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Lecture notes for undergraduate and first-year graduate students on digital watermarking and data embedding in multimedia data.

Based on lectures developed at University of Maryland, College Park, USA.


A brief introduction to the platform of India which
provides free access to quality online educational content for
Signal Processing. Experiences of creating courses related to Signal Processing,
supported by the European Union-funded project, MIELES.


Signals and systems is a well known fundamental course in signal processing. How this course is taught to a student can spell the difference between whether s/he pursues a career in this field or not. Giving due consideration to this matter, this paper reflects on the experiences in teaching this course. In addition, the authors share the experiences of creating and conducting a Massive Open Online Course (MOOC) on this subject under edX and subsequently following it up with deliberation among some students who did this course through the platform.


We give a brief history of the performance analysis of LMS.
Using averaging theory we show when and why the ‘independence
assumption’ ‘works’; we preface this with a fast
heuristic explanation of averaging methods, clarifying their
connection to the ‘ODE’ method. We then extend the discussion
to more recent distributed versions such as diffusion
LMS and consensus. While single node LMS is a single timescale
algorithm it turns out that distributed versions are twotime
scale systems, something that is not yet widely understood.


Simple but effective strategies for an undergraduate introductory course in signals and systems are described in this paper. These include peer facilitated tutorials, optional class tests, in-class only lab assessment and use of interactive animations. Peer facilitated tutorials were designed to support students to help other students. The optional class tests removed the stress and anxiety students face. With in-class only lab assessment the time students spent writing lab reports was replaced with time devoted to preparing and doing the lab together as a group.


Impact of online learning sequences to forecast course outcomes for an undergraduate digital signal processing (DSP) course is studied in this work. A multi-modal learning schema based on deep-learning techniques with learning sequences, psychometric measures, and personality traits as input features is developed in this work. The aim is to identify any underlying patterns in the learning sequences and subsequently forecast the learning outcomes.