Design of a real-time orientation sensor based on atmospheric polarization light.

In recent years, biomimetic polarization navigation has become a research hotspot in navigation fields because of its autonomy and concealment. Existing point-source polarization navigation sensors mainly use a logarithmic amplifier as the arithmetic unit to obtain polarization information. However, these sensors suffer from zero drift and low detection accuracy, which limits their application range. To address the above issues, a polarization navigation sensor based on a differential amplifier is designed as the operational unit. Based on the change of the arithmetic unit of the polarization signal, the algorithm for calculating the heading angle of the sensor is improved. The results of the orientation experiments with the designed sensor in clear weather indicate that the orientation error is ±1.243∘, and the standard deviation is 0.351°. The polarization navigation sensor can extract polarized light information and calculate the heading without accumulation of errors over time accurately and achieves good real-time performance.

An Epileptic Seizure Prediction Method Based on CBAM-3D CNN-LSTM Model.

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. Clinical and Translational Impact Statement-Timely prediction of epileptic seizures and intervention treatment can significantly reduce the accidental injury of patients and protect the life and health of patients.

Biomimetic Polarized Light Navigation Sensor: A Review.

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.

Skeleton-Based Human Pose Recognition Using Channel State Information: A Survey.

With the increasing demand for human-computer interaction and health monitoring, human behavior recognition with device-free patterns has attracted extensive attention. The fluctuations of the Wi-Fi signal caused by human actions in a Wi-Fi coverage area can be used to precisely identify the human skeleton and pose, which effectively overcomes the problems of the traditional solution. Although many promising results have been achieved, no survey summarizes the research progress. This paper aims to comprehensively investigate and analyze the latest applications of human behavior recognition based on channel state information (CSI) and the human skeleton. First, we review the human profile perception and skeleton recognition progress based on wireless perception technologies. Second, we summarize the general framework of precise pose recognition, including signal preprocessing methods, neural network models, and performance results. Then, we classify skeleton model generation methods into three categories and emphasize the crucial difference among these typical applications. Furthermore, we discuss two aspects, such as experimental scenarios and recognition targets. Finally, we conclude the paper by summarizing the issues in typical systems and the main research directions for the future.