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DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG
- A. Supratak, Hao Dong, Chao Wu, Yike Guo
- Computer Science, MathematicsIEEE Transactions on Neural Systems and…
- 12 March 2017
This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG, and utilizes convolutional neural networks to extract time-invariant features, and bidirectional-long short-term memory to learn transition rules among sleep stages automatically from EEG epochs.
Semantic Image Synthesis via Adversarial Learning
- Hao Dong, Simiao Yu, Chao Wu, Yike Guo
- Computer ScienceIEEE International Conference on Computer Vision…
- 21 July 2017
An end-to-end neural architecture that leverages adversarial learning to automatically learn implicit loss functions, which are optimized to fulfill the aforementioned two requirements of being realistic while matching the target text description.
Mixed Neural Network Approach for Temporal Sleep Stage Classification
- Hao Dong, A. Supratak, W. Pan, Chao Wu, P. Matthews, Yike Guo
- Psychology, BiologyIEEE Transactions on Neural Systems and…
- 15 October 2016
A comfortable configuration of a single-channel EEG on the forehead is found and it can be integrated with additional electrodes for simultaneous recording of the electro-oculogram, and use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical.
Unsupervised Image-to-Image Translation with Generative Adversarial Networks
This work develops a two step (unsupervised) learning method to translate images between different domains by using unlabeled images without specifying any correspondence between them, so that to avoid the cost of acquiring labeled data.
High dimensional biological data retrieval optimization with NoSQL technology
A new key-value pair data model better suited for high-dimensional data storage and querying, optimized for database scalability and performance is introduced and used as a basis for migrating tranSMART's implementation to a more scalable solution for Big Data.
Elastic Application Container: A Lightweight Approach for Cloud Resource Provisioning
- Sijin He, Li Guo, Yike Guo, Chao Wu, M. Ghanem, Rui Han
- Computer ScienceIEEE 26th International Conference on Advanced…
- 26 March 2012
The experiment results show that the proposed EAC-based resource management approach outperforms the VM-based approach in terms of feasibility and resource-efficiency.
Text-to-Image Synthesis via Visual-Memory Creative Adversarial Network
This paper proposes a method named visual-memory Creative Adversarial Network (vmCAN) to generate images depending on their corresponding narrative sentences that appropriately leverages an external visual knowledge memory in both multi-modal fusion and image synthesis.
Social networking federation: A position paper
A reference model of social networking federation system is designed, as well as some prototype application to demonstrate its paradigm, to establish a foundation of integrating and assimilating information within multiple social network systems.
Using Support Vector Machine on EEG for Advertisement Impact Assessment
- Zhen Wei, Chao Wu, Xiaoyi Wang, A. Supratak, Pan Wang, Yike Guo
- Computer Science, MedicineFront. Neurosci.
- 12 March 2018
This paper investigates a new approach to assess the impact of advertisement by utilizing low-cost EEG headbands to record and assess the measurable impact of advertising on the brain and believes the proposed SVM method can be further developed to a general and scalable methodology that can enable advertising agencies to assess impact rapidly, quantitatively, and without bias.
Concinnity: A Generic Platform for Big Sensor Data Applications
- Chao Wu, David Birch, Dilshan Silva, Chun-Hsiang Lee, Orestis Tsinalis, Yike Guo
- Computer ScienceIEEE Cloud Computing
- 15 October 2014
Concinnity takes sensor data from collection to final product via a cloud-based data repository and easy-to-use workflow system and supports rapid development of applications built on sensor data using data fusion and the integration and composition of models to form novel workflows.