J
Jiehan Zhou
Researcher at University of Oulu
Publications - 128
Citations - 1866
Jiehan Zhou is an academic researcher from University of Oulu. The author has contributed to research in topics: Ubiquitous computing & Computer science. The author has an hindex of 19, co-authored 113 publications receiving 1459 citations. Previous affiliations of Jiehan Zhou include Carleton University & College of Information Technology.
Papers
More filters
Proceedings ArticleDOI
CloudThings: A common architecture for integrating the Internet of Things with Cloud Computing
Jiehan Zhou,Teemu Leppänen,Erkki Harjula,Mika Ylianttila,Timo Ojala,Chen Yu,Hai Jin,Laurence T. Yang +7 more
TL;DR: This paper proposes the CloudThings architecture, a Cloud-based Internet of Things platform which accommodates CloudThings IaaS, PaaS and SaaS for accelerating IoT application, development, and management.
Proceedings ArticleDOI
Smart Home: Integrating Internet of Things with Web Services and Cloud Computing
TL;DR: This paper presents an approach to the development of Smart Home applications by integrating Internet of Things (IoT) with Web services and Cloud computing, and implements three use cases to demonstrate the approach's feasibility and efficiency.
Journal ArticleDOI
5G-Smart Diabetes: Toward Personalized Diabetes Diagnosis with Healthcare Big Data Clouds
TL;DR: The 5G-Smart Diabetes system is proposed, which combines the state-of-the-art technologies such as wearable 2.0, machine learning, and big data to generate comprehensive sensing and analysis for patients suffering from diabetes.
Journal ArticleDOI
LSTM-Based Analysis of Industrial IoT Equipment
TL;DR: This paper aims to develop a method of analyzing equipment working condition based on the sensed data and building a prediction model for working status forecasting and designing a deep neural network model to predict equipment running data and improving the prediction accuracy by systematic feature engineering and optimal hyperparameter searching.
Journal ArticleDOI
Emotion Recognition from Chinese Speech for Smart Affective Services Using a Combination of SVM and DBN.
TL;DR: A novel classification method that combines DBN and SVM (support vector machine) instead of using only one of them is proposed, which achieves an accuracy of 95.8%, which is higher than using either DBN or SVM separately.