Abstract
Digital Signal Processing (DSP) is a crucial field of research and study aimed at manipulating digital signals to enhance their accuracy, reliability, and efficiency. Over the past 5-10 years, advancements in DSP technology have yielded high performance across various fields including telecommunications, audio processing, medical imaging, and more. Recent innovations continue to expand the capabilities and applications of DSP. This paper highlights several new methods and trends in DSP that represent significant achievements in the field. Examples include: “integration of machine learning and deep learning,” “rare imaging and compression sensitivity,” “quantum signal processing,” “graph signal processing,” “advanced methods for signal reconstruction,” “real-time DSP in embedded systems,” and more. Overall, the field of digital signal processing is rapidly evolving, with new methods and technologies emerging to address the challenges of the digital world. The integration of machine learning, exploration of quantum computing, and adaptation to complex data structures are just a few directions in which DSP is expanding. As these new methods are refined and widely adopted, they are bound to open up new opportunities across a wide range of disciplines and fields.