In order to improve the communication efficiency, we in this paper propose the feature-based federated transfer learning as an innovative approach to reduce the uplink payload by more than five orders of magnitude compared to that of existing approaches.
In order to address this issue and improve the performance of any baseline 3D point classification or segmentation model, we propose a new module, referred to as the Recycling MaxPooling (RMP) module, to recycle and utilize the features of some of the discarded points.
In this setting, we develop an anomaly detection algorithm that chooses the processes to be observed at a given time instant, decides when to stop taking observations, and declares the decision on anomalous processes.
It is worth to note that our proposed RAA convolution is lightweight and compatible to be integrated into any CNN architecture used for detection from a BEV.
As the core module of the DPFA-Net, we propose a Feature Aggregation layer, in which features of the dynamic neighborhood of each point are aggregated via a self-attention mechanism.
In this paper, we address the anomaly detection problem where the objective is to find the anomalous processes among a given set of processes.
Deep reinforcement learning (DRL) has recently been used to perform efficient resource allocation in wireless communications.
Existing variations of CapsNets mainly focus on performance comparison with the original CapsNet, and have not outperformed CNN-based models on complex tasks.
Alzheimer's disease is one of the diseases that mostly affects older people without being a part of aging.
In order to improve the detection accuracy and reduce the delay in detection, we introduce a buffer zone in the operation of the proposed GAN-based detector.
As the applications of deep reinforcement learning (DRL) in wireless communications grow, sensitivity of DRL based wireless communication strategies against adversarial attacks has started to draw increasing attention.
Several research work in adversarial machine learning started to focus on the detection of AEs in autonomous driving.
Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i. e., they remain adversarial even against other models.
Anomaly detection is widely applied in a variety of domains, involving for instance, smart home systems, network traffic monitoring, IoT applications and sensor networks.
We consider both a single-user case and a scenario in which multiple users attempt to access channels simultaneously.
For instance, the sitting activity can be detected by IMU data, but it cannot be determined whether the subject has sat on a chair or a sofa, or where the subject is.
The growing demand on high-quality and low-latency multimedia services has led to much interest in edge caching techniques.
Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i. e., they maintain their effectiveness even against other models.
Dynamic programming is employed to implement the optimal offline and the initial online power control policies that minimize the transmit power consumption in the communication session.
In this paper, wireless video transmission to multiple users under total transmission power and minimum required video quality constraints is studied.
We consider the dynamic multichannel access problem, which can be formulated as a partially observable Markov decision process (POMDP).
The choice of parameters, and the design of the network architecture are important factors affecting the performance of deep neural networks.
Our proposed approach can be used to autonomously refine the parameters, and improve the accuracy of different deep neural network architectures.
With the advent of smartphones equipped with acceloremeter, gyroscope and camera; it is now possible to develop activity classification platforms everyone can use conveniently.