Ruiwen He

@article{zhu_3394,

title = {CarNet: A generative convolutional neural network-based line-of-sight/non-line-of-sight classifier for global navigation satellite systems by transforming multivariate time-series data into images},

author = {Ni ZHU and Ruiwen He and Zhiqiang Wang},

url = {https://www.sciencedirect.com/science/article/pii/S0952197625001605?via%3Dihub},

year  = {2025},

date = {2025-04-01},

journal = {Engineering Applications Of Artificial Intelligence},

volume = {145},

pages = {110160},

abstract = {Urban environments present significant challenges to commercial Global Navigation Satellite Systems (GNSS) receivers due to degraded satellite visibility and Non-line-of-sight (NLOS) receptions. Mitigating NLOS receptions for GNSS is essential, especially for safety-critical and reliability-critical location-based applications. Traditional physical error channel propagation modeling encountered bottlenecks since the NLOS and multipath errors cannot be modeled accurately in complex urban environments. Data-driven methods show significant potential for effectively classifying GNSS Line-of-sight (LOS) and NLOS. This paper proposes the CarNet - a generative Convolutional Neural Network (CNN)-based GNSS LOS/NLOS classifier by transforming multivariate time-series data into images. CarNet comprises two modules: an image generator and an image classifier. The image generator enriches and augments the original 1-dimension feature vector into 2-dimension feature maps and the image classifier uses an inception-based CNN to realize multi-scale feature extraction and classification. The proposed architecture is trained and tested on more than 6 h of real vehicle data collected in different challenging environments (about 1.6 million samples). A thorough benchmark is conducted, comparing CarNet against the existing mainstream Artificial Intelligence (AI) methods. The results with cross-validation on unseen data indicate that CarNet achieves the highest accuracy, i.e., 81.47% while maintaining the optimal balance between precision for both classes: 83.3% for LOS and 70.99% for NLOS. Finally, positioning accuracy is assessed using a reweighting strategy based on the LOS/NLOS information predicted by CarNet. The assessment of total datasets shows that CarNet weighting can achieve the best accuracy compared to the traditional weighting schemes based on signal-to-noise ratio or satellite elevation. CarNet shows strong potential for embedding into GNSS receivers to enhance positioning accuracy in complex urban environments, benefiting a wide range of location-based applications such as autonomous driving, emergency response, and urban logistics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{veldhuizen_3120,

title = {SHREC 2024: Recognition of dynamic hand motions molding clay},

author = {Ben Veldhuizen and Remco C. Veltkamp and Omar Ikne and Benjamin Allaert and Hazem Wannous and Marco Emporio and Andrea Giachetti and Joseph J. Laviola Jr. and Ruiwen He and Halim Benhabiles and Adnane Cabani and Anthony Fleury and Karim Hammoudi and Konstantinos Gavaldas and Christoforos Vlachos and Athanasios Papanikalaou and Ioannis Romanelis and Vlassis Fotis and Gerasimos Arvanitis and Konstantinos Moustakas and Christoph Von Tycowicz},

url = {https://www.sciencedirect.com/science/article/pii/S009784932400147X},

year  = {2024},

date = {2024-10-01},

journal = {Computers & Graphics-Uk},

volume = {123},

pages = {104012},

abstract = {Gesture recognition is a tool to enable novel interactions with different techniques and applications, like Mixed Reality and Virtual Reality environments. With all the recent advancements in gesture recognition from skeletal data, it is still unclear how well state-of-the-art techniques perform in a scenario using precise motions with two hands. This paper presents the results of the SHREC 2024 contest organized to evaluate methods for their recognition of highly similar hand motions using the skeletal spatial coordinate data of both hands. The task is the recognition of 7 motion classes given their spatial coordinates in a frame-by-frame motion. The skeletal data has been captured using a Vicon system and pre-processed into a coordinate system using Blender and Vicon Shogun Post. We created a small, novel dataset with a high variety of durations in frames. This paper shows the results of the contest, showing the techniques created by the 5 research groups on this challenging task and comparing them to our baseline method.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{yang_2796,

title = {A generalized deep learning-based framework for assistance to the human malaria diagnosis from microscopic images},

author = {Ziheng Yang and Halim Benhabiles and Karim Hammoudi and Feryal Windal and Ruiwen He and Dominique Collard},

url = {https://link.springer.com/article/10.1007/s00521-021-06604-4},

year  = {2022},

date = {2022-09-01},

journal = {Neural Computing & Applications},

volume = {34},

pages = {14223-14238},

abstract = {Malaria is an infectious disease caused by Plasmodium parasites and is potentially human life-threatening. Children under 5 years old are the most vulnerable group with approximately one death every two minutes, accounting for more than 65% of all malaria deaths. The World Health Organization (WHO) encourages the research of appropriate methods to treat malaria through rapid and economical diagnostic. In this paper, we present a deep learning-based framework for diagnosing human malaria infection from microscopic images of thin blood smears. The framework is based on a direct segmentation and classification approach which relies on the analysis of the parasite itself. The framework permits to segment the Plasmodium parasite in the images and to predict its species among four dominant classes: P. Falciparum, P. Malaria, P. Ovale, and P. Vivax. A high potential of generalization with a competitive performance of our framework on inter-class data is demonstrated through an experimental study considering several datasets. Our source code is publicly available on https://github.com/Benhabiles-JUNIA/MalariaNet.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{he_2794,

title = {A CNN-based methodology for cow heat analysis from endoscopic images},

author = {Ruiwen He},

url = {https://doi.org/10.1007/s10489-021-02910-5},

year  = {2022},

date = {2022-06-01},

journal = {Applied Intelligence},

volume = {52},

number = {1},

pages = {8372-8385},

abstract = {In cattle farming, the artificial insemination technique is a biotechnology that brings to farmers a wide range of benefits namely health security, genetic gain and economic costs. The main condition for the success of artificial insemination within cattle is the heat (or estrus) detection. In this context, several cow heat detection systems have been recently proposed in the literature to assist the farmer in this task. Nevertheless, they are mainly based on the analysis of the physical behavior of the cow which may be affected by several factors related to its health and its environment. In this paper, we present a new vision system for cow heat detection which is based on the analysis of the genital tract of the cow. The main core of our system is a CNN model that has been designed and tailored for analyzing endoscopic images collected using an innovative insemination technology named Eye breed. The conducted experiments on two datasets namely our own dataset and a public dataset show the high accuracy of our CNN model (more than 97% for both datasets) outperforming 19 methods from the state of the art. Moreover, we propose an optimized version of our model for an Android deployment by exploiting several techniques namely quantization, GPU acceleration and video downsampling. The conducted tests on a smart-phone shows that our heat detection system has a response time of a few seconds.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}