Felicity_S1E01 文本

IntroductionThe PI2001-EVAL1 allows the user to test the basic principle and operational characteristics of an Active ORing function in a redundant power architecture, while also experiencing the benefits and value of the PI2001 solution versus conventional Active ORing solutions. The PI2001-EVAL1 evaluation board is configured to receive two independent power source inputs,per a typical redundant power architecture, through two Active ORing channels that are combined to form aredundant power output. Each channel contains a PI2001controller and an N-channel power MOSFET. The MOSFET foot print can take an SO-8 or Power SO-8 MOSFET package.Each channel is capable of up to 20 A, for high current Active ORing, above 20 A, the two channels provided on the evaluation board can be paralleled in a master /slave configuration and OR’d with a second evaluation board.The PI2001-EVAL1 evaluation board is designed withoptimized PCB layout and component placement to represent a realistic high density final design for an embedded Active ORing solution for ≤7 Vbus applications requiring up to 20 A.This evaluation board is intended as an easy and simple way to test the electrical and thermal performance of the PI2001Active ORing controller.Both dynamic and steady state testing of the PI2001 can be completed on the PI2001-EVAL1 evaluation board, in addition to using the key features of the product. Dynamic testing can be completed under a variety of system level fault conditions to check for response time to faults.This document provides basic instructions for initial start-up and configuration of the evaluation board. Further information on the functionality of the PI2001 can be found in the PI2001 product data sheet.PI2001-EVAL1Cool-ORing ™ SeriesPI2001-EVAL1 Active ORingEvaluation Board User Guide®PI2001-EVAL1 Evaluation Board featuring the Cool-ORing PI2001 Universal Active ORing controller.Cool-ORing™ SeriesThe PI2001-EVAL1 Evaluation Board is intended to acquaint the user with the benefits and features of the Cool-ORing TM PI2001Universal Active ORing controller. It is not designed to be installed in end-use equipment.Please read this document before setting up the PI2001-EVAL1Evaluation Board and refer to the PI2001 product data sheet for device specifications, functional description and characteristics.During operation, the power devices and surrounding structures can be operated safely at high temperatures.•Remove power and use caution when connecting and disconnecting test probes and interface lines to avoid inadvertent short circuits and contact with hot surfaces.•When testing electronic products always use approved safety glasses. Follow good laboratory practice and procedures.The Cool-ORing PI2001 with an external industry standard N-channel MOSFET provides a complete Active ORing solution designed for use in redundant power systemarchitectures. The PI2001 controller with N-channel MOSFET enables extremely low power loss with fast dynamic response to fault conditions, critical for high availability systems. A master /slave feature allows the paralleling of PI2001solutions for high current Active ORing requirements. The PI2001 can also drive multiple paralleled MOSFETs.The PI2001 controller with a low Rds(on) N-channel MOSFET provides very high efficiency and low power loss during steady state operation. The PI2001 controller provides an active low fault flag output to the system during excessive forward current, light load, reverse current, over-voltage,under-voltage, and over-temperature fault conditions. A temperature sensing function indicates a fault if themaximum junction temperature exceeds 160°C. The under-voltage and over-voltage thresholds are programmable via an external resistor divider.Figure 1 shows a photo of the PI2001-EVAL1 evaluation board, with two PI2001 controllers and two N-channelMOSFETs used to form the two Active ORing channels. The board is built with two identical Active ORing circuits with options and features that enable the user to fully explore thecapabilities of the PI2001 universal Active ORing controller.Figure 1 –PI2001-EVAL1 Evaluation Board (1.8" x 1.8")Terminals RatingVin1, Vin28V / 24 AVaux1, Vaux2, (R2 = R4 = 10 Ω)-0.3 V to 17.3 V / 40 mA SL1, SL2-0.3 V to 8.0 V / 10 mA FT1, FT2-0.3 V to 17.3 V / 10 mATerminal DescriptionVin1Power Source Input #1or bus input designed to accommodate up to 20 A continuous current.Vaux1Auxiliary Input Voltage #1to supply PI2001 VC power. Vaux1 should be equal to Vin1 plus 5 V or higher. See details in Auxiliary Power Supply (Vaux) section of the PI2001 data sheet.Rtn1Vaux1 Return Connection: Connected to Ground planeGnd Vin & Vout Return Connection:Three Gnd connections are available and are connected to a common point, the Ground plane. Input supplies Vin1 & Vin2 and the output load at Vout should all be connected to their respective local Gnd connection.SL1PI2001 (U1) Slave Input-Output Pin: For monitoring U1 slave pin. When U1 is configured as the Master, this pin functions as an output that drives slaved PI2001 devices. When U1 is configured in Slave mode, SL1 serves as an input.SL2PI2001 (U2) Slave Input-Output Pin: For monitoring U2 slave pin. When U2 is configured as the Master, this pin functions as an output that drives slaved PI2001 devices. When U2 is configured in Slave mode, SL2 serves as an input.Vin2Power Source Input #2or bus input designed to accommodate up to 20 A continuous current.Vaux2Auxiliary Input Voltage #2to supply PI2001 VC power. Vaux2 should be equal to Vin2 plus 5 V or higher. See details in Auxiliary Power Supply (Vaux) section of the PI2001 data sheet.Rtn2Vaux2 Return Connection: Connected to Ground plane FT1PI2001 (U1) Fault Pin: Monitors U1 fault conditions FT2PI2001 (U2) Fault Pin: Monitors U2 fault conditionsVoutOutput: Q1 and Q2 MOSFET Drain pins connection, connect to the load high side.Table 1 –PI2001-EVAL1 Evaluation Board terminals descriptionCool-ORing TM PI2001 Product DescriptionJumper DescriptionJ1, J3BK Jumpers: Connect jumper across M for master mode and across S for slave mode. Remove jumper to adjust reverse fault blanking time using Rbk. Rbk is R7 for U1 and R14 for U2 shown in the schematic, Figure 2.J2Slave Jumper: Remove the jumper unless one of the PI2001 is configured in slave mode.Table 2 –PI2001-EVAL1 Evaluation Board jumpers descriptionFigure 2 –PI2001-EVAL1 Evaluation Board schematic.Item QTY Reference Designator Value Description Footprint Manufacturer 12C1, C2 1 µF Capacitor, MLCC X5R, 06031 µF,16 V21C322 µF Capacitor, MLCC X7R,121022 µF, 25 V34C4, C5, C6, C7Not installed120642D1, D2LED, Super Red THIN 0603Lite-On, Inc.,58FT1, FT2, Rtn1, Rtn2, Turret Test point TURRET-1528Keystone SL1, SL2, Vaux1, Vaux2Electronics67Gnd1, Gnd2, Gnd3, Gnd4,Turret Test point TURRET-1502Keystone Vin1, Vin2, Vout1, Vout2Electronics 72J1, J3Header Pins 0.1" pitch 2 x 3mm81J2Header Pins 0.1" pitch 2 x 2mm92Q1, Q2FDS8812NZSO-8Fairchild 30 V, 20 A, N-MOSFET102R1, R88.45 KΩResistor,8.45 KΩ,1%0603112R2, R913.3 KΩResistor,13.3 KΩ,1%0603122R3, R1010 ΩResistor,10 Ω,5%0603132R4, R11 4.99 KΩResistor, 4.99 KΩ,5%0603144R5, R6, R12, R13 2.00 KΩResistor, 2.00 KΩ,1%0603152R7, R14Not Installed0603162U1, U2PI2001Picor Universal Active ORing Controller3mmx3mm; 10-TDFN PICOR Table 3 –PI2001-EVAL1 Evaluation Board bill of materialsInitial Test Set UpTo test the PI2001-EVAL1 evaluation board it is necessary to configure the jumpers (J1, J2 and J3) first based on the required board configuration.Failure to configure the jumpers prior to the testing may result in improper circuit behaviorBaseline Test Procedure (Refer to Figure 3)1.0 Recommended Equipment1.1Two DC power supplies - 0-10 V; 25 A.1.2DC power supply 12 V; 100 mA.1.3DC electronic load - 50 A minimum.1.4Digital Multimeter 1.5Oscilloscope.1.6Appropriately sized interconnect cables.1.7Safety glasses.1.8PI2001 Product Data sheet.Figure 3 –Layout configuration for a typical redundant power application, using PI2001 with both solutions configured in Master Mode.Reference Designator Value Functional Description C1, C2 1 µF VC Bypass Capacitor C322 µF Output (Load) CapacitorC4, C5, C6, C7Not installedSnubber to reduce voltage ringing when the device turns off D1, D2LED To indicate a fault exist when it is onJ1, J3Jumper To select between Master and Slave Modes J2Jumper Connection between SL1 and SL2Q1, Q2N-MOSFET ORing Main SwitchR1, R88.45 K ΩUV Voltage Divider Resistor ( R2UV in Figure 4) R2, R913.3 K ΩOV Voltage Divider Resistor ( R2OV in Figure 4) R3, R1010 ΩVC Bias resistor R4, R11 4.99 K ΩLED Current LimiterR5, R6 2.00 K ΩUV Voltage Divider Resistor ( R1UV in Figure 4) R7, R14Not Installed BK Delay Timer Programmable Resistor U1, U2PI2001Universal Active ORing ControllerTable 4 –Component functional descriptionBefore initial power-up follow these steps to configure the evaluation board for specific end application requirements: 2.0 Undervoltage (UV) and Overvoltage (OV) resistors set up:2.1UV and OV programmable resistors are configuredfor a 3.3 V Vin (BUS voltage) application in atwo-resistor voltage divider configuration as shown inFigure 4. UV is set to 2.6 V and OV is set for 3.8 V,R1OV and R1UV are 2.00KΩ1%. If PI2121-EVAL1 isrequired to be used in a different Vin voltage . .application please follow the following steps tochange the resistor values.2.1.1It is important to consider the maximum currentthat will flow in the resistor divider andmaximum error due to UV and OV input .current.R1UV=V(UV TH)I RUV2.1.2Set R1UV and R1OV value based on systemallowable minimum current and 1% error;I RUV ≥ 100 µAR2UV=R1UV (V(UV)–1)V(UV TH)Where:V(UV TH) : UV threshold voltageV(UV) : UV voltage set (0.5 V typ)I RUV: R1UV currentR2OV=R1OV (V(OV)–1)V(OV TH)Where:V(OV TH) : OV threshold voltageV(OV) : OV voltage set (0.5 V typ)I ROV: R1OV currentFigure 4 –UV & OV two-resistor divider configuration2.1.3Example for 2.0 V Vin (BUS voltage), to set UV and OV for ±10% Vin set UV at 1.8 V and OV at 2.2 V.R2UV= R1UV (V(UV)–1) = 2.00 KΩ*( 1.8 V–1)= 5.20 KΩ(or 5.23 KΩ% standard value)V(UV TH)0.5 VR2OV= R1OV (V(OV)–1) = 2.00 KΩ*( 2.2 V–1)= 6.80 KΩ(or 6.81 KΩ% standard value)V(OV TH)0.5 V3.0Blanking timer setup:3.1The blanking timer provides noise filtering fortypical switching power conversion that mightcause premature reverse current detection bymasking the reverse fault condition. The shortestblanking time is 50 ns when the BK pin isconnected to ground. Connecting an externalresistor (R BK, reference designators R7 for U1 andR14 for U2) between the BK pin and ground willincrease the blanking time as shown in Figure 5.Where:R BK≤ 200 KΩNote:When BK is connected to VC for slave mode .operation,then the blanking time will be 270 ns typically.4.0Auxiliary Power Supply (Vaux):4.1The PI2001 Controller has a separate input (VC) thatprovides power to the control circuitry and the gatedriver. An internal voltage regulator (VC) clamps theVC voltage to 15.5 V typically.4.2Connect independent power source to Vaux inputs ofPI2001-EVAL1 Evaluation Board to supply power tothe VC input. The Vaux voltage should be 5V higherthan Vin (redundant power source output voltage) tofully enhance the MOSFET. If the MOSFET is replacedwith different MOSFET, make sure that Vaux= Vin +0.5 V + required voltage to be enhance the MOSFET.4.310 Ωbias resistors (Rbias, reference designators R3and R10) are installed on the PI2001-EVAL1 betweeneach Vaux input and VC pin of one of the PI2001controller. 4.4If Vaux is higher than the Clamp voltage, 15.5 Vtypical, the Rbias value has to be changed using thefollowing equations:4.4.1Select the value of Rbias using the followingequation:Rbias =Vaux min–VC clampMAXIC max4.4.2Calculate Rbias maximum power dissipation:Pd Rbias= (Vaux max–VC clampMIN)2RbiasWhere:Vaux min: Vaux minimum voltageVaux max: Vaux maximum voltageVC ClampMAX:Maximum controllerclamp voltage, 16.0 VVC ClampMIN:Minimum controllerclamp voltage, 14.0 VIC max: Controller maximum bias current,use 4.2 mA4.4.3For example, if the minimum Vaux = 22 Vand the maximum Vaux = 28 VRbias = Vaux min–VC clampMAX =22 V–16 V = 1.429 KΩIC max 4.2 mA,use 1.43 KΩ1% resistorPd Rbias= (Vaux max–VC clampMIN)2= (28 V–14.0 V)2=137 mWRbias 1.43 KΩNote:Minimize the resistor value for low Vaux voltagelevels to avoid a voltage drop that may reduce the VCvoltage lower than required to drive the gate of theinternal MOSFET. Figure 5 –BK Resistor selection versus Blanking Time5.0Hook Up of the Evaluation Board5.1OV and UV resistors values are configured for a 3.3 Vinput voltage. If you are using the evaluation board ina different input voltage level you have to adjust theresistor values by replacing R1, R2, R8 and R9, orremove R2, R5, R9 and R12 to disable UV and OV.Please refer to the UV/OV section for details to set R1,R2, R8 and R9 proper values.5.2Verify that the jumpers J1 and J3 are installed formaster mode [across M] and no Jumper on J2.5.3Connect the positive terminal of PS1 power supplyto Vin1. Connect the ground terminal of PS1 to itslocal Gnd. Set the power supply to 3.3 V.Keep PS1 output disabled (OFF).5.4Connect the positive terminal of PS2 power supplyto Vin2. Connect the ground terminal of PS2 to itslocal Gnd. Set the power supply to 3.3 V. Keep PS2output disabled (OFF).5.5Connect the positive terminal of PS3 power supplyto Vaux1 and Vaux2. Connect the ground terminalof this power supply to Rtn1 and Rtn2. Set thepower supply to 12 V. Keep PS3 output disabled(OFF).5.6Connect the electronic load to the output betweenVout and Gnd. Set the load current to 10 A.5.7Enable (turn ON) PS1 power supply output.5.8Turn on the electronic load.5.9Verify that the electronic load input voltagereading is one diode voltage drop below 3.3 V.5.10Enable (turn ON) PS3 power supply output.5.11Verify that the electronic load voltage readingincreases to a few millivolts below 3.3 V. This verifiesthat the MOSFET is in conduction mode.5.12D1 should be off. This verifies that there is nofault condition.5.13Reduce PS1 output voltage to 2 V,5.14D1 should turn on, this verifies that the circuit is inan under-voltage fault condition.5.15Increase PS1 output to 3.3 V, D1 should turn off, thenincrease PS1 output to 4 V, D1 should turn onindicating an over-voltage fault condition5.16Verify that Vin2 is at 0V. This verifies that thePI2001 (U2) FET (Q2) is off.5.17D2 should be on. This is due to a reverse voltagefault condition caused by the bus voltage beinghigh with respect to the input voltage (Vin2).5.18Enable (turn ON) PS2 output.5.19Verify that both PS1 and PS2 are sharing loadcurrent evenly by looking at the supply current.5.20Disable (turn OFF) PS1, PS2 and PS3 outputs.5.21Enable (turn ON) PS2 output then Enable PS3 output.5.22Verify that the electronic load voltage reading isfew millivolts below 3.3 V. This verifies that the PI2001(U2) MOSFET (Q2) is in conduction mode.5.23D2 should be off. This verifies that there is nofault condition.5.24Reduce PS2 output voltage to 2 V,5.25D2 should turn on, this verifies that the circuit is inan under-voltage fault condition.5.26Increase PS2 output to 3.3 V, D2 should turn off, thenincrease PS2 output to 4 V, D2 should turn onindicating an over voltage fault condition.5.27Verify that Vin1 is at 0V. This verifies that thePI2001 (U1) FET (Q1) is off.5.28D1 should be on. This is due to a reverse voltagefault condition caused by the bus voltage beinghigh with respect to the input voltage (Vin1).6.0Slave Mode: Slave Mode can be demonstrated in two setups; either by using one PI2001-EVAL1 evaluation board as a single ORing function with both PI2001 effectively in parallel or two PI2001-EVAL1 evaluation boards to demonstrate a true redundant 40A system. The following test steps use a single PI2001-EVAL1 in a slave mode application. Note:In this experiment U1 is configured in master mode andU2 is configured in slave mode.6.1BK pin (J1) of the master device will be connected toground [across M] while the slaved device BK pin (J3)is connected to VCC [across S]. Place a jumper across J2to connect slave pins together.6.2Connect the positive terminal of PS1 power supplyto Vin1. Connect the ground terminal of this powersupply to Gnd. Set the power supply to 3.3 V. KeepPS1 output disabled (OFF).6.3Connect the positive terminal of PS2 power supplyto Vin2. Connect the ground terminal of this powersupply to Gnd. Set the power supply to 3.3 V. KeepPS2 output disabled (OFF).6.4Connect the positive terminal of PS3 power supplyto Vaux1 and Vaux2. Connect the ground terminalof this power supply to Rtn1 and Rtn2. Set thepower supply to 12 V. Keep PS3 output disabled (OFF).6.5Connect the electronic load between Vout andGnd. Set the load current to 10 A.6.6Enable (Turn ON) PS2, and PS3 outputs, and keepPS1 output disabled (OFF).6.7Turn on the electronic load.6.8Verify that electronic load voltage drops to a diodedrop below PS2. This verifies that the Q2 is off due tothe Master (U1) not being on.6.9Enable (turn on) PS1 output:6.10Verify that the electronic load input voltage reading isa few millivolts below 3.3 V and PS1 and PS2 aresharing the load current evenly. This verifies that both MOSFET's, Q1 and Q2, are in conduction mode.7.0Input short circuit test7.1To emulate a real application, the BUS supplies for thistest should have a solid output source such as DC-DC converter that supplies high current and can be connected very close to the evaluation board to reduce stray parasitic inductance. Or use theprospective supply sources of the end application where the PI2001 will be used. 7.2Stray parasitic inductance in the circuit can contributeto significant voltage transient conditions, particularly when the MOSFET is turned-off after a reverse current fault has been detected. When a short is applied at the output of the input power sources and the .evaluation board input (Vin), a large reverse current is sourced from the evaluation board output through the ORing MOSFET. The reverse current in the MOSFET may reach over 60 A in some conditions before the MOSFET is turned off. Such high current conditionswill store high energy even in a small parasitic element, and can be represented as ½ Li2. A 1 nH parasitic inductance with 60 A reverse current will generate 1.8 µJ. When the MOSFET is turned off, the stored energy will be released and will produce a high negative voltage at the MOSFET source and high positive voltage at the MOSFET drain. This event will create a high voltage difference across the drain and source of the MOSFET.7.3Apply a short at one of the inputs (Vin1 or Vin2) whenthe evaluation board is configured with bothcontrollers (U1 and U2) in master mode. The short can be applied electronically using a MOSFET connected between Vin and Gnd or simply by connecting Vin to Gnd. Then measure the response time between when the short is applied and the MOSFET is disconnected (or turned off). An example for PI2001 response time to an input short circuit is shown in Figure 6.Figure 6 –Plot of PI2001 response time to reverse current detectionFigure 7a–PI2001-EVAL1 layout top layer. Scale 2.0:1 Figure 7b–PI2001-EVAL1 layout mid layer 2. Scale 2.0:1Figure 7c–PI2001-EVAL1 layout mid layer 1. Scale 2.0:1 Figure 7d–PI2001-EVAL1 layout Bottom layer. Scale 2.0:1Mechancial Drawing分销商库存信息: VICORPI2001-EVAL1。

N EW D IRECTIONS FOR T EACHING AND L EARNING , no. 128, Winter 2011 © Wiley Periodicals, Inc.Published online in Wiley Online Library (wileyonlinelibrary .com) • DOI: 10.1002/tl.4653Problem-Based LearningDeborah E. Allen, Richard S. Donham, Stephen A. BernhardtProblem-based learning (PBL) has wide currency on many college and uni-versity campuses, including our own, the University of Delaware. Although we would like to be able to claim clear evidence for PBL in terms of student learning outcomes, based on our review of the literature, we cannot state that research strongly favors a PBL approach, at least not if the primary evidence is subject matter learning.There is some evidence of PBL effectiveness in medical school settings where it began, and there are numerous accounts of PBL implementation in various undergraduate contexts, replete with persuasively positive data from course evaluations (Duch, Groh, and Allen, 2001). However, evidence for learning outcomes is still needed. In this chapter, we review the origins of PBL, outline its characteristic methods, and suggest why we believe PBL has a persistent and growing infl uence among educators.Origins of PBL in Medical SchoolsPBL was formalized by medical educators in the 1950s and 1960s to address the exponential expansion of medical knowledge while better aligning traditional classroom problem-solving approaches with those used in clinical practice (Barrows and Tamblyn, 1980; Boud, 1985). Traditional approaches were based on the bucket theory (Wood, 1994): If medical stu-dents were fi lled with the requisite foundational knowledge, they would be able to strategically retrieve and direct just the right subsets of it toward problems of clinical practice. PBL was designed to address the underlying fl aws of the bucket theory , especially leaky , overfl owing, or inappropriately 21In problem-based learning, students working in collaborative groupslearn by resolving complex, realistic problems under the guidance of faculty. In this chapter , we examine the evidence for effectiveness ofthe method to achieve its goals of fostering deep understandingsof content and discuss the potential for developing process skills:research, negotiation and teamwork, writing, and verbalcommunication.22E VIDENCE-B ASED T EACHINGfi lled buckets. By presenting complex case histories typical of real patients as the pretext for learning, PBL demanded that students call on an inte-grated, multidisciplinary knowledge base (Wood, 1994).In the idealized learning cycle of medical school PBL (Engle, 1999), students working in teams learn by solving real or realistic problems. Stu-dents grapple with a multistage, complex medical case history, which offers an engaging and memorable context for learning. As they defi ne the prob-lem’s scope and boundaries, student teams identify and organize relevant ideas and prior knowledge. The teams form questions based on self- identifi ed gaps in their knowledge, and they use these questions to guide subsequent independent research outside the classroom, with research tasks parceled out among team members. When the students reconvene, they present and discuss their fi ndings, integrating their new knowledge and skills into the problem context. As they move through the stages of a complex problem, they continue to defi ne new areas of needed learning in pursuit of a solution. In the case of this original PBL model, a solution is an accurate diagnosis and recommendation of successful treatment of the patient.PBL continues to be a favored method in many medical schools. What became evident in effectiveness studies was that there was no simple answer to the question “Is PBL better than traditional methods?” Several meta-analyses of the data suggested that PBL has modest or no benefi cial effect on student learning of content (from the United States Medical Licensing Examination [USMLE] Step 1—basic science understanding; Albanese and Mitchell, 1993; Nandi and others, 2000; Vernon and Blake, 1993). In fact, it appears that students in a traditional medical program sometimes, but not consistently, slightly outperform their PBL counterparts.However, disaggregation of the data suggests an underlying richness that is not captured simply by looking at student achievement on content recall exams. If, for example, scores on the USMLE Step 2 (knowledge of clinical practice) or ability to apply knowledge in the clinic after graduation are considered, medical school students with PBL experience frequently outperform their traditional counterparts (Albanese and Mitchell, 1993; Dochy, Segers, Van den Bossche, and Gijbels, 2003; Koh, Khoo, Wong, and Koh, 2008; Vernon and Blake, 1993). Recent meta-analyses have begun to tease apart some of the relative merits of PBL and suggest that the most positive effects are seen with student understanding of the organizing prin-ciples that link concepts in the knowledge domain being studied (Gijbels, Dochy, Van den Bossche, and Segers, 2005). Dochy and others (2003) reported a robust positive effect from PBL on the skills of students, noting that, intriguingly, students in PBL remember more acquired knowledge compared with their traditional counterparts. The early meta-analyses of PBL outcomes in the medical school setting (Albanese and Mitchell, 1993; V ernon and Blake, 1993) also document positive student attitudes about N EW D IRECTIONS FOR T EACHING AND L EARNING • DOI: 10.1002/tlP ROBLEM-B ASED L EARNING 23 learning, with students frequently viewing PBL as both a challenging and a motivating approach.Strategies for PBL ImplementationBecause PBL explicitly addresses some of the shortcomings of science edu-cation, it migrated into undergraduate science and engineering classrooms (Woods, 1985). It then expanded into basic as well as applied fi elds as well as into the humanities and social sciences (Duch and others, 2001). With the introduction of PBL to undergraduate courses, teachers modifi ed the method to accommodate larger class sizes, greater student diversity, timing and scheduling issues, multiple classroom groups, and lack of suitable classroom space (Allen, Duch, and Groh, 1996).PBL requires a shift in the educational paradigm for faculty. In PBL, the role of the instructor shifts from presenter of information to facilitator of a problem-solving process. Although the PBL process calls on students to become self-directed learners, faculty facilitators guide them by monitoring discussion and intervening when appropriate, asking questions that probe accuracy, relevance, and depth of information and analyses; raising new (or neglected) issues for consideration; and fostering full and even participa-tion (Mayo, Donnelly, and Schwartz, 1995).Instead of lecturing, PBL instructors must fi nd or create good prob-lems based on clear learning goals. Through these problems, instructors lead students to learn key concepts, facts, and processes related to core course content. PBL problems must be carefully constructed—not only to present students with issues and dilemmas that matter to them but also to foster their development of conceptual frameworks (Hung, Jonassen, and Liu, 2007). PBL problems may intentionally pose cognitive challenges by not providing all the information needed, thereby motivating a self-directed search for explanations. Instructors often allow students considerable lati-tude to make false starts and wrong turns. Well-developed, peer-reviewed problems can be found at the PBL Clearinghouse (University of Delaware, 2010).Successful implementation of PBL is critically dependent on the instructor’s scaffolding of students’ active learning and knowledge con-struction (Amador, Miles, and Peters, 2006; Duch and others, 2001). For example, PBL instructors can plan for intervals of class discussion or mini-lectures to help students navigate conceptual impasses, to dig more deeply into certain topics, or to fi nd useful resources. Instructors can enter team discussions to listen and pose questions (Hmelo-Silver, Duncan, and Chinn, 2007). They can also use student facilitators to extend their instruc-tional reach.Importantly, PBL can support the development of a range of “soft” skills: research skills, negotiation and teamwork, reading, writing, and oral communication. Cooperative learning strategies that foster effectiveN EW D IRECTIONS FOR T EACHING AND L EARNING • DOI: 10.1002/tl24 E VIDENCE -B ASED T EACHINGN EW D IRECTIONS FOR T EACHING AND L EARNING • DOI: 10.1002/tlteamwork become critical, as does the need for everyone to work to keep team members engaged and on track (Johnson, Johnson, and Smith, 1998). PBL classrooms are particularly well suited to the development of writing abilities. PBL instructors tend to rely on authentic assessment, with most problems leading up to a demonstration or presentation of learning, often taking the form of a written product: a solution, a recommendation, a sum-mary of what was learned, or some other form of group or individual reporting. To encourage development of writing skills, thinking skills, and learning in general, instructors can call for students to produce specifi c genres of writing: progress reports, schedules, task lists, meeting minutes, abstracts, literature reviews, proofs, lab reports, data analyses, and technical briefi ngs (Klein, 1999). Alaimo, Bean, Langenhan, and Nichols (2009) showed how to integrate writing as a core activity in an inquiry-based chemistry course, demonstrating strong learning outcomes in the process.Instructors must also encourage good team communication strategies. Teams must avoid reaching premature closure or succumbing to group-think—where a group seizes on a path because a team member is forceful or persuasive. The teams that perform best are those that generate and sus-tain consideration of multiple alternatives, engaging in and sustaining “substantive confl ict” (Burnett, 1991).Effectiveness of PBL on Content Learning in Undergraduate SettingsConfusion and lack of specifi cation about what PBL is as it is actually prac-ticed in the classroom hampers analysis of the effect of PBL on the acquisi-tion of content learning. In particular, PBL adopters in undergraduate settings, grappling with the diffi culties of monitoring multiple classroom groups, hybridize the method in various ways to incorporate aspects of discussion and case study method teaching (Silverman and Welty , 1990). Instructors tend to insert highly choreographed segments of instructor-centered, whole-class discussions into the PBL cycle and to interpose PBL problems intermittently throughout the course schedule, blended with more traditional instruction (Duch and others, 2001). As Newman (2003) noted, this hybridization of PBL makes it “diffi cult to distinguish between different types of PBL and even to distinguish between PBL and other edu-cational interventions” (p. 7).Nevertheless, there are scattered reports of positive outcomes. In a study of over 6,500 students, Hake (1998) found that interactive engage-ment methods (broadly defined as heads-on, hands-on activities with immediate feedback) were strongly superior to lecture-centered instruction in improving performance on valid and reliable mechanics tests used to assess students’ understanding of physics. Williams (2001) reported gains in the Force Concept Inventory for students in a PBL course that are con-sistent with the averages in other introductory physics courses that useP ROBLEM-B ASED L EARNING 25 interactive engagement methods. Palaez (2002) observed that students in a PBL biology course with an intensive writing component outperformed students in a course using traditional lecture-based instruction on exams that assessed conceptual understandings.Although there is less research on undergraduate learning than in medical education, the data support the broad conclusion that PBL may show only modest benefi ts on recalled content knowledge, but it positively infl uences integration of new knowledge with existing knowledge. How-ever, faculty members frequently adopt PBL to help students develop life-long learning skills. These skills are exercised routinely in the natural course of the PBL learning cycle. Given these additional but divergent stu-dent learning goals, many faculty members are satisfi ed with student con-tent learning that is similar or not signifi cantly decreased when using PBL. At the very least, these fi ndings assuage any residual concerns they or oth-ers may have that spending time on these ambitious process objectives undermines the learning of essential course content.Effectiveness of PBL on Process SkillsBecause PBL engages students in a range of soft skills, perhaps other posi-tive learning outcomes can be claimed for the method. A case in point is the benefi t of using cooperative learning groups on such general aspects of academic success as retention as well as on fostering positive student atti-tudes about learning (Springer, Stanne, and Donovan, 1999). Another is the use of writing-to-learn strategies in PBL. Incorporation of short, in-class writing assignments improves student performance on traditional concept and content-based exams (Butler, Phillmann, and Smart, 2001; Davidson and Pearce, 1990; Drabick, Weisberg, Paul, and Bubier, 2007; Stewart, Myers, and Culley, 2010).There is some evidence that systemic and sustained use of PBL in the classroom fosters cognitive growth. Downing and others (2009) followed two parallel cohorts of students in degree programs, one taught with PBL, the other by traditional methods, and found greater gains in metacognitive skills in the PBL group. Tiwari, Lai, So, and Yurn (2006) similarly reported signifi cant differences in the development of undergraduate nursing stu-dents’ critical thinking dispositions in a PBL versus a lecture-based course, as determined by comparisons of pre- and posttest scores on the California Critical Thinking Disposition Inventory.Effectiveness of PBL on Student EngagementWidespread agreement is emerging that at the core of effective teaching are activities that engage students by challenging them academically and involving them intensely, within supportive environments that provide multiple opportunities for interactions with faculty, peers, and members ofN EW D IRECTIONS FOR T EACHING AND L EARNING • DOI: 10.1002/tl26E VIDENCE-B ASED T EACHINGthe surrounding community (Smith, Sheppard, Johnson, and Johnson, 2005). Because PBL uses an assortment of methods associated with student engagement—active, collaborative, student-centered, and self-directed learning focused on realistic problems and authentic assessments—we might expect that it would lead to increased student engagement. By requiring students to talk to each other and collaborate on projects impor-tant to their academic success, PBL addresses student alienation and failure to form social networks, major reasons for students dropping out of college (Tinto, 1994). Two systematic analyses of students’ perceptions of the immediate and longer-term value and transferability of the reasoning and processing skills they developed during PBL courses (using the National Study of Student Engagement survey [NSSE] or a similarly designed instru-ment) in fact provide support for characterization of PBL as a pedagogy of engagement (Ahlfeldt, Mehta, and Sellnow, 2005; Murray and Summerlee, 2007).An important aspect of engagement is students’ ability to practice self-regulated or lifelong learning behaviors (Smith et al., 2005): the ability to defi ne what to learn and to effectively use the time and resource manage-ment needed to learn it. Blumberg’s (2000) review of the literature described numerous instances of documented gains in these areas that can be attributed to students’ PBL experiences.Incorporating writing tasks into PBL problems also shows promise for enhancing student engagement. Butler and others (2001) found that short, in-class microthemes increased positive motivation to attend class and increased student engagement. Additionally, Light (2001) found that writ-ing increases the time students spend on a course, increases the extent to which they are intellectually challenged, and increases their level of inter-est. Confi rming Light’s fi ndings are the very compelling data emerging from the NSSE (Gonyea, Anderson, Anson, and Paine, 2010). NSSE personnel worked with writing faculty to develop a special set of add-on questions concerning writing to the spring 2009 administration of NSSE. The data strongly supported writing as the single most important determinant of engaged, deep learning. When the independent variable is assigning mean-ing-constructing writing tasks, the NSSE data show moderate to strong effects on increased higher-order thinking, integrative learning, and refl ec-tive learning.ConclusionsThere is broad support for the conclusion that PBL methods enhance the affective domain of student learning, improve student performance on complex tasks, and foster better retention of knowledge. We would argue that more research is needed, research that is sensitive to the range of out-comes that we have discussed. For example, we would like to see addi-tional research into the effects of PBL on student performance on state N EW D IRECTIONS FOR T EACHING AND L EARNING • DOI: 10.1002/tlP ROBLEM-B ASED L EARNING 27N EW D IRECTIONS FOR T EACHING AND L EARNING • DOI: 10.1002/tlboard examinations and on students’ gains in problem-solving, critical thinking, motivation, and self-regulated learning. Another important area of future research would identify the particular PBL implementation meth-ods that lead to improved outcomes.PBL continues to enjoy popularity among a wide range of instructors across numerous disciplines at many institutions. Because PBL changes the nature of teaching and learning, many instructors embrace the method without clear, confi rming evidence of its effectiveness. In essence, they like being freed to work within a different classroom model, one where students are active and in control of learning. They like their role as consultant or facilitator better than their previous role of lecturer. The PBL classroom is, after all, a place that is lively with controversy , debate, and peer-to-peer communication—providing both faculty and students with immediate and unmistakable evidence of their competencies and understandings of and about what matters.ReferencesAhlfeldt, S., Mehta, S., and Sellnow, T. “Measurement and Analysis of Student Engagement in University Classes where Varying Levels of PBL Methods of Instruction Are in Use.” Higher Education Research and Development, 2005, 24, 5–20.Alaimo, P . J., Bean, J. C., Langenhan, J. M., and Nichols, L. “Eliminating Lab Reports: A Rhetorical Approach for Teaching the Scientific Paper in Sophomore Organic Chemistry .” WAC Journal, 2009, 20, 17–32.Albanese, M. S., and Mitchell, S. “Problem-Based Learning: A Review of Literature on Its Outcomes and Implementation Issues.” Academic Medicine, 1993, 68, 52–81.Allen, D. E., Duch, B. J., and Groh, S. E. “The Power of Problem-Based Learning in T eaching Introductory Science Courses.” In L. Wilkerson and W . H. Gijselaers (eds.), Bringing Problem-Based Learning to Higher Education: Theory and Practice . New Directions for T eaching and Learning Series, no. 68. San Francisco: Jossey-Bass, 1996.Amador, J. A., Miles, L., and Peters, C. B. The Practice of Problem-Based Learning: A Guide to Implementing PBL in the College Classroom. Bolton, Mass.: Anker, 2006.Barrows, H., and Tamblyn, R. Problem-based Learning: An Approach to Medical Education. New York: Springer, 1980. Blumberg, P . “Evaluating the Evidence that Problem-Based Learners Are Self-Directed Learners: Review of the Literature.” In D. H. Evensen and C. E. Hmelo (eds.), Problem-Based Learning: A Research Perspective on Learning Interactions (pp. 199–222). Mahwah, N.J.: Lawrence Erlbaum, 2000.Boud, D. J. “Problem-Based Learning in Perspective. In D. Boud (ed.), Problem-Based Learning in Education for the Professions (pp. 13–18). Sydney , Australia: HERDSA, 1985.Burnett, R. E. “Substantive Confl ict in a Cooperative Context: A Way to Improve the Collaborative Planning of Workplace Documents.” Technical Communication, 1991, 38, 532–539.Butler, A., Phillmann, K.-B., and Smart, L. “Active Learning within a Lecture: Assessing the Impact of Short, In-Class Writing Exercises.” Teaching of Psychology, 2001, 28, 57–59.Davidson, D., and Pearce, D. “Perspectives on Writing Activities in the Mathematics Classroom.” Mathematics Education Research Journal, 1990, 2, 15–22.28 E VIDENCE -B ASED T EACHINGN EW D IRECTIONS FOR T EACHING AND L EARNING • DOI: 10.1002/tlDochy , F ., Segers, M., Van den Bossche, P ., and Gijbels, D. “Effects of Problem-Based Learning: A Meta-Analysis.” Learning and Instruction, 2003, 13, 533–568.Downing, K., and others. “Problem-Based Learning and the Development of Metacognition.” Higher Education, 2009, 57, 609–621,Drabick, D.A.G., Weisberg, R., Paul, L., and Bubier, J. L. “Keeping It Short and Sweet: Brief, Ungraded Writing Assignments Facilitate Learning.” Teaching of Psychology, 2007, 34, 172–176. Duch, B., Groh, S. E., and Allen, D. E. (eds.). The Power of Problem-Based Learning: A Practical “How-to” for T eaching Undergraduate Courses in Any Discipline. Sterling, Va.: Stylus, 2001.Engle, C. E. “Not Just a Method but a Way of Learning.” In D. Boud and G. Feletti (eds.), The Challenge of Problem-Based Learning (pp. 17–27). London: Kogan Page, 1999.Gijbels, D., Dochy , F ., Van den Bossche, P ., and Segers, M. “Effects of Problem-Based Learning: A Meta-Analysis from the Angle of Assessment.” Review of Educational Research, 2005, 75, 27–61.Gonyea, R., Anderson, P ., Anson, C., and Paine, C. “Powering Up Your WAC Program: Practical, Productive Ways to Use Assessment Data from NSSE’s Consortium for the Study of Writing in College.” Paper presented at the 10th International Writing across the Curriculum Conference, Bloomington, Indiana, May 2010.Hake, R. “Interactive Engagement versus Traditional Methods: A Six Thousand-Student Survey of Mechanics Test Data for Introductory Physics Courses.” American Journal of Physics, 1998, 66, 64–74.Hmelo-Silver, C. E., Duncan, R. G., and Chinn, C. A. “Scaffolding and Achievement in Problem-based and Inquiry Learning: A Response to Kirschner, Sweller, and Clark (2006).” Educational Psychologist, 2007, 42, 99–107.Hung, W ., Jonassen, D. H., and Liu, R. “Problem-based Learning.” In J. M. Spector, J. van Merrienboer, M. D. Merrill, and M. P . Driscoll (eds.), Handbook of Research for Educational Communications and T echnology (pp. 485–505). Mahwah, N.J.: Lawrence Erlbaum, 2007.Johnson, D. W ., Johnson, R. T., and Smith, K. A. “Cooperative Learning Returns to College: What Evidence Is There that It Works?” Change , July-Aug. 1998, 27–35.Klein, P . D. “Reopening Inquiry into Cognitive Processes in Writing-to-Learn.” Educational Psychology Review , 1999, 11, 203–270.Koh, G.C.-H., Khoo, H. E., Wong, M. L., and Koh, D. “The Effects of Problem-Based Learning During Medical School on Physician Competency: A Systematic Review.” Canadian Medical Association Journal, 2008, 178, 34–41.Light, R. J. Making the Most of College . Cambridge, Mass.: Harvard University Press, 2001. Mayo, W . P ., Donnelly , M. B., and Schwartz, R. W . “Characteristics of the Ideal Problem-Based Learning T utor in Clinical Medicine.” Evaluation and the Health Professions, 1995, 18, 124–136.Murray, J., and Summerlee, A. “The Impact of Problem-based Learning in an Interdisciplinary First-Year Program on Student Learning Behaviour.” Canadian Journal of Higher Education, 2007, 37, 87–107.Nandi, P . L., and others. “Undergraduate Medical Education: Comparison of Problem-Based Learning and Conventional Teaching.” Hong Kong Medical Journal, 2000, 6, 301–306.Newman, M. “A Pilot Systematic Review and Meta-Analysis on the Effectiveness of Problem Based Learning.” On Behalf of Campbell Collaboration Systemic Review Group on the Effectiveness of Problem-based Learning. Newcastle upon T yne, U.K.: University of Newcastle upon Tyne, 2003. /static/uploads/resources /pbl_report.pdfP ROBLEM-B ASED L EARNING 29N EW D IRECTIONS FOR T EACHING AND L EARNING • DOI: 10.1002/tl Pelaez, N. J. “Problem-Based Writing with Peer Review Improves Academic Performance in Physiology .” Advances in Physiology Education, 2002, 26(3), 174–184.Silverman, R., and Welty , W . H. “Teaching with Cases.” Journal on Excellence in College T eaching, 1990, 1, 88–97.Smith, K. A., Sheppard, S. D., Johnson, D. W ., and Johnson, R. T. “Pedagogies of Engagement: Classroom-Based Practices.” Journal of Engineering Education, 2005, 94, 1–15.Springer, L., Stanne, M. E., and Donovan, S. S. “Measuring the Success of Small-Group Learning on Undergraduates in Science, Mathematics, Engineering and Technology: A Meta-Analysis.” Review of Educational Research, 1999, 69, 21–51.Stewart, T. L., Myers, A. C., and Culley , M. R. “Enhanced Learning and Retention Through ‘Writing to Learn’ in the Psychology Classroom.” Teaching of Psychology, 2010, 37, 46–49.Tinto, V . Leaving College: Rethinking the Causes and Cures of Student Attrition (2nd ed.). Chicago: University of Chicago Press, 1994.Tiwari, A., Lai, P ., So, M., and Yurn, K. “A Comparison of the Effects of Problem-based Learning and Lecturing on the Development of Students’ Critical Thinking.” Medical Education, 2006, 40, 547–554.University of Delaware. PBL Clearinghouse, 2010. https:///Pbl/Vernon, D.T.A., and Blake, R. L. “Does Problem-Based Learning Work? A Meta-Analysis of Evaluative Research.” Academic Medicine, 1993, 68, 550–563.Williams, B. A. “Introductory Physics: A Problem-Based Model.” In B. J. Duch, S. E. Groh, and D. E. Allen (eds.), The Power of Problem-Based Learning (pp. 251–269). Sterling, Va.: Stylus, 2001.Wood, E. J. “The Problems of Problem-Based Learning.” Biochemical Education, 1994, 22, 78–82.Woods, D. “Problem-Based Learning and Problem-Solving. In D. Boud (ed.), Problem-Based Learning for the Professions (pp. 19–42). Sydney , Australia: Higher Education Research and and Development Society of Australasia, 1985.D EBORAH E. A LLEN is an associate professor of biological sciences at the University of Delaware.R ICHARD S. D ONHAM is senior science education associate at the Mathematics and Science Education Resource Center at the University of Delaware.S TEPHEN A. B ERNHARDT is the Andrew B. Kirkpatrick Jr . chair in writing and professor of English at the University of Delaware.。

第一章引言ANSYS/LS-DYNA将显式有限元程序LS-DYNA和ANSYS程序强大的前后处理结合起来。

用LS-DYNA的显式算法能快速求解瞬时大变形动力学、大变形和多重非线性准静态问题以及复杂的接触碰撞问题。

使用本程序，可以用ANSYS建立模型，用LS-DYNA做显式求解，然后用标准的ANSYS后处理来观看结果。

也可以在ANSYS 和ANSYS-LS-DYNA之间传递几何信息和结果信息以执行连续的隐式－显式/显式－隐式分析，如坠落实验、回弹、及其它需要此类分析的应用。

1.1 显式动态分析求解步骤概述显式动态分析求解过程与ANSYS程序中其他分析过程类似，主要由三个步骤组成：1：建立模型（用PREP7前处理器）2：加载并求解（用SOLUTION处理器）3：查看结果（用POST1和POST26后处理器）本手册主要讲述了ANSYS/LS-DYNA显式动态分析过程的独特过程和概念。

没有详细论述上面的三个步骤。

如果熟悉ANSYS程序，已经知道怎样执行这些步骤，那么本手册将提供执行显式动态分析所需的其他信息。

如果从未用过ANSYS,就需通过以下两本手册了解基本的分析求解过程：·ANSYS Basic Analysis Guide·ANSYS Modeling and Meshing Guide使用ANSYS/LS-DYNA时，我们建议用户使用程序提供的缺省设置。

多数情况下，这些设置适合于所要求解的问题。

1.2 显式动态分析采用的命令在显式动态分析中，可以使用与其它ANSYS分析相同的命令来建立模型、执行求解。

同样，也可以采用ANSYS图形用户界面（GUI）中类似的选项来建模和求解。

然而，在显式动态分析中有一些独特的命令，如下：EDADAPT ：激活自适应网格EDASMP ：创建部件集合EDBOUND ：定义一个滑移或循环对称界面EDBVIS ：指定体积粘性系数EDBX ：创建接触定义中使用的箱形体EDCADAPT ：指定自适应网格控制EDCGEN ：指定接触参数EDCLIST ：列出接触实体定义EDCMORE ：为给定的接触指定附加接触参数EDCNSTR ：定义各种约束EDCONTACT ：指定接触面控制EDCPU ：指定CPU时间限制EDCRB ：合并两个刚体EDCSC ：定义是否使用子循环EDCTS ：定义质量缩放因子EDCURVE ：定义数据曲线EDDAMP ：定义系统阻尼EDDC ：删除或杀死/重激活接触实体定义EDDRELAX ：进行有预载荷几何模型的初始化或显式分析的动力松弛 EDDUMP ：指定重启动文件的输出频率（d3dump）EDENERGY ：定义能耗控制EDFPLOT ：指定载荷标记绘图EDHGLS ：定义沙漏系数EDHIST ：定义时间历程输出EDHTIME ：定义时间历程输出间隔EDINT ：定义输出积分点的数目EDIS ：定义完全重启动分析的应力初始化EDIPART ：定义刚体惯性EDLCS ：定义局部坐标系EDLOAD ：定义载荷EDMP ：定义材料特性EDNB ：定义无反射边界EDNDTSD ：清除噪声数据提供数据的图形化表示EDNROT ：应用旋转坐标节点约束EDOPT ：定义输出类型，ANSYS或LS-DYNAEDOUT ：定义LS-DYNA ASCII输出文件EDPART ：创建，更新，列出部件EDPC ：选择、显示接触实体EDPL ：绘制时间载荷曲线EDPVEL ：在部件或部件集合上施加初始速度EDRC ：指定刚体/变形体转换开关控制EDRD ：刚体和变形体之间的相互转换EDREAD ：把LS-DYNA的ASCII输出文件读入到POST26的变量中 EDRI ：为变形体转换成刚体时产生的刚体定义惯性特性 EDRST ：定义输出RST文件的时间间隔EDSHELL ：定义壳单元的计算控制EDSOLV ：把“显式动态分析”作为下一个状态主题EDSP ：定义接触实体的小穿透检查EDSTART ：定义分析状态（新分析或是重启动分析）EDTERM ：定义中断标准EDTP ：按照时间步长大小绘制单元EDVEL ：给节点或节点组元施加初始速度EDWELD ：定义无质量焊点或一般焊点EDWRITE ：将显式动态输入写成LS-DYNA输入文件PARTSEL ：选择部件集合RIMPORT ：把一个显式分析得到的初始应力输入到ANSYSREXPORT ：把一个隐式分析得到的位移输出到ANSYS/LS-DYNAUPGEOM ：相加以前分析得到的位移，更新几何模型为变形构型关于ANSYS命令按字母顺序排列的详细资料(包括每条命令的特定路径)，请参阅《ANSYS Commands Reference》。

MQL4 Reference MQL4命令手册（本手册采用Office2007编写）2010年2月目录MQL4 Reference (1)MQL4命令手册 (1)Basics基础 (12)Syntax语法 (12)Comments注释 (12)Identifiers标识符 (12)Reserved words保留字 (13)Data types数据类型 (13)Type casting类型转换 (14)Integer constants整数常量 (14)Literal constants字面常量 (14)Boolean constants布尔常量 (15)Floating-point number constants (double)浮点数常量（双精度） (15)String constants字符串常量 (15)Color constants颜色常数 (16)Datetime constants日期时间常数 (16)Operations & Expressions操作表达式 (17)Expressions表达式 (17)Arithmetical operations算术运算 (17)Assignment operation赋值操作 (17)Operations of relation操作关系 (18)Boolean operations布尔运算 (18)Bitwise operations位运算 (19)Other operations其他运算 (19)Precedence rules优先规则 (20)Operators操作符 (21)Compound operator复合操作符 (21)Expression operator表达式操作符 (21)Break operator终止操作符 (21)Continue operator继续操作符 (22)Return operator返回操作符 (22)Conditional operator if-else条件操作符 (23)Switch operator跳转操作符 (23)Cycle operator while循环操作符while (24)Cycle operator for循环操作符for (24)Functions函数 (25)Function call函数调用 (26)Special functions特殊函数 (27)Variables变量 (27)Local variables局部变量 (28)Formal parameters形式变量 (28)Static variables静态变量 (29)Global variables全局变量 (29)Defining extern variables外部定义变量 (30)Initialization of variables初始化变量 (30)External functions definition外部函数的定义 (30)Preprocessor预处理 (31)Constant declaration常量声明 (31)Controlling compilation编译控制 (32)Including of files包含文件 (32)Importing of functions导入功能 (33)Standard constants标准常数 (35)Series arrays系列数组 (35)Timeframes图表周期时间 (35)Trade operations交易操作 (36)Price constants价格常数 (36)MarketInfo市场信息识别符 (36)Drawing styles画线风格 (37)Arrow codes预定义箭头 (38)Wingdings宋体 (39)Web colors颜色常数 (39)Indicator lines指标线 (40)Ichimoku Kinko Hyo (41)Moving Average methods移动平均方法 (41)MessageBox信息箱 (41)Object types对象类型 (43)Object properties对象属性 (44)Object visibility (45)Uninitialize reason codes撤销初始化原因代码 (45)Special constants特别常数 (46)Error codes错误代码 (46)Predefined variables预定义变量 (50)Ask最新卖价 (50)Bars柱数 (50)Bid最新买价 (50)Close[]收盘价 (51)Digits汇率小数位 (51)High[]最高价 (51)Low[]最低价 (52)Open[]开盘价 (53)Point点值 (53)Time[]开盘时间 (53)Volume[]成交量 (54)Program Run程序运行 (56)Program Run程序运行 (56)Imported functions call输入函数调用 (57)Runtime errors运行错误 (57)Account information账户信息 (68)AccountBalance( )账户余额 (68)AccountCredit( )账户信用点数 (68)AccountCompany( )账户公司名 (68)AccountCurrency( )基本货币 (68)AccountEquity( )账户资产净值 (68)AccountFreeMargin( )账户免费保证金 (69)AccountFreeMarginCheck()账户当前价格自由保证金 (69)AccountFreeMarginMode( )账户免费保证金模式 (69)AccountLeverage( )账户杠杆 (69)AccountMargin( )账户保证金 (69)AccountName( )账户名称 (70)AccountNumber( )账户数字 (70)AccountProfit( )账户利润 (70)AccountServer( )账户连接服务器 (70)AccountStopoutLevel( )账户停止水平值 (70)AccountStopoutMode( )账户停止返回模式 (71)Array functions数组函数 (72)ArrayBsearch()数组搜索 (72)ArrayCopy()数组复制 (72)ArrayCopyRates()数组复制走势 (73)ArrayCopySeries()数组复制系列走势 (74)ArrayDimension()返回数组维数 (75)ArrayGetAsSeries()返回数组序列 (75)ArrayInitialize()数组初始化 (75)ArrayIsSeries()判断数组连续 (75)ArrayMaximum()数组最大值定位 (76)ArrayMinimum()数组最小值定位 (76)ArrayRange()返回数组指定维数数量 (76)ArrayResize()改变数组维数 (77)ArraySetAsSeries()设定系列数组 (77)ArraySize()返回数组项目数 (78)ArraySort()数组排序 (78)Checkup检查 (79)GetLastError( )返回最后错误 (79)IsConnected( )返回联机状态 (79)IsDemo( )返回模拟账户 (79)IsDllsAllowed( )返回dll允许调用 (80)IsExpertEnabled( )返回智能交易开启状态 (80)IsLibrariesAllowed( )返回数据库函数调用 (80)IsOptimization( )返回策略测试中优化模式 (81)IsStopped( )返回终止业务 (81)IsTesting( )返回测试模式状态 (81)IsTradeAllowed( )返回允许智能交易 (81)IsTradeContextBusy( )返回其他智能交易忙 (82)IsVisualMode( )返回智能交易“图片模式” (82)UninitializeReason( )返回智能交易初始化原因 (82)Client terminal客户端信息 (83)TerminalCompany( )返回客户端所属公司 (83)TerminalName( )返回客户端名称 (83)TerminalPath( )返回客户端文件路径 (83)Common functions常规命令函数 (84)Alert弹出警告窗口 (84)Comment显示信息在走势图左上角 (84)GetTickCount获取时间标记 (84)MarketInfo在市场观察窗口返回不同数据保证金列表 (85)MessageBox创建信息窗口 (85)PlaySound播放声音 (86)Print窗口中显示文本 (86)SendFTP设置FTP (86)SendMail设置Email (87)Sleep指定的时间间隔内暂停交易业务 (87)Conversion functions格式转换函数 (88)CharToStr字符转换成字符串 (88)DoubleToStr双精度浮点转换成字符串 (88)NormalizeDouble给出环绕浮点值的精确度 (88)StrToDouble字符串型转换成双精度浮点型 (89)StrToInteger字符串型转换成整型 (89)StrToTime字符串型转换成时间型 (89)TimeToStr时间类型转换为"yyyy.mm.dd hh:mi"格式 (89)Custom indicators自定义指标 (91)IndicatorBuffers (91)IndicatorCounted (92)IndicatorDigits (92)IndicatorShortName (93)SetIndexArrow (94)SetIndexBuffer (94)SetIndexDrawBegin (95)SetIndexEmptyValue (95)SetIndexLabel (96)SetIndexShift (97)SetIndexStyle (98)SetLevelStyle (98)SetLevelValue (99)Date & Time functions日期时间函数 (100)Day (100)DayOfWeek (100)Hour (100)Minute (101)Month (101)Seconds (101)TimeCurrent (101)TimeDay (102)TimeDayOfWeek (102)TimeDayOfYear (102)TimeHour (102)TimeLocal (102)TimeMinute (103)TimeMonth (103)TimeSeconds (103)TimeYear (103)Year (104)File functions文件函数 (105)FileClose关闭文件 (105)FileDelete删除文件 (105)FileFlush将缓存中的数据刷新到磁盘上去 (106)FileIsEnding文件结尾 (106)FileIsLineEnding (107)FileOpen打开文件 (107)FileOpenHistory历史目录中打开文件 (108)FileReadArray将二进制文件读取到数组中 (108)FileReadDouble从文件中读取浮点型数据 (109)FileReadInteger从当前二进制文件读取整形型数据 (109)FileReadNumber (109)FileReadString从当前文件位置读取字串符 (110)FileSeek文件指针移动 (110)FileSize文件大小 (111)FileTell文件指针的当前位置 (111)FileWrite写入文件 (112)FileWriteArray一个二进制文件写入数组 (112)FileWriteDouble一个二进制文件以浮动小数点写入双重值 (113)FileWriteInteger一个二进制文件写入整数值 (113)FileWriteString当前文件位置函数写入一个二进制文件字串符 (114)Global variables全局变量 (115)GlobalVariableCheck (115)GlobalVariableDel (115)GlobalVariableGet (115)GlobalVariableName (116)GlobalVariableSet (116)GlobalVariableSetOnCondition (116)GlobalVariablesTotal (117)Math & Trig数学和三角函数 (119)MathAbs (119)MathArccos (119)MathArcsin (119)MathArctan (120)MathCeil (120)MathCos (120)MathExp (121)MathFloor (121)MathLog (122)MathMax (122)MathMin (122)MathMod (122)MathPow (123)MathRand (123)MathRound (123)MathSin (124)MathSqrt (124)MathSrand (124)MathTan (125)Object functions目标函数 (126)ObjectCreate建立目标 (126)ObjectDelete删除目标 (127)ObjectDescription目标描述 (127)ObjectFind查找目标 (127)ObjectGet目标属性 (128)ObjectGetFiboDescription斐波纳契描述 (128)ObjectGetShiftByValue (128)ObjectGetValueByShift (129)ObjectMove移动目标 (129)ObjectName目标名 (129)ObjectsDeleteAll删除所有目标 (130)ObjectSet改变目标属性 (130)ObjectSetFiboDescription改变目标斐波纳契指标 (131)ObjectSetText改变目标说明 (131)ObjectsTotal返回目标总量 (131)ObjectType返回目标类型 (132)String functions字符串函数 (133)StringConcatenate字符串连接 (133)StringFind字符串搜索 (133)StringGetChar字符串指定位置代码 (133)StringLen字符串长度 (134)StringSubstr提取子字符串 (134)StringTrimLeft (135)StringTrimRight (135)Technical indicators技术指标 (136)iAC比尔.威廉斯的加速器或减速箱振荡器 (136)iAD离散指标 (136)iAlligator比尔・威廉斯的鳄鱼指标 (136)iADX移动定向索引 (137)iATR平均真实范围 (137)iAO比尔.威廉斯的振荡器 (138)iBearsPower熊功率指标 (138)iBands保力加通道技术指标 (138)iBandsOnArray保力加通道指标 (139)iBullsPower牛市指标 (139)iCCI商品通道索引指标 (139)iCCIOnArray商品通道索引指标 (140)iCustom指定的客户指标 (140)iDeMarker (140)iEnvelopes包络指标 (141)iEnvelopesOnArray包络指标 (141)iForce强力索引指标 (142)iFractals分形索引指标 (142)iGator随机震荡指标 (142)iIchimoku (143)iBWMFI比尔.威廉斯市场斐波纳契指标 (143)iMomentum动量索引指标 (143)iMomentumOnArray (144)iMFI资金流量索引指标 (144)iMA移动平均指标 (144)iMAOnArray (145)iOsMA移动振动平均震荡器指标 (145)iMACD移动平均数汇总/分离指标 (146)iOBV能量潮指标 (146)iSAR抛物线状止损和反转指标 (146)iRSI相对强弱索引指标 (147)iRSIOnArray (147)iRVI相对活力索引指标 (147)iStdDev标准偏差指标 (148)iStdDevOnArray (148)iStochastic随机震荡指标 (148)iWPR威廉指标 (149)Timeseries access时间序列图表数据 (150)iBars柱的数量 (150)iClose (150)iHigh (151)iHighest (151)iLow (152)iLowest (152)iOpen (152)iTime (153)iVolume (153)Trading functions交易函数 (155)Execution errors (155)OrderClose (157)OrderCloseBy (158)OrderClosePrice (158)OrderCloseTime (158)OrderComment (159)OrderCommission (159)OrderDelete (159)OrderExpiration (160)OrderLots (160)OrderMagicNumber (160)OrderModify (160)OrderOpenPrice (161)OrderOpenTime (161)OrderPrint (162)OrderProfit (162)OrderSelect (162)OrderSend (163)OrdersHistoryTotal (164)OrderStopLoss (164)OrdersTotal (164)OrderSwap (165)OrderSymbol (165)OrderTakeProfit (165)OrderTicket (166)OrderType (166)Window functions窗口函数 (167)HideTestIndicators隐藏指标 (167)Period使用周期 (167)RefreshRates刷新预定义变量和系列数组的数据 (167)Symbol当前货币对 (168)WindowBarsPerChart可见柱总数 (168)WindowExpertName智能交易系统名称 (169)WindowFind返回名称 (169)WindowFirstVisibleBar第一个可见柱 (169)WindowHandle (169)WindowIsVisible图表在子窗口中可见 (170)WindowOnDropped (170)WindowPriceMax (170)WindowPriceMin (171)WindowPriceOnDropped (171)WindowRedraw (172)WindowScreenShot (172)WindowTimeOnDropped (173)WindowsTotal指标窗口数 (173)WindowXOnDropped (173)WindowYOnDropped (174)Obsolete functions过时的函数 (175)MetaQuotes Language 4 (MQL4) 是一种新的内置型程序用来编写交易策略。

EMG AmplitudeEstimation ToolboxFor Use with MATLAB®(Version 5 for the PC)User's Guide Edward (Ted) A. ClancyAlpha Version 0.02a Worcester Polytechnic InstituteMay, 1999Copyright © 1999–2000, Edward A. Clancy.CreditsMATLAB is a registered trademark of:The MathWorks, Inc.24 Prime Park WayNatick, MA 01760Tel: 508-647-7000Fax: 508-647-7001E-mail: info@WWW: For more information, to report bugs (in detail, hopefully!), or to provide comments/constructive critique, please contact:Edward (Ted) A. ClancyDepartment of Electrical and Computer EngineeringWPI100 Institute RoadWorcester, MA 01609Phone: (508) 831-5778Fax: (508) 831-5491Email: ted@Various people have contributed to the analytic development and the analysis techniques in this Toolbox, as well as to the actual software development. Many thanks to these people, who include: Stéphane Bouchard, Kristin Farry, Neville Hogan, Denis Rancourt and Yves St-Amant.Forward to Version 0.02Welcome to the EMG Amplitude Estimation Toolbox! And thank you for helping me develop these tools. My eventual goal with this toolbox is to have a simple, efficient tool for implementing existing EMG processing algorithms and for developing new EMG processing algorithms. A frequent limitation in developing tools which are signal processing intensive is the time and effort that goes into software development and documentation. Hopefully, this toolbox will begin to allow all of us to share this effort, thereby minimizing the time that each of us must spend attending to the many details.This "alpha" version of the toolbox is likely to be incomplete in certain respects. I expect that more explanation, description and examples are needed in several areas. Also, I implore all users to independently test all modules that they use. While I have done my best to test everything in this toolbox, (1) my testing this time around has been a bit rushed---I found a few bugs during final testing (inevitably a few more must remain)--and (2) my philosophy is that any general-purpose software requires independent evaluation and testing. Different users will interact with the toolbox functions indifferent ways, thereby challenging the software in distinct ways. The most complete testing, and therefore the most robust software, is software which has been used (successfully) by many different users---such as you.I fully expect, and hope, that all of the user's of this version of the software will do so in collaboration with myself. I want the opportunity to fill in any gaps that remain in the documentation and make the toolbox most beneficial to each user. If you will agree to keep me involved in your use of the toolbox, I will do my best to improve the toolbox for your EMG needs. In the end, I want the toolbox to be a useful tool. For your part, I hope you will exercise some patience with any limitations in this initial version. As mentioned above, I hope you consider yourself as a co-developer. I also hope you will provide mature, written feedback (constructive criticism) on the toolbox. When doing so, please keep in mind that I would like to solve your problem in the context of a general tool which I hope to be useful for everyone.I hope that by working together, we can all improve our EMG processing work and do so in an efficient manner. Thank you for your help.Comments on Upward CompatibilityBecause EMG processing is an evolving field and this is a first "alpha" version of the toolbox, upward compatibility is not being assured. The plans at this time are that the default processing methods will be updated in future versions to reflect the best state of the art available. Thus, certain amounts of upward compatibility may be better served if explicit selection of all options is made. If upward compatibility is important, an alternative is to install the toolbox using a modified toolbox name. In particular, the installation instructions describe installing the toolbox using the name "emg". Forupward compatibility, the toolbox can be installed using the name "emg0_02". Thereafter, future versions could be installed using appropriate changes in the toolbox name. The desired toolbox can then be used by specifying the appropriate toolbox path to MATLAB. As the toolbox is used and feedback is received, the intent is to provide for upward compatibility at some point starting in the future.Comments on RAM UsageThe general scheme of MATLAB is to process an entire signal at one time. In addition, processing of the signal frequently requires replication of the signal while performing various filter operations. (E.g., most filtering operations are not performed "in place", thus the input vector and the output vector must exist simultaneously in RAM.) For signals of modest size, this scheme can require large amounts of RAM. There do exist certain programming styles which help to reduce RAM usage. Some effort has been made to reduce RAM requirements when it was easy to do so, however many operations were not optimized (i.e., minimized) for RAM usage. Some additional attention may be given to minimizing RAM usage in future versions of the toolbox. However, given the trend for cheaper and cheaper RAM modules in PCs (and other computers), efforts to minimize RAM requirements may prove unnecessary. For the near term, however, users should be aware that some EMG processing algorithms may be RAM intensive. Comments on DependenciesSome of the EMG functions are dependent upon other MATLAB toolboxes and/or MATLAB MEX files. For the toolboxes, the primary dependence is the Signal Processing Toolbox. Since most users of this EMG toolbox will rely heavily on signal processing algorithms for their research, this dependence will likely be permanent. Other dependencies (e.g. the routine e_uband relies on one function from the Statistics Toolbox) may eventually be removed. The dependency on MEX files is for implementation of limited, numerically intensive portions of certain algorithms. Thusfar, an alternative non-MEX file method had been made available. However, the non-MEX file methods will be quite slow. Thus, so long as MATLAB is running on a PC, the MEX files are recommended for use. If the toolbox is installed on other machines, it might be best to attempt to make MEX files for these machines. The MEX file source code (in the C programming language) is supplied with the toolbox.Table of ContentsFront PageCreditsForwardTable of ContentsInstalling and Uninstalling the EMG Amplitude Estimation Toolbox Introduction to EMG Processing and the EMG Amplitude Estimation Toolbox A Review of Methods for Estimating the EMG AmplitudeEMG Processing with Basic Functions of the EMG Amplitude Estimation ToolboxEMG Processing with Global Functions of the EMG Amplitude Estimation ToolboxEMG Amplitude Estimation Toolbox ReferenceInstalling and Uninstalling the EMG Amplitude Estimation Toolbox NOTE: These instructions are for a PC. No instructions yet exist for other machines (portions of the Toolbox will not function on other machines). InstallingAt present, there is no automated method for installing the Toolbox. Thus, the Toolbox must be installed using the steps and instructions which follow. The Toolbox exists onthe distribution media (e.g., floppy disk, CD ROM, Web archive, etc.) within the directory 'emgxxxxx', where xxxxx denotes the Toolbox version. For example, Toolbox version 1.01 would be contained within the directory named 'emg1_01'. Denote this directory as '$emg$'.Denote the directory on the PC in which MATLAB is installed as '$MATLAB$'. The default value of '$MATLAB$' is 'c:\MATLAB'.To install the toolbox, do the following:1. Uninstall Any Previous Versions: If any previous Toolbox versions are installed,they should first be uninstalled.2. Install M-Files:1. Copy the directory '$emg$\m_files' on the distribution media to directory'$MATLAB$\toolbox' on the PC.2. Rename directory '$MATLAB$\toolbox\m_files' on the PC to'$MATLAB$\toolbox\emg'.3. Add M-Files to Default Path:1. If file '$MATLAB$\bin\startup.m' does not exist, create it as an empty file.(*** For MATLAB 5.3, replace '$MATLAB$\bin' with'$MATLAB$\toolbox\local' for all occurrences on this page.***)2. Edit file '$MATLAB$\bin\startup.m', adding the line'$MATLAB$\toolbox\emg'.4. Install Help Files:1. Copy the directory '$emg$\html' on the distribution media to directory'$MATLAB$\help\toolbox' on the PC.2. Rename directory '$MATLAB$\help\toolbox\html' on the PC to'$MATLAB$\help\toolbox\emg'.UninstallingAgain, there is no automated method at present for uninstalling the Toolbox. The manual steps for uninstalling the Toolbox are:1. Delete Help Files: Delete the directory '$MATLAB$\help\toolbox\emg'.2. Remove M-Files from Default Path: Delete the line '$MATLAB$\toolbox\emg'from the file '$MATLAB$\bin\startup.m'.3. Delete M-Files: Delete the directory '$MATLAB$\toolbox\emg'.Introduction to EMG Processing and the EMG Amplitude Estimation Toolbox The EMG Amplitude Estimation Toolbox is intended to be a convenient tool for implementing a class of EMG amplitude estimators in MATLAB. This class of EMG processors has six general, sequential stages shown in the figure below. The six stagesare (1) noise rejection/filtering, (2) whitening, (3) multiple-channel combination(including gain scaling), (4) demodulation, (5) smoothing and (6) relinearization. Noise rejection/filtering generally consists of high pass filtering to suppress motion artifact inthe raw EMG signal. High pass filtering also removes the offset in the EMG signalwhich arises from offsets in the recording electronics and A/D converter. Whitening increases the statistical bandwidth [Bendat and Piersol, 1971] of the raw EMG. By temporally uncorrelating the EMG signal, the detection algorithm can operate on each whitened sample separately. Multiple-channel combination is used to combine the information from several electrode recordings made over the same muscle.Demodulation rectifies the whitened EMG and then raises the result to a power. Once demodulated, the information content of the signal changes from the signal standard deviation (prior to demodulation) to the signal mean (raised to a power - after demodulation). Smoothing filters the signal, increasing the signal-to-noise-ratio (albeit atthe expense of adding bias error to the estimate). Finally, relinearization inverts theeffect of the power law applied during the demodulation stage, returning the signal tounits of EMG amplitude.Users who are developing EMG processing algorithms or who wish to have detailed control over the EMG processing steps can do so by using the basic functions supplied in the EMG Amplitude Estimation Toolbox. These functions are described in the section titled "EMG Processing with Basic Functions of the EMG Amplitude Estimation Toolbox". Most user's, however, can control all of the EMG processing functions via two commands described in the section titled "EMG Processing with Global Functions of the EMG Amplitude Estimation Toolbox". The first command, e_cal(), is used to select all processing options and to perform calibration for an EMG processor. The second command, e_amp(), then processes all EMG amplitude estimates for that processor. Note that users must be familiar with the basic functions of the toolbox prior to using the two global functions.Three sections follow this introduction. The first section is a general review of EMG amplitude estimation processing. This section is not specific to the Toolbox. Rather, it serves as a background to the problem of amplitude estimation. The following sections then describe the functions of the toolbox; first the basic functions, then the global functions.Other EMG FunctionsThe toolbox includes one other function of use to EMG processing. Often it is useful to be able to generate simulated EMGs. One method for doing so is to generate a randomprocess, temporally and spatially filter the process, then modulate the output by thesimulated EMG amplitude. Toolbox function e_fsim is intended to provide a simple mechanism for performing this genre of simulation.The toolbox also includes some sample data for EMG processing. These data are described in the reference section titled e_data.ReferencesBendat, J. S. and Piersol, A. G. Random Data: Analysis and Measurement Procedures. New York: Wiley, 1971.A Review of Methods for Estimating theEMG AmplitudeThe amplitude of the surface electromyogram (EMG) is frequently used as the controlinput to myoelectric prostheses, as a measure of muscular effort, and has also been investigated as an indicator of muscle force. This paper will review the methods whichare used to estimate the EMG amplitude from recordings of the EMG. (Note that this review does not include the related area of EMG-to-force processing.) Historically,Inman et al. [1952] are credited with the first continuous EMG amplitude estimator.They implemented a full-wave rectifier followed by a simple resistor-capacitor (RC) low pass filter. Early investigators studied the type of non-linear detector which should be applied to the waveform. This work led to the routine use of analog rectify and smooth (low pass filter) processing and root-mean-square (RMS) processing of the EMG waveform to form an amplitude estimate. Ensuing investigation has shown the promiseof whitening individual EMG waveform channels, combining multiple waveformchannels into a single EMG amplitude estimate and adaptively tuning the smoothingwindow length. None of these techniques have been routinely incorporated into EMG amplitude estimators.Emerging from this work is a standard cascade of six sequential processing stages whichcan be used to form most any EMG processor. The six stages are (1) noiserejection/filtering, (2) whitening, (3) multiple-channel combination (including gain scaling), (4) demodulation, (5) smoothing and (6) relinearization. Noiserejection/filtering generally consists of high pass filtering to suppress motion artifact inthe raw EMG signal. High pass filtering also removes the offset in the EMG signalwhich arises from offsets in the recording electronics and A/D converter. Whitening increases the statistical bandwidth [Bendat and Piersol, 1971] of the raw EMG. By temporally uncorrelating the EMG signal, the whitening filter orthogonalizes the data samples, allowing the detection algorithm to operate on each whitened sample independently. Multiple-channel combination is used to combine the information from several electrode recordings made over the same muscle. Demodulation rectifies the whitened EMG and then raises the result to a power. Once demodulated, the information content of the signal changes from the signal standard deviation (prior to demodulation)to the signal mean (raised to a power - after demodulation). Smoothing filters the signal, increasing the signal-to-noise-ratio (albeit at the expense of adding bias error to the estimate). Finally, relinearization inverts the effect of the power law applied during the demodulation stage, returning the signal to units of EMG amplitude.Noise Rejection/FilteringHigh pass filtering serves to remove artifacts from the EMG, either during the recordingof the signal or during post-processing. High pass filtering rejects at least three established sources of artifact: offsets due to the recording apparatus, motion artifacts and electrocardiographic (ECG) artifacts. The recording apparatus - typically electrode-amplifiers, signal conditioning (hardware high and low pass filters, and individual channel gains), and analog-to-digital conversion - adds offsets to the normally zero-mean EMG. Thus, the signal mean is generally removed; a high pass filtering operation, albeit at a very low cut-off frequency (0 Hz).High pass filtering is also used to remove motion artifacts from the EMG. The frequency content of changes in joint angle and force fall below 5-10 Hz, while nearly all of the frequency content of the EMG is well above this range. Hence, high pass filters can be used to eliminate the frequencies associated with motion, with limited loss to EMG signal content. In general, initial high pass filtering is accomplished in hardware, either in the electrode-amplifiers, the signal conditioners, or both. A frequent strategy is for these filters to be of low order (2-4), but sufficient to prevent motion artifacts from causing the signal to saturate the recording apparatus. The lower order filters will roll-off slowly. Typical filter cut-off frequencies are between 10 and 30 Hz. With certain highmotion/vibration applications, higher order (6-10) analog filters are necessary. Thereafter, higher order high pass filtering can be performed during post-processing. For motion artifact rejection, high order high pass filters (e.g., tenth order) at 15-20 Hz may be sufficient.Finally, recordings over abdominal, chest and neck muscles, in particular, are frequently corrupted by ECG artifact. Redfern et al. [1993] found empirically that high pass filtering such signals, with a cut-off frequency at 30 Hz, greatly reduced the ECG artifact. This technique is likely acceptable if the filtered signal is used for amplitude estimation. However, if the shape of the power spectral density of the signal is analyzed during fatiguing contractions, then a cut-off frequency at 30 Hz may interfere with effected frequencies. In such cases, the cut-off frequency may need to be no higher than 20 Hz, if possible.WhiteningSeveral investigators have found that applying a whitening filter prior to demodulation and smoothing improves the amplitude estimate. A whitening filter outputs a constant-valued, or "whitened," power spectrum in response to an EMG input. If EMG is modeled as a discretely sampled Gaussian process, whitening orthogonalizes the data samples, allowing the detection algorithm to operate on each output sample independently. In addition, Zhang et al. [1990] discussed the advantages of whitening based on a model of EMG as the superposition of simulated motor unit action potentials. Kaiser and Peterson [1974] found that the shape of the whitening filter should be a function of the contraction level. They suggested that measurement noise, which is a larger portion of the entire signal from weaker contractions than from stronger contractions, may be a major determinant of the whitening filter's shape. They designed an adaptive analog filter to achieve the whitening that they desired. Hogan and Mann [1980a], [1980b] found thatreducing the outer edge spacing of a pair of rectangular electrodes from 20 mm to 10 mm whitened the EMG. During a constant 25% maximum voluntary contraction (MVC) trial, this electrode spacing gave a 35% signal-to-noise-ratio (SNR) improvement. Harba and Lynn [1981] used autoregressive (AR) modeling of the EMG power spectrum to form a whitening filter. Using a sixth-order AR model, they found only small contraction-level dependent changes in the whitening filter's shape. Whitening approximately doubled the probability of correctly differentiating between one of four discrete contraction levels.D'Alessio [1984] and Filligoi and Mandarini [1984] discussed whitening in functional mathematical models of the EMG. ("Functional" models are based on the observed signal rather than the underlying physiological process.) D'Alessio et al. [1987] proposed updating an AR model of the EMG PSD on a sample-by-sample basis. They used a forgetting factor so that the resulting PSD estimate represented only the most recent EMG signal. An adaptive whitening filter is then formed from the dynamic AR coefficients. With this technique, any change in the PSD structure which can be represented by an AR model can be accommodated. They implemented this technique using a sequential algorithm with a forgetting factor.Clancy and Hogan [1994] used moving-average digital whitening filters for constant-force contractions. They found that fourth-order whitening filters, calibrated from a short segment (<=5 s) of data, improved the SNR by 63% for 10%, 25%, 50% and 75% MVC's. These whitening filters, however, performed poorly for contractions less than 10% MVC, where additive measurement noise dominated the output of the whitening filters. Like Kaiser and Peterson [1974], Clancy and Hogan [1997], [1991] implemented an adaptive whitening filter which maintained the SNR performance improvement for contractions above 10% MVC, but reverted to unwhitened processing for lower contraction levels. Their implementation was a simple first-order adaptive filter. In quasi-constant-force, constant-angle, nonfatiguing EMG-to-torque estimation about the elbow, this simple adaptive whitening filter provided an error reduction of about 10% from the unwhitened technique.This result and the large performance improvements in EMG amplitude processing demonstrated for contractions above 10% MVC suggested that more formal approaches to adaptive whitening were well worth pursuing. Clancy and Farry [unpublished] recently developed a more formal adaptive whitening technique by modeling additive background noise in the measured EMG signal. Their adaptive whitener consists of cascading a non-adaptive whitening filter, an adaptive Wiener filter, and an adaptive gain correction. These stages can be calibrated from two, five second duration, constant-angle, constant-force contractions, one at a reference level [e.g., 50% maximum voluntary contraction (MVC)] and one at 0% MVC. In experimental studies of this technique, subjects tracked a randomly-moving target (projected on a computer screen) with real-time EMG amplitude estimates. With a 0.25 Hz bandwidth target, either adaptive whitening or multiple-channel processing reduced the tracking error to roughly half that of the error achieved using force feedback. At a 1.00 Hz bandwidth, all of the EMG processors had errors equivalent to that found with force feedback, reflecting that errors in this task were dominated by subjects’ inability to track targets at thisbandwidth. Increases in the additive noise level, smoothing window length, and tracking bandwidth diminished the advantages of whitening.Compared to the adaptive whitening technique of Clancy and Farry [unpublished], the technique of D'Alessio et al. [1987] is much less restrictive. It could provide better whitening if, or to the extent that, the true PSD shape changes with the EMG amplitude (or with localized muscle fatigue). However, since each PSD estimate is based on only a short segment of the most recent data, each PSD estimate (and, therefore, the resulting whitening filter) has a high variance. Hence, to the extent that the EMG PSD is truly amplitude modulated, the whitening method of Clancy and Farry is more stable and repeatable. For example, with the method of D'Alessio et al., a different whitening filter can result from different instances of EMG data which share the same PSD.Multiple-Channel CombinationThe first reported use of multiple sites for EMG amplitude estimation appears to be thatof Hogan and Mann [1980a, 1980b]. They suggested that dispersing multiple electrodes about a single muscle would provide a broader, more complete, measure of the underlying electrophysiologic activity, since a single differential electrode obtains mostof its signal energy from a small portion of muscle adjacent to the electrode. They derived an optimal amplitude estimator assuming that separate EMG sites were spatially correlated but temporally uncorrelated. Using four electrodes, they achieved an SNR performance improvement of approximately 91% compared to the single site rectify and low-pass filter estimator of Inman et al. [1952]. The combination of multiple sites and whitening via electrode geometry yielded an SNR performance improvement of approximately 176% compared to the estimator of Inman et al. The SNR performance of their algorithm was relatively insensitive to force levels over the range of 5-25% MVC. Hogan and Mann implemented their algorithm off-line on a digital computer and on-line with analog circuitry. Murray and Rolph [1985] implemented this algorithm in real time on a digital microprocessor. Harba and Lynn [1981] used four electrode pairs to improve the quality of an EMG processor which tried to differentiate between four discrete contraction levels. They were able to improve the probability of correctly differentiating between contraction levels by 40-70% (compared to using one electrode). Thusneyapan and Zahalak [1989] reported a nine site EMG amplitude estimator. Clancy and Hogan [1995] combined the techniques of waveform whitening and multiple channel combination. For contractions ranging from 10-75% MVC, a four channel, temporally whitened processor improved the SNR 187% compared to the estimator of Inman et al. [1952]. Eight whitened combined channels provided an SNR improvement of 309% compared to the estimator of Inman et al. Calibration of the optimal processor was achieved with a single five second contraction trial at 50% MVC. Continuing work has consistently found multiple channel amplitude estimators to be superior to single channel amplitude estimators [Clancy, in press; Clancy and Farry, unpublished]. Finally, Clancy [1997] noted that when multiple channels of EMG are recorded, the risk of failed recording channels (e.g., shorted electrodes, pick-up of large amounts of unwanted noise,etc.) grows with the number of electrodes. Automated methods for locating and managing failed channels may need to be developed.Demodulation and RelinearizationTreating the EMG as a zero mean, amplitude modulated signal, Inman et al. [1952] suggested demodulation with a full-wave rectifier (the analog equivalent of the first-power - absolute value - demodulator). In an attempt to improve on this demodulator, Kreifeldt and Yao [1974] experimentally investigated the performance of six non-linear demodulators. A second-power demodulator was found to be best for contraction levels of 10%, 25% and 50% MVC. A fourth-power demodulator was found to be best at 5% MVC. These power law demodulators improved the SNR performance of the full wave rectifier by 5-20%, depending on the force level.Hogan and Mann [1980a,1980b] used a functional mathematical model of EMG - based on a model of EMG as a Gaussian random process - to analytically predict that a second-power demodulator would give the best maximum likelihood estimate of the EMG amplitude for constant-force, constant-posture, nonfatiguing contractions. Theoretically, the SNR with this model is: SNR = ([2/(pi-2)]·N)1/2, where N is the number of degrees of freedom in the amplitude estimate. Experimentally, they found no SNR performance difference between the RMS processor (i.e., a second-power demodulator) and a full wave rectifier (i.e., a first-power demodulator). Similarly, Clancy [1991] consistently found full wave rectification to be a small improvement (2-8%) over RMS detection. These experimental results are contrary to the predictions from Gaussian theory. Although a Gaussian density for surface EMG is frequently assumed, evidence in the literature has demonstrated mixed results. Roesler [1974] measured the positive peak amplitudes from constant-force, constant-angle, fatiguing contractions of the biceps, triceps and forearm muscles. The Gaussian density precisely described the experimental density for various contraction strengths. For one biceps evaluation, the probability of deviation from a Gaussian distribution was less than 10-3, using a Chi-square test.Milner-Brown and Stein [1975] reported that the distribution of the EMG signal from constant-force, constant-angle contraction of the first dorsal interosseus muscle was more sharply peaked near zero than a Gaussian distribution. Recordings at higher force levels tended to appear less peaked than those at lower force levels. Parker et al. [1977], using fine wire electrodes inserted into biceps muscles, graphically compared the EMG probability density to a Gaussian density during light and moderate contraction levels. They concluded that the EMG is reasonably modeled as a Gaussian random process. Hunter et al. [1987], using surface electrodes on the biceps muscles, graphically compared the EMG probability density to a Gaussian density. Isometric, constant-force, nonfatiguing contractions were conducted at 30% MVC. They found that the density function departed considerably from the Gaussian form, being more sharply peaked near zero. More recently, Bilodeau et al. [1997] examined constant-angle, nonfatiguing contractions of the biceps muscles. Constant-force (20%, 40%, 60%, 80% MVC) and slowly-force-varying contractions were studied. Using a Shapiro-Wilk test, they generally found that EMG signals present a non-Gaussian amplitude distribution, being。

CIERSASSESS-1Cisco 360 CCIE R&S Workshop 1 Assessment Lab 1 Cisco 360 CCIE® R&S Advanced Workshop 1 introduces the blended learning approach for the CCIE preparation. Advanced Workshop 1 includes an Assessment Lab for the instructor and learner toassess learner readiness. This eight-hour, online Assessment Lab provides an assessment of thecandidate’s grasp of the technology and configuration skills needed for the core topics. You mustmaster the core technologies before moving on to multicast, quality of service (QoS), and security.The assessment lab tests interpretation skills using the hidden issues format. The in-depth reportsprovide not only the score, but also ample feedback to master those technologies where skills areweak. The user can compare performed work with the Master Answer Key and receives detailedexplanations in the answer key for any lost points. The user knows where to place study emphasisbefore continuing studies. This assessment saves many hours of study time by identifying “if what you think you know is what you actually know.”Cisco 360 CCIE R&S Workshop 1 Assessment Lab CIERSASSESS-1COPYRIGHT. 2008. CISCO SYSTEMS, INC. ALL RIGHTS RESERVED. ALL CONTENT ANDMATERIALS, INCLUDING WITHOUT LIMITATION, RECORDINGS, COURSE MATERIALS, HANDOUTSAND PRESENTATIONS AVAILABLE ON THIS PAGE, ARE PROTECTED BY COPYRIGHT LAWS.THESE MATERIALS ARE LICENSED EXCLUSIVELY TO REGISTERED STUDENTS FOR THEIRINDIVIDUAL PARTICIPATION IN THE SUBJECT COURSE. DOWNLOADING THESE MATERIALSSIGNIFIES YOUR AGREEMENT TO THE FOLLOWING: (1) YOU ARE PERMITTED TO PRINT THESEMATERIALS ONLY ONCE, AND OTHERWISE MAY NOT REPRODUCE THESE MATERIALS IN ANYFORM, OR BY ANY MEANS, WITHOUT PRIOR WRITTEN PERMISSION FROM CISCO; AND (2) YOUARE NOT PERMITTED TO SAVE ON ANY SYSTEM, MODIFY, DISTRIBUTE, REBROADCAST,PUBLISH, TRANSMIT, SHARE OR CREATE DERIVATIVE WORKS ANY OF THESE MATERIALS. IFYOU ARE NOT A REGISTERED STUDENT THAT HAS ACCEPTED THESE AND OTHER TERMSOUTLINED IN THE STUDENT AGREEMENT OR OTHERWISE AUTHORIZED BY CISCO, YOU ARE NOTAUTHORIZED TO ACCESS THESE MATERIALS.2 Cisco 360 CCIE R&S Workshop 1 Assessment Lab 1 © 2008 Cisco Systems, Inc.Table of ContentsCisco 360 CCIE R&S Workshop 1 Assessment Lab 1 (1)Cisco 360 CCIE R&S Workshop 1 Assessment Lab CIERSASSESS-1 (2)Table of Contents (3)Activity Objective (4)General Lab Instructions (4)Difficulty Level Explanation (5)CIERSASSESS-1 (6)Grading and Duration (6)Difficulty Level (6)Restrictions and Goals (6)1. Frame Relay and Serial Communications Section Total: 10 points (10)1.1. Configure Frame Relay interfaces (Basic: 4 points) (10)1.2. Control the Full Mesh with Static Maps (Basic: 3 points) (10)1.3. Verify Layer 3 Connectivity (Basic: 2 points) (10)1.4. Local Connectivity (Intermediate: 1 point) (10)2. Catalyst Switch Configuration Section Total: 20 points (10)2.1. Configure VLANs (Basic: 4 points) (10)2.2. Control Switch-To-Switch Links (Basic: 4 points) (11)2.3. Verify Connectivity (Basic: 4 points) (11)2.4. Load Share Using Trunks (Intermediate: 4 points) (11)2.5. Load Share Using Spanning-Tree (Intermediate: 4 points) (12)3. IPv4 OSPF Section Total: 18 points (12)3.1. Create OSPF Areas (Basic: 5 points) (12)3.2. Verify Connectivity (Basic: 4 points) (12)3.3. Add Connected Routes (Intermediate: 3 points) (12)3.4. Summarize (Intermediate: 3 points) (12)3.5. Add Routing Security (Intermediate: 3 points) (12)4. IPv4 EIGRP Section Total: 10 points (13)4.1. Create AS 35 (Basic: 3 points) (13)4.2. Verify Connectivity (Basic: 3 points) (13)4.3. Add a Connected Route (Intermediate: 2 points) (13)4.4. Summarize Routes (Intermediate: 2 points) (13)5. IPv4 RIP Section Total: 16 points (13)5.1. Enable RIP (Basic: 4 points) (13)5.2. Verify Connectivity (Basic: 3 points) (13)5.3. Filter Routes (Intermediate: 3 points) (13)5.4. Control Updates (Intermediate: 2 points) (14)5.5. Summarize Routes (Intermediate: 2 points) (14)5.6. Enable Routing Security (Intermediate: 2 points) (14)6. Redistribution Section Total: 16 points (14)6.1. RIP into OSPF on R1 (Basic: 2 points) (14)6.2. Between OSPF and EIGRP on SW3 and R3 (Basic: 4 points) (14)6.3. Verify Connectivity (Basic: 2 points) (14)6.4. Always Prefer Internal Paths to External (Intermediate: 4 points) (14)6.5. Protect Against Route Feedback (Intermediate: 4 points) (14)7. Border Gateway Protocol Section Total: 10 points (15)7.1. Configure Processes and Peers (Basic: 4 points) (15)7.2. Advertise Routes into BGP (Basic: 2 points) (15)7.3. Aggregate Routes (Intermediate: 4 points) (15)© 2008 Cisco Systems, Inc. Cisco 360 CCIE R&S Workshop 1 Assessment Lab 1 3Activity ObjectiveWhen performing any Assessment Lab, you will encounter a multitopic practice Routing and Switching CCIE lab. Each lab possesses a range of different internetworking topics. You have a predetermined set of hours to complete each Assessment Lab.When performing any Assessment Lab, formulate a test-taking strategy that includes the following activities. These very same activities should be conducted in the actual CCIE lab:Create a strategy for how to begin an Assessment LabCreate a checklist of best general practices to observe during the Assessment LabCreate a strong set of “issue spotting” skills to be able to uncover hidden and complex internetworking issuesDevelop time-management techniquesGeneral Lab InstructionsRead the instructions carefully. If you misinterpret any directions in the CCIE lab, very likely you will lose points. After you have read the “General Lab Instructions” section, read all of the other sectionsof the lab. Pay close attention to the “Restrictions and Goals” section.Your pod is cabled according to the diagram “Ethernet Cabling Topology” and “Frame Relay and Serial Cabling Topology.”All routers should have an initial IP configuration loaded.Frame Relay switching and the terminal server are preconfigured.If you experience any connectivity problems to the terminal server using multiple Telnet sessions, try to access the routers through the terminal server with Ctrl-Shift-6-x.Review all the tasks in the scenario.4 Cisco 360 CCIE R&S Workshop 1 Assessment Lab 1 © 2008 Cisco Systems, Inc.Difficulty Level ExplanationTasks are categorized as follows:Basic: These fundamental tasks are generally those that are needed to provide the basic functions of the protocol or feature. You must complete these tasks to provide reachabilityand to move forward in the lab.Intermediate: These tasks include protocol features like routing optimization, route filtering, optimal path selection, load sharing, and summarization. Failure to complete thesetasks will usually not affect later lab sections.Advanced: This category includes new Cisco IOS Software features and IP services, complex optimizations, and fine-tuning.Scenarios are categorized as follows based on task classifications:BasicBasic to IntermediateIntermediateIntermediate to AdvancedAdvanced© 2008 Cisco Systems, Inc. Cisco 360 CCIE R&S Workshop 1 Assessment Lab 1 56 Cisco 360 CCIE R&S Workshop 1 Assessment Lab 1 © 2008 Cisco Systems, Inc.CIERSASSESS-1Grading and DurationLab duration: 8 hours Maximum score: 100 pointsMinimum passing score:80 pointsDifficulty LevelDifficulty: Basic to IntermediateRestrictions and GoalsNote Read this section carefully.To receive any credit for a subsection, you must complete it. You will not get partial credit for partially completing the subsections.IP subnets assigned to Open Shortest Path First (OSPF) belong to network 172.110.0.0/16. IP subnets assigned to Routing Information Protocol (RIP) belong to network 172.120.0.0/16. IP subnets assigned to Enhanced Interior Gateway Routing Protocol (EIGRP) belong to the 172.90.0.0/16 subnet. You will lose points from multiple sections for failing to assign the correct IP addresses.Static routes are not allowed in this exercise.Advertise loopback interfaces with their original masks.All IP version 4 (IPv4) IP addresses involved in this scenario must be reachable, unless specified otherwise.Prefixes advertised from the backbone and interfaces connected to the shared equipment need not be reachable from within your pod.“N” represents the group number, “X” represents the pod number. Check your onlineinstructions for your number “NX”. Failure to assign the correct IP address could result in losing points in multiple sections.The backbone router BB1 is reachable via 160.100.10.10.Do not modify the hostname, console, or vty configuration unless specified otherwise. Do not modify the initial interface or IP address numbering.Do not use policy-based routing (PBR) in this lab.© 2008 Cisco Systems, Inc.Cisco 360 CCIE R&S Workshop 1 Assessment Lab 17Ethernet Cabling TopologyFa0/23Fa0/24Fa0/23Fa0/24Fa0/19Fa0/19Fa0/23Fa0/24Fa0/23Fa0/24Fa0/21Fa0/21Fa0/22Fa0/21Fa0/21Fa0/22Fa0/22Fa0/19Fa0/19Fa0/20Fa0/20Fa0/1Fa0/2Fa0/2Fa0/1Fa0/4Fa0/3Fa0/5Fa0/3Fa0/5Fa0/4Fa0/6Fa0/6Fa0/22Frame Relay and Serial Cabling TopologyFrame Relay DLCI AssignmentsRouterDLCI AssignmentsR1 Frame Relay interface102 103 104 R2 Frame Relay interface201 203 204 R3 Frame Relay interface301 302 304401 R4 Frame Relay interface4024038Cisco 360 CCIE R&S Workshop 1 Assessment Lab 1© 2008 Cisco Systems, Inc.© 2008 Cisco Systems, Inc.Cisco 360 CCIE R&S Workshop 1 Assessment Lab 19Lab IPv4 IGP DiagramR1R2R6R4R3R5BB1123.2/24102201103301Frame Relay10440135.3/2435.5/24Lo105:105.1/2414.4/24160.100.10.10/24160.100.10.NX/24Lo194:194.1/24Lo120:120.1/2463.6/2423.30/2435.30/24VLAN32VLAN31123.1/2432.10/2431.10/24RIPv2Fa0/1Fa0/1S0/0/0S0/0/0Fa0/0S0/0/0S0/0/0Fa0/1S0/0/0Fa0/0Fa0/0Fa0/0Fa0/063.30/2423.20/241. Frame Relay and Serial Communications Section Total: 10 points1.1. Configure Frame Relay interfaces (Basic: 4 points)Only one router must be configured with two and only two different type logical Frame Relay interfaces. The three remaining routers with Frame Relay interfaces must use physical interfacesonly. Supply IPv4 addresses on all required Frame Relay interfaces.1.2. Control the Full Mesh with Static Maps (Basic: 3 points)Use only the permanent virtual circuits (PVCs) listed on the Lab IPv4 diagram for user traffic. No dynamic entries are allowed in the Frame Relay map tables.1.3. Verify Layer 3 Connectivity (Basic: 2 points)Make sure that routers R2, R3, and R4 can ping R1 over respective Frame Relay PVCs.1.4. Local Connectivity (Intermediate: 1 point)Make sure that all routers can ping all attached, same-subnet Frame Relay IPv4 interfaces, including local IPv4 addresses.2. Catalyst Switch Configuration Section Total: 20 pointsNote Port 0/10 on switch SW4 is connected to the backbone. The configuration of this port shouldbe left as default. Do not change it; otherwise connectivity with the backbone will be lost.Healthy trunk status is displayed as following:Mode Encapsulation Statuson 802.1q trunkingDo not change any initially configured link speeds.2.1. Configure VLANs (Basic: 4 points)Create the VLANs referenced in the following table on switch SW1. The other switches in your pod must dynamically learn this information.VLANsVLAN VLAN NAMEVLAN26 GAMMAVLAN31 ALPHAVLAN32 BETAVLAN35 DELTAVLAN63 ZETAVLAN160 BBConfigure the following switch-to-router connections. Use dot1q trunk links where necessary.10 Cisco 360 CCIE R&S Workshop 1 Assessment Lab 1 © 2008 Cisco Systems, Inc.Switch-to-Router ConnectionsSwitch Router VLANSW2 R1 VLAN31VLAN32SW1 R2 VLAN26SW2 R3 VLAN35SW1 R4 VLAN160SW1 R5 VLAN35SW1 R6 VLAN26VLAN63Create the necessary switched virtual interfaces (SVIs) and assign the IP addresses specified in the diagram.Configure .1 as the host portion of IP addresses on R1 for VLAN 31 and VLAN 32.2.2. Control Switch-To-Switch Links (Basic: 4 points)Make sure that the ports specified in this table are administratively shut down:Switch PortSW1 0/20 0/21 0/22SW2 0/20 0/21Configure switch-to-switch links according to the following table. Do not use Inter-Switch Link (ISL) as a trunking protocol:Switch-to-Switch ConnectionsSwitch Port Switch Port ModeSW2 0/22 SW3 0/22 RoutedSW1 0/230/24SW20/230/24TrunkTrunkSW1 0/19 SW3 0/19 Trunk SW2 0/19 SW4 0/19 TrunkSW3 0/230/24SW40/230/24TrunkTrunkSW4 port 0/10 is your connection to the backbone; leave it at its default configuration.2.3. Verify Connectivity (Basic: 4 points)Make sure that all devices involved in this section can ping same-VLAN IPv4 addresses. Verify connectivity between R4 and BB1 on VLAN 160.2.4. Load Share Using Trunks (Intermediate: 4 points)On both SW3 and SW4, allow only traffic for VLANs 31, 35, and 63 to cross port 0/23.On both SW3 and SW4, allow only traffic for VLANs 26, 32, and 160 to cross port 0/24.2.5. Load Share Using Spanning-Tree (Intermediate: 4 points)Make SW1 the root bridge for VLANs 31, 35, and 63. Make SW2 the root bridge for VLANs 26, 32, and 160.Change the configuration on SW2 port 0/24 so that VLANs 31, 35, and 63 prefer the path across port 0/23 and VLANs 26, 32, and 160 prefer the path across port 0/24. If either link fails, trafficfor all of these VLANs should use the remaining link.All switches must operate in default spanning tree mode.3. IPv4 OSPF Section Total: 18 pointsNote Configure all OSPF routers with only one OSPF process ID (PID). You will lose points frommultiple sections for failing to assign one and only one OSPF PID on each specified router.Use your IGP diagram to help guide configuration.3.1. Create OSPF Areas (Basic: 5 points)Configure OSPF over Frame Relay between routers R1, R2, and R3. Use an OSPF network type that does not require a designated router (DR) or a backup designated router (BDR) and thatdiscovers neighbors via multicast. Make this Frame Relay network the OSPF backbone Area 0.Add subnet 172.110.103.0/24 on R3 to Area 0.On R2, place the three /30 loopback interfaces into OSPF Area 2.Place subnets 172.110.26.0/24, 172.110.106.0/24, and 172.110.63.0/24 into OSPF Area 236.Configure ABRs to provide connectivity to these routes from all other OSPF areas.Configure Area 23 on switches SW2 and SW3 to include subnets 172.110.130/24, 172.110.23.0/24, and 172.110.120.0/24.3.2. Verify Connectivity (Basic: 4 points)Verify that all internal OSPF prefixes can be reached from all devices in the OSPF domain.3.3. Add Connected Routes (Intermediate: 3 points)On R2, add subnet 172.20.10.0/24 into the OSPF routing process as either external type E1 or E2.Advertise subnet 172.110.102.0/24 as E1.3.4. Summarize (Intermediate: 3 points)Summarize subnet 172.20.10.0 to its classful boundary.Summarize the Area 2 prefixes using a 27-bit mask.Configure Area 23 so that switch SW3 sends only intra-area routes to SW2. Do not use any prefix-based filtering techniques.3.5. Add Routing Security (Intermediate: 3 points)Authenticate the OSPF backbone area. Do not use a cleartext password. Specify the authentication type at the area level only. Use the password san-fran.4. IPv4 EIGRP Section Total: 10 points4.1. Create AS 35 (Basic: 3 points)Configure EIGRP autonomous system (AS) 35 to include only the following subnets as internal prefixes:—172.90.35.0/24—172.90.55.36/30—172.90.55.40/30—172.90.55.44/304.2. Verify Connectivity (Basic: 3 points)All devices in the EIGRP domain must be able to reach all native EIGRP prefixes.4.3. Add a Connected Route (Intermediate: 2 points)On R5, add subnet 172.90.105.0/24 into EIGRP AS 35 as an external route. Make sure it is reachable from SW3 and R3.4.4. Summarize Routes (Intermediate: 2 points)On R5, configure a summary route that best represents the local /30 prefixes.Do not allow any EIGRP routes to reference a gateway of last resort.5. IPv4 RIP Section Total: 16 points5.1. Enable RIP (Basic: 4 points)Configure RIP Version 2 on R1, R4, and SW4 for network 172.120.0.0. Also enable RIP on R4 to learn routes from the backbone.5.2. Verify Connectivity (Basic: 3 points)Make sure that all addresses in the 172.120.0.0/16 range are reachable from R1, R4, SW1, and SW4 and that you are receiving routes from BB1.5.3. Filter Routes (Intermediate: 3 points)Allow only the following networks from the backbone into R4 via RIP:—192.168.105.0/24—192.168.106.0/24—192.168.107.0/24Use a minimal number of filtering statements to accomplish this task.Make sure no routers in your topology list 160.100.0.0/16 or any of its subnets as a dynamically learned entry.5.4. Control Updates (Intermediate: 2 points)Configure R1 and SW4 so that only unicast updates are seen on VLAN 31.5.5. Summarize Routes (Intermediate: 2 points)Make sure that R1 sees only a summary of the following routes:—172.120.192.0/24—172.120.193.0/24—172.120.194.0/24Configure R1 to send a default into the RIP domain. R1 should not have a default route in its local routing table.5.6. Enable Routing Security (Intermediate: 2 points)Authenticate RIP updates between R1 and SW4 with the Message Digest 5 (MD5)-encrypted password cisco.6. Redistribution Section Total: 16 points6.1. RIP into OSPF on R1 (Basic: 2 points)Redistribute RIP into OSPF on R1.6.2. Between OSPF and EIGRP on SW3 and R3 (Basic: 4 points)Mutually redistribute between OSPF and EIGRP on SW3 and R3.6.3. Verify Connectivity (Basic: 2 points)Verify complete IP version 4 (IPv4) unicast reachability within your pod.6.4. Always Prefer Internal Paths to External (Intermediate: 4 points)Use Administrative Distance to make sure that both SW3 and R3 prefer the direct path across VLAN 35 to subnet 172.90.105.0/24. Also make sure that OSPF external prefixes on R1 have ahigher administrative distance than RIP prefixes.6.5. Protect Against Route Feedback (Intermediate: 4 points)Protect the OSPF domain from learning its own prefixes through the EIGRP domain. Subnets 172.110.102.0/24 and 172.20.10.0/24 are considered to be OSPF domain prefixes.Protect the EIGRP domain from learning its own routes through the OSPF domain. Include subnet 172.90.105.0/24 as an EIGRP domain prefix.Implement your solution using route maps on both SW3 and R3.7. Border Gateway Protocol Section Total: 10 pointsNote The Border Gateway Protocol (BGP)table must display only networks advertised according to the BGP section specifications. IGP synchronization should be disabled.7.1. Configure Processes and Peers (Basic: 4 points)Configure BGP AS 4 on R3 and R4. Peer R3 and R4.Peer R4 with BB1. BB1 is in AS 9999.Configure BGP AS 44 on R1 and R2. Peer R1 with R2.Peer AS 4 and AS 44 using only R2 and R3.Use the nearest outside address, not loopbacks, for peering.7.2. Advertise Routes into BGP (Basic: 2 points)On R2, create one loopback and assign the following two addresses: 200.1.16.1/24 and 200.1.17.1/24.Advertise these two prefixes into BGP on R2, using network statements. They must be seen on all BGP speakers in your pod. These prefixes do not have to be reachable from non-BGP speakers.7.3. Aggregate Routes (Intermediate: 4 points)The backbone router is advertising the following prefixes via BGP:—179.200.37.0/24—179.200.39.0/24—179.200.41.0/24—179.200.43.0/24Summarize these routes on R3 using the single best longest match. Make sure this summary isseen on R1 and R2 as originating from the backbone AS. Suppress the longer matches.。