Moving Average Filters Chapitre 15 - Analog Devices - Revenir à l'accueil

Farnell Element 14 :

Farnell-NA555-NE555-..> 08-Sep-2014 07:33 1.5M

Farnell-AD9834-Rev-D..> 08-Sep-2014 07:32 1.2M

Farnell-MSP430F15x-M..> 08-Sep-2014 07:32 1.3M

Farnell-AD736-Rev-I-..> 08-Sep-2014 07:31 1.3M

Farnell-AD8307-Data-..> 08-Sep-2014 07:30 1.3M

Farnell-Single-Chip-..> 08-Sep-2014 07:30 1.5M

Farnell-Quadruple-2-..> 08-Sep-2014 07:29 1.5M

Farnell-ADE7758-Rev-..> 08-Sep-2014 07:28 1.7M

Farnell-MAX3221-Rev-..> 08-Sep-2014 07:28 1.8M

Farnell-USB-to-Seria..> 08-Sep-2014 07:27 2.0M

Farnell-AD8313-Analo..> 08-Sep-2014 07:26 2.0M

Farnell-SN54HC164-SN..> 08-Sep-2014 07:25 2.0M

Farnell-AD8310-Analo..> 08-Sep-2014 07:24 2.1M

Farnell-AD8361-Rev-D..> 08-Sep-2014 07:23 2.1M

Farnell-2N3906-Fairc..> 08-Sep-2014 07:22 2.1M

Farnell-AD584-Rev-C-..> 08-Sep-2014 07:20 2.2M

Farnell-ADE7753-Rev-..> 08-Sep-2014 07:20 2.3M

Farnell-TLV320AIC23B..> 08-Sep-2014 07:18 2.4M

Farnell-AD586BRZ-Ana..> 08-Sep-2014 07:17 1.6M

Farnell-STM32F405xxS..> 27-Aug-2014 18:27 1.8M

Farnell-MSP430-Hardw..> 29-Jul-2014 10:36 1.1M

Farnell-LM324-Texas-..> 29-Jul-2014 10:32 1.5M

Farnell-LM386-Low-Vo..> 29-Jul-2014 10:32 1.5M

Farnell-NE5532-Texas..> 29-Jul-2014 10:32 1.5M

Farnell-Hex-Inverter..> 29-Jul-2014 10:31 875K

Farnell-AT90USBKey-H..> 29-Jul-2014 10:31 902K

Farnell-AT89C5131-Ha..> 29-Jul-2014 10:31 1.2M

Farnell-MSP-EXP430F5..> 29-Jul-2014 10:31 1.2M

Farnell-Explorer-16-..> 29-Jul-2014 10:31 1.3M

Farnell-TMP006EVM-Us..> 29-Jul-2014 10:30 1.3M

Farnell-Gertboard-Us..> 29-Jul-2014 10:30 1.4M

Farnell-LMP91051-Use..> 29-Jul-2014 10:30 1.4M

Farnell-Thermometre-..> 29-Jul-2014 10:30 1.4M

Farnell-user-manuel-..> 29-Jul-2014 10:29 1.5M

Farnell-fx-3650P-fx-..> 29-Jul-2014 10:29 1.5M

Farnell-2-GBPS-Diffe..> 28-Jul-2014 17:42 2.7M

Farnell-LMT88-2.4V-1..> 28-Jul-2014 17:42 2.8M

Farnell-Octal-Genera..> 28-Jul-2014 17:42 2.8M

Farnell-Dual-MOSFET-..> 28-Jul-2014 17:41 2.8M

Farnell-TLV320AIC325..> 28-Jul-2014 17:41 2.9M

Farnell-SN54LV4053A-..> 28-Jul-2014 17:20 5.9M

Farnell-TAS1020B-USB..> 28-Jul-2014 17:19 6.2M

Farnell-TPS40060-Wid..> 28-Jul-2014 17:19 6.3M

Farnell-TL082-Wide-B..> 28-Jul-2014 17:16 6.3M

Farnell-RF-short-tra..> 28-Jul-2014 17:16 6.3M

Farnell-maxim-integr..> 28-Jul-2014 17:14 6.4M

Farnell-TSV6390-TSV6..> 28-Jul-2014 17:14 6.4M

Farnell-Fast-Charge-..> 28-Jul-2014 17:12 6.4M

Farnell-NVE-datashee..> 28-Jul-2014 17:12 6.5M

Farnell-Excalibur-Hi..> 28-Jul-2014 17:10 2.4M

Farnell-Excalibur-Hi..> 28-Jul-2014 17:10 2.4M

Farnell-REF102-10V-P..> 28-Jul-2014 17:09 2.4M

Farnell-TMS320F28055..> 28-Jul-2014 17:09 2.7M

Farnell-MULTICOMP-Ra..> 22-Jul-2014 12:35 5.9M

Farnell-RASPBERRY-PI..> 22-Jul-2014 12:35 5.9M

Farnell-Dremel-Exper..> 22-Jul-2014 12:34 1.6M

Farnell-STM32F103x8-..> 22-Jul-2014 12:33 1.6M

Farnell-BD6xxx-PDF.htm 22-Jul-2014 12:33 1.6M

Farnell-L78S-STMicro..> 22-Jul-2014 12:32 1.6M

Farnell-RaspiCam-Doc..> 22-Jul-2014 12:32 1.6M

Farnell-SB520-SB5100..> 22-Jul-2014 12:32 1.6M

Farnell-iServer-Micr..> 22-Jul-2014 12:32 1.6M

Farnell-LUMINARY-MIC..> 22-Jul-2014 12:31 3.6M

Farnell-TEXAS-INSTRU..> 22-Jul-2014 12:31 2.4M

Farnell-TEXAS-INSTRU..> 22-Jul-2014 12:30 4.6M

Farnell-CLASS 1-or-2..> 22-Jul-2014 12:30 4.7M

Farnell-TEXAS-INSTRU..> 22-Jul-2014 12:29 4.8M

Farnell-Evaluating-t..> 22-Jul-2014 12:28 4.9M

Farnell-LM3S6952-Mic..> 22-Jul-2014 12:27 5.9M

Farnell-Keyboard-Mou..> 22-Jul-2014 12:27 5.9M

Farnell-Full-Datashe..> 15-Jul-2014 17:08 951K

Farnell-pmbta13_pmbt..> 15-Jul-2014 17:06 959K

Farnell-EE-SPX303N-4..> 15-Jul-2014 17:06 969K

Farnell-Datasheet-NX..> 15-Jul-2014 17:06 1.0M

Farnell-Datasheet-Fa..> 15-Jul-2014 17:05 1.0M

Farnell-MIDAS-un-tra..> 15-Jul-2014 17:05 1.0M

Farnell-SERIAL-TFT-M..> 15-Jul-2014 17:05 1.0M

Farnell-MCOC1-Farnel..> 15-Jul-2014 17:05 1.0M

Farnell-TMR-2-series..> 15-Jul-2014 16:48 787K

Farnell-DC-DC-Conver..> 15-Jul-2014 16:48 781K

Farnell-Full-Datashe..> 15-Jul-2014 16:47 803K

Farnell-TMLM-Series-..> 15-Jul-2014 16:47 810K

Farnell-TEL-5-Series..> 15-Jul-2014 16:47 814K

Farnell-TXL-series-t..> 15-Jul-2014 16:47 829K

Farnell-TEP-150WI-Se..> 15-Jul-2014 16:47 837K

Farnell-AC-DC-Power-..> 15-Jul-2014 16:47 845K

Farnell-TIS-Instruct..> 15-Jul-2014 16:47 845K

Farnell-TOS-tracopow..> 15-Jul-2014 16:47 852K

Farnell-TCL-DC-traco..> 15-Jul-2014 16:46 858K

Farnell-TIS-series-t..> 15-Jul-2014 16:46 875K

Farnell-TMR-2-Series..> 15-Jul-2014 16:46 897K

Farnell-TMR-3-WI-Ser..> 15-Jul-2014 16:46 939K

Farnell-TEN-8-WI-Ser..> 15-Jul-2014 16:46 939K

Farnell-Full-Datashe..> 15-Jul-2014 16:46 947K

Farnell-HIP4081A-Int..> 07-Jul-2014 19:47 1.0M

Farnell-ISL6251-ISL6..> 07-Jul-2014 19:47 1.1M

Farnell-DG411-DG412-..> 07-Jul-2014 19:47 1.0M

Farnell-3367-ARALDIT..> 07-Jul-2014 19:46 1.2M

Farnell-ICM7228-Inte..> 07-Jul-2014 19:46 1.1M

Farnell-Data-Sheet-K..> 07-Jul-2014 19:46 1.2M

Farnell-Silica-Gel-M..> 07-Jul-2014 19:46 1.2M

Farnell-TKC2-Dusters..> 07-Jul-2014 19:46 1.2M

Farnell-CRC-HANDCLEA..> 07-Jul-2014 19:46 1.2M

Farnell-760G-French-..> 07-Jul-2014 19:45 1.2M

Farnell-Decapant-KF-..> 07-Jul-2014 19:45 1.2M

Farnell-1734-ARALDIT..> 07-Jul-2014 19:45 1.2M

Farnell-Araldite-Fus..> 07-Jul-2014 19:45 1.2M

Farnell-fiche-de-don..> 07-Jul-2014 19:44 1.4M

Farnell-safety-data-..> 07-Jul-2014 19:44 1.4M

Farnell-A-4-Hardener..> 07-Jul-2014 19:44 1.4M

Farnell-CC-Debugger-..> 07-Jul-2014 19:44 1.5M

Farnell-MSP430-Hardw..> 07-Jul-2014 19:43 1.8M

Farnell-SmartRF06-Ev..> 07-Jul-2014 19:43 1.6M

Farnell-CC2531-USB-H..> 07-Jul-2014 19:43 1.8M

Farnell-Alimentation..> 07-Jul-2014 19:43 1.8M

Farnell-BK889B-PONT-..> 07-Jul-2014 19:42 1.8M

Farnell-User-Guide-M..> 07-Jul-2014 19:41 2.0M

Farnell-T672-3000-Se..> 07-Jul-2014 19:41 2.0M

Farnell-0050375063-D..> 18-Jul-2014 17:03 2.5M

Farnell-Mini-Fit-Jr-..> 18-Jul-2014 17:03 2.5M

Farnell-43031-0002-M..> 18-Jul-2014 17:03 2.5M

Farnell-0433751001-D..> 18-Jul-2014 17:02 2.5M

Farnell-Cube-3D-Prin..> 18-Jul-2014 17:02 2.5M

Farnell-MTX-Compact-..> 18-Jul-2014 17:01 2.5M

Farnell-MTX-3250-MTX..> 18-Jul-2014 17:01 2.5M

Farnell-ATtiny26-L-A..> 18-Jul-2014 17:00 2.6M

Farnell-MCP3421-Micr..> 18-Jul-2014 17:00 1.2M

Farnell-LM19-Texas-I..> 18-Jul-2014 17:00 1.2M

Farnell-Data-Sheet-S..> 18-Jul-2014 17:00 1.2M

Farnell-LMH6518-Texa..> 18-Jul-2014 16:59 1.3M

Farnell-AD7719-Low-V..> 18-Jul-2014 16:59 1.4M

Farnell-DAC8143-Data..> 18-Jul-2014 16:59 1.5M

Farnell-BGA7124-400-..> 18-Jul-2014 16:59 1.5M

Farnell-SICK-OPTIC-E..> 18-Jul-2014 16:58 1.5M

Farnell-LT3757-Linea..> 18-Jul-2014 16:58 1.6M

Farnell-LT1961-Linea..> 18-Jul-2014 16:58 1.6M

Farnell-PIC18F2420-2..> 18-Jul-2014 16:57 2.5M

Farnell-DS3231-DS-PD..> 18-Jul-2014 16:57 2.5M

Farnell-RDS-80-PDF.htm 18-Jul-2014 16:57 1.3M

Farnell-AD8300-Data-..> 18-Jul-2014 16:56 1.3M

Farnell-LT6233-Linea..> 18-Jul-2014 16:56 1.3M

Farnell-MAX1365-MAX1..> 18-Jul-2014 16:56 1.4M

Farnell-XPSAF5130-PD..> 18-Jul-2014 16:56 1.4M

Farnell-DP83846A-DsP..> 18-Jul-2014 16:55 1.5M

Farnell-Dremel-Exper..> 18-Jul-2014 16:55 1.6M

Farnell-MCOC1-Farnel..> 16-Jul-2014 09:04 1.0M

Farnell-SL3S1203_121..> 16-Jul-2014 09:04 1.1M

Farnell-PN512-Full-N..> 16-Jul-2014 09:03 1.4M

Farnell-SL3S4011_402..> 16-Jul-2014 09:03 1.1M

Farnell-LPC408x-7x 3..> 16-Jul-2014 09:03 1.6M

Farnell-PCF8574-PCF8..> 16-Jul-2014 09:03 1.7M

Farnell-LPC81xM-32-b..> 16-Jul-2014 09:02 2.0M

Farnell-LPC1769-68-6..> 16-Jul-2014 09:02 1.9M

Farnell-Download-dat..> 16-Jul-2014 09:02 2.2M

Farnell-LPC3220-30-4..> 16-Jul-2014 09:02 2.2M

Farnell-LPC11U3x-32-..> 16-Jul-2014 09:01 2.4M

Farnell-SL3ICS1002-1..> 16-Jul-2014 09:01 2.5M

Farnell-T672-3000-Se..> 08-Jul-2014 18:59 2.0M

Farnell-tesaÂ®pack63..> 08-Jul-2014 18:56 2.0M

Farnell-Encodeur-USB..> 08-Jul-2014 18:56 2.0M

Farnell-CC2530ZDK-Us..> 08-Jul-2014 18:55 2.1M

Farnell-2020-Manuel-..> 08-Jul-2014 18:55 2.1M

Farnell-Synchronous-..> 08-Jul-2014 18:54 2.1M

Farnell-Arithmetic-L..> 08-Jul-2014 18:54 2.1M

Farnell-NA555-NE555-..> 08-Jul-2014 18:53 2.2M

Farnell-4-Bit-Magnit..> 08-Jul-2014 18:53 2.2M

Farnell-LM555-Timer-..> 08-Jul-2014 18:53 2.2M

Farnell-L293d-Texas-..> 08-Jul-2014 18:53 2.2M

Farnell-SN54HC244-SN..> 08-Jul-2014 18:52 2.3M

Farnell-MAX232-MAX23..> 08-Jul-2014 18:52 2.3M

Farnell-High-precisi..> 08-Jul-2014 18:51 2.3M

Farnell-SMU-Instrume..> 08-Jul-2014 18:51 2.3M

Farnell-900-Series-B..> 08-Jul-2014 18:50 2.3M

Farnell-BA-Series-Oh..> 08-Jul-2014 18:50 2.3M

Farnell-UTS-Series-S..> 08-Jul-2014 18:49 2.5M

Farnell-270-Series-O..> 08-Jul-2014 18:49 2.3M

Farnell-UTS-Series-S..> 08-Jul-2014 18:49 2.8M

Farnell-Tiva-C-Serie..> 08-Jul-2014 18:49 2.6M

Farnell-UTO-Souriau-..> 08-Jul-2014 18:48 2.8M

Farnell-Clipper-Seri..> 08-Jul-2014 18:48 2.8M

Farnell-SOURIAU-Cont..> 08-Jul-2014 18:47 3.0M

Farnell-851-Series-P..> 08-Jul-2014 18:47 3.0M

Farnell-SL59830-Inte..> 06-Jul-2014 10:07 1.0M

Farnell-ALF1210-PDF.htm 06-Jul-2014 10:06 4.0M

Farnell-AD7171-16-Bi..> 06-Jul-2014 10:06 1.0M

Farnell-Low-Noise-24..> 06-Jul-2014 10:05 1.0M

Farnell-ESCON-Featur..> 06-Jul-2014 10:05 938K

Farnell-74LCX573-Fai..> 06-Jul-2014 10:05 1.9M

Farnell-1N4148WS-Fai..> 06-Jul-2014 10:04 1.9M

Farnell-FAN6756-Fair..> 06-Jul-2014 10:04 850K

Farnell-Datasheet-Fa..> 06-Jul-2014 10:04 861K

Farnell-ES1F-ES1J-fi..> 06-Jul-2014 10:04 867K

Farnell-QRE1113-Fair..> 06-Jul-2014 10:03 879K

Farnell-2N7002DW-Fai..> 06-Jul-2014 10:03 886K

Farnell-FDC2512-Fair..> 06-Jul-2014 10:03 886K

Farnell-FDV301N-Digi..> 06-Jul-2014 10:03 886K

Farnell-S1A-Fairchil..> 06-Jul-2014 10:03 896K

Farnell-BAV99-Fairch..> 06-Jul-2014 10:03 896K

Farnell-74AC00-74ACT..> 06-Jul-2014 10:03 911K

Farnell-NaPiOn-Panas..> 06-Jul-2014 10:02 911K

Farnell-LQ-RELAYS-AL..> 06-Jul-2014 10:02 924K

Farnell-ev-relays-ae..> 06-Jul-2014 10:02 926K

Farnell-ESCON-Featur..> 06-Jul-2014 10:02 931K

Farnell-Amplifier-In..> 06-Jul-2014 10:02 940K

Farnell-Serial-File-..> 06-Jul-2014 10:02 941K

Farnell-Both-the-Del..> 06-Jul-2014 10:01 948K

Farnell-Videk-PDF.htm 06-Jul-2014 10:01 948K

Farnell-EPCOS-173438..> 04-Jul-2014 10:43 3.3M

Farnell-Sensorless-C..> 04-Jul-2014 10:42 3.3M

Farnell-197.31-KB-Te..> 04-Jul-2014 10:42 3.3M

Farnell-PIC12F609-61..> 04-Jul-2014 10:41 3.7M

Farnell-PADO-semi-au..> 04-Jul-2014 10:41 3.7M

Farnell-03-iec-runds..> 04-Jul-2014 10:40 3.7M

Farnell-ACC-Silicone..> 04-Jul-2014 10:40 3.7M

Farnell-Series-TDS10..> 04-Jul-2014 10:39 4.0M

Farnell-03-iec-runds..> 04-Jul-2014 10:40 3.7M

Farnell-0430300011-D..> 14-Jun-2014 18:13 2.0M

Farnell-06-6544-8-PD..> 26-Mar-2014 17:56 2.7M

Farnell-3M-Polyimide..> 21-Mar-2014 08:09 3.9M

Farnell-3M-VolitionT..> 25-Mar-2014 08:18 3.3M

Farnell-10BQ060-PDF.htm 14-Jun-2014 09:50 2.4M

Farnell-10TPB47M-End..> 14-Jun-2014 18:16 3.4M

Farnell-12mm-Size-In..> 14-Jun-2014 09:50 2.4M

Farnell-24AA024-24LC..> 23-Jun-2014 10:26 3.1M

Farnell-50A-High-Pow..> 20-Mar-2014 17:31 2.9M

Farnell-197.31-KB-Te..> 04-Jul-2014 10:42 3.3M

Farnell-1907-2006-PD..> 26-Mar-2014 17:56 2.7M

Farnell-5910-PDF.htm 25-Mar-2014 08:15 3.0M

Farnell-6517b-Electr..> 29-Mar-2014 11:12 3.3M

Farnell-A-True-Syste..> 29-Mar-2014 11:13 3.3M

Farnell-ACC-Silicone..> 04-Jul-2014 10:40 3.7M

Farnell-AD524-PDF.htm 20-Mar-2014 17:33 2.8M

Farnell-ADL6507-PDF.htm 14-Jun-2014 18:19 3.4M

Farnell-ADSP-21362-A..> 20-Mar-2014 17:34 2.8M

Farnell-ALF1210-PDF.htm 04-Jul-2014 10:39 4.0M

Farnell-ALF1225-12-V..> 01-Apr-2014 07:40 3.4M

Farnell-ALF2412-24-V..> 01-Apr-2014 07:39 3.4M

Farnell-AN10361-Phil..> 23-Jun-2014 10:29 2.1M

Farnell-ARADUR-HY-13..> 26-Mar-2014 17:55 2.8M

Farnell-ARALDITE-201..> 21-Mar-2014 08:12 3.7M

Farnell-ARALDITE-CW-..> 26-Mar-2014 17:56 2.7M

Farnell-ATMEL-8-bit-..> 19-Mar-2014 18:04 2.1M

Farnell-ATMEL-8-bit-..> 11-Mar-2014 07:55 2.1M

Farnell-ATmega640-VA..> 14-Jun-2014 09:49 2.5M

Farnell-ATtiny20-PDF..> 25-Mar-2014 08:19 3.6M

Farnell-ATtiny26-L-A..> 13-Jun-2014 18:40 1.8M

Farnell-Alimentation..> 14-Jun-2014 18:24 2.5M

Farnell-Alimentation..> 01-Apr-2014 07:42 3.4M

Farnell-Amplificateu..> 29-Mar-2014 11:11 3.3M

Farnell-An-Improved-..> 14-Jun-2014 09:49 2.5M

Farnell-Atmel-ATmega..> 19-Mar-2014 18:03 2.2M

Farnell-Avvertenze-e..> 14-Jun-2014 18:20 3.3M

Farnell-BC846DS-NXP-..> 13-Jun-2014 18:42 1.6M

Farnell-BC847DS-NXP-..> 23-Jun-2014 10:24 3.3M

Farnell-BF545A-BF545..> 23-Jun-2014 10:28 2.1M

Farnell-BK2650A-BK26..> 29-Mar-2014 11:10 3.3M

Farnell-BT151-650R-N..> 13-Jun-2014 18:40 1.7M

Farnell-BTA204-800C-..> 13-Jun-2014 18:42 1.6M

Farnell-BUJD203AX-NX..> 13-Jun-2014 18:41 1.7M

Farnell-BYV29F-600-N..> 13-Jun-2014 18:42 1.6M

Farnell-BYV79E-serie..> 10-Mar-2014 16:19 1.6M

Farnell-BZX384-serie..> 23-Jun-2014 10:29 2.1M

Farnell-Battery-GBA-..> 14-Jun-2014 18:13 2.0M

Farnell-C.A-6150-C.A..> 14-Jun-2014 18:24 2.5M

Farnell-C.A 8332B-C...> 01-Apr-2014 07:40 3.4M

Farnell-CC2560-Bluet..> 29-Mar-2014 11:14 2.8M

Farnell-CD4536B-Type..> 14-Jun-2014 18:13 2.0M

Farnell-CIRRUS-LOGIC..> 10-Mar-2014 17:20 2.1M

Farnell-CS5532-34-BS..> 01-Apr-2014 07:39 3.5M

Farnell-Cannon-ZD-PD..> 11-Mar-2014 08:13 2.8M

Farnell-Ceramic-tran..> 14-Jun-2014 18:19 3.4M

Farnell-Circuit-Note..> 26-Mar-2014 18:00 2.8M

Farnell-Circuit-Note..> 26-Mar-2014 18:00 2.8M

Farnell-Cles-electro..> 21-Mar-2014 08:13 3.9M

Farnell-Conception-d..> 11-Mar-2014 07:49 2.4M

Farnell-Connectors-N..> 14-Jun-2014 18:12 2.1M

Farnell-Construction..> 14-Jun-2014 18:25 2.5M

Farnell-Controle-de-..> 11-Mar-2014 08:16 2.8M

Farnell-Cordless-dri..> 14-Jun-2014 18:13 2.0M

Farnell-Current-Tran..> 26-Mar-2014 17:58 2.7M

Farnell-Current-Tran..> 26-Mar-2014 17:58 2.7M

Farnell-Current-Tran..> 26-Mar-2014 17:59 2.7M

Farnell-Current-Tran..> 26-Mar-2014 17:59 2.7M

Farnell-DC-Fan-type-..> 14-Jun-2014 09:48 2.5M

Farnell-DC-Fan-type-..> 14-Jun-2014 09:51 1.8M

Farnell-Davum-TMC-PD..> 14-Jun-2014 18:27 2.4M

Farnell-De-la-puissa..> 29-Mar-2014 11:10 3.3M

Farnell-Directive-re..> 25-Mar-2014 08:16 3.0M

Farnell-Documentatio..> 14-Jun-2014 18:26 2.5M

Farnell-Download-dat..> 13-Jun-2014 18:40 1.8M

Farnell-ECO-Series-T..> 20-Mar-2014 08:14 2.5M

Farnell-ELMA-PDF.htm 29-Mar-2014 11:13 3.3M

Farnell-EMC1182-PDF.htm 25-Mar-2014 08:17 3.0M

Farnell-EPCOS-173438..> 04-Jul-2014 10:43 3.3M

Farnell-EPCOS-Sample..> 11-Mar-2014 07:53 2.2M

Farnell-ES2333-PDF.htm 11-Mar-2014 08:14 2.8M

Farnell-Ed.081002-DA..> 19-Mar-2014 18:02 2.5M

Farnell-F28069-Picco..> 14-Jun-2014 18:14 2.0M

Farnell-F42202-PDF.htm 19-Mar-2014 18:00 2.5M

Farnell-FDS-ITW-Spra..> 14-Jun-2014 18:22 3.3M

Farnell-FICHE-DE-DON..> 10-Mar-2014 16:17 1.6M

Farnell-Fastrack-Sup..> 23-Jun-2014 10:25 3.3M

Farnell-Ferric-Chlor..> 29-Mar-2014 11:14 2.8M

Farnell-Fiche-de-don..> 14-Jun-2014 09:47 2.5M

Farnell-Fiche-de-don..> 14-Jun-2014 18:26 2.5M

Farnell-Fluke-1730-E..> 14-Jun-2014 18:23 2.5M

Farnell-GALVA-A-FROI..> 26-Mar-2014 17:56 2.7M

Farnell-GALVA-MAT-Re..> 26-Mar-2014 17:57 2.7M

Farnell-GN-RELAYS-AG..> 20-Mar-2014 08:11 2.6M

Farnell-HC49-4H-Crys..> 14-Jun-2014 18:20 3.3M

Farnell-HFE1600-Data..> 14-Jun-2014 18:22 3.3M

Farnell-HI-70300-Sol..> 14-Jun-2014 18:27 2.4M

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Sefram-SP270.pdf-PDF..> 29-Mar-2014 11:46 464KCHAPTER 15 EQUATION 15-1 Equation of the moving average filter. In this equation, x[ ] is the input signal, y[ ] is the output signal, and M is the number of points used in the moving average. This equation only uses points on one side of the output sample being calculated. y[i ] ’ 1 M j M&1 j’ 0 x [ i %j ] y [80] ’ x [80] % x [81] % x [82] % x [83] % x [84] 5 Moving Average Filters The moving average is the most common filter in DSP, mainly because it is the easiest digital filter to understand and use. In spite of its simplicity, the moving average filter is optimal for a common task: reducing random noise while retaining a sharp step response. This makes it the premier filter for time domain encoded signals. However, the moving average is the worst filter for frequency domain encoded signals, with little ability to separate one band of frequencies from another. Relatives of the moving average filter include the Gaussian, Blackman, and multiplepass moving average. These have slightly better performance in the frequency domain, at the expense of increased computation time. Implementation by Convolution As the name implies, the moving average filter operates by averaging a number of points from the input signal to produce each point in the output signal. In equation form, this is written: Where x [ ] is the input signal, y [ ] is the output signal, and M is the number of points in the average. For example, in a 5 point moving average filter, point 80 in the output signal is given by: 278 The Scientist and Engineer's Guide to Digital Signal Processing y [80] ’ x [78] % x [79] % x [80] % x [81] % x [82] 5 100 'MOVING AVERAGE FILTER 110 'This program filters 5000 samples with a 101 point moving 120 'average filter, resulting in 4900 samples of filtered data. 130 ' 140 DIM X[4999] 'X[ ] holds the input signal 150 DIM Y[4999] 'Y[ ] holds the output signal 160 ' 170 GOSUB XXXX 'Mythical subroutine to load X[ ] 180 ' 190 FOR I% = 50 TO 4949 'Loop for each point in the output signal 200 Y[I%] = 0 'Zero, so it can be used as an accumulator 210 FOR J% = -50 TO 50 'Calculate the summation 220 Y[I%] = Y[I%] + X(I%+J%] 230 NEXT J% 240 Y[I%] = Y[I%]/101 'Complete the average by dividing 250 NEXT I% 260 ' 270 END TABLE 15-1 As an alternative, the group of points from the input signal can be chosen symmetrically around the output point: This corresponds to changing the summation in Eq. 15-1 from: j ’ 0 to M&1 , to: j ’ &(M&1) /2 to (M&1) /2 . For instance, in an 11 point moving average filter, the index, j, can run from 0 to 11 (one side averaging) or -5 to 5 (symmetrical averaging). Symmetrical averaging requires that M be an odd number. Programming is slightly easier with the points on only one side; however, this produces a relative shift between the input and output signals. You should recognize that the moving average filter is a convolution using a very simple filter kernel. For example, a 5 point filter has the filter kernel: þ 0, 0, 1/5, 1/5, 1/5, 1/5, 1/5, 0, 0 þ . That is, the moving average filter is a convolution of the input signal with a rectangular pulse having an area of one. Table 15-1 shows a program to implement the moving average filter. Noise Reduction vs. Step Response Many scientists and engineers feel guilty about using the moving average filter. Because it is so very simple, the moving average filter is often the first thing tried when faced with a problem. Even if the problem is completely solved, there is still the feeling that something more should be done. This situation is truly ironic. Not only is the moving average filter very good for many applications, it is optimal for a common problem, reducing random white noise while keeping the sharpest step response. Chapter 15- Moving Average Filters 279 Sample number 0 100 200 300 400 500 -1 0 1 2 a. Original signal Sample number 0 100 200 300 400 500 -1 0 1 2 b. 11 point moving average FIGURE 15-1 Example of a moving average filter. In (a), a rectangular pulse is buried in random noise. In (b) and (c), this signal is filtered with 11 and 51 point moving average filters, respectively. As the number of points in the filter increases, the noise becomes lower; however, the edges becoming less sharp. The moving average filter is the optimal solution for this problem, providing the lowest noise possible for a given edge sharpness. Sample number 0 100 200 300 400 500 -1 0 1 2 c. 51 point moving average Amplitude Amplitude Amplitude Figure 15-1 shows an example of how this works. The signal in (a) is a pulse buried in random noise. In (b) and (c), the smoothing action of the moving average filter decreases the amplitude of the random noise (good), but also reduces the sharpness of the edges (bad). Of all the possible linear filters that could be used, the moving average produces the lowest noise for a given edge sharpness. The amount of noise reduction is equal to the square-root of the number of points in the average. For example, a 100 point moving average filter reduces the noise by a factor of 10. To understand why the moving average if the best solution, imagine we want to design a filter with a fixed edge sharpness. For example, let's assume we fix the edge sharpness by specifying that there are eleven points in the rise of the step response. This requires that the filter kernel have eleven points. The optimization question is: how do we choose the eleven values in the filter kernel to minimize the noise on the output signal? Since the noise we are trying to reduce is random, none of the input points is special; each is just as noisy as its neighbor. Therefore, it is useless to give preferential treatment to any one of the input points by assigning it a larger coefficient in the filter kernel. The lowest noise is obtained when all the input samples are treated equally, i.e., the moving average filter. (Later in this chapter we show that other filters are essentially as good. The point is, no filter is better than the simple moving average). 280 The Scientist and Engineer's Guide to Digital Signal Processing EQUATION 15-2 Frequency response of an M point moving average filter. The frequency, f, runs between 0 and 0.5. For f ’ 0, use: H[ f ] ’ 1 H [ f ] ’ sin(Bf M ) M sin(Bf ) Frequency 0 0.1 0.2 0.3 0.4 0.5 0.0 0.2 0.4 0.6 0.8 1.0 1.2 3 point 11 point 31 point FIGURE 15-2 Frequency response of the moving average filter. The moving average is a very poor low-pass filter, due to its slow roll-off and poor stopband attenuation. These curves are generated by Eq. 15-2. Amplitude Frequency Response Figure 15-2 shows the frequency response of the moving average filter. It is mathematically described by the Fourier transform of the rectangular pulse, as discussed in Chapter 11: The roll-off is very slow and the stopband attenuation is ghastly. Clearly, the moving average filter cannot separate one band of frequencies from another. Remember, good performance in the time domain results in poor performance in the frequency domain, and vice versa. In short, the moving average is an exceptionally good smoothing filter (the action in the time domain), but an exceptionally bad low-pass filter (the action in the frequency domain). Relatives of the Moving Average Filter In a perfect world, filter designers would only have to deal with time domain or frequency domain encoded information, but never a mixture of the two in the same signal. Unfortunately, there are some applications where both domains are simultaneously important. For instance, television signals fall into this nasty category. Video information is encoded in the time domain, that is, the shape of the waveform corresponds to the patterns of brightness in the image. However, during transmission the video signal is treated according to its frequency composition, such as its total bandwidth, how the carrier waves for sound & color are added, elimination & restoration of the DC component, etc. As another example, electromagnetic interference is best understood in the frequency domain, even if Chapter 15- Moving Average Filters 281 Sample number 0 6 12 18 24 0.0 0.1 0.2 2 pass 4 pass 1 pass a. Filter kernel Sample number 0 6 12 18 24 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1 pass 4 pass 2 pass b. Step response Frequency 0 0.1 0.2 0.3 0.4 0.5 -120 -100 -80 -60 -40 -20 0 20 40 1 pass 2 pass 4 pass d. Frequency response (dB) FIGURE 15-3 Characteristics of multiple-pass moving average filters. Figure (a) shows the filter kernels resulting from passing a seven point moving average filter over the data once, twice and four times. Figure (b) shows the corresponding step responses, while (c) and (d) show the corresponding frequency responses. FFT Integrate 20 Log( ) Amplitude Amplitude Frequency 0 0.1 0.2 0.3 0.4 0.5 0.00 0.25 0.50 0.75 1.00 1.25 1 pass 2 pass 4 pass c. Frequency response Amplitude (dB) Amplitude the signal's information is encoded in the time domain. For instance, the temperature monitor in a scientific experiment might be contaminated with 60 hertz from the power lines, 30 kHz from a switching power supply, or 1320 kHz from a local AM radio station. Relatives of the moving average filter have better frequency domain performance, and can be useful in these mixed domain applications. Multiple-pass moving average filters involve passing the input signal through a moving average filter two or more times. Figure 15-3a shows the overall filter kernel resulting from one, two and four passes. Two passes are equivalent to using a triangular filter kernel (a rectangular filter kernel convolved with itself). After four or more passes, the equivalent filter kernel looks like a Gaussian (recall the Central Limit Theorem). As shown in (b), multiple passes produce an "s" shaped step response, as compared to the straight line of the single pass. The frequency responses in (c) and (d) are given by Eq. 15-2 multiplied by itself for each pass. That is, each time domain convolution results in a multiplication of the frequency spectra. 282 The Scientist and Engineer's Guide to Digital Signal Processing Figure 15-4 shows the frequency response of two other relatives of the moving average filter. When a pure Gaussian is used as a filter kernel, the frequency response is also a Gaussian, as discussed in Chapter 11. The Gaussian is important because it is the impulse response of many natural and manmade systems. For example, a brief pulse of light entering a long fiber optic transmission line will exit as a Gaussian pulse, due to the different paths taken by the photons within the fiber. The Gaussian filter kernel is also used extensively in image processing because it has unique properties that allow fast two-dimensional convolutions (see Chapter 24). The second frequency response in Fig. 15-4 corresponds to using a Blackman window as a filter kernel. (The term window has no meaning here; it is simply part of the accepted name of this curve). The exact shape of the Blackman window is given in Chapter 16 (Eq. 16-2, Fig. 16-2); however, it looks much like a Gaussian. How are these relatives of the moving average filter better than the moving average filter itself? Three ways: First, and most important, these filters have better stopband attenuation than the moving average filter. Second, the filter kernels taper to a smaller amplitude near the ends. Recall that each point in the output signal is a weighted sum of a group of samples from the input. If the filter kernel tapers, samples in the input signal that are farther away are given less weight than those close by. Third, the step responses are smooth curves, rather than the abrupt straight line of the moving average. These last two are usually of limited benefit, although you might find applications where they are genuine advantages. The moving average filter and its relatives are all about the same at reducing random noise while maintaining a sharp step response. The ambiguity lies in how the risetime of the step response is measured. If the risetime is measured from 0% to 100% of the step, the moving average filter is the best you can do, as previously shown. In comparison, measuring the risetime from 10% to 90% makes the Blackman window better than the moving average filter. The point is, this is just theoretical squabbling; consider these filters equal in this parameter. The biggest difference in these filters is execution speed. Using a recursive algorithm (described next), the moving average filter will run like lightning in your computer. In fact, it is the fastest digital filter available. Multiple passes of the moving average will be correspondingly slower, but still very quick. In comparison, the Gaussian and Blackman filters are excruciatingly slow, because they must use convolution. Think a factor of ten times the number of points in the filter kernel (based on multiplication being about 10 times slower than addition). For example, expect a 100 point Gaussian to be 1000 times slower than a moving average using recursion. Recursive Implementation A tremendous advantage of the moving average filter is that it can be implemented with an algorithm that is very fast. To understand this Chapter 15- Moving Average Filters 283 FIGURE 15-4 Frequency response of the Blackman window and Gaussian filter kernels. Both these filters provide better stopband attenuation than the moving average filter. This has no advantage in removing random noise from time domain encoded signals, but it can be useful in mixed domain problems. The disadvantage of these filters is that they must use convolution, a terribly slow algorithm. Frequency 0 0.1 0.2 0.3 0.4 0.5 -140 -120 -100 -80 -60 -40 -20 0 20 Gaussian Blackman Amplitude (dB) y [50] ’ x [47] % x [48] % x [49] % x [50] % x [51] % x [52] % x [53] y [51] ’ x [48] % x [49] % x [50] % x [51] % x [52] % x [53] % x [54] y [51] ’ y [50] % x [54] & x [47] EQUATION 15-3 Recursive implementation of the moving average filter. In this equation, x[ ] is the input signal, y[ ] is the output signal, M is the number of points in the moving average (an odd number). Before this equation can be used, the first point in the signal must be calculated using a standard summation. y [i ] ’ y [i &1] % x [i %p] & x [i &q] q ’ p % 1 where: p ’ (M&1) /2 algorithm, imagine passing an input signal, x [ ], through a seven point moving average filter to form an output signal, y [ ]. Now look at how two adjacent output points, y [50] and y [51], are calculated: These are nearly the same calculation; points x [48] through x [53] must be added for y [50], and again for y [51]. If y [50] has already been calculated, the most efficient way to calculate y [51] is: Once y [51] has been found using y [50], then y [52] can be calculated from sample y [51], and so on. After the first point is calculated in y [ ], all of the other points can be found with only a single addition and subtraction per point. This can be expressed in the equation: Notice that this equation use two sources of data to calculate each point in the output: points from the input and previously calculated points from the output. This is called a recursive equation, meaning that the result of one calculation 284 The Scientist and Engineer's Guide to Digital Signal Processing 100 'MOVING AVERAGE FILTER IMPLEMENTED BY RECURSION 110 'This program filters 5000 samples with a 101 point moving 120 'average filter, resulting in 4900 samples of filtered data. 130 'A double precision accumulator is used to prevent round-off drift. 140 ' 150 DIM X[4999] 'X[ ] holds the input signal 160 DIM Y[4999] 'Y[ ] holds the output signal 170 DEFDBL ACC 'Define the variable ACC to be double precision 180 ' 190 GOSUB XXXX 'Mythical subroutine to load X[ ] 200 ' 210 ACC = 0 'Find Y[50] by averaging points X[0] to X[100] 220 FOR I% = 0 TO 100 230 ACC = ACC + X[I%] 240 NEXT I% 250 Y[[50] = ACC/101 260 ' 'Recursive moving average filter (Eq. 15-3) 270 FOR I% = 51 TO 4949 280 ACC = ACC + X[I%+50] - X[I%-51] 290 Y[I%] = ACC 300 NEXT I% 310 ' 320 END TABLE 15-2 CHAPTER 6 Convolution Convolution is a mathematical way of combining two signals to form a third signal. It is the single most important technique in Digital Signal Processing. Using the strategy of impulse decomposition, systems are described by a signal called the impulse response. Convolution is important because it relates the three signals of interest: the input signal, the output signal, and the impulse response. This chapter presents convolution from two different viewpoints, called the input side algorithm and the output side algorithm. Convolution provides the mathematical framework for DSP; there is nothing more important in this book. The Delta Function and Impulse Response The previous chapter describes how a signal can be decomposed into a group of components called impulses. An impulse is a signal composed of all zeros, except a single nonzero point. In effect, impulse decomposition provides a way to analyze signals one sample at a time. The previous chapter also presented the fundamental concept of DSP: the input signal is decomposed into simple additive components, each of these components is passed through a linear system, and the resulting output components are synthesized (added). The signal resulting from this divide-and-conquer procedure is identical to that obtained by directly passing the original signal through the system. While many different decompositions are possible, two form the backbone of signal processing: impulse decomposition and Fourier decomposition. When impulse decomposition is used, the procedure can be described by a mathematical operation called convolution. In this chapter (and most of the following ones) we will only be dealing with discrete signals. Convolution also applies to continuous signals, but the mathematics is more complicated. We will look at how continious signals are processed in Chapter 13. Figure 6-1 defines two important terms used in DSP. The first is the delta function, symbolized by the Greek letter delta, *[n]. The delta function is a normalized impulse, that is, sample number zero has a value of one, while 108 The Scientist and Engineer's Guide to Digital Signal Processing all other samples have a value of zero. For this reason, the delta function is frequently called the unit impulse. The second term defined in Fig. 6-1 is the impulse response. As the name suggests, the impulse response is the signal that exits a system when a delta function (unit impulse) is the input. If two systems are different in any way, they will have different impulse responses. Just as the input and output signals are often called x[n] and y[n] , the impulse response is usually given the symbol, h[n]. Of course, this can be changed if a more descriptive name is available, for instance, f [n] might be used to identify the impulse response of a filter. Any impulse can be represented as a shifted and scaled delta function. Consider a signal, a[n] , composed of all zeros except sample number 8, which has a value of -3. This is the same as a delta function shifted to the right by 8 samples, and multiplied by -3. In equation form: a[n] ’ &3*[n&8]. Make sure you understand this notation, it is used in nearly all DSP equations. If the input to a system is an impulse, such as &3*[n&8] , what is the system's output? This is where the properties of homogeneity and shift invariance are used. Scaling and shifting the input results in an identical scaling and shifting of the output. If *[n] results in h[n] , it follows that &3*[n&8] results in &3h[n&8] . In words, the output is a version of the impulse response that has been shifted and scaled by the same amount as the delta function on the input. If you know a system's impulse response, you immediately know how it will react to any impulse. Convolution Let's summarize this way of understanding how a system changes an input signal into an output signal. First, the input signal can be decomposed into a set of impulses, each of which can be viewed as a scaled and shifted delta function. Second, the output resulting from each impulse is a scaled and shifted version of the impulse response. Third, the overall output signal can be found by adding these scaled and shifted impulse responses. In other words, if we know a system's impulse response, then we can calculate what the output will be for any possible input signal. This means we know everything about the system. There is nothing more that can be learned about a linear system's characteristics. (However, in later chapters we will show that this information can be represented in different forms). The impulse response goes by a different name in some applications. If the system being considered is a filter, the impulse response is called the filter kernel, the convolution kernel, or simply, the kernel. In image processing, the impulse response is called the point spread function. While these terms are used in slightly different ways, they all mean the same thing, the signal produced by a system when the input is a delta function. Chapter 6- Convolution 109 System -2 -1 0 1 2 3 4 5 6 -1 0 1 2 -2 -1 0 1 2 3 4 5 6 -1 0 1 2 *[n] h[n] Delta Impulse Response Linear Function FIGURE 6-1 Definition of delta function and impulse response. The delta function is a normalized impulse. All of its samples have a value of zero, except for sample number zero, which has a value of one. The Greek letter delta, *[n] , is used to identify the delta function. The impulse response of a linear system, usually denoted by h[n] , is the output of the system when the input is a delta function. x[n] h[n] = y[n] x[n] y[n] Linear System h[n] FIGURE 6-2 How convolution is used in DSP. The output signal from a linear system is equal to the input signal convolved with the system's impulse response. Convolution is denoted by a star when writing equations. Convolution is a formal mathematical operation, just as multiplication, addition, and integration. Addition takes two numbers and produces a third number, while convolution takes two signals and produces a third signal. Convolution is used in the mathematics of many fields, such as probability and statistics. In linear systems, convolution is used to describe the relationship between three signals of interest: the input signal, the impulse response, and the output signal. Figure 6-2 shows the notation when convolution is used with linear systems. An input signal, x[n] , enters a linear system with an impulse response, h[n] , resulting in an output signal, y[n] . In equation form: x[n] t h[n] ’ y[n] . Expressed in words, the input signal convolved with the impulse response is equal to the output signal. Just as addition is represented by the plus, +, and multiplication by the cross, ×, convolution is represented by the star, t. It is unfortunate that most programming languages also use the star to indicate multiplication. A star in a computer program means multiplication, while a star in an equation means convolution. 110 The Scientist and Engineer's Guide to Digital Signal Processing Sample number 0 10 20 30 40 50 60 70 80 90 100 110 -2 -1 0 1 2 3 4 S 0 10 20 30 -0.25 0.00 0.25 0.50 0.75 1.00 1.25 S 0 10 20 30 -0.02 0.00 0.02 0.04 0.06 0.08 a. Low-pass Filter b. High-pass Filter Sample number 0 10 20 30 40 50 60 70 80 -2 -1 0 1 2 3 4 Sample number 0 10 20 30 40 50 60 70 80 90 100 110 -2 -1 0 1 2 3 4 Sample number 0 10 20 30 40 50 60 70 80 -2 -1 0 1 2 3 4 Sample number Sample number Input Signal Impulse Response Output Signal Amplitude Amplitude Amplitude Amplitude Amplitude Amplitude FIGURE 6-3 Examples of low-pass and high-pass filtering using convolution. In this example, the input signal is a few cycles of a sine wave plus a slowly rising ramp. These two components are separated by using properly selected impulse responses. Figure 6-3 shows convolution being used for low-pass and high-pass filtering. The example input signal is the sum of two components: three cycles of a sine wave (representing a high frequency), plus a slowly rising ramp (composed of low frequencies). In (a), the impulse response for the low-pass filter is a smooth arch, resulting in only the slowly changing ramp waveform being passed to the output. Similarly, the high-pass filter, (b), allows only the more rapidly changing sinusoid to pass. Figure 6-4 illustrates two additional examples of how convolution is used to process signals. The inverting attenuator, (a), flips the signal top-for-bottom, and reduces its amplitude. The discrete derivative (also called the first difference), shown in (b), results in an output signal related to the slope of the input signal. Notice the lengths of the signals in Figs. 6-3 and 6-4. The input signals are 81 samples long, while each impulse response is composed of 31 samples. In most DSP applications, the input signal is hundreds, thousands, or even millions of samples in length. The impulse response is usually much shorter, say, a few points to a few hundred points. The mathematics behind convolution doesn't restrict how long these signals are. It does, however, specify the length of the output signal. The length of the output signal is Chapter 6- Convolution 111 S 0 10 20 30 -2.00 -1.00 0.00 1.00 2.00 S 0 10 20 30 -2.00 -1.00 0.00 1.00 2.00 a. Inverting Attenuator b. Discrete Derivative Sample number 0 10 20 30 40 50 60 70 80 90 100 110 -2 -1 0 1 2 3 4 Sample number 0 10 20 30 40 50 60 70 80 90 100 110 -2 -1 0 1 2 3 4 Sample number 0 10 20 30 40 50 60 70 80 -2 -1 0 1 2 3 4 Sample number 0 10 20 30 40 50 60 70 80 -2 -1 0 1 2 3 4 Input Signal Impulse Response Output Signal Sample number Sample number Amplitude Amplitude Amplitude Amplitude Amplitude Amplitude FIGURE 6-4 Examples of signals being processed using convolution. Many signal processing tasks use very simple impulse responses. As shown in these examples, dramatic changes can be achieved with only a few nonzero points. equal to the length of the input signal, plus the length of the impulse response, minus one. For the signals in Figs. 6-3 and 6-4, each output signal is: 81% 31& 1 ’ 111 samples long. The input signal runs from sample 0 to 80, the impulse response from sample 0 to 30, and the output signal from sample 0 to 110. Now we come to the detailed mathematics of convolution. As used in Digital Signal Processing, convolution can be understood in two separate ways. The first looks at convolution from the viewpoint of the input signal. This involves analyzing how each sample in the input signal contributes to many points in the output signal. The second way looks at convolution from the viewpoint of the output signal. This examines how each sample in the output signal has received information from many points in the input signal. Keep in mind that these two perspectives are different ways of thinking about the same mathematical operation. The first viewpoint is important because it provides a conceptual understanding of how convolution pertains to DSP. The second viewpoint describes the mathematics of convolution. This typifies one of the most difficult tasks you will encounter in DSP: making your conceptual understanding fit with the jumble of mathematics used to communicate the ideas. 112 The Scientist and Engineer's Guide to Digital Signal Processing 0 1 2 3 4 5 6 7 8 9 10 11 -3 -2 -1 0 1 2 3 0 1 2 3 4 5 6 7 8 -3 -2 -1 0 1 2 3 0 1 2 3 -3 -2 -1 0 1 2 3 x[n] h[n] y[n] FIGURE 6-5 Example convolution problem. A nine point input signal, convolved with a four point impulse response, results in a twelve point output signal. Each point in the input signal contributes a scaled and shifted impulse response to the output signal. These nine scaled and shifted impulse responses are shown in Fig. 6-6. Now examine sample x[8] , the last point in the input signal. This sample is at index number eight, and has a value of -0.5. As shown in the lower-right graph of Fig. 6-6, x[8] results in an impulse response that has been shifted to the right by eight points and multiplied by -0.5. Place holding zeros have been added at points 0-7. Lastly, examine the effect of points x[0] and x[7] . Both these samples have a value of zero, and therefore produce output components consisting of all zeros. The Input Side Algorithm Figure 6-5 shows a simple convolution problem: a 9 point input signal, x[n] , is passed through a system with a 4 point impulse response, h[n] , resulting in a 9% 4& 1 ’ 12 point output signal, y[n] . In mathematical terms, x[n] is convolved with h[n] to produce y[n] . This first viewpoint of convolution is based on the fundamental concept of DSP: decompose the input, pass the components through the system, and synthesize the output. In this example, each of the nine samples in the input signal will contribute a scaled and shifted version of the impulse response to the output signal. These nine signals are shown in Fig. 6-6. Adding these nine signals produces the output signal, y[n] . Let's look at several of these nine signals in detail. We will start with sample number four in the input signal, i.e., x[4] . This sample is at index number four, and has a value of 1.4. When the signal is decomposed, this turns into an impulse represented as: 1.4*[n&4]. After passing through the system, the resulting output component will be: 1.4 h[n&4]. This signal is shown in the center box of the nine signals in Fig. 6-6. Notice that this is the impulse response, h[n] , multiplied by 1.4, and shifted four samples to the right. Zeros have been added at samples 0-3 and at samples 8-11 to serve as place holders. To make this more clear, Fig. 6-6 uses squares to represent the data points that come from the shifted and scaled impulse response, and diamonds for the added zeros. Chapter 6- Convolution 113 FIGURE 6-6 Output signal components for the convolution in Fig. 6-5. In these signals, each point that results from a scaled and shifted impulse response is represented by a square marker. The remaining data points, represented by diamonds, are zeros that have been added as place holders. 0 1 2 3 4 5 6 7 8 9 10 11 -3 -2 -1 0 1 2 3 contribution from x[ ] h[n- ] 0 1 2 3 4 5 6 7 8 9 10 11 -3 -2 -1 0 1 2 3 contribution from x[ ] h[n- ] 0 1 2 3 4 5 6 7 8 9 10 11 -3 -2 -1 0 1 2 3 contribution from x[ ] h[n- ] 0 1 2 3 4 5 6 7 8 9 10 11 -3 -2 -1 0 1 2 3 contribution from x[ ] h[n- ] 0 1 2 3 4 5 6 7 8 9 10 11 -3 -2 -1 0 1 2 3 contribution from x[ ] h[n- ] 0 1 2 3 4 5 6 7 8 9 10 11 -3 -2 -1 0 1 2 3 contribution from x[ ] h[n- ] 0 1 2 3 4 5 6 7 8 9 10 11 -3 -2 -1 0 1 2 3 contribution from x[ ] h[n- ] 0 1 2 3 4 5 6 7 8 9 10 11 -3 -2 -1 0 1 2 3 contribution from x[ ] h[n- ] 0 1 2 3 4 5 6 7 8 9 10 11 -3 -2 -1 0 1 2 3 contribution from x[ ] h[n- ] 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 In this example, x[n] is a nine point signal and h[n] is a four point signal. In our next example, shown in Fig. 6-7, we will reverse the situation by making x[n] a four point signal, and h[n] a nine point signal. The same two waveforms are used, they are just swapped. As shown by the output signal components, the four samples in x[n] result in four shifted and scaled versions of the nine point impulse response. Just as before, leading and trailing zeros are added as place holders. But wait just one moment! The output signal in Fig. 6-7 is identical to the output signal in Fig. 6-5. This isn't a mistake, but an important property. Convolution is commutative: a[n]tb[n] ’ b[n]ta[n] . The mathematics does not care which is the input signal and which is the impulse response, only that two signals are convolved with each other. Although the mathematics may allow it, exchanging the two signals has no physical meaning in system theory. The input signal and impulse response are two totally different things and exchanging them doesn't make sense. What the commutative property provides is a mathematical tool for manipulating equations to achieve various results. 114 The Scientist and Engineer's Guide to Digital Signal Processing TABLE 6-1 100 'CONVOLUTION USING THE INPUT SIDE ALGORITHM 110 ' 120 DIM X[80] 'The input signal, 81 points 130 DIM H[30] 'The impulse response, 31 points 140 DIM Y[110] 'The output signal, 111 points 150 ' 160 GOSUB XXXX 'Mythical subroutine to load X[ ] and H[ ] 170 ' 180 FOR I% = 0 TO 110 'Zero the output array 190 Y(I%) = 0 200 NEXT I% 210 ' 220 FOR I% = 0 TO 80 'Loop for each point in X[ ] 230 FOR J% = 0 TO 30 'Loop for each point in H[ ] 240 Y[I%+J%] = Y[I%+J%] + X[I%]tH[J%] 250 NEXT J% 260 NEXT I% '(remember, t is multiplication in programs!) 270 ' 280 GOSUB XXXX 'Mythical subroutine to store Y[ ] 290 ' 300 END A program for calculating convolutions using the input side algorithm is shown in Table 6-1. Remember, the programs in this book are meant to convey algorithms in the simplest form, even at the expense of good programming style. For instance, all of the input and output is handled in mythical subroutines (lines 160 and 280), meaning we do not define how these operations are conducted. Do not skip over these programs; they are a key part of the material and you need to understand them in detail. The program convolves an 81 point input signal, held in array X[ ], with a 31 point impulse response, held in array H[ ], resulting in a 111 point output signal, held in array Y[ ]. These are the same lengths shown in Figs. 6-3 and 6-4. Notice that the names of these arrays use upper case letters. This is a violation of the naming conventions previously discussed, because upper case letters are reserved for frequency domain signals. Unfortunately, the simple BASIC used in this book does not allow lower case variable names. Also notice that line 240 uses a star for multiplication. Remember, a star in a program means multiplication, while a star in an equation means convolution. A star in text (such as documentation or program comments) can mean either. The mythical subroutine in line 160 places the input signal into X[ ] and the impulse response into H[ ]. Lines 180-200 set all of the values in Y[ ] to zero. This is necessary because Y[ ] is used as an accumulator to sum the output components as they are calculated. Lines 220 to 260 are the heart of the program. The FOR statement in line 220 controls a loop that steps through each point in the input signal, X[ ]. For each sample in the input signal, an inner loop (lines 230-250) calculates a scaled and shifted version of the impulse response, and adds it to the array accumulating the output signal, Y[ ]. This nested loop structure (one loop within another loop) is a key characteristic of convolution programs; become familiar with it. Chapter 6- Convolution 115 FIGURE 6-7 A second example of convolution. The waveforms for the input signal and impulse response are exchanged from the example of Fig. 6-5. Since convolution is commutative, the output signals for the two examples are identical. 0 1 2 3 4 5 6 7 8 9 10 11 -3 -2 -1 0 1 2 3 0 1 2 3 4 5 6 7 8 -3 -2 -1 0 1 2 3 0 1 2 3 -3 -2 -1 0 1 2 3 x[n] h[n] y[n] 0 1 2 3 4 5 6 7 8 9 10 11 -3 -2 -1 0 1 2 3 contribution from x[ ] h[n- ] 0 1 2 3 4 5 6 7 8 9 10 11 -3 -2 -1 0 1 2 3 contribution from x[ ] h[n- ] 0 1 2 3 4 5 6 7 8 9 10 11 -3 -2 -1 0 1 2 3 contribution from x[ ] h[n- ] 0 1 2 3 4 5 6 7 8 9 10 11 -3 -2 -1 0 1 2 3 contribution from x[ ] h[n- ] 0 0 1 1 2 2 3 3 Output signal components Keeping the indexing straight in line 240 can drive you crazy! Let's say we are halfway through the execution of this program, so that we have just begun action on sample X[40], i.e., I% = 40. The inner loop runs through each point in the impulse response doing three things. First, the impulse response is scaled by multiplying it by the value of the input sample. If this were the only action taken by the inner loop, line 240 could be written, Y[J%] = X[40]tH[J%]. Second, the scaled impulse is shifted 40 samples to the right by adding this number to the index used in the output signal. This second action would change line 240 to: Y[40+J%] = X[40]tH[J%]. Third, Y[ ] must accumulate (synthesize) all the signals resulting from each sample in the input signal. Therefore, the new information must be added to the information that is already in the array. This results in the final command: Y[40+J%] = Y[40+J%] + X[40]tH[J%]. Study this carefully; it is very confusing, but very important. 116 The Scientist and Engineer's Guide to Digital Signal Processing The Output Side Algorithm The first viewpoint of convolution analyzes how each sample in the input signal affects many samples in the output signal. In this second viewpoint, we reverse this by looking at individual samples in the output signal, and finding the contributing points from the input. This is important from both mathematical and practical standpoints. Suppose that we are given some input signal and impulse response, and want to find the convolution of the two. The most straightforward method would be to write a program that loops through the output signal, calculating one sample on each loop cycle. Likewise, equations are written in the form: y[n] ’ some combination of other variables. That is, sample n in the output signal is equal to some combination of the many values in the input signal and impulse response. This requires a knowledge of how each sample in the output signal can be calculated independently of all other samples in the output signal. The output side algorithm provides this information. Let's look at an example of how a single point in the output signal is influenced by several points from the input. The example point we will use is y[6] in Fig. 6-5. This point is equal to the sum of all the sixth points in the nine output components, shown in Fig. 6-6. Now, look closely at these nine output components and identify which can affect y[6] . That is, find which of these nine signals contains a nonzero sample at the sixth position. Five of the output components only have added zeros (the diamond markers) at the sixth sample, and can therefore be ignored. Only four of the output components are capable of having a nonzero value in the sixth position. These are the output components generated from the input samples: x[3], x[4], x[5], and x[6] . By adding the sixth sample from each of these output components, y[6] is determined as: y[6] ’ x[3]h[3] % x[4]h[2] % x[5]h[1] % x[6]h[0] . That is, four samples from the input signal are multiplied by the four samples in the impulse response, and the products added. Figure 6-8 illustrates the output side algorithm as a convolution machine, a flow diagram of how convolution occurs. Think of the input signal, x[n] , and the output signal, y[n] , as fixed on the page. The convolution machine, everything inside the dashed box, is free to move left and right as needed. The convolution machine is positioned so that its output is aligned with the output sample being calculated. Four samples from the input signal fall into the inputs of the convolution machine. These values are multiplied by the indicated samples in the impulse response, and the products are added. This produces the value for the output signal, which drops into its proper place. For example, y[6] i s s h own b e i n g c a l c u l a t e d f r om t h e f o u r i n p u t s amp l e s : x[3], x[4], x[5], and x[6] . To calculate y[7] , the convolution machine moves one sample to the right. This results in another four samples entering the machine, x[4] through x[7] , and the value for y[7] dropping into the proper place. This process is repeated for all points in the output signal needing to be calculated. Chapter 6- Convolution 117 0 1 2 3 4 5 6 7 8 -3 -2 -1 0 1 2 3 -3 -2 -1 0 3.0 2.0 1.0 0.0 1.0 2.0 3.0 x[n] y[n] h[n] 0 -1 -2 -3 2 3 1 (flipped) FIGURE 6-8 The convolution machine. This is a flow diagram showing how each sample in the output signal is influenced by the input signal and impulse response. See the text for details. 0 1 2 3 4 5 6 7 8 9 10 11 -3 -2 -1 0 1 2 3 The arrangement of the impulse response inside the convolution machine is very important. The impulse response is flipped left-for-right. This places sample number zero on the right, and increasingly positive sample numbers running to the left. Compare this to the normal impulse response in Fig. 6-5 to understand the geometry of this flip. Why is this flip needed? It simply falls out of the mathematics. The impulse response describes how each point in the input signal affects the output signal. This results in each point in the output signal being affected by points in the input signal weighted by a flipped impulse response. 118 The Scientist and Engineer's Guide to Digital Signal Processing FIGURE 6-9 The convolution machine in action. Figures (a) through (d) show the convolution machine set to calculate four different output signal samples, y[0], y[3], y[8], and y[11]. 0 1 2 3 4 5 6 7 8 -3 -2 -1 0 1 2 3 -3 -2 -1 0 3.0 2.0 1.0 0.0 1.0 2.0 3.0 x[n] y[n] h[n] 0 -1 -2 -3 2 3 1 (flipped) a. Set to calculate y[0] 0 1 2 3 4 5 6 7 8 -3 -2 -1 0 1 2 3 -3 -2 -1 0 3.0 2.0 1.0 0.0 1.0 2.0 3.0 x[n] y[n] h[n] 0 -1 -2 -3 2 3 1 (flipped) b. Set to calculate y[3] 0 1 2 3 4 5 6 7 8 9 10 11 -3 -2 -1 0 1 2 3 0 1 2 3 4 5 6 7 8 9 10 11 -3 -2 -1 0 1 2 3 Figure 6-9 shows the convolution machine being used to calculate several samples in the output signal. This diagram also illustrates a real nuisance in convolution. In (a), the convolution machine is located fully to the left with its output aimed at y[0] . In this position, it is trying to receive input from samples: x[&3], x[&2], x[&1], and x[0] . The problem is, three of these samples: x[&3], x[&2], and x[&1] , do not exist! This same dilemma arises in (d), where the convolution machine tries to accept samples to the right of the defined input signal, points x[9], x[10], and x[11] . One way to handle this problem is by inventing the nonexistent samples. This involves adding samples to the ends of the input signal, with each of the added samples having a value of zero. This is called padding the signal with zeros. Instead of trying to access a nonexistent value, the convolution machine receives a sample that has a value of zero. Since this zero is eliminated during the multiplication, the result is mathematically the same as ignoring the nonexistent inputs. Chapter 6- Convolution 119 0 1 2 3 4 5 6 7 8 9 10 11 -3 -2 -1 0 1 2 3 0 1 2 3 4 5 6 7 8 9 10 11 -3 -2 -1 0 1 2 3 0 1 2 3 4 5 6 7 8 -3 -2 -1 0 1 2 3 -3 -2 -1 0 3.0 2.0 1.0 0.0 1.0 2.0 3.0 x[n] y[n] h[n] 0 -1 -2 -3 2 3 1 (flipped) c. Set to calculate y[8] 0 1 2 3 4 5 6 7 8 -3 -2 -1 0 1 2 3 -3 -2 -1 0 3.0 2.0 1.0 0.0 1.0 2.0 3.0 x[n] y[n] h[n] 0 -1 -2 -3 2 3 1 (flipped) d. Set to calculate y[11] Figure 6-9 (continued) The important part is that the far left and far right samples in the output signal are based on incomplete information. In DSP jargon, the impulse response is not fully immersed in the input signal. If the impulse response is M points in length, the first and last M&1 samples in the output signal are based on less information than the samples between. This is analogous to an electronic circuit requiring a certain amount of time to stabilize after the power is applied. The difference is that this transient is easy to ignore in electronics, but very prominent in DSP. Figure 6-10 shows an example of the trouble these end effects can cause. The input signal is a sine wave plus a DC component. The desire is to remove the DC part of the signal, while leaving the sine wave intact. This calls for a highpass filter, such as the impulse response shown in the figure. The problem is, the first and last 30 points are a mess! The shape of these end regions can be understood by imagining the input signal padded with 30 zeros on the left side, samples x[&1] through x[&30] , and 30 zeros on the right, samples x[81] through x[110] . The output signal can then be viewed as a filtered version of this longer waveform. These "end effect" problems are widespread in 120 The Scientist and Engineer's Guide to Digital Signal Processing EQUATION 6-1 The convolution summation. This is the formal definition of convolution, written in the shorthand: y [n] ’ x [n] t h[n]. In this equation, h[n] is an M point signal with indexes running from 0 to M-1. y [i ] ’ jM&1 j ’0 h[ j ] x [i&j ] DSP. As a general rule, expect that the beginning and ending samples in processed signals will be quite useless. Now the math. Using the convolution machine as a guideline, we can write the standard equation for convolution. If x[n] is an N point signal running from 0 to N-1, and h[n] is an M point signal running from 0 to M-1, the convolution of the two: y[n] ’ x[n] t h[n], is an N+M-1 point signal running from 0 to N+M-2, given by: This equation is called the convolution sum. It allows each point in the output signal to be calculated independently of all other points in the output signal. The index, i, determines which sample in the output signal is being calculated, and therefore corresponds to the left-right position of the convolution machine. In computer programs performing convolution, a loop makes this index run through each sample in the output signal. To calculate one of the output samples, the index, j, is used inside of the convolution machine. As j runs through 0 to M-1, each sample in the impulse response, h[ j], is multiplied by the proper sample from the input signal, x[i& j ]. All these products are added to produce the output sample being calculated. Study Eq. 6-1 until you fully understand how it is implemented by the convolution machine. Much of DSP is based on this equation. (Don't be confused by the n in y[n] ’ x[n] t h[n]. This is merely a place holder to indicate that some variable is the index into the array. Sometimes the equations are written: y[ ] ’ x[ ] t h[ ], just to avoid having to bring in a meaningless symbol). Table 6-2 shows a program for performing convolutions using the output side algorithm, a direct use of Eq. 6-1. This program produces the same output signal as the program for the input side algorithm, shown previously in Table 6-1. Notice the main difference between these two programs: the input side algorithm loops through each sample in the input signal (line 220 of Table 6- 1), while the output side algorithm loops through each sample in the output signal (line 180 of Table 6-2). Here is a detailed operation of this program. The FOR-NEXT loop in lines 180 to 250 steps through each sample in the output signal, using I% as the index. For each of these values, an inner loop, composed of lines 200 to 230, calculates the value of the output sample, Y[I%]. The value of Y[I%] is set to zero in line 190, allowing it to accumulate the products inside of the convolution machine. The FOR-NEXT loop in lines 200 to 240 provide a direct implementation of Eq. 6-1. The index, J%, steps through each Chapter 6- Convolution 121 sample in the impulse response. Line 230 provides the multiplication of each sample in the impulse response, H[J%], with the appropriate sample from the input signal, X[I%-J%], and adds the result to the accumulator. In line 230, the sample taken from the input signal is: X[I%-J%]. Lines 210 and 220 prevent this from being outside the defined array, X[0] to X[80]. In other words, this program handles undefined samples in the input signal by ignoring them. Another alternative would be to define the input signal's array from X[-30] to X[110], allowing 30 zeros to be padded on each side of the true data. As a third alternative, the FOR-NEXT loop in line 180 could be changed to run from 30 to 80, rather than 0 to 110. That is, the program would only calculate the samples in the output signal where the impulse response is fully immersed in the input signal. The important thing is that you must use one of these three techniques. If you don't, the program will crash when it tries to read the out-of-bounds data. S 0 10 20 30 -0.5 0.0 0.5 1.0 1.5 Sample number 0 10 20 30 40 50 60 70 80 -4 -2 0 2 4 Sample number 0 10 20 30 40 50 60 70 80 90 100 110 -4 -2 0 2 4 Input signal Impulse response Output signal unusable usable unusable Sample number Amplitude Amplitude Amplitude FIGURE 6-10 End effects in convolution. When an input signal is convolved with an M point impulse response, the first and last M-1 points in the output signal may not be usable. In this example, the impulse response is a high-pass filter used to remove the DC component from the input signal. 100 'CONVOLUTION USING THE OUTPUT SIDE ALGORITHM 110 ' 120 DIM X[80] 'The input signal, 81 points 130 DIM H[30] 'The impulse response, 31 points 140 DIM Y[110] 'The output signal, 111 points 150 ' 160 GOSUB XXXX 'Mythical subroutine to load X[ ] and H[ ] 170 ' 180 FOR I% = 0 TO 110 'Loop for each point in Y[ ] 190 Y[I%] = 0 'Zero the sample in the output array 200 FOR J% = 0 TO 30 'Loop for each point in H[ ] 210 IF (I%-J% < 0) THEN GOTO 240 220 IF (I%-J% > 80) THEN GOTO 240 230 Y(I%) = Y(I%) + H(J%) t X(I%-J%) 240 NEXT J% 250 NEXT I% 260 ' 270 GOSUB XXXX 'Mythical subroutine to store Y[ ] 280 ' 290 END TABLE 6-2 122 The Scientist and Engineer's Guide to Digital Signal Processing The Sum of Weighted Inputs The characteristics of a linear system are completely described by its impulse response. This is the basis of the input side algorithm: each point in the input signal contributes a scaled and shifted version of the impulse response to the output signal. The mathematical consequences of this lead to the output side algorithm: each point in the output signal receives a contribution from many points in the input signal, multiplied by a flipped impulse response. While this is all true, it doesn't provide the full story on why convolution is important in signal processing. Look back at the convolution machine in Fig. 6-8, and ignore that the signal inside the dotted box is an impulse response. Think of it as a set of weighing coefficients that happen to be embedded in the flow diagram. In this view, each sample in the output signal is equal to a sum of weighted inputs. Each sample in the output is influenced by a region of samples in the input signal, as determined by what the weighing coefficients are chosen to be. For example, imagine there are ten weighing coefficients, each with a value of onetenth. This makes each sample in the output signal the average of ten samples from the input. Taking this further, the weighing coefficients do not need to be restricted to the left side of the output sample being calculated. For instance, Fig. 6-8 shows y[6] being calculated from: x[3], x[4], x[5], and x[6] . Viewing the convolution machine as a sum of weighted inputs, the weighing coefficients could be chosen symmetrically around the output sample. For example, y[6] might receive contributions from: x[4], x[5], x[6], x[7], and x[8] . Using the same indexing notation as in Fig. 6-8, the weighing coefficients for these five inputs would be held in: h[2], h[1], h[0], h[&1], and h[&2] . In other words, the impulse response that corresponds to our selection of symmetrical weighing coefficients requires the use of negative indexes. We will return to this in the next chapter. Mathematically, there is only one concept here: convolution as defined by Eq. 6-1. However, science and engineering problems approach this single concept from two distinct directions. Sometimes you will want to think of a system in terms of what its impulse response looks like. Other times you will understand the system as a set of weighing coefficients. You need to become familiar with both views, and how to toggle between them. Digital Signal Processors Digital Signal Processing is carried out by mathematical operations. In comparison, word processing and similar programs merely rearrange stored data. This means that computers designed for business and other general applications are not optimized for algorithms such as digital filtering and Fourier analysis. Digital Signal Processors are microprocessors specifically designed to handle Digital Signal Processing tasks. These devices have seen tremendous growth in the last decade, finding use in everything from cellular telephones to advanced scientific instruments. In fact, hardware engineers use "DSP" to mean Digital Signal Processor, just as algorithm developers use "DSP" to mean Digital Signal Processing. This chapter looks at how DSPs are different from other types of microprocessors, how to decide if a DSP is right for your application, and how to get started in this exciting new field. In the next chapter we will take a more detailed look at one of these sophisticated products: the Analog Devices SHARC® family. How DSPs are Different from Other Microprocessors In the 1960s it was predicted that artificial intelligence would revolutionize the way humans interact with computers and other machines. It was believed that by the end of the century we would have robots cleaning our houses, computers driving our cars, and voice interfaces controlling the storage and retrieval of information. This hasn't happened; these abstract tasks are far more complicated than expected, and very difficult to carry out with the step-by-step logic provided by digital computers. However, the last forty years have shown that computers are extremely capable in two broad areas, (1) data manipulation, such as word processing and database management, and (2) mathematical calculation, used in science, engineering, and Digital Signal Processing. All microprocessors can perform both tasks; however, it is difficult (expensive) to make a device that is optimized for both. There are technical tradeoffs in the hardware design, such as the size of the instruction set and how interrupts are handled. Even 504 The Scientist and Engineer's Guide to Digital Signal Processing Data Manipulation Math Calculation Word processing, database management, spread sheets, operating sytems, etc. Digital Signal Processing, motion control, scientific and engineering simulations, etc. data movement (A º B) value testing (If A=B then ...) addition (A+B=C ) multiplication (A×B=C ) Typical Applications Main Operations FIGURE 28-1 Data manipulation versus mathematical calculation. Digital computers are useful for two general tasks: data manipulation and mathematical calculation. Data manipulation is based on moving data and testing inequalities, while mathematical calculation uses multiplication and addition. more important, there are marketing issues involved: development and manufacturing cost, competitive position, product lifetime, and so on. As a broad generalization, these factors have made traditional microprocessors, such as the Pentium®, primarily directed at data manipulation. Similarly, DSPs are designed to perform the mathematical calculations needed in Digital Signal Processing. Figure 28-1 lists the most important differences between these two categories. Data manipulation involves storing and sorting information. For instance, consider a word processing program. The basic task is to store the information (typed in by the operator), organize the information (cut and paste, spell checking, page layout, etc.), and then retrieve the information (such as saving the document on a floppy disk or printing it with a laser printer). These tasks are accomplished by moving data from one location to another, and testing for inequalities (A=B, A