Compressive sensing allows the reconstruction of original signals from a much smaller number of samples as compared to the Nyquist sampling rate. The effectiveness of compressive sensing motivated the researchers for its deployment in a variety of application areas. The use of an efficient sampling matrix for high-performance recovery algorithms improves the performance of the compressive sensing framework significantly. This paper presents the underlying concepts of compressive sensing as well as previous work done in targeted domains in accordance with the various application areas. To develop prospects within the available functional blocks of compressive sensing frameworks, a diverse range of application areas are investigated. The three fundamental elements of a compressive sensing framework (signal sparsity, subsampling, and reconstruction) are thoroughly reviewed in this work by becoming acquainted with the key research gaps previously identified by the research community. Similarly, the basic mathematical formulation is used to outline some primary performance evaluation metrics for 1D and 2D compressive sensing.

In the realm of supply chain management, the impact of Artificial Intelligence (AI) tools on optimizing commodity distribution is undeniable. This study presents the transformative potential of AI and computer vision in the field of commodity supply chain management. The capability of AI to reduce yield loss and enhance supply chain efficiency is a growing trend and vision-based commodity defect monitoring can be useful in this regard. We explored the employment of real-time computer vision techniques in supply chain flaw management, which include Detection Transformer (DETR), a type of Vision Transformer (ViT), and compared its performance with the You Only Look Once (YOLO) and other AI models. Computational feasibility is assessed, encompassing various computer vision and AI models, by using a dataset comprising images of commodity items used to substantiate our findings. The obtained results have shown the improved performance of DETR with a detection and classification accuracy of 96%, directly correlating with improved supply chain management. On the other hand, the higher computational burden imposed by DETR makes it less feasible for the higher constrained embedded applications. The practicality of AI algorithms for real-time defect identification reveals promising prospects for integration into supply chain systems. This research underscores AI’s potential to revolutionize commodity supply chain management, extending its benefits to various commodity distribution networks.