Recently, with the increasing difficulty of parking in cities, the wireless vehicle detectors (WVDs) based on Internet of Thing (IoT) technology and magnetic sensor have been widely used to obtain parking space status data, which lay the foundation for smart parking management system (SPMS). However, there is a weak magnetic region under every vehicle where the magnetic variation is even smaller than that distorted by adjacent vehicles, which leads to the accuracy decrease of the magnetometer-based vehicle detection algorithm. To improve the performance of the WVDs, an IoT-Driven vehicle detection method which combines the heterogeneous data feature of UWB channel with that of magnetic signal is proposed in this paper. In the proposed method, length of the propagation path and signature of the channel impulse response (CIR) with respect to the vehicles, which can be obtained from UWB modules, are introduced to achieve vehicle detection. Moreover, considering that the energy consumption of UWB modules is much higher than that of magnetic sensors, the UWB-based vehicle detection algorithm is only activated when the signature of magnetic signals cannot enable magnetometer-based vehicle detection algorithm to output an accurate decision. Finally, the proposed method was evaluated in a commercial parking lot under different conditions. The experiments show that it has a detection accuracy of 98.81% when the sampling rate of magnetic sensor is 1 Hz.

This letter presents a novel reconfigurable antenna with sum- and difference- patterns for the applications of wireless local area network (WLAN) access points. The proposed monopulse antenna consists of a 2×2 circularly-polarized (CP) stacked-patch antenna array and a reconfigurable monopulse comparator network. Utilizing this comparator network to excite the CP antenna array, sum, azimuth-difference, elevation-difference, and bidifference beams are produced and can be switched by controlling the dc voltages on the PIN diodes. Measurement results reveal that the antenna's return loss is better than 10 dB in the frequency ranges of 2.26—2.58 GHz, 2.24—2.62 GHz, 2.25—2.61 GHz, and 2—3 GHz at the sum, azimuth-difference, elevation-difference, and bidifference modes, respectively. The measured axial ratios, gains, and radiation patterns also show that this antenna can be readily applied to enhance the capability of communication-area coverage for WLAN access points.

An online neutrosophic similarity-based objectness tracking with a weighted multiple instance learning algorithm (NeutWMIL) is proposed. Each training sample is extracted surrounding the object location, and the distribution of these samples is symmetric. To provide a more robust weight for each sample in the positive bag, the asymmetry of the importance of the samples is considered. The neutrosophic similarity-based objectness estimation with object properties (super straddling) is applied. The neutrosophic theory is a new branch of philosophy for dealing with incomplete, indeterminate, and inconsistent information. By considering the surrounding information of the object, a single valued neutrosophic set (SVNS)-based segmentation parameter selection method is proposed, to produce a well-built set of superpixels which can better explain the object area at each frame. Then, the intersection and shape-distance criteria are proposed for weighting each superpixel in the SVNS domain, mainly via three membership functions, T (truth), I (indeterminacy), and F (falsity), for each criterion. After filtering out the superpixels with low response, the newly defined neutrosophic weights are utilized for weighting each sample. Furthermore, the objectness estimation information is also applied for estimating and alleviating the problem of tracking drift. Experimental results on challenging benchmark video sequences reveal the superior performance of our algorithm when confronting appearance changes and background clutters.

Fully convolutional networks (FCN) such as UNet and DeepLabv3+ are highly competitive when being applied in the detection of earthquake-damaged buildings in very high-resolution (VHR) remote sensing images. However, existing methods show some drawbacks, including incomplete extraction of different sizes of buildings and inaccurate boundary prediction. It is attributed to a deficiency in the global context-aware and inaccurate correlation mining in the spatial context as well as failure to consider the relative positional relationship between pixels and boundaries. Hence, a detection method for earthquake-damaged buildings based on the object contextual representations (OCR) and boundary enhanced loss (BE loss) was proposed. At first, the OCR module was separately embedded into high-level feature extractions of the two networks DeepLabv3+ and UNet in order to enhance the feature representation; in addition, a novel loss function, that is, BE loss, was designed according to the distance between the pixels and boundaries to force the networks to pay more attention to the learning of the boundary pixels. Finally, two improved networks (including OB-DeepLabv3+ and OB-UNet) were established according to the two strategies. To verify the performance of the proposed method, two benchmark datasets (including YSH and HTI) for detecting earthquake-damaged buildings were constructed according to the post-earthquake images in China and Haiti in 2010, respectively. The experimental results show that both the embedment of the OCR module and application of BE loss contribute to significantly increasing the detection accuracy of earthquake-damaged buildings and the two proposed networks are feasible and effective.

Freight train positioning and axle safety monitoring are the key to railway traffic supervision, which can be achieved by sensors based on Internet of Things (IoT) technology, and the energy harvesting technology is the best solution to those sensors due to neither electrical power nor communication system is available on railway wagons. This paper proposes a self-powered railway wagon monitoring sensor (SpRWMS) solution based on the geophone, which has the characteristics of mature technology and simple structure, and can convert mechanical vibration of wagons into electrical energy. Since the vibration is so weak that the voltage induced by the geophone is very weak, hence, this paper proposes an electromagnetic generator (EMG) constructed by geophone matrix and an energy management interface (EMI) to convert the induced potential into the voltage that can drive the circuit system of sensors. In this paper, the implementation and performance of EMI will be focused on, and the energy harvesting system analysis prior to developing prototypes and conducting field trail. The design principles and prototypes of EMG and EMI are proposed based on the parameters of vibration profiles collected by a data logger, and the performance of the SpRWMS prototype is evaluated on a trial run freight train. Experimental results show that the proposed EMI can extract the energy generated by EMG more effectively and increase the voltage across the storage capacitor from 2V to 3.3V, that is, our proposed EMI and EMG can convert the railway wagon vibrational energy into the electrical energy form required by the circuitry of sensors.

Optimizing anchor deployment is critical to ensure the performance and positioning stability of indoor positioning systems in real-world applications. In this paper, a new anchor deployment optimization method is proposed to enhance the positioning performance of range-based positioning systems in the Non-Line-of-Sight (NLOS) environment without increasing the application cost. Firstly a new fitness function is proposed by simultaneously considering the mean geometric dilution of precision (GDOP) and the coverage of available positioning area in the indoor NLOS environment. Then, a search architecture based on a particle swarm optimization (PSO) algorithm is proposed to optimize anchor deployment. The initialization method of swarm’s position and velocity is given, and the calculation process of the search architecture is introduced in detail. The results obtained from numerical simulations and experimental investigations verified that, for range-based positioning systems in NLOS environment, the accuracy and stability can be significantly improved by optimizing the anchor deployment through our proposed method.