We address three key challenges in learning continuous kernel representations: computational efficiency, parameter efficiency, and spectral bias. Continuous kernels have shown significant potential, but their practical adoption is often limited by high computational and memory demands. Additionally, these methods are prone to spectral bias, which impedes their ability to capture high-frequency details. To overcome these limitations, we propose a novel approach that leverages sparse learning in the Fourier domain. Our method enables the efficient scaling of continuous kernels, drastically reduces computational and memory requirements, and mitigates spectral bias by exploiting the Gibbs phenomenon.
The speed of packet forwarding is crucial for the overall transmission performance of network devices. A large number of studies have proposed an acceleration scheme for network traffic processing, which requires not only efficient routing lookup algorithms, but also high-performance architecture and forwarding control architecture. This paper proposes a fast packet forwarding framework based on the existing kernel to achieve the processing of network traffic acceleration. When the router receives the first packet of the same stream, it saves the content to be forwarded to the new fast forwarding table. In this process, it builds a database. The generated fast forwarding table can be used to guide packet forwarding. Once hit, it can directly extract the next hop information in the table, To guide the forwarding of messages. Experiments have shown that for frequently arriving traffic, it can be directly forwarded through a fast forwarding path. The performance testing experimental results show that building a fast forwarding framework and selecting appropriate routing lookup algorithms can directly forward packets that hit the table entry content when facing a large number of flows, greatly improving the forwarding efficiency.
The possibilistic fuzzy c-means clustering (PFCM) algorithm is a hybrid clustering algorithm based on the fuzzy c-means clustering (FCM) algorithm. It can improve the anti-noise ability of FCM and the stability of PCM. However, there are great challenges when it is applied in image segmentation for images with strong noise injection. Therefore, we propose an improved Possibilistic Fuzzy C-Means Clustering with adaptive region-level information (RIPFCM). The first is that both the region-level information and the original information are integrated into the objective function of PFCM. The second is that we introduce a method to adaptively set the weighted factors which are used to balance the influence of the region-level information and the original information. Finally, experiments of color image segmentation show that the proposed RIPFCM method achieves better results compared with several clustering algorithms.
With the continuous development of GNSS-R technology, its application scope is also expanding. At present, quite fruitful results have been achieved in moving target detection, remote sensing of Marine and land surface parameters. Power monitoring is very important for GNSS-R, and the temperature change of receiver antenna is an important reason affecting its power. Aiming at the problem that the antenna temperature of the receiver cannot be accurately controlled, this paper proposes a fuzzy neural network PID control algorithm through research, and compares the algorithm with the conventional PID algorithm by simulation. The results show that the step response curve of the system under the control of this algorithm can quickly reach the steady state value. And the system test, can be the constant temperature system temperature control within +/- 0.2 degrees C, to meet the high precision temperature control requirements, and the output noise power is stable.
Affected by haze, images often face color distortion, resolution reduction and other image quality degradation problems. The existing dehazing methods based on convolutional neural network(CNN) often perform well on large-scale synthetic datasets, but lack robustness in the processing of real haze images. This is because haze images in reality are often non-homogeneous. Due to the fact that the haze texture faced by the real haze processing is more complex, it is easier to destroy the texture details. Meanwhile, the paired training images are difficult to collect, and the small-scale data set is easy to lead to overfitting. To address these challenges, we propose a dehazing approach based on ensemble learning, DB-GAN, which uses Res2Net pre-trained by ImageNet as the encoder in the knowledge adaptation branch to improve the generalization ability of the network and avoid overfitting. In the data fitting branch, deep bilateral learning is used to learn the structure of the features from the full-resolution and low-resolution inputs, respectively, to better learn the color features and boundary features. We then map the different features by a fusion tail. Finally, we demonstrate the effectiveness of our approach through extensive experimental results.
