A Short term Training program (STTP)

on

SELECT TOPICS IN SIGNAL PROCESSING AND

MACHINE INTELLIGENCE WITH HANDS-ON

During 23rd

May 2016 to 3th

Jun 2016

College of Engineering Pune,Pune.

Co-ordinator: - Dr.VibhaVyas

Professor ,Electronics & Telecommunication Engg.,COEP

:

Attendant By.

1. Prof. Madhuri Babar (Assistant Professor Electronic & Telecommunication Engg.Department).

2. Prof.WaniPatil– (Assistant Professor Electronic Engg.Department.)

Day 1: 23rd May 2016

Capturing and Visualising the feeling of Virtual Touch Dr.SubhasisChaudhuri, Deputy Director(AIA),IIT Bombay

Human haptics-study of human sensing and manipulation through touch.

Machine haptics-study of design, construction, anduse of machine to replace or augment

human touch.

Computer haptics-deals with models and behavior of virtual objects together with

rendering algorithm for real time interaction

About Touching

Unlike others, touch is an active sensing mechanism.

Based on force feedback

Two types –kinesthetic and tactile

Our study is on kinesthetic aspect.

Haptic Rendering

The process associated with generating and displaying the touch and feel of virtual object to

human operator through a force reflecting device

Illustration of Haptic Rendering

(a)Mesh model of hand .(b)Visually and haptically rendered hand.

Introduction to pattern recognition Prof.PritiP.Rege,HoD (E&TC),COEP

What is pattern recognition?

“The assignment of a physical object or event to oneof several prespecified

categeries” -- Duda& Hart

• A pattern is an object, process or event that can be given a name.

• A pattern class (or category) is a set ofpatterns sharing common attributes and

usuallyoriginating from the same source.

• During recognition (or classification) given objects are assigned to prescribed

classes.

• A classifier is a machine which performs classification.

Pattern Classifiers and Measurement Metrics in Machine Learning

Prof.AshutoshMarathe,Professor, VIT College, Pune

Origin of Pattern Classifiers

Unsupervised Approach

• The objective is to discover the structure of the data set.

• Searching for Groups in the data and characteristics , making the

objects similar within group and different across the group.

• Different algorithms come up with different structures for the same

data.

• No ground truth to compare result against and „goodness‟ of results is

„subjective estimate‟ of the user

Supervised Approach

• Each object in the data set comes with a class label • Task is to train a classifier to do the labeling sensibly. • Designer supplies the machine with learning skills and presents the

labeled data to it. • The classification knowledge learned by the classifier would be

obscure. • The recognition accuracy of the classifier is the judge of its adequacy.

Day 2: 24th May 2016

Towards evolution of basis functions for data analytics

Dr.AdityaAbhankar,Dean,Faculty of Technology ,SavitribaiPhule,Pune University

PCA Algorithm

Zero mean data

Represent system using statistics

Use covariance (correlation)

Find out eigenvalues and eigenvectors

Represent in optimized way!

Understand dimensionality reduction

Use for applications like face recognition

Summary

Basis functions are the key pointidentifiers

All classification techniques eventually boildown to finding out basis functions

Not all systms have basis functions

Amongst different bases, it is important tofind out the most important basis

function

Speech Processing and Applications

Dr. S. P. Mahajan,Dept. of Electronics and Telecommunication ,College of Engineering Pune

• Speech wave conveys several kind of information

– Linguistic information that indicates the meaning speaker

wishes to impart.

– Individual information representing who is speaking.

– Emotion of the speaker

• Comparison of written language Vs. Spoken language.

– Preservation

– One way / Two way communication

Day 4: 26th May 2016

Wavelets Dr.ManeshKokre, Department of Electronics and Telecommunication Engineering ,S.G.G.S. Institute

of Engg and Technology, Nanded

What is a Transform and Why Do we Need One ?

How Does FT Work ?

The Wavelet Transform

Overcomes the present resolution problem of the STFT by using a variable length

window

Analysis windows of different lengths are used for different frequencies:

Analysis of high frequencies Use narrower windows for better time resolution

Analysis of low frequencies Use wider windows for better frequency

resolution

This works well, if the signal to be analyzed mainly consists of slowly varying

characteristics with occasional short high frequency bursts.

Heisenberg principle still holds!!!

The function used to window the signal is called the wavelet

Scale & Translation

The Mother of All Oscillatory Little Basis Functions

Continuous Wavelet Transform

Day 5: 27th May 2016

Affective Learning: Introduction and physiological signals

Suhas S. GajrePh.D.(IIT Delhi) Dept. of Electronics and Telecommunication Engineering

Shri Guru GobindSinghji Institute of Engineering and Technology, Nanded

InterPersonalNeuroBiology

Dr. R. R. Manthalkar Dept. of Electronics and Telecommunication Engineering

Shri Guru GobindSinghji Institute of Engineering and Technology, Nanded

Day 6: 28th May 2016

Signal Transforms

Dr.SumantraDutta Roy Department of Electrical Engineering

Indian Institute of Technology Delhi

Day 7: 29th May 2016

K-Means, MixtureModels & EM

Dr.SumantraDutta Roy Department of Electrical Engineering

Indian Institute of Technology Delhi

HANDS-ON LABORATORY SESSIONS

Day 3:25th

May 2015

Wavelet Group Ltd.

Blackfin Embedded Symmetric Multiprocessor

ADSP-BF561

GENERAL DESCRIPTION The ADSP-BF561 processor is a high performance member of the Blackfin family

of products targeting a variety of multimedia and telecommunications applications.

At the heart of this device are two independent Analog Devices Blackfin

processors. These Blackfin processors combine a dual-MAC state-of-the-art

signalprocessing engine, the advantage of a clean, orthogonal RISC-like

microprocessor instruction set, and single instruction, multiple data (SIMD)

multimedia capabilities in a single instruction set architecture. The ADSP-BF561

device integrates a general-purpose set of digital imaging peripherals.The ADSP-

BF561 processor has 328K bytes of on-chip memory.

Each Blackfin core includes:

• 16K bytes of Instruction SRAM/Cache

• 16K bytes of Instruction SRAM

• 32K bytes of Data SRAM/Cache

• 32K bytes of Data SRAM

• 4K bytes of Scratchpad SRAM

Additional on-chip memory peripherals include:

• 128K bytes of Low Latency On-Chip L2 SRAM

• Four-Channel Internal Memory DMA Controller

• External Memory Controller with Glueless Support for SDRAM, Mobile

SDRAM, SRAM, and Flash.

Day 8:1st

June 2016

EdGate Technologies Pvt. Ltd

OMAP-L138 DSP+ARM9Development Kit

Key features and benefits

• OMAP-L138 DSP+ARM9 software and development kit to jump-start real-time

signal processing innovation

• Reduces design work with downloadable and duplicable board schematics and

design files

• Fast and easy development of applications requiring fi ngerprint recognition and

face detection with embedded analytics

• Low-power OMAP-L138 DSP+ARM926EJ™ processor

• Scalable platform enables a variety of performance, power, peripheral and price

options

• 456-MB TMS320C674x DSP

• 456-MB ARM926EJ processor

• 128-MByte DDR2 SDRAM

• 128-MByte NAND Flash memory

• Micro SD/MMC slot

• USB and SD connectors

• Wide variety of peripheral interfaces

• Line in, headphone out, MIC-in ports

• Expansion connectors

• Includes Code Composer Studio™ IDE v4.0

• Full documentation on CD-ROM

Technical details The OMAP-L138 development kit is based on the OMAP-L138 DSP+ARM9 processor, a low-power applications processor based on an ARM926EJ-S and a TMS320C674x DSP core. It provides significantly lower powerthan other members of the TMS320C6000™ platform of DSPs. The OMAP-L138 processor enables developers to quickly design and develop devices featuring robust operating systems support and rich user interfaces with a fully integrated mixed-processor solution. The dual-core architecture of the device provides benefits of both DSP and Reduced InstructionSet Computer (RISC) technologies, enabling applications requiring a high-level operating system and more intensive digital signal processing.

With a wide variety of standard interfaces for connectivity and storage, the OMAP-L138 development kit enables developers to easily bring audio, video and other signals onto the board. Expansion headers allow customers to extend the functionality of the kit to include a camera sensor from Leopard Imaging or an LCD screen. Included interfaces are: • USB serial port • Fast Ethernet port (10/100 Mbps) • USB host port (USB 1.1) • USB OTG port (USB 2.0) • SATA port (3 Gbps) • VGA port (15-pin D-SUB) • LCD port (Beagleboard-XM connectors) • 3 audio ports 1 line in 1 line out 1 MIC in

• Composite in (RCA jack) • Leopard Imaging camera sensor input (32-pin ZIP connector) • Authentic fi ngerprint sensor

Easy to write and optimize DSP code Designers can readily target the OMAP-L138processor through TI’s robust and

comprehensive Code Composer Studio IDE. CCStudio IDE includes an efficient

optimizing C/C++ compiler assembler, linker, debugger; integrated CodeWright

editor with CodeSense technology for faster code creation; data visualization; a

profiler and a flexible project manager. CCStudio IDE also includes a DSP/BIOS™

real-time kernel and Chip SupportLibrary

Day 9 :2nd

June 2016

Benchmark Electronics Pvt.Ltd.

Matrox InspectorLibrary(MIL)

Interactive imaging Matrox Inspector is an interactive Windows®application for image

captureprocessing, analysis, display and archiving. Based on the Matrox Imaging

Library (MIL), it provides point-and-click access to an extensive set of optimized

functions for image processing, pattern recognition, blob analysis, edge extraction

and analysis, measurement, character recognition, 1D and 2D code reading,

calibration, and image compression. Matrox Inspector is designed to meet the

needs of MIL developers and end-users alike. MIL users employ Matrox Inspector

as a companion tool to facilitate application development by having access to all

MIL tools within a single interactive work environment. End-users, such as

scientists, technicians and engineers, rely on Matrox Inspector to perform and

automate image enhancement and measurement tasks.

Key features easy-to-use interactive work environment for Microsoft ®Windows®2000

and Windows ® XP

directly acquire images from a variety of video sources using Matrox

Imaging hardware

load and save images including sequences in many file formats extensive set

of accurate and robust image processing and analysis tools

calibrate images to correct visual distortions and perform measurements in

real-world units

operations are performed live or on archived images including sequences

track operation statistics (analysis results and speed)

configure analysis tools for use with Matrox Imaging Library (MIL)

applications

annotate images with text and graphics

control from and exchange data with other Microsoft ® Windows ®

applications

automate routines with Microsoft ® VBA compatible scripting

In addition to displaying images, Matrox Inspector presents processing and

analysis results as tables and/or graphs, including trend and distribution, which are

useful for tuning operation settings. The application also gives users the ability to

benchmark operations for accuracy and repeatability. In addition to making

annotations, users can draw into images to perform measurements as well as touch-

up and manually segment images.

Additional processing and analysis utilities

MIL includes a collection of interactive Windows-based utilities for each key

image processing and analysis tool. Intended for configuration and

experimentation, each tool supports live image capture and processing as well as

file I/O for individual or sequences of images. Tooltips over dialog controls

provide a convenient cross-reference to actual MIL function calls.

Griminess ofSTTP

Laboratory sessions

La

Rec

Group photo with all participatent of STTP