Multiview systems have become a key technology in modern computer vision, offering advanced capabilities in scene understanding and analysis. However, these systems face critical challenges in bandwidth limitations and computational constraints, particularly for resource-limited camera nodes. This paper presents a novel approach for communication-efficient distributed multiview detection and tracking using masked autoencoders (MAEs). We introduce a semantic-guided masking strategy that leverages pre-trained segmentation models and a tunable power function to prioritize informative image regions. This approach, combined with an MAE, reduces communication overhead while preserving essential visual information. We evaluate our method on both virtual and real-world multiview datasets, demonstrating comparable performance in terms of detection and tracking performance metrics compared to state-of-the-art techniques, even at high masking ratios. Our selective masking algorithm outperforms random masking, maintaining higher accuracy and precision as the masking ratio increases. Furthermore, our approach achieves a significant reduction in transmission data volume compared to baseline methods, thereby balancing multiview tracking performance with communication efficiency.

Relying on ground-based infrastructure for model training in Low Earth Orbit (LEO) constellations introduces significant challenges, including intermittent and costly spaceground communication. To address these challenges, this paper proposes a decentralized learning framework that enables model training directly within satellite constellations, utilizing intra-and inter-plane inter-satellite links (ISLs) for information exchange. The paper develops a geo-spatial filtering mechanism that selects the most relevant satellites to form a smaller, more efficient constellation. It then introduces a novel Adaptive Halving-Doubling (AHD) algorithm that enables collective communication within the emerging partial ring topology. Experimental results validate the framework's effectiveness, efficiency, and scalability. Notably, the transmission energy was observed to constitute less than 10% of traditional full constellation (FC) setups, even with increasing model sizes. Moreover, communication overhead relative to FC setups decreased with increasing constellation size, highlighting the operational efficiency of the framework.

Mega satellite networks recently emerged to com-plement the current terrestrial infrastructure to attain global coverage and faster communication links. However, with in-creased congestion in the radio spectrum, migrating satellite communication to high frequencies can provide higher data rates. Nevertheless, signal fading due to weather conditions and atmospheric losses becomes significant at such high frequencies. In this paper, we leverage tools from stochastic geometry to provide an analytical framework that captures the effect of high frequency fading for satellite constellations in the downlink. The analytical framework presented exploits the spatial diversity provided by the satellite network and examines the performance of the dominant satellite (satellite providing the best quality of service). Results show a close fit to the performance of practical networks and can thus provide network designers with an analytical benchmark to assist with network tuning.

With the exponential growth of the Internet of Things (IoT) landscape and the resulting spectrum congestion, innovative techniques for spectrum monitoring are crucial. This paper presents an approach to spectrum monitoring harnessing the power of spiking neural networks (SNNs) with a focus on image segmentation using the UNet architecture. Traditional methods, including energy detection, have been widely used but are not without challenges, especially in environments with varying signal-to-noise ratios. In contrast, the presented SNN approach in this paper demonstrates through simulations performance metrics that significantly surpass energy detection methods and closely align with conventional convolutional neural network techniques while also exhibiting favorable energy efficiency. Future explorations will delve into enhancing the framework using machine learning techniques for advanced feature extraction and multiclass segmentation.

Utilizing Low Earth Orbit (LEO) satellite networks equipped with Inter-Satellite Links (ISL) is envisioned to provide lower delay compared to traditional optical networks. However, LEO satellites have constrained energy resources as they rely on solar energy in their operations. Thus requiring special consideration when designing network topologies that do not only have low-delay link paths but also low-power consumption. In this paper, we study different satellite constellation types and network typologies and propose a novel power-efficient topology. As such, we compare three common satellite architectures, namely; (i) the theoretical random constellation, the widely deployed (ii) Walker-Delta, and (iii) Walker-Star constellations. The comparison is performed based on both the power efficiency and end - to-end delay. The results show that the proposed algorithm outperforms long-haul ISL paths in terms of energy efficiency with only a slight hit to delay performance relative to the conventional ISL topology.

The LoRa modulation scheme is becoming one of the most adopted Internet of Things wireless physical layer due to its ability to transmit data over long distances with low power requirements. Typical demodulation techniques for LoRa utilize variants of non-coherent Frequency Shift Keying demodulation. This paper aims to capitalize on the robustness of deep learning techniques, specifically by using convolutional neural networks to demodulate LoRa symbols. We achieve this by building a dataset consisting of emulated time-domain LoRa symbols across a range of channel impairments; namely, we examine additive white Gaussian noise together with carrier frequency offset and time offset. The presented results show an improvement when utilizing deep learning over typical non-coherent detection while performing very close to the optimal matched filter.