Machine vision, an interdisciplinary field that aims to replicate human visual perception in computers, has experienced rapid progress and significant contributions. This paper traces the origins of machine vision, from early image processing algorithms to its convergence with computer science, mathematics, and robotics, resulting in a distinct branch of artificial intelligence. The integration of machine learning techniques, particularly deep learning, has driven its growth and adoption in everyday devices. This study focuses on the objectives of computer vision systems: replicating human visual capabilities including recognition, comprehension, and interpretation. Notably, image classification, object detection, and image segmentation are crucial tasks requiring robust mathematical foundations. Despite the advancements, challenges persist, such as clarifying terminology related to artificial intelligence, machine learning, and deep learning. Precise definitions and interpretations are vital for establishing a solid research foundation. The evolution of machine vision reflects an ambitious journey to emulate human visual perception. Interdisciplinary collaboration and the integration of deep learning techniques have propelled remarkable advancements in emulating human behavior and perception. Through this research, the field of machine vision continues to shape the future of computer systems and artificial intelligence applications.

The discipline of Deep Learning has been recognized for its strong computational tools, which have been extensively used in data and signal processing, with innumerable promising results. Among the many commercial applications of Deep Learning, Music Signal Processing has received an increasing amount of attention over the last decade. This work reviews the most recent developments in Deep Learning in Music signal processing. Two main applications that are discussed are Music Information Retrieval, which spans a plethora of applications, and Music Generation, which can fit a range of musical styles. After a review of both topics, several emerging directions are identified for future research.

A modification of the classic logistic map is proposed, using fuzzy triangular numbers. The resulting map is analysed through its Lyapunov exponent (LE) and bifurcation diagrams. It shows higher complexity compared to the classic logistic map and showcases phenomena, like antimonotonicity and crisis. The map is then applied to the problem of pseudo random bit generation, using a simple rule to generate the bit sequence. The resulting random bit generator (RBG) successfully passes the National Institute of Standards and Technology (NIST) statistical tests, and it is then successfully applied to the problem of image encryption.

This work proposes a modified logistic map based on the system previously proposed by Han in 2019. The constructed map exhibits interesting chaos related phenomena like antimonotonicity, crisis, and coexisting attractors. In addition, the Lyapunov exponent of the map can achieve higher values, so the behavior of the proposed map is overall more complex compared to the original. The map is then successfully applied to the problem of random bit generation using techniques like the comparison between maps, $XOR$ , and bit reversal. The proposed algorithm passes all the NIST tests, shows good correlation characteristics, and has a high key space.

Currently, chaotic cryptography is mainly concentrated on image encryption. Once-popular stream-encryption algorithms, e.g., textual data, are now rarely considered. This article studies how chaotic image-encryption algorithms can be used to encrypt text. The proposed approach to this problem consists of two stages: in the first stage, the text message is intended for encryption into an image; in the second step, the selected image-encryption algorithm is used. An example illustrates the efficiency of this method. In addition, the article presents measures used in image-encryption analysis that confirm the security of the obtained cipher-image, such as entropy (value close to 8), correlation of adjacent pixels (values close to 0), or measures related to differential cryptanalysis. The proposed method offers a new look at text encryption using chaos, by applying image-encryption algorithms already known from the literature.