Examining Role of Mathematics in Hardware Engineering: Relationship Between Linear Algebra and Embedded Systems
Abstract
Linear algebra is often regarded as an abstract branch of mathematics, however, it has a myriad of implementations in the real world, particularly in the field of computer science and hardware engineering. Embedded systems, which represent small computing units integrated into various devices that we use on a daily basis, heavily rely on mathematical models in order to function properly. This article examines the role that fundamental linear algebra concepts such as vectors, matrices, eigenvalues and linear transformations play in embedded systems. Particularly, it outlines practical implementation of these concepts in signal processing, control systems, robotics and image processing, highlighting how they support core tasks and enable efficient performance. By bridging mathematical theory with engineering practice, this paper shows why linear algebra is indispensable for modern embedded system design and development.
References
[1] N. Ahmed, “Why Is Data Represented with Vectors & Matrices?” Medium. [Online]. Available:https://medium.com/@nahmed3536/why-is-data-represented-with-vectors-matrices-f0f72e08cbc9. [Accessed: Sep. 6, 2025] [2] “Vectors and Matrices in Signal Processing – Study Guide,” Fiveable Library, [Online]. Available: https://library.fiveable.me/bioengineering-signals-systems/unit-2/vectors-matrices-signal-processing/study-guide/oDLEQYPeJtuQXvq4. [Accessed: Sep. 6, 2025]. [3] E. W. Weisstein, “Eigenvalue,” Wolfram MathWorld. [Online]. Available: https://mathworld.wolfram.com/Eigenvalue.html. [Accessed: Sep. 6, 2025].
[4] Zakaria Jaadi ,“A Step-by-Step Explanation of Principal Component Analysis (PCA),” Built In, [Online]. Available: https://builtin.com/data-science/step-step-explanation-principal-component-analysis. [Accessed: Sep. 6, 2025].
[5] “Linear map,” Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Linear_map. [Accessed: Sep. 6, 2025].
[6] “Embedded System Essentials: Definitions and Uses,” Advantech, 2025. [Online]. Available: https://www.advantech.com/en-us/resources/industry-focus/embedded-system-essentials-definitions-and-uses. [Accessed: Sep. 7, 2025].
[7] “What is Convolution?,” MathWorks. [Online]. Available: https://www.mathworks.com/discovery/convolution.html. [Accessed: Sep. 7, 2025].
[8] “Matrix Filter Representations,” DSPRelated. [Online]. Available: https://www.dsprelated.com/freebooks/filters/Matrix_Filter_Representations.html. [Accessed: Sep. 7, 2025].
[9] “State-Space Representation,” Fiveable. [Online]. Available: https://fiveable.me/control-theory/unit-2/state-space-representation/study-guide/9uliQAjdyYtzzs83. [Accessed: Sep. 7, 2025].
[10] H. Zahid, “Embedded systems for motor control: From PWM to field-oriented control (FOC), ”Medium, Jul.24 ,2023. [Online]. Available: https://medium.com/@hammadzahid1010/embedded-systems-for-motor-control-from-pwm-to-field-oriented-control-foc-1937ed1c525e. [Accessed: Sep. 7, 2025].
[11] H. Zahid, “The matrix behind motion: Linear algebra in robotic kinematics,” Medium, Aug. 3, 2023. [Online]. Available: https://medium.com/@hammadzahid1010/the-matrix-behind-motion-linear-algebra-in-robotic-kinematics-27d61b2d7e23. [Accessed: Sep. 8, 2025].
[12] “Kernel (image processing),” Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Kernel_(image_processing). [Accessed: Sep. 9, 2025].
[13] M. Iba, “Linear algebra in computer vision,” Medium, Jan. 5, 2024. [Online]. Available: https://medium.com/@musadiq.iba/linear-algebra-in-computer-vision-9c5e0be73ae7. [Accessed: Sep. 9, 2025].






