RFID technology offers an affordable and user-friendly solution for contactless identification of objects and individuals. However, the widespread adoption of RFID systems raises concerns regarding security and privac...
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Deep learning-based solutions for the ill-posed problem of Monocular Depth Estimation (MDE) from 2D color images have shown potential in recent years, spurring a very active field of research. Most state-of-the-art pr...
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ISBN:
(纸本)9781665464383
Deep learning-based solutions for the ill-posed problem of Monocular Depth Estimation (MDE) from 2D color images have shown potential in recent years, spurring a very active field of research. Most state-of-the-art proposals focus on solving the problem in the context of automotive advanced driver assistance and/or autonomous driving systems. While presenting their own complexities and challenges, the vast majority of road environments exhibit a number of commonalities amongst themselves. The aerial domain in which modern Unmanned Aerial Vehicles (UAVs) operate is significantly different and features a large variety of possible scenes based on the specific mission carried out. The increasing number of applications for UAVs could benefit from more advanced learning-based MDE solutions for recovering 3D geometric information from the scene. In this paper, we conduct a study of existing research on the topic of MDE specifically tailored for aerial views, as well as presenting the datasets and tools currently supporting such research, high-lighting the challenges that remain. To the best of our knowledge, this is the first survey covering this field.
Existing relation extraction (RE) methods typically focus on extracting relational facts between entity pairs within single sentences or documents. However, a large quantity of relational facts in knowledge bases can ...
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Face presentation attack detection, also termed Face Anti-Spoofing (FAS) [item 1), 2) in the Appendix), is a hot and challenging research topic that has received much attention from the computervision and pattern rec...
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An underwater acoustic sensor network (UASN) is suitable for gathering data from aquatic environments, including lakes, rivers, seas, and oceans. This network faces several issues due to the distinct features of under...
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An underwater acoustic sensor network (UASN) is suitable for gathering data from aquatic environments, including lakes, rivers, seas, and oceans. This network faces several issues due to the distinct features of underwater environments and the limitations of acoustic channels. These challenges include energy limitations, unreliable communication links, and dynamic network topologies. Additionally, the difficulty of recharging or replacing batteries in underwater conditions makes energy optimization essential for prolonging the network lifespan. Currently, many energy-efficient approaches in UASNs emphasize node clustering and multi-hop communication, but most of these methods rely on distributed algorithms. This paper introduces a novel energy-efficient clustering framework called FHOEEC (Fire Hawk Optimization-based Energy-Efficient Clustering), which integrates both distributed and centralized strategies. The clustering process is divided into three stages: (1) cluster formation, (2) selection of cluster heads, and (3) cluster maintenance. During the periodic neighbor discovery phase, FHOEEC examines two key aspects: the format of the hello packet and its propagation process. FHOEEC aims to create an energy-efficient, cluster-based network structure. To achieve this, the sink node utilizes the fire hawk optimization (FHO) algorithm to decide on the optimal range and number of clusters. To establish these clusters, a fitness function considers a weighted combination of three sub-functions: intra-cluster and inter-cluster distances, the proportion of isolated clusters compared to others, and cluster density. In the final stage, intra-cluster and inter-cluster communication paths are established by focusing on energy balance. This ensures that nodes with energy levels below a specified threshold are excluded from serving as intermediate nodes. Simulation results and performance evaluations show that FHOEEC outperforms three existing clustering methods–CCCS, GTC, and
Recent advances in optical coherence tomography such as the development of high speed ultrahigh resolution scanners and corresponding signal processing techniques may reveal new potential biomarkers in retinal disease...
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Recent research on face analysis has demonstrated the richness of information embedded in feature vectors extracted from a deep convolutional neural network. Even though deep learning achieved a very high performance ...
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An important aim of research in medical imaging is the development of computer aided diagnosis (CAD) systems. A fundamental step in these systems is the image segmentation and convolutional neural networks (CNNs) are ...
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Graph Contrastive Learning (GCL) establishes a new paradigm for learning graph representations without human annotations. Although remarkable progress has been witnessed recently, the success behind GCL is still left ...
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Audio-visual learning,aimed at exploiting the relationship between audio and visual modalities,has drawn considerable attention since deep learning started to be used *** tend to leverage these two modalities to impro...
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Audio-visual learning,aimed at exploiting the relationship between audio and visual modalities,has drawn considerable attention since deep learning started to be used *** tend to leverage these two modalities to improve the performance of previously considered single-modality tasks or address new challenging *** this paper,we provide a comprehensive survey of recent audio-visual learning *** divide the current audio-visual learning tasks into four different subfields:audiovisual separation and localization,audio-visual correspondence learning,audio-visual generation,and audio-visual representation ***-of-the-art methods,as well as the remaining challenges of each subfield,are further ***,we summarize the commonly used datasets and challenges.
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