Cross View Capture for Stereo Image Super-Resolution

Zhu, Xiangyuan; Guo, Ke; Fang, Hui; Chen, Liang; Ren, Siming; Hu, Bin

doi:10.1109/tmm.2021.3092571

Cited by 77 publications

(23 citation statements)

References 50 publications

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“…Owing to its strong optimization capability, the proposed GOMGBO can also be applied to other optimization problems, such as regression tasks, 141 medical diagnosis, [142][143][144][145] covert communication systems, [146][147][148] service ecosystem, 149,150 image editing, [151][152][153] energy storage planning and scheduling, 154 social recommendation and quality-of-service (QoS)-aware service composition, [155][156][157] active surveillance, 158 pedestrian dead reckoning, 159 evaluation of human lower limb motions, 160 image super resolution, [161][162][163] sentiment classification, 164 data-to-text generation, 165 crowd sensing, 166 and feature selection. [167][168][169]…”

Section: Experimental Results Of the Mammographic Data Setmentioning

confidence: 99%

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Qiao

Shan

Jiang

et al. 2021

Int J of Intelligent Sys

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Gradient‐based optimizer (GBO) is a metaphor‐free mathematic‐based algorithm proposed in recent years. Encouraged by the gradient‐based Newton's method, this algorithm combines with population‐based evolutionary methods. The disadvantage of the traditional GBO algorithm is that the global search ability of the algorithm is too strong, and the local search ability is too weak; accordingly, it is difficult to obtain the global optimal solution efficiently. Therefore, a new improved GBO algorithm (GOMGBO) is developed to mitigate such performance concerns by introducing a Gaussian bare‐bones mechanism, an opposition‐based learning mechanism, and a moth spiral mechanism enhanced GBO algorithm. The proposed GOMGBO has been compared against many famous methods and improved variants on 30 benchmark functions. The experimental results show that GOMGBO has apparent advantages in convergence speed and precision. In addition, this paper analyzes the balance and diversity of the GOMGBO algorithm and compares GOMGBO with other algorithms on several engineering problems. The experimental results show that the GOMGBO algorithm is also better than the competitive algorithm in engineering problems. This study uses the GOMGBO algorithm to optimize kernel extreme learning machine (KELM), and a new GOMGBO‐KELM model is proposed. The model is used to deal with four clinical disease diagnosis problems. Compared with GBO‐KELM, back propagation neural network algorithm, and other models, comparative experiments show that GOMGBO‐KELM has high performance in dealing with practical cases. We invite the community to investigate further our method for solving problems more efficiently with reasonable speed and efficiency. Readers of this study can refer to https://aliasgharheidari.com for any guidance about the proposed GOMGBO method.

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Section: Experimental Results Of the Mammographic Data Setmentioning

confidence: 99%

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Qiao

Shan

Jiang

et al. 2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

show abstract

“…They evaluate the performance of SGuard in Mininet and the results show that SGuard is lightweight and efficient. Cheng et al proposed a DDoS attack detection method based on network flow grayscale matrix feature via multi-scale convolutional neural network (CNN) [18,19]. Meti et al utiliazed a support vector machine (SVM) classifier and a Neural Network (NN) classifier to detect suspicious and harmful connections [20].…”

Section: Related Workmentioning

confidence: 99%

DoS Attack Detection Based on Deep Factorization Machine in SDN

Wang¹,

Lei²,

Jiang³

et al. 2023

Computer Systems Science and Engineering

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Software-Defined Network (SDN) decouples the control plane of network devices from the data plane. While alleviating the problems presented in traditional network architectures, it also brings potential security risks, particularly network Denial-of-Service (DoS) attacks. While many research efforts have been devoted to identifying new features for DoS attack detection, detection methods are less accurate in detecting DoS attacks against client hosts due to the high stealth of such attacks. To solve this problem, a new method of DoS attack detection based on Deep Factorization Machine (DeepFM) is proposed in SDN. Firstly, we select the Growth Rate of Max Matched Packets (GRMMP) in SDN as detection feature. Then, the DeepFM algorithm is used to extract features from flow rules and classify them into dense and discrete features to detect DoS attacks. After training, the model can be used to infer whether SDN is under DoS attacks, and a DeepFM-based detection method for DoS attacks against client host is implemented. Simulation results show that our method can effectively detect DoS attacks in SDN. Compared with the K-Nearest Neighbor (K-NN), Artificial Neural Network (ANN) models, Support Vector Machine (SVM) and Random Forest models, our proposed method outperforms in accuracy, precision and F1 values.

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“…Thiagarajah et al [8] discussed the architecture and technical challenges of the cooperative HetNets to optimize and balance the networks' spectrum efficiency, energy efficiency, and quality of service (QoS). A deep learning algorithm in [17], [18] is a promising energy efficiency solution for HetNet. It adds another challenge to ensure high-energy details when requiring super-resolution multi-image sources.…”

Section: Introductionmentioning

confidence: 99%

Energy efficiency scheme for relay node placement in heterogeneous networks

et al. 2023

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Relay node (RN) placement expands the network coverage and capacity and significantly reduces the energy consumption of heterogeneous networks (HetNets). Energy efficiency is the system design parameter in HetNets as it determines network operators' energy consumption and economic value. Relay is one of the energy-saving methods, where it can reduce the transmit power by breaking a long transmission distance into several short transmissions. However, placing an RN without a proper transmission distance may lead to a waste of energy. Thus, investigating an optimum RN placement in HetNets is crucial to ensure energy efficiency and maintain network performance. This paper presents an energy efficiency scheme for the RN based on four commonly used network topologies of indoor HetNets. The minimum energy consumption algorithm is proposed based on a comparison of distance and links of the RN. The results show that the circular network topology is an optimal network model with an efficiency factor increase of 6% that can be used to design the energy efficiency indoor HetNet.

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Cross View Capture for Stereo Image Super-Resolution

Cited by 77 publications

References 50 publications

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

DoS Attack Detection Based on Deep Factorization Machine in SDN

Energy efficiency scheme for relay node placement in heterogeneous networks

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