The time required to finish N instructions in a pipeline with K stages can be calculated. Assume a cycle time of T for the overall instruction completion, and an equal T / K processing delay at each stage. With a pipeline scheme, the first instruction completes the pipeline after T , and there will be a new instruction out of the pipeline per stage delay T / K . Therefore, the delays of executing N instructions with and without pipelining, respectively, are (42.1) (42.2) There is an initial delay in the pipeline execution model before each stage has operations to execute. The initial delay is usually called pipeline start-up delay ( P ), and is equal to total execution time of one instruction. The speed-up of a pipelined machine relative to a nonpipelined machine is calculated as (42.3) When N is much larger than the number of pipestages P , the ideal speed-up approaches P . This is an intuitive result since there are P parts of the machine working in parallel, allowing the execution to go about P times faster in ideal conditions. The overlap of sequential instructions in a processor pipeline is shown in Fig. 42.4(b). The instruction pipeline becomes full after the pipeline delay of P = 5 cycles. Although the pipeline configuration executes operations in each stage of the processor, two important mechanisms are constructed to ensure correct functional operation between dependent instructions in the presence of data hazards. Data hazards occur when instructions in the pipeline generate results that are necessary for later instructions that are already started in the pipeline. In the pipeline configuration of Fig. 42.4(a), register operands are initially retrieved during the decode stage. However, the execute and memory stage can define register operands and contain the correct current value but are not able to update the register file until the later write-back execution stage. Forwarding (or bypassing) is the action of retrieving the correct operand value for an executing instruction between the initial register file access and any pending instruction’s register file updates. Interlocking is the action of stalling an operation in the pipeline when conditions cause necessary register operand results to be delayed. It is necessary to stall early stages of the machine so that the correct results are used, and the machine does not proceed with incorrect values for source operands. The primary causes of delay in pipeline execution are initiated due to instruction fetch delay and memory latency. Branch Prediction Branch instructions pose serious problems for pipelined processors by causing hardware to fetch and execute instructions until the branch instructions are completed. Executing incorrect instructions can result in severe performance degradation through the introduction of wasted cycles into the instruction stream. There are several methods for dealing with pipeline stalls caused by branch instructions. The simplest performance scheme handles branches by treating every branch as either taken or not taken . This treat- ment can be set for every branch or determined by the branch opcode. The designation allows the pipeline to continue to fetch instructions as if the branch was a normal instruction. However, the fetched instruction may need to be discarded and the instruction fetch restarted when the branch outcome is incorrect. Delayed branching is another scheme which treats the set of sequential instructions following a branch as delay slots. The delay-slot instructions are executed whether or not the branch instruction is taken. Limitations on delayed branches are caused by the compiler and program characteristics being unable to support numerous instructions that execute independent of the branch direction. Improvements have been introduced to provide nullifying branches, which include a predicted direction for the branch. When the prediction is incorrect, the delay-slot instructions are nullified. T * N() TT/k()*N 1–()+ P * N PN1–()+ ©2002 CRC Press LLC 43 Control with Embedded Computers and Programmable Logic Controllers 43.1 Introduction 43.2 Embedded Computers Hardware Platforms • Hardware Interfacing • Programming Languages 43.3 Programmable Logic Controllers Programming Languages • Interfacing • Advanced Capabilities 43.4 Conclusion 43.1 Introduction Modern control systems include some form of computer, most often an embedded computer or pro- grammable logic controller (PLC). An embedded computer is a microprocessor- or microcontroller- based system used for a specific task rather than general-purpose computing. It is normally hidden from the user, except for a control interface. A PLC is a form of embedded controller that has been designed for the control of industrial machinery. (See Fig. 43.1.) A block diagram of a typical control system is shown in Fig. 43.2. The controller monitors a process with sensors and affects it with actuators. A user interface allows a user or operator to direct and monitor the control system. Interfaces to other computers are used for purposes such as programming, remote monitoring, or coordination with another controller. When a computer is applied to a control application, there are a few required specifications. The system must always remain responsive and in control of the process. This requires that the control software be real-time so that it will respond to events within a given period of time, or at regular intervals. The systems are also required to fail safely. This is done with thermal monitoring for overheating, power level detection for imminent power loss, or with watchdog timers for unresponsive programs. 43.2 Embedded Computers An embedded computer is a microprocessor- or microcontroller-based system designed for dedicated functionality in a specialized (i.e., nongeneral-purpose) electronic device. Common examples of embed- ded computers can be found in cell phones, microwave ovens, handheld computing devices, automotive systems, answering machines, and many other systems. Hugh Jack Grand Valley State University Andrew Sterian Grand Valley State University ©2002 CRC Press LLC VI Software and Data Acquisition 44 Introduction to Data Acquistition Jace Curtis 45 Measurement Techniques: Sensors and Transducers Cecil Harrison Introduction • Motion and Force Transducers • Process Transducers • Transducer Performance • Loading and Transducer Compliance 46 A/D and D/A Conversion Mike Tyler Introduction • Sampling • ADC Specifications • DAC Specifications 47 Signal Conditioning Stephen A. Dyer Linear Operations • Nonlinear Operations 48 Computer-Based Instrumentation Systems Kris Fuller The Power of Software • Digitizing the Analog World • A Look Ahead 49 Software Design and Development Margaret H. Hamilton The Notion of Software • The Nature of Software Engineering • Development Before the Fact • Experience with DBTF • Conclusion 50 Data Recording and Logging Tom Magruder Overview • Historical Background • Data Logging Functional Requirements • Data-Logging Systems • Conclusions ©2002 CRC Press LLC 44 Introduction to Data Acquistition The purpose of a data acquisition system is to capture and analyze some sort of physical phenomenon from the real world. Light, temperature, pressure, and torque are a few of the many different types of signals that can interface to a data acquisition system. A data acquisition system may also produce electrical signals simultaneously. These signals can either intelligently control mechanical systems or provide a stimulus so that the data acquisition system can measure the response. A data acquisition system provides a way to empirically test designs, theories, and real world systems for validation or research. Figure 44.1 illustrates a typical computer-based data acquisition module. The design and the production of a modern car, for instance, relies heavily on data acquisition. Engineers will first use data acquisition to test the design of the car’s components. The frame can be monitored for mechanical stress, wind noise, and durability. The vibration and temperature of the engine can be acquired to evaluate the design quality. The researchers and engineers can then use this data to optimize the design of the first prototype of the car. The prototype can then be monitored under many different conditions on a test track while information is collected through data acquisition. After a few iterations of design changes and data acquisition, the car is ready for production. Data acquisition devices can monitor the machines that assemble the car, and they can test that the assembled car is within specifications. At first, data acquisition devices stood alone and were manually controlled by an operator. When the PC emerged, data acquisition devices and instruments could be connected to the computer through a serial port, parallel port, or some custom interface. A computer program could control the device automatically and retrieve data from the device for storage, analysis, or presentation. Now, instruments and data acquisition devices can be integrated into a computer through high-speed communication links, for tighter integration between the power and flexibility of the computer and the instrument or device. Since data acquisition devices acquire an electric signal, a transducer or a sensor must convert some physical phenomenon into an electrical signal. A common example of a transducer is a thermocouple. A thermocouple uses the material properties of dissimilar metals to convert a temperature into a voltage. As the temperature increases, the voltage produced by the thermocouple increases. A software program can then convert the voltage reading back into a temperature for analysis, presentation, and data logging. Many sensors produce currents instead of voltages. A current is often advantageous because the signal will not be corrupted by small amounts of resistance in the wires connecting the transducer to the data acquisition device. A disadvantage of current-producing transducers, though, is that most data acquisition devices measure voltage, not current. Generally, the data acquisition devices that can measure current use a very small resistance of a known value to convert the known current into a readable voltage. Ultimately, the device is then still acquiring a voltage. Jace Curtis National Instruments, Inc. ©2002 CRC Press LLC 45 Measurement Techniques: Sensors and Transducers 45.1 Introduction 45.2 Motion and Force Transducers Displacement (Position) Transducers • Velocity Transducers • Acceleration Transducers • Force Transducers 45.3 Process Transducers Fluid Pressure Transducers • Fluid Flow Transducers (Flowmeters) • Liquid Level Transducers • Temperature Transducers 45.4 Transducer Performance 45.5 Loading and Transducer Compliance 45.1 Introduction An automatic control system is said to be error actuated because the forward path components ( comparator, controller, actuator , and plant or process ) respond to the error signal (Fig. 45.1). The error signal is developed by comparing the measured value of the controlled output to some reference input , and so the accuracy and precision of the controlled output are largely dependent on the accuracy and precision with which the controlled output is measured. It follows then that measurement of the controlled output, accomplished by a system component called the transducer , is arguably the single most important function in an automatic control system. A transducer senses the magnitude or intensity of the controlled output and produces a proportional signal in an energy form suitable for transmission along the feedback path to the comparator. [The term proportional is used loosely here because the output of the transducer may not always be directly proportional to the controlled output; that is, the transducer may not be a linear component. In linear systems, if the output of the transducer (the measurement) is not linear, it is linearized by the signal conditioner.] The element of the transducer which senses the controlled output is called the sensor ; the remaining elements of a transducer serve to convert the sensor output to the energy form required by the feedback path . Possible configurations of the feedback path include: • Mechanical linkage • Fluid power (pneumatic or hydraulic) • Electrical, including optical coupling, RF propagation, magnetic coupling, or acoustic propagation Cecil Harrison University of Southern Mississippi ©2002 CRC Press LLC 46 A/D and D/A Conversion 46.1 Introduction 46.2 Sampling 46.3 ADC Specifications Range • Resolution • Coding Convention • Linear Errors • Nonlinear Errors • Aperture Errors • Noise • Dynamic Range • Types of ADCs • Flash • Successive- Approximation Register • Multistage • Integrating • Sigma-Delta • Digital-to-Analog Converters • Updating 46.4 DAC Specifications Range • Resolution • Monotonicity • Settling Time and Slew Rate • Offset Error and Gain Error • Architecture of DACs • Switching Network • Resistive Networks • Summing Amplifier 46.1 Introduction As computers began to gain popularity, engineers and scientists realized that computers could become a powerful tool. However, almost all real-world phenomena (such as light, pressure, velocity, temperature, etc.) are analog signals, and computers, on the other hand, rely on digital signals. Therefore, many companies began to invest in advancements in analog-to-digital and digital-to-analog converters (ADC and DAC). These devices have become the keystone in every measurement device. This chapter will examine the ADC and DAC on a functional level as well as discuss important specifications of each. 46.2 Sampling In order to convert an analog signal into a digital signal, the analog signal must first be sampled. Sampling involves converting one value of a signal at a particular interval of time. Generally, conversions happen uniformly in time. For example, a digitizing system may convert a signal every 5 µ s, or sample at 200 kS/s. Although it is not necessary to uniformly sample a signal, doing so provides certain benefits that will be discussed later. A typical sampling circuit contains two major components: a track-and-hold (T/H) circuit and the ADC. Since the actual conversion in the ADC takes some amount of time, it is necessary to hold constant the value of the signal being converted. At the instance the sample is to be taken, the T/H holds the sample value even if the signal is still changing. Once the conversion has been completed, the T/H releases the value it is currently storing and is ready to track the next value. One aspect of sampling that cannot be avoided is that some information is thrown away, meaning that an analog waveform actually has an infinite number of samples and there is no way to capture every value. Mike Tyler National Instruments, Inc. ©2002 CRC Press LLC 47 Signal Conditioning 47.1 Linear Operations Amplitude Scaling • Impedance Transformation • Linear Filtering 47.2 Nonlinear Operations Kelvin’s first rule of instrumentation states, in essence, that the measuring instrument must not alter the event being measured. For the present purposes, we can consider the instrument to consist of an input transducer followed by a signal-conditioning section, which in turn drives the data-processing and display section (the remainder of the instrument). We are using the term instrument in the broad sense, with the understanding that it may actually be a measurement subsystem within virtually any type of system. Certain requirements are imposed upon the transducer if it is to reproduce an event faithfully: It must exhibit amplitude linearity, phase linearity, and adequate frequency response. But it is the task of the signal conditioner to accept the output signal from the transducer and from it produce a signal in the form appropriate for introduction to the remainder of the instrument. Analog signal conditioning can involve strictly linear operations, strictly nonlinear operations, or some combination of the two. In addition, the signal conditioner may be called upon to provide auxiliary services, such as introducing electrical isolation, providing a reference of some sort for the transducer, or producing an excitation signal for the transducer. Important examples of linear operations include amplitude scaling, impedance transformation, linear filtering , and modulation . A few examples of nonlinear operations include obtaining the root-mean-square ( rms ) value, square root, absolute value , or logarithm of the input signal. There is a wide variety of building blocks available in either modular or integrated-circuit (IC) form for accomplishing analog signal conditioning. Such building blocks include operational amplifiers, instru- mentation amplifiers, isolation amplifiers, and a plethora of nonlinear processing circuits such as com- parators, analog multiplier/dividers, log/antilog amplifiers, rms-to-DC converters, and trigonometric function generators. Also available are complete signal-conditioning subsystems consisting of various plug-in input and output modules that can be interconnected via universal backplanes that can be either chassis- or rack-mounted. 47.1 Linear Operations Three categories of linear operations important to signal conditioning are amplitude scaling, impedance transformation, and linear filtering. Amplitude Scaling The amplitude of the signal output from a transducer must typically be scaled—either amplified or attenuated—before the signal can be processed. Stephen A. Dyer Kansas State University ©2002 CRC Press LLC . to hold constant the value of the signal being converted. At the instance the sample is to be taken, the T /H holds the sample value even if the signal is still changing. Once the conversion has. of the transducer (the measurement) is not linear, it is linearized by the signal conditioner.] The element of the transducer which senses the controlled output is called the sensor ; the remaining. branching is another scheme which treats the set of sequential instructions following a branch as delay slots. The delay-slot instructions are executed whether or not the branch instruction is