All Outputs (163)

Rewritable and sustainable 2D barcode for traceability application in smart IoT based fault-tolerant mechanism. (2020)
Journal Article
CHEN, R., YU, Y., XIE, S., ZHAO, H., LIU, S., REN, J. and TAN, H.-Z. 2020. Rewritable and sustainable 2D barcode for traceability application in smart IoT based fault-tolerant mechanism. Sustainability [online], 12(17), article 7192. Available from: https://doi.org/10.3390/su12177192

With the development of the Internet of Things (IoT) technology, two-dimensional (2D) barcodes are widely used in smart IoT applications as a perception portal. In industries with many circulations and testing links like traceability, since the exist... Read More about Rewritable and sustainable 2D barcode for traceability application in smart IoT based fault-tolerant mechanism..

Topological optimization of the DenseNet with pretrained-weights inheritance and genetic channel selection. (2020)
Journal Article
FANG, Z, REN, J., MARSHALL, S., ZHAO, H., WANG, S. and LI, X. 2021. Topological optimization of the DenseNet with pretrained-weights inheritance and genetic channel selection. Pattern recognition [online], 109, article ID 107608. Available from: https://doi.org/10.1016/j.patcog.2020.107608

Convolutional neural networks (CNNs) have been successfully applied in many computer vision applications, especially in image classification tasks, where most of the structures have been designed manually. With the aid of skip connection and dense co... Read More about Topological optimization of the DenseNet with pretrained-weights inheritance and genetic channel selection..

Semisupervised hypergraph discriminant learning for dimensionality reduction of hyperspectral image. (2020)
Journal Article
LUO, F., GUO, T., LIN, Z., REN, J. and ZHOU, X. 2020. Semisupervised hypergraph discriminant learning for dimensionality reduction of hyperspectral image. IEEE journal of selected topics in applied earth observations and remote sensing [online], 13, pages 4242-4256. Available from: https://doi.org/10.1109/jstars.2020.3011431

Semisupervised learning is an effective technique to represent the intrinsic features of a hyperspectral image (HSI), which can reduce the cost to obtain the labeled information of samples. However, traditional semisupervised learning methods fail to... Read More about Semisupervised hypergraph discriminant learning for dimensionality reduction of hyperspectral image..

STEM teaching for the Internet of Things maker course: a teaching model based on the iterative loop. (2020)
Journal Article
CHEN, R., ZHENG, Y., XU, X., ZHAO, H., REN, J. and TAN, H.-Z. 2020. STEM teaching for the Internet of Things maker course: a teaching model based on the interative loop. Sustainability [online], 12(14), article 5758. Available from: https://doi.org/10.3390/su12145758

As the key technology for 5G applications in the future, the Internet of Things (IoT) is developing rapidly, and the demand for the cultivation of engineering talents in the IoT is also expanding. The rise of maker education has brought new teaching... Read More about STEM teaching for the Internet of Things maker course: a teaching model based on the iterative loop..

A novel spectral-spatial singular spectrum analysis technique for near real-time in-situ feature extraction in hyperspectral imaging. (2020)
Journal Article
FU, H., SUN, G., ZABALZA, J., ZHANG, A., REN, J. and JIA, X. 2020. A novel spectral-spatial singular spectrum analysis technique for near real-time in-situ feature extraction in hyperspectral imaging. IEEE journal of selected topics in applied earth observations and remote sensing [online], 13, pages 2214-2225. Available from: https://doi.org/10.1109/JSTARS.2020.2992230

As a cutting-edge technique for denoising and feature extraction, singular spectrum analysis (SSA) has been applied successfully for feature mining in hyperspectral images (HSI). However, when applying SSA for in situ feature extraction in HSI, conve... Read More about A novel spectral-spatial singular spectrum analysis technique for near real-time in-situ feature extraction in hyperspectral imaging..

Content-sensitive superpixel generation with boundary adjustment. (2020)
Journal Article
ZHANG, D., XIE, G., REN, J., ZHANG, Z., BAO, W. and XU, X. 2020. Content-sensitive superpixel generation with boundary adjustment. Applied sciences [online], 10(9), article 3150. Available from: https://doi.org/10.3390/app10093150

Superpixel segmentation has become a crucial tool in many image processing and computer vision applications. In this paper, a novel content-sensitive superpixel generation algorithm with boundary adjustment is proposed. First, the image local entropy... Read More about Content-sensitive superpixel generation with boundary adjustment..

Spatial residual blocks combined parallel network for hyperspectral image classification. (2020)
Journal Article
ZHANG, B., QING, C., XU, X. and REN, J. 2020. Spatial residual blocks combined parallel network for hyperspectral image classification. IEEE access [online], 8, pages 74513-74524. Available from: https://doi.org/10.1109/ACCESS.2020.2988553

In hyperspectral image (HSI) classification, there are challenges of the spatial variation in spectral features and the lack of labeled samples. In this paper, a novel spatial residual blocks combined parallel network (SRPNet) is proposed for HSI cla... Read More about Spatial residual blocks combined parallel network for hyperspectral image classification..

Adaptive distance-based band hierarchy (ADBH) for effective hyperspectral band selection. (2020)
Journal Article
SUN, H., REN, J., ZHAO, H., SUN, G., LIAO, W., FANG, Z. and ZABALZA, J. 2022. Adaptive distance-based band hierarchy (ADBH) for effective hyperspectral band selection. IEEE transactions on cybernetics [online], 52(1), pages 215-227. Available from: https://doi.org/10.1109/TCYB.2020.2977750

Band selection has become a significant issue for the efficiency of the hyperspectral image (HSI) processing. Although many unsupervised band selection (UBS) approaches have been developed in the last decades, a flexible and robust method is still la... Read More about Adaptive distance-based band hierarchy (ADBH) for effective hyperspectral band selection..

Urban PM2.5 concentration prediction via attention-based CNN–LSTM. (2020)
Journal Article
LI, S., XIE, G., REN, J., GUO, L., YANG, Y. and XU, X. 2020. Urban PM2.5 concentration prediction via attention-based CNN–LSTM. Applied sciences [online], 10(6), article 1953. Available from: https://doi.org/10.3390/app10061953

Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the eff... Read More about Urban PM2.5 concentration prediction via attention-based CNN–LSTM..

Exemplar-supported representation for effective class-incremental learning. (2020)
Journal Article
GUO, L., XIE, G., XU, X. and REN, J. 2020. Exemplar-supported representation for effective class-incremental learning. IEEE access [online], 8, pages 51276-51284. Available from: https://doi.org/10.1109/ACCESS.2020.2980386

Catastrophic forgetting is a key challenge for class-incremental learning with deep neural networks, where the performance decreases considerably while dealing with long sequences of new classes. To tackle this issue, in this paper, we propose a new... Read More about Exemplar-supported representation for effective class-incremental learning..

Weakly supervised conditional random fields model for semantic segmentation with image patches. (2020)
Journal Article
XU, X., XUE, Y., HAN, X., ZHANG, Z., XIE, J. and REN, J. 2020. Weakly supervised conditional random fields model for semantic segmentation with image patches. Applied sciences [online], 10(5), article 1679. Available from: https://doi.org/10.3390/app10051679

Image semantic segmentation (ISS) is used to segment an image into regions with differently labeled semantic category. Most of the existing ISS methods are based on fully supervised learning, which requires pixel-level labeling for training the model... Read More about Weakly supervised conditional random fields model for semantic segmentation with image patches..

Varietal classification of rice seeds using RGB and hyperspectral images. (2020)
Journal Article
FABIYI, S.D., VU, H., TACHTATZIS, C., MURRAY, P., HARLE, D., DAO, T.K., ANDONOVIC, I., REN, J. and MARSHALL, S. 2020. Varietal classification of rice seeds using RGB and hyperspectral images. IEEE access [online], 8, pages 22493-22505. Available from: https://doi.org/10.1109/ACCESS.2020.2969847

Inspection of rice seeds is a crucial task for plant nurseries and farmers since it ensures seed quality when growing seedlings. Conventionally, this process is performed by expert inspectors who manually screen large samples of rice seeds to identif... Read More about Varietal classification of rice seeds using RGB and hyperspectral images..

MIMN-DPP: maximum-information and minimum-noise determinantal point processes for unsupervised hyperspectral band selection. (2020)
Journal Article
CHEN, W., YANG, Z., REN, J., CAO, J., CAI, N., ZHAO, H. and YUEN, P. 2020. MIMN-DPP: maximum-information and minimum-noise determinantal point processes for unsupervised hyperspectral band selection. Pattern recognition [online], 102, article 107213. Available from: https://doi.org/10.1016/j.patcog.2020.107213

Band selection plays an important role in hyperspectral imaging for reducing the data and improving the efficiency of data acquisition and analysis whilst significantly lowering the cost of the imaging system. Without the category labels, it is chall... Read More about MIMN-DPP: maximum-information and minimum-noise determinantal point processes for unsupervised hyperspectral band selection..

Automatic extraction of water inundation areas using Sentinel-1 data for large plain areas. (2020)
Journal Article
HU, S., QIN, J., REN, J., ZHAO, H., REN, J., and HONG, H. 2020. Automatic extraction of water inundation areas using sentinel-1 data for large plain areas. Remote sensing [online], 12(2), article 243. Available from: https://doi.org/10.3390/rs12020243

Accurately quantifying water inundation dynamics in terms of both spatial distributions and temporal variability is essential for water resources management. Currently, the water map is usually derived from synthetic aperture radar (SAR) data with th... Read More about Automatic extraction of water inundation areas using Sentinel-1 data for large plain areas..

Combining t-distributed stochastic neighbor embedding with convolutional neural networks for hyperspectral image classification. (2019)
Journal Article
GAO, L., GU, D., ZHUANG, L., REN, J., YANG, D. and ZHANG, B. 2020. Combining t-distributed stochastic neighbor embedding with convolutional neural networks for hyperspectral image classification. IEEE geoscience and remote sensing letters [online], 17(8), pages 1368-1372. Available from: https://doi.org/10.1109/LGRS.2019.2945122

Hyperspectral images (HSIs), featured by high spectral resolution over a wide range of electromagnetic spectra, have been widely used to characterize materials with subtle differences in the spectral domain. However, a large number of bands and an in... Read More about Combining t-distributed stochastic neighbor embedding with convolutional neural networks for hyperspectral image classification..

TGMCF: a tree-guided multi-modality correlation filter for visual tracking. (2019)
Journal Article
LIU, Q., LIU, W., WANG, Y., REN, J., DU, Q., LV, Y. and SUN, H. 2019. TGMCF: a tree-guided multi-modality correlation filter for visual tracking. IEEE access [online], 7, pages 166950-166963. Available from: https://doi.org/10.1109/ACCESS.2019.2943917

For updating the tracking models, most existing approaches have an assumption that the target changes smoothly over time. Despite their success in some cases, these approaches struggle in dealing with occlusion, illumination changes and abrupt motion... Read More about TGMCF: a tree-guided multi-modality correlation filter for visual tracking..

Multiscale spatial-spectral convolutional network with image-based framework for hyperspectral imagery classification. (2019)
Journal Article
CUI, X., ZHENG, K., GAO, L., ZHANG, B., YANG, D. and REN, J. 2019. Multiscale spatial-spectral convolutional network with image-based framework for hyperspectral imagery classification. Remote sensing [online], 11(19), article 2220. Available from: https://doi.org/10.3390/rs11192220

Jointly using spatial and spectral information has been widely applied to hyperspectral image (HSI) classification. Especially, convolutional neural networks (CNN) have gained attention in recent years due to their detailed representation of features... Read More about Multiscale spatial-spectral convolutional network with image-based framework for hyperspectral imagery classification..

Big data analytics and mining for effective visualization and trends forecasting of crime data. (2019)
Journal Article
FENG, M., ZHENG, J., REN, J., HUSSAIN, A., LI, X., XI, Y. and LIU, Q. 2019. Big data analytics and mining for effective visualization and trends forecasting of crime data. IEEE access [online], 7, pages 106111-106123. Available from: https://doi.org/10.1109/ACCESS.2019.2930410

Big data analytics (BDA) is a systematic approach for analyzing and identifying different patterns, relations, and trends within a large volume of data. In this paper, we apply BDA to criminal data where exploratory data analysis is conducted for vis... Read More about Big data analytics and mining for effective visualization and trends forecasting of crime data..

Deep learning based single image super-resolution: a survey. (2019)
Journal Article
HA, V.K., REN, J.-C., XU, X.-Y., ZHAO, S., XIE, G., MASERO, V. and HUSSAIN, A. 2019. Deep learning based single image super-resolution: a survey. International journal of automation and computing [online], 16(4), pages 413-426. Available from: https://doi.org/10.1007/s11633-019-1183-x

Single image super-resolution has attracted increasing attention and has a wide range of applications in satellite imaging, medical imaging, computer vision, security surveillance imaging, remote sensing, objection detection, and recognition. Recentl... Read More about Deep learning based single image super-resolution: a survey..

VIP-STB farm: scale-up village to county/province level to support science and technology at backyard (STB) program. (2019)
Presentation / Conference Contribution
YAN, Y., ZHAO, S., FANG, Y., LIU, Y., CHEN, Z. and REN, J. 2020. VIP-STB farm: scale-up village to county/province level to support science and technology at backyard (STB) program. In Ren, J., Hussain, A., Zhao, H., Huang, K., Zheng, J., Cai, J., Chen, R. and Xiao, Y. (eds.). 2020. Advances in brain inspired cognitive systems: proceedings of the 10th Brain inspired cognitive systems (BCIS) international conference 2019 (BCIS 2019), 13-14 July 2019, Guangzhou, China. Lecture notes in computer science, 11691. Cham: Springer [online], pages 283-292. Available from: https://doi.org/10.1007/978-3-030-39431-8_27

In this paper, we introduce a new concept in VIP-STB, a funded project through Agri-Tech in China: Newton Network+ (ATCNN), in developing feasible solutions towards scaling-up STB from village level to upper level via some generic models and systems.... Read More about VIP-STB farm: scale-up village to county/province level to support science and technology at backyard (STB) program..