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- research-articleJune 2024
On-device Online Learning and Semantic Management of TinyML Systems
ACM Transactions on Embedded Computing Systems (TECS), Volume 23, Issue 4Article No.: 55, Pages 1–32https://doi.org/10.1145/3665278Recent advances in Tiny Machine Learning (TinyML) empower low-footprint embedded devices for real-time on-device Machine Learning (ML). While many acknowledge the potential benefits of TinyML, its practical implementation presents unique challenges. This ...
- research-articleNovember 2022
Towards Semantic Management of On-Device Applications in Industrial IoT
ACM Transactions on Internet Technology (TOIT), Volume 22, Issue 4Article No.: 102, Pages 1–30https://doi.org/10.1145/3510820The Internet of Things (IoT) is revolutionizing the industry. Powered by pervasive embedded devices, the Industrial IoT (IIoT) provides a unique solution for retrieving and analyzing data near the source in real-time. Many emerging techniques, such as ...
- ArticleOctober 2022
SeLoC-ML: Semantic Low-Code Engineering for Machine Learning Applications in Industrial IoT
AbstractInternet of Things (IoT) is transforming the industry by bridging the gap between Information Technology (IT) and Operational Technology (OT). Machines are being integrated with connected sensors and managed by intelligent analytics applications, ...
- research-articleJune 2021
The synergy of complex event processing and tiny machine learning in industrial IoT
DEBS '21: Proceedings of the 15th ACM International Conference on Distributed and Event-based SystemsPages 126–135https://doi.org/10.1145/3465480.3466928Focusing on comprehensive networking, the Industrial Internet-of-Things (IIoT) facilitates efficiency and robustness in factory operations. Various intelligent sensors play a central role, as they generate a vast amount of real-time data that can ...
- research-articleDecember 2016
Object detection using boosted local binaries
Pattern Recognition (PATT), Volume 60, Issue CPages 793–801https://doi.org/10.1016/j.patcog.2016.07.010This paper presents a novel binary descriptor Boosted Local Binary (BLB) for object detection. The proposed descriptor encodes variable local neighbour regions in different scales and locations. Each region pair of the proposed descriptor is selected by ...
- ArticleDecember 2015
Object Detection Using Generalization and Efficiency Balanced Co-Occurrence Features
ICCV '15: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV)Pages 46–54https://doi.org/10.1109/ICCV.2015.14In this paper, we propose a high-accuracy object detector based on co-occurrence features. Firstly, we introduce three kinds of local co-occurrence features constructed by the traditional Haar, LBP, and HOG respectively. Then the boosted detectors are ...
- research-articleSeptember 2015
Object recognition based on deformable edge set
2015 IEEE International Conference on Image Processing (ICIP)Pages 2439–2443https://doi.org/10.1109/ICIP.2015.7351240We aim to solve the object recognition problem by a novel contour feature called Deformable Edge Set (DES). The DES consists of several Deformable Edge Features (DEF), which is deformed from an edge template to the actual object contour according to the ...
- ArticleAugust 2014
Gender Recognition Using Complexity-Aware Local Features
ICPR '14: Proceedings of the 2014 22nd International Conference on Pattern RecognitionPages 2389–2394https://doi.org/10.1109/ICPR.2014.414We propose a gender classifier using two types of local features, the gradient features which have strong discrimination capability on local patterns, and the Gabor wavelets which reflect the multi-scale directional information. The Real Ad a Boost ...
- ArticleAugust 2010
A Sample Pre-mapping Method Enhancing Boosting for Object Detection
ICPR '10: Proceedings of the 2010 20th International Conference on Pattern RecognitionPages 3005–3008https://doi.org/10.1109/ICPR.2010.736We propose a novel method to improve the training efficiency and accuracy of boosted classifiers for object detection. The key step of the proposed method is a sample pre-mapping on original space by referring to the selected ‘reference sample’ before ...