A Novel Multi-Stream Fusion Network for Underwater Image Enhancement

Due to the selective absorption of light and the existence of a large number of floating media in sea water, underwater images often suffer from color casts and detail blurs. It is therefore necessary to perform color correction and detail restoration. However,the existing enhancement algorithms cannot achieve the desired results. In order to solve the above problems, this paper proposes a multi-stream feature fusion network. First, an underwater image is preprocessed to obtain potential information from the illumination stream, color stream and structure stream by histogram equalization with contrast limitation, gamma correction and white balance, respectively. Next, these three streams and the original raw stream are sent to the residual blocks to extract the features. The features will be subsequently fused. It can enhance feature representation in underwater images. In the meantime, a composite loss function including three terms is used to ensure the quality of the enhanced image from the three aspects of color balance, structure preservation and image smoothness. Therefore, the enhanced image is more in line with human visual perception.Finally, the effectiveness of the proposed method is verified by comparison experiments with many stateof-the-art underwater image enhancement algorithms. Experimental results show that the proposed method provides superior results over them in terms of MSE,PSNR, SSIM, UIQM and UCIQE, and the enhanced images are more similar to their ground truth images.

Machine-Learning Based Packet Switching Method for Providing Stable High-Quality Video Streaming in Multi-Stream Transmission

Broadcasting gateway equipment generally uses a method of simply switching to a spare input stream when a failure occurs in a main input stream.However,when the transmission environment is unstable,problems such as reduction in the lifespan of equipment due to frequent switching and interruption,delay,and stoppage of services may occur.Therefore,applying a machine learning(ML)method,which is possible to automatically judge and classify network-related service anomaly,and switch multi-input signals without dropping or changing signals by predicting or quickly determining the time of error occurrence for smooth stream switching when there are problems such as transmission errors,is required.In this paper,we propose an intelligent packet switching method based on the ML method of classification,which is one of the supervised learning methods,that presents the risk level of abnormal multi-stream occurring in broadcasting gateway equipment based on data.Furthermore,we subdivide the risk levels obtained from classification techniques into probabilities and then derive vectorized representative values for each attribute value of the collected input data and continuously update them.The obtained reference vector value is used for switching judgment through the cosine similarity value between input data obtained when a dangerous situation occurs.In the broadcasting gateway equipment to which the proposed method is applied,it is possible to perform more stable and smarter switching than before by solving problems of reliability and broadcasting accidents of the equipment and can maintain stable video streaming as well.

High-Performance Transmission Mechanism Design of Multi-Stream Carrier Aggregation for 5G Non-Standalone Network

Multi-stream carrier aggregation is a key technology to expand bandwidth and improve the throughput of the fifth-generation wireless communication systems.However,due to the diversified propagation properties of different frequency bands,the traffic migration task is much more challenging,especially in hybrid sub-6 GHz and millimeter wave bands scenario.Existing schemes either neglected to consider the transmission rate difference between multistream carrier,or only consider simple low mobility scenario.In this paper,we propose a low-complexity traffic splitting algorithm based on fuzzy proportional integral derivative control mechanism.The proposed algorithm only relies on the local radio link control buffer information of sub-6 GHz and mmWave bands,while frequent feedback from user equipment(UE)side is minimized.As shown in the numerical examples,the proposed traffic splitting mechanism can achieve more than 90%link resource utilization ratio for different UE transmission requirements with different mobilities,which corresponds to 10%improvement if compared with conventional baselines.

Multi-Stream CNN-Based Personal Recognition Method Using Surface Electromyogram for 5G Security

As fifth generation technology standard(5G)technology develops,the possibility of being exposed to the risk of cyber-attacks that exploits vulnerabilities in the 5G environment is increasing.The existing personal recognitionmethod used for granting permission is a password-basedmethod,which causes security problems.Therefore,personal recognition studies using bio-signals are being conducted as a method to access control to devices.Among bio-signal,surface electromyogram(sEMG)can solve the existing personal recognition problem that was unable to the modification of registered information owing to the characteristic changes in its signal according to the performed operation.Furthermore,as an advantage,sEMG can be conveniently measured from arms and legs.This paper proposes a personal recognition method using sEMG,based on a multi-stream convolutional neural network(CNN).The proposed method decomposes sEMG signals into intrinsic mode functions(IMF)using empirical mode decomposition(EMD)and transforms each IMF into a spectrogram.Personal recognition is performed by analyzing time–frequency features from the spectrogram transformed intomulti-streamCNN.The database(DB)adopted in this paper is the Ninapro DB,which is a benchmark EMG DB.The experimental results indicate that the personal recognition performance of the multi-stream CNN using the IMF spectrogram improved by 1.91%,compared with the singlestream CNN using the spectrogram of raw sEMG.

Performance Evaluation Methods for Multi-stream Plate-Fin Heat Exchanger

Mathematical model of cross type multi-stream plate-fin heat exchanger is established.Meanwhile,mean square error of accumulative heat load is normalized by dimensionless,and the equations of temperature-difference uniformity factor are improved.Evaluation factors above and performance of heat exchanger are compared and analyzed by taking aircraft three-stream condenser as an example.The results demonstrate that the mean square error of accumulative heat load is common result of total heat load and excess heat load between passages.So it can be influenced by passage arrangement,flow inlet parameters as well as flow patterns.Dimensionless parameter of mean square error of accumulative heat load can reflect the influence of passage arrangement to heat exchange performance and will not change dramatically with the variation of flow inlet parameters and flow patterns.Temperature-difference uniformity factor is influenced by passage arrangement and flow patterns.It remains basically unchanged under a certain range of flow inlet parameters.

基于多维观测特征的MF-HMM模型识别新型LDoS驱动的高分散低速率QoS侵犯

针对新型LDo S驱动的高分散低速率Qo S侵犯,提出一种新颖的基于网络微观和宏观多维特征的识别方法。在网络微观方面,加权计算了反应TCP包头内部微观变化的Flag控制位,以及计算了反应LDo S固有周期特性的I-I-P 3元组的功率谱密度PSD特征;在网络宏观方面,引入反应网络发送流和确认流比值变化的R特征,共同构成多维观测序列,采用多维隐马尔科夫混合模型multi-stream fused HMM(MF-HMM)自动识别Qo S侵犯。同时,应用Kaufman算法动态调整阈值。大量实验表明,提出的方法有效降低了识别的误报率和漏报率,特别针对新型LDo S驱动的高分散低速率Qo S侵犯,在复杂网络背景流量下依然具有很高的识别率。

Grid Generation and Numerical Analysis of Multi-Stream Flow in the Complex Channel with a Forced Mixer Lobe

The co-located grid, SIMPLEC and Chen-Kim modified k - E turbulence model are applied to investigate numerically the multi-stream flow and temperature fields in the complex channel with a forced mixer lobe at room temperature and at elevated temperature. The body-fitted coordinate grids are generated respectively in sub-domains according to the shapes of the channel by solving Poisson’s equations to compose the whole grid of the domain. The large viscosity, linear and simultaneous under-relaxation factors are used to solve the coupling of fluid and solid. The solid grid is complemented at the upper inlet of the secondary flow to keep the same node number at the inlet and at double-wall sub-domains. The numerical results and experimental data show good agreement at room temperature. It is illustrated that the cooling air ejected into the slot between the double plates decreases the temperature of the wall.