Clark Hochgraf Headshot

Clark Hochgraf

Associate Professor
Department of Electrical, Computer, and Telecommunications Engineering Technology
College of Engineering Technology

585-475-3167
Office Location

Clark Hochgraf

Associate Professor
Department of Electrical, Computer, and Telecommunications Engineering Technology
College of Engineering Technology

Education

BS, State University of New York at Buffalo; Ph.D., University of Wisconsin at Madison

585-475-3167

Currently Teaching

EEET-425
4 Credits
Develops the knowledge and ability to process signals using Digital Signal Processing (DSP) techniques. Starts with foundational concepts in sampling, probability, statistics, noise, fixed and floating point number systems, and describes how they affect real world performance of DSP systems. Fundamental principles of convolution, linearity, duality, impulse responses, and discrete fourier transforms are used to develop FIR and IIR digital filters and to explain DSP techniques such as windowing. Students get an integrated lab experience writing DSP code that executes in real-time on DSP hardware.
EEET-112
1 Credits
Develops skills and practice in the design, fabrication, measurement and analysis of practical DC circuits used in electronic devices. Topics include the measurement relative to: resistance, current, and voltage with circuit techniques of Ohm's Law; current and voltage division; simplification of series, parallel, series-parallel circuits: bridge and ladder networks: Kirchhoff's Laws; power; and transient circuit behavior. Laboratory verification of DC analytical and techniques is included. Printed circuit board (PCB) design, fabrication, and assembly is also included emphasizing the development of soldering skill proficiency.
EEET-520
3 Credits
Machine learning has applications in a wide variety of fields ranging from medicine and finance to telecommunications and autonomous self-driving vehicles. This course introduces machine learning and gives you the knowledge to understand and apply machine learning to solve problems in a variety of application areas. The course covers neural net structures, deep learning, support vector machines, training and testing methods, clustering, classification, and prediction with applications across a variety of fields. The focus will be on developing a foundation from which a variety of machine learning methods can be applied. Students may only receive credit for this course or TCET-620, not both.
EEET-427
4 Credits
Develops the knowledge of control system concepts and applies them to electromechanical systems. Systems are characterized and modeled using linear systems methods, focused with a controls perspective. Impulse responses, step responses, and transfer functions are reviewed. Principles of stability and damping are developed and applied to the specification and design of open and closed loop compensators to deliver specific input-output performance. During the course’s laboratory component, students design and implement compensators for electromechanical systems.
TCET-620
3 Credits
Machine learning has applications in a wide variety of fields ranging from medicine and finance to telecommunications and autonomous self-driving vehicles. This course introduces machine learning and gives you the knowledge to understand and apply machine learning to solve problems in a variety of application areas. The course covers neural net structures, deep learning, support vector machines, training and testing methods, clustering, classification, and prediction with applications across a variety of fields. The focus will be on developing a foundation from which a variety of machine learning methods can be applied. Students may only receive credit for this course or EEET-520, not both.
EEET-598
1 - 3 Credits
Special Topics is an experimental upper-division course intended as a means for offering innovative topics not currently reflected in either the computer or electrical engineering technology curriculums.

Select Scholarship

Published Conference Proceedings
Hochgraf, Clark, et al. "Providing First Responders with Real-time Status of Cellular Networks During a Disaster." Proceedings of the 2018 IEEE International Symposium on Technologies for Homeland Security (HST). Ed. IEEE. Woburn, MA: IEEE, 2018. Web.
Nygate, Joseph, et al. "Applying Machine Learning in Managing Deployable Systems." Proceedings of the 2018 IEEE International Symposium on Technologies for Homeland Security (HST). Ed. IEEE. Woburn, MA: IEEE, 2018. Web.
Journal Paper
Hochgraf, Clark G., et al. "Effect of Ultracapacitor-modified PHEV Protocol on Performance Degradation in Lithium-ion Cells." Journal of Power Sources. (2012): 5. Print.
Published Article
Hochgraf, C., R. Tripathi, and S. Herzberg. “Smart Grid Charger for Electric Vehicles Using ExistingCellular Networks and Sms Text Messages.”Smart Grid Communications (SmartGridComm), 2010 First IEEE International Conference, 4-6 Oct. 2010. n.p. Print. " «
Hochgraf, C. “What If the Prius Wasn’t a Hybrid? What If the Corolla Were? An Analysis Based on Vehicle Limited Fuel Consumption and Powertrain and Braking Efficiency.” 2010 World Congress & Exhibition. Society of Automotive Engineers, 2010. n.p. Print. É «
Bellinger, Scott, and Clark Hochgraf. “Using NationalCompetitions to Focus Student Clubs.” ASEE Annual Conference and Exposition, 2010. n.p. Web. «