Sam Siewert has studied at University of California Berkeley, University of Notre Dame, University of Houston and University of Colorado Boulder and has a BS in Aerospace and Mechanical Engineering and MS/Ph.D. in Computer Science. He has worked in the computer engineering industry for twenty four years before starting an academic career in 2012. Half of his time was spent on NASA space exploration programs including the Spitzer space telescope, Space Shuttle mission control, and deep space programs. The other half of that time he has spent on commercial product development. His commercial work has ranged from I/O chip firmware architecture to scalable systems design of storage and networking solutions for high performance computing. In 2014 Dr. Siewert joined Embry Riddle Aeronautical University Prescott as full time faculty and retains an adjunct professor role in addition with University of Colorado Boulder. Overall, his focus has been embedded systems with an emphasis on autonomous systems, computer and machine vision, hybrid reconfigurable architecture and operating systems. Related research interests include real-time theory, digital media and fundamental computer architecture. Dr. Siewert has published numerous research, industry, and educational papers on these topics.
Both small satellites (e.g. CubeSats) and sUAS (small Unoccupied Aerial Systems) have a rapidly g... more Both small satellites (e.g. CubeSats) and sUAS (small Unoccupied Aerial Systems) have a rapidly growing number of potential missions and can serve as platforms for a wide range of uses in science, communications, and remote sensing alone or together. The challenge is integrating these systems quickly and reliably with efficient payload operations. While much work has been completed to define and make flight control and operations available as open systems, fewer options exist for instrumentation. Some options are available for specific domains. For example, ROS (Robot Operating System) is used for many UAV (Unoccupied Aerial Vehicle) projects, but interdisciplinary use cases are non-trivial for projects that migrate payloads between UAVs and CubeSats or coordinate UAV and CubeSat joint missions. Common objectives and careful flight and ground configuration for both UAVs and CubeSats could provide a Common Instrument Stack Architecture (CISA). Many of the basic instrument integration objectives for CubeSats and sUAS share common requirements. The goal for the research architectures we are investigating, is to compare combinations of stack layers including: 1) the use of open source real-time operating systems (RTOS), such as FreeRTOS and Linux with real-time extensions, with 2) open middleware, such as ROS, cFS (core Flight System) or F Prime, and 3) image co-processing firmware. Previously developed and tested FPGA (Field Programmable Gate Array) and GP-GPU (General Purpose Graphics Processing Units) firmware for sUAS sensor networks will be integrated with CISA for performance testing. Ideally, we envision this architecture working for sensor networked flight instruments (sUAS and CubeSat) as well as ground systems (e.g. sUAS traffic management and CubeSat ground operations). To start, we are installing and evaluating the leading open-source middleware options in use for both UAVs and CubeSats integrated with our multi-core, GP-GPU, DPS, and FPGA image processing use cases to assess throughput, memory, I/O, storage, and power consumption. Our intent is to evaluate the relative merits of the underlying hardware architecture by running our prototype CISA (Common Instrument System Architecture) on off-the-shelf multi-core, GP-GPU, and FPGA SoCs (Systems-on-Chip) that have flown or are proposed for space flight, are in use with UAVs and provide best power efficiency and performance. This reference CISA stack composed of an SoC, operating system, co-processing, and middleware is hypothesized to allow future projects to migrate instrumentation and sensor payloads from UAVs to CubeSats easily and efficiently. Migration would provide continuity and scaling for research and science missions as well as joint operations. Along with objective metrics such as power and performance, the project is evaluating subjective metrics such as simplicity of integration, configuration, and interfacing common sensors.
2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC), 2019
Millions of small Unmanned Aerial Systems (sUAS), or drones weighing less than 55 pounds, will fl... more Millions of small Unmanned Aerial Systems (sUAS), or drones weighing less than 55 pounds, will fly in urban airspaces within a decade. This is in addition to goals for increased Urban Air Mobility (UAM): larger transportation UAS and piloted air vehicles, including air taxi service and innovative aviation designs to revolutionize transportation. A significant problem is the lack of current management systems for this extremely large volume of air traffic, composed of heterogeneous drone shapes, sizes, and onboard equipment capabilities, flying adjacent to larger UAM vehicles in spaces previously unoccupied by General Aviation (GA). Robust and resilient detection, identification, localization, and tracking of sUAS sharing UAM airspace is complicated by urban factors such as non-transmitting, non-cooperative drones. Exacerbating these challenges, current technological limitations and vulnerabilities in Global Positioning System (GPS), Automatic Dependent Surveillance-Broadcast (ADS-B), and Inertial Navigation Systems (INS) can pose safety and security threats. To streamline a vision of safe and secure sUAS-UAM shared airspace, we propose Drone Net, a UAS Traffic Management (UTM) network of multi-modal ground and flight instruments. Drone Net is an architecture fusing a network of passive visual and acoustic sensor nodes with active Radio Detection and Ranging (radar) and Light Detection and Ranging (lidar) sensing methods for UTM, designed for integration with existing Air Traffic Control (ATC) and future UAM systems. The purpose of the Drone Net system is to evaluate use of Electro-Optical/Infrared (EO/IR) and acoustic arrays networked within an urban UAS operating region such as uncontrolled Class-G and waiver-granted Class-D airspace. The Drone Net approach combines detection, tracking, and localization estimation from radar, ADS-B, and EO/IR such that the design is robust to sensor errors, sample loss, and spoofing or other types of attacks. The system experimental design allows for emulation of corrupted ADS-B, GPS, and INS data on our flight platform including capability to communicate with ground radar and EO/IR to recover from flight instrument major and minor malfunctions such that a Drone Net cooperative UAS can be engineered to be fail-safe and fail-secure. We hypothesize that the Drone Net network of rooftop sensors, shown below, provides this conceptualized fail-safe, fail-secure property based upon the combination of self-localization with backup and confirming data from the ground sensor network. Further, with ground-system sensor fusion as well as UTM flight plan and registration information fusion, the overall system can manage small UAS that are both compliant and non-compliant alongside GA and UAM more safely than a single mode like ADS-B alone. To test our hypothesis, we envision two experiments this year. First, a test to confirm that we can emulate ADS-B, GPS, and INS sensor data corruption, leading to recovery using backup flight (lidar, EO/IR) or ground (radar, EO/IR) data to safely land an ALTA6 or other test UAS. Second, a series of Sense-and-Avoid (SAA) experiments between our test UAS and a tethered aerial obstacle. In the future, we will use this experience with fail-safe, fail-secure methods and software to further test more advanced scenarios between multiple UAS. The network of Drone Net instruments in a local area as well as regional and more global cloud-based networks will allow for heterogeneous information fusion and algorithm development for multi-sensor drone detection, classification, and identification with more accuracy than a single database or sensor system. The latent power of the Drone Net project is in its open-design ground and flight sensor network with data sharing capability to improve data mining and machine learning over time for analysis and security applications. More concrete applications for Drone Net include UTM integration with ATC and UAM, but also airspace safety and security for campus and public venues in general. Herein, we will present our experimental design and current test data from Embry-Riddle Aeronautical University in collaboration with University of Colorado Boulder toward completing the open specification for ground and flight instruments. We would additionally like to invite others to participate in growing the network and the data available for UTM, UAM, and GA shared airspace research.
Collaboration technology calls for new, innovative techniques for supporting informal communicati... more Collaboration technology calls for new, innovative techniques for supporting informal communication and coordinated work. Distributed virtual environments provide one avenue for supporting this aspect of computer-supported work. We have built a multiperson distributed virtual environment using low-cost workstations interconnected with relatively high-speed networks. This domain makes use of interactive and on-demand continuous media in addition to a number of other tasks that fall on a spectrum between hard real-time and best-effort response. A brute force technique for implementing applications in this type of domain demands excessive system resources, even though the actual requirements by different parts of the application vary according to the way the virtual environment is being used at the moment. A more sophisticated approach is to provide applications with the ability to dynamically adjust resource requirements according to their current needs and the availability of system ...
Sam Siewert, Gary Nutt, and Marty Humphrey* Department of Computer Science University of Colorado... more Sam Siewert, Gary Nutt, and Marty Humphrey* Department of Computer Science University of Colorado, Boulder, CO 80309-0430 siewerts@rodin.colorado.edu 1.0 Introduction The RT-PCIP ("Real-Time Parametrically Controlled In-Kernel Pipeline") mechanism introduced in this paper is intended to provide time-critical applications with quantifiable assurance of system response using a simple EPA ("Execution-Performance Agent") interface to the deadline monotonic scheduling algorithm. In addition, the RT-PCIP EPA provides a system call and signal interface which allows applications to monitor and control pipeline real-time performance on-line, and therefore significantly extends existing work on "in-kernel" pipelines. The set of applications requiring this type of performance negotiation support from an operating system is increasing with the emergence of virtual reality environments [Nu95], continuous media [Co94], multimedia [Ste95], digital control [Tr95], and ...
1.0 Introduction The RT-PCIP (“Real-Time Parametrically Controlled In-Kernel Pipeline”) mechanism... more 1.0 Introduction The RT-PCIP (“Real-Time Parametrically Controlled In-Kernel Pipeline”) mechanism introduced in this paper is intended to provide time-critical applications with quantifiable assurance of system response using a simple EPA (“Execution-Performance Agent”) interface to the deadline monotonic scheduling algorithm. In addition, the RT-PCIP EPA provides a system call and signal interface which allows applications to monitor and control pipeline real-time performance on-line, and therefore significantly extends existing work on “in-kernel” pipelines. The set of applications requiring this type of performance negotiation support from an operating system is increasing with the emergence of virtual reality environments [Nu95], continuous media [Co94], multimedia [Ste95], digital control [Tör95], and “shared-control” automation [Bru93][SiNu96]. The RT-PCIP mechanism and EPA are being implemented in the RT-Mach microkernel, and will be tested on a UAV (“Unoccupied AirVehicle”) ...
A Faculty Learning Community (FLC) in any university provides an excellent way for faculty to bot... more A Faculty Learning Community (FLC) in any university provides an excellent way for faculty to both innovate and improve teaching methods and styles. When our FLC, consisting of seven faculty members and two staff members, convened, it became apparent across academic disciplines that undergraduate research warranted emphasis. Undergraduate research integration into curriculum promises benefits: student engagement and development of employer-desired skills such as communication, teamwork, analytical reasoning, and the application of knowledge to real-world settings. This paper details the FLC’s efforts to incorporate more research into seven undergraduate classes by using discovery learning pedagogies and to begin compiling a list of best practices to share with others. The fact that these efforts span different undergraduate grade levels and disciplines offers key insights for any undergraduate program. Further, discussions about the formation and collaboration of the FLC at this uni...
Cloud Data Scaling Total data produced per year surpassed one zettabyte in 2010 and continues to ... more Cloud Data Scaling Total data produced per year surpassed one zettabyte in 2010 and continues to more than double every two years.3 To put this in perspective, about two zettabytes (ZB) of digital universe will be created (per IDC) in 2011 is two million terabytes, or over one million new 3.5" SATA disks with two terabyte (TB) capacities. At the same time, not only is total data stored growing rapidly, but so is storage density. For example, Seagate demonstrated HAMR (Heat Assisted Magnetic Recording) technology with a terabit per square inch hard disk drive that is expected to lead EXECUTIVE SUMMARY The growth in unstructured data is pushing the limits of data center scalability at the same time that disk drive vendors are pushing the limits of data density at tolerable device level bit error rates (BER).1 For organizations delivering Cloud-hosted services
Both small satellites (e.g. CubeSats) and sUAS (small Unoccupied Aerial Systems) have a rapidly g... more Both small satellites (e.g. CubeSats) and sUAS (small Unoccupied Aerial Systems) have a rapidly growing number of potential missions and can serve as platforms for a wide range of uses in science, communications, and remote sensing alone or together. The challenge is integrating these systems quickly and reliably with efficient payload operations. While much work has been completed to define and make flight control and operations available as open systems, fewer options exist for instrumentation. Some options are available for specific domains. For example, ROS (Robot Operating System) is used for many UAV (Unoccupied Aerial Vehicle) projects, but interdisciplinary use cases are non-trivial for projects that migrate payloads between UAVs and CubeSats or coordinate UAV and CubeSat joint missions. Common objectives and careful flight and ground configuration for both UAVs and CubeSats could provide a Common Instrument Stack Architecture (CISA). Many of the basic instrument integration objectives for CubeSats and sUAS share common requirements. The goal for the research architectures we are investigating, is to compare combinations of stack layers including: 1) the use of open source real-time operating systems (RTOS), such as FreeRTOS and Linux with real-time extensions, with 2) open middleware, such as ROS, cFS (core Flight System) or F Prime, and 3) image co-processing firmware. Previously developed and tested FPGA (Field Programmable Gate Array) and GP-GPU (General Purpose Graphics Processing Units) firmware for sUAS sensor networks will be integrated with CISA for performance testing. Ideally, we envision this architecture working for sensor networked flight instruments (sUAS and CubeSat) as well as ground systems (e.g. sUAS traffic management and CubeSat ground operations). To start, we are installing and evaluating the leading open-source middleware options in use for both UAVs and CubeSats integrated with our multi-core, GP-GPU, DPS, and FPGA image processing use cases to assess throughput, memory, I/O, storage, and power consumption. Our intent is to evaluate the relative merits of the underlying hardware architecture by running our prototype CISA (Common Instrument System Architecture) on off-the-shelf multi-core, GP-GPU, and FPGA SoCs (Systems-on-Chip) that have flown or are proposed for space flight, are in use with UAVs and provide best power efficiency and performance. This reference CISA stack composed of an SoC, operating system, co-processing, and middleware is hypothesized to allow future projects to migrate instrumentation and sensor payloads from UAVs to CubeSats easily and efficiently. Migration would provide continuity and scaling for research and science missions as well as joint operations. Along with objective metrics such as power and performance, the project is evaluating subjective metrics such as simplicity of integration, configuration, and interfacing common sensors.
2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC), 2019
Millions of small Unmanned Aerial Systems (sUAS), or drones weighing less than 55 pounds, will fl... more Millions of small Unmanned Aerial Systems (sUAS), or drones weighing less than 55 pounds, will fly in urban airspaces within a decade. This is in addition to goals for increased Urban Air Mobility (UAM): larger transportation UAS and piloted air vehicles, including air taxi service and innovative aviation designs to revolutionize transportation. A significant problem is the lack of current management systems for this extremely large volume of air traffic, composed of heterogeneous drone shapes, sizes, and onboard equipment capabilities, flying adjacent to larger UAM vehicles in spaces previously unoccupied by General Aviation (GA). Robust and resilient detection, identification, localization, and tracking of sUAS sharing UAM airspace is complicated by urban factors such as non-transmitting, non-cooperative drones. Exacerbating these challenges, current technological limitations and vulnerabilities in Global Positioning System (GPS), Automatic Dependent Surveillance-Broadcast (ADS-B), and Inertial Navigation Systems (INS) can pose safety and security threats. To streamline a vision of safe and secure sUAS-UAM shared airspace, we propose Drone Net, a UAS Traffic Management (UTM) network of multi-modal ground and flight instruments. Drone Net is an architecture fusing a network of passive visual and acoustic sensor nodes with active Radio Detection and Ranging (radar) and Light Detection and Ranging (lidar) sensing methods for UTM, designed for integration with existing Air Traffic Control (ATC) and future UAM systems. The purpose of the Drone Net system is to evaluate use of Electro-Optical/Infrared (EO/IR) and acoustic arrays networked within an urban UAS operating region such as uncontrolled Class-G and waiver-granted Class-D airspace. The Drone Net approach combines detection, tracking, and localization estimation from radar, ADS-B, and EO/IR such that the design is robust to sensor errors, sample loss, and spoofing or other types of attacks. The system experimental design allows for emulation of corrupted ADS-B, GPS, and INS data on our flight platform including capability to communicate with ground radar and EO/IR to recover from flight instrument major and minor malfunctions such that a Drone Net cooperative UAS can be engineered to be fail-safe and fail-secure. We hypothesize that the Drone Net network of rooftop sensors, shown below, provides this conceptualized fail-safe, fail-secure property based upon the combination of self-localization with backup and confirming data from the ground sensor network. Further, with ground-system sensor fusion as well as UTM flight plan and registration information fusion, the overall system can manage small UAS that are both compliant and non-compliant alongside GA and UAM more safely than a single mode like ADS-B alone. To test our hypothesis, we envision two experiments this year. First, a test to confirm that we can emulate ADS-B, GPS, and INS sensor data corruption, leading to recovery using backup flight (lidar, EO/IR) or ground (radar, EO/IR) data to safely land an ALTA6 or other test UAS. Second, a series of Sense-and-Avoid (SAA) experiments between our test UAS and a tethered aerial obstacle. In the future, we will use this experience with fail-safe, fail-secure methods and software to further test more advanced scenarios between multiple UAS. The network of Drone Net instruments in a local area as well as regional and more global cloud-based networks will allow for heterogeneous information fusion and algorithm development for multi-sensor drone detection, classification, and identification with more accuracy than a single database or sensor system. The latent power of the Drone Net project is in its open-design ground and flight sensor network with data sharing capability to improve data mining and machine learning over time for analysis and security applications. More concrete applications for Drone Net include UTM integration with ATC and UAM, but also airspace safety and security for campus and public venues in general. Herein, we will present our experimental design and current test data from Embry-Riddle Aeronautical University in collaboration with University of Colorado Boulder toward completing the open specification for ground and flight instruments. We would additionally like to invite others to participate in growing the network and the data available for UTM, UAM, and GA shared airspace research.
Collaboration technology calls for new, innovative techniques for supporting informal communicati... more Collaboration technology calls for new, innovative techniques for supporting informal communication and coordinated work. Distributed virtual environments provide one avenue for supporting this aspect of computer-supported work. We have built a multiperson distributed virtual environment using low-cost workstations interconnected with relatively high-speed networks. This domain makes use of interactive and on-demand continuous media in addition to a number of other tasks that fall on a spectrum between hard real-time and best-effort response. A brute force technique for implementing applications in this type of domain demands excessive system resources, even though the actual requirements by different parts of the application vary according to the way the virtual environment is being used at the moment. A more sophisticated approach is to provide applications with the ability to dynamically adjust resource requirements according to their current needs and the availability of system ...
Sam Siewert, Gary Nutt, and Marty Humphrey* Department of Computer Science University of Colorado... more Sam Siewert, Gary Nutt, and Marty Humphrey* Department of Computer Science University of Colorado, Boulder, CO 80309-0430 siewerts@rodin.colorado.edu 1.0 Introduction The RT-PCIP ("Real-Time Parametrically Controlled In-Kernel Pipeline") mechanism introduced in this paper is intended to provide time-critical applications with quantifiable assurance of system response using a simple EPA ("Execution-Performance Agent") interface to the deadline monotonic scheduling algorithm. In addition, the RT-PCIP EPA provides a system call and signal interface which allows applications to monitor and control pipeline real-time performance on-line, and therefore significantly extends existing work on "in-kernel" pipelines. The set of applications requiring this type of performance negotiation support from an operating system is increasing with the emergence of virtual reality environments [Nu95], continuous media [Co94], multimedia [Ste95], digital control [Tr95], and ...
1.0 Introduction The RT-PCIP (“Real-Time Parametrically Controlled In-Kernel Pipeline”) mechanism... more 1.0 Introduction The RT-PCIP (“Real-Time Parametrically Controlled In-Kernel Pipeline”) mechanism introduced in this paper is intended to provide time-critical applications with quantifiable assurance of system response using a simple EPA (“Execution-Performance Agent”) interface to the deadline monotonic scheduling algorithm. In addition, the RT-PCIP EPA provides a system call and signal interface which allows applications to monitor and control pipeline real-time performance on-line, and therefore significantly extends existing work on “in-kernel” pipelines. The set of applications requiring this type of performance negotiation support from an operating system is increasing with the emergence of virtual reality environments [Nu95], continuous media [Co94], multimedia [Ste95], digital control [Tör95], and “shared-control” automation [Bru93][SiNu96]. The RT-PCIP mechanism and EPA are being implemented in the RT-Mach microkernel, and will be tested on a UAV (“Unoccupied AirVehicle”) ...
A Faculty Learning Community (FLC) in any university provides an excellent way for faculty to bot... more A Faculty Learning Community (FLC) in any university provides an excellent way for faculty to both innovate and improve teaching methods and styles. When our FLC, consisting of seven faculty members and two staff members, convened, it became apparent across academic disciplines that undergraduate research warranted emphasis. Undergraduate research integration into curriculum promises benefits: student engagement and development of employer-desired skills such as communication, teamwork, analytical reasoning, and the application of knowledge to real-world settings. This paper details the FLC’s efforts to incorporate more research into seven undergraduate classes by using discovery learning pedagogies and to begin compiling a list of best practices to share with others. The fact that these efforts span different undergraduate grade levels and disciplines offers key insights for any undergraduate program. Further, discussions about the formation and collaboration of the FLC at this uni...
Cloud Data Scaling Total data produced per year surpassed one zettabyte in 2010 and continues to ... more Cloud Data Scaling Total data produced per year surpassed one zettabyte in 2010 and continues to more than double every two years.3 To put this in perspective, about two zettabytes (ZB) of digital universe will be created (per IDC) in 2011 is two million terabytes, or over one million new 3.5" SATA disks with two terabyte (TB) capacities. At the same time, not only is total data stored growing rapidly, but so is storage density. For example, Seagate demonstrated HAMR (Heat Assisted Magnetic Recording) technology with a terabit per square inch hard disk drive that is expected to lead EXECUTIVE SUMMARY The growth in unstructured data is pushing the limits of data center scalability at the same time that disk drive vendors are pushing the limits of data density at tolerable device level bit error rates (BER).1 For organizations delivering Cloud-hosted services
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