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Found 12 papers, 2 papers with code
Towards Low-Energy Adaptive Personalization for Resource-Constrained Devices
For each type, we fine-tune different blocks of the model to achieve optimal performance with reduced energy costs.
MicroT: Low-Energy and Adaptive Models for MCUs
On MCUs, compared to the standard full model inference, MicroT can save up to about 29. 13% in energy consumption.
Effective Abnormal Activity Detection on Multivariate Time Series Healthcare Data
Multivariate time series (MTS) data collected from multiple sensors provide the potential for accurate abnormal activity detection in smart healthcare scenarios.
Information Theory Inspired Pattern Analysis for Time-series Data
For applications with stochastic state transitions, features are developed based on Shannon's entropy of Markov chains, entropy rates of Markov chains, entropy production of Markov chains, and von Neumann entropy of Markov chains.
PRISM: Privacy Preserving Healthcare Internet of Things Security Management
In this paper, we propose PRISM, an edge-based system for experimenting with in-home smart healthcare devices.
Using Entropy Measures for Monitoring the Evolution of Activity Patterns
The study uses Internet of Things (IoT) enabled solutions for continuous monitoring of in-home activity, sleep, and physiology to develop care and early intervention solutions to support people living with dementia (PLWD) in their own homes.
Towards Battery-Free Machine Learning and Inference in Underwater Environments
This paper is motivated by a simple question: Can we design and build battery-free devices capable of machine learning and inference in underwater environments?
Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data
Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia which can negatively impact the Activities of Daily Living (ADL) and the independence of individuals.
Multimodal Federated Learning on IoT Data
Federated learning is proposed as an alternative to centralized machine learning since its client-server structure provides better privacy protection and scalability in real-world applications.
Preparation of superconducting thin films of infinite-layer nickelate Nd$_{0.8}$Sr$_{0.2}$NiO$_{2}$
The recent observation of superconductivity in infinite-layer nickelate Nd$_{0. 8}$Sr$_{0. 2}$NiO$_{2}$ has received considerable attention.
Superconductivity Strongly Correlated Electrons
Semi-supervised Federated Learning for Activity Recognition
In this paper, we propose an activity recognition system that uses semi-supervised federated learning, wherein clients conduct unsupervised learning on autoencoders with unlabelled local data to learn general representations, and a cloud server conducts supervised learning on an activity classifier with labelled data.
A Library for Constraint Consistent Learning
This paper introduces the first, open source software library for Constraint Consistent Learning (CCL).
