Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 12 of 12 for “"Edge intelligence"”.
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Complexity for Edge Intelligence in 6G
… fondamentale è quella di Multi Access Edge Computing (MEC) che rappresenta una tecnologia abilitante al fine di costruire la cosiddetta “edge intelligence” di rete distribuita e collettiva, puntando su meccanismi di trasferimento di attività computazionali tra i nodi edge (offloading) …
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Circuit and algorithm design to enable edge intelligence
“Edge Intelligence” (EI) is a promising alternative to a centralized could-IoT paradigm that has inherent advantages with communication cost, processing latency, data security, network robustness, and so on. However, EI Design is essentially challenging to support ever-demanding artificial …
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Power Efficient Wireless Sensor Node through Edge Intelligence
Edge intelligence can reduce power dissipation to enable power-hungry long-range wireless applications. This work applies edge intelligence to quantify the reduction in power dissipation. We designed a wireless sensor node with a LoRa radio and implemented a decision tree classifier, in situ, to …
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Efficient Edge Intelligence in the Era of Big Data
… years, there has also been a growing interest in edge intelligence for emerging instantaneous big data inference. However, the inference algorithms, especially deep learning, usually require heavy computation requirements, thereby greatly limiting their deployment on the edge. We take special …
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Acies-OS: a twin-assisted systems architecture for edge intelligence
The rapid proliferation of Artificial Intelligence (AI) within the Internet of Things (IoT) and Cyber-Physical Systems (CPS) has created new opportunities for intelligent sensing, perception, and control at the network edge. However, deploying deep learning-based intelligence on embedded platforms …
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IntelliEdgent: device-server collaborative deep learning model composition for resource-efficient edge intelligence
… approach for deep learning execution, based on edge computing, called IntelliEdgent, which intelligently splits the computation workload across both the device and the server, so that the resultant execution latency is optimal. At first, we study the problem of detecting Out-Of-Distribution …
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From Neuron Models to Edge Intelligence: A Multi-Level Exploration of Neuromorphic Computing
L'abstract è presente nell'allegato / the abstract is in the attachment
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AI-based Edge Computing System for Event Based Analytics
… we have witnessed advanced research for edge computing and its potential benefits of reducing latency, desirable availability, and privacy protection. However, cloud-based AI solutions are not readily deployable to the edge in IoT's data-driven world because of the difficulties of dealing …
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Digital Twin for Machine Tools and Manufacturing Systems
… the challenges of model fidelity, knowledge extraction and cost- effective deployment in industrial environments. The study makes three key contributions. Firstly, it proposes a lightweight hybrid modelling approach that fuses finite element analysis with neural networks to predict …
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Enable Intelligence on Billion Devices with Deep Learning
<p>With the proliferation of edge computing and Internet of Things (IoT), billions of edge devices (e.g., smartphone, AR/VR headset, autonomous car, etc) are deployed in our daily life and constantly generating the gigantic amount of data at the network edge. Bringing deep learning to such huge …
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GNN-Enhanced Hierarchical Federated Learning in Device-to-Device Networks
… growth of data generated at the network edge, creating new opportunities for data-driven services while posing fundamental challenges in privacy preservation, communication efficiency, and adaptability. Federated Learning (FL) provides a privacy-preserving paradigm for collaborative model …
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Utilizing GAN and Sequence Based LSTMs on Post-RF Metadata for Near Real Time Analysis
Wireless anomaly detection is a mature field with several unique solutions. This thesis aims to describe a novel way of detecting wireless anomalies using metadata analysis based methods. The metadata is processed and analyzed by a LSTM based Autoencoder and a LSTM based feature analyzer to produce …