Answer

Correct option: (ii) Modify the sensor signal for ADC

Explanation

Signal conditioning prepares a raw sensor signal so that it is suitable for accurate measurement and conversion by an ADC.

Common signal-conditioning operations include:

• Amplification – increases a weak sensor signal.
• Filtering – removes unwanted noise and interference.
• Isolation – protects circuits and reduces ground-loop problems.
• Linearization – improves the relationship between sensor output and measured quantity.
• Level shifting – adjusts the signal to the ADC input range.

For example, if a sensor produces 0–100 mV while an ADC accepts 0–5 V, an amplifier can increase the sensor signal before conversion.

Therefore, the correct answer is (ii).

Answer

Correct option: (ii) High-frequency noise

Explanation

A filter is a signal-conditioning circuit that allows desired frequency components to pass while attenuating unwanted frequency components.

In DAQ systems, filters are commonly used to remove high-frequency noise, electrical interference and other unwanted components before the signal reaches the ADC.

For example, a low-pass filter allows low-frequency measurement signals to pass while reducing high-frequency noise.

Importance in DAQ

Filtering improves:

1. Signal quality.
2. Measurement accuracy.
3. ADC performance.
4. Resistance to unwanted interference.
5. Protection against aliasing when used as an anti-aliasing filter before sampling.

Therefore, the correct answer is (ii).

Answer

Correct option: (i) One resistor and one capacitor

Explanation

A basic first-order RC filter is made using one resistor and one capacitor. It has one energy-storage element and therefore has a roll-off of approximately 20 dB/decade beyond its cutoff frequency.

For a simple RC low-pass filter, the cutoff frequency is:

$$f_c = \frac{1}{2\pi RC}$$

Below the cutoff frequency, the signal passes with relatively little attenuation. Above the cutoff frequency, higher-frequency components are increasingly attenuated.

Therefore, the correct answer is (i).

Solution: Signal Conditioning Process and Real-Time Applications

Definition

Signal conditioning is the process of modifying a sensor's raw electrical output so that it becomes suitable for accurate measurement, analog-to-digital conversion, processing, and control.

A typical chain is:

Sensor → Amplification → Filtering → Isolation / Linearization → ADC → Processor

Why Signal Conditioning is Required

Sensor outputs are often:

• Very small in amplitude.
• Noisy.
• Non-linear.
• Outside the input range of the ADC.
• Sensitive to electrical interference.
• Referenced to a different ground potential.

Signal conditioning solves these problems before digitization.

Major Signal-Conditioning Operations

1. Amplification

A weak sensor signal is amplified to use the available ADC input range effectively.

For an amplifier:

$$V_{out}=G V_{in}$$

where $G$ is the voltage gain.

2. Filtering

Filters remove unwanted frequency components and noise.

A low-pass filter is commonly placed before an ADC to remove high-frequency noise and prevent aliasing.

For a first-order RC low-pass filter:

$$f_c=\frac{1}{2\pi RC}$$

3. Isolation

Isolation electrically separates the sensor side from the measurement electronics. It helps protect circuits and reduce ground-loop interference.

Common techniques include transformers, opto-isolators and isolation amplifiers.

4. Linearization

Some sensors have non-linear output characteristics. Linearization converts the sensor response into a more useful relationship with the measured physical quantity.

This may be performed using analog circuits or digitally in the microcontroller.

5. Level Shifting and Scaling

The signal is adjusted to match the ADC input range.

For example, if a sensor produces 0–100 mV and the ADC accepts 0–5 V, an amplifier can scale the signal so that the ADC uses more of its resolution.

6. Excitation

Some sensors such as resistive bridges require an excitation voltage or current. The signal-conditioning system may provide this excitation and measure the resulting response.

Real-Time DAQ Applications

Temperature Monitoring

Temperature Sensor → Amplifier/Filter → ADC → Microcontroller → Display/Alarm

The microcontroller continuously samples the sensor and can activate an alarm if temperature exceeds a limit.

Motor-Speed Monitoring

A speed sensor produces pulses proportional to motor speed. Conditioning removes noise and shapes the pulses before the microcontroller counts them.

Heart-Rate Monitoring

A biomedical sensor produces a small physiological signal. Amplification and filtering remove unwanted noise and interference. The conditioned signal is digitized and processed to calculate heart rate.

Importance in Real-Time Systems

Proper signal conditioning provides:

1. Better measurement accuracy.
2. Improved signal-to-noise ratio.
3. Protection of ADC and processing electronics.
4. Better use of ADC resolution.
5. Reliable real-time decision-making.
6. Reduced aliasing and interference.

Conclusion

Signal conditioning is a critical stage between the sensor and ADC. By amplifying, filtering, isolating, scaling and linearizing the sensor signal, it ensures that the DAQ system receives a clean and measurable signal suitable for reliable real-time monitoring and control.

Solution: Types of Signal Conditioning in DAQ Systems

Signal conditioning prepares a sensor signal for accurate measurement and ADC conversion.

1. Amplification

Weak sensor outputs are increased to a suitable voltage range.

$$V_{out}=G V_{in}$$

Operational amplifiers and instrumentation amplifiers are commonly used.

2. Filtering

Filters remove unwanted frequency components and noise.

• Low-pass: removes high-frequency noise.
• High-pass: removes unwanted low-frequency/DC components.
• Band-pass: passes a selected frequency band.
• Notch: rejects a narrow interfering frequency such as 50/60 Hz.

3. Isolation

Electrical isolation prevents unwanted current paths and protects the measurement system. Isolation amplifiers and opto-isolators are common methods.

4. Linearization

Some sensors have nonlinear input-output characteristics. Linearization converts the response into a more convenient relationship, either using analog circuits or software.

5. Level Shifting and Scaling

The signal is shifted or scaled to match the ADC input range. For example, a 0–100 mV sensor can be amplified to 0–5 V.

6. Excitation

Resistive and bridge-type sensors may require a stable excitation voltage or current before their output can be measured.

7. Signal Conversion

A conditioning stage may convert one signal form to another, such as current-to-voltage conversion for a 4–20 mA transmitter.

8. Sample-and-Hold / Anti-Aliasing

A sample-and-hold circuit maintains the signal during conversion, while an analog anti-aliasing filter limits frequencies above the useful measurement bandwidth.

Conclusion

The major conditioning functions are amplification, filtering, isolation, linearization, scaling, excitation and signal conversion. Proper conditioning improves signal-to-noise ratio, ADC utilization, accuracy and reliability.

Solution: Noise Sources and Noise Reduction Techniques

Noise

Noise is an unwanted electrical signal that interferes with the desired measurement signal and reduces measurement accuracy.

Major Noise Sources

1. Thermal Noise

Produced by random motion of charge carriers in resistive components. Its noise power increases with temperature and bandwidth.

2. Shot Noise

Caused by the random arrival of charge carriers, especially in semiconductor devices and junctions.

3. Flicker Noise

Also called 1/f noise. It becomes important at low frequencies and is associated with electronic devices and materials.

4. Electromagnetic Interference (EMI)

Motors, transformers, switching circuits, mobile/radio transmitters and power lines can induce unwanted signals.

5. Ground Loops

Different ground potentials can create unwanted circulating currents and 50/60 Hz interference.

6. Crosstalk

A signal from one channel can couple into another through electrical or electromagnetic coupling.

7. Quantization Noise

Introduced when an analog value is represented by a finite number of ADC levels.

Noise Reduction Techniques

1. Shielding

Use shielded cables and conductive enclosures to reduce electromagnetic pickup.

2. Proper Grounding

Use a carefully designed grounding scheme and avoid unnecessary ground loops.

3. Twisted-Pair Cables

Twisting reduces pickup from external electromagnetic fields, especially for differential signals.

4. Differential Measurement

Measure the voltage difference between two signal terminals rather than relying on a common ground reference. This improves rejection of common-mode interference.

5. Filtering

Use low-pass, high-pass, band-pass or notch filters according to the measurement bandwidth.

6. Isolation

Isolation amplifiers, transformers or digital isolators can break unwanted electrical paths.

7. Short and Proper Wiring

Keep sensitive signal wires short, route them away from high-power cables and separate analog and noisy digital wiring.

8. Averaging and Digital Filtering

Repeated measurements can be averaged, and digital filters can reduce random noise after acquisition.

Conclusion

Noise reduction requires a combination of shielding, grounding, differential measurement, filtering, isolation and proper wiring. The best technique depends on the source and frequency of the interference.

Solution: ADC Resolution and Calibration Error

Given:

• ADC resolution = 14 bits
• ADC counts = $0$ to $16383$
• Input range = 0–2.500 V
• Temperature range = 0–200°C
• Sensor output = 0–2.5 V linearly
• Measured code at 100°C = 6550

(i) Ideal LSB and Ideal Code at 100°C

Step 1: Calculate ideal LSB

A 14-bit ADC has:

$$2^{14}=16384$$

quantization intervals.

Therefore:

$$LSB=\frac{2.500}{16384}$$

$$LSB=0.0001525879\text{ V}$$

Hence:

$$\boxed{LSB\approx152.59\,\mu V}$$

Step 2: Ideal voltage at 100°C

The sensor is linear from 0 V at 0°C to 2.5 V at 200°C.

At 100°C, which is half of the temperature range:

$$V_{100}=2.5\times\frac{100}{200}=1.25\text{ V}$$

Step 3: Ideal ADC code

Using the ideal ADC transfer convention:

$$Code_{ideal}=\frac{V_{100}}{V_{FS}}(2^{14}-1)$$

$$=\frac{1.25}{2.5}\times16383$$

$$=8191.5$$

So the ideal code is approximately:

$$\boxed{8192}$$

(ii) Offset Error

Measured code:

$$Code_{measured}=6550$$

Ideal code:

$$Code_{ideal}\approx8192$$

Code error:

$$\Delta Code=6550-8192=-1642$$

Convert this code error into voltage using the LSB:

$$\Delta V=\Delta Code\times LSB$$

$$=-1642\times0.0001525879$$

$$\boxed{\Delta V\approx-0.25055\text{ V}}$$

So the measured result is approximately 0.251 V below the ideal value.

Equivalent temperature error

The sensor has a range of:

$$2.5\text{ V}\leftrightarrow200°C$$

Therefore its sensitivity is:

$$\frac{200}{2.5}=80°C/V$$

Hence:

$$\Delta T=\Delta V\times80$$

$$=-0.25055\times80$$

$$\boxed{\Delta T\approx-20.04°C}$$

Therefore, at the 100°C reference point, the calibrated reading corresponds to an error of approximately −20°C relative to the ideal transfer.

Cross-check Using Measured Code

Measured voltage corresponding to code 6550:

$$V_{measured}\approx6550\times\frac{2.5}{16384}$$

$$\approx0.99945\text{ V}$$

This corresponds to:

$$T_{measured}\approx\frac{0.99945}{2.5}\times200$$

$$\approx79.96°C$$

So a true 100°C input is being reported at approximately 79.96°C, giving an error of approximately:

$$79.96-100\approx-20.04°C$$

Final Answer

Quantity	Result
ADC resolution	14 bit
Ideal LSB	152.59 μV
Ideal code at 100°C	≈ 8192
Measured code	6550
Code error	−1642 counts
Offset error	≈ −0.2506 V
Equivalent temperature error	≈ −20.04°C
Measured temperature equivalent	≈ 79.96°C

Note: Strictly, a single calibration point reveals a combined offset/gain error unless the gain is independently known. Under the exam's stated linear-sensor assumption, the difference at the 100°C reference is treated as the offset error.