In recent years, deep learning-based methods for motor imagery EEG classification have become increasingly popular in the field of brain-computer interfaces. However, most of the studies tend to use sequence-structured classification networks to extract spatial features when dealing with motor imagery EEG signal classification tasks, ignoring the fact that EEG signals as time-series signals contain rich temporal information and features between neural network layers, resulting in poor classification performance. Therefore, this paper proposes a feature fusion network called ResCNNBiGRU, which consists of ResNet-based residual convolutional neural network (ResCNN) and bidirectional gated recurrent unit (BiGRU) connected in parallel. The two branches use different forms of EEG signal feature representation as input, the input to the ResCNN branch is a wavelet transformed time-frequency image, and the input to the BiGRU branch is EEG data in a two-dimensional matrix format. ResCNN extracts spatial features and utilizes interlayer features through residual connections. It also introduces convolutional block attention module (CBAM) to avoid introducing too much useless low-level feature information during interlayer feature fusion. BiGRU extracts temporal features. Finally, experiments are conducted on the authoritative four-category motor imagery dataset BCI Competition IV 2a to verify the performance of the proposed algorithm.
Simultaneous localization and mapping (SLAM) technology is widely used in smart factories, logistics, unmanned vehicles, high precision mapping and other fields, where inaccurate positioning or even failure is the key problem to be improved and solved by LiDAR SLAM algorithm. In this paper, we propose a loosely coupled SLAM algorithm based on lidar and inertial measurement unit (IMU). Firstly, linear interpolation of the laser point cloud is performed by introducing IMU data to correct the point cloud distortion. Second, a height threshold and a dynamic clustering threshold are introduced in the point cloud segmentation part to determine the classification. Again, the cumulative error is effectively avoided by using Scan-Context descriptors, and the next step is executed to find matching loopback frames based on the Euclidean distance, and the loopback constraints are added after the successful verification of both. Finally, a local map consisting of key frames is aligned with the current frame to obtain the inter-frame matching error, and then a joint optimization function is constructed jointly with the IMU pre-integration error and the loopback detection constraint to solve the laser odometer pose. The results on the KITTI dataset show that the improved laser odometer accuracy is higher than that of the LeGO-LOAM scheme in compliance with the odometer real-time requirements.
In order to solve the problem of inaccurate scale optimization of visual inertial odometer (VIO) algorithm under uniform motion, this paper presents a Visual-Inertial-Encoder Tightly-Coupled Odometry (VIETO) algorithm, and describes VIETO initialization as an optimal estimation problem in the sense of maximum-a-posteriori (MAP) estimation. Firstly, the pre-integration theory of encoder is introduced in this paper so that the scale and velocity information can be obtained by using the encoder to measure the pre-integration during the visual MAP estimation, which provides a good initial value for the optimal estimation of IMU parameters. Secondly, the encoder error term and random plane constraint are introduced into the visual inertia optimization framework to further constrain pose estimation. Finally, we apply VIETO to the monocular inertial ORB-SLAM3 system. By comparing the algorithm with other similar algorithms on the DS dataset, the results prove the effectiveness of the system.
The variable head pose and low-quality eye images in natural scenes can lead to low accuracy of gaze estimation. In this paper, we propose a multi-feature fusion gaze estimation model based on the attention mechanism. First, face and eye feature extractors based on the group convolution channel and spatial attention mechanism (GCCSAM) are designed to use channel and spatial information to adaptively select and enhance important features in face images and two eye images, and suppress information irrelevant to gaze estimation. Then we design two feature fusion networks to fuse the features of face, two eyes and pupil center position, thus avoiding the effects of two-eye asymmetry and inaccurate head pose estimation on gaze estimation. The average angular error of the proposed method is 4.1° on MPIIGaze and 5.2° on EyeDiap. Compared with the current mainstream methods, our method effectively improves the accuracy and robustness of gaze estimation in natural scenes.
The Point feature and line feature have been widely used in visual SLAM(simultaneous localization and mapping) algorithm. But most of these methods assume that the environments are static, ignoring that there are often dynamic objects in real world, which can degrade the SLAM performance. In order to solve this problem, a line-expanded visual odometry is proposed. It calculates optical flow between two adjacent frames to identify and eliminate dynamic point features in dynamic objects, and use the rest of point features to find the collinear relationship to expand line features for visual SLAM algorithm based on point features. Final it use the rest of point features and line features to estimate the camera pose. The proposed method not only reduces the influence of dynamic objects, but also avoids the tracking failure caused by few point features. The experiments are carried out on a TUM dataset. Compared with state-of-the-art methods like ORB (oriented FAST and rotated BRIEF) method and ORB add optical flow method, the results demonstrate that the proposed method reduces the tracking error and improve the robustness and accuracy of visual odometry in dynamic environments.
A mode-locked 2 μm fiber laser with a simple ring cavity is experimentally demonstrated. By inserting the Tm/Ho-doped fiber saturable absorber into a laser cavity pumped by a 793 nm diode laser, a stable mode-locked pulse at a central wavelength of 2010.11 nm was obtained. The repetition rate, maximum average output power, 3 dB spectral bandwidth, pulse width, and signal-to-noise ratio are 3.76 MHz, 9.63 mW, 0.16 nm, 28.2 ns, and 37 dB, respectively. To the best of our knowledge, this is the first reported mode-locked 2 μm fiber laser using a Tm/Ho-doped fiber saturable absorber.
We describe the contents of an advanced undergraduate course on photonics at School of Electrical Engineering, Chongqing University of Posts and Telecommunications. The main goal of the course is to equip the student with the necessary theoretical and practical knowledge to participate in photonics-related industry and further graduate level study and research if they choose. The prerequisites include college-level physics and higher mathematics which a general engineering student has already had in his/her first and second year college study. Although applications of photonics are ubiquitous such as telecommunications, photonic computing, spectroscopy, military technology, and biophotonics etc. Telecommunication information system application is more emphasized in our course considering about the potential job chances for our students.
In order to cultivate the innovative talents with the comprehensive development to meet the talents demand for development of economic society, Chongqing University of Posts and Telecommunications implements cultivation based on broadening basic education and enrolment in large category of general education. Optoelectronic information science and engineering major belongs to the electronic engineering category. The "2 +2" mode is utilized for personnel training, where students are without major in the first and second year and assigned to a major within the major categories in the end of the second year. In the context of the comprehensive cultivation, for the changes in the demand for professionals in the global competitive environment with the currently rapid development, especially the demand for the professional engineering technology personnel suitable to industry and development of local economic society, the concept of CDIO engineering ability cultivation is used for reference. Thus the curriculum system for the three-node structure optoelectronic information science and engineering major is proposed, which attaches great importance to engineering practice and innovation cultivation under the background of the comprehensive cultivation. The conformity between the curriculum system and the personnel training objectives is guaranteed effectively, and the consistency between the teaching philosophy and the teaching behavior is enhanced. Therefore, the idea of major construction is clear with specific characteristics.
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