Neural-Network-Based Localization Method for Wi-Fi Fingerprint Indoor Localization.

Despite the high demand for Internet location service applications, Wi-Fi indoor localization often suffers from time- and labor-intensive data collection processes. This study proposes a novel indoor localization model that utilizes fingerprinting technology based on a convolutional neural network to address this issue. The aim is to enhance Wi-Fi indoor localization by streamlining the data collection process. The proposed indoor localization model leverages a 3D ray-tracing technique to simulate the wireless received signal strength intensity (RSSI) across the field. By incorporating this advanced technique, the model aims to improve the accuracy and efficiency of Wi-Fi indoor localization. In addition, an RSSI heatmap fingerprint dataset generated from the ray-tracing simulation is trained on the proposed indoor localization model. To optimize and evaluate the model's performance in real-world scenarios, experiments were conducted using simulated datasets obtained from the publicly available databases of UJIIndoorLoc and Wireless InSite. The results show that the new approach solves the problem of resource limitation while achieving a verification accuracy of up to 99.09%.

Research and Application of Energy-Efficient Management Approach for Wireless Sensor Networks.

Wireless sensor networks (WSNs) are widely used in industrial applications. However, many of them have limited lifetimes, which has been a considerable constraint on their widespread use. As a typical application of WSNs, distributed measurement of the electric field under high-voltage direct-current (HVDC) transmission lines also suffers from this issue. This paper first introduces the composition of the electric-field measurement system (EFMS) and its working principle. Considering the actual power supply of the system, this paper mainly introduces the composition of the wireless sensor node (WSND) and analyzes the power consumption and potential working state transformation of the WSND, together with a comprehensive study on parameters affecting the power consumption of the wireless communication unit. Moreover, an energy-efficient scheduling approach is proposed after specially designing a working sequence and the study on system parameters. The proposed approach is verified by experiments on not only the experimental line of the national HVDC test base, but also a commercial operation HVDC transmission line with the challenge of long endurance, which is considered in this paper with a new strategy. The results show that the proposed method can greatly extend the lifetime of the WSND.

A survey of mechanical antennas applied for low-frequency transmitting.

The mechanical antenna (MA) is a new type of low-frequency (LF) transmitting antenna that generates an alternating electromagnetic (EM) signal through the mechanical movement of electric charges or magnetic dipoles, which is an interdisciplinary field including not only antennas but also electromagnetics, materials science, and dynamics. This principle of signaling makes it possible to break the constraints on physical dimensions decided by the wavelength of the traditional antenna so as to achieve LF communications with a smaller size and to provide a novel solution for long-range, underwater, and underground communications, navigation over the horizon, and geological exploring. Therefore, MA has become a research hotspot in the field of LF communications in recent 5 years, and this work proposed a survey on this topic of MA applied for LF transmitting. Firstly, we briefly review traditional low-frequency transmitting antennas and summarize the defect; then we introduce research progress of different implementation schemes for MA, comparing the signaling performance, advantages, and disadvantages of each scheme. Furthermore, we discuss the experiment setup, results, and related technology for MA including signal modulation methods. Finally, we explore prospects for future research about MA. This work presents a comprehensive and critical survey of small LF transmitters based on MA to help the readers to understand and identify the background, status, and challenges of research in this field.

A miniaturized mechanical antenna based on FEP/THV unipolar electrets for extremely low frequency transmission.

An electret-based mechanical antenna (EBMA), which can transmit extremely low frequency (ELF) electromagnetic signals, has the advantages of miniaturization and high transmitting efficiency, with great potential applications in air, underwater, and underground communications. To improve the charge density of the electret, which is a key factor in determining the radiation performance of an EBMA, this work proposes a fluorinated ethylene propylene/terpolymer of tetrafluoroethylene, hexafluoropropylene and vinylidene fluoride (FEP/THV) unipolar electret exhibiting negative polarity, reaching a total charge density up to -0.46 mC/m2 for each layer of electret. Long transmission distances can be achieved in sea water, soil, and air using a 3-layer-FEP/THV-based EBMA with a compact volume of 5 × 10-4 m3. As an application demonstration, binary ASCII-coded ELF information of "BUAA" is successfully transmitted with a power consumption < 5 W.

Developing an Intelligent Monitoring Technology for Airport Stone Column Machines.

Most of the construction machinery for vibro-sinking stone columns, which are widely used in China, needs to be improved in terms of degree of automation. Engineering quality control is mainly carried out post-inspection; consequently, it is difficult to control the construction quality in real time. According to the construction characteristics of traditional stone column machines, we established the theory and model for the real-time monitoring of stone column construction, as well as put forward an intelligent monitoring method for stone column machines. With the comprehensive application of critical technologies such as the Global Navigation Satellite System (GNSS) measurement technology, laser ranging sensors, and massive data processing, an intelligent data acquisition technique and associated monitoring equipment for stone column construction machines are developed. The data acquisition and storage of crucial construction parameters, such as pile depth, pile point co-ordinates, bearing layer current, and reverse insertion times, are realized. A large number of actual construction data are collected and the construction quality parameters of stone column machines are obtained. By comparison with third-party detection data, it is verified that the intelligent monitoring technique for stone column machines proposed in this paper is feasible.

A Novel Water-Shore-Line Detection Method for USV Autonomous Navigation.

For the navigation of an unmanned surface vehicle (USV), detection and recognition of the water-shore-line (WSL) is an important part of its intellectualization. Current research on this issue mainly focuses on the straight WSL obtained by straight line fitting. However, the WSL in the image acquired by boat-borne vision is not always in a straight line, especially in an inland river waterway. In this paper, a novel three-step approach for WSL detection is therefore proposed to solve this problem through the information of an image sequence. Firstly, the initial line segment pool is built by the line segment detector (LSD) algorithm. Then, the coarse-to-fine strategy is used to obtain the onshore line segment pool, including the rough selection of water area instability and the fine selection of the epipolar constraint between image frames, both of which are demonstrated in detail in the text. Finally, the complete shore area is generated by an onshore line segment pool of multi-frame images, and the lower boundary of the area is the desired WSL. In order to verify the accuracy and robustness of the proposed method, field experiments were carried out in the inland river scene. Compared with other detection algorithms based on image processing, the results demonstrate that this method is more adaptable, and can detect not only the straight WSL, but also the curved WSL.

Autonomous Visual Perception for Unmanned Surface Vehicle Navigation in an Unknown Environment.

Robust detection and recognition of water surfaces are critical for autonomous navigation of unmanned surface vehicles (USVs), since any none-water region is likely an obstacle posing a potential danger to the sailing vehicle. A novel water region visual detection method is proposed in this paper. First, the input image pixels are clustered into different regions and each pixel is assigned a label tag and a confidence value by adaptive multistage segmentation algorithm. Then the resulting label map and associated confidence map are fed into a convolutional neural network (CNN) as training samples to train the network online. Finally, the online trained CNN is used to segment the input image again but with greater precision and stronger robustness. Compared with other deep-learning image segmentation algorithms, the proposed method has two advantages. Firstly, it dispenses with the need of manual labeling training samples which is a costly and painful task. Secondly, it allows real-time online training for CNN, making the network adaptive to the navigational environment. Another contribution of this work relates to the training process of neuro network. An effective network training method is designed to learn from the imperfect training data. We present the experiments in the lake with a various scene and demonstrate that our proposed method could be applied to recognize the water region in the unknown navigation environment automatically.

A Multi-Feature and Multi-Level Matching Algorithm Using Aerial Image and AIS for Vessel Identification.

In order to monitor and manage vessels in channels effectively, identification and tracking are very necessary. This work developed a maritime unmanned aerial vehicle (Mar-UAV) system equipped with a high-resolution camera and an Automatic Identification System (AIS). A multi-feature and multi-level matching algorithm using the spatiotemporal characteristics of aerial images and AIS information was proposed to detect and identify field vessels. Specifically, multi-feature information, including position, scale, heading, speed, etc., are used to match between real-time image and AIS message. Additionally, the matching algorithm is divided into two levels, point matching and trajectory matching, for the accurate identification of surface vessels. Through such a matching algorithm, the Mar-UAV system is able to automatically identify the vessel's vision, which improves the autonomy of the UAV in maritime tasks. The multi-feature and multi-level matching algorithm has been employed for the developed Mar-UAV system, and some field experiments have been implemented in the Yangzi River. The results indicated that the proposed matching algorithm and the Mar-UAV system are very significant for achieving autonomous maritime supervision.

Enhanced charge extraction with all-carbon electrodes for inorganic CsPbBr3 perovskite solar cells.

Perovskite-structured cesium lead bromide (CsPbBr3) halide provides new opportunities for promoting the commercialization of perovskite solar cells (PSC) due to its high carrier mobility and light absorption coefficient as well as remarkable environmental stability at high humidity and high temperatures. Herein, all-carbon electrodes from multi-walled carbon nanotubes (MWCNT) and carbon black (CB) were prepared for all-inorganic CsPbBr3 PSCs with the configuration of FTO/c-TiO2/m-TiO2/CsPbBr3/carbon. The as-prepared electrodes were free of hole-transporting layers and precious metals. The work function and electrical conductivity of the carbon electrode were tuned by changing the MWCNT/CB ratio to reduce charge recombination at the perovskite/carbon interface. The optimal all-inorganic PSC achieves a maximum power conversion efficiency of 7.62% using the MWCNT (75 wt%)/CB (25 wt%) electrode in comparison with 6.24% for the pure MWCNT-based device. Upon persistent attack by 80% RH in air atmosphere, the solar cell retains 95% of its initial efficiency over 1100 h.

Two-dimensional hybrid layered materials: strain engineering on the band structure of MoS2/WSe2 hetero-multilayers.

In this paper, we report a comprehensive modeling and simulation study of constructing hybrid layered materials by alternately stacking MoS2 and WSe2 monolayers. Such hybrid MoS2/WSe2 hetero-multilayers exhibited direct bandgap semiconductor characteristics with bandgap energy (E g) in a range of 0.45-0.55 eV at room temperature, very attractive for optoelectronics (wavelength range 2.5-2.75 µm) based on thicker two-dimensional (2D) materials. It was also found that the interlayer distance has a significant impact on the electronic properties of the hetero-multilayers, for example a five orders of magnitude change in the conductance was observed. Three material phases, direct bandgap semiconductor, indirect bandgap semiconductor, and metal were observed in MoS2/WSe2 hetero-multilayers, as the interlayer distance decreased from its relaxed (i.e., equilibrium) value of about 6.73 Å down to 5.50 Å, representing a vertical pressure of about 0.8 GPa for the bilayer and 1.5 GPa for the trilayer. Such new hybrid layered materials are very interesting for future nanoelectronic pressure sensor and nanophotonic applications. This study describes a new approach to explore and engineer the construction and application of tunable 2D semiconductors.

WSN-Based Space Charge Density Measurement System.

It is generally acknowledged that high voltage direct current (HVDC) transmission line endures the drawback of large area, because of which the utilization of cable for space charge density monitoring system is of inconvenience. Compared with the traditional communication network, wireless sensor network (WSN) shows advantages in small volume, high flexibility and strong self-organization, thereby presenting great potential in solving the problem. Additionally, WSN is more suitable for the construction of distributed space charge density monitoring system as it has longer distance and higher mobility. A distributed wireless system is designed for collecting and monitoring the space charge density under HVDC transmission lines, which has been widely applied in both Chinese state grid HVDC test base and power transmission projects. Experimental results of the measuring system demonstrated its adaptability in the complex electromagnetic environment under the transmission lines and the ability in realizing accurate, flexible, and stable demands for the measurement of space charge density.

Development and Application of a Wireless Sensor for Space Charge Density Measurement in an Ultra-High-Voltage, Direct-Current Environment.

A space charge density wireless measurement system based on the idea of distributed measurement is proposed for collecting and monitoring the space charge density in an ultra-high-voltage direct-current (UHVDC) environment. The proposed system architecture is composed of a number of wireless nodes connected with space charge density sensors and a base station. The space charge density sensor based on atmospheric ion counter method is elaborated and developed, and the ARM microprocessor and Zigbee radio frequency module are applied. The wireless network communication quality and the relationship between energy consumption and transmission distance in the complicated electromagnetic environment is tested. Based on the experimental results, the proposed measurement system demonstrates that it can adapt to the complex electromagnetic environment under the UHVDC transmission lines and can accurately measure the space charge density.

Relative localization in wireless sensor networks for measurement of electric fields under HVDC transmission lines.

In the wireless sensor networks (WSNs) for electric field measurement system under the High-Voltage Direct Current (HVDC) transmission lines, it is necessary to obtain the electric field distribution with multiple sensors. The location information of each sensor is essential to the correct analysis of measurement results. Compared with the existing approach which gathers the location information by manually labelling sensors during deployment, the automatic localization can reduce the workload and improve the measurement efficiency. A novel and practical range-free localization algorithm for the localization of one-dimensional linear topology wireless networks in the electric field measurement system is presented. The algorithm utilizes unknown nodes' neighbor lists based on the Received Signal Strength Indicator (RSSI) values to determine the relative locations of nodes. The algorithm is able to handle the exceptional situation of the output permutation which can effectively improve the accuracy of localization. The performance of this algorithm under real circumstances has been evaluated through several experiments with different numbers of nodes and different node deployments in the China State Grid HVDC test base. Results show that the proposed algorithm achieves an accuracy of over 96% under different conditions.