IoT and Sensor Networks Wireless Communications Industry 4.0 Applications Carbon Capture and Storage Systems Recent Trends in Publications : Her work focuses on distributed detection algorithms, federated learning for wireless systems, fault diagnosis in critical infrastructure (e.g., natural gas pipelines), and RIS-aided communication networks. Recent 2025 publications emphasize region-specific channel estimation in RIS-enabled networks and fault diagnosis in CO2 pipelines. Awards & Recognition : TCS Research Fellowship (IIT Kanpur) Qualcomm Innovation Fellowship finalist (2019) Grants & Projects : Associated with the SIGNIFY project on sensor validation for digital twins of safety-critical systems. Labs & Teams : Collaborates with NTNU's Electronic Systems group and international teams on federated sensor networks and industrial IoT applications.

Scott L. Millerمشاهده پروفایل

استاد

• Communication Theory
• Signal Processing
• Wireless Communications
• +۶ مورد دیگر

Scott L. Miller is a Professor of Electrical and Computer Engineering at Texas A&M University, holding the Debbie and Dennis Segers '75 Professorship. He serves as a faculty fellow in the Eugene E. Webb ’43 program and is affiliated with the Department of Electrical and Computer Engineering. His work focuses on communication theory, signal processing, and wireless systems design. Educational Background: B.S. in Electrical Engineering, UC San Diego (1985) M.S. in Communication Theory and Systems, UC San Diego (1986) Ph.D. in Communication Theory and Systems, UC San Diego (1988) Research interests include physical layer security, optical communication systems (VLC, FSO-mmWave), acoustic wave propagation for downhole telemetry, and interference mitigation in multiuser systems. His recent work addresses secured signal processing in emerging wireless paradigms and hybrid communication systems. He holds the IEEE Fellow distinction. Awards: IEEE Fellow Advising and grants involve mentoring graduate students in cutting-edge communication technologies. Notable contributions include experimental demonstrations of VLC-based downhole systems and analytical frameworks for IQ imbalance compensation in uplink systems.

• Quantum Communications
• Quantum Cryptography
• Quantum Information Theory
• +۳ مورد دیگر

Imre Sándor is a Professor at the Budapest University of Technology and Economics (BME), affiliated with the Department of Network Systems and Services within the Faculty of Electrical Engineering and Computer Science. He has been a core member of the Doctoral School of Informatics since 2002 and has held roles in the Department of Information Technology from 2000 to 2012. His research focuses on quantum communication systems, quantum cryptography, and optimization algorithms, with a particular emphasis on integrating quantum technologies into telecommunications networks. Sándor has co-organized conferences and workshops, such as the 1st Workshop on Intelligent Infocommunication Networks, Systems and Services (WI2NS2) in 2023. His work spans theoretical and applied aspects of quantum information processing, including quantum key distribution, quantum OFDM protocols, and error mitigation strategies for quantum networks. He has authored over 60 publications, often collaborating with researchers in areas like entanglement management, quantum genetic algorithms, and 5G network optimization. His research interests include developing novel quantum communication protocols, enhancing network security through quantum technologies, and optimizing resource allocation in large-scale systems. Notable contributions include advancements in multiuser quantum key distribution, space weather impact modeling for satellite quantum communication, and energy-efficient MIMO systems using quantum-inspired optimization. Sándor’s publications frequently explore the intersection of quantum computing and telecommunications, addressing challenges in signal processing, network architecture, and environmental resilience.

Wei Yuمشاهده پروفایل

استاد

• Network Information Theory
• Wireless Communications
• Optimization
• +۶ مورد دیگر

Professor Wei Yu is a Canada Research Chair (Tier 1) in Information Theory and Wireless Communications at the University of Toronto , Department of Electrical and Computer Engineering. He received his Ph.D. in Electrical Engineering from Stanford University (2002) and B.A.Sc. in Computer Engineering and Mathematics from University of Waterloo (1997). Key roles: IEEE Fellow, Fellow of Canadian Academy of Engineering, Member of Royal Society of Canada Editorial leadership: IEEE Transactions on Wireless Communications, IEEE Transactions on Information Theory Research Interests span network information theory, convex optimization, wireless communications, and broadband access networks. His work focuses on multiuser information theory, MIMO systems, and 5G/6G network optimization. Publications reflect expertise in reconfigurable intelligent surfaces (RIS), covariance-based activity detection, and fractional programming for signal processing. Scientific Recognition includes multiple IEEE Best Paper Awards (2008, 2017, 2021), NSERC Steacie Fellowship (2015), and the 2024 IEEE Joint Communications/Information Theory Society Paper Award. He is a Clarivate Highly Cited Researcher. Teaching Contributions include ECE1502 (Information Theory), ECE431 (Digital Signal Processing), and ECE1505 (Convex Optimization), with a focus on mathematical foundations and practical applications in communications.

Raviraj Adveمشاهده پروفایل

استاد

• Wireless Communications
• Radar Signal Processing
• Cooperative Networks
• +۷ مورد دیگر

Professor Raviraj Adve is affiliated with the University of Toronto , working in the Department of Electrical and Computer Engineering and cross-listed with the Electromagnetics Group . He holds a PhD in Electrical Engineering from Syracuse University (1996) and previously worked at Research Associates for Defense Conversion Inc. (1997–2000). Education: B.Tech, Indian Institute of Technology Bombay (1990) PhD, Syracuse University (1996) Research Interests span two main areas: Wireless Communications (heterogeneous networks, massive MIMO, cooperative communication, resource allocation) and Radar Signal Processing (waveform diversity, space-time adaptive processing, HF radar systems). His work emphasizes practical algorithm development for real-world constraints. Scientific Contributions include 15 recent articles covering cooperative networks , radar clutter modeling , low-complexity MIMO algorithms , and molecular communication . Notable awards: IEEE Fellow (2017), Young Radar Engineer of the Year (2009), and multiple teaching awards. Leadership Roles: Vice-chair of IEEE Radar Systems Panel, Associate Editor for IEEE Trans. on Aerospace and Electronic Systems , and editorial board member for IET Trans. on Radar, Sonar and Navigation .

• Signal Processing
• Digital Communications
• Machine Learning Applications
• +۳ مورد دیگر

Rafael Boloix Tortosa is an Associate Professor in the Department of Signal Theory and Communications at the University of Seville's School of Engineering, where he leads significant research in complex-valued signal processing and its applications. His academic career spans over two decades with continuous involvement in national and international research projects. His research focuses on advancing complex-valued signal processing methodologies, particularly complex Gaussian processes, kernel methods, and widely linear estimation techniques. These theoretical advances are applied to diverse domains including medical imaging (especially burn wound analysis), wireless communications (UWB positioning, OFDM systems), and art conservation. His work bridges fundamental signal processing theory with practical engineering solutions. Professor Boloix Tortosa maintains an active publication record with significant contributions in top IEEE journals, particularly in signal processing and machine learning. His recent work (2025-2019) demonstrates continued innovation in complex-valued machine learning methods, while his earlier publications established foundational work in blind source separation and multiuser detection. He actively participates in multiple research projects including EU-funded initiatives like COURAGEOUS (counter-UAS solutions) and ARIADNE (agricultural drones), alongside national projects in medical imaging and communications. His research group has received consistent funding through the TIC-155 consolidation incentives. His laboratory work centers around the Signal Processing and Communications Research Group, where he develops both theoretical frameworks and practical implementations for signal processing challenges. The group maintains strong connections between theoretical advances and real-world applications across multiple engineering domains.

• Signal Processing
• Digital Communications
• Wireless Communications
• +۳ مورد دیگر

Francisco Javier Payán Somet is a Full Professor in the Department of Signal Theory and Communications at the University of Seville. His research focuses on signal processing, digital communications, and wireless systems, with a strong emphasis on OFDM systems, Gaussian processes, and digital filter design. Research Group: Signal Processing and Communications Projects: Flexible Automated Software for Testing, CITIUS, PLATINO, and others. His research interests include Signal Processing , Digital Communications , Wireless Systems , Gaussian Processes , and Recursive Digital Filters . His work spans theoretical and applied domains, including hardware development for antennas and GPS equipment. The trends in his recent articles highlight advancements in complex-valued signal analysis , blind equalization schemes , and higher-order statistics for communication systems . Earlier works focus on low-frequency oscillators and digital filter stability . He has participated in numerous projects, including the Consolider-Ingenio Program and grants for the TIC-155 Research Group . His research support includes international collaboration incentives. He is affiliated with the Signal Processing and Communications research group at the University of Seville, contributing to both academic and industrial applications in telecommunications.

Ricardo Eirisمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Construction Engineering
• Educational Technology
• +۱۲ مورد دیگر

Ricardo Eiris is an Assistant Professor at Arizona State University's School of Sustainable Engineering and the Built Environment. His research integrates emerging technologies to enhance human performance in construction workforce training and education. Education Ph.D. in Design, Construction, and Planning, University of Florida (2020) M.S. in Digital Arts and Science, University of Florida (2020) M.S. in Construction Management, University of Florida (2016) M.S. in Civil Engineering, University of Florida (2016) B.S. in Civil Engineering, Universidad Metropolitana - Venezuela (2013) Research Focus His work spans three interconnected domains: (1) AI-Enabled Metaverse for workforce education transformation, (2) Extended Reality (AR/VR/MR) for hazard visualization, and (3) Drone Operations for inspection training. He develops immersive environments like iVisit and DroneSim to address safety and training gaps. Scientific Contributions 2024: 360-degree digital twins for task detection 2023: Social equity in PPE accessibility 2022: Multiuser VR site visit frameworks 2021: Drone flight visualization systems 2020: Comparative VR vs traditional safety methods Honors & Awards ASC VDC Faculty Boot Camp Fellowship (2024) ASC International Best Conference Paper Award (2023) ASC Mechanical & Electrical Faculty Fellowship (2022) ASCE ExCEEd Fellowship (2021) National Academies STEM Education Competition Winner (2020) Teaching & Leadership He teaches courses in construction technology and advanced research methods. Leads the Human-Centered Technology in Construction (HCTC) Research Group, focusing on technology-driven safety and workforce development.

Xiaohua Liمشاهده پروفایل

دانشیار

• Signal Processing
• Wireless Communications
• Cognitive Radio Networks
• +۶ مورد دیگر

Xiaohua (Edward) Li is an Associate Professor of Electrical and Computer Engineering at the State University of New York at Binghamton. He holds a BS and MS from Shanghai Jiao Tong University and a PhD from the University of Cincinnati. His research focuses on signal processing for communications, cognitive radio networks, wireless security, and machine learning applications in networked systems. Education: Bachelor of Science, Control Engineering, Shanghai Jiao Tong University Master of Science, Biomedical Engineering, Shanghai Jiao Tong University Doctor of Philosophy, Electrical and Computer Engineering, University of Cincinnati Research Interests: Dr. Li's work spans adaptive signal processing, cognitive radio networks, blind equalization, and secure wireless communications. He has pioneered methods for integrating machine learning into signal processing frameworks and developed novel approaches for 5G imaging and communication systems. His research emphasizes practical applications, including anti-jamming designs, spectrum sharing, and secure physical-layer transmissions. Grants & Projects: AFOSR: Integrated 5G Imaging and Communication Surveillance (2021–2024) NSF: Intelligence Measure of Cognitive Radio Networks (2015–2017) AFRL: Jamming and spectrum mutation in cognitive radio networks (2013–2015) Awards: Best Paper Award at MMM-ACNS 2017 (Warsaw, Poland) Best Reviewer Award at ETRI Journal 2013 Teaching: Dr. Li teaches advanced courses including Digital Signal Processing II, Neural Networks and Deep Learning, and Wireless Communications. He advises students on projects ranging from cooperative networks to smart grid security.

• Multiuser Communication Systems
• Reinforcement Learning
• Wireless Sensor Networks
• +۳ مورد دیگر

Associate Professor Lu Jin is affiliated with the School of Engineering and the Department of Electrical, Electronic and Computer Engineering at The University of Western Australia. His research focuses on multiuser communication systems, reinforcement learning applications, wireless sensor networks, and acoustic communication challenges in complex environments. He has contributed to educational research in signal processing pedagogy and materials science optimization for construction applications. Research Highlights: Developed dichotomous search-based frequency estimation algorithms Conducted convergence analysis of Markov Chain Monte Carlo methods for concrete mix design Explored blind adaptive multi-user detection techniques for under-ice acoustic networks His publications span engineering education methodologies, signal processing innovations, and interdisciplinary materials research. No scientific awards were explicitly listed in the provided information.

Khaled Altuwairgiمشاهده پروفایل

پژوهشگر

• Wireless Communications
• Signal Processing
• Telecommunications
• +۴ مورد دیگر

Khaled Altuwairgi is a Researcher in the Department of Electrical & Electronic Engineering. His work focuses on advancing wireless communication systems, particularly in the domains of Intelligent Reflecting Surfaces (IRS), multi-user networks, and energy-efficient communication protocols. He has contributed to cutting-edge research on IRS-assisted mmWave systems, THz-NOMA under beam misalignment, and ambient backscatter communications. His research interests include signal processing for next-generation networks, performance analysis of emerging communication technologies, and hardware-imperfection mitigation strategies. Altuwairgi has published extensively in IEEE conferences and journals, with notable contributions in 2024 addressing proximity-based users in mmWave systems and energy detection in backscatter networks. No academic awards or student advisement records are explicitly mentioned. His ORCID identifier (0000-0001-6476-0779) links to his research outputs across multiple venues, highlighting interdisciplinary collaborations in telecommunications engineering.

• Wireless Communications
• Massive Random Access Systems
• Machine Learning in Communications
• +۶ مورد دیگر

Mohammad Kazemi is a Marie Curie Research Fellow at the Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London. His research focuses on wireless communications, machine learning applications in communication systems, and massive random access systems. He holds a PhD from Amirkabir University of Technology (2017), and has held research positions at Bilkent University (2019-2023) and Amirkabir University's MMWCL lab (2017-2019). He served as Editorial Assistant for IEEE Transactions on Communications (2020-2023). Education: B.S. and M.S. in Electrical Engineering, K.N. Toosi University of Technology (2007, 2010) PhD in Electrical Engineering, Amirkabir University of Technology (2017) His research interests emphasize practical solutions for massive machine-to-machine (M2M) communications through EU-funded DeepMARA project. Key areas include fundamental limits of deletion/insertion channels, federated edge learning architectures, and secure mmWave communications. He explores applications in smart healthcare and transportation sectors using AI-driven communication frameworks. Projects & Grants: DeepMARA: EU/UKRI-funded project developing reinforcement learning-based strategies for massive random access systems Over-the-air federated learning with hierarchical clustering Labs & Teams: Member of the Information Processing and Communications Lab (IPC-Lab) at Imperial College London.

• Signal Processing
• Financial Signal Analysis
• Machine Learning
• +۵ مورد دیگر

Ali Akansu is a Professor in the Department of Electrical and Computer Engineering at NJIT. His academic career spans multiple decades, with significant contributions to signal processing, financial engineering, and multimedia communications. He has led or co-led four major NSF-funded projects, including the Center for Digital Video & Media Research and the Laboratory for Multimedia Signal Processing and Communication. His research focuses on topics such as eigenanalysis for financial portfolios, machine learning in network analysis, and multimodal affective state assessment using EEG and fNIRS. Key research areas include financial signal processing, portfolio optimization, eigensubspace applications, and data hiding techniques. Notable projects involve FPGA-based real-time risk analysis systems and GPU implementations for eigenportfolio analysis. He has published over 190 peer-reviewed articles, with impactful work in journals like Journal of Investment Strategies and Brain Sciences . His recent work combines machine learning with signal processing to address challenges in network traffic forecasting and financial market dynamics. He has also contributed to biomedical signal processing through studies on affective state assessment using neuroimaging techniques. Awards and recognitions include a h-index of 27 and over 2,694 citations. Grants: National Science Foundation (1991-1993, 1997-2002): Real-Time Color Video Workstation, Multimedia Signal Processing Lab, and Digital Video Research Center Labs/Teams: Leadership in NJIT's Laboratory for Multimedia Signal Processing and Financial Signal Processing groups

• Massive MIMO
• Aeronautical Communications
• Optimization Algorithms
• +۴ مورد دیگر

Jiankang Zhang is a Senior Lecturer in Computer Science at Bournemouth University, UK. He previously held positions at the University of Southampton (Senior Research Fellow) and Zhengzhou University (Lecturer/Associate Professor). His research focuses on massive MIMO , aeronautical communications , optimization algorithms , and edge computing , with applications to UAV networks, visible light communication (VLC), and physical layer security. Education: PhD in Communication and Information Systems (Zhengzhou University, 2012), BSc in Mathematics and Applied Mathematics (Beijing University of Posts and Telecommunications, 2006) His recent work explores reconfigurable intelligent surfaces (RIS) for secure transmissions, UAV-assisted networks , and multi-agent reinforcement learning for resource allocation in NOMA-V2X systems. He contributes to IEEE journals as an Associate Editor and Guest Editor. Scientific Awards: IEEE Senior Member (2018), Outstanding Associate Editor (IEEE Access, 2020), Workshop Chair (IRSCN), Guest Editor roles His article trends emphasize 5G/6G , secure wireless communication , machine learning-aided network optimization , and UAV mobility , with a focus on balancing security vs. efficiency in next-generation networks.