Documents Found!
As seen in
Less Work, Better Grades
Join
Course Hero
Access
best resources
Ace
your classes
Ace your courses with Course Hero!
|
|
|
Limited, unformatted preview (showing 61 of 1319 words):
...Networks: Wireless MAC and Routing Nick Feamster CS 7260 April 10, 2006 Today s Lecture Overview What s different about wireless networks? Challenges Media Access Control (MAC) Hidden terminal and Exposed terminal CSMA/CD Reservation-based MAC: RTS/CTS Routing Traditional routing protocols Biswas et al., Opportunistic Routing in Multi-hop Wireless Networks 2 What is a Wireless Network? Wireless: without wires Many ways to communicate without...
Study Smarter, Score Higher
Here are the top 5 related documents
...CS 345
Types and Parametric Polymorphism
Vitaly Shmatikov
slide 1
Quote of the Day
Types are the leaven of computer programming; they make it digestible. - Robin Milner
slide 2
Reading Assignment
Tucker and Noonan, Chapter 5 C Reference Manual,...
...CS 345
Programming Paradigms
Vitaly Shmatikov
slide 1
Quote of the Day
These machines have no common sense; they have not yet learned to `think, and they do exactly as they are told, no more and no less. This fact is the hardest concept to grasp ...
...CS 345 - Programming Languages Spring 2008 Homework #5
Due: 2pm CDT (in class), March 27, 2008
YOUR NAME: Collaboration policy
No collaboration is permitted on this assignment. Any cheating (e.g., submitting another persons work as your own, or perm...
...CS 345
Functions
Vitaly Shmatikov
slide 1
Quote of the Day
"It is better to have 100 functions operate on one data structure than 10 functions on 10 data structures." - Alan Perlis
slide 2
Reading Assignment
Tucker and Noonan, Chapter 9 C Refer...
Document Content (unformatted)
Course Hero has millions of student submitted documents similar to the one
below including study guides, homework solutions, papers, exam answer keys and textbook solutions.
Networks: Wireless MAC and Routing Nick Feamster CS 7260 April 10, 2006 Today s Lecture Overview What s different about wireless networks? Challenges Media Access Control (MAC) Hidden terminal and Exposed terminal CSMA/CD Reservation-based MAC: RTS/CTS Routing Traditional routing protocols Biswas et al., Opportunistic Routing in Multi-hop Wireless Networks 2 What is a Wireless Network? Wireless: without wires Many ways to communicate without wires Optical Acoustic Radio Frequency (RF) Many possible configurations Point-to-point (e.g., microwave communications links) Point-to-multipoint (e.g., cellular communications) Ad-hoc, (e.g., sensor networks) 3 Wireless Communications Networks Wireless LANs: 802.11 Cellular Networks 2G, 3G, 4G Networks Voice and data (e.g., EVDO) Point-to-Point Microwave Networks Satellite Communications Short-Range: Bluetooth, etc. Ultra-wideband Networks 4 Applications of Wireless Networking Cellular phones Wireless LANs Metro-area networks Medical equipment 5 802.11 ( Wi-Fi ) Overview Throughput 802.11b 11 Mb/s 802.11a 54 Mb/s Frequency Bands of Operation 802.11b, g: 2.400 2.4835 GHz 802.11a: 5-6 GHz range RF Formats 802.11b utilizes frequency hopping, CDMA, and CCK modulations 802.11a utilizes Orthogonal Frequency Division Multiplexing (OFDM) 6 Differences from the Wired Network Sharing and resource management Wired network: no interference below network layer Wireless networks: interference can occur at the physical layer Closest analog in the wired network: Ethernet on a hub-based network Difference: Collision detection easier in wireless network 7 Challenges in Wireless Networking Resource sharing Routing Challenge: coping with probabilistic packet reception Achieving high throughput Challenge: determining capacity of a wireless network Mobility TCP performance Energy-efficiency 8 Carrier Sense Multiple Access (CSMA) Listen to medium and wait until it is free (no one else is talking) Wait a random backoff time Advantage: Simple to implement Disadvantage: Cannot recover from a collision 9 Wireless Interference Two transmitting stations interfere with each other at the receiver Receiver gets garbage A B C 10 Carrier Sense Multiple Access with Collision Detection (CSMA-CD) Procedure Listen to medium and wait until it is free Start talking, but listen to see if someone else starts talking too If collision, stop; start talking after a random backoff time Used for hub-based Ethernet Advantage: More efficient than basic CSMA Disadvantage: Requires ability to detect collisions More difficult in wireless scenario 11 Collision Detection in Wireless No fate sharing of the link High loss rates Variable channel conditions Radios are not full duplex Cannot simultaneously transmit and receive Transmit signal is stronger than received signal 12 Solution: Link-Layer Acknowledgments Absence of ACK from receiver signals packet loss to sender Sender interprets packet loss as being caused by collision Problem: Does not handle hidden terminal cases. 13 Carrier Sense Multiple Access with Collision Avoidance (CSMA-CA) Similar to CSMA but control frames are exchanged instead of data packets RTS: request to send CTS: clear to send DATA: actual packet ACK: acknowledgement 14 Carrier Sense Multiple Access with Collision Avoidance (CSMA-CA) Small control frames lessen the cost of collisions (when data is large) RTS + CTS provide virtual carrier sense protects against hidden terminal A B 15 Random Contention Access Slotted contention period Used by all carrier sense variants Provides random access to the channel Operation Each node selects a random backoff number Waits that number of slots monitoring the channel If channel stays idle and reaches zero then transmit If channel becomes active wait until transmission is over then start counting again 16 802.11 DCF B1 = 25 wait data B2 = 20 B2 = 15 B1 = 5 data wait B2 = 10 cw = 31 B1 and B2 are backoff intervals at nodes 1 and 2 17 802.11 Contention Window Random number selected from [0,cw] Tradeoffs in setting cw Less wasted idle time Large number of collisions with multiple senders (two or more stations reach zero at once) Optimal cw for known number of contenders, packet size Computed by minimizing expected time wastage (by both collisions and empty slots) Problem: can t estimate number of contenders very easily Number of contenders could also change 18 802.11 Adaptive Contention Window 802.11 adaptively sets cw Starts with cw = 31 If no CTS or ACK then increase to 2*cw+1 (63, 127, 255) Reset to 31 on successful transmission 802.11 adaptive scheme is unfair Under contention, unlucky nodes will use cw larger than lucky nodes (due to straight reset after a success) Lucky nodes may be able to transmit several packets while unlucky nodes are counting down for access Fair schemes should use same cw for all contending nodes (better for high congestion too) 19 802.11 DCF (CSMA-CA) Full exchange with virtual carrier sense Other nodes defer access for NAV A Sender Sender Receiver A B RTS CTS NAV (RTS) NAV (CTS) 20 B Receiver DATA ACK Virtual Carrier Sense Provided by RTS & CTS Prevents hidden terminal collisions Typically unnecessary RTS CTS A B C 21 Physical Carrier Sense Energy detection threshold Monitors channel during idle times between packets to measure the noise floor Energy levels above the this noise floor by a threshold trigger carrier sense DSSS correlation threshold Monitors the channel for Direct Sequence Spread Spectrum (DSSS) coded signal Triggers carrier sense if the correlation peak is above a threshold More sensitive than energy detection (but only works for 802.11 transmissions) High BER disrupts transmission but not detection 22 Physical Carrier Sense Range Carrier can be sensed at lower levels than packets can be received Results in larger carrier sense range than transmission range More than double the range in NS2 802.11 simulations Receive Range Carrier Sense Range Long carrier sense range helps protect from interference 23 Hidden Terminal Revisited Virtual carrier sense no longer needed in this situation RTS CTS A B C Physical Carrier Sense 24 Initial approach: Traditional routing packet packet A B packet src C dst Identify a route, forward over links Abstract radio to look like a wired link ExOR Slides adapted from http://pdos.csail.mit.edu/papers/roofnet:exor-sigcomm05/ 25 Radios aren t wires A B 3 4 56 56 1 2 3 45 6 s C d Every packet is broadcast Reception is probabilistic 26 ExOR: Probabilistic Broadcast packet A B src packet dst C Decide who forwards after reception Goal: only closest receiver should forward Challenge: agree efficiently and avoid duplicate transmissions 27 Why ExOR might increase throughput src 75% N1 50% 25% N2 N3 N4 N5 dst Best traditional route over 50% hops: 3(1/0.5) = 6 tx Throughput 1/# transmissions ExOR exploits lucky long receptions: 4 transmissions Assumes probability falls off gradually with distance 28 Why ExOR might increase throughput N1 5% 2 25% N2 N3 N4 src 25% 25 % 0% 100% 100% 10 dst % 00 1 Traditional routing: 1/0.25 + 1 = 5 tx ExOR: 1/(1 (1 0.25)4) + 1 = 2.5 transmissions Assumes independent losses 29 Batch Maps 100 tx: 9 src rx: 88 99 N1 tx: 8 rx: 57 85 N2 tx: 57 -23 24 N3 N4 rx: 23 53 tx: 23 rx: 0 40 tx: 0 dst rx: 0 22 Challenge: finding the closest node to have rx d Send batches of packets for efficiency Node closest to the dst sends first Other nodes listen, send remaining packets in turn Repeat schedule until dst has whole batch 30 Reliable summaries s t N s N N N d t s Repeat summaries in every data packet Cumulative: what all previous nodes rx d This is a gossip mechanism for summaries 31 Priority ordering N s N N N d Goal: nodes closest to the destination send first Sort by ETX metric to dst Nodes periodically flood ETX link state measurements Path ETX is weighted shortest path (Dijkstra s algorithm) Source sorts, includes list in ExOR header Details in the paper 32 Using ExOR with TCP C T N P T G W E E W P Batching requires more packets than typical TCP window 33 ExOR Evaluation Does ExOR increase throughput? When/why does it work well? 34 65 Roofnet node pairs 1 kilometer 35 Evaluation Details 65 Node pairs 1.0MByte file transfer 1 Mbit/s 802.11 bit rate 1 KByte packets Traditional Routing 802.11 unicast with linklevel retransmissions Hop-by-hop batching UDP, sending as MAC allows ExOR 802.11 broadcasts 100 packet batch size 36 ExOR: 2x overall improvement Cumulative Fraction of Node Pairs 1.0 0.8 0.6 0.4 0.2 ExOR Traditional 0 0 200 400 600 Throughput (Kbits/sec) 800 Median throughputs: 240 Kbits/sec for ExOR, 121 Kbits/sec for Traditional 37 25 Highest throughput pairs 3 Traditional Hops 2.3x Throughput (Kbits/sec) 1000 800 600 400 200 0 Node Pair ExOR Traditional Routing 2 Traditional Hops 1 Traditional Hop 1.7x 1.14x 38 25 Lowest throughput pairs Throughput (Kbits/sec) 1000 800 600 400 200 0 ExOR Traditional Routing 4 Traditional Hops 3.3x Node Pair Longer Routes 39 ExOR uses links in parallel Traditional Routing 3 forwarders 4 links ExOR 7 forwarders 18 links 40 ExOR moves packets farther 58% of Traditional Routing transmissions Fraction of Transmissions 0.6 ExOR Traditional Routing 0.2 0.1 0 25% of ExOR transmissions 0 100 200 300 400 500 600 700 800 900 1000 Distance (meters) ExOR average: 422 meters/transmission Traditional Routing average: 205 meters/tx 41
Find millions of documents here - Study Guides, Homework Solutions, Papers, Exam Answer Keys and more.
Course Hero has millions of course related materials that will enable you to learn better,
faster and get an A in all your courses.
Below is a small sample set of documents:
Below is a small sample set of documents:
Georgia Tech >> CS >> 7641 (Fall, 2008)
Journal of Arti cial Intelligence Research 4 1996 237-285 Submitted 9 95; published 5 96 Reinforcement Learning: A Survey Computer Science Department, Box 1910, Brown University Providence, RI 02912-1910 USA Leslie Pack Kaelbling Michael L. Littma...
Georgia Tech >> CS >> 3600 (Fall, 2008)
Artificial Intelligence Chapter 1 Chapter 1 1 Outline What is AI? A brief history The state of the art Chapter 1 2 What is AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act ratio...
Georgia Tech >> CS >> 3600 (Fall, 2008)
Intelligent Agents Chapter 2 Chapter 2 1 Reminders Assignment 0 (lisp refresher) due 1/28 Lisp/emacs/AIMA tutorial: 11-1 today and Monday, 271 Soda Chapter 2 2 Outline Agents and environments Rationality PEAS (Performance measure, Environme...
Georgia Tech >> CS >> 3600 (Fall, 2008)
Problem solving and search Chapter 3 Chapter 3 1 Reminders Assignment 0 due 5pm today Assignment 1 posted, due 2/9 Section 105 will move to 9-10am starting next week Chapter 3 2 Outline Problem-solving agents Problem types Problem formulati...
Georgia Tech >> CS >> 3600 (Fall, 2008)
Informed search algorithms Chapter 4, Sections 12 Chapter 4, Sections 12 1 Outline Best-rst search A search Heuristics Chapter 4, Sections 12 2 Review: Tree search function Tree-Search( problem, fringe) returns a solution, or failure fringe...
Georgia Tech >> CS >> 3600 (Fall, 2008)
Constraint Satisfaction Problems Chapter 5 Chapter 5 1 Outline CSP examples Backtracking search for CSPs Problem structure and problem decomposition Local search for CSPs Chapter 5 2 Constraint satisfaction problems (CSPs) Standard search ...
Georgia Tech >> CS >> 3600 (Fall, 2008)
Game playing Chapter 6 Chapter 6 1 Outline Games Perfect play minimax decisions pruning Resource limits and approximate evaluation Games of chance Games of imperfect information Chapter 6 2 Games vs. search problems Unpredictable oppon...
Georgia Tech >> CS >> 3600 (Fall, 2008)
First-order logic Chapter 8 Chapter 8 1 Outline Why FOL? Syntax and semantics of FOL Fun with sentences Wumpus world in FOL Chapter 8 2 Pros and cons of propositional logic Propositional logic is declarative: pieces of syntax correspond to...
Georgia Tech >> CS >> 3600 (Fall, 2008)
Inference in first-order logic Chapter 9 Chapter 9 1 Outline Reducing rst-order inference to propositional inference Unication Generalized Modus Ponens Forward and backward chaining Logic programming Resolution Chapter 9 2 A brief histor...
Georgia Tech >> CS >> 3600 (Fall, 2008)
Bayesian networks Chapter 14.13 Chapter 14.13 1 Outline Syntax Semantics Parameterized distributions Chapter 14.13 2 Bayesian networks A simple, graphical notation for conditional independence assertions and hence for compact specification ...
Georgia Tech >> CS >> 3600 (Fall, 2008)
Inference in Bayesian networks Chapter 14.45 Chapter 14.45 1 Outline Exact inference by enumeration Exact inference by variable elimination Approximate inference by stochastic simulation Approximate inference by Markov chain Monte Carlo Chap...
Georgia Tech >> CS >> 3600 (Fall, 2008)
Speech recognition (briefly) Chapter 15, Section 6 Chapter 15, Section 6 1 Outline Speech as probabilistic inference Speech sounds Word pronunciation Word sequences Chapter 15, Section 6 2 Speech as probabilistic inference Its not easy to ...
Georgia Tech >> CS >> 3600 (Fall, 2008)
Rational decisions Chapter 16 Chapter 16 1 Outline Rational preferences Utilities Money Multiattribute utilities Decision networks Value of information Chapter 16 2 Preferences An agent chooses among prizes (A, B, etc.) and lotteries, i....
Georgia Tech >> CS >> 3600 (Fall, 2008)
Learning from Observations Chapter 18, Sections 13 Chapter 18, Sections 13 1 Outline Learning agents Inductive learning Decision tree learning Measuring learning performance Chapter 18, Sections 13 2 Learning Learning is essential for unkn...
Georgia Tech >> CS >> 3600 (Fall, 2008)
Statistical learning Chapter 20, Sections 13 Chapter 20, Sections 13 1 Outline Bayesian learning Maximum a posteriori and maximum likelihood learning Bayes net learning ML parameter learning with complete data linear regression Chapter 20, ...
Georgia Tech >> CS >> 3600 (Fall, 2008)
Neural networks Chapter 20, Section 5 Chapter 20, Section 5 1 Outline Brains Neural networks Perceptrons Multilayer perceptrons Applications of neural networks Chapter 20, Section 5 2 Brains 1011 neurons of > 20 types, 1014 synapses, 1ms1...
Georgia Tech >> CS >> 3600 (Fall, 2008)
Communication and Language Chapter 22 Chapter 22 1 Outline Communication Grammar Syntactic analysis Problems Chapter 22 2 Communication Classical view (pre-1953): language consists of sentences that are true/false (cf. logic) Modern view (...
Georgia Tech >> CS >> 6290 (Fall, 2008)
Name: GTid: Homework 3 Prof. Loh CS6290 - Fall 2006 Handed Out: 12 Oct 2006 (Thu) Due: 26 Oct 2006 (Thu) 1. Instruction Scheduling In a dynamically scheduled processor, it is possible that in a given cycle there are more instructions ready to execu...
Georgia Tech >> CS >> 6455 (Fall, 2008)
Work, Ethnography and System Design Bob Anderson Technical Report EPC-1996-103 Published in: The Encyclopedia of Microcomputers, Vol. 20, A. Kent and J.G. Williams (eds.), Marcel Dekker, New York, 1997, pp 159-183. Copyright Rank Xerox Ltd 1996....
Georgia Tech >> CS >> 1301 (Fall, 2008)
CS 1301 Fall 2008 Lab 1 Email & Newsgroups Due: Friday, September 5th by 6 pm (Local Time) (Emails/newsgroup posts must be RECEIVED, not sent, by this time, so do not wait until 5:59 to send them) Files to submit: NONE! Just send the email and post ...
Georgia Tech >> CS >> 6750 (Fall, 2008)
...
Georgia Tech >> CS >> 7260 (Fall, 2008)
Measurement: Techniques, Strategies, and Pitfalls Nick Feamster CS 7260 February 7, 2007 Internet Measurement Process of collecting data that measure certain phenomena about the network Should be a science Today: closer to an art form Key goal:...
Georgia Tech >> CS >> 7260 (Fall, 2008)
Defenses, Application-Level Attacks, etc. Nick Feamster CS 7260 April 4, 2007 IP Traceback R R A R7 R5 R3 R1 V R2 R6 R R R4 R R R Logging Challenges Attack path reconstruction is difficult Packet may be transformed as it moves through the netw...
Georgia Tech >> CS >> 8803 (Fall, 2008)
First-order logic Chapter 8 Chapter 8 1 Outline Why FOL? Syntax and semantics of FOL Fun with sentences Wumpus world in FOL Chapter 8 2 Pros and cons of propositional logic Propositional logic is declarative: pieces of syntax correspond to...
Georgia Tech >> CS >> 1371 (Fall, 2008)
CS1371 Introduction to Computing for Engineers Iteration 1 9/4/2003 Control Flow Statements Learning Objectives Learn about how to control the sequence of expressions that are evaluated in a program. Topics FOR statements WHILE statements 2 1...
Georgia Tech >> CS >> 1371 (Fall, 2008)
CS1371 Introduction to Computing for Engineers Recursion 1 Background We will review dynamic memory allocation We will start with the familiar iterative approach This works when processing simple, linear collections It will not work at all on ...
Gettysburg >> CS >> 111 (Fall, 2009)
INSERT INTO \"Seats\" VALUES ( 1, \'Aisle\', \'Economy\', 0 ); INSERT INTO \"Seats\" VALUES ( 2, \'Aisle\', \'Economy\', 0 ); INSERT INTO \"Seats\" VALUES ( 3, \'Aisle\', \'First\', 0 ); INSERT INTO \"Seats\" VALUES ( 4, \'Middle\', \'Economy\', 0 ); INSERT INTO \"Seats\" VAL...
UCSD >> BICD >> 123 (Spring, 2008)
kDa 200 116.3 66.2 45 31 21.5 14.4 6.5 QuickTime and a TIFF (LZW) decompressor are needed to see this picture. ...
UCSD >> BICD >> 123 (Spring, 2008)
bp Lane 1 2 3 4 5 6 7 12000 2000 1650 1000 850 650 500 400 300 200 100 AP3 PCR gel. Lane 1 is the 1 kb plus ladder, lanes 2 4 are the PCR product from wildtype DNA extracts, lanes 5 7 are the PCR products from DNA extracts from the ap3 mut...
UCSD >> BICD >> 123 (Spring, 2008)
progress Guard cell abscisic acid signalling and engineering drought hardiness in plants Julian I. Schroeder, June M. Kwak & Gethyn J. Allen Cell and Developmental Biology Section, Division of Biology and Center for Molecular Genetics, University of...
UCSD >> BICD >> 123 (Spring, 2008)
DNA isolation and labeling Genomic DNA was isolated by a modified CTAB method. 2 g of tissue frozen in liquid nitrogen was ground up and suspended in 30 ml extraction buffer (0.35 M sorbitol, 0.1 M Tris pH 8.0, 50 mM EDTA). After centrifugation, the ...
UCSD >> BICD >> 100 (Fall, 2008)
BICD100 Genetics Fall 2004 MIDTERM 1 ANSWER KEY Reinagel Question 1. (5 points) You perform a cross between a black-eyed mouse and a red-eyed mouse of unknown genotypes, and all the progeny have black eyes (F1). You cross the F1 progeny with one a...
UCSD >> BIMM >> 110 (Fall, 2008)
BIMM 110 Spring 2007 ANSWERS TO EXERCISES OF WEEK 3 Symbols: The symbol designates an advanced problem that uses complex conditional probability. You will not have to do this kind of calculation on the final exam, but you should briefly look over the...
UCSD >> BIMM >> 110 (Fall, 2008)
BIMM 110 Spring 2005 ANSWERS FOR PROBLEMS OF WEEK 4 (April 18-22) next page CBB Genetics Immo E. Scheffler Fall 2001 Lecture II Answers 1. If human genome contains about 3 x 109 base pairs (per haploid set) and human DNA is about 40% GC. About ho...
UCSD >> BIPN >> 140 (Fall, 2008)
RESEARCH ARTICLES The Structure of the Potassium Channel: Molecular Basis of K Conduction and Selectivity Declan A. Doyle, Joao Morais Cabral, Richard A. Pfuetzner, ~ Anling Kuo, Jacqueline M. Gulbis, Steven L. Cohen, Brian T. Chait, Roderick MacKin...
UCSD >> BICD >> 100 (Fall, 2008)
What was so special about what Mendel did? Performed controlled, reciprocal crosses Worked with true-breeding lines Analyzed characters showing only two different forms (e.g. seed shape round or wrinkled, seed color yellow or green) Fortuitously,...
Loyola Chicago >> CS >> 447 (Fall, 2009)
Exact Set Matching Problem Molecular Sequence Algorithms, Spring 2004 In an exact set matching problem we locate occurrences of , in text any pattern of a set Let Lecture 4: Set Matching and Aho-Corasick Algorithm Pekka Kilpelainen Univers...
UNC Wilmington >> MSA >> 540 (Fall, 2009)
STATISTICS FOR SIC CODE 15 Building Construction-General Contractors and Operative Builders This Industry Comprises 23 Companies Industry Description This major group includes general contractors and operative builders primarily engaged in the constr...
Grinnell >> CSC >> 151 (Fall, 2009)
Fundamentals of Computer Science I: Media Computing (CS151.01 2008S) Front Door Introduction Welcome to one of the Spring 2008 sessions of Grinnell College\'s CSC 151, Fundamentals of Computer Science I, which is described relatively briefly in the o...
Grinnell >> CS >> 302 (Spring, 2006)
CSC302 2006S, Class 39: Presentations (3) FP Admin: * Late again: Dimitar Overview * Background of FP * Example of Difference * The FP /Background of FP/ [Angeline\'s presentation would have benefitted from an outline (Powerpoint, whatever), not o...
Grinnell >> CS >> 302 (Spring, 2006)
CSC302 2006S, Class 42: Wrapup Admin: * Sorry about last night. Overview: * Grading. * About the final. * About the midsem. * Wrapup. * Evaluation forms. Final Topics: * Languages * Prolog: Probably small program ; Possibly hybrid * Haskell: F...
Grinnell >> CS >> 302 (Spring, 2006)
CSC302 2006S, Class 28: Declarative Programming Admin: * Post-mid-semester examination distributed. * Sick family + spring cleaning + exam writing = no grading. Sorry. * One of you argued that Saturday\'s Harris Party should count for EC. * Talk tom...
Grinnell >> CS >> 302 (Spring, 2006)
CSC302 2006S, Class 34: Haskell (1): The Basics Admin: * More Prolog? * No reading for Wednesday * Presentation proposals due Wednesday. * Desired form for final? * Take-home portion of final distributed Monday of 14th week, due Tuesday of finals ...
Grinnell >> CS >> 302 (Spring, 2006)
CSC302 2006S, Class 19: Introspection and Reflection in Java Admin: * Thoughts on \"Growing a Language\"? * Presents * New homework: Figure out anonymous inner classes. * Questions on semantics homework? Overview: * What are reflection and introspect...
Grinnell >> CS >> 302 (Spring, 2006)
CSC302 2006S, Class 17: Java as Object-Oriented Language Admin: * Congratulations to Peter! * Reading: Beyond Java 4, 5, 8. (Skipping Ruby.) * Reading for Monday: Forthcoming. * Homework: Semantics of D. * Friday: Movie about Java. (Guy Steele: Gro...
Grinnell >> CS >> 302 (Spring, 2006)
CSC302 2006S, Class 32: Logic Programming (2): Prolog Basics. Admin: * Questions on the exam? * I\'ll grade four out of five. * Problem One: * Yes, you need to turn in a new semantics (and grammar and .) * Why is the syntax abstract? ...
Grinnell >> CS >> 302 (Spring, 2006)
CSC302 2006S, Class 33: Logic Programming (3): Playing with Prolog Admin: * Due: Exam * Read the first five sections (Introduction; Values, Types, and Other Goodies; Functions; Case Expressions and Pattern Matching; Type Classes and Overloading) of...
Grinnell >> CS >> 302 (Spring, 2006)
CSC302 2006S, Class 37: Presentations (1) Ruby Admin: * Sorry for not giving proper support to today\'s walkout. * Student presentation today (Brown, Nettling, Tasev). * I\'ll take notes (and insert comments) on the eboard. * Late: Tasev, Ventresca O...
Grinnell >> CS >> 302 (Spring, 2006)
CSC302 2006S, Class 38: Presentations (2): Objects in Ruby Admin: * Presentation by Michael Claveria, Bradley Miller, David Ventresca * Reading for Friday? * SamR takes notes in EBoard again. * Really Late: Alex * Moral: \"Ruby is Awesome\" Overview:...
Grinnell >> CS >> 302 (Spring, 2006)
CSC302 2006S, Class 39: Presentations (3) FP Admin: * Late again: Dimitar Overview * Background of FP * Example of Difference * The FP /Background of FP/ [Angeline\'s presentation would have benefitted from an outline (Powerpoint, whatever), not o...
Grinnell >> CS >> 302 (Spring, 2006)
CSC302 2006S, Class 38: Presentations (2): Objects in Ruby Admin: * Presentation by Michael Claveria, Bradley Miller, David Ventresca * Reading for Friday? * SamR takes notes in EBoard again. * Really Late: Alex * Moral: \"Ruby is Awesome\" Overview:...
Grinnell >> CS >> 302 (Spring, 2006)
CSC302 2006S, Class 38: Presentations (2): Objects in Ruby Admin: * Presentation by Michael Claveria, Bradley Miller, David Ventresca * Reading for Friday? * SamR takes notes in EBoard again. * Really Late: Alex * Moral: \"Ruby is Awesome\" Overview:...
Grinnell >> CS >> 302 (Spring, 2006)
CSC302 2006S, Class 37: Presentations (1) Ruby Admin: * Sorry for not giving proper support to today\'s walkout. * Student presentation today (Brown, Nettling, Tasev). * I\'ll take notes (and insert comments) on the eboard. * Late: Tasev, Ventresca O...
Grinnell >> CS >> 302 (Spring, 2006)
CSC302 2006S, Class 12: Scheme Syntax Admin: * Homework 1 due at 5:00 p.m. * No non-reading (+question) homework next week to give you time to read carefully. Please take that time. * About the symbols after your name in readings. * What do you kno...
Grinnell >> CS >> 302 (Spring, 2006)
CSC302 2006S, Class 13: Scheme Semantics (1) Admin: * This week, we\'re clearly doing stuff that looks like Math. * Reading for Wednesday: The meaning of lambda (three equations, plus any auxiliaries). * Revised assignment: * Why three lambda def...
Grinnell >> CS >> 302 (Spring, 2006)
CSC302 2006S, Class 29: SQL(1): The Relational Model Admin: * Neither grading nor answer keys are available. Sorry. * Are there questions on the exam? * The reading for Friday is the preliminary paper on SEQUEL. * Late: DAVE Overview: * Context. * ...
Grinnell >> CS >> 302 (Spring, 2006)
CSC302 2006S, Class 27: Types (3): Type Equivalence Admin: * Monday will be return homework and distribute exam day. * There is therefore no reading for Monday. * We may, nonetheless, begin our coverage of declarative languages on Monday. * EC for a...
Grinnell >> CS >> 302 (Spring, 2006)
CSC302 2006S, Class 6: Why Functional Programming? Admin: * Absent: Alex (sick) * No readings. Yay! * Real homework (due next Friday) * Strange/fun example today * Discuss your solutions to \'homework\' Overview: * Key points from Graham and McCarth...
Grinnell >> CS >> 152 (Fall, 2004)
CSC152 2006S, Class 56! Course Wrapup and Evaluations Admin: * Last-minute EC * EC Con Brio, 7 pm, Younker Lounge * EC M&8 - 10 or 10:30 in Harris * EC Dance Show 8 pm in Harris * Rant about end-of-course evalutions linked from today\'s page O...
Grinnell >> CS >> 152 (Fall, 2004)
CSC152 2006S, Class 26: Arrays and Vectors Admin: * Homework: * Do the vectors lab on your own. (Nothing to submit.) * Read all about (a) linear structures; (b) stacks. * Request: Play a silly game. Overview: * Basics of vectors and arrays. * ...
Grinnell >> CS >> 152 (Fall, 2004)
CSC152 2006S, Class 10: Binary Representation Admin: * Syllabus rearranged * No programming homework for tomorrow * Reading: Conditionals in Java (ready later today) * Don\'t worry about understanding all of Javadoc; just be able to extract key metho...
Grinnell >> CS >> 152 (Fall, 2004)
CSC152 2006S, Class 38: Dictionaries (2): Binary Search Trees Admin: * Are there questions on the exam? Please keep them short today. * The hint about dealing with removal in the random vector can be found in the work we did in the first day on p...
Grinnell >> CS >> 152 (Fall, 2004)
CSC152 2006S, Class 39: Dictionaries (3): Experimenting with Binary Search Trees Admin: * Are there questions on the exam? * Question 1c. is now optional. (Extra credit, no negative extra credit.) * No reading for Friday (since you wouldn\'t do it ...
Grinnell >> CS >> 152 (Fall, 2004)
CSC152 2006S, Class 40: Dictionaries (4): Hash Tables Admin: * Monday: Discussion of this exam. * Late exams will be graded very late. * No reading or homework for Monday. * What do you think of the structure of Wednesday\'s class? Overview: * The I...
Grinnell >> CS >> 152 (Fall, 2004)
CSC152 2006S, Class 30: Notes on Exam 1 Admin: * Homework: Think about how to implement priority queues. * Sorry about confusion on Friday\'s lab. Overview: * General notes * Problem 4: Edit Distance * Problem 1: Building Fractions * Problem 2: Comp...
Grinnell >> CS >> 152 (Fall, 2004)
CSC152 2006S, Class 25: Parameterized Types (Generics) Admin * Exams due! * Sorry for any frustration. * I expect to return them next Tuesday. * Silly gifts from the trip. * Read \"Arrays and Vectors\" (available this afternoon). Overview: * A ...
Grinnell >> CS >> 152 (Fall, 2004)
CSC152 2006S, Class 2: About Object-Oriented Programming Admin: * HW1 due * Assignments: * HW2: Course Basics * Read \"An Introduction to Unix\" * Read \"Basics of Object-Oriented Problem Solving\" (available by 5 pm) * Sam is sarcastic. Complain...
Grinnell >> CS >> 152 (Fall, 2004)
CSC152 2006S, Class 46: Sorting Admin: * Homework 16: Approximate String Matching. * We\'ll try the mini-Titular Head again tomorrow. * Advance notice: Exam 3 distributed Friday. * Taking more CS? * Natural fall course: CSC223 (Software Design) (St...
Grinnell >> CS >> 152 (Fall, 2004)
CSC152 2006S, Class 18: Writing Your Own Template Classes (1) Admin: * No written hw for tomorrow! * The reading is actually ready! * Stupid story * Today is lecture/discussion * EC: Hitting Daniel Zamora with a bowling pin (IanBR) * EC: Attending C...
Grinnell >> CS >> 152 (Fall, 2004)
CSC152 2006S, Class 19: Object Basics (2): Lab Admin: * New reading for Friday: Standard Object Methods * Homework for Friday: Simplify Fractions * Today is a lab day. Whee! * Advance warning: I will be away a lot in the next two weeks. Wednesda...
What are you waiting for?