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 11 of 11 for “"tinyML"”.
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Taming TinyML: deep learning inference at computational extremes
… tackled by the emerging research field called *TinyML*. In this thesis, I develop model discovery and compression methodology whose common threads are automation and holistic optimisation of network architectures and their execution software, informed by the computational limitations of …
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Efficient Deep Learning Computing: From TinyML to LargeLM
… two extremes of scaling: tiny machine learning (TinyML) and large language models (LLMs). TinyML aims to run deep learning models on low-power IoT devices with tight memory constraints. Weexplored a system-algorithm co-design approach to remove redundant memory usage and enable real-life …
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From Passive Data Collection to Sensor-Level Intelligence
… these limitations, this thesis integrates TinyML into resource-constrained IoT devices, enabling sensor-level intelligence that reduces reliance on continuous data transmission while extending device lifetime, conserving bandwidth, and preserving efficiency. By performing local analytics, …
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Deep learning for DDoS attack detection in mobile edge computing
… MEC. Also, in this category this study proposes TinyML based DDoS detection model which can be used in an embedded device with low energy and bandwidth consumption. Secondly, the study proposes a hybrid deep learning algorithm (AE-MLP) and a cloud edge collaboration where training is done in the …
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Machine Learning Applications for Time Series Data: Motor Anomaly Detection and Mean Arterial Blood Pressure Estimation
… two such applications using timeseries data: (1) TinyML for Anomalous Motor Operation Detection, and (2) Estimation of Mean Arterial Blood Pressure (MAP) from ultrasound measurements. In the first application, we explore different algorithms for detecting anomalous fan motor operation on a small …
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Edge Device Speaker Verification
… The field of edge device machine learning (TinyML) is an active area of research. Our contribution demonstrates the possibility of building systems which can perform inference on a form small microcontroller, accepting the trade-offs inherit in the problem.</p>
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Efficient convolutional neural network inference on microcontrollers
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01
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Hardware-Aware Cross-Layer Optimizations of Deep Neural Networks for Embedded Systems
L'abstract è presente nell'allegato / the abstract is in the attachment
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Lightweight edge AI vision models for IoT-based insect monitoring
Smart automated insect monitoring is essential for early detection of insect pest infestations in orchards. It assists farmers in controlling insect pest populations in their fields and preventing crop losses and improving crop quality. Traditional approaches relying on manual inspections are …
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Methods and Applications for Low-power Deep Neural Networks on Edge Devices
L'abstract è presente nell'allegato / the abstract is in the attachment
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Efficient Continual Learning and On-Device Training for Mobile and IoT Devices
The surge in mobile phones, wearables, and Internet of Things (IoT) devices has resulted in an abundance of sensor data. This played a pivotal role in the widespread adoption of deep neural networks (DNN) to support various real-world scenarios in mobile computing, including personalising user …