IEEE Boston Section

Upcoming Events!

Jun
15
Tue
BOSTON MADE: FROM REVOLUTION TO ROBOTICS, INNOVATIONS THAT …
Jun 15 @ 7:00 pm – 8:30 pm
Consultants’ Network Bob Krim, Associate Professor & Founder, Entrepreneur Innovation Center at Framingham State University Online only via MS Zoom; no in-person gathering (RSVP & Join on our website below) The link to register click here:  A fascinating look at how Boston became, and remains, a global center for innovation, told through 50 world-changing inventions. Since the 1600’s Boston has been at the forefront of world-changing innovation from inventing the telephone, through the electrical breakthroughs which transformed public transportation with the invention of the subway, through becoming the first state to end slavery, and giving birth to organ transplant, as well as the medical breakthroughs like the anti-covid vaccines which have transformed our lives this Spring. In total Boston-area inventors have contributed more than four hundred stand-out social and scientific innovations, and uncounted numbers that are less well known.  Robert Krim & Alan Earls Boston made is the first time that the story has been laid out to the public. In fact, fresh waves of innovation have brought he city back from major economic collapses.  Krim sets out the five innovation drivers, including strong entrepreneurship, local funding, and networking.   From boom and back to boom, Boston has maintained an ability to reinvent, and build anew. https://www.amazon.com/Boston-Made-Revolution-Robotics-Innovations/dp/1623545358/ref=sr_1_2?dchild=1&keywords=Robert%2BKrim&qid=1621848140&sr=8-2 Dr. Robert Krim, son of a longtime IEEE member, is the leading expert on the factors that have driven and continue to drive unparalleled innovation in Massachusetts; he is the author of the just published Boston Made From Revolution to Robotics. As founder and leader of the Boston History & Innovation Collaborative, he spearheaded a multi-organization, 20-year study covering four centuries and involving hundreds of experts on the forces that drive these innovations. Most recently his revelations about Boston have prompted the development of a permanent exhibit at Boston’s Logan Airport, From Massachusetts to the World: Four Centuries of Innovation. He is a Professor of Innovation at Framingham State University.  Krim has degrees in US History, Economics, Business Administration from Harvard, University of California, Berkeley, and Boston College. He lives in Newton with his wife and children. Free and Open to the Public; RSVP is appreciated. Visit the IEEE BOSTON CONSULTANTS’ NETWORK @ http://boston-consult.com/
Planning for A Successful Exit @ Webinar
Jun 15 @ 7:00 pm – 9:00 pm

Entrepreneurs’ Network

Register:  https://boston-enet.org/event-3892698/Registration

REGISTRATION FOR THIS EVENT WILL CLOSE ON TUESDAY, June 15, AT 3:00 PM (EDT)

You are one of them…a dedicated entrepreneur, who has poured heart and sweat into the venture.

Does this focused dedication to launch and grow the business also include planning for potential future exits?

An exit strategy is an important “early on“ element of the overall business strategy that describes the vision of how you will eventually capitalize on your investment.  The decisions about how you structure and operate the business can have huge implications down the road.

Hear from expert panelists, who have prepared companies and participated in successful exits!

They will talk about exit strategy planning and, just as important, what exits look and feel like in real life to a serial entrepreneur, an investor and legal M& A advisors.

Agenda:

6:00 – 6:45 – Networking on Grapevine.today

7:00 – 7:10 PM – ENET Chairperson’s announcements

7:10 – 7:25 PM – eMinute Pitch  – Up to 3 Startup companies’ presentations

7:25 – 8:10 PM – 3 expert speakers on the night’s topic

8:10 – 8:30 PM – Moderator and Audience Q & A with the speakers

8:30 – 9:00 PM – Networking on Grapevine.today

(all times are USA Eastern Daylight time) (all times are USA Eastern Daylight time)

A question and answer session will follow the panel discussion, and panelists will be available afterward for responses to individual questions.

Panelists:

Jeff Behrens, CEO and Co-Founder, LabShares Newton, CEO, GelMEDIX, Inc.

Jeff Behrens is a serial biotech entrepreneur. He is currently CEO of GelMEDIX, an opthamology startup based on work from Mass Eye and Ear and UCLA. He is also founder and CEO of LabShares Newton, a biotech incubator for biotech startups run by biotech entrepreneurs.

Formerly, Jeff was President and CEO of Siamab Therapeutics, a biotech company focused on developing antibodies targeting glycan targets in cancer that exited in 2019. Siamab’s lead drug candidate initiated clinical trials in early 2021. Previously, Jeff served as Senior Director, Business Development and Operations at Edimer Pharmaceuticals (funded by Third Rock Ventures) and also worked at Alnylam and Biogen Idec, where co-founded Biogen’s Innovation Incubator.

In 2003 Jeff sold his healthcare IT company, The Telluride Group, to mindSHIFT Technologies, a Fidelity-funded rollup.

Jeff has a PhD from EPFL (Lausanne, Switzerland), an MS from the Harvard/MIT Division of Health Sciences and Technology (HST), an MBA from MIT Sloan, and graduated from Harvard College. He teaches HST590, a PhD level course at MIT.

Katherine Hill Ritchie, Director and Board Member Nottingham Spirk

Mrs. Hill Ritchie has 18 years of investment and financial services experience. She worked internally or as an advisor to 8 family offices directly or through her firm, Private Capital Investments, LLC.

Her current role is Director and Board Member for Nottingham Spirk Family Office, and her past roles include: Simon Group Holdings, Eden Capital, and PEX Global. She spent 7 years in Switzerland where she was a Managing Director at Palladio Alternative Research and Senior Analyst and Investment Committee Member for the Saad family office’s $3.5 billion investment portfolio. She was a Director for Wedge Alternatives, and also Hedgefund.net. She is an Angel Investor and is on the investment committee for University Impact, a social impact VC fund.

Katherine received her MBA from Fordham University and her BS in Psychology from University of Maryland. Her Board member activities include: Chair of the ACG New York Family Office Committee, Board Member of ACG NY, and Family Office Advisory Board of TriState Capital. Her past philanthropic volunteer activities include: Global Co-Chair of The Guild, the Philanthropy and Education Committees of 100 Women in Finance, Fordham MBA Overseers Board.

Daniel T. Janis, Partner at Davis, Malm & D’Agostine, P.C.

Dan Janis is a corporate attorney who focuses on mergers and acquisitions. He also represents public and private companies in a range of general and transactional matters. Dan’s clients rely on his experience in corporate finance, private equity placements, venture capital financings, syndicated commercial credit facilities, joint ventures and day-to-day business counseling to help them achieve their objectives.

Prior to joining Davis Malm in 2009, Dan cut his teeth as a corporate associate in the Boston offices of Skadden Arps and Goulston & Storrs. Dan prides himself on his pragmatic, efficient resolution of whatever issues confront his clients.

David J. Powsner, Partner at Davis, Malm & D’Agostine, P.C.

Dave is an intellectual property attorney, advising high-tech companies on a range of complex matters. His physics degree from MIT and experience in computer programming, combined with his legal experience, enables him to understand, analyze and provide practical guidance on patent, copyright, trade secret, trademark, licensing and litigation matters. Dave represents companies with products in a variety of tech markets, from medical devices to computer software to networking to consumer electronics.

Co-Organizers /Moderators

Kristin King, MBA., VP Corporate Development & Strategy, Defibtech, LLC. Boston Harbor Angel Investor

Kristin is an accomplished MedTech executive, serial intrapreneur, investor and strategic advisor to startups developing biotech solutions.  With over 20 years spanning technical, commercial and business skills as well as Boston Harbor Angel Investor, she covers broad expertise transforming technologies from early concept to successful global divisions.

Kristin holds a B.S. in bioengineering from Syracuse University, MBA in Finance & Marketing from NYU Stern.

Dave Hall, Founder & CEO, DLH Technology, Advisors

Startup Strategy & Venture Capital Consulting

Dave is Founder and CEO of DLH Technology Advisors.  He is a startup & strategy Executive, Innovation Consultant, Advisory Board Member, Connector, Evangelist and Speaker for growth companies looking to implement, optimize, and fund their Go-to-Market plan.

DLH Technology Advisors offers consulting services including Startup Strategy, & Frameworks, Business Development, Startup Marketing, Go-to-Market Strategy, Executive Coaching and Startup Funding Channels. DLH has resources for CRM development services including Salesforce implementation, AppExchange App Development, QuickStarts, Lead Architect and Admin Services for the entire Salesforce product line – www.DLHsales.com.

Jun
17
Thu
Digital Signal Processing for Wireless Communications (DSP) – Webinar Course @ Webinar
Jun 17 @ 7:00 pm – 8:00 pm

Registration – click here:

Course Start Date:  May 14, 2021, videos released weekly 2×1.5 hours

Workshops:  Thursdays:  May 20, 27, June 3, 10,17, 2021

Each workshop is scheduled from 7:00PM – 8:00PM

Speaker:  Dan Boschen

IEEE Member Fee:  $190.00

Non-Member Fee:  $210.00

Decision to run/cancel course:  Tuesday, May 11, 2021

COURSE DESCRIPTION

New Format Combining Live Workshops with Pre-recorded Video

This is a hands-on course providing pre-recorded lectures that students can watch on their own schedule and an unlimited number of times prior to live Q&A/Workshop sessions with the instructor. Ten 1.5 hour videos released 2 per week while the course is in session will be available for up to two months after the conclusion of the course.

Course Summary

This course is a fresh view of the fundamental and practical concepts of digital signal processing applicable to the design of mixed signal design with A/D conversion, digital filters, operations with the FFT, and multi-rate signal processing.  This course will build an intuitive understanding of the underlying mathematics through the use of graphics, visual demonstrations, and applications in GPS and mixed signal (analog/digital) modern transceivers. This course is applicable to DSP algorithm development with a focus on meeting practical hardware development challenges in both the analog and digital domains, and not a tutorial on working with specific DSP processor hardware.

Now with Jupyter Notebooks!

This long-running IEEE Course has been updated to include Jupyter Notebooks which incorporates graphics together with Python simulation code to provide a “take-it-with-you” interactive user experience. No knowledge of Python is required but the notebooks will provide a basic framework for proceeding with further signal processing development using that tools for those that have interest in doing so.

This course will not be teaching Python, but using it for demonstration. A more detailed course on Python itself is covered in a separate IEEE Course “Python Applications for Digital Design and Signal Processing”.

Students will be encouraged but not required to load all the Python tools needed, and all set-up information for installation will be provided prior to the start of class.

Target Audience:

All engineers involved in or interested in signal processing applications. Engineers with significant experience with DSP will also appreciate this opportunity for an in-depth review of the fundamental DSP concepts from a different perspective than that given in a traditional introductory DSP course.

Benefits of Attending/ Goals of Course:

Attendees will build a stronger intuitive understanding of the fundamental signal processing concepts involved with digital filtering and mixed signal analog and digital design. With this, attendees will be able to implement more creative and efficient signal processing architectures in both the analog and digital domains. The knowledge gained from this course will have immediate practical value for any work in the signal processing field.

Topics / Schedule:

Class 1: Correlation, Fourier Transform, Laplace Transform

Class 2: Sampling and A/D Conversion, Z –transform, D/A Conversion

Class 3: IIR and FIR Digital filters, Direct Fourier Transform

Class 4: Windowing, Digital Filter Design, Fixed Point vs Floating Point

Class 5: Fast Fourier Transform, Multi-rate Signal Processing, Multi-rate Filters

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. Dan 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 at: http://www.linkedin.com/in/danboschen

Jun
19
Sat
Introduction to Practical Neural Networks and Deep Learning (Part I) @ A live, interactive webinar 
Jun 19 @ 9:00 am – 12:30 pm

(3 hours of instruction!)

IEEE Members $110
Non-members $130
Decision to (Run/Cancel) Date for this course is Tuesday, June 15, 2021

 

 

Speaker:  CL Kim

Reference book: “Neural Networks and Deep Learning” by Michael Nielsen, http://neuralnetworksanddeeplearning.com

Series Overview:   From the book introduction: “Neural networks and deep learning currently provides the best solutions to many problems in image recognition, speech recognition, and natural language processing.”

This Part 1 and the planned Part 2 (winter or spring 2022, to be confirmed) series of courses will teach many of the core concepts behind neural networks and deep learning.

More from the book introduction:  “We’ll learn the core principles behind neural networks and deep learning by attacking a concrete problem: the problem of teaching a computer to recognize handwritten digits. …it can be solved pretty well using a simple neural network, with just a few tens of lines of code, and no special libraries.”

“But you don’t need to be a professional programmer.”

The code provided is in Python, which even if you don’t program in Python, should be easy to understand with just a little effort.

Benefits of attending the series:

* Learn the core principles behind neural network and deep learning.

* See a simple Python program that solves a concrete problem: teaching a computer to recognize a handwritten digit.

* Improve the result through incorporating more and more of core ideas about neural networks and deep learning.

* Understand the theory, with worked-out proofs of fundamental equations of backpropagation for those interested.

* Run straightforward Python demo code example.

The demo Python program (updated from version provided in the book) can be downloaded from the speaker’s GitHub account. The demo program is run in a Docker container that runs on your Mac, Windows, or Linux personal computer; we plan to provide instructions on doing that in advance of the class.

(That would be one good reason to register early if you plan to attend, in order that you can receive the straightforward instructions and leave yourself with plenty of time to prepare the Git and Docker software that are widely used among software professionals.)

Course Background and Content:   This is a live instructor-led introductory course on Neural Networks and Deep Learning. It is planned to be a two-part series of courses. The first course is complete by itself and covers a feedforward neural network (but not convolutional neural network in Part 1). It will be a pre-requisite for the planned Part 2 second course. The class material is mostly from the highly-regarded and free online book “Neural Networks and Deep Learning” by Michael Nielsen, plus additional material such as some proofs of fundamental equations not provided in the book.

Outline:

Introduction to Practical Neural Networks and Deep Learning (Part 1)

Feedforward Neural Networks.

* Simple (Python) Network to classify a handwritten digit

* Learning with Gradient Descent

* How the backpropagation algorithm works

* Improving the way neural networks learn:

** Cross-entropy cost function

** Softmax activation function and log-likelihood cost function

** Rectified Linear Unit

** Overfitting and Regularization:

*** L2 regularization

*** Dropout

*** Artificially expanding data set

*** Hyper-parameters

Pre-requisites: There is some heavier mathematics in learning the four fundamental equations behind backpropagation, so a basic familiarity with multivariable calculus and matrix algebra is expected, but nothing advanced is required. (The backpropagation equations can be also just accepted without bothering with the proofs since the provided Python code for the simple network just make use of the equations.) Basic familiarity with Python or similar computer language.

Speaker Background:     CL Kim works in Software Engineering at CarGurus, Inc. He has graduate degrees in Business Administration and in Computer and Information Science from the University of Pennsylvania. He had previously taught for a few years the well-rated IEEE Boston Section class on introduction to the Android platform and API.

Decision to (Run/Cancel) Date for this course is Tuesday, June 15, 2021

IEEE Members $110
Non-members $130
Jun
21
Mon
Artificial Intelligence (AI) What’s in it for me? – AI Webinar Series – Session 6 @ Webinar
Jun 21 @ 12:00 pm – 1:30 pm

Session 6:  AI in Finances – Manuela Veloso and JP Morgan

For more information and registration click here: 

Jul
20
Tue
Information Theory for Electronic Communication with MATLAB Applications @ Live Webinar
Jul 20 @ 10:00 am – 11:30 am

Speaker:   Orhan Gazi, Cankaya University, Ankara-Turkey

Course Format: Live Webinar, 8 sessions, 1.5 hours per session

Times and Dates: 10 AM – 11:30 AM ET – July 20, 22, 27, 29, August 3, 5, 10, 12.

Decision (Run/Cancel) Date for this Course is:  Wednesday, July 14, 2021

Registration Fees:

IEEE Member:  $250.00

Non-Member:  $300.00

 

 

Introduction:  Information theory was born with the publication of Shannon’s paper, a mathematical theory of communication, in 1948. In his paper, Shannon defined the terms entropy, mutual information, and channel capacity which is the maximum reliable transmission speed for a given signal-to-noise ratio. Shannon also stated ‘channel coding theorem’ in his paper, which opened another research area, design of channel codes, in communication field. The concept of data compression aroused after Shannon’s paper. Any engineer working in the communication industry must have some knowledge about information theory. Especially knowledge of capacity is very critical to compare the performance of communication systems with each other. In this course, we will provide information about basic concept of information theory. We will also provide some practical applications using MATLAB platform.

Prerequisite: The one who is interested in taking this course should have basic knowledge of probability and random variables. He or She should be familiar with the terms probability mass function, probability density function, random variable, expected value, variance, etc.

  • Discrete Entropy, Mutual Information for Discrete Channels, Information Channels
  • MATLAB Applications for Entropy and Mutual Information
  • Entropy for Continuous Random Variables, Discrete and Continuous Channel Capacities
  • Bounds and Limiting Cases for AWGN Channel Capacity
  • MATLAB Applications for Channel Capacities
  • Typical Sequences and Data Compression
  • MATLAB Applications for Data Compressions
  • Channel Coding Theorem

Target Audience:  Electronic and Communication Engineers, electronic engineers, computer engineers, engineers working in communication industry

Benefits of Attending Course: 

1) The participant will have an idea about Shannon’s information theory.

2) The participant will have an idea about transmission channel capacity.

3) The participant will learn the logic behind the data compression concept.

4) The participant will be able to compare the performances of two different communication systems.

5) The participant will have an idea about the factors affecting maximum transmission speed.

Speaker Bio:  Prof. Orhan Gazi is the author of the book “Information Theory for Electrical Engineers” https://www.springer.com/gp/book/9789811084317  Prof. Orhan Gazi is the sole author of 10 books written in electrical engineering subjects.

He is also the author of the book “Polar Codes.  A Non-Trivial Approach to Channel Coding” which can be reached from https://www.springer.com/gp/book/9789811307362

The book is selected by IEEE COMSOC as one of the best readings in polar codes, https://www.comsoc.org/publications/best-readings/polar-coding

He is also the single author of the book “Forward Error Correction via Channel Coding” https://www.springer.com/gp/book/9783030333799

The research area of Prof. Orhan Gazi involves “channel coding”, and “digital communication subjects”.  Recently, he focuses on over capacity data transmission using polar codes. He is also interested in practical applications of communication systems involving FPGA devices. He is delivering courses with titles “VHDL circuit design”, “interface design using VHDL for FPGA devices” and “system on chip design”.

Materials to be included:  Lecture slides will be provided.

Decision (Run/Cancel) Date for this Course is:  Wednesday, July 14, 2021

Registration Fees:

IEEE Member:  $250.00

Non-Member:  $300.00

“Simulating the Performance of Ocean-Observing Imaging Payloads for Nanosatellites” @ Zoom
Jul 20 @ 6:00 pm – 7:00 pm

Geoscience & Remote Sensing Society

Zoom link: 

Registration click here: 

Speaker:  Candence Brea Payne

Abstract:

Earth’s oceans are the largest defining feature of our planet and arguably an invaluable resource. Consequences of climate change threaten to have substantial and irreversible negative effects on our oceans, making it crucial to quickly understand and quantify behavioral changes resulting from increased human impact. Near-continuous, large-scale monitoring from space is revolutionizing methods for monitoring and forecasting ocean behavior. Nanosatellite platforms offer a potential solution for large-scale deployment of ocean-sensing instruments that provide detailed measurements of critical characteristics. M​onitoring these key features provides valuable insight to behavioral changes within the context of our shifting climate.

Constellations of nanosatellites that target key ocean characteristics could provide continuous ocean monitoring with high spatiotemporal resolution. Compared with current state-of-the-art ocean-observing spacecraft, such as NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) with a repeat cycle of 16 days, nanosatellites in Low-Earth Orbit (LEO) can observe the same ground scene roughly once every five days. While spacecraft such as NASA’s Geostationary Operational.  Environmental Satellite (GOES) achieves high temporal resolution, imaging the same scene every 30 seconds to 15 minutes depending on target region size, they are limited to imaging a single ground scene due to their stationary placement. Constellations of nanosatellites offer opportunities for measurement improvement including reducing revisit rates down from several days to hours, as well as increasing surface coverage through placement in orbital planes of varying inclinations. Informative, emergent information such as sea surface salinity, front location, and fauna concentrations (namely phytoplankton) are derived from measuring key characteristics such as ocean color and Sea Surface Temperature (SST). Existing nanosatellite constellations such as Planet’s Flock-3p, composed of 88 3U (10 x 10 x 30 cm​) CubeSats, provide daily coverage of Earth’s land mass; however, they do not yet target oceans and coastal regions, nor tailor their imaging bands for these specific measurement needs. We present a concise set of ocean measurement band centers for an imaging payload targeting ocean color, a key behavioral feature. We assume narrow-band (10 – 15 nm bandwidth) ocean color measurements (​390 nm – 865 nm) and constrain the payload to within the volume of a U-class (3U / 6U / 12U) nanosatellite located in LEO ​(~ 450 km altitude)​. A radiometric link approach is used to develop a tool that compares the performance of multiple different available Commercial

Off-the-Shelf (COTS) detectors, as well as different detector and optical front-end combinations. As detector sensitivity performance is driven primarily by aperture size and focal length, the imaging payload is assumed to have a scalable aperture (e.g.,diameter, focal length) and tunable sensor parameters (e.g., pixel pitch, number of pixels, sensor format). We simulate the sensor’s performance primarily by scaling the aperture from 0.5 cm to 20 cm diameter, suitable for 0.5U – 12U CubeSat volumes. Simulation results determine key “cut-off” regions where collected data no longer achieve the desired measured sensitivity of the target feature. A discussion of the radiometric approach, including definition of the measurement and detector parameter trade-space, is provided, along with preliminarily results of the simulated performance.

Bio:

Cadence Payne is a 4th year PhD student in the department of Aeronautics and Astronautics in the Space Telecommunications, Astronomy, and Radiation Laboratory advised by Dr. Kerri Cahoy. Her research at MIT focuses on technology development for small, Earth-observing spacecraft called CubeSats. She is currently the lead Systems Engineer for the Auroral Emission Radio Observer (AERO), a 3U CubeSat that uses a 4-meter vector sensor antenna to probe low-frequency emission from the Earth's aurora. She is also supporting AEROS, a joint mission with MIT Portugal that collects data for climate and weather monitoring via ocean observations. ​She graduated from Morehead State University in 2017 with a BS in Space Science and a minor in astronomy.

 

 

 

 

Jul
22
Thu
Information Theory for Electronic Communication with MATLAB Applications @ Live Webinar
Jul 22 @ 10:00 am – 11:30 am

Speaker:   Orhan Gazi, Cankaya University, Ankara-Turkey

Course Format: Live Webinar, 8 sessions, 1.5 hours per session

Times and Dates: 10 AM – 11:30 AM ET – July 20, 22, 27, 29, August 3, 5, 10, 12.

Decision (Run/Cancel) Date for this Course is:  Wednesday, July 14, 2021

Registration Fees:

IEEE Member:  $250.00

Non-Member:  $300.00

 

 

Introduction:  Information theory was born with the publication of Shannon’s paper, a mathematical theory of communication, in 1948. In his paper, Shannon defined the terms entropy, mutual information, and channel capacity which is the maximum reliable transmission speed for a given signal-to-noise ratio. Shannon also stated ‘channel coding theorem’ in his paper, which opened another research area, design of channel codes, in communication field. The concept of data compression aroused after Shannon’s paper. Any engineer working in the communication industry must have some knowledge about information theory. Especially knowledge of capacity is very critical to compare the performance of communication systems with each other. In this course, we will provide information about basic concept of information theory. We will also provide some practical applications using MATLAB platform.

Prerequisite: The one who is interested in taking this course should have basic knowledge of probability and random variables. He or She should be familiar with the terms probability mass function, probability density function, random variable, expected value, variance, etc.

  • Discrete Entropy, Mutual Information for Discrete Channels, Information Channels
  • MATLAB Applications for Entropy and Mutual Information
  • Entropy for Continuous Random Variables, Discrete and Continuous Channel Capacities
  • Bounds and Limiting Cases for AWGN Channel Capacity
  • MATLAB Applications for Channel Capacities
  • Typical Sequences and Data Compression
  • MATLAB Applications for Data Compressions
  • Channel Coding Theorem

Target Audience:  Electronic and Communication Engineers, electronic engineers, computer engineers, engineers working in communication industry

Benefits of Attending Course: 

1) The participant will have an idea about Shannon’s information theory.

2) The participant will have an idea about transmission channel capacity.

3) The participant will learn the logic behind the data compression concept.

4) The participant will be able to compare the performances of two different communication systems.

5) The participant will have an idea about the factors affecting maximum transmission speed.

Speaker Bio:  Prof. Orhan Gazi is the author of the book “Information Theory for Electrical Engineers” https://www.springer.com/gp/book/9789811084317  Prof. Orhan Gazi is the sole author of 10 books written in electrical engineering subjects.

He is also the author of the book “Polar Codes.  A Non-Trivial Approach to Channel Coding” which can be reached from https://www.springer.com/gp/book/9789811307362

The book is selected by IEEE COMSOC as one of the best readings in polar codes, https://www.comsoc.org/publications/best-readings/polar-coding

He is also the single author of the book “Forward Error Correction via Channel Coding” https://www.springer.com/gp/book/9783030333799

The research area of Prof. Orhan Gazi involves “channel coding”, and “digital communication subjects”.  Recently, he focuses on over capacity data transmission using polar codes. He is also interested in practical applications of communication systems involving FPGA devices. He is delivering courses with titles “VHDL circuit design”, “interface design using VHDL for FPGA devices” and “system on chip design”.

Materials to be included:  Lecture slides will be provided.

Decision (Run/Cancel) Date for this Course is:  Wednesday, July 14, 2021

Registration Fees:

IEEE Member:  $250.00

Non-Member:  $300.00

Jul
27
Tue
Information Theory for Electronic Communication with MATLAB Applications @ Live Webinar
Jul 27 @ 10:00 am – 11:30 am

Speaker:   Orhan Gazi, Cankaya University, Ankara-Turkey

Course Format: Live Webinar, 8 sessions, 1.5 hours per session

Times and Dates: 10 AM – 11:30 AM ET – July 20, 22, 27, 29, August 3, 5, 10, 12.

Decision (Run/Cancel) Date for this Course is:  Wednesday, July 14, 2021

Registration Fees:

IEEE Member:  $250.00

Non-Member:  $300.00

 

 

Introduction:  Information theory was born with the publication of Shannon’s paper, a mathematical theory of communication, in 1948. In his paper, Shannon defined the terms entropy, mutual information, and channel capacity which is the maximum reliable transmission speed for a given signal-to-noise ratio. Shannon also stated ‘channel coding theorem’ in his paper, which opened another research area, design of channel codes, in communication field. The concept of data compression aroused after Shannon’s paper. Any engineer working in the communication industry must have some knowledge about information theory. Especially knowledge of capacity is very critical to compare the performance of communication systems with each other. In this course, we will provide information about basic concept of information theory. We will also provide some practical applications using MATLAB platform.

Prerequisite: The one who is interested in taking this course should have basic knowledge of probability and random variables. He or She should be familiar with the terms probability mass function, probability density function, random variable, expected value, variance, etc.

  • Discrete Entropy, Mutual Information for Discrete Channels, Information Channels
  • MATLAB Applications for Entropy and Mutual Information
  • Entropy for Continuous Random Variables, Discrete and Continuous Channel Capacities
  • Bounds and Limiting Cases for AWGN Channel Capacity
  • MATLAB Applications for Channel Capacities
  • Typical Sequences and Data Compression
  • MATLAB Applications for Data Compressions
  • Channel Coding Theorem

Target Audience:  Electronic and Communication Engineers, electronic engineers, computer engineers, engineers working in communication industry

Benefits of Attending Course: 

1) The participant will have an idea about Shannon’s information theory.

2) The participant will have an idea about transmission channel capacity.

3) The participant will learn the logic behind the data compression concept.

4) The participant will be able to compare the performances of two different communication systems.

5) The participant will have an idea about the factors affecting maximum transmission speed.

Speaker Bio:  Prof. Orhan Gazi is the author of the book “Information Theory for Electrical Engineers” https://www.springer.com/gp/book/9789811084317  Prof. Orhan Gazi is the sole author of 10 books written in electrical engineering subjects.

He is also the author of the book “Polar Codes.  A Non-Trivial Approach to Channel Coding” which can be reached from https://www.springer.com/gp/book/9789811307362

The book is selected by IEEE COMSOC as one of the best readings in polar codes, https://www.comsoc.org/publications/best-readings/polar-coding

He is also the single author of the book “Forward Error Correction via Channel Coding” https://www.springer.com/gp/book/9783030333799

The research area of Prof. Orhan Gazi involves “channel coding”, and “digital communication subjects”.  Recently, he focuses on over capacity data transmission using polar codes. He is also interested in practical applications of communication systems involving FPGA devices. He is delivering courses with titles “VHDL circuit design”, “interface design using VHDL for FPGA devices” and “system on chip design”.

Materials to be included:  Lecture slides will be provided.

Decision (Run/Cancel) Date for this Course is:  Wednesday, July 14, 2021

Registration Fees:

IEEE Member:  $250.00

Non-Member:  $300.00

Jul
29
Thu
Information Theory for Electronic Communication with MATLAB Applications @ Live Webinar
Jul 29 @ 10:00 am – 11:30 am

Speaker:   Orhan Gazi, Cankaya University, Ankara-Turkey

Course Format: Live Webinar, 8 sessions, 1.5 hours per session

Times and Dates: 10 AM – 11:30 AM ET – July 20, 22, 27, 29, August 3, 5, 10, 12.

Decision (Run/Cancel) Date for this Course is:  Wednesday, July 14, 2021

Registration Fees:

IEEE Member:  $250.00

Non-Member:  $300.00

 

 

Introduction:  Information theory was born with the publication of Shannon’s paper, a mathematical theory of communication, in 1948. In his paper, Shannon defined the terms entropy, mutual information, and channel capacity which is the maximum reliable transmission speed for a given signal-to-noise ratio. Shannon also stated ‘channel coding theorem’ in his paper, which opened another research area, design of channel codes, in communication field. The concept of data compression aroused after Shannon’s paper. Any engineer working in the communication industry must have some knowledge about information theory. Especially knowledge of capacity is very critical to compare the performance of communication systems with each other. In this course, we will provide information about basic concept of information theory. We will also provide some practical applications using MATLAB platform.

Prerequisite: The one who is interested in taking this course should have basic knowledge of probability and random variables. He or She should be familiar with the terms probability mass function, probability density function, random variable, expected value, variance, etc.

  • Discrete Entropy, Mutual Information for Discrete Channels, Information Channels
  • MATLAB Applications for Entropy and Mutual Information
  • Entropy for Continuous Random Variables, Discrete and Continuous Channel Capacities
  • Bounds and Limiting Cases for AWGN Channel Capacity
  • MATLAB Applications for Channel Capacities
  • Typical Sequences and Data Compression
  • MATLAB Applications for Data Compressions
  • Channel Coding Theorem

Target Audience:  Electronic and Communication Engineers, electronic engineers, computer engineers, engineers working in communication industry

Benefits of Attending Course: 

1) The participant will have an idea about Shannon’s information theory.

2) The participant will have an idea about transmission channel capacity.

3) The participant will learn the logic behind the data compression concept.

4) The participant will be able to compare the performances of two different communication systems.

5) The participant will have an idea about the factors affecting maximum transmission speed.

Speaker Bio:  Prof. Orhan Gazi is the author of the book “Information Theory for Electrical Engineers” https://www.springer.com/gp/book/9789811084317  Prof. Orhan Gazi is the sole author of 10 books written in electrical engineering subjects.

He is also the author of the book “Polar Codes.  A Non-Trivial Approach to Channel Coding” which can be reached from https://www.springer.com/gp/book/9789811307362

The book is selected by IEEE COMSOC as one of the best readings in polar codes, https://www.comsoc.org/publications/best-readings/polar-coding

He is also the single author of the book “Forward Error Correction via Channel Coding” https://www.springer.com/gp/book/9783030333799

The research area of Prof. Orhan Gazi involves “channel coding”, and “digital communication subjects”.  Recently, he focuses on over capacity data transmission using polar codes. He is also interested in practical applications of communication systems involving FPGA devices. He is delivering courses with titles “VHDL circuit design”, “interface design using VHDL for FPGA devices” and “system on chip design”.

Materials to be included:  Lecture slides will be provided.

Decision (Run/Cancel) Date for this Course is:  Wednesday, July 14, 2021

Registration Fees:

IEEE Member:  $250.00

Non-Member:  $300.00

SPECIAL NOTICE – CORONAVIRUS (COVID-19)

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