Human action recognition (HAR) based on visible light videos is prone to expose users privacy. Infrared videos and radio signals are well known invisible sensing approaches for HAR with the constraint of privacy preservation. However, either of them holds some inherent weaknesses, which strongly hinder the HAR performance improvement. Considering the complementarity of the different modal data, we suggest to explore both infrared and radio clues to improve the performance of HAR. Specifically, we design a two-branch heterogenous deep neural network to extract IR and WiFi features respectively. Rather than traditional methods, we propose a deep feature fusion approach which nonlinearly projects IR and WiFi features into a common subspace to maximize the correlation between two modalities. To validate our novel ideas, we build a human action dataset comprising IR and WiFi samples. Based on this dataset, a series of elaborate experiments were carried out, which prove that multi modal sensing can significantly improve HAR performance with the constraint of privacy preservation. The proposed feature fusion strategy outperforms other state-of-the-art methods obviously in experiments.

How to accurately locate the information required by researchers in the massive patent resources is an urgent problem to be solved in the patent database. Traditional collaborative filtering algorithms, mainly based on the users' rating scores, ignore their interests or direction changes and the active user's effect on recommended precision. To solve these problems, this paper reconstructs the recommendation weight with the consideration of time factor and item similarity, and proposed a practical method for calculating the similarity of item. This method can obtain a more reliable item similarity and better-recommended results by weakening the influence of active users on similarity calculation. The experimental results show that the proposed method obviously improves the recommendation performance. Compared with several traditional recommendation algorithms and deep learning algorithms in five datasets, the precision and recall rate improve by 5.2% and 9.5% on average, respectively.

The past decade witnesses a great progress of personalized recommendation systems, which extract potential characteristics of users and items based on users' ratings information on items, and then recommend other items that may be of interest to users. However, traditional recommendation algorithms ignore the users social relationships and suffer with the cold start problem. In this paper, we propose a recommendation algorithm based on similarity calculation matrix factorization (SCMF) with the consideration of users social relationships and items associated relationships when ratings information is available. At the same time, when ratings information is unavailable, the proposed algorithm can obtain potential feature relationships and makes personalized recommendations. We also introduce a penalty factor of positive items to the similarity calculation equation in case of over fitting. By this way, the SCFM algorithm can improve the prediction accuracy and alleviate the problem of cold start of sparse data. We compared our approach with other 4 algorithms and conducted experiments on 4 common used data sets. Regarding the experimental results, the recommendation precision of SCMF can be improved by 2% to 9% compared with the traditional best algorithm, and by 0.17% to 18% for cold start users. Additionally, our approaches were applied to patent data resource exploitation, provided by Wanfang patent retrieval system. The experimental results also verify the effectiveness of SCMF algorithm in improving recommendation performance.

Wireless sensor networks as the base support for the Internet of things have been a large number of popularity and application. Such as intelligent agriculture, we have to use the sensor network to obtain the growing environment data of crops and others. However, the difficulty of power supply of wireless nodes has seriously hindered the application and development of Internet of things. In order to solve this problem, people use low-power sleep scheduling and other energy-saving methods on the nodes. Although these methods can prolong the working time of nodes, they will eventually become invalid because of the exhaustion of energy. The use of solar energy, wind energy, and wireless signals in the environment to obtain energy is another way to solve the energy problem of nodes. However, these methods are affected by weather, environment, and other factors, and they are unstable. Thus, the discontinuity work of the node is caused. In recent years, the development of wireless power transfer (WPT) has brought another solution to this problem. In this paper, a three-layer framework is proposed for mobile station data collection in rechargeable wireless sensor networks to keep the node running forever, named TLFW which includes the sensor layer, cluster head layer, and mobile station layer. And the framework can minimize the total energy consumption of the system. The simulation results show that the scheme can reduce the energy consumption of the entire system, compared with a Mobile Station in a Rechargeable Sensor Network (MSiRSN).

Wireless sensor networks (WSNs) have been used for many long-term monitoring applications with the strip topology that is ubiquitous in the real-world deployment, such as pipeline monitoring, water quality monitoring, vehicle monitoring, and Great Wall monitoring. The efficiency of routing strategy has been playing a key role in serving such monitoring applications. In this article, we first present a robust geographic opportunistic routing (GOR) approach—LIght Propagation Selection (LIPS)—that can provide a short path with low energy consumption, communication overhead, and packet loss. To overcome the complication caused by the multi-turning point structure, we propose the virtual Plane mirror (VPM) algorithm, inspired by the light propagation, which is to map the strip topology into the straight one logically. We then select partial neighbors as the candidates to avoid blindly involving all next-hop neighbors and ensure the data transmission along the correct direction. Two implementation problems of VPM—transmission spread angle and the communication range—are thoroughly analyzed based on the percolation theory. Based on the preceding candidate selection algorithms, we propose a GOR algorithm in the strip topology network. By theoretical analysis and extensive simulation, we illustrate the validity and higher transmission performance of LIPS in strip WSNs. In addition, we have proved that the length of the path in LIPS is two times the length of the shortest path via geometrical analysis. Simulation results show that the transmission success rate of our approach is 26.37% higher than the state-of-the-art approach, and the communication overhead and energy consumption rate are 33.11% and 40.23% lower, respectively.

Target material identification is playing an important role in our everyday life. This paper introduces a device-free target material identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate material sensing. Comprehensive real-life experiments demonstrate that we can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors.