Export Citations
Save this search
Please login to be able to save your searches and receive alerts for new content matching your search criteria.
- research-articleSeptember 2024
Utilizing Self-Supervised Learning for Recognizing Human Activity in Older Adults through Labeling Applications in Real-World Smart Homes
- Hui Chen,
- Charles Gouin-Vallerand,
- Kévin Bouchard,
- Sébastien Gaboury,
- Hubert Kenfack Ngankam,
- Maxime Lussier,
- Mélanie Couture,
- Nathalie Bier,
- Sylvain Giroux
GoodIT '24: Proceedings of the 2024 International Conference on Information Technology for Social GoodSeptember 2024, Pages 275–283https://doi.org/10.1145/3677525.3678671Deep learning models have significantly contributed to recognizing older adults’ daily activities for telemonitoring and assistance. However, recognizing human activities in real-world smart homes over the long term presents substantial challenges. ...
- research-articleJuly 2024
3D surface segmentation from point clouds via quadric fits based on DBSCAN clustering
AbstractExtracting surfaces from 3D point clouds is significant in reconstructing and transforming these discrete points into their corresponding models. Scanned point clouds are often accompanied by noise, and the existing methods mainly rely on local ...
Highlights- A normal estimation method based on neighborhood information reconstruction is proposed to provide reliable feature information for subsequent segmentation.
- A subdivision strategy is designed to extend the DBSCAN clustering algorithm ...
- research-articleJuly 2024
A sparse time-frequency reconstruction approach from the synchroextracting domain
Highlights- Derive a signal reconstruction approach (SRA) in the synchroextracting domain.
- Evaluate the SRA's reconstruction ability, noise robustness, and the impact of window length.
- Verify the SRA's advantage compared with advanced ...
Synchroextracting, including frequency-extracting and transient-extracting, offers a sparse time-frequency (TF) representation for multi-component signals. However, it is susceptible to amplitude modulation (AM) and frequency modulation (FM) when ...
- research-articleMay 2024
Erasure-Coded Hybrid Writes Based on Data Delta
International Journal of Parallel Programming (IJPP), Volume 52, Issue 4Aug 2024, Pages 231–252https://doi.org/10.1007/s10766-024-00773-0AbstractErasure coding is extensively deployed in today’s data centers to tackle prevalent failures, because it can offer higher reliability at lower storage overhead than data replication. However, for each small write, erasure-coded storage systems have ...
- research-articleMay 2024
Quintuple-based Representation Learning for Bipartite Heterogeneous Networks
ACM Transactions on Intelligent Systems and Technology (TIST), Volume 15, Issue 3Article No.: 61, Pages 1–19https://doi.org/10.1145/3653978Recent years have seen rapid progress in network representation learning, which removes the need for burdensome feature engineering and facilitates downstream network-based tasks. In reality, networks often exhibit heterogeneity, which means there may ...
-
- articleMay 2024
Research on Removing Image Noise and Distortion in Machine Dial Recognition
International Journal of Information Technologies and Systems Approach (IJITSA-IGI), Volume 17, Issue 1Jan 2024, Pages 1–20https://doi.org/10.4018/IJITSA.343047This study aims to address the issues of image noise and distortion in machine dial recognition via initial denoising. A wavelet local threshold denoising method that combines high-frequency wavelet coefficients with wavelet decomposition coefficients in ...
- research-articleJuly 2024
Enhancing seismic data by edge-preserving geometrical mode decomposition
Digital Signal Processing (DISP), Volume 148, Issue CMay 2024https://doi.org/10.1016/j.dsp.2024.104442AbstractReal-time seismic signals are intertwined with different types of noises during the generation, acquisition, and transmission process. The enhanced data with high resolution assists to interpret and analyze records more accurately. In this paper, ...
- ArticleApril 2024
RCPM_RLM: A Regional Co-location Pattern Mining Method Based on Representation Learning Model
AbstractDue to the heterogeneity of spatial data, spatial co-location patterns are not all global prevalent patterns. There are regional prevalent patterns that can only appear in specific local areas. Regional co-location pattern mining (RCPM) is ...
- research-articleApril 2024
An efficient Bayesian method with intrusive homotopy surrogate model for stochastic model updating
Computer-Aided Civil and Infrastructure Engineering (MICE), Volume 39, Issue 1615 August 2024, Pages 2500–2516https://doi.org/10.1111/mice.13206AbstractThis paper proposes a new stochastic model updating method based on the homotopy surrogate model (HSM) and Bayesian sampling. As a novel intrusive surrogate model, the HSM is established by the homotopy stochastic finite element (FE) method. ...
- research-articleJuly 2024
Anti-interrupted sampling repeater jamming based on intra-pulse frequency modulation slope agile radar waveform joint FrFT
Digital Signal Processing (DISP), Volume 147, Issue CApr 2024https://doi.org/10.1016/j.dsp.2024.104418AbstractInterrupted sampling repeater jamming (ISRJ) is coherent with the radar-transmitted signal, which can form multi-false targets at the radar receiver. Therefore, ISRJ suppression is a compelling task in electronic counter-countermeasure. Intra-...
Graphical abstractDisplay Omitted
- research-articleApril 2024
An improved binary dandelion algorithm using sine cosine operator and restart strategy for feature selection
Expert Systems with Applications: An International Journal (EXWA), Volume 239, Issue CApr 2024https://doi.org/10.1016/j.eswa.2023.122390Highlights- A feature selection method based on binary dandelion algorithm is proposed.
- The algorithm applies a sine-cosine operator and a restart strategy.
- Mutual information and quick bit mutation improve the performance of the algorithm.
Feature selection (FS) is an important data preprocessing technology for machine learning and data mining. Metaheuristic algorithm (MH) has been widely used in feature selection because of its powerful search function. This paper presents an ...
- research-articleJuly 2024
Development and validation of a deep interpretable network for continuous acute kidney injury prediction in critically ill patients
- Meicheng Yang,
- Songqiao Liu,
- Tong Hao,
- Caiyun Ma,
- Hui Chen,
- Yuwen Li,
- Changde Wu,
- Jianfeng Xie,
- Haibo Qiu,
- Jianqing Li,
- Yi Yang,
- Chengyu Liu
Artificial Intelligence in Medicine (AIIM), Volume 149, Issue CMar 2024https://doi.org/10.1016/j.artmed.2024.102785AbstractEarly detection of acute kidney injury (AKI) may provide a crucial window of opportunity to prevent further injury, which helps improve clinical outcomes. This study aimed to develop a deep interpretable network for continuously predicting the 24-...
Highlights- A deep neural network developed for continuously predicting the 24-hour AKI risk
- The model interpretability help the understanding of AKI risk at individual patient levels
- Generalisability in AI-based models is important when ...
- research-articleMay 2024
CTFCD: Channel transformer based on full convolutional decoder for single image deraining
Journal of Visual Communication and Image Representation (JVCIR), Volume 98, Issue CFeb 2024https://doi.org/10.1016/j.jvcir.2023.103992AbstractAlthough convolutional neural network and visual transformer have been successfully applied in various field of computer vision, there is little work combining them to construct an efficient network model to solve image deraining tasks. ...
- research-articleApril 2024
Pearls Hide behind Linearity: Simplifying Deep Convolutional Networks for Embedded Hardware Systems via Linearity Grafting
ASPDAC '24: Proceedings of the 29th Asia and South Pacific Design Automation ConferenceJanuary 2024, Pages 250–255https://doi.org/10.1109/ASP-DAC58780.2024.10473971The increasing complexity of convolutional neural networks (CNNs) has fueled a huge demand for compression. Nonetheless, network pruning, as the most effective knob, fails to deliver Pareto-optimal networks. To tackle this issue, we introduce a novel ...
- research-articleJune 2024
Predicting Postoperative Complications of TAVR Using Multimodal Feature Alignment Network
CVDL '24: Proceedings of the International Conference on Computer Vision and Deep LearningJanuary 2024, Article No.: 21, Pages 1–6https://doi.org/10.1145/3653781.3653805This study proposes a multimodal feature alignment network for early prediction of new-onset conduction disturbance after the Transcatheter Aortic Valve Replacement (TAVR) surgery. Based on medical prior knowledge, the baseline clinical information, ...
- research-articleJuly 2024
Information accuracy verification system of invoice metadata based on data warehouse
International Journal of Business Intelligence and Data Mining (IJBIDM), Volume 25, Issue 12024, Pages 34–51https://doi.org/10.1504/ijbidm.2024.139451This paper aims to explore the information accuracy verification system based on the invoice metadata of the data warehouse. Metadata defines the role of the data warehouse and specifies the content and location of the data warehouse, and describes the ...
- research-articleFebruary 2024
Spatio-temporal features for fast early warning of unplanned self-extubation in ICU
- Yang Chen,
- Ling Wang,
- Guorong Wang,
- Shuang Yang,
- Yingying Wang,
- MingFang Xiang,
- Xuan Zhang,
- Hui Chen,
- Dekun Hu,
- Hong Cheng
Engineering Applications of Artificial Intelligence (EAAI), Volume 127, Issue PAJan 2024https://doi.org/10.1016/j.engappai.2023.107294AbstractPatients’ behaviors in the Intensive Care Units (ICU) have garnered research attention, particularly regarding the impact of Unplanned Extubation (UEX). However, there is currently no existing report on methods for early warning of UEX action in ...
Graphical abstractDisplay Omitted
Highlights- Early warning for Unplanned self-Extubation in ICU.
- Non-contacted action prediction method in ICU.
- A new Spatio-Temporal feature for action recognition.
- research-articleDecember 2023
GPro3D: Deriving 3D BBox from ground plane in monocular 3D object detection
AbstractConsidering the inherent ill-posed nature, monocular 3D object detection (M3OD) is extremely challenging. The ground plane prior is a highly informative geometry clue in M3OD. However, it has been neglected by most mainstream methods. This paper ...
- research-articleDecember 2023
DAG-Order: An Order-Based Dynamic DAG Scheduling for Real-Time Networks-on-Chip
ACM Transactions on Architecture and Code Optimization (TACO), Volume 21, Issue 1Article No.: 2, Pages 1–24https://doi.org/10.1145/3631527With the high-performance requirement of safety-critical real-time tasks, the platforms of many-core processors with high parallelism are widely utilized, where network-on-chip (NoC) is generally employed for inter-core communication due to its ...