Prof. Shahram Latifi
(Keynote Speaker)

IEEE Fellow

University of Nevada, Las Vegas, USA

Speech Title: Advanced Biometrics: Covert, Stand-off and Reliable

Abstract:Biometrics have long been used to secure lives and investments. Most of the biometrics techniques are image-based and have their own merits and demerits offering a tradeoff among various factors such as ease of use, resilience, reliability and cost-effectiveness. Inspired by recent advances in technology such as high processing power and high resolution cameras, current research focuses on moving biometrics techniques from an overt mode to covert, touch-based to stand-off capture-based, and single-mode to multibiometrics. In this talk, an overview of advanced biometrics techniques is presented. We also present our research results on partial iris recognition to be employed in a covert or stand-off mode. We also address a multimodal biometrics technique obtained by fusing iris and retina images which gives a more reliable and accurate result than each of the unimodals mentioned above.

Biography: Shahram Latifi, an IEEE Fellow, received the Master of Science and the PhD degrees both in Electrical and Computer Engineering from Louisiana State University, Baton Rouge, in 1986 and 1989, respectively. He is currently a Professor of Electrical Engineering at the University of Nevada, Las Vegas. Dr. Latifi is the co-director of the Center for Information Technology and Algorithms (CITA) at UNLV. He has designed and taught undergraduate and graduate courses in the broad spectrum of Computer Science and Engineering in the past four decades. He has given keynotes and seminars on machine learning/AI and IT-related topics all over the world. He has authored over 250 technical articles in the areas of networking, cybersecurity, image processing, biosurveillance, biometrics, document analysis, fault tolerant computing, parallel processing, and data compression. His research has been funded by NSF, NASA, DOE, DoD, Boeing, Lockheed and Cray Inc. Dr. Latifi was an Associate Editor of the IEEE Transactions on Computers (1999-2006), an IEEE Distinguished Speaker (1997-2000), and Co-founder and General Chair of the IEEE Int'l Conf. on Information Technology (2004-2015). Dr. Latifi is the recipient of several research awards, the most recent being the Barrick Distinguished Research Award (2021). Dr. Latifi was recognized to be among the top 2% researchers around the world in December 2020, according to Stanford top 2% list (publication data in Scopus, Mendeley). He is a Registered Professional Engineer in the State of Nevada.


Prof. Joel J. P. C. Rodrigues
(Keynote Speaker)


China University of Petroleum (East China), Qingdao, China
Instituto de Telecomunicações, Covilhã, Portugal

Speech Title: Enabling Smart Environments through Internet of Things

Abstract: This keynote addresses a hot topic focusing on enabling Smart Environments through Internet of Things, considering their major issues and research perspectives. It starts with the introduction to Internet of Things and their typical application scenarios and their main verticals. Several projects are presented to illustrated different solutions based on an IoT approach. IoT Opportunities to “change the World” are discussed. The communications ends with new trends and challenges on Internet of Things, suggesting further research topics.

Biography: Joel J. P. C. Rodrigues [Fellow, IEEE & AAIA] is with the College of Computer Science and Technology, China University of Petroleum, Qingdao, China; Senac Faculty of Ceará, Brazil; and senior researcher at the Instituto de Telecomunicações, Portugal. Prof. Rodrigues is an Highly Cited Researcher (Clarivate), N. 1 of the top scientists in computer science in Brazil (, the leader of the Next Generation Networks and Applications (NetGNA) research group (CNPq), Member Representative of the IEEE Communications Society on the IEEE Biometrics Council, and the President of the scientific council at ParkUrbis – Covilhã Science and Technology Park. He was Director for Conference Development - IEEE ComSoc Board of Governors, an IEEE Distinguished Lecturer, Technical Activities Committee Chair of the IEEE ComSoc Latin America Region Board, a Past-Chair of the IEEE ComSoc Technical Committee (TC) on eHealth and the TC on Communications Software, a Steering Committee member of the IEEE Life Sciences Technical Community and Publications co-Chair. He is the editor-in-chief of the International Journal of E-Health and Medical Communications and editorial board member of several high-reputed journals (mainly, from IEEE). He has been general chair and TPC Chair of many international conferences, including IEEE ICC, IEEE GLOBECOM, IEEE HEALTHCOM, and IEEE LatinCom. He has authored or coauthored about 1100 papers in refereed international journals and conferences, 3 books, 2 patents, and 1 ITU-T Recommendation. He had been awarded several Outstanding Leadership and Outstanding Service Awards by IEEE Communications Society and several best papers awards. Prof. Rodrigues is a member of the Internet Society, a senior member ACM, and Fellow of AAIA and IEEE.


Prof. Yudong Zhang
(Keynote Speaker)

Senior Member of IEEE and ACM

The University of Leicester, UK

Speech Title: Data Mining Theories and Techniques for COVID-19 Diagnosis

Abstract: COVID-19 is a pandemic disease that caused more than 6.65 million deaths until 12/Dec/2022. X-ray and CT scans are two popular medical imaging technique used in radiology to get detailed images of the body noninvasively for diagnostic purposes. Traditional manual labeling of X-ray or CT-based scans is tedious and error-prone. To solve the problem, our lab develops new data mining theories and techniques, such as advanced pooling-based networks, graph convolutional networks, attention neural networks, weakly supervised networks, etc. We also use cloud computing techniques to run our developed app on the remote server to help doctors in the suburban area. Two other chest-related diseases: secondary pulmonary tuberculosis and community-acquired pneumonia, will be covered in this talk.

Biography: Prof. Yudong Zhang is a Chair Professor at the School of Computing and Mathematical Sciences, University of Leicester, UK. His research interests include deep learning and medical image analysis. He is the Fellow of IET, Fellow of EAI, and Fellow of BCS. He is the Senior Member of IEEE and ACM. He is the Distinguished Speaker of ACM. He was the 2019, 2021 & 2022 recipient of Clarivate Highly Cited Researcher. He has (co)authored over 400 peer-reviewed articles. There are more than 60 ESI Highly Cited Papers and 5 ESI Hot Papers in his (co)authored publications. His citation reached 23250 in Google Scholar (h-index 84). He is the editor of Neural Networks, IEEE TITS, IEEE TCSVT, etc. He has conducted many successful industrial projects and academic grants from NIH, Royal Society, GCRF, EPSRC, MRC, Hope, British Council, and NSFC. He has served as (Co-)Chair for more than 60 international conferences (including more than 20 IEEE or ACM conferences). More than 50 news presses have reported his research outputs, such as Reuters, BBC, Telegraph, Physics World, UK Today News, etc.


Assoc. Prof. João Alexandre Lôbo Marques
(Keynote Speaker)

University of Saint Joseph, Taipa, Macau SAR, China

Speech Title: Intelligent Data Fusion System for Assessing and Classifying the Long-Term Effects of Exposure to COVID-19 in Pregnancy (Long Covid): Associated Neurophysiological and Epigenetic Mechanisms and Consequences for Infant Development

Abstract: The Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) is responsible for the most significant global public health crisis of the last 50 years, having brought severe damage to mental health, with about 36% of patients presenting neuropsychiatric symptoms during or in cases of “Post-Acute Sequelae of COVID-19” (PASC). Among these, we can mention cognitive impairment, fatigue, sleep disturbances, depression, post-traumatic stress, and substance use disorders observed for more than 6 months after infection in patients who required hospitalization and non-hospitalized in percentages ranging from 3-47%. The main objective of this work is to develop and validate an intelligent system based on data fusion and multiple Artificial Intelligence approaches to assess PASC neuropsychiatric symptoms in women exposed to the SARS-CoV-2 virus during pregnancy with PASC neuropsychiatric symptoms, comparing the exposed non-pregnant women and the involvement of neurophysiological and epigenetic mechanisms, as well as determining the consequences of PASC for the development of babies. The project provides the follow-up for 12 months of women with long-term COVID treated at the HUWC (University Hospital), performing Neuropsychological and Sleep Assessments carried out through directed anamnesis, and polysomnography, in addition to various tests. Additionally, non-invasive ambulatory monitoring of heart rate is carried out, and computerized analysis of its variability is carried out. Metrics will be extracted in the time domains (SDNN, SDANN, RMSSD, pNN50, among others), frequency (HF, LF, LF /HF, among others), geometric and non-linear (entropy, chaotic, among others). The data collected in the research are from multiple sources and of different types, including laboratory analysis, DNA methylation and RNA sequencing, questionnaires, and monitoring tests such as polysomnography, EEG, EOG, and ECG. The data is consolidated, integrated, and validated in a structured and unstructured data lake, which allows classifying the project as a Big Data solution. The system also provides a layer for viewing and interpreting the information obtained, either through diagnostic support dashboards or through the use of eXplainable Artificial Intelligence (XAI) techniques to indicate the most relevant attributes for certain types of analysis.

Biography: Associate Professor, Head of Department, Research Coordinator at the University of Saint Joseph, Macau, SAR China. Founder of the Laboratory of Applied Neurosciences (LAN/USJ). PostDoctorate and Honorary Research Fellow at the University of Leicester - UK. Visiting Associate Professor at the University of the Chinese Academy of Sciences (UCAS) - Shenzhen Institutes of Advanced Technologies (SIAT). PhD in Engineering Federal University of Ceará (2010). Associate Professor and Software Department Chief at University Gregorio Semedo (UGS), Angola (2016). Associate Professor at University Lusiada of Angola (2009-2015). Master in Artificial Intelligence UFC (2007). Has large experience in Artificial Intelligence, Bioengineering, and Applied Computer Science, focusing on signal and image processing. RESEARCH AREAS - Neuroscience applied to management (marketing, leadership, performance) - Business Analytics - Big Data Applications - Theory of Constraints - Project management - Digital Signal Processing - Bioengineering / Computer-Aided Diagnostic Systems - Artificial Intelligence - Deep Learning - Nonlinear analysis and dynamics of time series.


Prof. Miaowen Wen
(Keynote Speaker)

Senior Member of IEEE

South China University of Technology, China

Speech Title: Channel Estimation and Diversity Reception for RIS-Empowered Broadband Wireless Systems

Abstract: Channel estimation is a challenging problem for reconfigurable intelligent surface (RIS) empowered wireless communications, especially for broadband channels. In this talk, a cyclic-prefixed single-carrier (CPSC) transmission scheme with PSK signaling for this purpose is first introduced. We will see that different cyclically delayed versions of the incident signal can be created by wisely configuring the RIS according to the transmitted PSK symbols. Based on this, a practical and efficient channel estimator can be easily developed and multipath diversity can be harvested. By resorting to the concept of index modulation (IM), this talk will then introduce how to extend CPSC-RIS for improved spectral efficiency. Finally, BER results will be shown to verify the superiority of the presented scheme over the orthogonal frequency division multiplexing (OFDM) solution.

Biography: Miaowen Wen received the Ph.D. degree from Peking University, Beijing, China, in 2014. From 2012 to 2013, he was a Visiting Student Research Collaborator with Princeton University, Princeton, NJ, USA. From 2019 to 2021, he was with the Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, as a Post-Doctoral Research Fellow. He is currently a Professor with South China University of Technology, Guangzhou, China. He has published two Springer books entitled Index Modulation for 5G Wireless Communications and Index Modulation for OFDM Communications Systems, as well as 250+ research papers, which include 170+ journal papers and 70+ conference papers. His research interests include a variety of topics in the areas of wireless and molecular communications.
Dr. Wen was a recipient of the IEEE Asia-Pacific (AP) Outstanding Young Researcher Award in 2020, and five Best Paper Awards from the IEEE ITST’12, the IEEE ITSC’14, the IEEE ICNC’16, the IEEE ICCT’19, and the EAI QSHINE’22. He was the winner in data bakeoff competition (Molecular MIMO) at IEEE CTW'19, Selfoss, Iceland. He served as a Guest Editor for the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS and for the IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING. Currently, he is serving on the Editorial Boards of the IEEE TRANSACTIONS ON COMMUNICATIONS, the IEEE TRANSACTIONS ON MOLECULAR, BIOLOGICAL, AND MULTISCALE COMMUNICATIONS, and the IEEE COMMUNICATIONS LETTERS. Personal Webpage




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