IEEE North Jersey Section

Microstrip antennas are in massive demand for several emerging applications such as biomedical devices, ultrawideband communication devices, radar applications, etc. Microstrip antennas are inherently narrowband and low gain antennas. Since their inception, various methods have been introduced to improve them. Most of the methods depend on the software that needs unlimited optimization runs, which increases the antenna design's computation cost. Theory of Characteristic Modes (TCM) is the powerful analysis technique that made antenna structure easy to understand and comprehend. As it gives physical insight into the antenna's structure, the analysis and design of the antenna may not need many optimization cycles. It was first introduced in the 1970s, but it was not till 2007 that this method hit the modern world again for planar antenna design. The reason behind the unmatched success of this method was the details it gives about the surface current behavior of the antenna in a user-friendly manner. Since then, CMA (Characteristic Mode Analysis) has been successfully applied to analyze planar structures and helps design and improve specific characteristics of antennas. Most of my research focused on designing and optimizing the UWB antennas using CMA. CMA can fully illustrate antenna structure with a finite number of resonant modes. Hence, it has the potential of simplifying complicated and tedious antenna design procedures.This talk will focus on the following: (a) Can characteristic modes analysis define the future of antenna design procedures by helping to reduce the complexity of antenna design procedure? (b) the possibilities of producing the compact and efficient antenna design using characteristic modes. (c) how surface currents can be controlled to produce optimized antenna designs.Speaker(s): Dr. Mahrukh Khan, Ph.D., Virtual: https://events.vtools.ieee.org/m/313636

IEEE North Jersey Section: RAS Chapter and SMC Chapter joint seminar onHuman-Robot Collaborative Learning of Human Welder Intelligence for Enhancing Human Welder Skills and Robotizing Complex Welding ProcessesYuMing ZhangJames R. Boyd Professor of Electrical Engineering, University of KentuckPlace: https://njit.webex.com/meet/zhouTime: 9:30am-10:30am, May 21, 2022For precision joining, skilled welders currently overperform welding robots due to their adaptation to the process. This is a kind of human intelligence that may be used to equip welding robots for them to become more intelligent and help welders to reduce the needed training time. A major challenge in learning such intelligence arises from the specular nature of pool surface that disqualifies diffuse reflection-based laser triangulation methods. To overcome this issue, the mirror surface is advantageously used to reflect a laser pattern away from the arc, simultaneously eliminating the arc illumination problem. To allow welders to freely demonstrate their skills, a human-robot collaborative system has been established where a welder carries a virtual torch, similarly as operating an actual one, without a sensor. The movement is measured at the virtual system and then followed by a robot which carries the sensor and performs the actual welding. The measured weld pool is displayed to the operator at the virtual site such that the welder can observe the change in the operation result to adjust his/her torch movement and other parameters. The true intelligence of the welder is thus contained in and can thus be extracted from the resultant data. For more complex welding processes that require operations of multiple welding torches/tools, their robotization are more challenging complex. A possible solution is also to learn from human welders as they are quicker learners who can adjust their operations to stabilize the complex welding process. This involves simultaneous operations from multiple welders and capture of the data needed to learn. Human-robot collaboration again provides an environment to enable the operation and capture the “true” data for learning of the intelligence needed despite the complexity of multiple operations.Bio: Dr. YuMing Zhang’s research focuses on intelligent robotic and human-robot collaborative welding systems. His research has been supported by the NSF, Navy, National Labs and industry, brought him 12 US patents, and over 200 journal publications. His recognition includes Fellow of the American Welding Society (AWS), Fellow of the American Society of Mechanical Engineers (ASME), and Fellow of the Society of Manufacturing Engineers (SME); Dean’s Award for Excellence in Research from the College of Engineering. Five of his graduate students won the IIW (International Welding Institute) Henry Granjon Prize on behalf of the US against IIW member countries’ national winners for dissertation/thesis research. Dr. Zhang is currently one of the two Editors for the Journal of Manufacturing Processes published by the SME. He is, and has been, Associate Editor/Editorial Board Member for a number of major international journals including the IEEE Transactions on Automation Science and Engineering.Speaker(s): Yuming Zhang, 323 Dr Martin Luther King Jr Blvd., ECE-NJIT, Newark, New Jersey, United States, 07102-1982

The IEEE North Jersey Section's Executive Committee (EXCOM) monthly meeting will be held ONLINE using Zoom.The EXCOM meeting starts at 6:00 pm EST and typically ends at 8:30 pm.The meeting is meant to discuss and coordinate the activities of the Section and its Chapters and Groups, as well as new initiatives.Everyone is welcome to attend this meeting.Please register in advance for this meeting using vTools.You can change/cancel the registration if your plans change.For more information, please contact our IEEE North Jersey Section Chair Ajay Poddar ([email protected]), or Secretary, Adriaan van Wijngaarden, ([email protected]).Zoom meeting information: https://us02web.zoom.us/j/89067811667Meeting ID: 890 6781 1667Note: If you are unable to join the meeting, please send your chapter activity report to the section chair at [email protected]: 06:00 pm - 06:30 pm Networking06:30 pm - 08:30 pm IEEE North Jersey Section Executive Committee MeetingThe meeting agenda typically includes news related to the IEEE and the IEEE North Jersey Section, Secretary and Treasurer Reports, Chapter and Affinity Group Reports, Committee Reports, and reports by various Chairs and Representatives, Committee Chairs, and planning, and new initiatives.Virtual: https://events.vtools.ieee.org/m/314373