OBJECTIVE: Accurate inter-subject pain intensity assessment using EEG remains a major challenge due to substantial inter-subject variability. This study introduces a novel framework that leverages pain-related brain dynamics and transfer learning to enable reliable inter-subject pain intensity classification. METHODS: The proposed method first quantifies pain sensitivity from resting-state EEG to identify source subjects with comparable neural pain signatures. High-activation pain brain sources are subsequently localized and remapped between source and target subjects. A classifier is trained to evaluate transfer suitability across subjects, and balanced distribution adaptation is applied to align brain source features, mitigating inter-subject variability. The adapted model infers pseudo-labels for the target EEG, which guide the pain response extraction. Final classification is determined by selecting the model exhibiting the minimal cross-domain discrepancy between brain source and pain-evoked EEG features. RESULTS: Experimental evaluations on real EEG datasets demonstrate that the proposed method significantly outperforms three existing approaches in inter-subject pain intensity classification. SIGNIFICANCE: The proposed method effectively overcomes the problem of poor reliability in inter-subject pain intensity classification, providing a robust and clinically viable solution.

OBJECTIVE: The latest development in low-cost single-channel Electroencephalography (EEG) devices is gaining widespread attention because it reduces hardware complexity. Discrete wavelet transform (DWT) has been a popular solution to eliminate the blink artifacts in EEG signals. However, the existing DWT-based methods share the same wavelet function among subjects, which ignores the individual difference. To remedy this deficiency, this article proposes a novel approach to eliminate the blink artifacts in single-channel EEG signals. METHODS: Firstly, the forward-backward low-pass filter (FBLPF) and a fixed-length window are used to detect blink artifact intervals. Secondly, the adaptive bi-orthogonal wavelet (ABOW) is constructed based on the most representative blink signal. Thirdly, these detected signals are filtered by ABOW-DWT. The DWT's decomposition depth is automatically chosen by a similarity-based method. RESULTS: Compared to eight state-of-the-art methods, experiments on semi-simulated and real EEG signals demonstrate the proposed method's superiority in removing the blink artifacts with less neural information loss. SIGNIFICANCE: To filter the blink artifacts in single-channel EEG signals, the innovative idea of constructing an adaptive wavelet function based on the signal characteristics rather than using the conventional wavelet is proposed for the first time.

In this study, the authors propose a novel approach for single image super‐resolution. Their method is based on the idea of learning a mapping function, which can reveal the intrinsic relationship between sparse coefficients of low‐resolution (LR) and high‐resolution (HR) image patch pairs with respect to their individual dictionaries. Adaptive regularised l 2 ‐boosting algorithm is proposed to learn this type of mapping function. Specifically, to reduce time consumption, the authors cluster training patches into several clusters. Within each cluster, a pair of dictionaries for LR and HR image patches is jointly trained. Adaptive regularised l 2 ‐boosting algorithm is then employed to obtain the function. Thus, in a reconstruction stage, for each given input LR image patch, the authors can effectively estimate its corresponding HR image patch. Their extensive experimental results demonstrated that the proposed method achieves a performance of similar quality performance to that of the top methods.

Dictionary learning for sparse representation classifiers (SRC) has demonstrated great success for many classification problems, i.e., face recognition, object detection, etc. However, it has not enjoyed a similar reception in the facial expression recognition literature. In this paper, we applied dictionary learning methods to the task of facial expression recognition, which is then compared with SVM. In addition, we introduce a new dictionary learning method that incorporates side information, which is contained in the training data but not available in the testing phase. In particular, we introduce a new soft constraint derived from side information and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution to the objective function is efficiently obtained using the K-SVD algorithm. Our algorithm learns the dictionary and an optimal linear classifier jointly. Experimental part demonstrates the effectiveness of the sparse representation classifiers for facial expression recognition problem, and dictionary learning with side information method achieves further improvement on low resolution facial expression recognition.

Facial action recognition is concerned with recognizing the local facial motions from image or video. In recent years, besides the development of facial feature extraction techniques and classification techniques, prior models have been introduced to capture the dynamic and semantic relationships among facial action units. Previous works have shown that combining the prior models with the image measurements can yield improved performance in AU recognition. Most of these prior models, however, are learned from data, and their performance hence largely depends on both the quality and quantity of the training data. These data-trained prior models cannot generalize well to new databases, where the learned AU relationships are not present. To alleviate this problem, we propose a knowledge-driven prior model for AU recognition, which is learned exclusively from the generic domain knowledge that governs AU behaviors, and no training data are used. Experimental results show that, with no training data but generic domain knowledge, the proposed knowledge-driven model achieves comparable results to the data-driven model for specific database and significantly outperforms the data-driven models when generalizing to new data set.

The tracking and recognition of facial activities from images or videos have attracted great attention in computer vision field. Facial activities are characterized by three levels. First, in the bottom level, facial feature points around each facial component, i.e., eyebrow, mouth, etc., capture the detailed face shape information. Second, in the middle level, facial action units, defined in the facial action coding system, represent the contraction of a specific set of facial muscles, i.e., lid tightener, eyebrow raiser, etc. Finally, in the top level, six prototypical facial expressions represent the global facial muscle movement and are commonly used to describe the human emotion states. In contrast to the mainstream approaches, which usually only focus on one or two levels of facial activities, and track (or recognize) them separately, this paper introduces a unified probabilistic framework based on the dynamic Bayesian network to simultaneously and coherently represent the facial evolvement in different levels, their interactions and their observations. Advanced machine learning methods are introduced to learn the model based on both training data and subjective prior knowledge. Given the model and the measurements of facial motions, all three levels of facial activities are simultaneously recognized through a probabilistic inference. Extensive experiments are performed to illustrate the feasibility and effectiveness of the proposed model on all three level facial activities.

In this paper, the law of change of aircraft center of gravity (CG) during fuel consumption is studied. The change of aircraft CG is discussed based on three different structures, such as single tank system, dual-symmetrical tank system and dual-asymmetrical tank system. Based on dual-symmetrical tank system, two fuel consumption modes are considered including the same consumption rate and different consumption rate on both tanks. The curves of change of aircraft CG on different conditions are obtained. The character and trend of change of CG is analyzed.

Transmission on-off valve is an important component in the aircraft fuel system. It is directly related to the fuel supply security during the flight. Erosive wear is the main form of transmission on-off valve failure. it is said that Mathematical modeling of the dynamic characteristics of hydro-mechanical shut-off valves in typical aircraft fuel system, and the failure to model caused by Erosive wear.

A critical shortcoming of determining co-occurrence probability texture features using Haralick's popular grey level co-occurrence matrix (GLCM) is the excessive computational burden. Here, a more robust algorithm (the grey level cooccurrence integrated algorithm or GLCIA) to perform this task is presented. The GLCIA is created by integrating the preferred aspects of two algorithms: the grey level cooccurrence hybrid structure (GLCHS) and the grey level cooccurrence hybrid histogram (GLCHH). The GLCHS utilizes a dedicated 2-d data structure to quickly generate the probabilities and apply statistics to generate the features. The GLCHH uses a more efficient 1-d data structure to perform the same tasks. Since the GLCHH is faster than the GLCHS yet the GLCHH is not able to calculate features using all available statistics, the integration of these two methods generates a superior algorithm (the GLCIA). The computational gains vary as a function of window size, quantization level, and statistics selected. The GLCIA computational time relative to that of the standard GLCM method ranges from 0.04% to 16%. The GLCIA is a highly recommended technique for anyone wishing to calculate co-occurrence probability texture features, especially from large-scale digital imagery.