Keynote Speaker I

1547013190798067.png

Prof. Ian Robert McAndrew

Capitol Technology University, Maryland, USA


Title: Aviation Cybersecurity Issues for Unmanned Vehicles to fly Beyond the Visual Line of Sight

Abstract.: Aviation is now remote in real terms with unmanned vehicles in the air, land and sea. It is now more critical that the Cybersecurity issues have implications for safety, reliability and efficient operations. This research discusses how and why data transfer within these vehicles and with those around now with ever more invasive hacking what practical solutions are available for all ends of the economic market. In particular, the advancements in cyber programming that can be integrated to ensure unmanned vehicles are safe from external hacking. This one issue is the single most items needing solutions before legislation will support their use beyond the visual line of sight.

Bio.

Prof. Ian R. McAndrew PhD is a Mechanical Engineer that has worked in education for over 27 years. His teaching and research has been globally, starting in London and now with Capitol Technology University where he is the Dean of Doctoral Programs. He has taught in over 20 countries and published with many academics from all over the world. He has 6 degrees, also a qualified Electrical Engineer and FRAeS. He has supervised over 50 PhDs and has almost 60 peer reviewed publications. His current research is in aerodynamics and low speed flight. He is a keen supporter of conferences as this is where junior researchers can develop their skills for a life in research. He is frequently invited to deliver Keynote speeches and is the Chair of several International Conferences. Additionally, he is the editor or assistant editor in chief of several International Journals.


Keynote Speaker II

1554206290796416.png

Dr. Chiman Kwan, President 

Signal Processing, Inc., Rockville, Maryland, USA


Title: Practical Issues in Contingency Planning for UAVs with Engine Failures

Abstract.: Unmanned Air Vehicles (UAV), also known as Unmanned Air Systems (UAS), are gaining more attention in recent years. Some potential commercial applications may include cargo transfer between major cities, package and food delivery to individual households, etc.  However, it is well-known that UAVs are much less reliable and have far more accidents than manned aircraft. This is probably one of the most important reasons that FAA is hesitant to open up the national airspace (NAS) and imposes tight restrictions to UAVs. Reliability of UAVs can be strengthened using durable engines and communication equipment, strong structural materials, advanced conditioned based maintenance and structural health monitoring procedures, accurate fault diagnostic algorithms, and robust  and fault tolerant controllers. Despite the above measures, some equipment failures such as engine and communication equipment failures may still occur. In this talk, we present some recent research results done by our team to deal with engines failures, which are the most challenging contingency in UAVs. There is limited hanging time and the mishap UAV needs to land in an unpopulated area. In particular, some practical issues such as landing site selection, contingency waypoint selection, wind effects, etc. are explicitly addressed in our approach. A contingency planning software prototype has been developed that can deal with engine failures. Architectures of contingency planning software and some exemplar application scenarios will be discussed throughout the talk. 

Bio.

Chiman Kwan received his BS (honors) with major in electronics and minor in mathematics from the Chinese University of Hong Kong in 1988, and MS and Ph.D. degrees in electrical engineering from the University of Texas at Arlington in 1989 and 1993, respectively. He is the founder and Chief Technology Officer of Signal Processing, Inc. and Applied Research LLC, leading research and development effort in real-time control, chemical agent detection, biometrics, speech processing, image fusion, remote sensing, mission planning for UAVs, and fault diagnostics and prognostics.

From April 1991 to February 1994, he worked in the Beam Instrumentation Department of the SSC (Superconducting Super Collider National Laboratory) in Dallas, Texas, where he was heavily involved in the modeling, simulation and design of modern digital controllers and signal processing algorithms for the beam control and synchronization system. He later joined the Automation and Robotics Research Institute in Fort Worth, where he applied intelligent control methods such as neural networks and fuzzy logic to the control of power systems, robots, and motors. Between July 1995 and April 2006, he was the Vice President of Intelligent Automation, Inc. in Rockville, Maryland.

Over the past 28 years, Dr. Kwan has served as Principal Investigator/Program Manager for more than 115 competitively selected projects with total funding more than 36 million dollars from various government agencies and private companies such as Ford, Motorola, Boeing, Honeywell, and Stanford Telecom. He has 15 issued and pending patents, 55 invention disclosures, 325 journal and conference papers, and 500+ proprietary technical reports. He received numerous awards and recognitions from NASA, US Navy, US Air Force, and IEEE.


Keynote Speaker III

1553506105729517.png

Prof. Wenbing Zhao

Department of Electrical Engineering and Computer Science, Cleveland State University, USA


Title: Complex Human Activity Recognition in Human Patient Simulation

Abstract.: Human patient simulation (HPS) has been used for over 40 years in medical and nursing education. HPS is a training and feedback method in which learners practice tasks and processes in lifelike circumstances using models. While HPS has been widely adopted in nursing programs gloablly, its effectiveness is severely handicapped by the lack of reliable and efficient methods to provide objective assessment and feedback to students. In this talk, Dr. Zhao will introduce his team’s preliminary work on building a system that aims to automatically recognize the complex activities involved in HPS and provide realtime as well as offline on-demand precise feedback regarding the trainee’s performance during the HPS. 

Bio.: Prof. Zhao is a Professor at the Department of Electrical Engineering and Computer Science, Cleveland State University. He earned his Ph.D. at University of California, Santa Barbara in 2002. He has over 170 peer-reviewed publications. Prof. Zhao’s research spans from dependable distributed systems to human centered smart systems. His research has been funded by the US NSF, US Department of Transportation, Ohio Bureau of Workers’ Compensation, Ohio Department of Higher Education, and Ohio Development Services Agency. He has delivered more than 10 keynotes, tutorials, public talks and demonstrations in various conferences, industry and academic venues. Prof. Zhao is an associate editor for IEEE Access, MDPI Computers, and Peer Computer Science, and a member of the editorial board of several international journals, including Computers, Applied System Innovation, Internal Journal of Parallel, Emergent and Distributed Systems. He is 5 currently an IEEE Senior Member and serves on the executive committee of the IEEE Cleveland Section.


Keynote Speaker IV

1559618256168682.png

Prof. Hesham H.Ali

University of Nebraska at Omaha Omaha, USA


Title: Innovative Population Based Approaches for Analyzing Big Biological and Mobility Data in Biomedical Informatics 

Abstract.: With continuous advancements of biomedical instruments and the associated ability to collect diverse types of valuable biological data, research studies have been recently focused on how to best extract useful information from the ‘Big Data’ currently available. The currently available data is not only massive in size, but it also exhibits all the features of complex big data systems with a high degree of variability, veracity and velocity. In addition, the last several years have witnessed major advancements in the development of sensor technologies and wearable devices with the goal of collecting data to be used in various application domains. Although these developments are certainly welcomed, so much left to be done to take full-advantage of the data gathered by such devices. The most critical missing component is the lack of advanced data analytics. How to leverage this raw data to advance biomedical research and improve health care through personalized and targeted medicine, can be considered one of the most exciting scientific challenges of our generation. In this talk, we propose new big data population-based algorithms and tools to analyze different types of biological and mobility data for the purpose of advancing biomedical research and improving healthcare. We employ a population analysis model to assess health levels of individuals as well as to predict health hazards in various medical applications. We also utilize graph-theoretic mechanisms to zoom in and out of the population networks and extract different types of information at various granularity levels to help with prevention, early diagnosis and treatment of infectious and neurodegenerative diseases. 

Bio.: Hesham H. Ali is a Professor of Computer Science and Lee and Wilma Seemann Distinguished Dean of the College of Information Science and Technology at the University of Nebraska at Omaha (UNO). He also serves as the director of the UNO Bioinformatics Core Facility that supports a large number of biomedical research projects in Nebraska. He has published numerous articles in various IT areas including scheduling, distributed systems, data analytics, wireless networks, and Bioinformatics. He has also published two books in scheduling and graph algorithms, and several book chapters in Bioinformatics. He has been serving as the PI or Co-PI of several projects funded by NSF, NIH and Nebraska Research Initiative in the areas of data analytics, wireless networks and Bioinformatics. He has also been leading a Research Group that focuses on developing innovative computational approaches to model complex biomedical systems and analyze big bioinformatics data. The research group is currently developing several next generation big data analytics tools based on the concept of population analysis for mining various types of large-scale biological and medical data. This includes the development of new graph theoretic models for analyzing large heterogeneous biological and health data associated with various biomedical research areas, particularly projects associated with infectious and neurodegenerative diseases, microbiome studies and aging research. He has also been leading two projects for developing secure and energy-aware wireless infrastructure to address tracking and monitoring problems in medical environments, particularly to study mobility profiling for advancing personalized healthcare.