DMA, EDMA Programming?
Configuring L1p, L1D, L2 memory?
Only mirrors are placed on this server
Pattern Recognition in Medical Imaging - mirror
compiles and organizes a complete range of proven and cutting-edge new methods, which are playing a leading role in the improvement of image quality, analysis and interpretation in modern medical imaging
2. Technologies in Robots
3. Industrial Robots
4. Industrial Manipulators and its Kinematics
5. Parallel Manipulators
6. Grippers Manipulators
7. Electric Actuators
8. Actuators - Electric, Hydraulic, Pneumatic
9. Internal State Sensors
10. Internal State Sensors
11. External State Sensors
12. Trajectory planning
13. Trajectory Planning
14. Trajectory Planning
15. Trajectory Planning
16. Trajectory Planning
17. Trajectory Planning
18. Trajectory Planning
19. Trajectory Planning
20. Forward Position Control
21. Inverse Problem
22. Velocity Analysis
23. Velocity Analysis
24. Dynamic Analysis
25. Image Processing
26. Image Processing
27. Image Processing
28. Image Processing
29. Image Processing
30. Image Processing
31. Robot Dynamics and Control
32. Robot Dynamics and Control
33. Robot Dynamics and Control
34. Robot Dynamics and Control
35. Robot Dynamics and Control
36. Robot Dynamics and Control
37. Futuristic Topics in Robotics
Lecture - 38
Lecture - 39
Lecture - 40 Futuristic Topics in Robotics
The MathWorks invites you to a free Webinar;
Date: Wednesday Aug 26, 2009
Time: 4:00 p.m. India Standard Time
Attendees will discover how MATLAB and featured Toolboxes enable users to more effectively solve problems encountered in analysis, design, implementation and verification of signal processing systems.
Through demonstrations, we will showcase features and capabilities of the Signal Processing Toolbox, Filter Design Toolbox, Fixed-Point Toolbox, Embedded MATLAB and related products and show how these features help users tackle a wide range of signal processing problems and challenges. You will learn about:
- Statistical signal processing, with a focus on spectral analysis
- Design of digital filters including multirate and adaptive filters
- Specification-based filter design methodology using Filterbuilder
- Estimating the computational complexity of filter structures
- Converting a design from a floating-point to a fixed-point representation
- Generating C code from MATLAB using Embedded MATLAB
- Accelerating fixed-point M-code execution speed through automatic C code generation
- Generating synthesizable VHDL or Verilog code from filters in MATLAB
Signal Processing and Communications
The MathWorks Inc.
A Q&A session will follow the presentation and demos.
Please register for this webinar – https://mathworksevents.webex.
We look forward to having you attend.
MathWorks India Pvt. Ltd.
2.Types of Wireless communication
3.The modern wireless Communication Systems
4.The cellular concept. System Design issues
5.Cell capacity and reuse
6.Interference and System capacity
7.Improving coverage and system capacity
8.Mobile Radio Propagation
9.Mobile Radio Propagation Contd
10.Mobile Radio Propagation Contd
11.Mobile Radio Propagation Contd
12.Mobile Radio Propagation Contd
13.Mobile Radio Propagation Contd
14.Mobile Radio Propagation II
15.Mobile Radio Propagation II Contd
16.Mobile Radio Propagation II Contd
17.Mobile Radio Propagation II Contd
18.Mobile Radio Propagation II Contd
19.Mobile Radio Propagation II Contd
20.Mobile Radio Propagation II
21.Modulation Techniques for Mobile Communication
22.Modulation Techniques for Mobile Communication
23.Modulation Techniques Contd
24.Modulation Techniques Contd
25.Modulation Techniques Contd
26.Modulation Techniques Contd
27.Modulation Techniques Contd
28.Modulation Techniques for Mobile Communications
29.Equalization and Diversity Techniques
30.Equalization and Diversity Techniques
31.Equalization and Diversity Techniques Contd
32.Equalization and Diversity Techniques Contd
33.Coding Techniques for Mobile Communications
34.Coding Techniques for Mobile Communications
35.Coding Techniques for Mobile Contd
36.Coding Techniques for Mobile Communications
38.GSM and CDMA
39.GSM and CDMA Contd
IEEE Signal Processing Society, Bangalore Chapter is organizing a one day workshop on Biomedical Signal Processing’.
Biomedical signal processing constitutes measurement and analysis of signals and images in clinical medicine and the biological sciences. Engineers, scientists and clinicians play an important role in this interdisciplinary field to solve the growing clinical problems that may improve the efficiency of diagnosis, treatment and therapy. This workshop will give the audience an excellent opportunity to know the latest trends in the industry, recent academic research and clinician’s perspective on current problems in healthcare.
The panel discussion will expose the audience to the opportunities and challenges in Indian healthcare. This event will also be a forum to meet peers in industry & academia and pave the way for potential collaborations.
Prominent speakers who will take part in the workshop include:
Dr. Ravi Malladi, India Technology Growth leader, GE Global Research
Prof. Vijay Chandru, Chairman and CEO, Strand Life Sciences
Prof. R.M. Vasu, Indian Institute of Science
Dr. K.G. Kallur, Bangalore Institute of Oncology and Health Care Global
Dr. Amit Kale, Siemens Corporate Technology
Dr. Manjiri Bakre, Philips Research Asia - Bangalore
The panel will include a number of eminent people, including Mr. S Bhaskaran, Sr Director, Philips Healthcare, Philips Innovation Campus.
The workshop will be conducted on Saturday, the 5th of September 2009 at the following venue:
M S Ramaiah Institute of Technology
Vidya Soudha, MSRIT Post MSR Nagar,
Bangalore - 560054.
Phone: 23600822, 23606934, 23607473
To register online for this event and for periodic updates, please visit http://ieeesp-blr.org. Considering the limited number of seats available, preference will be given to members of IEEE and to people who register online on a first come first serve basis. The registration fee can be paid via crossed cheque or demand draft drawn in favor of *“IEEE SP Society, Bangalore Chapter”* and sent to the following address by 25th August 2009:
Dr. Lokesh Boregowda
Honeywell Technology Solutions Lab
151/1, Doraisanipalya, Bannerghatta Road
Bangalore - 560 076, India
IEEE Student Members – Rs. 200
Students – Rs. 300
IEEE SPS Members – Rs. 400
IEEE Members – Rs. 500
Others – Rs. 800
Silver: Rs. 25,000* 2 free registrations, and logo on brochure
Gold: Rs. 50,000* 4 free registrations and logo on banner on main stage, mementos and brochure
Contact Information (for other queries)
Vishnu Makkapati, Philips Research Asia – Bangalore
Prof. T.V. Sreenivas, Indian Institute of Science
Prof. A. G. Ramakrishnan, Indian Institute of Science
Sriram Sethuraman, Ittiam Systems
Prabindh Sundareson, Texas Instruments
Lokesh Boregowda, Honeywell Technology Solutions Lab
Chetan Vinchhi, Texas Instruments
Phaneendra Yalavarthy, Indian Institute of Science
Karthick NG, GE Healthcare
Satyadhyan Chickerur, M.S.Ramaiah Institute of Technology
2 Discrete source encoding
3 Memory-less sources, prefix free codes, and entropy
4 Entropy and asymptotic equipartition property
5 Markov sources and Lempel-Ziv universal codes
7 High rate quantizers and waveform encoding
8 Measure, fourier series, and fourier transforms
9 Discrete-time fourier transforms and sampling theorem
10 Degrees of freedom, orthonormal expansions, and aliasing
11 Signal space, projection theorem, and modulation
12 Nyquist theory, pulse amplitude modulation (PAM), quadrature amplitude modulation (QAM), and frequency translation
13 Random processes
14 Jointly Gaussian random vectors and processes and white Gaussian noise (WGN)
15 Linear functionals and filtering of random processes
16 Review; introduction to detection
17 Detection for random vectors and processes
18 Theorem of irrelevance, M-ary detection, and coding
19 Baseband detection and complex Gaussian processes
20 Introduction of wireless communication
21 Doppler spread, time spread, coherence time, and coherence frequency
22 Discrete-time baseband models for wireless channels
23 Detection for flat rayleigh fading and incoherent channels, and rake receivers
24 Case study — code division multiple access (CDMA)
Lecture - 2 Problem Solving by Search
Lecture - 3 Searching with Costs
Lecture - 4 Informed State Space Search
Lecture - 5 Heuristic Search: A* and Beyond
Lecture - 6 Problem Reduction Search: AND/OR Graphs
Lecture - 7 Searching Game Trees
Lecture - 8 Knowledge Based Systems: Logic and Deduction
Lecture - 9 First Order Logic
Lecture - 10 Inference in First Order Logic
Lecture - 11 Resolution - Refutation Proofs
Lecture - 12 Resolution Refutation Proofs
Lecture - 13 Logic Programming Prolog
Lecture - 14 Prolog Programming
Lecture - 15 Prolog: Exercising Control
Lecture - 16 Additional Topics
Lecture - 17 Introduction to Planning
Lecture - 18 Partial Order Planning
Lecture - 19 GraphPLAN and SATPlan
Lecture - 20 SATPlan
Lecture - 21 Reasoning Under Uncertainity
Lecture - 22 Bayesian Networks
Lecture - 23 Reasoning with Bayes Networks
Lecture - 24 Reasoning with Bayes networks (Contd.)
Lecture - 25 Reasoning Under Uncertainity Issues
Lecture - 26 Learning : Decision Trees
Lecture - 27 Learning : Neural Networks
Lecture - 28 Back Propagation Learning
2.Introduction to Digital Signal Processing Contd
4.Characterization Description,Testing of Digital Syst
5.LTI Systems Step & Impulse Responses,Convolution
6.Inverse Systems,Stability,FIR & IIR
7.FIR & IIR; Recursive & Non Recursive
9.Discrete Fourier Transform (DFT)
11.DFT (Contd.) and Introduction to Z Transform
12.Z Transform part1
13.Z Transform part2
14.Discrete Time Systems in the Frequency Domain
15.Simple Digital Filters
16.All Pass Filters,Com.Filters
17.Linear Phase filters,Complementary Transfer Fn
18.Compensatary Transfer Functions, (Contd.),
19.Test for Stability using All Pass Functions
20. Digital Processing of Continuous Time Signals
21. Problem Solving Session: FT, DFT,& Z Transforms
22. Problem Solving Session: FT,DFT, & Z Transforms
23. Analog Filter Design
24. Analog Chebyshev LPF Design
25. Analog Filter Design (Contd.): Transformations
26. Analog frequency Transformation;
27. Problem Solving Session on Discrete Time System
28. Digital Filter Structures
29. IIR Realizations
30. All Pass Realizations
31. Lattice Synthesis (Contd.)
32. FIR Lattice Synthesis
33. FIR Lattice (Contd.) and Digital Filter Design
34. IIR Filter Design
35. IIR Design by Bilinear Transformation
36. IIR Design Examples
37. Digital to Digital Frequency Transformation
38. FIR Design
39. FIR Digital Filter Design by Windowing
40. FIR Design by Windowing & Frequency Sampling
41. Solving Problems on DSP Structures
42. FIR Design by Frequency Sampling
43. FIR Design by Frequency Sampling (Contd.)
Topics Covered by title:
- What is Compression?
- Basic Need of Compression.
- Types of Compression.
- JPEG Encoder explained.
What is Compression?
Representing the data (Image, Audio, Video, Speech or Voice..) with the fewer number of bits than what it exactly requires to represent.Basic Need of Compression
Effectively utilizing transmission bandwidth .Utilization of the storage media to the maximum.
Types of Compression:
- Lossless Compressing the data which almost resembles the original Input data when decompressed.
- Lossy Compressing the data with some lose of information(Keeping in mind of Human Visual and Psychoacoustic system) i.e. neglecting the higher frequency components which are very less sensitive to human visual system.
In general image is nothing but a group of pixels. Pixel holds the brightness and color information of the image at a particular coordinate.
Red, Green and Blue are the primary color components of the color image.With the help of these color combinations we can get the color that we deserve.
Step by Step procedure in compressing input Image data.
- Input: Reading MxN (In general 8x8) block of input RGB Image each color component is of 8-bit.
- RBG->YcbCr: Converting MxN RBG samples to YCbCr (Luma and Chroma components).
- DCT:Performing Discrete Cosine Transform (DCT) on each of the MxN Luma and Chroma components.
- Scaling:Performing quantization of the resultant ouiput coefficient matrix given by DCT which of same size as input to this block.
- Scanning:Zig-Zag scaning of the resultant MxN matrix to a single dimensional array.
- Huffmancoding:Performing Huffman Coding on the resultant 1-D array.
- BitStream:And finally the resultant bitstream will be the JPEG encoder output.
Reading 8x8 matrix of Red, Green and Blue components of input image. Converting each one of the RGB components to YCbCr (Luma and Chroam components) using below equation
Y = (0.299R + 0.587G + 0.114B) + 64
Cb = (-0.1687R - 0.3313G + 0.5B) + 512
Cr = (0.5R - 0.4187G - 0.0813B) + 512
And then performing 2D - DCT on each of the 8x8 matrix of Y, Cb and Cr
ref the link below.
DCT will give the lower frequency coefficients matrix 8x8 (Y,Cb and Cr).
which are more sensitive to human eye.
And then performing scalar quntization (scaling of resultant DCT output array) of each of the luma and chroma matrices.
This quantization is the major step in which the actual compression takes place and this is module which consumes more number of cycles in JPEG compression.
And performing zig-zag scanning on the resultant scaled arrays(Y, Cb and Cr).
Performing Huffman Run length coding on the resultant 1D array got after zig-zag coding.
The resultant array is the output bitstream of the JPEG encoder.