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Due to environmentally friendly operation and on-site productivity, electrocatalytic singlet oxygen ( 1 O 2 ) production via O 2 gas is of immense interest in environment purification. However, the side-on configuration of O 2 on the catalysts surface will lead to the formation of H 2 O, which seriously limits the selectivity and activity of 1 O 2 production. Herein, we show a robust N-doped CuO (N–CuO) with Pauling-type (end-on) adsorption of O 2 at the N–Cu–O 3 sites for the selective generation of 1 O 2 under direct-current electric field. We propose that Pauling-type configuration of O 2 not only lowers the overall activation energy barrier, but also alters the reaction pathway to form 1 O 2 instead of H 2 O, which is the key feature determining selectivity for the dissociation of Cu–O bonds rather than the O–O bonds. The proposed N dopant strategy is applicable to a series of transition metal oxides, providing a universal electrocatalysts design scheme for existing high-performance electrocatalytic 1 O 2 production. Side-on configuration of O 2 on the catalysts conventionally leads to reduction of O 2 to water. Here, the authors propose a nitrogen doping strategy with Pauling-type adsorption of O 2 for selective electrocatalytic singlet oxygen production.

A novel low-energy hybrid capacitor switching scheme for a low-power successive approximation register (SAR) analogue-to-digital converter (ADC) is presented. The proposed switching scheme combines a new switch method and the monotonic technique. The new switch method can achieve no switching energy consumption in the first three comparison cycles. Furthermore, a low-energy monotonic procedure is performed for the rest of the comparisons. The average switching energy is reduced by 98.83% compared with the conventional architecture, resulting in the most energy-efficient switching scheme among the existing switching techniques. Besides the significant energy saving, the proposed switching scheme also achieves a 75% reduction of the capacitors over the conventional scheme.

This paper proposes a scattering area model for processing multipath parameters achieve single base station positioning. First of all, we construct a scattering area model based on the spatial layout of obstacles near the base station and then collect the multipath signals needed for positioning and extract parameters. Second, we use the joint clustering algorithm improved by k-means clustering and mean shift clustering algorithm to process the parameters and extract useful information. Third, the processed information is combined with the spatial layout information of the scattering area model to construct equations, and then the solving problem of equations is converted into a least-squares optimization problem. Finally, the Levenberg-Marquardt (LM) algorithm is used to solve the optimal solution and estimate the mobile target position. The simulation results show that the positioning algorithm in this paper can be used by a single base station to locate the target in an outdoor non-line-of-sight (NLOS) environment, and the accuracy is improved compared with the traditional positioning algorithm.

Smart environment sensing and other applications play a more and more important role along with the rapid growth of device-free sensing-based services, and extracting parameters contained in channel state information (CSI) accurately is the basis of these applications. However, antenna arrays in wireless devices are all planar arrays whose antenna spacing does not meet the spatial sampling theorem while the existing parameter estimation methods are almost based on the array satisfying the spatial sampling theorem. In this paper, we propose a parameter estimation algorithm to estimate the signal parameters of angle of arrival (AoA), time of flight (ToF), and Doppler frequency shift (DFS) based on the service antenna array, which does not satisfy the spatial sampling theorem. Firstly, the service antenna array is mapped to a virtual linear array and the array manifold of the virtual linear array is calculated. Secondly, the virtual linear array is applied to estimate the multi-dimensional parameters of the signal. Finally, by calculating the geometric relationship between the service antenna and the virtual linear array, the parameters of the signal incident on the service antenna can be obtained. Therefore, the service antenna can not only use the communication channel for information communication, but also sense the surrounding environment and provide related remote sensing and other wireless sensing application services. Simulation results show that the proposed parameter estimation algorithm can accurately estimate the signal parameters when the array antenna spacing does not meet the spatial sampling theorem. Compared with TWPalo, the proposed algorithm can estimate AoA within 3∘, while the error of ToF and DFS parameter estimation is within 1 ns and 1 m/s.

In recent years, the behavior recognition technology based on Wi-Fi devices has been favored by many researchers. Existing Wi-Fi-based human behavior recognition technology mainly uses classification algorithms to construct classification models, which has problems such as inaccurate behavior segmentation, failure to extract deep-level features from the original data and design classification models matching the proposed features in the process of behavior recognition. In order to solve above problems, this paper proposes a window variance comparison method by combining the adaptive thresholds calculated to achieve effective segmentation of multiple discontinuous human behaviors, then uses the short-time Fourier algorithm to extract time-frequency features of individual behavior, and extracts the graph structure data from the autocorrelation matrix of time-frequency features and the features themselves. A graph neural network is built for behavior recognition. The experimental results show that the segmentation accuracy of the behavior segmentation method in the two scenes is 0.964 and 0.993, which is better than the existing threshold-based behavior segmentation methods. In addition, this paper extracts graph structure data by spectral energy change method and builds behavior recognition model by using graph neural network, and the recognition accuracy is significantly improved compared with the traditional classification algorithm.

With the development of human–computer interaction(s) (HCI), hand gestures are playing increasingly important roles in our daily lives. With hand gesture recognition (HGR), users can play virtual games together, control the smart equipment, etc. As a result, this paper presents a multi-hand gesture recognition system using automotive frequency modulated continuous wave (FMCW) radar. Specifically, we first constructed the range-Doppler map (RDM) and range-angle map (RAM), and then suppressed the spectral leakage, and dynamic and static interferences. Since the received echo signals with multi-hand gestures are mixed together, we propose a spatiotemporal path selection algorithm to separate the mixed multi-hand gestures. A dual 3D convolutional neural network-based feature fusion network is proposed for feature extraction and classification. We developed the FMCW radar-based platform to evaluate the performance of the proposed multi-hand gesture recognition method; the experimental results show that the proposed method can achieve an average recognition accuracy of 93.12% when eight gestures with two hands are performed simultaneously.

The demand for indoor localization is becoming urgent, but the traditional location fingerprint approach takes a lot of manpower and time to construct a fine-grained location fingerprint database. To address this problem, we propose to use the approach of combining dynamic collection of fingerprint samples with Radial Basis Function (RBF) interpolation. Specifically, the raw sparse fingerprint database is constructed from a small number of fingerprints collected on a few paths, in which the pedestrian track correction algorithm improves the validity and accuracy of the sparse fingerprint database. Then, the RBF interpolation approach is applied to enrich the sparse fingerprint database, in which the Genetic Algorithm (GA) is used to optimize the free shape parameter and the cut-off radius is determined according to the experimental results. Extensive experiments show that the proposed approach guarantees high interpolation and localization accuracy and also significantly reduces the effort of manual collection of fingerprint samples.

Although the Micro Electro Mechanical System (MEMS) sensors are capable of providing short-term high positioning accuracy, every positioning result significantly depends on the historical ones, which inevitably leads to the long-term error accumulation. The Bluetooth Low Energy (BLE) is independent of the accumulative error, but the positioning accuracy is suffered by the irregular jump error resulted from the Received Signal Strength Indicator (RSSI) jitter. Considering the requirement of accurate, seamless, and consecutive positioning by the existing commercial systems, we propose a new integrated BLE and MEMS Wireless (BMW) system for multi-floor positioning. In concrete terms, first of all, the way of fingerprint database construction with the reduced workload is introduced. Second, the fingerprint database is denoised by the process of affinity propagation clustering, outlier detection, and RSSI filtering. Third, the robust M estimation-based extended Kalman filter is applied to estimate the two-dimensional coordinates of the target on each floor. Finally, the barometer data are used to calculate the height of the target. The extensive experimental results show that the proposed system can not only restrain the accumulative error caused by the MEMS sensors but also eliminate the irregular jump error from the BLE RSSI jitter. In an actual multi-floor environment, the proposed system is verified to be able to achieve the Root Mean Square (RMS) positioning error within 1 m.

With the rapid development of artificial intelligence, hand gesture detection has gradually become a research hotspot in human–computer interaction. However, the traditional hand gesture detection has low robustness and detection accuracy, and brings the problem of privacy protection. Therefore, this paper proposes a Faster Region-based Convolutional Neural Network (F-RCNN) hand gesture detection method based on Frequency Modulated Continuous Wave (FMCW) radar with 3-Dimensions deep convolutional Generative Adversarial Networks (3-DCGAN). In particular, the range Doppler and angle of the hand gestures are calculated by FMCW radar. Then, the semantic label maps of the Range-Time-Map, Doppler-Time-Map and Angle-Time-Map images are respectively sent to the 3-DCGAN to expand the datasets. After that, the original and the 3-DCGAN generated images are sent to F-RCNN for jointly training. The classifier function is designed, and a learning ranking based assessment called Rank based Quality Score (RQS) is applied to improve the detection performance. The experimental results show that the mean average precision reaches to 80.8%. Moreover, the RQS for hand gesture detection is as high as 96.3%, which is increased by 5.8% compared to the traditional F-RCNN.

A low power clock generator with self-calibration for UHF RFID tags compatible with the EPCglobal Class-1 Gen-2 (EPC Gen2) standard is presented. By utilizing the timing information of the reader to tag (R ⇒ T) link symbols, the frequency accuracy of the calibrated clock can meet the stringent requirement of the standard. Designed in a 0.18 μ m standard CMOS technology, simulation results show that the frequency error of the clock source is only from − 2.07 to +  1.4% of the target frequency of 2.56 MHz. The power consumption is only 720 nW.