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Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

The purpose of this paper is to solve the azimuth sensitivity of a high-resolution range profile (HRRP), which is one of the biggest obstacles faced by a radar automatic target recognition (RATR) system.

Aimed at addressing the shortcomings of the equal angular-sector segmentation based on the scatterer model, an adaptive angular-sector segmentation is proposed on the basis of grey incidence analysis (GIA).

The main conclusions reached are as follows. First, the adaptive angular-sector segmentation in terms of GIA is suitable for RATR based on the HRRP; and, second, the adaptive angular-sector segmentation based on the type-B degree of grey incidence model is better than the Deng-Si degree of grey incidence model and the degree of grey slope incidence model.

The outcome obtained in this paper can be selected for the RATR application.

This paper has been built on the basis of previous research achievements, and a new RATR method of adaptive angular-sector segmentation is presented based on the GIA.

With the development of social networking service and WeMedia, virtual brand community has become a typical platform of value co-creation and customers have become a core subject of value co-creation. The high proportion of negative members and even zombie members has become an obstacle to the sustainable development of many communities. Then, how to maintain and promote the sustainable development of virtual brand community? The purpose of this article is to examine how customers prefer community or brand.

This paper obtained data through an online questionnaire survey to test the proposed hypotheses of this study.

The findings of this study indicated that practical and social values not only promote customers to be loyal toward community but also drive customers to be loyal toward brand and their influence on community loyalty is greater than on brand loyalty, but entertainment value does not significantly affect community and brand loyalty. The effect of practical and social values on community loyalty is fully mediated by the effect of brand loyalty, and the effect of practical and social values on brand loyalty is fully mediated by the effect of community loyalty.

This research enriches the research study’s results of value co-creation theory and customer assets, thus providing a new perspective for research on customer loyalty.

This paper aims to understand the underlying motivations and factors that drive millennials to embrace smartwatches as fashionable accessories, health monitoring tools and eco-friendly alternatives.

In June–July 2022, a cross-sectional study was conducted, gathering 285 complete responses through an online survey using convenience sampling. These responses were then analyzed to obtain valuable insights using structural equation modeling.

This study’s findings confirm the mediation effect of fashion innovativeness on the relationship between subjective norms and attitudes toward smartwatch usage (b = 0.034, lower limit confidence interval (LLCI) = 0.007, upper limit confidence interval (ULCI) = 0.086, p = 0.015). In addition, it highlights the mediating role of healthology in the association between subjective norms and attitudes toward using smartwatches (b = 0.062, LLCI = 0.006, ULCI = 0.151, p = 0.029).

This research has limitations in terms of sample representativeness, self-reported data, cultural and regional factors and technological advancement.

Understanding millennials’ motivations behind smartwatch usage has implications for marketers, designers and manufacturers in targeting this generation effectively. By highlighting smartwatches’ fashion-forward and health-conscious aspects, companies can appeal to millennials’ preferences and develop innovative features that align with their values.

This study provides validation for the increased level of environmental concerns among millennials, emphasizing its substantial influence on their purchasing decisions when it comes to smartwatches. Furthermore, it highlights that health-consciousness holds greater significance than fashion-forwardness as a determining factor for consumers of smartwatches.

This pioneering study explores the adoption intentions of smartwatch usage, examining it from the unique perspectives of health theology and environmental concerns. By delving into these novel dimensions, the research fills a significant gap in the existing literature. It contributes to a deeper understanding of the factors influencing millennials’ decision-making processes when embracing smartwatches.