Recently, device-free human behavior recognition has become a hot research topic and has achieved significant progress in the field of ubiquitous computing. Among various implementation, behavior recognition based on WiFi CSI (channel state information) has drawn wide attention due to its major advantages. This paper investigates more than 100 latest CSI based behavior recognition applications within the last 6 years and presents a comprehensive survey from every aspect of human behavior recognition. Firstly, this paper reviews general behavior recognition applications using the WiFi signal and presents the basic concept of CSI and the fundamental principle of CSI-based behavior recognition. This paper analyzes the key components and core characteristics of the system architecture of human behavior recognition using CSI. Afterward, we divide the sensing procedures into many steps and summarize the typical studies from these steps, including base signal selection, signal preprocessing, and identification approaches. Next, based on the recognition technique, we classify the applications into three groups, including patternbased, model-based, and deep learning-based approach. In every group, we categorize the state-of-the-art applications into three groups, including coarse-grained specific behavior recognition, fine-grained specific behavior recognition, and activity inference. It elaborates the typical behavior recognition applications from five aspects, including experimental equipment, experimental scenario, behavior, classifier, and system performance. Then, this paper presents comprehensive discussions about representative applications from the implementation view and outlines the major consideration when developing a recognition system. Finally, this article concludes by analyzing the open issues of CSI-based behavior recognition applications and pointing out future research directions. INDEX TERMS Channel state information (CSI), deep learning, human behavior recognition, model, pattern, WiFi.

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Heteroatom doping is identified as an effective strategy to enhance the electrochemical performance of hard carbon. However, the effect of oxygen on biomass-based hard carbon is usually ignored. Herein, oxygen-doped carbon microspheres are fabricated with a lignin-based oxygen source, endowing the carbon microspheres with a high capacitance-controlled capacity and stable cycling performance. In addition, the quality of oxygen doping and microsphere morphology is regulated by using different ratios of lignin. As an anode for sodium-ion batteries, the optimal oxygen-doped carbon microspheres can deliver a high reversible capacity (310.4 mAh g −1 at 25 mA g −1 ) and long life span. Moreover, when paired with Na-Ni 1/3 Fe 1/3 Mn 1/3 O 2 , the full cells can display a capacity of 313.3 mAh g −1 at 25 mA g −1 and an excellent rate performance (218.7 mAh g −1 at 500 mA g −1 ). Based on first principles, the oxygen functional groups provided by lignin exhibit a strong Na + adsorption capability, demonstrating that oxygen functional groups, especially CO, play a pivotal role in the capacitancecontrolled process. This work presents an idea for the material design and heteroatom doping of high-performance sodium-ion battery anode materials.

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To investigate the potential of conventional and deep learning techniques to recognize the species and distribution of mould in unhulled paddy, samples were inoculated and cultivated with five species of mould, and sample images were captured. The mould recognition methods were built using support vector machine (SVM), back-propagation neural network (BPNN), convolutional neural network (CNN), and deep belief network (DBN) models. An accuracy rate of 100% was achieved by using the DBN model to identify the mould species in the sample images based on selected colour-histogram parameters, followed by the SVM and BPNN models. A pitch segmentation recognition method combined with different classification models was developed to recognize the mould colony areas in the image. The accuracy rates of the SVM and CNN models for pitch classification were approximately 90% and were higher than those of the BPNN and DBN models. The CNN and DBN models showed quicker calculation speeds for recognizing all of the pitches segmented from a single sample image. Finally, an efficient uniform CNN pitch classification model for all five types of sample images was built. This work compares multiple classification models and provides feasible recognition methods for mouldy unhulled paddy recognition.

Two-dimensional heterostructure manipulation is promising to overcome the high recombination rates and limited redox abilities of photogenerated electron–hole pairs in a single photocatalyst. The built-in electric field (E hetero) in the type-II heterojunction is normally unfavorable for the desired charge transfer, which is an important but easily neglected issue that needs to be solved. Here, on the basis of the density functional theory (DFT) and the nonadiabatic molecular dynamics (NAMD) calculations, we obtain a type-II band alignment in Janus-MoSSe/WS2 heterostructure, which meets the band-edge position requirement for water splitting. Importantly, the intrinsic self-build electric field (E self) of Janus-MoSSe can effectively weaken the hindrance effect of E hetero for charge transfer by constructing a suitable Se–S stacking configuration, improving charge separation efficiency in the Janus-MoSSe/WS2 heterostructure. Our work provides a materials-by-design paradigm and interlayer charge-transfer dynamics understanding of heterostructure engineering against asymmetric structures lacking reflection symmetry.