Conditioning in Real-time Systems

In a real-time system, there are various components in it. It has sensor, actuator, conditioning units and interface units. The signals generated by the sensor can’t be used directly by the interface unit. Similarly the signals produced by the computer can’t be used directly by the actuator in the real-time system. Therefore, a process is required to change the signals from one from to another and this process is known as conditioning in real-time systems.
Types of Conditioning in Real-time Systems: Following are the conditioning performed on different signals in real-time systems:

• Voltage Amplification – Voltage amplification is basically performed to match the scale of voltage of sensor output signal with the scale of voltage of interface input signal. The output voltage of sensor needs to be scaled full with the full scale of interface output voltage. For example, sensor may generate signal voltage in milli volts range whereas the interface input may need signal voltage in volts range. In this case voltage amplification is done.
• Voltage Level Shifting – Voltage level shifting is required to assign the voltage level generated by the sensor that can be acceptable by the interface unit. Same thing is with the voltage level shifting of signals from interface unit to the actuator in real-time systems. For example, sensor may generate signal voltage from -0.05 to +0.5 volts range but interface unit accepts only in 0 to 1 volts range signals. In this case sensor voltage is level shifted to match with the interface unit.
• Frequency Range Shifting – Just like the level shifting of voltage, frequency range shifting is also carried out in real-time systems. This is carried out to match the frequency of the signals generated by sensor and signals that may be accepted by the interface input. In the same way, range of signals generated by the computer is shifted to match the frequency range of actuator.
• Noise Filtering – Many types of noise may occur in the real-time system and signals may be affected by these noise. Hence in order to reduce the noise in signals, frequency filtering is used. It reduces the noise component in the signal and signals are shifted from noise bands.
• Signal Mode Conversion – Signal mode conversion in conditioning is basically carried out to change the alternating current to direct current and vice-versa. Signal mode conversion is used in one more way – changing of analog signals to digital and vice-versa. When the signals transmits from sensor to interface unit, signals are changed from analog to digital and when the signals are transmitted from computer to actuator then signals are changed from digital to analog.
• Signal Calibration – Signal calibration is an important step in conditioning where the output signal from the sensor is calibrated to a known standard. This is necessary to ensure that the signal accurately represents the quantity being measured. Calibration is often performed using calibration standards and equipment.
• Signal Conditioning for Nonlinearities – In real-time systems, it is common for the output signal from the sensor to have nonlinearities. Signal conditioning techniques such as linearization and compensation are used to address these nonlinearities and obtain a more accurate representation of the measured quantity.
• Filtering for Signal Smoothing – In some real-time systems, the signal from the sensor may contain random variations or noise that can cause problems in the system. Signal filtering techniques such as averaging and low-pass filtering are used to smooth out the signal and reduce the effects of noise.
• Data Compression – In some real-time systems, it may be necessary to transmit large amounts of data over a limited bandwidth channel. Data compression techniques are used to reduce the amount of data that needs to be transmitted while maintaining the accuracy of the signal.
• Data Encryption – In some real-time systems, the transmitted data may need to be protected from unauthorized access or tampering. Data encryption techniques are used to secure the data and ensure its confidentiality and integrity.
• Sensor Fusion – Sensor fusion is a technique used in real-time systems where data from multiple sensors is combined to obtain a more accurate representation of the environment. Sensor fusion techniques such as Kalman filtering and particle filtering are commonly used in robotics, autonomous vehicles, and other real-time systems.