Dynamic adaptive streaming over HTTP (DASH) has emerged as a foundational technology for video delivery over heterogeneous networks, particularly within 5G environments characterized by high bandwidth variability, ultralow-latency requirements, and dense mobility patterns. This survey provides a comprehensive analysis of DASH from a system-level perspective, emphasizing the interaction between adaptive bitrate (ABR) algorithms, transport-layer evolution, edge computing paradigms, and radio access dynamics. The study synthesizes existing literature across four primary dimensions: performance optimization, latency reduction, energy efficiency, and security resilience. It highlights how traditional ABR strategies—throughput-based, buffer-based, hybrid, and machine learning-driven approaches—exhibit distinct tradeoffs in quality of experience (QoE), stability, computational overhead, and power consumption. Furthermore, the evolution of HTTP protocols (HTTP/2 and HTTP/3) and the integration of multiaccess edge computing (MEC), content delivery network (CDN) caching, and device-to-device communication are shown to significantly influence streaming efficiency, albeit with new architectural complexities. A key contribution of this work is the explicit framing of DASH in 5G as a cross-layer optimization problem, where QoE, energy efficiency, and security are tightly coupled rather than independently optimizable. The survey also examines emerging security vulnerabilities across media presentation description (MPD) manipulation, segment injection, and CDN poisoning, emphasizing that DASH’s HTTP-based design inherently expands the attack surface in distributed deployments. Finally, the paper identifies persistent gaps in current research, including the dominance of simulation-based evaluations, limited real-world validation, lack of unified cross-layer frameworks, and insufficient integration of security-aware and energy-aware adaptation mechanisms. The findings suggest that future DASH systems must evolve toward coordinated, intelligence-driven architectures that jointly optimize performance, robustness, and resource efficiency in dynamic 5G and beyond networks.
With recent advancements in sensory technologies and computational power, multimodel analysis and learning have gained significant attention in the machine learning (ML) community and have been applied across a wide array of domains. Nevertheless, the effective integration of multimodal data sources poses significant challenges to the extraction of discriminative representations, as well as the design of robust fusion strategies capable of capturing complex intermodal relationships. To address these challenges, in this work, a multimodal based representation learning solution, the deep discriminative multiple canonical correlation analysis (DDMCCA), is proposed. To verify the generic naturalness and effectiveness of DDMCCA, we conduct experiments on three databases with different types of input data sources, including face recognition, handwritten digital recognition, and object recognition. Experimental results validate the superiority of the presented solution over state-of-the-art (SOTA).
This work presents a method for car license plate (LP) detection utilizing machine learning techniques that ensures rapid image processing and high detection accuracy. Three primary contributions distinguish this research. First, we introduce a novel image representation known as the “Integral Image,” which facilitates the rapid computation of features for our detector. The system is trained using a diverse set of positive (LP) and negative (non-LP) images and is validated against various real-world scenes. Second, we implement an AdaBoost-based learning algorithm to identify a minimal subset of critical visual features, leading to the development of efficient classifiers. Last, our cascade method integrates increasingly complex classifiers, enabling the rapid elimination of background regions while focusing computational efforts on promising object-like areas. The Open Computer Vision (CV) Library and Python are employed to demonstrate this implementation. We evaluated our algorithm on a dataset of 3599 images sourced from traffic video footage, achieving an impressive success rate of 95.8% in detecting Hungarian LPs.
