ABSTRACT We propose an efficient image compression scheme leveraging Vision Mamba and dynamic convolution, addressing the limitations of existing methods, such as failure to capture long‐range pixel dependencies and high computational complexity. Our approach improves both global and local information learning with reduced computational cost. Experimental results on the Kodak, Tecnick and CLIC datasets show that our model achieves competitive performance with lower algorithm complexity. Our code is available on: https://github.com/Lynxsx/ICVM .

In this study, the authors propose a generalisedswitching protocol (GSP) for increasing the flexibility of energy harvesting (EH) protocol and enhancing the security of amplify‐and‐forward (AF) multi‐antenna relaying system. The proposed GSP‐based relaying [generalised switching relaying (GSR)] system is established with target node assisted interference and EH technologies. In phase I, while the source transmits certain signal to the relay, the destination transmits an artificial noise to interfere the passive eavesdropper. In this phase, the relay harvests energy from the two nodes. In phase II, the relay node processes the two different received signals, which are from the source and destination, then AF them by utilising the harvested energy. The authors also derive a new analytical formula for the proposed protocol on ergodic secrecy capacity. The effect of the number of relay antennas N , the source transmitted power , the destination transmitted power and EH efficiency factors and on ergodic secrecy capacity are investigated. The simulation results show that as N , , , and increase, the ergodic secrecy capacity of GSR system increases accordingly. Moreover, the GSR protocol can provide higher ergodic secrecy capacity than both time switching relaying and powersplitting relaying protocols.

We first generalize the extended orthogonal space time block codes (EO-STBCs). Then, based on the generalized EO-STBCs (GEO-STBCs) and channel state information (CSI), the feedback bits are generated at the receiver. By using the feedback bits from the legitimate receiver, we propose a new advantage distillation scheme for multiple-input multiple-output (MIMO) wiretap channels.

A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

In this correspondence, the linear complexity and minimal polynomial of new generalized cyclotomic sequences of order two are determined. Our results show that these sequences also have high linear complexity.