Zobrazeno 1 - 10
of 578
pro vyhledávání: '"Cao, Hung"'
This paper presents a Multimodal Ambient Context-enriched Intelligence Platform (MACeIP) for Smart Cities, a comprehensive system designed to enhance urban management and citizen engagement. Our platform integrates advanced technologies, including In
Externí odkaz:
http://arxiv.org/abs/2409.15243
Rodents have long been established as the premier model for behavioral studies, traditionally raised and maintained in conventional cage environments. However, these settings often limit rodents' ability to exhibit their full range of intrinsic behav
Externí odkaz:
http://arxiv.org/abs/2409.01618
Recent advancements in deep learning have significantly improved visual quality inspection and predictive maintenance within industrial settings. However, deploying these technologies on low-resource edge devices poses substantial challenges due to t
Externí odkaz:
http://arxiv.org/abs/2407.11771
Autor:
Azghadi, Seyed Alireza Rahimi, Mih, Atah Nuh, Kawnine, Asfia, Wachowicz, Monica, Palma, Francis, Cao, Hung
Indoor localization plays a vital role in the era of the IoT and robotics, with WiFi technology being a prominent choice due to its ubiquity. We present a method for creating WiFi fingerprinting datasets to enhance indoor localization systems and add
Externí odkaz:
http://arxiv.org/abs/2407.09242
Autor:
Nguyen, Quoc Khanh, Nguyen, Truong Thanh Hung, Nguyen, Vo Thanh Khang, Truong, Van Binh, Phan, Tuong, Cao, Hung
To address the challenges of providing quick and plausible explanations in Explainable AI (XAI) for object detection models, we introduce the Gaussian Class Activation Mapping Explainer (G-CAME). Our method efficiently generates concise saliency maps
Externí odkaz:
http://arxiv.org/abs/2404.13417
Autor:
Mih, Atah Nuh, Rahimi, Alireza, Kawnine, Asfia, Palma, Francis, Wachowicz, Monica, Dubay, Rickey, Cao, Hung
This paper proposes an optimization of an existing Deep Neural Network (DNN) that improves its hardware utilization and facilitates on-device training for resource-constrained edge environments. We implement efficient parameter reduction strategies o
Externí odkaz:
http://arxiv.org/abs/2403.10569
Autor:
Nguyen, Truong Thanh Hung, Clement, Tobias, Nguyen, Phuc Truong Loc, Kemmerzell, Nils, Truong, Van Binh, Nguyen, Vo Thanh Khang, Abdelaal, Mohamed, Cao, Hung
LangXAI is a framework that integrates Explainable Artificial Intelligence (XAI) with advanced vision models to generate textual explanations for visual recognition tasks. Despite XAI advancements, an understanding gap persists for end-users with lim
Externí odkaz:
http://arxiv.org/abs/2402.12525
Autor:
Naderi, Amir Mohammad, Casey, Jennifer G., Huang, Mao-Hsiang, Victorio, Rachelle, Chiang, David Y., MacRae, Calum, Cao, Hung, Gupta, Vandana A.
Quantifying cardiovascular parameters like ejection fraction in zebrafish as a host of biological investigations has been extensively studied. Since current manual monitoring techniques are time-consuming and fallible, several image processing framew
Externí odkaz:
http://arxiv.org/abs/2402.09658
Resource constraints have restricted several EdgeAI applications to machine learning inference approaches, where models are trained on the cloud and deployed to the edge device. This poses challenges such as bandwidth, latency, and privacy associated
Externí odkaz:
http://arxiv.org/abs/2401.05355
The use of edge devices together with cloud provides a collaborative relationship between both classes of devices where one complements the shortcomings of the other. Resource-constraint edge devices can benefit from the abundant computing power prov
Externí odkaz:
http://arxiv.org/abs/2310.03823