What is Signal Processing? A Complete Guide

Also referred to as digital signal processing (DSP), signal processing is a series of processes required to transform real-world observations like voltage readings, temperature measurements, and vibrations in machines into a format that makes analysis possible and accurate.

Physical measurements like temperature, voltage, and vibration are analog parameters that must first be converted into digital values/formats to be compatible with modern computers or digital device systems. Analog signals can be noisy or potentially hazardous without proper filtering, with these being some reasons for conversion.

DSP is primarily a software activity where digital signal processing software running on graphics cards (GPUs) or processors of desktop computers or smart devices performs various signal processing tasks, such as filtering, transformation, and analysis, to extract insights and meaning from analog signals.

For more demanding DSP applications, Dedicated hardware such as FPGAs (Field Programmable Gate Arrays) or chips like ASICs (Applications Specific Integrated Circuits) are used.

Signal acquisition and conditioning come before signal processing. In the acquisition stage, a DAQ device, like a data logger, collects quantities such as temperature or vibration. The next stage in the sequence is where conditioning (or cleaning) of acquired signals takes place. This involves filtering, amplifying, attenuating, and isolating signals for quality and accuracy.

There are different types of signal processing or approach to the processing task. This is based on domain, processing style, application, and adaptability. Here are some signal processing types:

• Analog Signal Processing: Signal processing was historically done entirely in the analog domain. Analog components like capacitors, resistors, and transistors were used to build filters like RLC filters, through which analog (or continuous-time) signals were processed. However, due to limitations like distortion and noise, signal processing has necessarily transitioned to the digital domain.
• Digital Signal Processing: Because our modern world has become increasingly digitized, with smartphones, computers, and audio devices becoming part of everyday life, DSP software, signal processors, and circuitry have revolutionized signal processing, offering improved accuracy, reduced noise, and compatibility with modern systems.
• Real-time Signal Processing: This type of signal is based on the grouping of real-time processing. This is important for making timely decisions and responding to changing conditions. Think of the pathway of sound vibrations from a microphone to a speaker.

Signal processing cutsacross numerous applications in various industries. For example, Signal processing units or circuits in industrial data acquisition systems help in process control and automation.

Automation-powered robots in food, medical, and automotive facilities can receive visual signals (images) and ultrasonic signals (distance) through sensors. Signal processing units help to filter these signals, making them more accurate and understandable for the digital control systems that translate the signals into the robot's movements and actions.

To improve these systems, DAQ devices are used to measure parameters like vibrations, current, and more. KEYENCE offers data acquisition systems for different measurement applications. Our DAQ hardware, like the NR-500 multi-input data logger, is PC-compatible, unlike conventional data loggers. Its compact design makes it usable even in limited inspection spaces.

Even for new and advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML), signal processing in machine learning is a relevant concept. Many machine learning algorithms rely on DSP techniques to extract relevant features and patterns from data.

Other signal-processing applications include:

• Seismic studies and data analysis
• Digital image and graphics processing
• Data acquisition and signal processing
• Ultrasound and MRI imaging
• Wearable devices and healthcare monitoring

Many signal processing techniques are available. Key techniques include:

• Filtering: This is the process of completely or partially removing unwanted features or components from a signal. Usually, filtering reduces noise and suppresses interfering signals.
• Compression: This process helps remove unnecessary or redundant information in digital signals without removing essential information. Without compression, signals become too big for digital systems to store and transmit.
• Modulation: This process encodes information in a transmitted signal. The technique impresses information such as pictures, voice, or music onto a high-frequency carrier wave.
• Demodulation: This process extracts the original signal or impressed information from the modulated carrier. Demodulation helps remove the high-frequency carrier wave leaving only the originally transmitted signal for further processing.

Signal processing tools refer to software (or programming language) and hardware resources used to perform analog-to-digital signal conversion. These include:

• MATLAB: This is a high-level programming language and environment for performing signal processing and analysis. The software environment allows numerical computations, data analysis, visualization, and algorithm development. It has functions for signal processing techniques like filtering and can interface with other programming languages such as Python, C, and Java.
• Python: Python is a popular, open-source programming language with libraries like NumPy and Pytorch that have impressive machine learning and numerical computation capabilities that are useful in signal processing. Engineers and programmers use Python to develop better signal-processing algorithms and build applications.
• Data Logger: Data loggers are examples of hardware resource components required in signal processing. Newer data loggers have built-in signal-processing circuits, as well as the ability to monitor physical measurements like temperature, voltage, current, and strain. Data loggers are used in various industries, including automotive manufacturing to improve electric vehicles' efficiency and battery performance.