Introduction to Mixed Signals PDF

INTRODUCTION TO MIXED SIGNALS Signal Adetectable (or measurable) physical quantity or impulse (as voltage, current, or magnetic field strength) by which messages or information can be transmitted. (by Webster's New Collegiate Dictionary) Signal Characteristics Signals are Physical Quantities Si...

INTRODUCTION TO MIXED SIGNALS Signal Adetectable (or measurable) physical quantity or impulse (as voltage, current, or magnetic field strength) by which messages or information can be transmitted. (by Webster's New Collegiate Dictionary) Signal Characteristics Signals are Physical Quantities Signals are Measurable Signals Contain Information All Signals are Analog Units of Measurement Temperature: °C Pressure: Newtons/m2 Mass: kg Voltage: Volts Current: Amps Power: Watts Classification of Signals Analog Digital Analog Signals (or real-world) variables in nature include all measurable physical quantities are generally limited to electrical variables, their rates of change, and their associated energy or power levels. Digital Signals the actual signal has been conditioned and formatted into a digit. signals may or may not be related to real-world analog variables. Examples include the data transmitted over local area networks (LANs) or other high speed networks. Signal Conditioning deals with preparing real-world signals for processing and includes such topics as sensors (temperature and pressure, for example), isolation and instrumentation amplifiers, etc. Digital Signal Processing the analog signal is converted into binary form by a device known as an analog-to- digital converter (ADC). The output of the ADC is a binary representation of the analog signal and is manipulated arithmetically by the Digital Signal Processor. After processing, the information obtained from the signal may be converted back into analog form using a digital-to-analog converter (DAC). Some Signals result in response to other signals. A good example is the returned signal from a radar or ultrasound imaging system, both of which result from a known transmitted signal. Reasons for Processing Real—World Signals to extract information from them. This information normally exists in the form of signal amplitude (absolute or relative), frequency or spectral content, phase, or timing relationships with respect to other signals. Once the desired information is extracted from the signal, it may be used in a number of ways. to reformat the information contained in a signal. This would be the case in the transmission of a voice signal over a frequency division multiple access (FDMA) telephone system. In this case, analog techniques are used to "stack" voice channels in the frequency spectrum for transmission via microwave relay, coaxial cable, or fiber. Reasons for Processing Real—World Signals to compress the frequency content of the signal (without losing significant information) then format and transmit the information at lower data rates, thereby achieving a reduction in required channel bandwidth. High speed modems and adaptive pulse code modulation systems (ADPCM) make extensive use of data reduction algorithms, as do digital mobile radio systems, MPEG recording and playback, and High Definition Television (HDTV). Digital Transmission Link the analog voice information is first converted into digital using a ADC. The digital information representing the individual voice channels is multiplexed in time (time division multiple access, or TDMA) and transmitted over a serial digital transmission link (as in the T-Carrier system). Industrial Data Acquisition and Control Systems make use of information extracted from sensors to develop appropriate feedback signals which in turn control the process itself. Note that these systems require both ADCs and DACs as well as sensors, signal conditioners, and the DSP (or microcontroller). Signal Recovery In some cases, the signal containing the information is buried in noise, and the primary objective is signal recovery. Techniques such as filtering, auto-correlation, convolution, etc. are often used to accomplish this task in both the analog and digital domains. Reasons for Signal Processing Extract Information About the Signal (Amplitude, Phase, Frequency, Spectral Content, Timing Relationships) Reformat the Signal (FDMA, TDMA, CDMA Telephony) Compress Data (Modems, Cellular Telephone, HDTV, MPEG) Generate Feedback Control Signal (Industrial Process Control) Extract Signal from Noise (Filtering, Autocorrelation, Convolution)  Capture and Store Signal in Digital Format for Analysis (FFT Techniques) Generation of Real-World Signals In most of the given examples (the ones requiring DSP techniques), both ADCs and DACs are required. In some cases, however, only DACs are required where real-world analog signals may be generated directly using DSP and DACs. Generation of Real-World Signals Example 1. Video raster scan display systems (The digitally generated signal drives a video or RAMDAC. 2. artificially synthesized music and speech. In reality, however, the real-world analog signals generated using purely digital techniques do rely on information previously derived from the real-world equivalent analog signals. 3. In display systems, the data from the display must convey the appropriate information to the operator. 4. In synthesized audio systems, the statistical properties of the sounds being generated have been previously derived using extensive DSP analysis (i.e. sound source, microphone, preamp, ADC, etc.). Methods and Technologies Available for Processing Real-world Signals Signals may be processed using: analog techniques (analog signal processing, or ASP), digital techniques (digital signal processing, or DSP), or a combination of analog and digital techniques (mixed signal processing, or MSP). In some cases, the choice of techniques is clear; in others, there is no clear cut choice, and second-order considerations may be used to make the final decision. Mixed Signal Processing implies that both analog and digital processing is done as part of the system.  The system may be implemented in the form of a printed circuit board, hybrid microcircuit, or a single integrated circuit chip. In the context of this broad definition, ADCs and DACs are considered to be mixed signal processors, since both analog and digital functions are implemented in each. Recent advances in Very Large Scale Integration (VLSI) processing technology allow complex digital processing as well as analog processing to be performed on the same chip. The very nature of DSP itself implies that these functions can be performed in real- time. Analog versus Digital Signal Processing Today's engineer faces a challenge in selecting the proper mix of analog and digital techniques to solve the signal processing task at hand. It is impossible to process real-world analog signals using purely digital techniques, since all sensors (microphones, thermocouples, strain gages, microphones, piezoelectric crystals, disk drive heads, etc.) are analog sensors. Therefore, some sort of signal conditioning circuitry is required in order to prepare the sensor output for further signal processing, whether it be analog or digital. Signal Conditioning Circuits in reality, analog signal processors, performs such functions as multiplication (gain), isolation (instrumentation amplifiers and isolation amplifiers), detection in the presence of noise (high common-mode instrumentation amplifiers, line drivers, and line receivers), dynamic range compression (log amps, LOGDACs, and programmable gain amplifiers), and filtering (both passive and active). Several Methods of Accomplishing Signal Processing The top portion of the figure shows the purely approach. analog The latter parts of the figure show the DSP approach.  Note that once the decision has been made to use DSP techniques, the next decision must be where to place the ADC in the signal path. Digital Signal Processing With respect to DSP, the factor that distinguishes it from traditional computer analysis of data is its speed and efficiency in performing sophisticated digital processing functions such as filtering, FFT analysis, and data compression in real time. Sensors are used to convert other physical quantities (temperature, pressure, etc.) to electrical signals. Practical Example h a cutoff frequency of 1kHz. The digital filter is implemented in a typical sampled data system shown in Figure 1. Note that there are several implicit requirements in the diagram. First, it is assumed that an ADC/DAC combination is available with sufficient sampling frequency, resolution, and dynamic range to accurately process the signal. Second, the DSP must be fast enough to complete all its calculations within the sampling interval, 1/fs. Third, analog filters are still required at the ADC input and DAC output for antialiasing and anti-imaging, but the performance demands are not as great. Assuming these conditions have been met, the following offers a comparison between the digital and analog filters. Practical Example The required cutoff frequency of both filters is 1kHz. The analog filter is realized as a 6- pole Chebyshev Type 1 filter (ripple in passband, no ripple in stopband), and the response is shown in Figure 2. In practice, this filter would probably be realized using three 2-pole stages, each of which requires an op amp, and several resistors and capacitors. Modern filter design CAD packages make the 6-pole design relatively straightforward, but maintaining the 0.5dB ripple specification requires accurate component selection and matching. On the other hand, the 129-tap digital FIR filter shown has only 0.002dB passband ripple, linear phase, and a much sharper roll off. In fact, it could not be realized using analog techniques! Another obvious advantage is that the digital filter requires no component matching, and it is not sensitive to drift since the clock frequencies are crystal controlled. The 129-tap filter requires 129 multiply-accumulates (MAC) in order to compute an output sample. This processing must be completed within the sampling interval, 1/fs, in order to maintain real-time operation. In this example, the sampling frequency is 10kSPS, therefore 100µs is available for processing, assuming no significant additional overhead requirement. The ADSP-21xx-family of DSPs can complete the entire multiply-accumulate process (and other functions necessary for the filter) in a single instruction cycle. Therefore, a 129-tap filter requires that the instruction rate be greater than 129/100µs = 1.3 million instructions per second (MIPS). DSPs are available with instruction rates much greater than this, so the DSP certainly is not the limiting factor in this application. The ADSP-218x 16-bit fixed point series offers instruction rates up to 75MIPS. The assembly language code to implement the filter on the ADSP-21xx-family of DSPs is shown in Figure 3. Note that the actual lines of operating code have been marked with arrows; the rest are comments. In a practical application, there are certainly many other factors to consider when evaluating analog versus digital filters, or analog versus digital signal processing in general. Most modern signal processing systems use a combination of analog and digital techniques in order to accomplish the desired function and take advantage of the best of both the analog and the digital world. SIGNALS AND SENSORS SIGNAL  An electronic impulse or radio wave transmitted or received An electrical or electromagnetic current that is used for carrying data from one device or network to another. A function that conveys information about a phenomenon. SIGNALS CLASSIFICATION 1. Continuous-time and discrete-time signals 2. Analogue and digital signals 3. Periodic and aperiodic signals 4. Energy and power signals 5. Deterministic and probabilistic signals 6. Casual and non-casual 7. Even and Odd signals May use functions other than a rectangular pulse. Here are three example functions: 1. Exponential 2. Triangular 3. Gaussian Exponential function- is very important in signals and systems, and the parameter 𝑠 is a complex variable Complex frequency- Therefore the complex variable 𝑠 = 𝜎 + 𝑗𝜔 is the ________. Function 𝒆𝒔𝒕 - can be used to describe a very large class of signals and functions. Real function 𝒙𝒆 (𝒕) – is a said to be an even function of t Real function 𝒙𝒐 (𝒕) – is said to be an odd function of t Even and odd functions have the following properties:  Even x Odd = Odd  Odd x Odd= Even  Even x Even = Even SENSOR  A device that produces an output signal for the purpose of sensing a physical phenomenon. SENSORS AND TRANSDUCERS MEASUREMENT Measurement is an important subsystem of a mechatronics system. Its main function is to collect the information on system status and to feed it to the microprocessor(s) for controlling the whole system. MEASUREMENT SYSTEM It comprises of the following: sensors, transducers signal processing devices Note: For a mechatronics system designer it is quite difficult sensors/transducers to for choose the suitable desired application(s). It is therefore essential to learn the principle of working of commonly used 4 sensors/transducers. SENSORS IN MANUFACTURING They are basically employed to automatically carry out the production operations as well as process monitoring activities. SENSOR TECHNOLOGY -It has the following important advantages in transforming a conventional manufacturing unit into a modern one. Sensors alarm the system operators about the failure of any of the sub units of manufacturing system. It helps operators to reduce the downtime of complete manufacturing system by carrying out the preventative measures. Reduces requirement of skilled and experienced labors. Ultra-precision in product quality can be achieved. SENSOR It is defined as an element which produces signal relating to the quantity being measured According to the Instrument Society of America, sensor can be defined as “A device which provides a usable output in response to a specified measurand.” Note: Here, the output is usually an ‘electrical quantity’ and measurand is a ‘physical quantity, property or condition which is to be measured’. Thus in the case of, say, a variable inductance displacement element, the quantity being measured is displacement and the sensor transforms an input of displacement into a change in inductance. TRANSDUCER It is defined as an element when subjected to some physical change experiences a related change or an element which converts a specified measurand into a usable output by using a transduction principle. It can also be defined as a device that converts a signal from one form of energy to another form. Note: A wire of Constantan alloy (copper-nickel 5545% alloy) can be called as a sensor because variation in mechanical displacement (tension or compression) can be sensed as change in electric resistance. This wire becomes a transducer with appropriate electrodes and input-output mechanism attached to it. Thus we can say that ‘sensors are transducers’. TRANSDUCER Transducers or measurement systems are not perfect systems. Mechatronics design engineer must know the capability and shortcoming of a transducer or measurement system to properly assess its performance. There are a number of performance related parameters of a transducer or measurement system. These parameters are called as sensor specifications. SENSOR/TRANSDUCERS SPECIFICATIONS Sensor specifications inform the user to the about deviations from the ideal behavior of the sensors. Following are the various specifications of a sensor/transducer system: 1. Range The range of a sensor indicates the limits between which the input can vary. For example, a thermocouple for the measurement of temperature might have a range of 25-225 ℃. 2. Span The span is difference between the maximum and minimum values of the input. Thus, the above-mentioned thermocouple will have a span of 200 ℃. 3. Error Error is the difference between the result of the measurement and the true value of the quantity being measured. A sensor might give a displacement reading of 29.8 mm, when the actual displacement had been 30 mm, then the error is -0.2 mm. 4. Accuracy The accuracy defines the closeness of the agreement between the actual measurement result and a true value of the measurand. It is often expressed as a percentage of the full range output or full-scale deflection. A piezoelectric transducer used to evaluate dynamic pressure phenomena associated with explosions, pulsations, or dynamic pressure conditions in motors, rocket engines, compressors, and other pressurized devices is capable to detect pressures between 0.1 and 10,000 psig (0.7 KPa to 70 MPa). 5. Sensitivity Sensitivity of a sensor is defined as the ratio of change in output value of a sensor to the per unit change in input value that causes the output change. For example, a general purpose thermocouple may have a sensitivity of 41 u V/ºC. 6. Nonlinearity indicates the maximum deviation of the actual measured curve of a sensor from the ideal curve. Linearity is often specified in terms of percentage of nonlinearity, which is defined as: -Nonlinearity (%) = Maximum deviation in input / Maximum full scale input 7. Hysteresis is an error of a sensor which is defined as the maximum difference in output at any measurement value within the sensor’s specified range when approaching the point first with increasing and then with decreasing the input parameter.  Figure 2 shows the hysteresis error might have occurred during measurement of temperature using a thermocouple.  The hysteresis error value is normally specified as a positive or negative percentage of the specified input range.  8. Resolution Resolution is the smallest detectable incremental change of input parameter that can be detected in the output signal. Resolution can be expressed either as a proportion of the full-scale reading or in absolute terms. For example, if a LVDT sensor measures a displacement up to 20 mm and it provides an output as a number between 1 and 100 then the resolution of the sensor device is 0.2 mm. 9. Stability Stability is the ability of a sensor device to give same output when used to measure a constant input over a period of time. The term 'drift' is used to indicate the change in output that occurs over a period of time. It is expressed as the percentage of full range output. 10. Dead band/time The dead band or dead space of a transducer is the range of input values for which there is no output. The dead time of a sensor device is the time duration from the application of an input until the output begins to respond or change. 11. Repeatability It specifies the ability of a sensor to give same output for repeated applications of same input value. It is usually expressed as a percentage of the full range output: Repeatability = (maximum - minimum values given) X 100 / full range. 12. Response time Response time describes the speed of change in the output on a step-wise change of the measurand. It is always specified with an indication of input step and the output range for which the response time is defined. CLASSIFICATION OF SENSORS Sensors can be classified into various groups according to the factors such as:  measurand,  application fields,  conversion principle,  energy domain of the measurand  thermodynamic considerations DETAIL CLASSIFICATION OF SENSORS in view of their applications in manufacturing is as Follows: A. Displacement, position and proximity sensors   Potentiometer  Strain-gauged element  Capacitive element  Differential transformers  Eddy current proximity sensors  Inductive proximity switch  Optical encoders  Pneumatic sensors  Proximity switches (magnetic)  Hall effect sensors B. Velocity and motion  Incremental encoder  Tachogenerator  Pyroelectric sensors C. Force  Strain gauge load cell D. Fluid pressure  Diaphragm pressure gauge  Capsules, bellows, pressure tubes  Piezoelectric sensors  Tactile sensor E. Liquid flow  Orifice plate  Turbine meter F. Liquid level  Floats  Differential pressure G. Temperature  Bimetallic strips  Resistance temperature detectors  Thermistors  Thermo-diodes and transistors  Thermocouples  Light sensors  Photo diodes  Photo resistors  Photo transistor