Xiangchen Lai
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Xiangchen Lai has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Jiawen Liao
Chengdu University of Technology
1 shared publication - Keke Kong
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
1 shared publication - Yao Li
Tianfu Jiangxi Laboratory, Chengdu 641419, China
1 shared publication - Yijia Chen
Chengdu University of Technology
1 shared publication - Yong Jia
Chengdu University of Technology
1 shared publication
Latest Publications
- Open Access
- 2 September 2026
- Published by MDPI AG in Electronics
- Vol. 15 (17) , 3953
A Novel Multi-Scale Dual-Modality CNN Approach Leveraging Light and Dark Fingerprints for RSS-Based Indoor Positioning
Traditional indoor localization using received signal strength (RSS) fingerprinting commonly uses measured RSS values as location-related inputs. This paper investigates a paired light–dark representation of an RSS fingerprint image and a cross-modality multi-scale fusion model for small-scale indoor localization. Specifically, each RSS fingerprint image is inverted in pixel intensity, and the original and inverted images are retained as synchronized representations of the same RSS measurement rather than as independent physical modalities or separate augmented samples. The paired images are processed by two parallel convolutional neural network (CNN) paths combined with a cross-modality fusion (CMF) module, which extracts and fuses multi-scale feature representations. The fused features are fed into fully connected layers to predict the coordinate vector. Experimental comparisons with traditional and advanced methods on four public datasets show positioning errors comparable to the strongest advanced benchmark and lower errors than the considered traditional baselines. The proposed model uses approximately 30.8% fewer parameters than the advanced benchmark.
- 2 September 2026
- Published by MDPI AG in Electronics
- Vol. 15 (17) , 3953
Open AccessA Novel Multi-Scale Dual-Modality CNN Approach Leveraging Light and Dark Fingerprints for RSS-Based Indoor Positioning
Traditional indoor localization using received signal strength (RSS) fingerprinting commonly uses measured RSS values as location-related inputs. This paper investigates a paired light–dark representation of an RSS fingerprint image and a cross-modality multi-scale fusion model for small-scale indoor localization. Specifically, each RSS fingerprint image is inverted in pixel intensity, and the original and inverted images are retained as synchronized representations of the same RSS measurement rather than as independent physical modalities or separate augmented samples. The paired images are processed by two parallel convolutional neural network (CNN) paths combined with a cross-modality fusion (CMF) module, which extracts and fuses multi-scale feature representations. The fused features are fed into fully connected layers to predict the coordinate vector. Experimental comparisons with traditional and advanced methods on four public datasets show positioning errors comparable to the strongest advanced benchmark and lower errors than the considered traditional baselines. The proposed model uses approximately 30.8% fewer parameters than the advanced benchmark.