# Hands-On Fundamentals of 1D Convolutional Neural Networks--A Tutorial for Beginner Users
## 1 Introduction  
Recently, there has been a surge of interest in deep learning (DL), with numerous review papers on the topic published in recent years [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. DL architectures, such as Deep Neural Networks (DNNs), Deep Belief Networks (DBNs), Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Transformers, have found compelling applications across various fields. These include computer vision, speech recognition [15; 16; 17; 18; 19; 20; 21], natural language processing [22; 23; 24; 25; 26; 27; 28; 29], bioinformatics [30; 31; 32; 33; 34; 35; 36; 37], medical image analysis [38; 39; 40; 41; 42; 43; 44; 45; 46; 47], climate science [48; 50; 51; 52; 53; 54; 55; 56; 57; 58], and material inspection [59; 60; 61; 62; 63; 64; 65; 66; 67]. Focusing specifically on one-dimensional problems, CNN-based architectures are still the most widely used in the literature compared to other, more recent, and potentially more effective architectures for certain problems. This observation is based on research conducted using prominent databases and search engines (e.g., Scopus, IEExplore, ScienceDirect, SpringerLink, Web of Science, ACM Digital Library, PubMed). Relevant results have emerged regarding the frequency with which a specific deep learning architecture, associated with one-dimensional problems, appears in paper titles published between 2018 and 2023. Although this research is not exhaustive, it highlights that discussions on 1D CNNs remain highly active in the literature. However, within the realm of CNNs applications, it is important to note that those focusing on one-dimensional data are less frequent compared to those dealing with multidimensional data (2D and 3D). This discrepancy in research activity can be observed through a simple Google search. For example, a search for terms like \"3D CNN\", \"2D CNN\", or \"1D CNN\" reveals a significant drop in the number of results as the dimensionality of the datafed into the network decreases. Although searches include web pages and blog posts, which are generally not peer-reviewed, a similar trend is evident in the peer-reviewed literature as well. The relatively limited number of published papers on 1D CNNs, compared to those on 2D and 3D, suggests an under-explored area that could stimulate further research [68]. One possible outcome of this could be an increase in review papers specifically focusing on 1D CNNs, which are currently quite limited [69; 70; 71]. 1D CNNs offer promising solutions across various sectors. They are particularly well-suited for real-time applications [72; 73; 74; 75] due to their low computational requirements. In fact, continuous online monitoring generates a large quantity of data that needs to be processed and analyzed rapidly and accurately to efficiently detect specific patterns, assets or even diseases. This is the case of Raman spectra detection, in which the use of 1D CNNs has proven effective at identifying in real-time one pure unknown Raman instance from thousands of classes with higher accuracy over traditional multivariate analyses [72]. In the context of real-time applications for 5G communications, the increased bandwidth has posed a significant challenge in linearization of power amplifier over a wide bandwidth, which has been faced implementing 1D CNNs that have proven to be highly effective for real-time digital pre-distortion operation, surpassing traditional methods like Volterra series [73]. The use of 1D CNNs has also been proposed as a computationally efficient solution for fast and accurate diagnostics in the power industry, particularly for monitoring electromagnetic interference. This approach has enabled highly accurate automatic condition monitoring, significantly aiding in the identification of insulation and mechanical faults in high-voltage electrical power assets [74].