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Ldpc_225c
School: Berkeley
Low Density Parity Check Decoder Architecture Engling Yeo yeo@eecs.berkeley.edu Department of Electrical Engineering and Computer Sciences University of California, Berkeley Engling Yeo University of California, Berkeley 1 Lowdensity Parity Chec

Lec24_06
School: Berkeley

Lec11_06
School: Berkeley

Lec26
School: Berkeley
Transform Transmitter Image Coding  ,.  Transform T,(k., k2) "'" 1,(k" k2) Quantization " Codeword assignment Receiver Inverse transform I T,(k"k2) Decoder What is exploited: Most of the image energy is concentrated in a small nu

Lec19
School: Berkeley

L1magic
School: Berkeley
1 magic : Recovery of Sparse Signals via Convex Programming Emmanuel Cand`s and Justin Romberg, Caltech e October 2005 1 Seven problems A recent series of papers [38] develops a theory of signal recovery from highly incomplete information. The

Lec26
School: Berkeley
Transform Transmitter Image Coding  ,.  Transform T,(k., k2) "'" 1,(k" k2) Quantization " Codeword assignment Receiver Inverse transform I T,(k"k2) Decoder What is exploited: Most of the image energy is concentrated in a small nu

Lec15
School: Berkeley
N ~ 1\ I j j J , ~. . \ . r  b ! ! , f . .  ,  o. V i I I \ '3 " '?,. '?' nJ I" .( j r7 ('\t c:a1 ."  . .; + o 4Q. w . .s ,i . , <; \ \ 'T ) c:c. ."..J , '.0 ;)  " . ~ . 4 ~ ~ ., '. . ! J i

Lec9
School: Berkeley

Lec7a
School: Berkeley

Lec7
School: Berkeley

Homework4_probs
School: Berkeley

EE225C_Midterm_sheets
School: Berkeley
Adaptive Pilot Detect and Coarse Timing Acquisition Block Timing Recovery Unit for a 1.6 Mbps DSSS Receiver EE225C Project Midterm Report Submitted by Mike Sheets msheets@eecs.berkeley.edu November 6, 2000 Design characteristics Power Delay Area 14

Lec20_06
School: Berkeley

EE225C_Proposal_ammer_sheets
School: Berkeley
Timing Recovery Unit for a 1.6 Mbps DSSS Receiver EE225C Project Proposal Submitted by Josie Ammer and Mike Sheets September 21, 2000 System Components: Adaptive Pilot Detect and Course Timing Acquisition (APD&CTA) Mike Sheets (msheets@eecs.berk

OFDM_SVD_dejan
School: Berkeley
OFDM Receiver Design: Singular Value Decomposition for Channel Estimation Dejan Markovi dejan@eecs.berkeley.edu EE225C Midterm Report 7 November 2000 SVD Block Description Tx Encoding & Modulatio n Channel z'1 z'4 U Rx x' V x y y' U Demod

TB_2
School: Berkeley
 Test Bench use STD.textio.all; library IEEE; use IEEE.std_logic_1164.all; use IEEE.std_logic_signed.all; use IEEE.std_logic_arith.all; entity TB is end TB; architecture stimulus of TB is component Sub_tr is port( Zr : out std_logic_vect

Ffc_tutorial
School: Berkeley
A Tutorial on Using SimulinkTM and XilinxTM System Generator to Build Floatingpoint and Fixedpoint Communication Systems For EE225c, 2003 By Changchun Shi Last Updated: March 10, 2003 Berkeley Wireless Research Center EECS Department, University of

Midterm_pres
School: Berkeley
32Point Fully Parallel FFT Kevin Camera kcamera@eecs.berkeley.edu FFT Block Description 32points 15MHz input rate 12b words, 5b coef. Optional truncation and saturation Microarchitectures Pure ripple, no booth Carrysave Booth multipliers

Lec16_ofdm
School: Berkeley
E225C Lecture 16 OFDM Introduction EE225C Multipath can be described in two domains: time and frequency Time domain: Impulse response time time time Impulse response Frequency domain: Frequency response time time time Sinusoidal signal as inpu

Comsoc
School: Berkeley
IEEE SCV Communications Society Lecture CMOS for Ultra Wideband and 60 GHz Communications Bob Brodersen Dept. of EECS Univ. of Calif. Berkeley http:/bwrc.eecs.berkeley.edu Berkeley Wireless Research Center FCC  Unlicensed Spectra UWB ISM 0 UPCS U

Final_pres
School: Berkeley
Polyphase Filter Bank Architectures for a Spacebased Radar Receiver Kevin Camera (kcamera@eecs.berkeley.edu) Changchun Shi (ccshi@eecs.berkeley.edu) EE225C, Fall 2000 Prof. Borivoje Nikolic Prof. Bob Brodersen Outline Project motivation Existing a

Hw5
School: Berkeley
EE 225C VLSI Signal Processing Homework 5 Due on April, 2003 The goal of this problem set will be to implement a single carrier, 20 Mbit/sec QPSK transmitter and receiver that uses a synchronizer to compensate for the channel impairments. A prototype

Lab8
School: Berkeley
University of California, Berkeley College of Engineering Department of Electrical Engineering and Computer Sciences Prof. A. Zakhor EE225b Digital Image Processing Assignment #8 Image Restoration Overview: Spring 2003 In this assignment, you wi

Lab7
School: Berkeley
University of California, Berkeley College of Engineering Department of Electrical Engineering and Computer Sciences Prof. A. Zakhor EE225b Digital Image Processing Assignment #7 Image Enhancement Overview: Spring 2003 A problem frequently encou

Lab6
School: Berkeley
University of California, Berkeley College of Engineering Department of Electrical Engineering and Computer Sciences Prof. A. Zakhor EE225b Digital Image Processing Assignment #6 2DFIR filter design J. S. Lim, TwoDimensional Signal and Image Pro

Lab3
School: Berkeley
University of California, Berkeley College of Engineering Department of Electrical Engineering and Computer Sciences Prof. A. Zakhor EE225b Digital Image Processing Lab Assignment #3 Tomography Overview: Spring 2003 In this assignment, you explo

Lab2
School: Berkeley
University of California, Berkeley College of Engineering Department of Electrical Engineering and Computer Sciences Prof. A. Zakhor EE225b Digital Image Processing Lab Assignment #1 Phaseonly image reconstruction Overview: Spring 2003 In this

Lecture29
School: Berkeley

Lecture28
School: Berkeley

Lecture24
School: Berkeley

Lecture23
School: Berkeley

Lecture22
School: Berkeley

Lecture21
School: Berkeley

Lecture16
School: Berkeley

Lecture15
School: Berkeley

Lecture14
School: Berkeley

Lecture13
School: Berkeley

Lecture11
School: Berkeley

Lecture10
School: Berkeley

Lecture8
School: Berkeley

Lecture7
School: Berkeley

Lecture6
School: Berkeley

Lecture5
School: Berkeley

Lecture4
School: Berkeley

Lecture2
School: Berkeley

Lecture1
School: Berkeley

Ps2
School: Berkeley
UNIVERSITY OF CALIFORNIA AT BERKELEY PROF. EDWARD A. LEE COLLEGE OF ENGINEERING DEPARTMENT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 518 CORY HALL BERKELEY, CALIFORNIA 94720 TEL: (510) 6420455 FAX: (510) 6422739 EMAIL: eal@eecs.Berkeley.EDU

Lec.7_sfg
School: Berkeley

Info225a
School: Berkeley
EECS 225A Digital Signal Processing Gastpar University of California, Berkeley: Spring 2008 January 7, 2008 Course Information (Preliminary Version) 1 Logistics Michael Gastpar, 265 Cory Hall, gastpar@eecs.berkeley.edu, OH Tue Thu 12:401:30 Tues

Hw5_appendix2
School: Berkeley
Appendix II (extracted from Peimin Chis MS thesis) Correlator After differential demodulation, the next task is to correlate against the local preamble. This can be mathematically written as q[n] = r[n  i] * p * [i] i =0 N 1 = (rI [n  i] + j r

1.30
School: Berkeley

Lec19
School: Berkeley

Lec17
School: Berkeley

Info225a
School: Berkeley
EECS 225A Digital Signal Processing Gastpar University of California, Berkeley: Spring 2007 January 16, 2007 Course Information (Preliminary Version) 1 Logistics Michael Gastpar, 265 Cory Hall, gastpar@eecs.berkeley.edu, OH Tue Thu 12:401:30 Tue

Hw5_appendix3
School: Berkeley
Appendix III (extracted from Peimin Chi's MS thesis) CORDIC Due to frequency offset, the maximum correlation value is Ne j2fT when there is no noise, as shown in (2.11). To estimate the frequency offset, we need to estimate the phase of the max corre

Hw2
School: Berkeley
EE 225C VLSI Signal Processing Homework 2 Due on March 5, 2003 1. Architectural tradeoffs (a) Calculate the energy efficiency and area efficiency metrics, MOPS/mW and MOPS/mm2 , for the following four chips that appeared in the 2003 ISSCC. (b) Compar

Lec16_ofdm
School: Berkeley
E225C Lecture 16 OFDM Introduction EE225C Introduction to OFDM l Basic idea Using a large number of parallel narrowband subcarriers instead of a single wideband carrier to transport information l Advantages Very easy and efficient in dealing

Lab7
School: Berkeley
University of California, Berkeley College of Engineering Department of Electrical Engineering and Computer Sciences Prof. A. Zakhor EE225b Digital Image Processing Assignment #5 Image Enhancement Overview: Spring 2008 A problem frequently encou

EE225C_Proposal_ammer_sheets
School: Berkeley
Timing Recovery Unit for a 1.6 Mbps DSSS Receiver EE225C Project Proposal Submitted by Josie Ammer and Mike Sheets September 21, 2000 System Components: Adaptive Pilot Detect and Course Timing Acquisition (APD&CTA) o Mike Sheets (msheets@eecs.berk

Lec25
School: Berkeley
1 (Wireless) Networks are more than communications. Part II Prof. Adam Wolisz TUBerlin/Vsiting Scholar UCB EE 225C Spring 2003 Intro . Who: Adam Wolisz Professor of EE&CS at the Technische Universitt Berlin (TUB) Germany Chair of Telecommunic

Wireless_channels
School: Berkeley
Wireless Channels Ada Poon, Bob Brodersen Berkeley Wireless Research Center University of California, Berkeley EarthIonospheric Waveguide 3 30 kHz, very low frequency (VLF) Large wavelength (>10 km) Wave cant penetrate to the lowest layer of ionos

Icd_proposal
School: Berkeley
I.ADMINISTRATIVE TITLE: Iterative LDPCCoded Channel Decoder ABSTRACT: This proposal includes algorithms and implementation of iterative decoders based on a Low Density Parity Check (LDPC) coded partial response channel for high speed applications, a

Hmwk05
School: Berkeley
EECS 225A Spring 2005 Homework 5 Due: February 24. Solutions will be posted on that date and you will selfgrade your homework. 1. In the following, Z (k ) = R(k ) + j I (k ) is a zeromean Gaussian random process (meaning R (k ) and I (k m) are ze

Lec13_06
School: Berkeley

Lec7_06
School: Berkeley

Lec23_06
School: Berkeley

Lec25_06
School: Berkeley

Lec18_06
School: Berkeley

Lec5_06
School: Berkeley

Solnmidterm1
School: Berkeley
EE 225A Spring 2005 First Midterm Exam: Solutions 1. A function of a complex variable z is analytic in a region if (check one): It is continuous at every point in the region. It is differentiable with respect to z at every point in the region. It

Hmwk02
School: Berkeley
EECS 225A Spring 2005 Homework 2 Due: February 3. Solutions will be presented on that date and you will selfgrade your homework. Note: In all homework problems you are encouraged to use the numeric and/or symbolic capabilities of Matlab or similar f

Hmwk03
School: Berkeley
EECS 225A Spring 2005 Homework 3 Due: February 10. Solutions will be presented on that date and you will selfgrade your homework. Note: In all homework problems you are encouraged to use the numeric and/or symbolic capabilities of Matlab or similar

Soln04
School: Berkeley
EECS 225A Spring 2005 Homework 4 solutions 1. As shown below, a random variable X is the input to a cascade of two systems with random variable outputs Y1 and Y2 . You are given the joint PDF p X ,Y1 ,Y2 ( x, y1 , y 2 ) and told that it satisfies pY

Hmwk07
School: Berkeley
EECS 225A Spring 2005 Homework 7 Due: Date March 10. Solutions will be presented on that date and you will selfgrade your homework. Note: In all homework problems you are encouraged to use the numeric and/or symbolic capabilities of Matlab or simila

Hmwk01
School: Berkeley
EECS 225A Spring 2005 Homework 1 Due: January 27. Solutions will be presented on that date and you will selfgrade your homework. Note: In all homework problems you are encouraged to use the numeric and/or symbolic capabilities of Matlab or similar f

Soln05
School: Berkeley
EECS 225A Spring 2005 Homework 5 solutions 1. In the following, Z (k ) = R(k ) + j I (k ) is a zeromean Gaussian random process (meaning R(k ) and I (k m) are zeromean jointly Gaussian for all k and m ) and widesense stationary with autocorrelat

Soln03
School: Berkeley
EECS 225A Spring 2005 Homework 3 solutions 1. In lecture the unitsample response of a secondorder allpass filter was illustrated. a. This filter has only a single independent parameter, which is the location of one of the two poles. Why? b. Startin

Soln02
School: Berkeley
EECS 225A Spring 2005 Homework 2 solutions 1. Consider a complex signal {z k ,1 k n} . The goal is to find the best approximation to this signal in terms of a complex exponential with some fixed frequency ; that is, the complex coefficients u and

Matrixinversionlemma
School: Berkeley
EECS 225A Spring 2005 Matrix inversion lemma David G Messerschmitt Version 1.0, May 4, 2005 When both A and (A uv H ) are invertible (where A is a square matrix and u and v are column vectors), the matrix inversion lemma states that (A uv H ) 1 A

Singularlinearequations
School: Berkeley
Linear equations: Case of singular square matrix David G Messerschmitt Version 1.1, March 2, 2005 For a set of linear equations Ax = b when A is square but singular, there are two cases: Case I: There are no solutions (so we look for the best approx

Soln10
School: Berkeley
EECS 225A Spring 2005 Selected book problem solutions Chapters 7, 8, and 9 The following problem solutions should assist you in studying for the second midterm. You should learn more if you give each problem a go before looking at the solution.

Soln01
School: Berkeley
EECS 225A Spring 2005 Homework 1 1. Choose any two of the identities involving finite summations in Table 2.3 of Hayes. a. Verify those identities numerically for 0 N 1000 using Matlab. b. Verify those identities for all N using (and trusting) the

Review
School: Berkeley
EECS 225A Spring 2005 Common themes Complex variables Real functions of a complex variable contains z * , not analytic Stationary points * = 0 z Gradient * = 0 z Linear timeinvariant systems Complex exponentials Impulse response, transfer functio

Soln08
School: Berkeley
EECS 225A Spring 2005 Homework 8 solutions 1. In class we showed that if the transfer function for the n  1 order lattice filter An1 ( z ) is minimum phase, and n < 1 then An (z ) is also minimum phase. a. Loosen the assumption and assume that only

Soln07
School: Berkeley
EECS 225A Spring 2005 Homework 7 solutions 1. You wish to design a leastsquares inverse filter that realizes (or if necessary approximates) g (k ) hN (k ) = d (k ) , 0 k < M . However, battery power limitations restrict the value of N (number of F

Soln09
School: Berkeley
EECS 225A Spring 2005 Homework 9 solutions 1. Given an N N autocorrelation matrix R (N) , you are told that the eigenvalues of this matrix, asymptotically as N , approach the values (  a < 1 is realvalued) 1 2n 1 + a 2a cos N 2 , 0 n

Lec2a
School: Berkeley

Hayeserratatoproblems
School: Berkeley
1 Problem 2.13: The matrix A should be ERRATA in Problems (First Printing) A = ;0 1 1 0 x(n) = A cos(n! + ) Problem 3.8: De ne the process x(n) as follows and, in part (c), let ! be a random variable that is uniformly distributed over the interval

Lec17
School: Berkeley

Lab8
School: Berkeley
University of California, Berkeley College of Engineering Department of Electrical Engineering and Computer Sciences Prof. A. Zakhor EE225b Digital Image Processing Assignment #8 Image Restoration Overview: Spring 2007 In this assignment, you wi

Lab7
School: Berkeley
University of California, Berkeley College of Engineering Department of Electrical Engineering and Computer Sciences Prof. A. Zakhor EE225b Digital Image Processing Assignment #7 Image Enhancement Overview: Spring 2007 A problem frequently encou

Hmwk08
School: Berkeley
EECS 225A Spring 2005 Homework 8 Due: March 31. Solutions will be presented on that date and you will selfgrade your homework. 1. In class we showed that if the transfer function for the n 1 order lattice filter An1 ( z ) is minimum phase, and n <

Hmwk09
School: Berkeley
EECS 225A Spring 2005 Homework 9 Due: April 7, 2005. Solutions will be presented on that date and you will selfgrade your homework. 1. Given an N N autocorrelation matrix R (N) , you are told that the eigenvalues of this matrix, asymptotically as N

Hmwk06
School: Berkeley
EECS 225A Spring 2005 Homework 6 Due March 3. Solutions will be presented on that date and you will selfgrade your homework. Note: In all homework problems you are encouraged to use the numeric and/or symbolic capabilities of Matlab or similar facil

Hmwk04
School: Berkeley
EECS 225A Spring 2005 Homework 4 Due: February 17 1. As shown below, a random variable X is the input to a cascade of two systems with random variable outputs Y1 and Y2 . You are given the joint PDF p X ,Y1 ,Y2 ( x, y1 , y 2 ) and told that it satisf