Epilepsy as a common disease of the nervous system, with high incidence, sudden and recurrent characteristics. Therefore, timely prediction of seizures and intervention treatment can significantly reduce the accidental injury of patients and protect the life and health of patients. Epilepsy seizures is the result of temporal and spatial evolution, Existing deep learning methods often ignore its spatial features, in order to make better use of the temporal and spatial characteristics of epileptic EEG signals. We propose a CBAM-3D CNN-LSTM model to predict epilepsy seizures. First, we apply short-time Fourier transform(STFT) to preprocess EEG signals. Secondly, the 3D CNN model was used to extract the features of preictal stage and interictal stage from the preprocessed signals. Thirdly, Bi-LSTM is connected to 3D CNN for classification. Finally CBAM is introduced into the model. Different attention is given to the data channel and space to extract key information, so that the model can accurately extract interictal and pre-ictal features. Our proposed approach achieved an accuracy of 97.95%, a sensitivity of 98.40%, and a false alarm rate of 0.017 h ^-1 on 11 patients from the public CHB-MIT scalp EEG dataset.

The structure design of an autonomous/remote underwater vehicle, Intelligence Ocean I(IO-I), capable of operating in ROV or AUV mode and acquiring marine environment information and its own status data are outlined first in this paper. The dynamics equations of underwater vehicles in horizontal and vertical are given to describe the movement of an underwater vehicle, taking the gravity, buoyancy, thrust and hydrodynamic force into account. Furthermore, in order to realize high precision automatic control of robot, the fuzzy control algorithm which belongs to the intelligent control is combined with the cascade PID control algorithm to realize an modified underwater vehicle control algorithm, which improves the applicability of the control algorithm and reduces the difficulty and workload of parameter adjustment.The proposed control algorithm is recognized in numerical simulations as well as in experimental tests which confirm the good performance of the controller and the prototype.

Path planning is a key technology for autonomous underwater vehicle (AUV) navigation. With the emphasis and research on AUV, AUV path planning technology is continuously developing. Path planning techniques generally include environment modelling methods and path planning algorithms. Based on a brief description of the environment modelling methods, this paper focuses on the path planning algorithms commonly used by AUV. According to the basic principles of the algorithm, the AUV path planning algorithms are divided into four categories: artificial potential field methods, geometric model search methods, random sampling methods, and intelligent bionic methods. In this review, we summarize in detail the development and application of various path planning algorithms in recent years. Meanwhile, we analyse the advantages and disadvantages of various algorithms and their improvement methods. Obstacles, ocean currents, and undersea terrain have an impact on AUV path planning. Therefore, how to deal with the complex underwater environment adds some limits to AUV path planning algorithms. In addition to the external environment, path planning algorithms also need to consider AUV’s physical constraints, such as energy constraints and motion constraints. Then, we analyse the motion constraints in AUV path planning. Finally, we discuss the development direction of AUV path planning algorithm. Time-varying ocean currents, special obstacles, multiobjective constraints, and practicability will be the problems that AUV path planning algorithms need to solve.

A polarized light sensor is applied to the front-end detection of a biomimetic polarized light navigation system, which is an important part of analyzing the atmospheric polarization mode and realizing biomimetic polarized light navigation, having received extensive attention in recent years. In this paper, biomimetic polarized light navigation in nature, the mechanism of polarized light navigation, point source sensor, imaging sensor, and a sensor based on micro nano machining technology are compared and analyzed, which provides a basis for the optimal selection of different polarized light sensors. The comparison results show that the point source sensor can be divided into basic point source sensor with simple structure and a point source sensor applied to integrated navigation. The imaging sensor can be divided into a simple time-sharing imaging sensor, a real-time amplitude splitting sensor that can detect images of multi-directional polarization angles, a real-time aperture splitting sensor that uses a light field camera, and a real-time focal plane light splitting sensor with high integration. In recent years, with the development of micro and nano machining technology, polarized light sensors are developing towards miniaturization and integration. In view of this, this paper also summarizes the latest progress of polarized light sensors based on micro and nano machining technology. Finally, this paper summarizes the possible future prospects and current challenges of polarized light sensor design, providing a reference for the feasibility selection of different polarized light sensors.

In the soft fault diagnosis of nonlinear analog filter circuits, the single feature can’t maximally reveal the behaviors hidden in signals. In order to overcome such shortcomings, a fusion algorithm weighted feature from multi-group is proposed. This method use reliefF algorithm to optimize canonical correlation analysis combines support vector machine(RCCA-SVM) for diagnosis. The fault characteristics used in this method are extracted from the time-domain, statistical features and frequency-domain by wavelet packet transform (WPT). And then the CCA algorithm is used to improve the correlation of features according to the weights of the features. Finally, the fusion features are dimension reduced by principal component analysis(PCA), support vector machine(SVM) is the classifier of the diagnosis. The simulations show that the proposed method has a good diagnostic effect on circuit fault diagnosis of non-linear analog circuits.

Wi-Fi sensing has garnered increasing interest for its significant advantages, primarily leveraging Wi-Fi signal fluctuations induced by human activities and advanced neural network algorithms. However, its application faces challenges due to limited generalizability, necessitating frequent data recollection and neural network retraining for adaptation to new environments. To address these limitations, some researchers introduced few-shot learning into Wi-Fi sensing applications because it offers a promising solution with its ability to achieve remarkable performance in novel scenarios using minimal training samples. Despite its potential, a comprehensive review of its applications within this domain remains absent. This study endeavors to fill this gap by exploring prominent Wi-Fi sensing applications that incorporate few-shot learning, aiming to delineate their key features. We categorize few-shot learning approaches into three distinct methodologies: transfer learning, metric learning, and meta-learning, based on their neural network training strategies. Through this classification, we examine representative systems from an application perspective and elucidate the principles of few-shot learning implementation. These systems are evaluated in terms of learning methodology, data modality, and recognition accuracy. Finally, this paper highlights the challenges and future directions for few-shot learning in Channel State Information (CSI) based human sensing, providing a valuable resource for researchers in the field of Wi-Fi human sensing leveraging few-shot learning.