Standard distribution middleware has traditionally been perceived as complex software which is not suitable for satisfying the highest certification criteria in safety-critical environments. However, this idea is slow...
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ISBN:
(纸本)9798350387964;9798350387957
Standard distribution middleware has traditionally been perceived as complex software which is not suitable for satisfying the highest certification criteria in safety-critical environments. However, this idea is slowly changing and there are efforts such as the Future Airborne Capability Environment (FACE) consortium to integrate standard distribution middleware into the development of avionic systems. This integration facilitates the interoperability and portability of avionic applications, but there are still challenges that need to be addressed before full success can be achieved. To this end, this paper explores the usage of the Data Distribution Service for Real-Time systems (DDS) on top of a partitioned system with a communication network based on the ARINC 664 specification (precisely, the AFDX network). This work specifically identifies the incompatibilities between the two standards and also proposes potential solutions. A set of overhead metrics of using DDS in a distributed partitioned platform is also provided.
With the rapid development of wireless sensor networks (WSNs) and the Internet of Things (IoT), increasing computing tasks are sinking to mobile edge networks, such as distributed learning systems. These systems benef...
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With the rapid development of wireless sensor networks (WSNs) and the Internet of Things (IoT), increasing computing tasks are sinking to mobile edge networks, such as distributed learning systems. These systems benefit from the massive amounts of data and computing power on mobile devices and can learn qualified models on the premise of protecting user privacy. In fact, coordinating mobile devices to participate in computing is challenging. On the one hand, the heterogeneous performance of devices makes it difficult to guarantee computing efficiency. On the other hand, there are unreliable factors in the mobile network, which will destroy the stability of the distributed learning. Therefore, we design a three-layer framework called an edge-intelligence-based distributed learning system (EIDLS). Specifically, a novel multilayer perceptron-based device availability evaluation model is proposed to select devices with good performance. The evaluation model performs online learning and optimization according to the resources (CPU, battery, etc.) of devices. Meanwhile, we propose a dynamic trust evaluation algorithm to reduce the side effects of unreliable devices. The experimental results of some commonly used datasets validate that the proposed EIDLS dramatically minimizes the energy consumption and communication cost and improves the calculation accuracy and the stability of the system.
The proceedings contain 76 papers. The topics discussed include: data informativity of continuous-time systems by sampling using linear functionals;pattern recognition tools for output-based classification of synchron...
The proceedings contain 76 papers. The topics discussed include: data informativity of continuous-time systems by sampling using linear functionals;pattern recognition tools for output-based classification of synchronized Kuramoto states;effects of time delay on the resonance frequency of Andronov-Hopf bifurcations in neuromorphic devices;on the optimal communication weights in distributed optimization algorithms;polynomial stability of coupled waves with weak indirect damping;stability analysis of an SIR model with general transmission rates;bridging robustness and resilience for dynamical systems in nature;a priori parameter identifiability of enzymatic reaction networks;integer transformation of Boolean networks and its topological implications;statistical methods to evaluate discrete Boolean mathematical models from systems biology experimental data sets;and robust control of linear stochastic systems with affine plus integral state feedback.
As the demand for seamless connectivity across distributednetworks increases, traditional federated learning (FL) models struggle to maintain accuracy and efficiency in dynamic environment. Conventional approaches, s...
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The proceedings contain 4 papers. The topics discussed include: quantum transfer learning for sentiment analysis: an experiment on an Italian corpus;multi-class quantum convolutional neural networks;practical implemen...
ISBN:
(纸本)9798400706462
The proceedings contain 4 papers. The topics discussed include: quantum transfer learning for sentiment analysis: an experiment on an Italian corpus;multi-class quantum convolutional neural networks;practical implementation of a quantum string matching algorithm;and speeding up answer set programming by quantum computing.
The proceedings contain 82 papers. The topics discussed include: utilizing confidence in localization predictions for improved spectrum management;I2S attack: exploring MITM attack on satellite communications by spect...
ISBN:
(纸本)9798350317640
The proceedings contain 82 papers. The topics discussed include: utilizing confidence in localization predictions for improved spectrum management;I2S attack: exploring MITM attack on satellite communications by spectrum shared IoTs;3D spectrum awareness for radio dynamic zones using kriging and matrix completion;Pri-Share: enabling inter-SAS privacy protection via secure multi-party spectrum allocation;application of Mamdani fuzzy inference systems to interference assessments;satellite orbit prediction processor: mitigating satellite RFI to radio astronomy;evaluating cooperative spectrum sensing a hardware-in-the-loop approach;economic and market design challenges for spectrum zone management systems;and using signals of opportunity to establish trust in distributed spectrum monitoring systems.
The development of practical Brain-computer Interface (BCI) systems has been hindered by significant issues related to data, specifically the lack of sufficient data needed for training. To address this challenge, gen...
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ISBN:
(纸本)9798350312249
The development of practical Brain-computer Interface (BCI) systems has been hindered by significant issues related to data, specifically the lack of sufficient data needed for training. To address this challenge, generating synthetic data that mimics real recorded data has been proposed to augment the real data. One promising technique for data augmentation is through the use of Generative Adversarial networks (GANs), which have been successfully applied in many other fields. This paper proposes a novel GAN-based approach for generating synthetic spectrum images of Motor Imagery (MI) Electroencephalogram (EEG). The proposed GAN is examined with two Convolutional Neural Network (CNN) architectures in the context of MI classification. Using the public dataset BCI competition IV, our findings reveal that the generated EEG spectrum images using GANs exhibit temporal, spectral, and spatial characteristics similar to the real ones. The average classification accuracy of right-hand versus left-hand MI using the proposed GAN/CNN models has improved to 76.71% with an enhancement of 2.5% in comparison to using the CNN applied to the real data only. These results suggest that using GANs could improve MI BCI systems with limited data.
Edge computing is crucial for IoT applications, especially those needing quick, private data handling. However, these applications are resource-intensive, and edge computing resources are limited compared to cloud cap...
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In India, farmers often face significant challenges in selecting the appropriate crop variety for specific soil types, leading to substantial productivity losses. This project leverages precision agriculture by utiliz...
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This paper presents a robust adaptive diffusion algorithm based on exponential hyperbolic cosine cost function to enhance the distributed estimation performance. Mathematical analysis shows the convergence criteria of...
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