Engineering Colloquium Fall 2022
Each spring and fall, the Engineering Department at SSU presents a series of colloquia on a wide range of engineering topics and trend of technologies. The talks are generally very high-level and designed for the general audience. The Engineering Colloquium was established in Fall 2006 and was initially sponsored by former Agilent Technologies (now Keysight Technologies) and local industries.
Days & Dates: First and Third Thursday of every month
Lecture: 4:00 to 5:00 p.m. including Q &A
For more information, please contact the ES Department at (707) 664-2030 or firstname.lastname@example.org.
The Coming 6th Generation of Mobile Wireless
Mr. Roger Nickols
6G Program Manager
Keysight Technologies, Santa Rosa, CA
Abstract: The first commercial 5G deployments were in March of 2019—barely three years ago and the path to 6G is already a few years under way. It is without a doubt that 6G will be evolution and revolution beyond 5G, but some of the differences are already quite clear. Not only is the technology going to be different, the change in commercial and government approach to commercial wireless systems has already begun. This talk will cover what remains to be realized from the original 5G vision and what to expect from the work on 6G during the next decade.
Bio: Roger Nichols is an acknowledged subject matter expert in mobile wireless communications design and measurement technologies. He has 37 years of engineering and management experience at Hewlett-Packard, Agilent, and Keysight Technologies spanning roles in R&D, marketing, and manufacturing. He has managed projects, programs, and departments beginning with analog cellular radio evolving to 6G and on every standard in between. He directed Keysight’s 5G program starting in 2014 and has been directing Keysight’s 6G program since its inception in 2019. He is a member of the FCC Technical Advisory Council and is also the strategic director of Keysight’s work in wireless standards. Roger holds a BSEE from the University of Colorado, Boulder.
AI Inference with Intel® FPGA AI Suite
Mr. Kevin Drake
Abstract: Intel® FPGAs enable real-time, low-latency, and low-power deep learning inference combined with the following advantages:
- I/O flexibility
- Ease of integration into custom platforms
- Long lifetime
Intel® FPGA AI Suite was developed with the vision of ease-of-use of artificial intelligence (AI) inference on Intel® FPGAs. The suite enables FPGA designers, machine learning engineers, and software developers to create optimized FPGA AI platforms efficiently.
Utilities in the Intel FPGA AI Suite speed up FPGA development for AI inference using familiar and popular industry frameworks such as TensorFlow* or PyTorch* and OpenVINO toolkit, while also leveraging robust and proven FPGA development flows with the Intel Quartus Prime Software. The Intel® FPGA AI Suite tool flow works with the OpenVINO toolkit, an open-source project to optimize inference on a variety of hardware architectures. The OpenVINO toolkit takes Deep Learning models from all the major Deep Learning frameworks (such as TensorFlow, PyTorch, Keras*) and optimizes them for inference on a variety of hardware architectures, including various CPUs, CPU+GPU, and FPGAs.
Bio: Kevin is an Application Engineer at Intel Corporation. He holds a bachelor’s degree in computer science from Sonoma State University and graduated in 2021. He is currently pursuing his master’s degree in data science at Worcester Polytechnic Institute. At Intel, Kevin works with AI Inference and Acceleration using FPGAs and other ASIC devices. He has also worked in the Intel FPGA University Program to help students learn FPGA design and development in a healthy and equitable environment. Prior to Intel, Kevin was an education and training manager for the US Air Force. During this time, he managed 14 career fields’ training and operational readiness.
Class Modular Sensors: Used in Outdoor IoT Monitoring
Mr. Neil Hancock
Abstract: The low-cost Internet of Things architecture is well suited for remote environmental monitoring. This talk covers a device designed to be low cost, solar powered, and used outdoors, for critical stream level monitoring in the drought stricken North Bay. The “open source” Stroud Water Research Center's "Modular Sensors" software and the Mayfly microcomputer facilitate environmental scientists collecting physical measurements from the “great outdoors”. Low-cost board level sensors using ADC, I2C or 1Wire are well understood and relatively low power. External transducers have a wider range of sensing, and require interfaces using the newer USGS SDI-12 three wire water world protocol, and the older stable Modbus 4wire RS485. Each brings new powering challenges. The software code and hardware definitions are stored in git, a distributed “Version Control System” allowing easy incremental traceable improvements. The package comes with technical debt that needs characterizing and analyzing.
Bio: Neil Hancock is a Chartered Engineer, active in circuit design and firmware for 40 years. For the first 20 years he worked with large companies in telecom equipment design and more recently in Internet of Things design for environmental monitoring. Neil volunteers as a Planning Commissioner for the City of Cotati, an Industry Advisor for SSU, is a long-term IEEE member, and enjoys short bicycle rides to coffee shops in Sonoma County. He received his BSc in Electronic Engineering, from City, University of London, United Kingdom.
Chartered Engineers (CEng) develop solutions to engineering problems using new or existing technologies, through innovation, creativity and change and/or they may have technical accountability for complex systems with significant levels of risk.
Photovoltaic Microinverters and Energy Storage Systems, 2022
Mr. Mark Baldassari
Director of Codes and Standards
Enphase Energy, Petaluma, CA
Abstract: The presentation will cover how solar photovoltaic cells convert solar energy into usable power. The effects on energy production due to insolation (sunlight) and temperature. Simple solar systems cover traditional string inverters versus modern microinverters and discusses the safety benefits. Energy storage is discussed starting with why storage is needed for several applications. A simple single line diagram is presented showing how PV and energy storage can be incorporated into a home. A quiz is presented to test the audience on the major topics presented.
Bio: Mr. Baldassari has over 39 years experience in engineering and product development and over 14 years with Enphase Energy, where he holds the position of Director, Codes and Standards. Currently, he actively participates in several Codes and Standards development groups both internationally and domestically. He is involved with the development of the National Electrical Code, product safety standards with Underwriters Laboratory, and international installation and safety standards. Mr. Baldassari has a bachelor’s degree in electrical and Electronic Engineering from California State University Sacramento.
Learning Preferences for Interactive Autonomy
Dr. Erdem Bıyık
Center for Human-Compatible Artificial Intelligence, UC, Berkeley, CA
Abstract: In human-robot interaction or more generally multi-agent systems, we often have decentralized agents that need to perform a task together. In such settings, it is crucial to have the ability to anticipate the actions of other agents. Without this ability, the agents are often doomed to perform very poorly. Humans are usually good at this, and it is mostly because we can have good estimates of what other agents are trying to do. We want to give such an ability to robots through reward learning and partner modeling. In this talk, I am going to talk about active learning approaches to this problem and how we can leverage preference data to learn objectives. I am going to show how preferences can help reward learning in the settings where demonstration data may fail, and how partner-modeling enables decentralized agents to cooperate efficiently.
Bio: Dr. Erdem Bıyık is a postdoctoral researcher at the Center for Human-Compatible Artificial Intelligence at University of California, Berkeley. He has received his B.Sc. degree from Bilkent University, Turkey, in 2017; and Ph.D. degree from Stanford University in 2022. His research interests lie in the intersection of robotics, artificial intelligence, machine learning and game theory. He is interested in enabling robots to actively learn from various forms of human feedback and designing robot policies to improve the efficiency of multi-agent systems both in cooperative and competitive settings. He also worked at Google as a research intern in 2021 where he adapted his active robot learning algorithms to recommender systems. He will join the University of Southern California as an assistant professor in 2023.
Snap-Shot 3D Cameras
Dr. Weijian Yang
Department of Electrical and Computer Engineering, University of California, Davis
Abstract: Three-dimensional (3D) imaging through a compact device could enable many applications in mobile consumer electronics and biomedical endoscopy. Conventional 3D cameras are bulky and require taking multiple photos to synthesize a 3D scene. Here, I will introduce a new type of 3D camera. It replaces all the bulk optics by a single layer of optical mask and can be made very compact. It takes a snap-shot of the 3D object or scene, and then recovers the 3D information through computational algorithms. Here, I will discuss the two 3D cameras that we recently developed, which can image 3D microscopic objects or 3D macroscopic scenes. Both cameras are composed of a single piece of randomly positioned microlens array and an image sensor. We develop highly-efficient computational algorithms to reconstruct the 3D objects and 3D scenes from a single camera exposure. Our 3D cameras open new avenues for high speed 3D imaging with a compact device footprint.
Bio: Weijian Yang is an associate professor at the Department of Electrical and Computer Engineering at the University of California, Davis. He received his undergraduate degree from Peking University and a PhD from the University of California, Berkeley, all in Electrical Engineering. After postdoctoral training in neuroscience at Columbia University, Dr. Yang started his own laboratory at UC Davis in late 2017. His current research aims to develop advanced optical methods and neurotechnologies to interrogate and modulate brain activity, with a goal to understand how neural circuits organize and function and how behaviors emerge from neuronal activity. He is a recipient of the Career Awards at the Scientific Interface from Burroughs Wellcome Fund in 2016, the Early Career Award from National Science Foundation in 2019, and the Science and PINS Prize for Neuromodulation (second prize) from American Association for the Advancement of Science in 2021.