Impulsive noise is a significant concern for Single Input Multiple Output (SIMO) wireless communication systems. This paper evaluates the effectiveness of hybrid combining techniques in mitigating the impact of impulsive noise caused by both man-made and natural phenomena. The Selection combining-Equal Gain Combining (SC-EGC) and Selection Combining Maximal Ratio Combining (SC-MRC) hybrid techniques are considered in the presence of three different noise models: Additive White Gaussian Noise (AWGN), Middleton Class-A impulsive noise model, and Symmetric Alpha Stable (SαS) impulsive noise model for the Rayleigh and Rician fading channels. Bit Error Rate (BER) is used to assess channel performance. The research contribution of this paper is the comprehensive evaluation of hybrid combining techniques in a set of noise models that can arise in wireless communication systems, including both man-made and natural sources of impulsive noise. The findings suggest that impulsive noise has a greater impact on channel performance than AWGN noise, and hybrid combining techniques are more effective in mitigating its effects. The results have significant research significance and practical implications for SIMO wireless communication systems, and can guide the development of more robust and reliable communication systems in the presence of impulsive noise.

Steady-State Visual Evoked Potentials (SSVEP) have garnered significant attention due to their promising applications in brain-computer interfaces (BCI), medical diagnostics, and several other domains. Enhancing the characteristics of SSVEP signals through intricate signal processing has emerged as a pivotal research focus for more efficient signal extraction. In this work, we introduce a novel layered enhancement algorithm for SSVEP electroencephalogram (SSVEP-EEG) signals based on fractional-order differentiation operators. This innovative approach marries brain signal analysis with image processing methodologies. By utilizing fractional-order differentiation operators in tandem with the Laplace pyramid, the signal undergoes hierarchical enhancement. This amplified signal is then reconstructed, which facilitates an in-depth extraction of image intricacies and attributes, ultimately accentuating the distinctiveness of SSVEP features. To validate the efficacy of the proposed method, we applied it to three recognized target identification algorithms: Canonical Correlation Analysis (CCA), Filter Bank Canonical Correlation Analysis (FBCCA), and Task-Related Component Analysis (TRCA) using a publicly available dataset. Experimental outcomes underscore that, in contrast to contemporary techniques, our proposed algorithm not only effectively attenuates the trend components of SSVEP signals but also substantially elevates the recognition precision of CCA, FBCCA, and TRCA.

In order to solve the problems of the current wireless cable monitoring effect is not ideal and the prediction method is difficult to deal with the nonlinear data of cables, a power cable monitoring method based on UHF-RFID and deep learning in the edge computing environment is proposed. First, based on edge computing, a power cable monitoring system is designed to migrate the analysis of massive data to the edge of the network to improve the monitoring efficiency. Then, the temperature sensing chip and RFID chip were integrated to design a UHF-RFID temperature tag, which was fixed at the cable temperature measurement point to achieve passive wireless monitoring of the cable. Finally, the parameters of the GRNN model are optimized using the beetle antennae search algorithm, and the EEMD decomposed data is input into the BAS-GRNN model for learning to output temperature prediction results and determine whether it is over temperature. The proposed method was demonstrated, and results showed that the maximum error between UHF-RFID temperature tag temperature measurement results and the thermocouple was within 0.3℃. The average relative error of the proposed method was only 0.01, and the time was 3.59s, which can meet the actual usage requirements.

During the cyclic operation process of hydraulic excavators, based on manual experience, issues such as low work efficiency and high energy consumption can cause significantly high wearing of the joints of the excavator; this markedly affects the service life of the excavator. To solve such issues, we propose a secondary trajectory optimization method based on time–energy consumption that enables the excavator to conduct smooth, efficient, and low-energy-consumption work. Considering different weight coefficient values and obtaining the optimal time–energy trajectory, we compare the above-mentioned results with those obtained through skilled manual operation of hydraulic excavators. The experimental results demonstrate that through reasonable and effective planning of each joint movement, the large jerk that occurs during joint movement processes may be effectively avoided, the working efficiency of the excavator is improved, unnecessary energy consumption is reduced, and the excavator can operate autonomously under stable conditions. These results verify the effectiveness and feasibility of the proposed method.

Capacity crunch has become critical in recent years as commercial communication systems approach their theoretical data rate limits. This work presents a low-complexity digital backpropagation (DBP) implementation approach based on step size distribution that uses a binary logarithmic step size method to achieve high data rate optical transmission. The proposed scheme shows performance improvements (∆Q) of 2.36 dB, 1.19 dB, and 0.71 dB over linear compensation, constant step size DBP, and logarithmic step size DBP techniques in a 2400 km 112 Gbit/s DP-16QAM system, respectively. At 13 dBm, a high performance (Q) of 10.9 dB (BER = 2.25×10-4) is achieved, above the 3.80×10-3 hard-decision forward error correction (HD-FEC) limit, using the proposed scheme. Also, the allowable transmission distance is extended by 960 km at the HD-FEC limit over the linear compensation technique. The optimization achieves a 38% savings in the number of DBP calculation steps compared to the constant step size DBP, which considerably reduces the computational cost since a few steps are required for effective nonlinearity compensation.

With the increasing demand for electricity, power distribution utilities must provide an efficient and appropriate Service Restoration (SR) strategy to restore customers as soon as possible after power outages. The heuristic SR algorithm presented in this article is a bi-stage algorithm that initially re-energizes some loads quickly by remote-controlled switches in the first stage and then proceeds to restore the rest of the network in the second stage. In addition to solving the restoration problem in a bi-stage algorithm, determining the optimal Switching Sequences (SSs) and applying the Energy Not Supplied (EENS) as objective function included in this study. Also, taking into account the time of occurrence of the failure and the daily load curve, the position of the maintenance team, load transfer capability and the traffic conditions of the network has increased the attractiveness and practicality of the proposed method. The heuristic SR algorithm was applied on a standard IEEE 69-bus system in various scenarios. The results indicated a significant difference in the solutions of the problem and the ENS in different scenarios. Finally, it was found that using this heuristic method would lead to optimal, accurate, and applicable solutions for SR in distribution networks.

Abstract: The consumption of coal in winter heating period in northern China is large, and the combustion generates greenhouse gases that pollute the environment. At the same time, wind abandonment is widespread in northern China, causing waste of energy. In order to solve these problems, this paper proposes to apply clean energy heating and waste wind power generation for heating, and build a multi-objective optimal dispatching model under the goal of considering Customer satisfaction and operating costs. Finally, taking a region in the north of China as an example, the improved genetic algorithm is used to solve the model. The example results verify that the use of clean energy heating can increase the wind power consumption space and reduce the heating cost in winter.