Emerging applications have imposed stringent demands on Internet quality of service (QoS). To ensure that various data streams within different Internet services receive the appropriate QoS, advancements in flow class...
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Unmanned surface vehicles (USVs) are increasingly used in bathymetric survey, maritime surveillance, and maintenance applications. However, detecting obstacles in the maritime environment poses significant challenges ...
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ISBN:
(纸本)9798350332261
Unmanned surface vehicles (USVs) are increasingly used in bathymetric survey, maritime surveillance, and maintenance applications. However, detecting obstacles in the maritime environment poses significant challenges due to sea clutter, background variability, and other factors. Although various sensors such as radar, AIS, LiDAR, and camera have been used for obstacle detection, they have limitations in terms of range and effectiveness, especially at different USV speeds. In this paper, we present a real-time obstacle detection system for USVs in the maritime environment by explaining the used sensors, the architecture and the implemented algorithms. We focus on evaluating the performance of the YOLOV7 network, which forms the basis of our fusion algorithm. Our results demonstrate that our system can effectively detect maritime objects in real-time, providing improved safety and efficiency for USV operations. Additionally, we provide an open solution for visualizing the detected targets on a chart plotter navigation.
Multimodal deep sensorfusion has the potential to enable autonomous vehicles to visually understand their surrounding environments in all weather conditions. However, existing deep sensorfusion methods usually emplo...
Multimodal deep sensorfusion has the potential to enable autonomous vehicles to visually understand their surrounding environments in all weather conditions. However, existing deep sensorfusion methods usually employ convoluted architectures with intermingled multimodal features, requiring large coregistered multimodal datasets for training. In this work, we present an efficient and modular RGB-X fusion network that can leverage and fuse pre-trained single-modal models via scene-specific fusion modules, thereby enabling joint input-adaptive network architectures to be created using small, coregistered multimodal datasets. Our experiments demonstrate the superiority of our method compared to existing works on RGB-thermal and RGB-gated datasets, performing fusion using only a small amount of additional parameters. Our code is available at https://***/dsriaditya999/RGBXfusion.
River floods stand among the natural disasters with far-reaching impacts, affecting human lives, the economy, infrastructure, agriculture, and more. Substantial investments by organizations are directed toward innovat...
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ISBN:
(纸本)9798350319439
River floods stand among the natural disasters with far-reaching impacts, affecting human lives, the economy, infrastructure, agriculture, and more. Substantial investments by organizations are directed toward innovative strategies for flood prevention. The concept of Artificial Intelligence of Things (AIoT), a fusion of Artificial Intelligence and Internet of Things technologies, has showcased its prowess across various domains. In this paper, we introduce an AIoT framework where river flood sensors, located in every region, transmit their data using LoRaWAN technology to local broadcast centers in proximity. These broadcast centers subsequently forward the data via 4G/5G networks to a centralized cloud server. The server employs efficient AI algorithms to analyze the data and predict river conditions nationwide, contributing to proactive flood prevention. This approach has demonstrated effectiveness on multiple fronts. LoRaWAN-based communication between sensor nodes and broadcast centers offers reduced energy consumption and expanded coverage, while AI-driven data analysis enhances the accuracy of river flood predictions.
The real-time track association and fusion task(the AF task) aims to integrate the tracks of maritime targets collected by sensors into a coherent track. In real-world scenarios, track data collected by a single senso...
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Due to the massive increase in data sets in contemporary applications and the difficulty of their processing in various approaches, data fusion remains a predominant way to obtain outstanding results, in terms of reli...
Due to the massive increase in data sets in contemporary applications and the difficulty of their processing in various approaches, data fusion remains a predominant way to obtain outstanding results, in terms of reliability, efficiency, and precision. The following document presents an overview of data fusion in wireless sensor networks; in which, various concepts to applications are considered to generate a base document for future research. This article gives a comprehensive and current view of models or architectures, techniques, methodologies or algorithms, and a current review of applications in different areas.
With the continuous growth of intelligent driving applications, the accuracy of vehicle target detection is of critical importance. The limitations of a single sensor cannot meet the stringent requirements for vehicle...
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This paper presents a prototype two-in-one, dense-plus-sparse depth module designed for Mixed Reality headsets that uses a novel approach to sparse depth imaging by combining indirect Time-of-Flight (iToF) and triangu...
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ISBN:
(纸本)9781510686069;9781510686076
This paper presents a prototype two-in-one, dense-plus-sparse depth module designed for Mixed Reality headsets that uses a novel approach to sparse depth imaging by combining indirect Time-of-Flight (iToF) and triangulation modalities to provide improved range and accuracy. The module uses an ADI ADSD3030 VGA iToF sensor and reduces module size from 62 x 24 x 13 mm in HoloLens 2 to 33.5 x 15 x 7.2 mm. Sparse frames have temporal noise of <1% of distance at 3% reflectivity, 3 klx sunlight equivalent spectrum and can image 95% reflectivity targets out to the ambiguity distance of 21 m while consuming 14.7 mJ per-frame. New low-compute algorithms that combine iToF and triangulation are presented and are demonstrated to mitigate systematic errors such as multipath and subsurface diffusion as well as extend dynamic range under full-sunlight and high-signal conditions. sensorapplications include scene understanding, object capture and hand-tracking.
The distributed adaptive signal fusion (DASF) framework has been proposed as a generic method to solve spatial filtering and signal fusion problems in a distributed fashion over a wireless sensor network, reducing the...
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The research on magnetic field sensing and data fusion technology in the power system aims to enhance the monitoring and management capabilities of the power system. With the continuous growth of electricity demand, t...
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