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Summary In the past several years, a series of breakthrough research advancements have been achieved by leveraging wireless signals such as Wi‐Fi in various emerging applications, including healthcare, behavior recognition, positioning, and target detection. Compared to traditional human behavior sensing methods, Wi‐Fi signals human behavior sensing technology has many advantages, including non‐line‐of‐sight, sensor device‐free sensing, passive sensing, ease of deployment, and no need for lights. Data mining undoubtedly plays a critical role in making Wi‐Fi‐based human behavior detection intelligent enough to facilitate convenient services and environments. We study Wi‐Fi signals mining using the data mining process and review the developmental process of Wi‐Fi data mining. This covers the methods of Wi‐Fi data mining, including signal acquisition, preprocessing, feature extraction to training, and classification. We then propose WHSecurity, a whole home intrusion detection and tracking system that is based on all of the methods covered above. Finally, WHSecurity includes a deep learning‐based data mining process called multiview learning for the decision‐making on intrusion detection and tracking. Experimental outcomes show that the WHSecurity approach performs superior in terms of intrusion detection and tracking performance. [ABSTRACT FROM AUTHOR]

In recent years, a third-generation neural network, namely, spiking neural network, has received plethora of attention in the broad areas of Machine learning and Artificial Intelligence. In this paper, a novel differential-based encoding method is proposed and new spike-based learning rules for backpropagation is derived by constraining the addition of bias voltage in spiking neurons. The proposed differential encoding method can effectively exploit the correlation between the data and improve the performance of the proposed model, and the new learning rule can take complete advantage of the modulation properties of bias on the spike firing threshold. We experiment with the proposed model on the environmental sound dataset RWCP and the image dataset MNIST and Fashion-MNIST, respectively, and assign various conditions to test the learning ability and robustness of the proposed model. The experimental results demonstrate that the proposed model achieves near-optimal results with a smaller time step by maintaining the highest accuracy and robustness with less training data. Among them, in MNIST dataset, compared with the original spiking neural network with the same network structure, we achieved a 0.39% accuracy improvement. [ABSTRACT FROM AUTHOR]