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Area monitoring and surveillance are some of the main applications for Unmanned Aerial Vehicle (UAV) networks. The scientific problem that arises from this application concerns the way the area must be covered to fulfill the mission requirements. One of the main challenges is to determine the paths for the UAVs that optimize the usage of resources while minimizing the mission time. Different approaches rely on area partitioning strategies. Depending on the size and complexity of the area to monitor, it is possible to decompose it exactly or approximately. This paper proposes a partitioning method called Parallel Partitioning along a Side (PPS). In the proposed method, grid-mapping and grid-subdivision of the area, as well as area partitioning are performed to plan the UAVs path. An extra challenge, also tackled in this work, is the presence of non-flying zones (NFZs). These zones are areas that UAVs must not cover or pass over it. The proposal is extensively evaluated, in comparison with existing approaches, to show that it enables UAVs to plan paths with minimum energy consumption, number of turns and completion time while at the same time increases the quality of coverage.

Obstacle avoidance is crucial for the successful completion of UAV missions. Static and dynamic obstacles, such as trees, buildings, flying birds, or other UAVs, can threaten these missions. As a result, safe path planning is essential, particularly for missions involving multiple UAVs. Collision-free paths can be designed in either 2D or 3D environments, depending on the scenario. This study provides an overview of recent advancements in obstacle avoidance and path planning for UAVs. These methods are compared based on various criteria, including avoidance techniques, obstacle types, the environment explored, sensor equipment, map types, and path statuses. Additionally, this paper includes a process addressing obstacle detection and avoidance and reviews the evolution of obstacle detection and avoidance (ODA) techniques in UAVs over the past decade.

The presence of obstacles like a tree, buildings, or birds along the path of a drone has the ability to endanger and harm the UAV’s flight mission. Avoiding obstacles is one of the critical challenging keys to successfully achieve a UAV’s mission. The path planning needs to be adapted to make intelligent and accurate avoidance online and in time. In this paper, we propose an energy-aware grid based solution for obstacle avoidance (EAOA). Our work is based on two phases: in the first one, a trajectory path is generated offline using the area top-view. The second phase depends on the path obtained in the first phase. A camera captures a frontal view of the scene that contains the obstacle, then the algorithm determines the new position where the drone has to move to, in order to bypass the obstacle. In this paper, the obstacles are static. The results show a gain in energy and completion time using 3D scene information compared to 2D scene information.

The internet of Things (IoT) has attracted significant attention in many applications in both academic and industrial areas. In IoT, each object can have the capabilities of sensing, identifying, networking and processing to communicate with ubiquitous objects and services. Often this paradigm (IoT) using Wireless Sensor Networks must cover large area of interest (AoI) with huge number of devices. As these devices might be battery powered and randomly deployed, their long-term availability and connectivity for area coverage is very important, in particular in harsh environments. Moreover, a poor distribution of devices may lead to coverage holes and degradation to the quality of service. In this paper, we propose an approach for self-organization and coverage maximization. We present a distributed algorithm for “Maintaining Connectivity and Coverage Maximization” called M C C M . The algorithm operates on different movable devices in homogeneous and heterogeneous distribution. It does not require high computational complexity. The main goal is to keep the movement of devices as minimal as possible to save energy. Another goal is to reduce the overlapping areas covered by different devices to increase the coverage while maintaining connectivity. Simulation results show that the proposed algorithm can achieve higher coverage and lower nodes’ movement over existing algorithms in the state of the art.

This article presents a comprehensive review of the state-of-the-art applications and methodologies related to the use of unmanned aerial vehicles (UAVs) in the healthcare sector, with a particular focus on path planning. UAVs have gained remarkable attention in healthcare during the outbreak of COVID-19, and this study explores their potential as a viable option for medical transportation. The survey categorizes existing studies by mission type, challenges addressed, and performance metrics to provide a clearer picture of the path planning problems and potential directions for future research. It highlights the importance of addressing the path planning problem and the challenges that UAVs may face during their missions, including the UAV delivery range limitation, and discusses recent solutions in this field. The study concludes by encouraging researchers to conduct their studies in a realistic environment to reveal UAVs’ real potential, usability, and feasibility in the healthcare domain.

Automatically predicting ICD-10 codes from clinical notes using machine learning models can reduce the burden of manual coding. However, existing methods often overlook the semantic relationships between ICD-10 codes, resulting in inaccurate evaluations when clinically similar codes are considered completely different. Traditional evaluation metrics, which rely on equality-based matching, fail to capture the clinical relevance of predicted codes. This study introduces NNBSVR (Neural Network-Based Semantic Vector Representations), a novel approach for generating semantic-based vector representations of ICD-10 codes. Unlike traditional approaches that rely on exact code matching, NNBSVR incorporates contextual and hierarchical information to enhance both prediction accuracy and evaluation methods. We validate NNBSVR using intrinsic and extrinsic evaluation methods. Intrinsic evaluation assesses the vectors’ ability to reconstruct the ICD-10 hierarchy and identify clinically meaningful clusters. Extrinsic evaluation compares our relevancy-based approach, which includes customized evaluation metrics, to traditional equality-based metrics on an ICD-10 code prediction task using a 9.57 million clinical notes corpus. NNBSVR demonstrates significant improvements over equality-based metrics, achieving a 9.81% gain in micro-F1 score on the training set and a 12.73% gain on the test set. A manual review by medical experts on a sample of 10,000 predictions confirms an accuracy of 92.58%, further validating our approach. This study makes two significant contributions: first, the development of semantic-based vector representations that encapsulate ICD-10 code relationships and context; second, the customization of evaluation metrics to incorporate clinical relevance. By addressing the limitations of traditional equality-based evaluation metrics, NNBSVR enhances the automated assignment of ICD-10 codes in clinical settings, demonstrating superior performance over existing methods.

Automatically predicting ICD-10 codes from clinical notes using machine learning models can reduce the burden of manual coding. However, existing methods often overlook the semantic relationships between ICD-10 codes, resulting in inaccurate evaluations when clinically similar codes are considered completely different. Traditional evaluation metrics, which rely on equality-based matching, fail to capture the clinical relevance of predicted codes. This study introduces NNBSVR (Neural Network-Based Semantic Vector Representations), a novel approach for generating semantic-based vector representations of ICD-10 codes. Unlike traditional approaches that rely on exact code matching, NNBSVR incorporates contextual and hierarchical information to enhance both prediction accuracy and evaluation methods. We validate NNBSVR using intrinsic and extrinsic evaluation methods. Intrinsic evaluation assesses the vectors’ ability to reconstruct the ICD-10 hierarchy and identify clinically meaningful clusters. Extrinsic evaluation compares our relevancy-based approach, which includes customized evaluation metrics, to traditional equality-based metrics on an ICD-10 code prediction task using a 9.57 million clinical notes corpus. NNBSVR demonstrates significant improvements over equality-based metrics, achieving a 9.81% gain in micro-F1 score on the training set and a 12.73% gain on the test set. A manual review by medical experts on a sample of 10,000 predictions confirms an accuracy of 92.58%, further validating our approach. This study makes two significant contributions: first, the development of semantic-based vector representations that encapsulate ICD-10 code relationships and context; second, the customization of evaluation metrics to incorporate clinical relevance. By addressing the limitations of traditional equality-based evaluation metrics, NNBSVR enhances the automated assignment of ICD-10 codes in clinical settings, demonstrating superior performance over existing methods.

Multi-view video systems are designed to allow users to watch 3D videos or a scene recorded by multiple cameras from multiple viewpoints. They are actually used by crowd sourced journalism services or to cover events using a set of wireless drones/sensors filming the same scene, etc. Multi-view videos are captured by multiple cameras at different positions with significant correlations between neighboring views. Owing to the increased data volume of multi-view video, highly efficient encoding techniques are needed. The common idea for Multi-View Video Coding (MVC) is to further exploit the redundancy between adjacent views. In this paper, we focus on the acquisition phase of the multi-view video system. We propose a Mobile Multi-view Video Streaming scheme with Constant Order Encoding (MMVS/COE). It encodes by exploiting the inter/intra-view dependency to reduce redundancy and optimize the tradeoff between traffic volume (bite rate) and video quality. Evaluations’ results show that MMVS/COE reduces traffic, compared to existing methods, mainly MVC/MC, by decreasing redundancies among video streams while maintaining video quality.

An adequate usage of energy on nodes with restricted energy source is one of the major challenges in WSNs. Since data transmission is the main task that shortens node's lifetime, it is very necessary to balance the transmission of data among the network paths. Cluster-based architecture in WSNs is one of the keys to improve energy efficiency and extend network lifetime. It reduces the number of messages transmitted towards the sink or Base Station. This is due to restricting the communication with the sink to few nodes, called Cluster Head nodes (CHs). One of the major concerns is how to choose cluster heads and route data through energy-efficient paths towards destination. In this paper, we propose R-MUCH a clustering routing algorithm. It is a multi-hop version of MUCH algorithm (Multi-Criteria Cluster Head Delegation Based on Fuzzy Logic). CHs send data in a multi-hop fashion to the sink by choosing the path that has the lowest cost in terms of energy consumption. R-MUCH chooses for each CH its next-hop. It uses fuzzy logic and relies on three factors: the distance, the node's remaining energy and the number of times the node has served as next hop. Simulation shows that network lifetime is increased over existing approaches in the state of the art.