Sensors & Transducers Volume 136, Issue 1 January 2012
www.sensorsportal.com
ISSN 1726-5479
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Schneider, John K., Ultra-Scan Corporation, USA Sengupta, Deepak, Advance Bio-Photonics, India Seif, Selemani, Alabama A & M University, USA Seifter, Achim, Los Alamos National Laboratory, USA Shah, Kriyang, La Trobe University, Australia Sankarraj, Anand, Detector Electronics Corp., USA Silva Girao, Pedro, Technical University of Lisbon, Portugal Singh, V. R., National Physical Laboratory, India Slomovitz, Daniel, UTE, Uruguay Smith, Martin, Open University, UK Soleymanpour, Ahmad, Damghan Basic Science University, Iran Somani, Prakash R., Centre for Materials for Electronics Technol., India Sridharan, M., Sastra University, India Srinivas, Talabattula, Indian Institute of Science, Bangalore, India Srivastava, Arvind K., NanoSonix Inc., USA Stefan-van Staden, Raluca-Ioana, University of Pretoria, South Africa Stefanescu, Dan Mihai, Romanian Measurement Society, Romania Sumriddetchka, Sarun, National Electronics and Computer Technology Center, Thailand Sun, Chengliang, Polytechnic University, Hong-Kong Sun, Dongming, Jilin University, China Sun, Junhua, Beijing University of Aeronautics and Astronautics, China Sun, Zhiqiang, Central South University, China Suri, C. Raman, Institute of Microbial Technology, India Sysoev, Victor, Saratov State Technical University, Russia Szewczyk, Roman, Industrial Research Inst. for Automation and Measurement, Poland Tan, Ooi Kiang, Nanyang Technological University, Singapore, Tang, Dianping, Southwest University, China Tang, Jaw-Luen, National Chung Cheng University, Taiwan Teker, Kasif, Frostburg State University, USA Thirunavukkarasu, I., Manipal University Karnataka, India Thumbavanam Pad, Kartik, Carnegie Mellon University, USA Tian, Gui Yun, University of Newcastle, UK Tsiantos, Vassilios, Technological Educational Institute of Kaval, Greece Tsigara, Anna, National Hellenic Research Foundation, Greece Twomey, Karen, University College Cork, Ireland Valente, Antonio, University, Vila Real, - U.T.A.D., Portugal Vanga, Raghav Rao, Summit Technology Services, Inc., USA Vaseashta, Ashok, Marshall University, USA Vazquez, Carmen, Carlos III University in Madrid, Spain Vieira, Manuela, Instituto Superior de Engenharia de Lisboa, Portugal Vigna, Benedetto, STMicroelectronics, Italy Vrba, Radimir, Brno University of Technology, Czech Republic Wandelt, Barbara, Technical University of Lodz, Poland Wang, Jiangping, Xi'an Shiyou University, China Wang, Kedong, Beihang University, China Wang, Liang, Pacific Northwest National Laboratory, USA Wang, Mi, University of Leeds, UK Wang, Shinn-Fwu, Ching Yun University, Taiwan Wang, Wei-Chih, University of Washington, USA Wang, Wensheng, University of Pennsylvania, USA Watson, Steven, Center for NanoSpace Technologies Inc., USA Weiping, Yan, Dalian University of Technology, China Wells, Stephen, Southern Company Services, USA Wolkenberg, Andrzej, Institute of Electron Technology, Poland Woods, R. Clive, Louisiana State University, USA Wu, DerHo, National Pingtung Univ. of Science and Technology, Taiwan Wu, Zhaoyang, Hunan University, China Xiu Tao, Ge, Chuzhou University, China Xu, Lisheng, The Chinese University of Hong Kong, Hong Kong Xu, Sen, Drexel University, USA Xu, Tao, University of California, Irvine, USA Yang, Dongfang, National Research Council, Canada Yang, Shuang-Hua, Loughborough University, UK Yang, Wuqiang, The University of Manchester, UK Yang, Xiaoling, University of Georgia, Athens, GA, USA Yaping Dan, Harvard University, USA Ymeti, Aurel, University of Twente, Netherland Yong Zhao, Northeastern University, China Yu, Haihu, Wuhan University of Technology, China Yuan, Yong, Massey University, New Zealand Yufera Garcia, Alberto, Seville University, Spain Zakaria, Zulkarnay, University Malaysia Perlis, Malaysia Zagnoni, Michele, University of Southampton, UK Zamani, Cyrus, Universitat de Barcelona, Spain Zeni, Luigi, Second University of Naples, Italy Zhang, Minglong, Shanghai University, China Zhang, Qintao, University of California at Berkeley, USA Zhang, Weiping, Shanghai Jiao Tong University, China Zhang, Wenming, Shanghai Jiao Tong University, China Zhang, Xueji, World Precision Instruments, Inc., USA Zhong, Haoxiang, Henan Normal University, China Zhu, Qing, Fujifilm Dimatix, Inc., USA Zorzano, Luis, Universidad de La Rioja, Spain Zourob, Mohammed, University of Cambridge, UK
Sensors & Transducers Journal (ISSN 1726-5479) is a peer review international journal published monthly online by International Frequency Sensor Association (IFSA). Available in electronic and on CD. Copyright © 2012 by International Frequency Sensor Association. All rights reserved.
Sensors & Transducers Journal
Contents Volume 136 Issue 1 January 2012
www.sensorsportal.com
ISSN 1726-5479
Research Articles Digital Sensors and Sensor Systems: Practical Design Book Review…………………………………………………………………………………………………..
I
Fast and Simple Measurement of Position Changes White Paper, iC-Haus GmbH………………………………………………………………………………..
IV
A Novel Method of Linearizing Thermistor Characteristic Using Voltage Controlled Oscillator Narayana K. V. L and Bhujanga Rao A..............................................................................................
1
A Data Acquisition System Based on DSP for Mechanical Nanoscale Displacement Sensor Yong Yu, Qian Wu, Hanyu Sun, Zhengwei Li and Yunjian Ge ..........................................................
12
Modified AC Wheatstone Bridge Network for Accurate Measurement of Pressure Using Strain Gauge Type Pressure Sensor Subrata Chattopadhyay, Mahuya Banerjee and Sagarika Pal...........................................................
25
Fingerprint Sensors: Liveness Detection Issue and Hardware based Solutions Shahzad Memon, Nadarajah Manivannan, Azad Noor, Wamadeva Balachadran, Nikolaos V. Boulgouris .......................................................................................................................
35
Fiber Optic Vibration Sensor Using Pmma Fiber for Real Time Monitoring P. Kishore, D. Dinakar , D. Sen Gupta, P. Saidi Reddy, M. Sai Shankar, K. Srimannarayana .........
50
ARM Processor Based Multisensor System Design for the Measurement of Environmental Parameters NarasimhaMurthy Yayavaram, Soundara Rajan, Vishnu Vardhan ....................................................
59
A Decoupling Algorithm Based on Homotopy Theory for 3-D Tactile Sensor Arrays Junxiang Ding, Yunjian Ge, Yuan Wang, Zhaohui Wang ..................................................................
72
Digital Imaging and Piezo-dispenser Actuator in Automatic Flocculation Control Jani Tomperi, Markus Honkanen, Pasi Kallio, Kauko Leiviskä, Pentti Saarenrinne, Iiris Joensuu, Marjatta Piironen ................................................................................................................................
83
An Embedded Web based Real Time Application for Remote Monitoring & Controlling of MST RADAR Transmitters Nagabhushan Raju Konduru, Lakshmi Narayana Roshanna, Rajendra Prasad Thommundru, Chandrasekhar Reddy Devanna ........................................................................................................
96
Advanced Oscilloscope Triggering Based on Signal Frequency Shakeb A. Khan, Alka Nigam, A. K. Agarwala, Mini S. Thomas, T. Islam
105
Pyramidal Traceability Hierarchy for Pressure Measurements and Calibrations at NIS- Egypt A. A. Eltawil.........................................................................................................................................
118
Fuzzy Logic Based Autonomous Traffic Control System Muhammad Abbas, M. Saleem Khan, Nasir Ali and Syed Fazil ........................................................
132
Potential of Piezoelectric Sensors in Bio-signal Acquisition Dipali Bansal.......................................................................................................................................
147
Measurement and Analysis of Sodium in Vegetables Using ATmega16 Microcontroller Based Spectrophotometer K. Murugananthan and P. Neelamegam.. ..........................................................................................
158
Authors are encouraged to submit article in MS Word (doc) and Acrobat (pdf) formats by e-mail:
[email protected] Please visit journal’s webpage with preparation instructions: http://www.sensorsportal.com/HTML/DIGEST/Submition.htm International Frequency Sensor Association (IFSA).
Sensors & Transducers Journal, Vol. 136, Issue 1, January 2012, pp. 105-117
Sensors & Transducers ISSN 1726-5479 © 2012 by IFSA http://www.sensorsportal.com
Advanced Oscilloscope Triggering Based on Signal Frequency 1
Shakeb A. Khan, 1 Alka Nigam, 2 A. K. Agarwala, 1 Mini S. 1 Thomas, T. Islam 1
Jamia Milia Islamia, Department of Electrical Engineering, New Delhi-110025, India Tel.: +919650087393, +919810877220, +919810424609, +918800902585, fax:+91-11-26981261 2 IDDC, IIT, New Delhi-110016, India Tel./fax: +91-11-26591428 E-mail:
[email protected],
[email protected],
[email protected],
[email protected],
[email protected]
Received: 8 December 2012 /Accepted: 24 January 2012 /Published: 30 January 2012
Abstract: This paper presents a novel frequency based technique for triggering of the oscilloscope. Fast Fourier Transform (FFT) has the capability of analyzing waveforms in the frequency domain and this has opened up a new set of opportunities in digital oscilloscopes. The proposed FFT based technique can provide stable triggering even in the case of complex periodic signals. In this technique, the frequency of all harmonics present in the test signal is retrieved using FFT and the base repetition rate of the signal is found by selecting the frequency of the lowest harmonic present. This base frequency signal can be used to trigger the oscilloscope to obtain a stable waveform display. The efficacy of the method is evaluated by implementing the technique using DAQ (Data Acquisition) card and LabVIEW ® software. Robustness of the technique is evaluated by simulated addition of random noise. It is found that the proposed technique is quite effective in detecting the base repetition rate of complex as well as simple waveforms and is quite robust. Copyright © 2012 IFSA. Keywords: FFT, LabVIEW, MATLAB, Oscilloscope, Triggering.
1. Introduction Triggering of the horizontal sweep plays a very important role in stable display of time varying waveforms. Oscilloscopes use basic voltage level triggering schemes along with extensions like variable hold off for the purpose, but these schemes have limitation in triggering of the horizontal sweep for complex waveforms. Furthermore, no automatic scheme (akin to an “AUTOSET” button which automates the vertical and horizontal axis settings) is available for the stable display of complex waveforms. 105
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This paper outlines a method of oscilloscope triggering based on the application of the FFT algorithm to the estimation of frequency spectra and hence the lowest harmonic component, which is the key for the success of the proposed scheme. For the stable display of time varying signals on oscilloscopes, the horizontal sweep must be synchronized with the input signal, which is called triggering. The trigger unit contains the circuitry that generates the timing signals to synchronize the start of sweep, with timing pulses generated from the input signal (internal trigger), or from an external signal (external trigger). The triggering function is essential to achieve a stable display. Without proper triggering, successive horizontal sweeps start at different instants of the input signal, leading to an incoherent jumble or a moving image on the screen. The frequency and amplitude modulated waveforms are typical examples of complex waveforms. Proper triggering of oscilloscopes for such types of waveforms is still a challenging task. To handle the above mentioned limitation of level triggering scheme for complex waveforms, different advanced triggering techniques have been proposed [1–3]. The whd (Weighted Hamming Distance) based technique is discussed in [1] and auto correlation based technique is discussed in [2]. In [3–4], the performance of these two techniques is compared in terms of their robustness. In both the techniques, the base repetition rate of the waveform is determined by finding the rate of repetition of a captured reference pattern. It is also necessary that the capture reference pattern must contain at least one base cycle of the test waveform. It is difficult to ensure this condition, which is essential for the success of both the schemes. In this paper, a frequency based triggering technique is proposed which overcomes the limitation associated with the advanced triggering schemes mentioned above. In this technique, frequency of all signals present in the test signal is retrieved using FFT and the base repetition rate of the signal is found by selecting the frequency of the lowest signal present. This proposed technique has been implemented using LabVIEW ® software and DAQ (Data Acquisition) card. The FFT tool of LabVIEW® software is utilized to retrieve the frequency spectrum of test waveforms and this signal frequency spectrum is utilized to calculate the base frequency of the signal. Performance of the proposed technique is evaluated by triggering the oscilloscope externally in real time for different selected complex and simple test waveforms. Subsequently, the robustness of the technique is evaluated by simulated addition of noise in the signal. With the initial results, it is proved experimentally that this approach is equally effective in getting the stable display of the complex as well as simple periodic waveforms and quite effective in handling all kind of waveforms even in noisy environment. In section II, the proposed advanced triggering technique has been explained with the help of a flowchart. The complete block diagram of the implementation modules has been presented in section III and validation results for noiseless case are also presented in this section. In section IV the results of robustness testing of the proposed technique is discussed. Finally section V concludes the paper with scope for future research in this area.
2. FFT Based Technique for Oscilloscope Triggering The most popular computer algorithm for generating a frequency spectrum is the FFT. As the name implies, the FFT is very efficient but it does have one quirk that affects the way it is used. FFT can only process a sampled waveform where N (number of samples) is a power of 2. FFT is based on the concept that real world signals can be approximated by a sum of sinusoids [5]. Plotting the harmonic magnitudes on the y-axis and the frequency of the harmonic on the x-axis generates a frequency spectrum. The spectrum is a set of vertical lines or bars because harmonics can only have frequencies that are integer multiples of the original signal frequency [6]. The use of weighted windowing can 106
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improve the quality of the spectrum. In situations where major frequency components are multiples of one another, the best approach is to adjust the sampling process so that a full period of the input signal lines up with the sampling window. If this can be done and if the input signal is periodic, then the best window is rectangular [7]. In composite waveforms the lowest harmonics frequency is same as the basic input frequency in the spectrum and in modulated waveforms the spacing between harmonics is equal to the base frequency of the signal. The value of harmonic spacing is the key parameter in the proposed triggering technique. The harmonic spacing of the selected test waveform is calculated using the following formula fb = |f1 – f2| ,
(1)
where fb is the base frequency of the considered test waveform. f1, f2 are frequencies of any two adjacent harmonics or central frequency and frequency of one of the adjacent harmonics. The flow-chart of the proposed technique is shown in Fig. 1 which explains the involved steps. In this scheme, first of all, suitable number of samples of the test waveform is captured and the frequency components present in the waveform are computed using FFT. Subsequently, base frequency of the waveform is calculated using equation (1). At this base frequency, triggering pulses are generated. If generated triggering pulses fail to give stable display of waveform on the oscilloscope, then window size is increased till the clear frequency spectrum is observed and the whole process is repeated until stable display is achieved.
3. Implementation and Results Proposed advanced oscilloscope triggering technique is implemented using LabVIEW® software and DAQ (Data Acquisition) card. The block diagram, illustrated in Fig. 2 (a) and experimental set up of the scheme is shown in Fig. 2 (b). Test waveforms are generated in the virtual environment of LabVIEW® software (V 8.5) and analysis of the harmonic components has been carried out using its FFT tool. Tektronix TDS-210 oscilloscope is triggered externally for the selected complex test waveforms to evaluate the performance of the scheme. The real time implementation of the scheme has been done using NI PCI-6221 DAQ card. The base time period measurement scheme using FFT is implemented in virtual environment of LabVIEW®, the VI of the implemented scheme is shown in Fig. 3. Performance of the proposed technique is evaluated using four different test waveforms; AM (Amplitude Modulated), FM (Frequency Modulated), composite and sinusoidal waveforms, which represent four different kind of test waveforms. Virtual waveform generator along with modulation toolkit is used to generate the modulated test waveforms. Frequency spectrum of the test waveform is retrieved using FFT tool and finally equation (1) is used to compute base frequency of the signal. At this frequency, pulses are generated virtually and extracted in real time using DAQ card. At the same time, virtually generated test waveform is also extracted out in real time and applied to one of the channels of the oscilloscope. Finally, the test waveform is triggered externally using the generated pulses. Different types of selected test waveforms along with triggering pulses generated are shown in Figures 4 - 7.
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Start
Initialize Waveform
Initalize sampling frequency and window length L1
Compute window size in terms of next nearest power of 2 for L1=L
Compute FFT for defined window size
Plot Frequency spectrum
Compute fundamental frequency fb =| fc - f1|
fb =| fc - f1|
Synchronized triggering pulses generated and fed externally to oscilloscope
NO
Stable Display YES
YES
Stop
Fig. 1. Flow-chart of the proposed FFT based triggering scheme.
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Fig. 2 (a). Block diagram of frequency based triggering scheme.
Fig. 2 (b). Set up of the frequency based triggering scheme.
Fig. 3. VI of the frequency based triggering scheme.
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In the first case, simple sinusoidal waveform, of frequency 1 kHz is considered as the test waveform. For stable display of the waveform, frequency of triggering pulses should be 1 kHz. The retrieved frequency spectrum of the test waveform comprises no side bands and central frequency is 1 kHz. Therefore, from equation (1) above, the base frequency of the test waveform is 1 kHz .The result for simple sinusoidal test waveform is shown in Fig. 4. It is clearly seen from the diagram that the frequency of the triggering pulses is 1 kHz as desired.
Fig. 4. Result 1: Frequency spectrum and stable display results for sinusoidal waveform.
In the second case, 100 Hz composite multi-tone waveform is considered as the test waveform. Therefore, for stable triggering of this waveform, triggering pulses of 100 Hz frequency is required. The test waveform and its retrieved frequency spectrum is shown in Fig. 5.
Fig. 5. Result 2: Frequency spectrum and stable display results for Multitone composite waveform.
For composite waveform, from retrieved frequency spectrum, f1 is 100 Hz and f2 is 200 Hz. The computed value of base frequency (fb) is 100 Hz that is the required value. The result for composite waveform is shown in Fig. 5. It is clearly seen from the diagram that the frequency of the generated triggering pulses is 100 Hz as desired. Further, an AM waveform is selected to evaluate the performance of the technique. In conventional voltage level triggering scheme, if selected trigger voltage level is less than the peak value, then multiple crossing of trigger level will cause unstable triggering. This waveform is the sum of three sinusoids of different frequencies as illustrated in the retrieved frequency spectrum that is shown in 110
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Fig. 6. The frequency of modulation signal is 10 Hz. Therefore, for stable display of the waveform, it should be triggered at 10 Hz.
Fig. 6. Result 3: Frequency spectrum and stable display results for amplitude modulated waveform.
This AM test waveform has the modulating signal frequency of 10 Hz and carrier frequency of 200 Hz. For the AM test waveform, from the retrieved frequency spectrum, f1 is 200 Hz and f2 is 190 or 210 Hz. The computed value of base frequency (fb) is 10 Hz that is equal to the required value. The result for AM waveform is shown in Fig. 6. It is clearly shown in the diagram that frequency of generated triggering pulses is 10 Hz as desired. In contrast to amplitude modulation, angular modulation of a sine wave carrier with a single sine wave yields an infinite number of sidebands spaced by modulating signal frequency. In other words AM is a linear process, whereas FM is a nonlinear process. The spectral components (including the carrier component) change their amplitudes, when the modulation index m is varied [8]. Frequency spectrum of a FM waveform is shown in Fig. 7. The base repetition rate of the considered FM waveform is 100 Hz. As per equation (1), the base frequency can be found by having an absolute difference of any two neighboring band frequencies that is equal to 100 Hz as required. FM test waveform and triggering pulses are shown in Fig.7.
Fig. 7. Result 4: Frequency spectrum and stable display results for frequency modulated waveform.
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4. Robustness Testing In [1-3], Whd and autocorrelation based techniques are discussed and their performances are compared in terms of their robustness. In this section, performance of the frequency based triggering technique is compared with the other two advanced triggering techniques in terms of their robustness. Variation in Whd min and COCmax in the presence of different levels of random noise is studied to determine the robustness of the techniques [3]. To avoid jitter in the presence of noise, there should be sufficient distance between Whd min and second lowest value of Whd. Similarly COCmax should also have sufficient distance from second maximum value of COC. Therefore, these distances are considered as parameters for robustness analysis and are calculated over 10000 cycles of considered test waveforms. Random noise of different levels, 5 %, 10 % and 15 % of the peak to peak values, are introduced in the captured samples and resultant samples are used as test vectors to both the techniques [3]. Same test vectors of AM of 1 kHz with 25 samples per cycles have been used for the validation of the FFT based technique. Fig. 8, illustrates the process involved in robustness testing of the proposed technique.
Fig. 8. Robustness testing procedure.
In this case, real time test vectors of the test waveform are captured and stored in the host computer. Different levels of noise are added to these test vectors and noisy samples are used to retrieve the frequency spectrum of the test waveform and the whole scheme is implemented in MATLAB® environment. The advantage of the proposed scheme over the reported advanced techniques is that this scheme is basically dependent on the frequency of the test waveform and frequency of the test waveform is insensitive to the level of noise. Both the reported techniques Whd and COC are amplitude based techniques and are extremely sensitive to the noise level. The only shortcoming of the proposed technique is that it requires sufficient number of cycles to generate the correct frequency spectrum. Different window sizes are tried to estimate optimum window size for the AM test waveform. Results are shown in Fig. 9, 9 (a) shows frequency spectrum for 128 samples, 9 (b) for 256 samples, 9 (c) for 1024 samples and 9 (d) for 2,50000 samples. It can be seen from the diagram that the required frequency spectrum can be found with 256 samples.
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(d) Fig. 9. Frequency spectrum of 1 kHz AM test waveform for different window length. 113
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Further, the performance of the technique is studied in noisy environment by adding 5 %, 10 % and 15 % random noise to the considered test wave samples. Retrieved spectrums with window size of 10 K cycles are shown in Fig. 10. For the noisy waveform the retrieved frequency spectrums are shown in Fig. 10 (a), (b) and (c) for 5 %, 10 % and 15 % noise levels respectively. The spectrum reveals the base frequency of the AM waveform, this can be computed very easily from these spectrums because all the frequency bands are clearly visible in all the three figures.
Fig. 10. Frequency spectrum of AM of 1 kHz for different noise level for 10 K cycles (a) 5% (b) 10% and (c) for 15% noise level.
Table 1. Results of AM waveform of 1 kHz in MATLAB for different numbers of samples, the sampling frequency is 250000 samples/sec, carrier frequency is 10 kHz. S. No. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.
Noise level 0% 5% 10 % 15 % 0% 5% 10 % 15 % 0% 5% 10 % 15 % 0% 5% 10 % 15 %
No. of Samples 256 256 256 256 512 512 512 512 1024 1024 1024 1024 250000 250000 250000 250000
fc 10 kHz 10 kHz 10 kHz 10 kHz 10 kHz 10 kHz 10 kHz 10 kHz 10 kHz 10 kHz 10 kHz 10 kHz 10 kHz 10 kHz 10 kHz 10 kHz
f1 11 kHz 11 kHz 11 kHz 11 kHz 11 kHz 11 kHz 11 kHz 11 kHz 11 kHz 11 kHz 11 kHz 11 kHz 11 kHz 11 kHz 11 kHz 11 kHz
fb=f1-fc 1 kHz 1 kHz 1 kHz 1 kHz 1 kHz 1 kHz 1 kHz 1 kHz 1 kHz 1 kHz 1 kHz 1 kHz 1 kHz 1 kHz 1 kHz 1 kHz 114
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Finally, the above results are validated in real time using LabVIEW® and DAQ card [9]. VI of the online robustness testing scheme is shown in Fig. 11. In this case, AM and FM waveforms are generated and different levels (5 %, 10 %, 15 %) of noises are added and base frequency is measured in virtual environment. Further, triggering pulses are generated, which are utilized to trigger the test waveform in real time on the oscilloscope.
Fig. 11. VI of the frequency based triggering scheme for robustness testing.
Results, with 15% noise level, are shown in Fig. 12 (a) and 12 (b) for AM and FM waveforms respectively. In case of AM waveform, the base frequency is 10 Hz. It can be seen in the result that despite the presence of 15% noise, the proposed technique is able to detect base frequency of the AM test waveform.
Fig 12 (a). Frequency spectrum and stable display results of DSO of AM waveform for 15 % noise. 115
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Fig. 12 (b). Frequency spectrum and stable display results of DSO of FM waveform for 15 % noise.
In general, the spectrum of an FM signal is not infinite. The sideband amplitudes become negligibly small beyond a certain frequency offset from the carrier, depending on the magnitude of m the modulation index. The bandwidth can be determined for low distortion transmission by counting the number of significant sidebands (significant sidebands usually indicate all those sidebands that have a voltage at least 1 percent (-40 dB) of the voltage of the unmodulated carrier), Fig. 12(b) shows the results of FM waveform with the maximum noise level of 15 %, the carrier signal frequency is 500 Hz and modulating signal frequency is 100 Hz. The base repetition rate of the considered FM waveform is 100 Hz.
5. Conclusions In this paper a frequency based triggering scheme for oscilloscopes has been proposed. The proposed technique is successfully implemented using a DAQ card and LabVIEW® software. Performance of the technique is evaluated using certain selected simple and complex test waveforms. Robustness of the technique is also tested by simulated addition of random noise in the test signal. The main conclusion emerging from this work can be summarized as follows. 1. The proposed scheme is based on frequency, which remains unaffected with the addition of noise, as it affects the amplitude of the signal and both associative memory based weighted hamming distance and autocorrelation based algorithms reported earlier are amplitude sensitive. 2. This technique provides a simple and fast solution for the complex waveforms triggering problem, The frequency of all harmonics present in the test signal is retrieved using FFT and the base repetition rate of the signal is found by selecting the frequency of the lowest harmonics present. Frequency based triggering technique is successfully implemented in real time using LabVIEW® software and DAQ card for noisy and noiseless environment. 3. One of the advantage of frequency based triggering scheme is that one can find the triggering frequency for horizontal sweep by using inbuilt FFT function that is present in most of the advanced digital oscilloscopes. The effectiveness of this technique has been successfully validated both by simulation and hardware implementation and the robustness of the technique has been tested on a large variety of periodic waveforms. It is found that this technique is more suitable than other reported techniques. 116
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References [1]. Khan Shakeb A, Agarwala A. K., Shahani D. T. and Alam Mahfooz M., Advanced oscilloscope Triggering, IEEE Transaction on Instrumentation and Measurement, Vol. 56, No. 3, June 2007, pp. 944-953. [2]. M. G. D'Elia, C. Liguori, V. Paciello, A. Pietrosanto, Software customization to provide digital oscilloscope with enhanced period-measurement features, IEEE Transactions on Instrumentation and Measurement, Vol. 55, Issue 2, April 2006, pp. 493 – 450. [3]. K. P. S Rana., R. Singh and K. S. Sayann, Correlation based novel technique for real time oscilloscope triggering for complex waveforms, Measurement, 43, 2010, pp. 299- 311. [4]. Khan Shakeb A, Nigam Alka, Agarwala A. K., Thomas Mini S., Performance Evaluation and Robustness Testing of Advanced Oscilloscope Triggering Schemes, Sensors & Transducers, Vol. 112, Issue 1, January 2010, pp. 95-106. [5]. Knowledge-base Fourier Analysis and FFT, Astro-Med, Inc. of Test and Measurement, Group, February 14, 2007. [6]. FFT Tutorials of University of Rhode Iceland Department of Electrical and Computer Engineering, ELE 436: Communication System. [7]. Harris F. J., On the use of windows for harmonic analysis with the discrete Fourier transform, Proceedings of IEEE, Vol. 66, pp. 51-83, Jan 1978. [8]. Application Note 150-1, Agilent Spectrum Analysis Amplitude and Frequency Modulation, cp. literature.agilent.com/litweb/pdf/5954-9130.pdf. [9]. [NI 2009] SCB-68 User Manual for Advanced Functions. [10].XYZ of Oscilloscopes, http://www.tektronix.com ___________________ 2012 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved. (http://www.sensorsportal.com)
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