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Dan Boschen

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.

Fixed-Point Filters – Modelling and Verification Using Python

Status: Available Now

NEW: All files related to this workshop have been zipped and can be downloaded by clicking on the link in the left column "Click Here to Download Slides (PDF)"

Digital filters are commonly used in the processing of signals, whether they be wireless waveforms, captured sounds, and biomedical signals such as ECG; typically for the purpose of passing certain frequencies and suppressing others. Fixed-point implementation is attractive for lowest power lowest cost solutions when it is critical to make the most out of limited computing resources, however there can be significant performance challenges when implementing filters in fixed-point binary arithmetic. When a fixed-point implementation is required, a typical design process is to start with a floating-point design that has been validated to meet all performance requirements, and then simulate a fixed-point implementation of that design while modifying the precision used to ensure the requirements are met.

In this workshop, Dan takes you through the practical process of simulating a fixed-point digital filter using open-source Python libraries. This is of interest to participants wanting to see a motivating example for learning Python as well as those with experience using Python. Also included: a quick recap of basic filter structures and filter performance concerns.  A significant background in Digital Signal Processing (DSP) or digital filter design is not required. Having taken an undergraduate Signals and Systems course is sufficient. For a more detailed review of binary fixed-point operations and notations that will be used in this workshop, please attend Dan's Theatre Talk "Fixed-Point Made Easy: A Guide for Newcomers and Seasoned Engineers" that will be scheduled before this. After attending this talk, the participants will be equipped to confidently convert a given filter implementation to fixed-point prior to detailed implementation. If you have a floating-point filter design and need to implement it in fixed-point, this workshop is for you!

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Fixed-Point Made Easy: A Guide for Newcomers and Seasoned Engineers

Status: Available Now

Fixed-point implementation is popular for lowest power, lowest cost solutions when it is critical to make the most out of limited computing resources. However, the jargon and rules can be overwhelming to newcomers and seasoned engineers alike.

In this theatre talk, Dan will guide you through the common representations and rules for working with binary fixed point. This will include the Q notation for fractional number representation, two's complement, signed and unsigned numbers, considerations for truncation, rounding and overflow, and easy to follow rules for binary arithmetic. There will be plenty of fun examples to demonstrate the key concepts and practical use of the methodologies. If you are new to fixed-point or rusty and would like a refresher, this talk is for you!  This would particularly apply to anyone that needs a recap on fixed-point and is interested in attending Dan's talk "Fixed-Point Filters - Modelling and Verification Using Python". 

Even those exposed to fixed point in the past will appreciate this work-out session to quickly get back in top fixed-point shape!

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