lab2 - State University of New York at Stony Brook...

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State University of New York at Stony Brook ESE 211 Electronics Laboratory A Department of Electrical and Computer Engineering 2009 ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 1 Lab 2: Measurement of DC voltages and currents 1. Objectives. 1) Familiarization with bar-code resistor marking. Measurement of resistance using the DMM. 2) Setting up the DC power supply for operation as a voltage source and for operation as a current source. Selection of a ground node. 3) Measurement of voltages and currents using DMM. Measurement of I-V characteristics. 2. Introduction. There is no such thing as a perfect measurement. Each measurement contains a degree of uncertainty due to either fundamental reasons or, more often, due to limitations of the methodologies, instruments and the people using them. Random error present in the results of the measurement can be minimized by averaging over multiple measurements. Assuming normally distributed data, the error bars for the results of measurements of R using ohmmeter can be calculated based on standard deviation σ R . () N R R , 1 - N R R σ N 1 i i N 1 i 2 i R = = = = , ( 1 ) where N is number of measurements (for instance, the number of times you measure the same resistance using ohmmeter). Each time you might get slightly different value R i . After averaging over N (ideally N should be rather big number) experiments one can calculate an average value R and the actual value of the resistance will be with 68% of certainty in the range from R σ - R t o R σ R + . There is 95% likelihood that the actual value is within range from R σ 2 - R to R σ 2 R + . From equation (1) one can see that precision of measurement improves as square root of number of experiments. Hence, in order to minimize the contribution of the random noise it is advised to perform multiple measurements and average the results. There is also systematic errors that can affect the accuracy of the measurements (precision can be improved by averaging but final result could still be not accurate since the errors are not necessarily come in the form of random noise). Good example of the systematic error are offset voltages and currents, i.e. when R is calculate based on I and V there is constant shift in either one or even in both of these parameters, hence averaging alone could not improve the accuracy. One way to deal with this is to use known functional dependences of one
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This note was uploaded on 02/13/2012 for the course ESE 211 taught by Professor Belenky during the Spring '07 term at SUNY Stony Brook.

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lab2 - State University of New York at Stony Brook...

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