The diagnosis of major depressive disorder predominantly relies on subjective methods， including physician consultations and scale assessments， which may lead to misdiagnosis. EEG （ElectroEncephaloGraphy） offers advantages such as high temporal resolution， low cost， ease of setup， and non-invasiveness， making it a potential quantitative measurement tool for psychiatric disorders， including depressive disorder. Recently， deep learning algorithms have been diversely applied to EEG signals， notably in the diagnosis and classification of depressive disorder. Due to significant redundancy is observed when processing EEG signals through a self-attention mechanism， a convolutional neural network leveraging a Probabilistic sparse Self-Attention mechanism （PSANet） was proposed. Firstly， a limited number of pivotal attention points were chosen in the self-attention mechanism based on the sampling factor， addressing the high computational cost and facilitating its application to extensive EEG data sequences； concurrently， EEG data was amalgamated with patients’ physiological scales for a comprehensive diagnosis. Experiments were executed on a dataset encompassing both depressive disorder patients and a healthy control group. Experimental results show that PSANet exhibits superior classification accuracy and a reduced number of parameters relative to alternative methodologies such as EEGNet.

Focused on the existing diagnosis methods of Parkinson's Disease （PD）， the auxiliary diagnosis methods of PD based on gait analysis was reviewed. In clinical practice， the common diagnosis method of gait assessment for PD is based on scales， which is simple and convenient， but is highly subjective and requires well-experienced clinical doctors. With the development of computer technology， more methods of gait analysis are provided. Firstly， PD and its abnormal manifestations in gait were summarized. Then， the common methods of auxiliary diagnosis for PD based on gait analysis were reviewed. These methods were able to be roughly divided into two types： methods based on wearable or non-wearable devices. Wearable devices are small and have high accuracy for diagnosis， and with the use of them， the gait status of patients can be monitored for a long time. With the use of non-wearable devices， human gait data is captured through video sensors such as Microsoft Kinect， without wearing related devices and restricting patients' movements. Finally， the deficiencies in the existing gait analysis methods were pointed out， and the possible development trends in the future were discussed.

Aiming at the problem of insufficient adaptive ability of network intrusion detection models， the large-scale fast search ability of Sparrow Search Algorithm （SSA） was introduced into Particle Swarm Optimization （PSO） algorithm， and a network intrusion detection algorithm based on Sparrow Search Algorithm and improved Particle Swarm Optimization Algorithm （SSAPSO） was proposed. In the algorithm， by optimizing the parameters that are difficult to set in Light Gradient Boosting Machine （LightGBM） algorithm， PSO algorithm converged quickly while ensuring the optimization accuracy， and an optimal network intrusion detection model was obtained. Simulation results show that on the four benchmark functions， SSAPSO converged faster than basic PSO algorithm. Compared with Categorical features+gradient Boosting （CatBoost） algorithm， SSAPSO optimized LightGBM （SSAPSO-LightGBM） has the accuracy， recall， precision and F1_score improved by 15.12%， 3.25%， 21.26% and 12.25% respectively on KDDCUP99 dataset. Compared with LightGBM algorithm， SSAPSO-LightGBM has the detection accuracy for Normal， Remote-to-Login （R2L） attack， User-to-Root （U2R） attack and Probeing （PROBE） attack on the above dataset improved by 0.61%， 3.14%， 4.24%， 1.04% and 5.03% respectively.

Convolutional Neural Network （CNN） is one of the important research directions in the field of computer vision based on deep learning at present. It performs well in applications such as image classification and segmentation， target detection. Its powerful feature learning and feature representation capability are admired by researchers increasingly. However， CNN still has problems such as incomplete feature extraction and overfitting of sample training. Aiming at these issues， the development of CNN， classical CNN network models and their components were introduced， and the methods to solve the above issues were provided. By reviewing the current status of research on CNN models in image classification， the suggestions were provided for further development and research directions of CNN.

In view of the difficulty of traditional mechanical fault diagnosis methods to solve the problem of the uncertainty of manual extraction， a large number of deep learning feature extraction methods have been proposed， which greatly promotes the development of mechanical fault diagnosis. As a typical representative of deep learning， convolution neural networks have made significant developments in image classification， target detection， image semantic segmentation and other fields. There is also a lot of literature in the field of mechanical fault diagnosis. In view of the published literature， in order to further understand the problem of mechanical fault diagnosis by using the method of convolutional neural network， on the basis of a brief introduction to the relevant theories of convolution neural network， and then from the aspects such as data input type， transfer learning， and prediction， the applications of convolution neural network in mechanical fault diagnosis were summarized. Finally， the development directions of convolution neural network and its applications in mechanical fault diagnosis were prospected.

Wearable ElectroEncephaloGram （EEG） device is a wireless EEG system to daily real-time monitoring. It is developed rapidly and widely applied because of its portability， real-time performance， non-invasiveness， and low-cost advantages. This system is mainly composed of hardware parts such as signal acquisition module， signal processing module， micro-control module， communication module and power supply module， and software parts such as mobile terminal module and cloud storage module. The key technologies of wearable EEG devices were discussed. First， the improvement of EEG signal acquisition module was explained. In addition， the comparisons of wearable EEG device signal preprocessing module， signal noise reduction， artifact processing and feature extraction technology were performed. Then， the advantages and disadvantages of machine learning and deep learning classification algorithms were analyzed， and the application fields of wearable EEG device were summarized. Finally， future development trends of the key technologies of wearable EEG device were proposed.

To solve the long tail problem caused by low coverage and diversity in recommendation system， a recommendation framework for long tail items and a recommendation algorithm named FLTI （Focusing on Long Tail Item） were proposed. The recommendation framework for long tail items was built based on Convolutional Neural Network （CNN） model with three layers， including data processing layer， recommendation algorithm layer and recommendation list generation layer. The FLTI algorithm was added to the recommendation algorithm layer of the framework， and calculated the frequent recommendation items and the infrequent recommendation items at first， then replaced the frequent recommendation items by the long-tail items to meet the specified proportion of long-tail items in the system. Experimental results on Movielens 1M and BookCrossing datasets show that compared to traditional User-Based Collaborative Filtering （UserCF） algorithm， Item-based Collaborative Filtering （ItemCF） algorithm， Singular Value Decomposition （SVD） recommendation algorithm and Colloborative Denosing Auto-Encoder （CDAE） algorithm， the coverage of FLTI algorithm is improved up to 51%， and the diversity of FLTI algorithm is improved up to 59% .

OOFDM (Orthogonal Frequency Division Multiplexing) is a focus in research of broadband communications now. The combination of OFDM with ROF (Radio Over Fiber) system can overcome optical dispersion and improve the performance of ROF system。 A generation and transmission system of optical millimeter-wave carrying OFDM signal based on phase modulator was proposed. In the center station, Double SideBand with Suppressed Carrier (DSB-SC) optical millimeter wave signal was generated utilizing phase modulator along with optical filter, while OFDM signal was modulated only onto one of the two optical carriers and transported along optical fiber. The paper set a model about optical carrier OFDM generation and analyzed fiber dispersion influence on phase, and proved the feasibility of the system through the simulation test. The result show the system's eye chart is very clear over 95km optical fiber.