Digital Phase Sensitive Detector

We are basically making Digital Phase Sensitive Detectors that will help extract very low amplitude signals from signals having very high noise content. Phase Sensitive Detector is a well-developed technique for measuring signals hidden in the noise. Nowadays, instrumentation for the latest t

2025-06-28 16:26:43 - Adil Khan

Project Title

Digital Phase Sensitive Detector

Project Area of Specialization Electrical/Electronic EngineeringProject Summary

We are basically making Digital Phase Sensitive Detectors that will help extract very low amplitude signals from signals having very high noise content.

Phase Sensitive Detector is a well-developed technique for measuring signals hidden in the noise. Nowadays, instrumentation for the latest technologies like THz detection, Magnetic Resonance, and Laser Applications use built-in DSP phase-sensitive detection.

A phase-sensitive detector is an instrument used to extract features of a sinusoidal component of a known frequency from a signal; accurate measurement may be made even when the small signal is obscured by noise sources many times larger. Given a measured signal and a frequency of interest, the instrument returns estimates of the amplitude and phase of the sinusoidal component of that frequency.

The digital technique eliminates many of the problems associated with analog phase-sensitive detectors like harmonic rejection, output offsets, limited dynamic reserves, and gains error. Fixed point mathematics will be used and it will be implemented using a DSP kit.

The achievement of this project will be a step in modern electronics systems development.

Project Objectives

The main objective of our project is to be able to improve the signal-to-noise ratio in a signal with high noise content. Also, we want to implement digitally using the processor. This will help us change filter coefficients much easier than analog ones.

Project Implementation Method

General Idea:

It will be implemented using DSP Kit QQ2812. Data signal will be taken in real-time and after processing, the noise will be removed using the DSP kit mentioned above and the result will be shown on the function generator.

How does it work?
An input signal is given to our design through the Signal In node. After this, we have a low noise differential amplifier which is used to reduce external interference. Then we multiply the input signal with a gain to increase its magnitude however note that noise is also multiplied with the same gain and thus enhanced. Next, a local oscillator is used that generates a cosine wave and also passes it through a 90 degrees phase shift register to generate a sine wave. At this point, we have a sine and cosine wave and 2 multipliers, and also the input signal. What we do next is that one input to each multiplier is our signal after the gain block while the other input is cosine in one case and sine in the other.

The above is implemented in MATLAB using the DSP toolbox so far. Now, we will implement it practically on the DSP kit using assembly language. We will also make GUI using C-sharp language in Visual studio to make our project presentable. To check our results we will take signals from sound cards and function generators. 

Benefits of the Project

This project will help detect very low amplitude signals from signals that are covered with noise. Now, this is extremely helpful for mobile phone communication, GPS, and all sorts of communication.

Applications:

After adding feedback in PSD, we can convert it into a Lock-in Amplifier which is an extension of our project. A number of important applications have been overlooked!

  1. Absorption spectroscopy
  2. A.C. bridges
  3. Antenna patterns
  4. Astronomical spectroscopy
  5. Atomic absorption
  6. Audio amplifier frequency response
  7. Audiometry
  8. Auger spectroscopy
  9. Biomedical stimuli response measurements
  10. Bode plots
  11. Cochlear microphonics
  12. Common mode rejection measurements
  13. Complex impedance measurements
  14. Contact potential measurements
  15. Crosstalk in cables, amplifiers, etc.
  16. C- V plotting
  17. Cube interferometry
  18. De Haas Van Alphen effect
  19. Densitometry
  20. Detective compensation
  21. Displacement measurements
  22. Doppler measurements
  23. Dual-beam optical measurements
  24. Eddy-current flaw testing
  25. Edge shift in GaAs
  26. Electrochemistry
  27. Electroluminescence
  28. Emission spectroscopy
  29. E.P.R./e.s.r. spectroscopy
  30. Filter calibration
  31. Fluorescence spectroscopy
  32. Frequency-response measurements
  33. Frequency -shift measurements
  34. Hall effect: single frequency
  35. Hall effect: double frequency
  36. Infra-red (near and far) spectroscopy
  37. Interferometry
  38. Klystron stabilization
  39. Laser research
  40. Line ripple measurement in the amplifier
  41. power supplies
  42. Magnetic-field measurements
  43. Magnetometry
  44. Magnetoresistance studies
  45. Marx gauging
  46. Mass spectroscopy
  47. Microphone calibration
  48. Microwave reflections, attenuation
  49. Microwave spectroscopy
  50. Moisture content measurement (C-G)
  51. Molecular-beam spectroscopy
  52. N.M.R. spectroscopy
  53. N.O.R. spectroscopy
  54. Nyquist plots
  55. Operational amplifier gain measurement
  56. Optical derivative measurements
  57. Photometry
  58. Plasma-physics research
  59. Pyrometry
  60. Radiometry
  61. Raman spectroscopy
  62. Ratiometric measurements
  63. Resistance thermometry
  64. R.F. measurements
  65. Second sound
  66. Seismic measurements
  67. Semiconductor research
  68. Source compensation
  69. Spectrophotometry
  70. Strain gauging
  71. Stress-strain measurements
  72. Temperature control
  73. Temperature measurement
  74. Torque measurements
  75. Ultra-violet spectroscopy
  76. Visible spectroscopy
  77. Whistler signal measurements
  78. Work function measurements
  79. Young’s modulus
  80. Zeeman effect
Technical Details of Final Deliverable

We will be using MATLAB for software implementation. This is because of the flexibility of use and wide range of applications of MATLAB. Now in order to program our project, we will be using the coding portion instead of Simulink so that we can see what is actually happening at each step and can review the errors accordingly.

Next, we will do all the coding using fixed-point mathematics after which we will check the whole algorithm using data from self-generated algos, microphone, and offline data from a function generator. After successful implementation and getting correct results, we will move on to the next step.

Next using C sharp, a GUI will be developed through which we will take real-time data. after getting the data we will not only perform the arithmetic operations on it like multiplication/ addition required in our project but we will also show the graphs and FFT of the data on the screen. 

In the end, we will be using DSP kit QQ2812 for the practical implementation of our project. We will use the application associated with it for the coding purpose i.e. code composer. And all the coding will be done in assembly language so that we can know from the basics what actually is happening. after this we will use real-time data to test our code and will successfully implement it.

Final Deliverable of the Project HW/SW integrated systemCore Industry TelecommunicationOther IndustriesCore Technology Internet of Things (IoT)Other TechnologiesSustainable Development Goals Industry, Innovation and InfrastructureRequired Resources
Item Name Type No. of Units Per Unit Cost (in Rs) Total (in Rs)
Total in (Rs) 65000
DSP Kit QQ2812 Equipment16000060000
Stationery, printing and overheads Miscellaneous 150005000

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