IEEE Boston Section
Upcoming Events!
Jupyter Notebooks: This course makes extensive use of Jupyter Notebooks which combines running Python code with interactive plots and graphics for a rich user experience. Jupyter Notebooks is an open-source web-based application (that can be run locally) that allows users to create and share visually appealing documents containing code, graphics, visualizations and interactive plots. Students will be able to interact with the notebook contents and use “take-it-with-you” results for future applications in signal processing. 
Target Audience: This course is targeted toward users with little to no prior experience in Python, however familiarity with other modern programming languages and an exposure to object-oriented constructs is very helpful. Students should be comfortable with basic signal processing concepts in the frequency and time domain. Familiarity with Matlab or Octave is not required, but the equivalent operations in Python using the NumPy package will be provided for those students that do currently use Matlab and/or Octave for signal processing applications. 
Benefits of Attending / Goals of Course: Attendees will gain an overall appreciation of using Python and quickly get up to speed in best practice use of Python and related tools specific to modeling and simulation for signal processing analysis and design. All set-up information for the installation of all tools will be provided before the start of class. 
Topics / Schedule: Pre-recorded lectures (3 hours each) will be distributed Friday prior to all Workshop dates. Workshop/ Q&A Sessions are 7pm-8pm on the dates listed below: Tuesday, September 13 Topic 1: Intro to Jupyter Notebooks, the Spyder IDE and the course design examples. Core Python constructs. Tuesday, September 20 Topic 2: Core Python constructs; iterators, functions, reading writing data files. Tuesday, September 27 Topic 3: Signal processing simulation with popular packages including NumPy, SciPy, and Matplotlib. Tuesday, October 4 Topic 4: Bit/cycle accurate modelling and analysis using the design examples and simulation packages
Speaker’s Bio:
Dan Boschen has a MS in Communications and Signal Processing from Northeastern University, with over 25 years of experience in system and hardware design for radio transceivers and modems. He has held various positions at Signal Technologies, MITRE, Airvana and Hittite Microwave designing and developing transceiver hardware from baseband to antenna for wireless communications systems and has taught courses on DSP to international audiences for over 15 years. Dan is a contributor to Signal Processing Stack Exchange https://dsp.stackexchange.com/, and is currently at Microchip (formerly Microsemi and Symmetricom) leading design efforts for advanced frequency and time solutions. For more background information, please view Dan’s Linked-In page.HPEC is the largest computing conference in New England and is the premier conference in the world on the convergence of High Performance and Embedded Computing. We are passionate about performance. Our community is interested in computing hardware, software, systems and applications where performance matters. We welcome experts and people who are new to the field.
The 2021 HPEC technical committee seeks new presentations that clearly describe advances in high performance extreme computing technologies.
The HPEC submission site is open. Submission Deadline is: July 9, 2022!
COURSE DESCRIPTION
First Video Release, Wednesday, September 7, 2022. Additional videos released weekly in advance of that week’s live session!
Live Workshops: 6:00PM – 7:30PM EDT, Tuesdays, September 13, 20, 27, October 4
Attendees will have access to the recorded session and exercises for two months (until December 4) after the live session ends!
IEEE Member Fee: $190.00
Non-Member Fee: $210.00
Decision to run/cancel course: Friday, September 2, 2022
Speaker: Dan Boschen
This is a hands-on course combining pre-recorded lectures with live Q&A and workshop sessions in the popular and powerful open-source Python programming language.
New Format with Pre-Recorded Videos: The course format has been updated to release pre-recorded video lectures that students can watch on their own schedule, and an unlimited number of times, prior to live Q&A workshop sessions on Zoom with the instructor. The videos will also be available to the students for viewing for up to two months after the conclusion of the course.
Overview: Dan provides simple, straight-forward navigation through the multiple configurations and options, providing a best-practices approach for quickly getting up to speed using Python for modelling and analysis for applications in signal processing and digital design verification. Students will be using the Anaconda distribution, which combines Python with the most popular data science applications, and Jupyter Notebooks for a rich, interactive experience.
The course begins with basic Python data structures and constructs, including key “Pythonic” concepts, followed by an overview and use of popular packages for scientific computing enabling rapid prototyping for system design.
During the course students will create example designs including a sigma delta converter and direct digital synthesizer both in floating point and fixed point. This will include considerations for cycle and bit accurate models useful for digital design verification (FPGA/ASIC), while bringing forward the signal processing tools for frequency and time domain analysis.
Jupyter Notebooks: This course makes extensive use of Jupyter Notebooks which combines running Python code with interactive plots and graphics for a rich user experience. Jupyter Notebooks is an open-source web-based application (that can be run locally) that allows users to create and share visually appealing documents containing code, graphics, visualizations and interactive plots. Students will be able to interact with the notebook contents and use “take-it-with-you” results for future applications in signal processing.
Target Audience: This course is targeted toward users with little to no prior experience in Python, however familiarity with other modern programming languages and an exposure to object-oriented constructs is very helpful. Students should be comfortable with basic signal processing concepts in the frequency and time domain. Familiarity with Matlab or Octave is not required, but the equivalent operations in Python using the NumPy package will be provided for those students that do currently use Matlab and/or Octave for signal processing applications.
Benefits of Attending / Goals of Course: Attendees will gain an overall appreciation of using Python and quickly get up to speed in best practice use of Python and related tools specific to modeling and simulation for signal processing analysis and design.
All set-up information for the installation of all tools will be provided before the start of class.
Topics / Schedule:
Pre-recorded lectures (3 hours each) will be distributed Friday prior to all Workshop dates. Workshop/ Q&A Sessions are 7pm-8pm on the dates listed below:
Tuesday, September 13
Topic 1: Intro to Jupyter Notebooks, the Spyder IDE and the course design examples. Core Python constructs.
Tuesday, September 20
Topic 2: Core Python constructs; iterators, functions, reading writing data files.
Tuesday, September 27
Topic 3: Signal processing simulation with popular packages including NumPy, SciPy, and Matplotlib.
Tuesday, October 4
Topic 4: Bit/cycle accurate modelling and analysis using the design examples and simulation packages
Speaker’s Bio:
Dan Boschen has a MS in Communications and Signal Processing from Northeastern University, with over 25 years of experience in system and hardware design for radio transceivers and modems. He has held various positions at Signal Technologies, MITRE, Airvana and Hittite Microwave designing and developing transceiver hardware from baseband to antenna for wireless communications systems and has taught courses on DSP to international audiences for over 15 years. Dan is a contributor to Signal Processing Stack Exchange https://dsp.stackexchange.com/, and is currently at Microchip (formerly Microsemi and Symmetricom) leading design efforts for advanced frequency and time solutions.
For more background information, please view Dan’s Linked-In page.
COURSE DESCRIPTION
First Video Release, Wednesday, September 7, 2022. Additional videos released weekly in advance of that week’s live session!
Live Workshops: 6:00PM – 7:30PM EDT, Tuesdays, September 13, 20, 27, October 4
Attendees will have access to the recorded session and exercises for two months (until December 4) after the live session ends!
IEEE Member Fee: $190.00
Non-Member Fee: $210.00
Decision to run/cancel course: Friday, September 2, 2022
Speaker: Dan Boschen
This is a hands-on course combining pre-recorded lectures with live Q&A and workshop sessions in the popular and powerful open-source Python programming language.
New Format with Pre-Recorded Videos: The course format has been updated to release pre-recorded video lectures that students can watch on their own schedule, and an unlimited number of times, prior to live Q&A workshop sessions on Zoom with the instructor. The videos will also be available to the students for viewing for up to two months after the conclusion of the course.
Overview: Dan provides simple, straight-forward navigation through the multiple configurations and options, providing a best-practices approach for quickly getting up to speed using Python for modelling and analysis for applications in signal processing and digital design verification. Students will be using the Anaconda distribution, which combines Python with the most popular data science applications, and Jupyter Notebooks for a rich, interactive experience.
The course begins with basic Python data structures and constructs, including key “Pythonic” concepts, followed by an overview and use of popular packages for scientific computing enabling rapid prototyping for system design.
During the course students will create example designs including a sigma delta converter and direct digital synthesizer both in floating point and fixed point. This will include considerations for cycle and bit accurate models useful for digital design verification (FPGA/ASIC), while bringing forward the signal processing tools for frequency and time domain analysis.
Jupyter Notebooks: This course makes extensive use of Jupyter Notebooks which combines running Python code with interactive plots and graphics for a rich user experience. Jupyter Notebooks is an open-source web-based application (that can be run locally) that allows users to create and share visually appealing documents containing code, graphics, visualizations and interactive plots. Students will be able to interact with the notebook contents and use “take-it-with-you” results for future applications in signal processing.
Target Audience: This course is targeted toward users with little to no prior experience in Python, however familiarity with other modern programming languages and an exposure to object-oriented constructs is very helpful. Students should be comfortable with basic signal processing concepts in the frequency and time domain. Familiarity with Matlab or Octave is not required, but the equivalent operations in Python using the NumPy package will be provided for those students that do currently use Matlab and/or Octave for signal processing applications.
Benefits of Attending / Goals of Course: Attendees will gain an overall appreciation of using Python and quickly get up to speed in best practice use of Python and related tools specific to modeling and simulation for signal processing analysis and design.
All set-up information for the installation of all tools will be provided before the start of class.
Topics / Schedule:
Pre-recorded lectures (3 hours each) will be distributed Friday prior to all Workshop dates. Workshop/ Q&A Sessions are 7pm-8pm on the dates listed below:
Tuesday, September 13
Topic 1: Intro to Jupyter Notebooks, the Spyder IDE and the course design examples. Core Python constructs.
Tuesday, September 20
Topic 2: Core Python constructs; iterators, functions, reading writing data files.
Tuesday, September 27
Topic 3: Signal processing simulation with popular packages including NumPy, SciPy, and Matplotlib.
Tuesday, October 4
Topic 4: Bit/cycle accurate modelling and analysis using the design examples and simulation packages
Speaker’s Bio:
Dan Boschen has a MS in Communications and Signal Processing from Northeastern University, with over 25 years of experience in system and hardware design for radio transceivers and modems. He has held various positions at Signal Technologies, MITRE, Airvana and Hittite Microwave designing and developing transceiver hardware from baseband to antenna for wireless communications systems and has taught courses on DSP to international audiences for over 15 years. Dan is a contributor to Signal Processing Stack Exchange https://dsp.stackexchange.com/, and is currently at Microchip (formerly Microsemi and Symmetricom) leading design efforts for advanced frequency and time solutions.
For more background information, please view Dan’s Linked-In page.
COURSE DESCRIPTION
First Video Release, Wednesday, September 7, 2022. Additional videos released weekly in advance of that week’s live session!
Live Workshops: 6:00PM – 7:30PM EDT, Tuesdays, September 13, 20, 27, October 4
Attendees will have access to the recorded session and exercises for two months (until December 4) after the live session ends!
IEEE Member Fee: $190.00
Non-Member Fee: $210.00
Decision to run/cancel course: Friday, September 2, 2022
Speaker: Dan Boschen
This is a hands-on course combining pre-recorded lectures with live Q&A and workshop sessions in the popular and powerful open-source Python programming language.
New Format with Pre-Recorded Videos: The course format has been updated to release pre-recorded video lectures that students can watch on their own schedule, and an unlimited number of times, prior to live Q&A workshop sessions on Zoom with the instructor. The videos will also be available to the students for viewing for up to two months after the conclusion of the course.
Overview: Dan provides simple, straight-forward navigation through the multiple configurations and options, providing a best-practices approach for quickly getting up to speed using Python for modelling and analysis for applications in signal processing and digital design verification. Students will be using the Anaconda distribution, which combines Python with the most popular data science applications, and Jupyter Notebooks for a rich, interactive experience.
The course begins with basic Python data structures and constructs, including key “Pythonic” concepts, followed by an overview and use of popular packages for scientific computing enabling rapid prototyping for system design.
During the course students will create example designs including a sigma delta converter and direct digital synthesizer both in floating point and fixed point. This will include considerations for cycle and bit accurate models useful for digital design verification (FPGA/ASIC), while bringing forward the signal processing tools for frequency and time domain analysis.
Jupyter Notebooks: This course makes extensive use of Jupyter Notebooks which combines running Python code with interactive plots and graphics for a rich user experience. Jupyter Notebooks is an open-source web-based application (that can be run locally) that allows users to create and share visually appealing documents containing code, graphics, visualizations and interactive plots. Students will be able to interact with the notebook contents and use “take-it-with-you” results for future applications in signal processing.
Target Audience: This course is targeted toward users with little to no prior experience in Python, however familiarity with other modern programming languages and an exposure to object-oriented constructs is very helpful. Students should be comfortable with basic signal processing concepts in the frequency and time domain. Familiarity with Matlab or Octave is not required, but the equivalent operations in Python using the NumPy package will be provided for those students that do currently use Matlab and/or Octave for signal processing applications.
Benefits of Attending / Goals of Course: Attendees will gain an overall appreciation of using Python and quickly get up to speed in best practice use of Python and related tools specific to modeling and simulation for signal processing analysis and design.
All set-up information for the installation of all tools will be provided before the start of class.
Topics / Schedule:
Pre-recorded lectures (3 hours each) will be distributed Friday prior to all Workshop dates. Workshop/ Q&A Sessions are 7pm-8pm on the dates listed below:
Tuesday, September 13
Topic 1: Intro to Jupyter Notebooks, the Spyder IDE and the course design examples. Core Python constructs.
Tuesday, September 20
Topic 2: Core Python constructs; iterators, functions, reading writing data files.
Tuesday, September 27
Topic 3: Signal processing simulation with popular packages including NumPy, SciPy, and Matplotlib.
Tuesday, October 4
Topic 4: Bit/cycle accurate modelling and analysis using the design examples and simulation packages
Speaker’s Bio:
Dan Boschen has a MS in Communications and Signal Processing from Northeastern University, with over 25 years of experience in system and hardware design for radio transceivers and modems. He has held various positions at Signal Technologies, MITRE, Airvana and Hittite Microwave designing and developing transceiver hardware from baseband to antenna for wireless communications systems and has taught courses on DSP to international audiences for over 15 years. Dan is a contributor to Signal Processing Stack Exchange https://dsp.stackexchange.com/, and is currently at Microchip (formerly Microsemi and Symmetricom) leading design efforts for advanced frequency and time solutions.
For more background information, please view Dan’s Linked-In page.
News!
SPECIAL NOTICE – CORONAVIRUS (COVID-19)
IEEE Boston Section recognized for Excellence in Membership Recruitment Performance
IEEE Boston Section was founded Feb 13, 1903, and serves more than 8,500 members of the IEEE. There are 29 chapters and affinity groups covering topics of interest from Aerospace & Electronic Systems, to Entrepreneur Network to Women in Engineering to Young Professionals. The chapters and affinity groups organize more than 100 meetings a year. In addition to the IEEE organization activities, the Boston Section organizes and sponsors up to seven conferences in any given year, as well as more than 45 short courses. The Boston Section publishes a bi-weekly newsletter and, currently, a monthly Digital Reflector newspaper included in IEEE membership.
The IEEE Boston Section also offers social programs such as the section annual meeting, Milestone events, and other non-technical professional activities to round out the local events. The Section also hosts one of the largest and longest running entrepreneurial support groups in IEEE.
More than 150 volunteers help create and coordinate events throughout the year.