This paper presents a deep learning-based intelligent web application for the early detection of eye diseases using retinal fundus images. The dataset used in this study consists of 4,216 retinal fundus images collected from Kaggle, representing multiple eye disease categories. Multiple deep learning architectures, including CNN, DenseNet, InceptionV3, and ResNet, were evaluated and compared with a proposed modified MobileNetV2 architecture. The proposed architecture enhances the baseline MobileNetV2 by optimizing feature extraction and classification layers for improved performance in multi-class eye disease detection. Experimental results show that the proposed model achieved an overall cl... More >

Urban traffic congestion is a major issue that negatively affects mobility efficiency, environmental sustainability and road safety. Many recent methods for controlling traffic signals have used methods based on deep reinforcement learning (DRL) and provided positive results. However, it focused primarily on vehicle flow and have not taken into account pedestrian dynamics due to inherent difficulty related to accurately sensing all pedestrians. As a result of these limitations, recent advances in sixth-generation (6G) localisation technology will provide new opportunities to provide precise, low-latency tracking of pedestrians at signalized intersections, allowing for improved control of ped... More >

Urban parking inefficiency has become a critical challenge in modern cities, leading to increased traffic congestion, higher fuel consumption, and greater environmental impact. This paper proposes an intelligent smart parking management system that integrates hardware sensing, machine learning, and computer vision to enable real-time parking monitoring and automated vehicle identification. The system combines infrared sensors, camera modules, and microcontroller-based control with vision-based parking space detection and automatic license plate recognition (ALPR). Experimental results demonstrate that the parking space detection module achieves an accuracy of 93.97%, while the license plate... More >

Wireless communication technologies are becoming increasingly important in today's rapidly developing technological world. The widespread use of the Internet, the popularity of smartphones and other mobile devices, and the rise in industrial applications have all contributed to the start of a new wireless communication technology revolution. As a result, 6th generation (6G) wireless communication technology is intended to provide enhanced performance features such as faster speeds, larger bandwidths, lower latency, higher connection density, and improved reliability. mmWave-band communications are already having a vital impact on 5G. Similarly, communications in the TeraHertz (THz)-band will... More >

Unmanned aerial vehicles (UAVs) have great potential in 5G and 6G networks due to their communication capabilities, low-cost, and adaptable deployment opportunities. The use of multiple Unmanned Aerial Vehicles (UAV) systems is a more cost-effective and operationally efficient approach compared to using a single UAV system. Compared to a large UAV, flying ad hoc networks (FANETs) with small UAVs have many advantages. This UAV-based architecture typically uses clusters of UAVs, where the UAV acts as a cluster head (CH), collecting information from other UAVs and sending it to the emergency communication vehicle (ECV). Although this model works well in open areas, Non-line-of-sight (NLoS) issu... More >

Device-to-Device (D2D) communication serves as a pivotal technology in the evolution of cellular networks, presenting opportunities to markedly improve spectral efficiency, minimize latency, and optimize energy consumption through direct interactions between user devices. Nonetheless, the implementation of spectrum reuse by D2D pairs presents significant interference challenges, particularly in underlay communication contexts. This paper presents a robust and energy-efficient resource allocation mechanism utilizing a polynomial-time proportional fair (PF) scheduling algorithm. The framework provides minimum rate assurances for cellular users while adaptively distributing multiple resource bl... More >

Aiming to move from conventional throughput-centric paradigms to intelligent, context-aware systems able of perception and autonomous decision-making, sixth-generation (6G) wireless networks is seeking. Driven by recent developments in deep learning and edge artificial intelligence, computer vision (CV) proves to be a key enabler for such perceptive 6G systems. This paper offers a thorough overview bringing together the scattered terrain of CV-enabled 6G technologies. It benchmarks current models against major 6G performance criteria, evaluates architectural paradigms including federated and split learning, and presents a disciplined taxonomy of use cases. This study also notes the possibili... More >

The emergence of ultra-wideband (UWB) technology, following the FCC’s allocation of the 3.1-10.6 GHz spectrum for unlicensed use, has significantly advanced short-range wireless communication, particularly in biomedical telemetry. This paper proposes a compact UWB microstrip patch antenna (32×32×1.6 mm³) designed for biomedical applications such as brain tumor detection, cancer screening, and health monitoring. The antenna incorporates a miniaturized radiating patch with stair-step and serrated slot structures, as well as a defected ground configuration to enhance impedance bandwidth and reduce electromagnetic interference. It achieves effective operation across the 2.48-10.8 GHz range... More >