Search Results (7)

Precise identification of water bodies in agricultural watersheds is crucial for irrigation, water resource management, and flood disaster prevention. However, the spectral noise caused by complex light and shadow interference and water quality differences, combined with the diverse shapes of water bodies and the high computational cost of image processing, severely limits the accuracy of water body recognition in agricultural watersheds. This paper proposed a lightweight and efficient learnable Kalman filter and Deformable Convolutional Attention Network (LKF-DCANet). The encoder is built using a shallow Channel Attention-Enhanced Deformable Convolution module (CADCN), while the decoder combines a Convolutional Additive Token Mixer (CATM) and a learnable Kalman filter (LKF) to achieve adaptive noise suppression and enhance global context modeling. Additionally, a feature-based knowledge distillation strategy is employed to further improve the representational capacity of the lightweight model. Experimental results show that LKF-DCANet achieves an Intersection over Union (IoU) of 85.95% with only 0.22 M parameters on a public dataset. When transferred to a self-constructed UAV dataset, it achieves an IoU of 96.28%, demonstrating strong generalization ability. All experiments are conducted on RGB optical imagery, confirming that LKF-DCANet offers an efficient and highly versatile solution for water body segmentation in precision agriculture. Full article

Vessel detection is critical for ensuring maritime transportation and navigational safety, creating a pressing need for detection methodologies that are more efficient, precise, and intelligent in the maritime domain. Nonetheless, accurately detecting vessels across multiple scales remains challenging due to the diversity in vessel types and locations, similarities in ship hull shapes, and disturbances from complex environmental conditions. To address these issues, we introduce an innovative FSN-YOLO framework that utilizes enhanced YOLOv8 with multi-layer attention feature fusion. Specifically, FSN-YOLO employs the backbone structure of FasterNet, enriching feature representations through super-resolution processing with a lightweight Convolutional Neural Network (CNN), thereby achieving a balance between processing speed and model size without compromising accuracy. Furthermore, FSN-YOLO uses the Receptive-Field Attention (RFA) mechanism to adaptively fine-tune the feature responses between channels, significantly boosting the network’s capacity to capture critical information and, in turn, improve the model’s overall performance and enrich the discriminative feature representation of ships. Experimental validation on the Seaship7000 dataset showed that, compared to the baseline YOLOv8l approach, FSN-YOLO considerably increased accuracy, recall rates, and mAP@0.5:0.95 by absolute margins of 0.82%, 1.54%, and 1.56%, respectively, positioning it at the forefront of current state-of-the-art models. Full article

The main goal of this paper is the proposal of a key-based steganographic system in which the ratio of capacity and image quality metrics that represents the stego object while reducing the detectability of hidden content was improved. The main contribution of the proposed steganographic system is a new algorithm for selecting stego areas. The area selection algorithm is based on clustering the pixels of the cover object into a predetermined number of clusters. The goal of this selection of areas (clusters) is to group as many homogeneous parts of the image as possible in order to cover these areas with as few rectangular shapes as possible. Since the data on the defined rectangles represent the key of the system, the capacity of the additional secret channel is minimized in this way. On the obtained stego-carriers, an embedding of test random content is performed in order to estimate its detectability. By combining the proposed area selection method with the Minimal Decimal Difference steganographic method, a system was created with an optimal trade-off between detectability of secret content, quality and capacity of the carrier, and the length of the stego-key. Finally, a comparison of the obtained results with relevant adaptive steganographic methods is presented. The proposed concept obtains its verification in one practical system for secure file transfer of controlled cryptographic strength. Full article

Current developments and the need for high-performance devices that provide safe and reliable communications present a future perspective by using visible light as an alternative solution that can substantially improve road and pedestrian safety. The daily use of smartphones is imperative; thus one can build on this premise a system dedicated to the aforementioned problem. However, the problem of the visible light communication channel (VLC) is highly dynamic and becomes extremely unpredictable in terms of exposure to noise sources. Developing applications dedicated to direct communications with infrastructure and vehicles using portable devices is becoming a challenge and at the same time a necessary solution. The article proposes the shaping of an emission–reception architecture dedicated to adaptive fuse light communications using OCC (optical camera communication) but also standard VLC communications using ambient light sensors via an Android application. This approach aims to provide a first step in shaping information-sharing applications using VLC communications. As far as we know, this approach has not been implemented in external VLC systems. The performance of the architecture and the application was demonstrated by practical tests that confirmed the capacity of the technology even if we are in the first stage. Full article

Industrial Internet of Things (IoTs) applications will have higher demands in terms of system capacity and wireless transmission performance in the future. Reconfigurable intelligent surfaces (RIS) for the wireless transmission environment reconstruction is currently a research hotspot in the field of wireless communication. In this paper, RIS is applied to industrial IoTs scenarios, and an innovative infrastructure based on shape-adaptive RIS suitable for smart industrial IoTs is proposed. Based on the many advantages of RIS, the proposed infrastructure can meet the significant demands of industrial IoTs in terms of network coverage, system capacity, transmission rate, and communication security. Further, in order to achieve flexible RIS phase adjustment in wider ranges, in this paper, a shape-adaptive RIS mechanism is proposed to regulate RIS-reflected waves in the shape domain, which can be achieved by scalable RIS unit sizes and macroscopically changeable physical shapes. This mechanism can reduce the requirements for RIS-controller and artificial-unit design and expand the spatial distribution of reflected beams, whereas this is difficult to achieve by conventional regulations in the spatial, time, and frequency domains. The simulation results show that, in terms of system-coverage probability, the system performance of RIS is better than that of general communication systems, and the performance of the deformable RIS-based system is the best. In terms of channel gain, the performance of the deformable RIS is related to its surface size. Moreover, the path loss caused by the long-distance transmission of wireless signals can be compensated for by increasing the RIS surface area. Full article

Devices in wireless sensor networks are typically powered by batteries, which must last as long as possible to reduce both the total cost of ownership and potentially pollutant wastes when disposed of. By lowering the duty cycle to the bare minimum, time slotted channel hopping manages to achieve very low power consumption, which makes it a very interesting option for saving energy, e.g., at the perception layer of the Internet of Things. In this paper, a mechanism based on probabilistic blacklisting is proposed for such networks, which permits to lower power consumption further. In particular, channels suffering from non-negligible disturbance may be skipped based on the perceived quality of communication so as to increase reliability and decrease the likelihood that retransmissions have to be performed. The only downside of this approach is that the transmission latency may grow, but this is mostly irrelevant for systems where the sampling rates are low enough. Full article

The demand for video traffic is increasing over mobile networks that are taking another shape by its heterogeneity. However, the wireless link capacity cannot cope with the traffic demand. This is due to the interference problem that can be considered as the most important challenge in heterogeneous networks. Consequently, it will result in poor service for the quality of video streaming such as the bad quality delivery, service interruption, etc. In this paper, we propose a solution for interference mitigation in the context of heterogeneous networks through power control mechanism, while guaranteeing the Quality of Service of the video streaming. We derive a model for adapting the video bit rate to match the channel’s achievable bit rate. Our results demonstrate a high satisfaction for video streaming in terms of delay and throughput. Full article