ISAX heterogeneous processors integrate cores that share a common base ISA, with certain cores offering extension ISAs (e.g., vector extension) to accelerate computation. ISAX balances performance and energy efficiency while facilitating the reuse of existing software ecosystems. RISC-V, which adopts the ISAX architecture, has gained extensive attention in both industry and academia. Binary translation via binary rewriting enables transparent ISAX heterogeneous computing by translating extension instructions when migrating a program to cores without extension support. However, current binary rewriting methods still struggle to achieve both high performance and correctness.

Spectrum resources play a key supporting role in the communication system and are the basic guarantee for the normal operation of the communication system. In mobile communication and the Internet of Things scenarios, efficient coordination of spectrum resources is a necessary process for the communication process to exert its effectiveness. However, although the traditional central model-based spectrum resource allocation method has the advantages of global resource grasp and resource utilization, it may lead to a decrease in allocation efficiency due to frequent synchronization of spectrum resource demand, while the distributed spectrum resource allocation method faces the difficulties of local game and poor allocation effect. In view of this, this paper proposes a set of spectrum resource planning blockchain scheme based on a beneficial consensus mechanism, which gives full play to the planning ability of each device node while ensuring that the investment of computing power and other resources is effectively returned. This paper designs the formation of block cycle and equipment resource allocation process in the scheme, and analyzes the characteristics of the proposed scheme through four aspects: security, activity, complexity and adaptability, and finally relies on simulations to prove the effectiveness, feasibility and advantages of the proposed blockchain spectrum resource allocation scheme.

Nuclear magnetic resonance (NMR) relaxometry, a noninvasive and nondestructive method, is a key technique for unconventional reservoir evaluation. Echo data detected from NMR instrument, a kind of weak signal, however, is characterized by a low signal-to-noise ratio (SNR). The achievement of NMR relaxation spectra inversion of a high precision, for echo data with a low SNR, is a challenge, which will also affect the unconventional reservoir evaluation of NMR logging. In this article, a variational mode decomposition (VMD) method was proposed for NMR echo data denoising. NMR echo data were decomposed into an ensemble of intrinsic mode functions (IMFs) by the VMD method. The IMF number is an important parameter for VMD, and VMD results are different with various IMF numbers. An optimal selection method for the IMF number was proposed. The decomposed IMFs compose of signals with different frequencies. Noise is from high-frequency signal, but valuable data are from low-frequency signal. The effective IMFs in VMD were selected and summed as the denoised echo data. Numerical simulations and field NMR logging data processing were undertaken to evaluate the NMR echo data denoising effectiveness and practicality of the proposed VMD-based method. The results showed that the inverted NMR spectra exhibit a higher quality after the VMD-based denoise, compared with those for raw echo data and after the empirical mode decomposition (EMD) denoise. This indicates that a higher denoising quality is achieved by the VMD-based method than by the EMD-based method for NMR echo data.