Case 2:17-cv-00932-JLR Document 61-3 Filed 03/09/18 Page 1 of 25
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`EXHIBIT C
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`EXHIBIT C
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`Case 2:17-cv-00932-JLR Document 61-3 Filed 03/09/18 Page 2 of 25
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`IN THE UNITED STATES DISTRICT COURT
`FOR THE WESTERN DISTRICT OF WASHINGTON
`SEATTLE DIVISION
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` CASE NO. 2:17-cv-00932
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`JURY TRIAL DEMANDED
`
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`CYWEE GROUP LTD.,
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`Plaintiff,
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`HTC CORPORATION
`and
`HTC AMERICA, INC.,
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`Defendants.
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`
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`DECLARATION OF NICHOLAS GANS, PH.D.
`I, Nicholas Gans, Ph.D., hereby declare as follows:
`I have been asked by counsel for Plaintiff CyWee Group Ltd. (“CyWee”) to offer
`1.
`information and my opinions as to the technologies disclosed in U.S. Patent No. 8,441,438 (the
`“’438 patent”) and U.S. Patent No. 8,552,978 (the “’978 Patent”).
`In connection with the preparation of this Declaration, I have reviewed the
`2.
`materials listed below:
`The ’438 patent;
`•
`•
`The file wrapper for the ’438 patent;
`•
`The ’978 patent; and
`•
`The file wrapper for the ’978 patent.
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`Exhibit C, 17-cv-932-JLR-001
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`Shore Chan DePumpo LLP
`901 Main St., Ste. 3300
`Dallas, TX 75202
`214.593.9110
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`Case 2:17-cv-00932-JLR Document 61-3 Filed 03/09/18 Page 3 of 25
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`All of the opinions stated in this declaration are based on my personal knowledge
`3.
`and professional judgment. If called as a witness, I am prepared to testify competently about
`them.
`I.
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`EXPERIENCE AND QUALIFICATIONS
`I am a Clinical Associate Professor with the Department of Electrical Engineering
`4.
`at the University of Texas at Dallas.
`I received my doctorate in Systems and Entrepreneurial Engineering from the
`5.
`University of Illinois at Urbana-Champaign, with dissertation research in the fields of robotics,
`controls, and estimation. I continue to research and teach these topics in my capacity as a
`Professor, with over 100 peer reviewed publications and three patents. I have authored multiple
`papers on the topic of Inertial Measurement Units and related sensors and fusion algorithms.
`A more complete list of my qualifications is set forth in my curriculum vitae, a
`6.
`copy of which is attached hereto as Exhibit A.
`I am being compensated for work in this matter. My compensation in no way
`7.
`depends on the outcome of this litigation, nor do I have a personal interest in the outcome of this
`litigation.
`NATURE OF THE DISCLOSED TECHNOLOGIES
`II.
`The ’438 patent and ’978 patent disclose devices and methods for tracking the
`8.
`motion of a device in 3D space and compensating for accumulated errors. That is, at a high level,
`the patented inventions teach how to determine a device’s current orientation based on motion
`data detected by its motion sensors, such as an accelerometer, gyroscope, and magnetometer.
`There are different types of motion sensors, including accelerometers, gyroscopes,
`9.
`and magnetometers. Accelerometers measure accelerations. For example, airbags use
`accelerometers, such that the airbag is triggered based on sudden deceleration. Accelerometers
`can also measure forces due to gravity. Gyroscopes measure rotation rates or angular velocities.
`Magnetometers measure magnetism, including the strength of a magnetic field along a particular
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`Exhibit C, 17-cv-932-JLR-002
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`Shore Chan DePumpo LLP
`901 Main St., Ste. 3300
`Dallas, TX 75202
`214.593.9110
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`Case 2:17-cv-00932-JLR Document 61-3 Filed 03/09/18 Page 4 of 25
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`direction. Each type of motion sensor is subject to inaccuracies. For example, a gyroscope sensor
`has a small, added offset or bias. This bias will accumulate over time and lead to large drift error.
`Similarly, magnetometers are subject to interference from natural and manmade sources (e.g.
`power electronics). Additionally, errors can accumulate over time. These sensors typically take
`measurements along a single direction. To accurately measure motions along an arbitrary axis,
`three like sensors are grouped together and aligned at right angles. Such a sensor set is generally
`referred to as a 3-axis sensor.
`To incorporate the data from multiple sensors and compensate for the errors
`10.
`described above, the ’438 patent and ’978 patent each disclose a sensor fusion technology.
`Specifically, the ’438 patent discloses an enhanced sensor fusion technology and application for
`calculating orientation (including tilting angles along all three spatial axes) by using
`measurements from both a 3-axis accelerometer and a 3-axis gyroscope; furthermore, it can
`correct or eliminate errors associated with the motion sensors. This technology is especially
`suited for accurately representing a mobile device’s orientation in 3D space on a 2D display
`screen by mapping the yaw, pitch, and roll angles relating to movement along the three spatial
`axes to a 2D display reference frame. Simply put, the ’438 patent discloses an improved system
`and method to capture motion of the device and for eliminating or correcting errors based on
`movements and rotations of the device.
`Likewise, the ’978 patent discloses a similar enhanced sensor fusion technology
`11.
`for calculating orientation. Unlike the ’438 patent, which discloses and claims using two motion
`sensors—an accelerometer and gyroscope—the ’978 patent discloses and claims using a third
`sensor—a magnetometer.
`Orientation information returned by the claimed inventions of the ’438 and ’978
`12.
`patents has many uses, particularly for mobile cellular devices, such as navigation, gaming, and
`augmented/virtual reality applications. Navigation applications can use orientation information to
`determine the heading of the phone, indicate what direction the user is facing, and automatically
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`Exhibit C, 17-cv-932-JLR-003
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`Shore Chan DePumpo LLP
`901 Main St., Ste. 3300
`Dallas, TX 75202
`214.593.9110
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`

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`Case 2:17-cv-00932-JLR Document 61-3 Filed 03/09/18 Page 5 of 25
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`orient the map to align with the cardinal directions. Increasing numbers of games and other
`applications use the motion of the phone to input commands, such as tilting the mobile device
`like a steering wheel. Augmented and virtual reality applications rely on accurate estimation of
`the device orientation in order to render graphics and images at the proper locations on the
`screen.
`III. OPINION REGARDING ADVANTAGES OVER PRIOR ART
`In the past, motion sensors had limited applicability to handheld pointing devices
`13.
`due to a variety of technological hurdles. For example, different types of acceleration (e.g.,
`linear, centrifugal, gravitational) could not be readily distinguished from one another, and rapid,
`dynamic, and unexpected movements caused significant errors and inaccuracies. These
`difficulties were compounded by the miniaturization of the sensors necessary to incorporate them
`in handheld devices. With the development of micro-electromechanical systems, or “MEMS,”
`miniaturized motion sensors could be manufactured and incorporated on a semiconductor chip,
`but such MEMS sensors had significant limitations.
`For example, it is impossible for MEMS accelerometers to distinguish different
`14.
`types of acceleration (e.g., linear, centrifugal, gravitational). When a MEMS accelerometer is
`used to estimate orientation, it must measure force along the direction of gravity (i.e., down), but
`that gravitational measurement can be “interfused” with other accelerations and forces (e.g.,
`vibration or movement by the person holding the device). Thus, non-gravitational accelerations
`and forces must be estimated and subtracted from the MEMS accelerometer measurement to
`yield an accurate result. A MEMS gyroscope is prone to drift, which will accumulate increasing
`errors over time if not corrected by another sensor or recalibrated. A MEMS magnetometer is
`highly sensitive to not only the earth’s magnetic fields, but other sources of magnetism (e.g.,
`power lines and transformers) and can thereby suffer inaccuracies from environmental sources of
`interference that vary both in existence and intensity from location to location.
`Additionally, orientation cannot be accurately calculated using only one type of
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`Exhibit C, 17-cv-932-JLR-004
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`Shore Chan DePumpo LLP
`901 Main St., Ste. 3300
`Dallas, TX 75202
`214.593.9110
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`Case 2:17-cv-00932-JLR Document 61-3 Filed 03/09/18 Page 6 of 25
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`MEMS sensor. For example, if only a 3-axis MEMS accelerometer is used to measure
`orientation, pitch and yaw can be measured, but not roll. If only a MEMS gyroscope is used to
`measure angular velocity, only relative changes in orientation can be measured, not absolute
`orientation.
`The ’438 patent and ’978 patent technologies overcome technological hurdles
`16.
`such as those discussed above in a unique and novel way by incorporating measurements from
`multiple sensors for increased accuracy and algorithms to compensate for accumulated errors.
`The ’438 patent discloses a system and method for interactively and iteratively fusing angular
`velocity measurements in the x, y, and z directions and axial acceleration measurements in the x,
`y, and z directions such that the measurements complement each other according to a specific
`algorithm. The limitations of the specific measurements and accumulated errors therein are
`overcome and eliminated. The ’978 patent builds upon the ’438 patent and further incorporates
`magnetism measurements in the x, y, and z directions to further compliment the accuracy.
`17. Without orientation information, mobile device apps would be limited to very
`static operation. This was the scenario with initial smart phones and other mobile devices.
`Navigation aids could render a map and indicate the location of the device using GPS. However,
`these maps would orient with North on the map pointing to the top of the screen. The user could
`rotate the map using touch commands, but the map would not rotate automatically as the user
`turned. Nor could the device indicate what direction the device was facing.
`18. Many games use motion of the device to control the game. A common control
`scheme, especially for driving and piloting games, is to have the user rotate the phone or device
`like a steering wheel to indicate the direction the vehicle should move. Some puzzle games also
`use motions to cause elements of the game to move. As discussed previously, accelerometers
`measure acceleration, which is a very noisy signal. Acceleration is the derivative of velocity,
`which is the derivative of position. Small magnitude noise can have large derivatives, which
`means that small levels of noise from vibration or electrical fluctuations will be magnified at the
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`Exhibit C, 17-cv-932-JLR-005
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`Shore Chan DePumpo LLP
`901 Main St., Ste. 3300
`Dallas, TX 75202
`214.593.9110
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`Case 2:17-cv-00932-JLR Document 61-3 Filed 03/09/18 Page 7 of 25
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`acceleration level. Even a stationary device will have notable noise measured by an
`accelerometer. A moving device will only amplify this noise. Since accelerometers measure
`linear and centripetal accelerations as well as the acceleration of gravity, orientation estimates on
`a moving device will not be accurate. In Figure 11, we conducted an experiment where an
`accelerometer was placed on a moving pendulum with a very accurate angle measurement from
`an optical encoder. When the pendulum is rotated slowly, the accelerometer is fairly accurate,
`but during moderate or fast motion it shows significant errors.
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`Acceleration
`Encoder
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`Angle (radians)
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`If only an accelerometer is used, a coarse estimate of the device orientation can be
`19.
`obtained by averaging or numerically filtering the results. Essentially, the device can determine
`if it is tilted left or right, up or down, but the exact angle cannot be estimated accurately while in
`motion. This is suitable for games to move a character or steer a vehicle in a particular direction,
`but generally cannot utilize the magnitude of tilt to move at corresponding faster or slower
`speeds.
`20. Without orientation sensors, games could be controlled using traditional
`“joystick” type inputs. For smart phones with touch screens, commands are given by having the
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`1 T. R. Bennett, R. Jafari and N. Gans, "Motion Based Acceleration Correction for Improved
`Sensor Orientation Estimates," 2014 11th International Conference on Wearable and Implantable
`Body Sensor Networks, Zurich, 2014, pp. 109-114.
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`Exhibit C, 17-cv-932-JLR-006
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`Shore Chan DePumpo LLP
`901 Main St., Ste. 3300
`Dallas, TX 75202
`214.593.9110
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`

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`Case 2:17-cv-00932-JLR Document 61-3 Filed 03/09/18 Page 8 of 25
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`user touch specific parts of the screen.
`Augmented reality (AR) and virtual reality (VR) are new and growing classes of
`21.
`applications for smart phones and mobile devices. In AR, the device camera provides live video
`feed to the screen, and the application overlays generated graphics onto the screen at specific
`locations. AR navigation apps can draw signs or labels to indicate what specific places or objects
`are, or can render arrows or other indicators. AR games and teaching applications can label
`objects or draw characters or items such that they appear as if they are in the real world seen in
`the video. Virtual reality is similar but does not use the camera, rather it completely renders an
`artificial 3D environment on the screen. VR most often requires a head set such that the user only
`sees the screen. Mobile devices and smart phones used for VR generally split the screen and
`display to two side-by-side images of the rendered environment that are slightly offset to
`simulate a left and right eye. The device then sits in a headset with lenses such that the user has
`each eye see only one of the split-screen images and has a sense of stereo (3D) vision.
`22. Without orientation sensing, AR and VR applications cannot work. The system
`will have no ability to understand the orientation of the device and know where to draw objects
`and/or the scene. The rough orientation estimate provided by an accelerometer (ideally with a
`magnetometer) will not be sufficient to track during typical head motions. It has been
`demonstrated the VR applications that use an accelerometer often cause motion sickness, as the
`rendered images do track with the head motions. An AR application with the use of a gyroscope
`and fusion algorithm will not render objects at the correct locations, and may obscure the view
`rather than provide helpful information.
`There are ways to estimate orientation other than the approaches presented in the
`23.
`’438 patent or ’978 patent, which involve algorithms that filter and fuse measurements from
`inertial and magnetic sensors. Most such methods are based on cameras and computer vision
`algorithms. However, the limitations of these methods render them unusable for mobile devices.
`For example, there are a variety of motion capture systems that use cameras arrayed around an
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`Exhibit C, 17-cv-932-JLR-007
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`Shore Chan DePumpo LLP
`901 Main St., Ste. 3300
`Dallas, TX 75202
`214.593.9110
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`Case 2:17-cv-00932-JLR Document 61-3 Filed 03/09/18 Page 9 of 25
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`environment. Markers (e.g. reflective balls) can be placed on objects, and the cameras can locate
`the markers, often to sub-mm accuracy. If an object has three or more markers on it, the
`orientation of the object can be determined with sub-degree accuracy. This method is very
`accurate, but quite expensive (often about $100,000). The cameras are fixed in place, and the
`estimation can only work within a small space (a box of dimensions on the order of tens of
`meters). Clearly, this is not suitable for the vast majority of mobile device users or applications.
`Some high-end VR systems use such an approach, but this is not feasible for general consumers.
`A camera on a mobile device, such as a smart phone, can be used to estimate
`24.
`orientation of the phone. One class of approaches to this problem uses special patterns or
`markers in the environment. These often have the appearance of a QR code or 2D UPC. Taking a
`picture of the pattern, computer vision algorithms can determine the position and orientation of
`the camera with respect to the marker. AR applications have placed the patterns on specific
`objects or consumer products so the device can render images and graphics with respect to the
`pattern. AR games have included patterned mats that are placed on a table or other flat surface,
`and the device renders characters and objects as if they were on the surface. An example of a
`game using different patterned mats is seen in Figure 22.
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`2 https://pokemondb.net/spinoff/pokedex-3d
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`Exhibit C, 17-cv-932-JLR-008
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`Shore Chan DePumpo LLP
`901 Main St., Ste. 3300
`Dallas, TX 75202
`214.593.9110
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`Case 2:17-cv-00932-JLR Document 61-3 Filed 03/09/18 Page 10 of 25
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`Figure 2
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`25. Multiple unique patterns can be placed around an environment; so long as one is
`always in view, the camera can maintain an estimate of the orientation and position. In this way,
`it can be used for navigation. An example is seen in Figure 33. The necessity of placing patterns
`would make this approach useless for a majority of applications, particularly outdoors. The
`camera would also need to remain on at all times, which would cause severe battery drain.
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`3 This is an unpublished image from Dr. Gans’ research laboratory.
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`Exhibit C, 17-cv-932-JLR-009
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`Shore Chan DePumpo LLP
`901 Main St., Ste. 3300
`Dallas, TX 75202
`214.593.9110
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`

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`Case 2:17-cv-00932-JLR Document 61-3 Filed 03/09/18 Page 11 of 25
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`Figure 3
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`26. Orientation of the camera can also be estimated over an indefinite amount of time
`using vision algorithms known as visual odometry. In visual odometry, changes in the image
`over time are used to estimate the camera velocity. This velocity can be integrated over time to
`estimate the change in orientation. While these methods are well understood, they can only track
`change in relative orientation, not give absolute orientation. They also require the camera to be
`on at all times, which will greatly reduce battery life.”
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`Dated: July 6, 2017
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`______________________________
`Nicholas Gans, Ph.D.
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`Exhibit C, 17-cv-932-JLR-010
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`Shore Chan DePumpo LLP
`901 Main St., Ste. 3300
`Dallas, TX 75202
`214.593.9110
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`

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`Case 2:17-cv-00932-JLR Document 61-3 Filed 03/09/18 Page 12 of 25
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`EXHIBIT A
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`Exhibit C, 17-cv-932-JLR-011
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`Shore Chan DePumpo LLP
`901 Main St., Ste. 3300
`Dallas, TX 75202
`214.593.9110
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`

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`Case 2:17-cv-00932-JLR Document 61-3 Filed 03/09/18 Page 13 of 25
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`Nicholas R. Gans
`Curriculum Vitae
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`Phone: (972) 883-6755
`Fax: (972) 883-2710
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`ngans@utdallas.edu, nrgans@gmail.com
`Department of Electrical Engineering, MS: EC33
`The University of Texas at Dallas
`800 W. Campbell Rd.
`Richardson, TX 75080, USA
`http://www.utdallas.edu/~ngans
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`CURRENT POSITION
`• Assistant Professor – Department of Electrical Engineering,
`University of Texas at Dallas, Aug. 2009 – present
`
`EDUCATION
`• Ph.D. Systems and Entrepreneurial Engineering
`University of Illinois Urbana Champaign - December 2005
`Dissertation: Hybrid Switched System Visual Servo Control
`Advisor: Seth Hutchinson
`• M.S. Electrical and Computer Engineering
`University of Illinois Urbana Champaign - May 2002
`Thesis: Performance Tests of Partitioned Approaches To Visual Servo Control
`Advisor: Seth Hutchinson
`• B.S., Cum Laude, Electrical Engineering and Applied Physics, Minor in Philosophy
`Case Western Reserve University - May 1999
`
`RESEARCH INTERESTS
`• Vision-Based Control and State Estimation
`• Mobile Robot Control
`• Multi-Agent/Distributed Control and Estimation
`• Self-Optimizing Systems
`• Nonlinear Control
`• Hybrid Switched-System Control
`• Computer Vision
`• Human/Machine Interaction
`• Engineering Ethics Education
`
`RESEARCH EXPERIENCE
`• Postdoctoral Associate – National Research Council/Air Force Research Laboratory
`Eglin Air Force Base, Sep. 2008 – July 2009
`Directed by David Jeffcoat
`• Postdoctoral Researcher - Nonlinear Controls and Robotics Lab
`University of Florida, Jan. 2006 – Aug. 2008
`Directed by Dr. Warren Dixon
`• Graduate Research Assistant - Robot Motion Planning and Control Lab
`University of Illinois Urbana Champaign, Jan. 2002 - Dec. 2005
`Directed by Dr. Seth Hutchinson
`
`Nicholas Gans
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`Curriculum Vitae
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`Page | 1
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`Exhibit C, 17-cv-932-JLR-012
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`Shore Chan DePumpo LLP
`901 Main St., Ste. 3300
`Dallas, TX 75202
`214.593.9110
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`

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`Case 2:17-cv-00932-JLR Document 61-3 Filed 03/09/18 Page 14 of 25
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`CURRENT GRANTS
` PI, “Distributed Control and Vision-Based Estimation for UAS Autonomy,” Air Force Research
`Laboratory Munitions Directorate, 5/01/2017 – 4/31/2019, $75,000
` PI, “Engineering Projects in Community Service,“ UT Dallas Center for Teaching and Learning
`Instructional Improvement Grant, 9/1/2016-8/31/2017, $5000
` PI, “Vision-Based Control of Quadrotor UAVs”, Kasling Aircraft, 9/1/2016-12/1/2016, $8,098
` PI, “GOALI: Adaptive Control of Inkjet Printing on 3D Curved Surfaces”, National Science
`Foundation, 5/1/2016 – 4/30/2019, $294,452
` PI, “Vision-Based Surface Shape & Condition Estimation”, Texas Instruments, 12/1/15 -
`11/30/17, $70,000
` PI, “ICC for Olalekan Ogunmolu” (Soft for RadioSurgery), UT Southwestern Medical Center,
`9/1/2014 – 5/31/2016, $13,320
` Co-PI, “Engineering Ethics as an Expert Guided and Socially Situated Activity,” NSF,
`9/1/2013-8/31/2017, $299,967
`
`
`PREVIOUS GRANTS
`
` PI, “Smart City Transportation System: Real-Time Multi-Objective Optimization of
`Autonomous Multi-Agent Controllers in an Orchestrated Resource Environment for Adaptive
`and Responsive Traffic Management,” Texas Research Alliance, 10/1/2015-6/1/2016, $40,000
` PI, “Human Machine Interface for Including Out-of Sequence Information in UAV Target
`Search,” Air Force Research Laboratory, 9/23/2013 – 5/23/2016, $159,998
` Co-PI, “Cooperative Robot Manipulation”, Daegu Gyeongbuk Institute of Science And
`Technology, 3/1/2013 12/31/2015, $272,961
` PI, “3D Interactive Camera/Projector Display Unit for Human/Robot Interaction”, Texas
`Instruments, 1/1/13 - 12/28/15, $105,000
` PI, “Development of Algorithms and an Instructional Lab for Actin Robot Control Software
`and Cyton Robot Arm ,” Energid Technologies Corporation, 1/15/13 – 6-15-13, $23,556
` Co-PI, “Development of an Adaptive Radar Laboratory,” Mustang Technology Group, $35,000
` PI, “System Characterizations of the Scanning Tunneling Microscope”, Zyvex Corporation,
`5/30/2011 - 8/30/2011, $3,084
` PI, “Super-resolution of Target Details for Improved Target State Estimation and
`Classification,” University of Texas Catalyst Grant, June 2010-August 2011, $40,000
` PI, “Hardware in the Loop Simulation for Vision-Based Control of Autonomous Vehicles,” Air
`Force Research Lab grant FA8651-10-1-003, January 2010-September 2010, $10,000
`
`
`TEACHING EXPERIENCE
`•Course Instructor University of Texas at Dallas
`o Engineering Projects in Community Service – Spring 2016, Fall 2016
`Linear Systems and Signals (EECs 6331) – Spring 2010, Spring 2012, Spring 2013, Spring
`2014, Spring 2015, Spring 2016
`o Introduction to Robotics (EEGR 5V80, ENGR 5375) – Spring 2011, Fall 2013, Fall 2014,
`Fall 2015, Fall 2015
`
`Nicholas Gans
`
`Curriculum Vitae
`
`Page | 2
`
`Exhibit C, 17-cv-932-JLR-013
`
`Shore Chan DePumpo LLP
`901 Main St., Ste. 3300
`Dallas, TX 75202
`214.593.9110
`
`

`

`Case 2:17-cv-00932-JLR Document 61-3 Filed 03/09/18 Page 15 of 25
`
`o Senior Design 2 (EE 4389) – Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring
`2016
`o Vision-Based Estimation and Control (EESC 7V85 ) – Fall 2010, Fall 2012
`o Electronic Circuits Laboratory (EE 3111) – Summer 2011, Summer 2012
`o Systems and Controls (EE 4310) – Fall 2011
`
`
`
`
`
`• One Day Workshop Recent Advances in Extremum Seeking Control and its Applications
`19th IFAC World Congress, Capetown, South Africa, Sep. 2014
`• One Day Workshop Vision-Based Estimation and Control
`University of Johannesburg, Johannesburg, South Africa, Nov. 2009
`
` •
`
` Short Course - Vision-Based Control for Autonomous Vehicles
`NASA Johnson Space Center, Houston, TX, Aug. 2012
`AIAA Guidance, Navigation and Control Conference, Chicago, IL, Aug. 2009
`AIAA Guidance, Navigation and Control Conference, Hilton Head, SC, Aug. 2007
`A two day short course offered through AIAA. I taught sections on cameras and imaging,
`pose estimation through epipolar geometry, visual servoing, chained pose estimation, and
`hardware in the loop simulation.
`
`
`STUDENT SUPERVISION
`Doctoral advisement/direction:
`• Jinglin Shen, PhD awarded August 2013, “Multi-View Systems for Robot-Human Interaction
`and Object Grasping”
`• Yinghua Zhang, PhD awarded May 2014, “Improving Global Properties of Real-Time
`Optimization With Applications in Robotic Visual Search”
`• Jingfu Jin, PhD awarded December 2015, “Unified Formation Control, Heading Consensus
`and Obstacle Avoidance for Heterogeneous Mobile Robots with Nonholonomic Constraints”
`• J.-Pablo Ramirez, PhD awarded May 2016, “Mobile Sensor Guidance for Optimal
`Information Acquisition Under Out-Of-Sequence and Soft Measurements”
`• Terrell Bennet, PhD awarded August 2016, “Algorithms for Enabling Wearable Sensors in
`the Internet of Things”
`
`
`
`
`Masters advisement/direction:
`• Keveh Fathian, MS awarded December 2012, “Virtual Thermal Sensing and Control of Heat
`Distribution Using State Estimation”
`• David Tick, MS awarded May 2011, “Fusion of Discrete and Continuous Epipolar Geometry
`With Wheel and IMU Odometry for Localization of Mobile Robots”
`• Wen Yu, MS awarded August 2011, “Interactive Camera/Projector Display Unit Using Image
`Homography”
`
`
`AWARDS
`•Selected as one of 2014’s IEEE Transactions on Robotics Outstanding Reviewers
`•Nominated for the Provost's Award for Faculty Excellence in Undergraduate Research
`Mentoring, The University of Texas at Dallas, 2014
`
`Nicholas Gans
`
`Curriculum Vitae
`
`Page | 3
`
`Exhibit C, 17-cv-932-JLR-014
`
`Shore Chan DePumpo LLP
`901 Main St., Ste. 3300
`Dallas, TX 75202
`214.593.9110
`
`

`

`Case 2:17-cv-00932-JLR Document 61-3 Filed 03/09/18 Page 16 of 25
`
`
`
`•Best Paper of the Session, K. Fathian, J. Jin and N. Gans, “A New Approach for Solving the
`Five-Point Relative Pose Problem for Vision-Based estimation and Control,” Proc. American
`Control Conference 2014
`•Best doctoral colloquium award (work in progress): Quality Enhancement Framework for
`Wearable Computers - Terrell R. Bennett, (my PhD student), 2014 Proc. Body Sensor Network
`Conference
`•Best Paper of the Session, T. Bennett, R. Jafari, and N. Gans, “An Extended Kalman Filter to
`Estimate Human Gait Parameters and Walking Distance,” 2013 Proc. American Controls
`Conference
`•Best Student Paper, D. Q. Tick, J. Shen, and N.R. Gans, "Fusion of Discrete and Continuous
`Epipolar Geometry for Visual Odometry and Localization," 2010 IEEE International
`Workshop on Robotic and Sensors Environments
`• National Research Council/US Air Force Office of Scientific Research Associateship
`•Best Paper of the Session, K. Kaiser, N. Gans and W. E. Dixon, “Localization and Control of
`an Aerial Vehicle through Chained, Vision-Based Pose Reconstruction,” 2007 American
`Control Conference
`
`
`
`
`PROFESSIONAL ACTIVITIES
`Memberships
`• IEEE Senior Member
`• IEEE Robotics and Automation Society
`• IEEE Control Systems Society
`• AIAA
`• ASME
`
`Conference Organizing Committees
`• Local Arrangements Chair - IEEE Conference on Automation Science and Engineering
`(CASE) 2016
`• Exhibitions Chair - IEEE Conference on Automation Science and Engineering (CASE) 2016
`• Exhibitions CoChair - IEEE Int’l Conf. on Intelligent Robots and Systems (IROS) 2014
`
`
`International Program Committee Member
`• IEEE Int’l Conf. on Intelligent Robots and Systems (IROS): 2006
`• IEEE Int’l Conf. on Intelligent Robots and Systems (IROS), Associate Editor: 2007-2014
`• IEEE/IFAC Int’l Conf. on Inform. in Control, Autom. & Robotics (ICINCO): 2008-2014
`• American Controls Conference (ACC) 2009-2014
`
`Workshop/Invited Session Organizer/Lecturer
`• 6o Taller de Robótica y Planificación de Movimientos, Plenary Speaker, Decentralized
`Formation Control and Obstacle Avoidance for Mobile Robots, Centro de Investigación en
`Matemáticas, Guanajuato, Mexico, April. 2016
`• 19th IFAC World Congress, Organizer and Lecturer at One Day Workshop, Recent Advances
`in Extremum Seeking Control and its Applications, Capetown, South Africa, Sep. 2014
`• University of Johannesburg, Organizer and Lecturer at One Day Workshop, Vision-Based
`Estimation and Control, Johannesburg, South Africa, Nov. 2009
`
`Nicholas Gans
`
`Curriculum Vitae
`
`Page | 4
`
`Exhibit C, 17-cv-932-JLR-015
`
`Shore Chan DePumpo LLP
`901 Main St., Ste. 3300
`Dallas, TX 75202
`214.593.9110
`
`

`

`Case 2:17-cv-00932-JLR Document 61-3 Filed 03/09/18 Page 17 of 25
`
`
`
`• AIAA Guidance, Navigation and Control Conference (GNC), Organizer and Lecturer at Two
`Day Workshop, Vision-Based Control for Autonomous Vehicles, Chicago, IL, Aug. 2009
`•International Conference on Pattern Recognition (ICPR), Presenter at One Day Workshop,
`Visual Observation and Analysis of Animal and Insect Behavior, Tampa, FL, Dec. 2008
`• IEEE International Symposium on Intelligent Control (ISIC), Invited Session Organizer and
`Lecturer: Current Topics Vision-Based Control, San Antonio, TX, Sep. 2008
`• AIAA Guidance, Navigation and Control Conference (GNC), Organizer and Lecturer at Two
`Day Workshop, Vision-Based Control for Autonomous Vehicles, Hilton Head, SC, Aug. 2007
`
`Review Panels
`• National Science Foundation
`• NASA
`
`Publication Reviewer
`• IEEE Transactions on Control (TAC)
`• IEEE Transactions on Robotics (TRO)
`• IEEE Transactions on Systems, Man and Cybernetics (TSMC)
`• IEEE Transactions on Controls System Technology (TCST)
`• ASME Journal of Dynamic Systems, Measurement and Control
`• International Journal of Computer Vision (IJCV)
`• International Journal of Robotics Research (IJRR)
`• Journal of Intelligent and Robotic Systems
`• European Journal of Control (EJC)
`• IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
`• IEEE Conference on Robotics and Automation (ICRA)
`• IEEE Conference on Decision and Control (CDC)
`• American Controls Conference (ACC)
`• ASME Dynamic Systems and Control Conference (DSCC)
`• IEEE Conference on Control, Automation, Robotics and Vision (ICARV)
`• IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)
`• International Conference on Intelligent Autonomous Systems (IAS)
`• IEEE Potentials
`
`
`INVITED LECTURES
`
`•“Novel Algorithms for Estimating Camera Pose and Target Structure” – Lecture at Robotics
`Graduate Student Seminars, University of Illinois at Urbana Champaign, October 21, 2016
`•“Novel Algorithms for Estimating Camera Pose and Target Structure” – Lecture at Computer
`Science and Engineering Graduate Student Seminars, Texas A&M University, September 12,
`2016
`•“Decentralized Formation Control and Obstacle Avoidance for Mobile Robots” – Plenary Talk,
`The 6th Robotics Workshop and Planning Movements, El Centro de Investigación en