Understanding Signal Processing
A form of data analysis, signal processing seeks to refine, enhance and filter various signals such as sounds,
images or even scientific data, to add clarity to the information being transmitted. That could be a cleaner sound,
a sharper image or in the case of raw data, removing extraneous readings to focus on the core result.
For instance, sensors within vehicles produce vast quantities of data in real time , together they may seem a jumbled
mess of contradictory elements, but when processed, provide clear information about performance , location , speed and
so on.
By providing this clarity from available data, signal processing allows systems to monitor and control vehicle
functions much better, contributing to increased safety, higher performance and efficiency as well as improved
reliability.
What is Digital Signal Processing?
A type of signal processing, Digital Signal Processing (DSP) uses a range of digital tools and algorithms to convert
analogue signals to a digital format, and then process them with more precision than would otherwise be possible.
DSP is often used in communications and video transmission but is perhaps most commonly found in the audio industry,
where digitization and processing allows for endless options when creating music.
Signal Processing in Practice
With data driving so many industries today, it won’t be a surprise to find that signal processing is used in a
diverse range of applications. One of the leading adopters of signal processing technology is the automotive
industry, where we can find all kinds of applications, including:
Engine management — Signal Processing is used within the ECU to interpret data
and optimize
performance in real time.
Safety Systems — Analyzing data inputs in braking and collision avoidance systems to ensure
accurate and effective performance in emergency situations.
Driving Aids — Technology such as lane assistance and adaptive cruise control make use of
signal processing for reliable performance. These same systems power autonomous vehicles too.
Infotainment
Telematics Systems
EV Performance — Signal Processing allows for more accurate real-time insight into battery
state and energy consumption, allowing for more precise control and improved efficiency.
Maintenance Scheduling — Processing vehicle data allows for more accurate
insight into vehicle
wear, and enables more accurate predictions for maintenance needs.
Conclusion
A crucial technology in many industries, signal processing has become a pivotal element in the automotive industry
today. As vehicles use increasing volumes of data to maintain the efficiency and performance demanded by consumers
and fleet operators, signal processing enables more effective use of data in a wide range of applications.
From driver aids to pro-active maintenance, signal processing enables a better driver experience, more effective
management and improved performance in vehicles of all types.
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