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The high-impact scenario of UAV live uplink streaming is gaining significant interest in diverse applications, such as ambient monitoring, disaster rescue, and smart surveillance. This paper addresses the problem of uplink streaming by a fleet of camera-equipped UAVs, with one UAV acting as the sink, collecting and transmitting videos from the others. We demonstrate that performing video stabilization at the source UAVs or the sink enhances video quality and reduces required communication throughput, leading to bandwidth savings. We analyze the UAV live uplink streaming architecture to identify the most effective stabilization point within the network in a distributed manner. Using a reinforcement learning framework, we develop a method to dynamically optimize the stabilization gain-cost trade-off, pinpointing the optimal node for stabilization tasks. Through targeted numerical simulations under different system conditions we identify when and where stabilization should be applied to maximize efficiency. Our results show that video stabilization improves system performance in terms of media quality, battery life, and bandwidth usage.

Comprehensive understanding of the human protein-protein interaction network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Here the authors summarize the community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict protein-protein interactions.

This paper tackles the problem of predicting the protein-protein interactions that arise in all living systems. Inference of protein-protein interactions is of paramount importance for understanding fundamental biological phenomena, including cross-species protein-protein interactions, such as those causing the 2020-21 pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Furthermore, it is relevant also for applications such as drug repurposing, where a known authorized drug is applied to novel diseases. On the other hand, a large fraction of existing protein interactions are not known, and their experimental measurement is resource consuming. To this purpose, we adopt a Graph Signal Processing based approach modeling the protein-protein interaction (PPI) network (a.k.a. the interactome) as a graph and some connectivity related node features as a signal on the graph. We then leverage the signal on graph features to infer links between graph nodes, corresponding to interactions between proteins. Specifically, we develop a Markovian model of the signal on graph that enables the representation of connectivity properties of the nodes, and exploit it to derive an algorithm to infer the graph edges. Performance assessment by several metrics recognized in the literature proves that the proposed approach, named GRAph signal processing Based PPI prediction (GRABP), effectively captures underlying biologically grounded properties of the PPI network.

Point clouds (PCs) provide fundamental tools for digital representation of 3D surfaces, which have a growing interest in recent applications, such as e-health or autonomous means of transport. However, the estimation of 3D coordinates on the surface as well as the signal defined on the surface points (vertices) is affected by noise. The presence of perturbations can jeopardize the application of PCs in real scenarios. Here, we propose a novel visually driven point cloud denoising algorithm (VIPDA) inspired by visually driven filtering approaches. VIPDA leverages recent results on local harmonic angular filters extending image processing tools to the PC domain. In more detail, the VIPDA method applies a harmonic angular analysis of the PC shape so as to associate each vertex of the PC to suit a set of neighbors and to drive the denoising in accordance with the local PC variability. The performance of VIPDA is assessed by numerical simulations on synthetic and real data corrupted by Gaussian noise. We also compare our results with state-of-the-art methods, and we verify that VIPDA outperforms the others in terms of the signal-to-noise ratio (SNR). We demonstrate that our method has strong potential in denoising the point clouds by leveraging a visually driven approach to the analysis of 3D surfaces.

A comprehensive representation of the road pavement state of health is of great interest. In recent years, automated data collection and processing technology has been used for pavement inspection. In this paper, a new signal on graph (SoG) model of road pavement distresses is presented with the aim of improving automatic pavement distress detection systems. A novel nonlinear Bayesian estimator in recovering distress metrics is also derived. The performance of the methodology was evaluated on a large dataset of pavement distress values collected in field tests conducted in Kazakhstan. The application of the proposed methodology is effective in recovering acquisition errors, improving road failure detection. Moreover, the output of the Bayesian estimator can be used to identify sections where the measurement acquired by the 3D laser technology is unreliable. Therefore, the presented model could be used to schedule road section maintenance in a better way.

Unmanned aerial vehicles (UAVs) are supposed to be used to provide different services from video surveillance to communication facilities during critical and high-demanding scenarios. Augmented reality streaming services are especially demanding in terms of required throughput, computing resources at the user device, as well as user data collection for advanced applications, for example, location-based or interactive ones. This work is focused on the experimental utilization of a framework adopting reinforcement learning (RL) approaches to define the paths crossed by UAVs in delivering resources for augmented reality services. We develop an OpenAI Gym-based simulator that is tuned and tested to study the behavior of UAVs trained with RL to fly around a given area and serve augmented reality users. We provide abstractions for the environment, the UAVs, the users, and their requests. A reward function is then defined to encompass several quality-of-experience parameters. We train our agents and observe how they behave as a function of the number of UAVs and users at different hours of the day.

Small cells (SCs) mounted on top of the unmanned aerial vehicles (UAVs) are a promising solution to boost the capacity in hotspot areas. However, the adoption of UAV-SCs involves the planning of their missions over time, which includes the scheduling of recharging actions of each UAV-SC at ground sites. Typically, the energy needed to recharge UAV-SCs is derived from the grid, which can be coupled with microgeneration exploiting renewable energy sources (e.g., solar panels). In this architecture, the energy that is locally produced can be either sold to the grid or used to recharge the UAV-SCs. On the other hand, when the energy from microgeneration is insufficient for recharging the UAV-SCs, additional energy can be bought from the grid. In this paper, we investigate the trade-off between maximizing the throughput provided by the UAV-SCs over a set of areas, maximizing energy sold to the grid, and maximizing energy bought from the grid. The proposed model, MaxUAVProfit, is designed to (i) plan the UAV-SCs missions as a sequence of positions and actions in 3D space vs. time, (ii) manage the grid-connected microgeneration, and (iii) control the amount of throughput received by each hotspot. We then evaluate the MaxUAVProfit in a realistic scenario, which is based on the measurement of real cellular metrics and a realistic UAV-SC energy consumption model. Our findings demonstrate the superiority of the MaxUAVProfit with respect to other competing solutions, which include either optimization of microgeneration or maximization of the area throughput.

In this paper, we address the problem of green Compressed Sensing (CS) reconstruction within Internet of Things (IoT) networks, both in terms of computing architecture and reconstruction algorithms. The approach is novel since, unlike most of the literature dealing with energy efficient gathering of the CS measurements, we focus on the energy efficiency of the signal reconstruction stage given the CS measurements. As a first novel contribution, we present an analysis of the energy consumption within the IoT network under two computing architectures. In the first one, reconstruction takes place within the IoT network and the reconstructed data are encoded and transmitted out of the IoT network; in the second one, all the CS measurements are forwarded to off-network devices for reconstruction and storage, i.e., reconstruction is off-loaded. Our analysis shows that the two architectures significantly differ in terms of consumed energy, and it outlines a theoretically motivated criterion to select a green CS reconstruction computing architecture. Specifically, we present a suitable decision function to determine which architecture outperforms the other in terms of energy efficiency. The presented decision function depends on a few IoT network features, such as the network size, the sink connectivity, and other systems’ parameters. As a second novel contribution, we show how to overcome classical performance comparison of different CS reconstruction algorithms usually carried out w.r.t. the achieved accuracy. Specifically, we consider the consumed energy and analyze the energy vs. accuracy trade-off. The herein presented approach, jointly considering signal processing and IoT network issues, is a relevant contribution for designing green compressive sampling architectures in IoT networks.