Jiabao Li - IEEE Xplore Author Profile

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Achieving land cover change detection (LCCD) through remotely sensed images (RSIs) is important in the observation of the changes on the Earth’s surface. In such detection, spectral-reflectance noise and the uncertainty of the imaging external conditions for the bitemporal RSIs usually cause some salt-and-pepper noisy pixels in the results and reduce the change detection accuracy. In this article,...Show More
This paper investigates a centralized cooperative cognitive radio network (CCRN) where a primary base station (PBS) transmits a message to a primary user while a secondary user transmitter (SU-Tx) function as a friendly jammer. The jammer sends jamming signals to protect the PBS’s messages from a potential eavesdropper (Eve). However, the SU-Tx also attempts to covertly transmit its own messages t...Show More
Deep learning methods excel in Polarimetric SAR (PolSAR) image classification. However, existing methods typically sample an image block for each pixel with a fixed-size square window, which always contains inconsistent/incomplete content with the central pixel, resulting in many misclassifications especially in boundary and heterogeneous regions. So, a size-fixed square window is not enough for r...Show More
Multispectral filter array (MSFA) imaging with one single sensor is a fast, portable, and inexpensive means of acquiring spectral images. The most challenging task for MSFA imaging is the multispectral demosaicing with the aim of reconstructing the captured raw/mosaic image, especially for the systems with many bands which results in the higher sparseness of the raw data. In this paper, a global c...Show More
With the development of deep sequencing, recent studies indicate that a miRNA precursor can generate multiple miRNA isoforms (isomiRs). The family prediction of canonical miRNAs and isomiRs could provide a basis for miRNA functional research. In this study, we propose a novel method for family identification of canonical miRNA and isomiRs based on incremental learning. First, a benchmark dataset i...Show More
This work studies the joint transmit power control and receive beamforming in uplink rate splitting multiple access (RSMA)-based low earth orbit (LEO) satellite networks, using both generative diffusion model and proximal policy optimization (PPO) learning framework. In particular, using RSMA, interference is partially decoded and partially treated as noise, thereby improving the spectral efficien...Show More
Ensuring signal confidentiality against eavesdroppers is particularly challenging, especially with imperfect channel state information (CSI). To address this, we propose a novel approach leveraging reconfigurable intelligent surfaces (RISs) to enhance security and optimize transmission performance. This paper focuses on secure communication in symbiotic radio (SR) systems by investigating cooperat...Show More
Human activity recognition (HAR) based on wearable sensors is a hot topic in health detection and motion management. Nonetheless, conventional identification methods necessitate substantial labeled datasets, and the acquisition of high-quality labeled data crucial for human activity recognition is both time-consuming and costly. To tackle this problem, transfer learning is used to annotating unlab...Show More
Deep learning networks can automatically acquire high-level semantic features for polarimetric SAR image classification, while it involves a blind learning procedure without explicit guidance. In contrast, sparse representation methods represent effective non-deep models with a robust mathematical mechanism serving as guidance. However, they can’t capture complex image features and semantic inform...Show More
Affective analysis is a technology that aims to understand human sentiment states, and it is widely applied in human–computer interaction and social sentiment analysis. Compared to unimodal, multimodal sentiment analysis (MSA) focuses more on the complementary information and differences from multimodalities, which can better represent the actual sentiment expressed by humans. Existing MSA methods...Show More
This paper examines integrating jamming and secondary signals for covert communications in cognitive radio networks (CRNs), aiming to enhance covertness by using jamming and secondary signals in an overlay cooperative CRN. The scenario involves a primary base station (PBS) transmitting to a primary user (PU), with a secondary user transmitter (SU-Tx) acting as a cooperative jammer to obscure the m...Show More
Queue counting using WiFi channel state information (CSI) faces challenges due to susceptibility to external factors and relies on ideal testing environments for current methods. We propose an efficient CSI recurrence plot (RP)-based framework for queue counting (CRPF-QC), containing a transformation module and a recognition module. The conversion module transforms the CSI into RP, distinct from t...Show More
Non-intrusive load monitoring (NILM) monitors the operating status and power consumption of residential appliances with only one main meter, providing a new measure for energy management. Deep learning (DL) shows outperformance in NILM. However, the lack of appliance data caused by rapid growth appliance types and high-cost data sampling reduces the DL-based load recognition accuracy. Transfer lea...Show More
With recent advances of Industrial Internet of Things (IIoT), the connectivity and data collection capabilities of industrial equipment have be significantly enhanced, yet bringing new challenges for the remaining useful life (RUL) prediction. To fulfill the RUL predicting demand in multivariate time series, this work proposes an encoder–decoder model termed as dual-scale transformer model (DSForm...Show More
Laplacian dimensionality reduction can effectively achieve feature transformation and preserve the important structure of high-dimensional features. However, the trained model with this method usually require better generalization ability to new samples. Hence, a human activity recognition method based on kernel-supervised laplacian eigenmaps (KSLE) by combining the kernel method, laplacian mappin...Show More
The methods based on multimodal representation learning enhance discriminable sentiment expression for multimodal sentiment analysis(MSA). The modal invariant and specific features serve different purposes in sentiment learning and the diversity of inter-sample and inter-category relationships takes less consideration in previous advances. In this paper, we propose a fine-grained tri-modal interac...Show More
For the consumer cameras with Bayer filter array, raw color filter array (CFA) data collected in real-world is sampled with signal-dependent noise. Various joint denoising and demosaicking (JDD) methods are utilized to reconstruct full-color and noise-free images. However, some artifacts (e.g., remaining noise, color distortion, and fuzzy details) still exist in the reconstructed images by most JD...Show More
With the development of intelligent transportation systems, most human objects can be accurately detected in normal road scenes. However, the detection accuracy usually decreases sharply when the pedestrians are merged into the background with very similar colors or textures. In this paper, a camouflaged object detection method is proposed to detect the pedestrians or vehicles from the highly simi...Show More
Land cover change detection (LCCD) with bitemporal remote sensing images has been widely used in practical applications. However, when the bitemporal images are multimodal remote sensing images (MRSIs) which are acquired with different sensors, the change detection performance may be unsatisfactory, because MRSIs cannot be compared directly to generate a change magnitude and obtain a change detect...Show More
Cyber-physical energy systems (CPES) are a crucial component of smart grids (SGs), and as such, they represent a specialized subset of cyber-physical systems. CPES provides essential services for pricing decisions and automatic generation control through short-term load forecasting (STLF), making the accuracy of STLF critical to optimizing their operation. However, due to the numerous communicatio...Show More
Gesture recognition has become a research hotspot in the fields of human–computer interaction, sign language recognition, rehabilitation training, sports medicine, etc. The surface electromyography (sEMG) signal contains abundant motion intention features which can be used for gesture recognition. Existing feature engineering methods are commonly used to extract linear features while ignoring the ...Show More
In this work, we design a covert jamming scheme against an intelligent Eve towards physical layer security in a cooperative cognitive radio networks. To protect the primary message from being decoded by an intelligent eavesdropper (Eve), a secondary user is picked as a friendly jammer that transmits artificial noise (AN) to the Eve. However, the intelligent Eve can detect the existence of AN, and ...Show More
Spectral–spatial features are important for ground target identification and classification with high spatial resolution remotely sensed (HSRRS) Imagery. In this article, two novel features, named the Gaussian-weighting spectral (GWS) feature and the area shape index (ASI) feature, are proposed to complement the deficiency of the basic image feature for land cover classification with HSRRS imagery...Show More
Motor dysfunction (e.g., incoordination of upper or lower limb) significantly limits the individuals’ ability of daily living, and thus the provisioning of a motion coordination assessment method becomes of vital importance. As a quantitative indicator, intermuscular coupling strength could assess limb coordination. Since surface electromyography (sEMG) signals cover nonlinear coupling characteris...Show More
When clustering gene expression, it is expected that correlation coefficients of genes in the same clusters are high, and that gene ontology (GO) enrichment analysis of most clusters will be significant. However, existing short-term gene expression clustering algorithms have limitations. To address this problem, we proposed a novel clustering process based on angular features for short-term gene e...Show More