Signals and systems, a foundational domain in modern engineering, are integral to diverse fields such as communications, control, electronics, healthcare, and multimedia processing. Research focuses on modeling, processing, and analyzing signals, encompassing continuous or discrete-time signals and system properties like linearity, time invariance, and causality. Frequency-domain tools, including Fourier, Laplace, and Z-transforms, enable spectral analysis of signals and system frequency responses. Advances in digital signal processing have driven innovations in discrete-time systems, particularly in digital communications and image processing. Furthermore, integration with artificial intelligence has expanded applications in speech recognition, image classification, and cross-disciplinary engineering solutions, underscoring the field’s evolving interdisciplinary impact. This paper aims to explore some key theories and applications in signals and systems, and analyze how to improve system performance and solve practical engineering problems through reasonable system design and signal processing methods, based on the latest research results and technological advances.

Jianguo Jia, Fangdi Mao, Jiangbo Chen, Yuting Zhang, Qinyang Zhu , Cai Weiling, Qinxi Lin. The Application of Signals in Monitoring and Transmission. International Journal of Multimedia Computing (2025), Vol. 6, Issue 1: 64-70. https://doi.org/10.38007/IJMC.2025.060106.

[1] Y. Li, Z. Ma, A.h. Yang, Study on Theory of Signal and System in Modern Communication Technology, in: Y. Yang, M. Ma (Eds.) Proceedings of the 2nd International Conference on Green Communications and Networks 2012 (GCN 2012): Volume 3, Springer Berlin Heidelberg, Berlin, Heidelberg, 2013, pp. 561-567.

[2] A. Ortega, P. Frossard, J. Kovaдeviд┤, J.M.F. Moura, P. Vandergheynst, Graph Signal Processing: Overview, Challenges, and Applications, Proceedings of the IEEE 106(5) (2018) 808-828.

[3] J. Yu, J. Zhang, Recent progress on high-speed optical transmission, Digital Communications and Networks 2(2) (2016) 65-76.

[4] D. Yu, L. Deng, Deep Learning and Its Applications to Signal and Information Processing [Exploratory DSP], IEEE Signal Processing Magazine 28(1) (2011) 145-154.

[5] A.J. Flanagin, The Conduct and Consequence of Research on Digital Communication, Journal of Computer-Mediated Communication 25(1) (2020) 23-31.

[6] Q. Zhang, M. Zhang, T. Chen, Z. Sun, Y. Ma, B. Yu, Recent advances in convolutional neural network acceleration, Neurocomputing 323 (2019) 37-51.

[7] A. Vazeou, Continuous blood glucose monitoring in diabetes treatment, Diabetes Research and Clinical Practice 93 (2011) S125-S130.

[8] C.L. Philip Chen, C.-Y. Zhang, Data-intensive applications, challenges, techniques and technologies: A survey on Big Data, Information Sciences 275 (2014) 314-347.

[9] H. Li, D. Yuan, X. Ma, D. Cui, L. Cao, Genetic algorithm for the optimization of features and neural networks in ECG signals classification, Scientific Reports 7(1) (2017) 41011.

[10] R. Rodríguez, A. Mexicano, J. Bila, S. Cervantes, R. Ponce, Feature Extraction of Electrocardiogram Signals by Applying Adaptive Threshold and Principal Component Analysis, Journal of Applied Research and Technology 13(2) (2015) 261-269.

[11] A. Gacek, An Introduction to ECG Signal Processing and Analysis, in: A. Gacek, W. Pedrycz (Eds.), ECG Signal Processing, Classification and Interpretation: A Comprehensive Framework of Computational Intelligence, Springer London, London, 2012, pp. 21-46.

[12] F. Badilini, M. Vaglio, R. Dubois, P. Roussel, N. Sarapa, I. Denjoy, F. Extramiana, P. Maison-Blanche, Automatic analysis of cardiac repolarization morphology using Gaussian mesa function modeling, Journal of Electrocardiology 41(6) (2008) 588-594.