Visible light communication (VLC) is considered a solution to the scarcity of radio frequency communication resources due to its abundant spectrum resources and rapid intensity modulation capability. It has a wide ran...
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ISBN:
(数字)9781728190549
ISBN:
(纸本)9781728190556
Visible light communication (VLC) is considered a solution to the scarcity of radio frequency communication resources due to its abundant spectrum resources and rapid intensity modulation capability. It has a wide range of applications in indoor positioning and intelligent transport systems. For example, in Connected and Autonomous Vehicle scenarios, VLC uses traffic lights to warn vehicles at different distances in abnormal situations, thus preventing potential traffic accidents. To facilitate fast, long-range VLC communication in such one-to-many communication scenarios, current systems typically use optical cameras or digital micro-mirror devices as receivers. However, there are several challenges associated with these devices. Optical cameras have a limited sampling rate, resulting in reduced effective throughput. Other receivers, such as digital micro-mirror devices, are relatively costly, which hinders their widespread use. In this paper, we propose a novel, low-cost, and high-speed VLC scheme. We use a low-cost material called Polymer-Dispersed Liquid Crystal as the measurement matrix, reducing the cost by 99% compared to digital micro-mirror devices. We implement hierarchical coding based on compressive sensing to reduce data redundancy and thus improve communication throughput. Empirical experiments conducted using four pho-to diodes at the receiver show a 120% improvement in overall throughput compared to existing one-to-many VLC systems.
We present a randomized differential testing approach to test OpenMP implementations. In contrast to previous work that manually creates dozens of verification and validation tests, our approach is able to randomly ge...
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We consider a communication system where a group of users, interconnected in a bidirectional gossip network, wishes to follow a time-varying source, e.g., updates on an event, in real-time. The users wish to maintain ...
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ISBN:
(数字)9798350393187
ISBN:
(纸本)9798350393194
We consider a communication system where a group of users, interconnected in a bidirectional gossip network, wishes to follow a time-varying source, e.g., updates on an event, in real-time. The users wish to maintain their expected version ages below a threshold, and can either rely on gossip from their neighbors or directly subscribe to a server publishing about the event, if the former option does not meet the timeliness requirements. The server wishes to maximize its profit by increasing subscriptions from users and minimizing event sampling frequency to reduce costs. This leads to a Stackelberg game between the server and the users where the sender is the leader deciding its sampling frequency and the users are the followers deciding their subscription strategies. We investigate equilibrium strategies for low-connectivity and high-connectivity topologies.
Abuses of forgery techniques have created a considerable problem of misinformation on social media. Although scholars devote many efforts to face forgery detection (a.k.a DeepFake detection) and achieve some results, ...
Abuses of forgery techniques have created a considerable problem of misinformation on social media. Although scholars devote many efforts to face forgery detection (a.k.a DeepFake detection) and achieve some results, two issues still hinder the practical application. 1) Most detectors do not generalize well to unseen datasets. 2) In a supervised manner, most previous works require a considerable amount of manually labeled data. To address these problems, we propose a simple contrastive pertaining framework for DeepFake detection (DFCP), which works in a finetuning-after-pretraining manner, and requires only a few labels (5%). Specifically, we design a two-stream framework to simultaneously learn high-frequency texture features and high-level semantics information during pretraining. In addition, a video-based frame sampling strategy is proposed to mitigate potential noise data in the instance-discriminative contrastive learning to achieve better performance. Experimental results on several downstream datasets show the state-of-the-art performance of the proposed DFCP, which works at frame-level (w/o temporal reasoning) with high efficiency but outperforms video-level methods.
DL techniques have increased the efficiency of decision making in different areas. However, in the case of the presence of uncertainties in the data or in the environment, decision-making requires the explainability o...
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ISBN:
(数字)9798350350180
ISBN:
(纸本)9798350350197
DL techniques have increased the efficiency of decision making in different areas. However, in the case of the presence of uncertainties in the data or in the environment, decision-making requires the explainability of the model, especially for high-stakes decision making such as medical image analysis area. This paper focuses on medical image classification CNN based deep learning models and aims to apply and compare three popular explainable AI approaches LIME, SHAP and *** results on a Pneumonia and Alzheimer’s datasets for disease detection show that the Grad-CAM method seems to outperform LIME and SHAP and able to enhance the interpretability of DL models, identify automatically the most important features that contribute to the model’s decision.
Encoding constraints into neural networks is attractive. This paper studies how to introduce the popular positive linear satisfiability to neural networks. We propose the first differentiable satisfiability layer base...
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Behind the scenes, local municipalities and environmental agencies conduct the water testing needed to ensure public safety. While this is a critical service, it can also be resource-intensive and timeconsuming, as ma...
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Behind the scenes, local municipalities and environmental agencies conduct the water testing needed to ensure public safety. While this is a critical service, it can also be resource-intensive and timeconsuming, as many programs rely on manual sampling and data collection. This can mean that some water bodies are sampled only once a year, or even less.
Cloud segmentation is a critical challenge in remote sensing image interpretation, as its accuracy directly impacts the effectiveness of subsequent data processing and analysis. Recently, vision foundation models (VFM...
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We consider a communication system consisting of a server that tracks and publishes updates about a time-varying data source or event, and a gossip network of users interested in closely tracking the event. The timeli...
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ISBN:
(数字)9798350351255
ISBN:
(纸本)9798350351262
We consider a communication system consisting of a server that tracks and publishes updates about a time-varying data source or event, and a gossip network of users interested in closely tracking the event. The timeliness of the information is measured through the version age of information. The users wish to have their expected version ages remain below a threshold, and have the option to either rely on gossip from their neighbors or subscribe to the server directly to follow updates about the event if the former option does not meet the timeliness requirements. The server wishes to maximize its profit by increasing the number of subscribers and reducing costs associated with the frequent sampling of the event. We model the problem setup as a Stackelberg game between the server and the users, where the server commits to a frequency of sampling the event, and the users make decisions on whether to subscribe or not. As an initial work, we focus on directed networks with unidirectional flow of information and obtain the optimal equilibrium strategies for all the players. We provide simulation results to confirm the theoretical findings and provide additional insights.
The classification of medical images has greatly advanced due to improvements in imaging technologies and the application of deep learning. This study presents an automatic system for classifying peripheral blood cell...
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ISBN:
(数字)9798350386448
ISBN:
(纸本)9798350386455
The classification of medical images has greatly advanced due to improvements in imaging technologies and the application of deep learning. This study presents an automatic system for classifying peripheral blood cells, leveraging deep learning and transfer learning techniques to enhance performance and efficiency. We developed three designs combining convolutional architectures: VGG16, InceptionV3and ResNet50. The first design combines VGG16 and InceptionV3, the second concatenates InceptionV3and ResNet50, and the third associates VGG16 and ResNet50. Experiments were conducted on the Peripheral Blood Cell (PBC) dataset, containing 17,092 images across eight distinct classes. The results demonstrate the effectiveness of our approach, achieving a maximum accuracy of 99%.
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