Ytterbium-doped femtosecond fiber lasers are widely used in scientific research, industrial processing, and other fields due to their high quantum efficiency, wide gain bandwidth, and compact structure. This article addresses the problems of low processing efficiency and difficulty in increasing the average power of femtosecond lasers. A high repetition rate fiber chirped pulse amplification system is built, which uses a high repetition rate Figure-9 fiber laser as the seed source and an acousto-optic modulator (AOM) to shape the dense pulse train in the time domain. The main amplification stage uses a large mode field ytterbium-doped fiber to achieve full fiberization of the amplification system, and a volume grating (VBG) is selected as the pulse compressor to make the laser system highly integrated. When the repetition rate is 67.5 MHz, the compressed output laser has an average power of 20.5 W, a pulse width of 447 fs, a pulse train energy of 750 μJ, a spot ellipticity of 0.96, and a beam quality M2 better than 1.4 (Mx2=1.33, My2=1.16).

Fiber-terahertz communication is regarded as one of the best candidates for future 6G mobile communication systems. Using fiber optic-assisted terahertz communication can solve the problem of large-capacity terahertz coverage. Still, it also leads to more complex and variable channel environments. This makes channel estimation for fiber terahertz communication challenging. In this paper, we use the conditional generative adversarial networks (CGAN) to solve channel estimation problems with their excellent mapping ability to complex relationships. Meanwhile, we further improved the performance of the network by using a residual structure and an unrolled structure. Compared with the traditional recurrent neural networks (RNN) and convolutional neural networks (CNN), the normalized mean squared error (NMSE) of CGAN-based fiber terahertz channel estimation is reduced by up to 1.93 dB and 1.02 dB, respectively.

Trimap is required for most image matting algorithms, as it provides partial regions with known opacity. As capturing elaborate trimaps is a time-consuming process, in practice, users prefer to provide coarse trimaps with large unknown regions. However, extant image matting algorithms cannot provide high-quality alpha mattes based on coarse trimaps. Although some matting algorithms include trimap expansion in the pre-processing stage, if this is done by directly comparing the similarity of image features between pixels, errors and omissions can easily occur. To overcome this issue, in this paper, a coarse trimap expansion model based on one-class classification is presented, in which the problem is treated as a process of reclassifying pixels in unknown regions. For this purpose, a coarse trimap expansion method denoted as CTE-OC is proposed, in which the similarity between pixels is reliably determined by measuring semantic features, allowing newly developed one-class classifiers to adequately classify pixels in entire unknown regions. The validity of these strategies is tested experimentally, and the results show that CTE-OC can significantly improve the quality of alpha mattes obtained by extant image matting methods when provided with coarse trimaps.

This paper presents a novel lidar SLAM system for localizing a mobile robot to build a map of the environment. To identify the unknown transform matrix, we design a new scan-matching approach, in which a point cloud segmentation algorithm is additionally integrated. Different from the traditional normal distribution transform algorithm for point cloud registration, our newly proposed one additionally incorporates a ground point remover and a point cloud segmentation method. By employing the point cloud segmentation algorithm to divide the point cloud space into different cells, the newly proposed algorithm can guarantee the continuity and convergence of the cost function. To tackle the recognition difficulties that the camera-based loop-closure detection heavily depends on the environment’s appearance, a depth-completion algorithm is introduced to fuse sensor data to ensure the robustness of the algorithm. Moreover, the bags of binary words (DBoW) are adopted to improve the image-matching quality. Finally, experimental results are presented to illustrate the effectiveness of the proposed system.

This paper considers a digital twin network (DTN) assisted mobile edge computing (MEC) system. When wireless device (WD) users request services from MECs, we assume in the system there are a cloud server, storing all of service entities, and a lightweight digital twin network, providing the digital replicas of state information of all MECs instead of service entities. We aim to maximize the number of service requests served by the MECs, or, equivalently, to minimize the load of the cloud. This is formulated as a mixed-integer non-convex optimization problem. We propose a service placement algorithm based on hash-based data structure called Merkle tree to solve the problem. The introduction of candidate mode pruning effectively reduces the time complexity of the algorithm in iterations. Simulation results show that our proposed method has a better performance compared with the other benchmarks.

The intensities and phase profiles of the elliptic optics vortex beam both are determined by the topological charge (TC) and ellipticity. This paper presents an efficient and simple method for measuring the TC and ellipticity of elliptic vortex. By observing the diffraction patterns, the TC modulus and ellipticity can be probed with the suitable rectangular aperture, and the TC sign can be detected with the suitable right triangular aperture. The scheme works well even for high-order elliptic vortex with topological charge value as high as ± 18, and ellipticity range from 0.5 to 2 at least. Obviously, the measurement of TC and ellipticity have important research value for promoting the application of elliptical vortex.

The Normal Distributions Transform (NDT) is currently a very practical laser scan matching algorithm. In this paper, a NDT variant algorithm is designed for SLAM which contains a ground point remover and a effective point cloud data segmentation. Its primary goal isto improve the robustness oftheNDTalgorithm. By extracting points belonging to the ground and segmenting 3D scan data into different objects, the proposed method solve the problem that the cost function of the standard NDT is not continuous enough. Simultaneously, the computation speed of the algorithm increased significantly. Final ly, experimental results are presented to illustrate the effectiveness of the proposed algorithm.

In recent years, affective computing based on electroencephalogram (EEG) data has attracted increased attention. As a classic EEG feature extraction model, Granger causality analysis has been widely used in emotion classification models, which construct a brain network by calculating the causal relationships between EEG sensors and select the key EEG features. Traditional EEG Granger causality analysis uses the L 2 norm to extract features from the data, and so the results are susceptible to EEG artifacts. Recently, several researchers have proposed Granger causality analysis models based on the least absolute shrinkage and selection operator (LASSO) and the L 1/2 norm to solve this problem. However, the conventional sparse Granger causality analysis model assumes that the connections between each sensor have the same prior probability. This paper shows that if the correlation between the EEG data from each sensor can be added to the Granger causality network as prior knowledge, the EEG feature selection ability and emotional classification ability of the sparse Granger causality model can be enhanced. Based on this idea, we propose a new emotional computing model, named the sparse Granger causality analysis model based on sensor correlation (SC-SGA). SC-SGA integrates the correlation between sensors as prior knowledge into the Granger causality analysis based on the L 1/2 norm framework for feature extraction, and uses L 2 norm logistic regression as the emotional classification algorithm. We report the results of experiments using two real EEG emotion datasets. These results demonstrate that the emotion classification accuracy of the SC-SGA model is better than that of existing models by 2.46–21.81%.

This paper focuses on the mode purity and beam propagation properties of Gaussian vortex beams which are Gaussian beams modulated by the spiral phase plates (SPP). For a given topological charge, the Laguerre-Gaussian beam has constant mode purity during its free space propagation. However, with the increase of the topological charge, the value of the highest mode purity decays. Our results imply that the modulation of SPP to the Gaussian beam is suitable for producing Laguerre-Gaussian beams with low topological charge, rather than with high topological charge.

In this paper, a self-calibration method for a linear structured light 3D measurement system and its advantages are presented. According to the mathematical model of the linear-structured light 3D measurement system, the calibration problem is a highdimensional optimization problem. The principle of this self-calibration method can be drawn from a traditional calibration method. In this method, quantum genetic algorithm and feature matching are applied to self-calibration. Feature matching is used to derive two points with fixed spatial relationships, and an optimal solution of system parameters can be obtained by quantum genetic algorithm. Finally, experimental results are given. The measurement error is 0.05 mm and the ratio of the error to scanning distance is 1.54e-4.In this paper, a self-calibration method for a linear structured light 3D measurement system and its advantages are presented. According to the mathematical model of the linear-structured light 3D measurement system, the calibration problem is a high-dimensional optimization problem. The principle of this self-calibration method can be drawn from a traditional calibration method. In this method, quantum genetic algorithm and feature matching are applied to selfcalibration. Feature matching is used to derive two points with fixed spatial relationships, and an optimal solution of system parameters can be obtained by quantum genetic algorithm. Finally, experimental results are given. The measurement error is 0.05 mm and the ratio of the error to scanning distance is 1.54e-4.

This paper studied the propagations of fractional vortex beams in nonlocal nonlinear media, and found that quasi-stable solitons can form when the nonlocality is strong enough and the initial power is equal to the critical power. The propagation properties, including intensity patterns, phase structures, transverse energy flows, and rotation period, were all investigated. The critical power of solitons with different topological charges is the same, however, the orbital angular momentum (OAM) of that increases with the increase of fractional topological charge. In addition, we revealed that fractional vortex solitons have lateral shifts in the y-direction, and the shift values can be controlled by the fractional topological charge. Obviously, the quasi-stable fractional vortex solitons have important academic value and potential application to optical switches, optical wrenches and optical communications, etc.

新经济、新产业的发展迫切需要新工科人才的支撑.按照“以研促学、知行融合、研赛相长”的构建思路,以服务粤港澳大湾区重点产业集群为目标,积极探索“立地式”创新型人才培养实践,在实践平台建设、人才培养体系、激励与保障机制建设等方面进行了整体统筹和规划,经过3年多的建设实践,取得较好的成效.

With the dramatic increase in the number of IOT devices the network access protocol of the devices has also become diverse., which has led to the need for IoT platform to support heterogeneous network access. However, the existing IoT platform framework does not support heterogeneous network access well. Therefore, to solve the issue mentioned above, in this paper, we propose a Flexi-IoT platform, a novel IoT framework for heterogeneous network access specially. Flexi-IoT platform overcomes the drawbacks of the existing IoT platform framework in supporting heterogeneous network access. Furthermore, the paper also designs a method for controlling devices via fuzzy logic. And the implementation procedure of Flexi-IoT platform is demonstrated with an application example.

At present, in the field of electroencephalogram (EEG) signal recognition, the classification and recognition in complex scenarios with more categories of EEG signals have gained more attention. Based on the joint fast Fourier transform (FFT) and support vector machine (SVM) methods, this study proposed a novel EEG signal-processing joint method for the complex scenarios with 10 classifications of EEG signals. Moreover, a comprehensive efficiency formula was put forward. The formula considered the accuracy and time consumption of the joint method. This new joint method could improve the accuracy and comprehensive efficiency of multiclass EEG signal recognition. The new joint approach used standardization for data preprocessing. Feature extraction was performed by combining FFT and principal component analysis methods. EEG signals were classified using the weighted k-nearest nenighbour method. In this study, experiments were conducted using public datasets of brainwave 0-9 digits classification. The result demonstrated that the accuracy and comprehensive efficiency of the novel joint method were 84% and 87%, respectively, which were better than those of the existing methods. The precision rate, recall rate, and F1 score of the novel joint method were 89%, 85%, and 0.85, respectively. In conclusion, the proposed joint method was effective in a complex scenario for multiclass EEG signal recognition.

借助"数字逻辑及数字系统综合实验实训平台",结合"LED点阵显示汉字"设计案例,用FPGA实现数字电路综合设计教学改革.在目标任务的达成过程中,学生会渐渐意识到不同设计方法的优劣,从而迫切想要改变自己的设计方法,提高自己的设计水平.为更高效率地完成设计,学生能够自觉主动地探索新的知识领域,如饥似渴地学习新的设计方法,从而不断激发学习兴趣,拓宽和延伸设计思路,加强实践动手能力,提升成长型学习思维能力,提高应用型人才的培养质量.

Feature extraction of electroencephalography (EEG) signals plays a significant role in the wearable computing field. Due to the practical applications of EEG emotion calculation, researchers often use edge calculation to reduce data transmission times, however, as EEG involves a large amount of data, determining how to effectively extract features and reduce the amount of calculation is still the focus of abundant research. Researchers have proposed many EEG feature extraction methods. However, these methods have problems such as high time complexity and insufficient precision. The main purpose of this paper is to introduce an innovative method for obtaining reliable distinguishing features from EEG signals. This feature extraction method combines differential entropy with Linear Discriminant Analysis (LDA) that can be applied in feature extraction of emotional EEG signals. We use a three-category sentiment EEG dataset to conduct experiments. The experimental results show that the proposed feature extraction method can significantly improve the performance of the EEG classification: Compared with the result of the original dataset, the average accuracy increases by 68%, which is 7% higher than the result obtained when only using differential entropy in feature extraction. The total execution time shows that the proposed method has a lower time complexity.

According to the applied talents training objectives and the digital circuit's problems in the teaching reform,from the perspective of curriculum of digital circuit and EDA cources,this paper proposes a plaform of integrated experiment training with digital logic and digital system from the perspective of digital circuit and EDA course integration.Fusion of FPGA-based schematic design method and the traditional digital circuit design technology based on students from basic learing-a profound understanding-practical design-to enhance the application of innovative training.Practice shows that the integration of curriculums gradually improves the training quality of applied talents.

在数字逻辑电路课堂教学中,现有仿真软件常常存在展示效果不佳、非电路的教学内容不能模拟等缺点,为了弥补这些不足设计开发数字逻辑电路教学软件.结合实例,对数字逻辑电路教学软件的设计思路和方法做了归纳,包括针对非电路知识点软件的设计,基本电路工作原理、工程案例等内容的模拟方法.自行开发教学软件可以因应教学需要设计,具有较好的灵活性,对帮助学生理解和应用知识点,提升课堂教学效果具有积极意义.