Edge Computing(EC)pushes computational capability to the Terrestrial Devices(TDs),providing more efficient and faster computing *** Aerial Vehicles(UAVs)equipped with EC servers can be flexibly deployed,even in comple...
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Edge Computing(EC)pushes computational capability to the Terrestrial Devices(TDs),providing more efficient and faster computing *** Aerial Vehicles(UAVs)equipped with EC servers can be flexibly deployed,even in complex terrains,to provide mobile computing services at all ***,UAVs can establish an air-to-ground line-of-sight link with TDs to improve the quality of communication ***,the UAV-to-TD link may be obstructed by ground obstacles such as buildings or trees,leading to sub-optimal data transmission *** surmount this issue,Reconfigurable Intelligent Surfaces(RISs)emerge as a promising technology capable of intelligently reflecting signals to enhance communication quality between UAVs and *** this paper,we consider the RISs-assisted multi-UAVs collaborative edge Computing Network(RUCN)in urban environment,in which we study the computational offloading *** goal is to maximize the overall energy efficiency of UAVs by jointly optimizing the flight duration and trajectories of UAVs,and the phase shifts of *** is worth noting that this problem has been formally established as ***,we propose the Deep Deterministic Policy Gradients based UAV Trajectory and RIS Phase shift optimization algorithm(UTRP-DDPG)to solve this complex optimization *** results of extensive numerical experiments show that the proposed algorithm outperforms the other benchmark algorithms under various parameter ***,the UTRP-DDPG algorithm improves the UAV energy efficiency by at least 2%compared to DQN algorithm.
Artificial intelligence and blockchain are quickly integrating in daily life and business applications. When numerous information systems must access and analyze data in real-time in centralized systems and applicatio...
Artificial intelligence and blockchain are quickly integrating in daily life and business applications. When numerous information systems must access and analyze data in real-time in centralized systems and applications, such as healthcare, a bottleneck develops. This issue would be resolved by blockchain's decentralized database architecture, safe data storage, data exchange, and authentication. Additionally, ai can be present at the very top of the blockchain and produce insights from the shared data that is created and applied to forecasts. Blockchain is a cutting-edge Cybersecurity technology that creates chains by mutual consent between nodes that chronologically link new blocks to those previously stored in nodes. A number of industries, including banking, insurance, cybersecurity, forecasting, healthcare, and cryptocurrencies, among others, are growing more quickly as a result of technology convergence. The likelihood of these systems being hacked increases as more digital technologies are used and these businesses provide services. Blockchain technology and artificial intelligence can work together to create a strong defense against these dangers and security problems. We'll examine how blockchain and ai are merged in cybersecurity in this chapter. We'll go into more detail about how they help secure cyber-physical systems.
Medical image analysis systems can help radiologists in reading images accurately and promptly. The systems are expected to be robust to images obtained with any imaging systems by different vendors and at different f...
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A dictionary learning (DL)-based method for analyzing multiple functional magnetic resonance imaging (fMRI) data sets is developed. The algorithm can incorporate subject group information to extract neural activation ...
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ISBN:
(数字)9798331520526
ISBN:
(纸本)9798331520533
A dictionary learning (DL)-based method for analyzing multiple functional magnetic resonance imaging (fMRI) data sets is developed. The algorithm can incorporate subject group information to extract neural activation maps indicative of the group differences and the maps shared across groups. Furthermore, multiple data sets are jointly analyzed to obtain the maps common across data sets and those unique to individual data sets. For this, a novel structured supervised DL problem is formulated. Numerical tests on synthetic and real fMRI data sets verify the effectiveness of the proposed method
Objectives: CT perfusion (CTP) imaging is vital in treating acute ischemic stroke by identifying salvageable tissue and the infarcted core. CTP images allow quantitative estimation of CT perfusion parameters, which ca...
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Functional network connectivity (FNC) is a valuable measure for assessing the temporal interdependence and intrinsic functional relationships among brain networks. While longitudinal research on intrinsic functional c...
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ISBN:
(数字)9798331520526
ISBN:
(纸本)9798331520533
Functional network connectivity (FNC) is a valuable measure for assessing the temporal interdependence and intrinsic functional relationships among brain networks. While longitudinal research on intrinsic functional connectivity has been useful for characterizing age-related changes across whole-brain networks, there has been limited focus on capturing multivariate patterns of FNC changes during brain development. In this study, we employed a previously proposed method that utilizes FNC matrices to estimate multiple overlapping brain functional change patterns (FCPs). This method was applied to the extensive Adolescent Brain and Cognitive Development (ABCD) dataset. Our findings reveal several well-structured FCPs indicating significant changes over a four-year period, including brain functional connectivity between visual network (VSN) and sensorimotor network (SMN) domains. This FNC expression pattern strengthens with age. Additionally, we observed a distinct pattern of changes between male and female individuals for two components including males show stronger increasing coupling between the default mode network (DMN) and VSN compared to females.
This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast, with a focus on partial differential equations. These methods have attracte...
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To solve the problem that it is difficult to accurately estimate the coherent parameters of distributed aperture radar under the condition of low signal-to-noise ratio, a distributed coherent synthesis method based on...
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ISBN:
(数字)9798331515669
ISBN:
(纸本)9798331515676
To solve the problem that it is difficult to accurately estimate the coherent parameters of distributed aperture radar under the condition of low signal-to-noise ratio, a distributed coherent synthesis method based on two-stage compensation is proposed in this paper. This method utilizes prior information such as system layout, beam pointing, and inertial navigation station location to perform coarse alignment of echoes at the first stage in time, and then uses cross-correlation method to estimate coherent parameters of delay and phase within a small range, and perform precise compensation of time and phase at the second stage, so as to realize the coherent synthesis of multiple radar echoes. Experimental results show that the proposed method can effectively realize distributed coherent synthesis processing under low signal-to-noise ratio.
Transformer-based models have unlocked a plethora of powerful intelligent applications at the edge, such as voice assistant in smart home. Traditional deployment approaches offload the inference workloads to the remot...
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ISBN:
(数字)9798350383508
ISBN:
(纸本)9798350383515
Transformer-based models have unlocked a plethora of powerful intelligent applications at the edge, such as voice assistant in smart home. Traditional deployment approaches offload the inference workloads to the remote cloud server, which would induce substantial pressure on the backbone network as well as raise users’ privacy concerns. To address that, in-situ inference has been recently recognized for edge intelligence, but it still confronts significant challenges stemming from the conflict between intensive workloads and limited on-device computing resources. In this paper, we leverage our observation that many edge environments usually comprise a rich set of accompanying trusted edge devices with idle resources and propose Galaxy, a collaborative edge ai system that breaks the resource walls across heterogeneous edge devices for efficient Transformer inference acceleration. Galaxy introduces a novel hybrid model parallelism to orchestrate collaborative inference, along with a heterogeneity-aware parallelism planning for fully exploiting the resource potential. Furthermore, Galaxy devises a tile-based fine-grained overlapping of communication and computation to mitigate the impact of tensor synchronizations on inference latency under bandwidth-constrained edge environments. Extensive evaluation based on prototype implementation demonstrates that Galaxy remarkably outperforms state-of-the-art approaches under various edge environment setups, achieving up to 2.5× end-to-end latency reduction.
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