Chenfei Liao, Wensong Wang, Zichen Wen, Xu Zheng, Yiyu Wang, Haocong He, Yuanhuiyi Lyu, Lutao Jiang, Xin Zou, Yuqian Fu, Bin Ren, Linfeng Zhang, Xuming Hu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

A wireless microwave monitoring system for measuring the pollutants of triclosan and glyphosate in water is proposed in this manuscript. The proposed wireless microwave monitoring system is a long-range, low-power internet of thighs (IoT) system, which can serve as a sensing node for monitoring water quality in a large-scale water area. The microwave sensing system and the LoRa-based wireless communication module constitute the real-time online wireless microwave monitoring network. The microwave sensing system is composed of the multiple stepped impedance transformer (MSIT) with an open-ended stub, radio frequency (RF) oscillator, and frequency demodulation circuit. To suppress the external interference, the microwave sensing system is designed as the differential configuration. The test channel of microwave sensing system outputs the pollutant concentration-dependent DC voltage, transmits this DC voltage to a laptop terminal via LoRa transceiver module, and the host computer system on the laptop terminal can display the concentrations of triclosan and glyphosate in distilled water by the trained BP-NN model. In measurement, a sensitivity of 1.33553mV/(μg/mL) and a limit of detection (LOD) of 1.001 μg/mLare calculated for water-triclosan mixture. In contrast, a sensitivity of 0.87638mV/(μg/mL) and a LOD of 1.525 μg/mLare obtained for water-glyphosate mixture. The measured results demonstrate that the proposed wireless microwave monitoring system is an attractive role for industrial application.

Extracting high-fidelity mesh surfaces from Signed Distance Fields (SDFs) has become a fundamental operation in geometry processing. Despite significant progress over the past decades, key challenges remain-namely, how to automatically capture the intricate geometric and topological structures encoded in the zero level set of SDFs. In this paper, we present a novel isosurface extraction algorithm that introduces two key innovations: 1) An incrementally constructed power diagram through the addition of sample points, which enables repeated updates to the extracted surface via its dual-regular Delaunay tetrahedralization; and 2) An adaptive point insertion strategy that identifies regions exhibiting the greatest discrepancy between the current mesh and the underlying continuous surface. As Fig. 1 shows, our framework progressively refines the extracted mesh with minimal computational cost until it sufficiently approximates the underlying surface. Experimental results demonstrate that our approach outperforms state-of-the-art methods, particularly for models with intricate geometric variations and complex topologies.

This article presents a cryogenic fractional-N 4–5.9-GHz cascaded phase-locked loop (PLL) operating down to 4 K for a transmon control system. The main PLL stage employs a supply-boost constant charging current sampling phase detector (SBCC-SPD) to achieve ultralow rms jitter and fractional spur from 300 to 4 K. The first-stage PLL operates in integer-N mode employing an inverter-based sampling PD and a Class-F voltage-controlled oscillator (VCO) to achieve low output phase noise, with a frequency-tuning range of 11.8–15.9 GHz. The second-stage PLL operates in fractional-N mode using double-multimodule divider (MMD) with shared delta-sigma-modulator (DSM) architecture. It incorporates an SBCC-SPD and Class-B VCO, with a frequency-tuning range of 4–5.9 GHz for transmon control application. The reference signal of SBCC-SPD is from the divided signal of the first-stage PLL, which is modulated by the same DSM used in the feedback path of the second-stage PLL to compensate for the quantization error. Fabricated in a 28-nm Bulk CMOS process, the PLL achieves an rms jitter of 58.8 fs at 300 K and 36.9 fs at 4 K, with a fractional spur of −71.8 dBc at 300 K and −69.1 dBc at 4 K. The chip consumes a power consumption of 24.8 mW at 300 K and 14.2 mW at 4K corresponding to a figure of merit (FOM) of −257.1 dB. This chip occupies an area of 0.344 mm2.

Partial discharge (PD) detection is critical for diagnosing and maintaining insulation systems of high-voltage equipment. In this article, a novel broadband three-directional (3-D) omnidirectional magnetic induction-based sensor module is proposed for noncontacting PD detection of high-voltage equipment in the industrial Internet of Things (IIoT) application. The equivalent circuit models of a bandpass filter and planar spiral coil are comparatively investigated. Both broadband and filtering performance are realized by combining multiple spiral coils associated with the lumped capacitors. Moreover, five coils are wrapped around the dielectric cube to construct a 3-D omnidirectional sensor. Benefiting from the subcoil interactions, the magnetic field vector is redistributed to produce a transverse component that can detect the fields parallel to the coil’s plane, thus contributing to omnidirectional sensing performance. To verify the proposed measurement technique, an IIoT-based real-time monitoring system prototype is manufactured, and the designed sensor is fabricated by using 3-D printing technology. The experiments show that the designed sensor module can achieve an ultrawide impedance bandwidth ranging from 4 to 70 MHz (|$S_{11} {\unicode {0x007C}}$¡ −10 dB) while the out-of-band interference can be suppressed. Besides, the proposed system has been demonstrated for 3-D omnidirectional real-time monitoring and intelligent analysis for PD events, showcasing its significant potential in IIOT applications.

This article proposes a novel millimeter-wave liquid-crystal (LC)-based reconfigurable folded reflectarray antenna (RFRAA) tailored for Internet of Vehicles (IoV) sensing and communication. It offers 2-D beam scanning capabilities. The RFRAA consists of a reflectarray (RA), a$2\times 2$feeding array, and a polarization-selective surface (PSS). The unit cell of the RA is designed using a slot-coupled patch array with a cross-shaped slot. The differential-driven structure and meandered microstrip line are added into the unit cell. They produce a 90° polarization rotation of the reflected wave relative to the incident wave and provide phase compensation for in-phase radiation. LC substrate is injected into the unit cell and can adjust the phase delay of the meandered microstrip line to meet the beam scanning. To minimize cross-polarization, an array of metallic strip along x-axis is employed as the PSS, it can only transmit x-polarized wave. A prototyped RFRAA with$15\times 15$-unit cells is fabricated and measured. The RFRAA achieves 2-D beam steering within ±60° in both the E-plane and H-plane. It demonstrates a peak aperture efficiency (AE) of 29.6% at 24 GHz and a height-to-dimension (H/D) ratio of 0.29. These features make it a promising candidate for IoV sensing and communication applications.

This paper proposes a dual-band dual-circularly polarized multibeam tensor holographic metasurface antenna (THMSA) designed for Internet of Vehicles (IoV) sensing and communication applications. It comprises two distinct HMSAs and a monopole antenna. The two HMSAs are arranged in a back-to-back configuration, each featuring independent beam-steering capabilities. Each HMSA is generated through holographic interference between the desired circularly polarized (CP) beam and a reference wave. To reduce the antenna profile, a metal sleeve encases the monopole antenna, which serves as a surface wave launcher to generate the omnidirectional reference wave on the two HMSAs. This design enables the simultaneous generation of dual-circularly polarized beams across two frequency bands simultaneously. More importantly, the CP beams operate without interfering with each other. To verify the concept of the design, the proposed THMSA is fabricated and measured. This demonstrates its capability to generate LHCP and RHCP beams in different directions at 3.9 GHz and 4.5 GHz. The impedance bandwidth (|S11|< -10dB) spans from 3.62 GHz to 5.5 GHz and axial ratio (AR) bandwidths are from 3.87 GHz to 4.15 GHz and 4.38 GHz to 4.58 GHz, respectively. The proposed THMSA is highly suitable for vehicle-based 5G, vehicle-to-base station, and vehicle-to-satellite communications.

The industrial Internet of Things (IIoT) significantly enhances mechanical fault diagnosis. However, IIoT-based intelligent diagnostic models struggle with sample insufficiency and high energy consumption due to collection costs and limited computing resources. Therefore, this article proposes an energy-efficient mechanical fault diagnosis method based on the neural-dynamics-inspired metric SpikingFormer (MSF) to achieve accurate fault recognition under insufficient samples. The design and construction of a data acquisition system based on the aircraft engine platform and the ship water jet propulsion platform effectively support the operation of the developed diagnostic algorithm. Specifically, an event-driven multiscale mask spiking self-attention (MMSSA) mechanism is designed to focus critical spatiotemporal features from different scales under low computational complexity. Meanwhile, a rate encoding metric classifier (REMC) is constructed to bridge spiking learning and prototype representation, thereby accurately classifying fault under insufficient samples. Finally, a customized backpropagation strategy based on neural dynamics is developed to enable the MSF to learn effectively and be stable. The superiority of the MSF in energy consumption and diagnostic accuracy is verified through comparison with six authoritative methods across standard, laboratory-acquired, and real-world datasets. The results showed that the parameter count of MSF is$7.04\times $and$20.46\times $less than the strong baseline method, respectively, and the diagnostic accuracy on the two real datasets is 4.52% and 6.91% higher than the latest method, respectively.

Accurate partial discharge (PD) detection and insulation diagnosis are essential for ensuring the operational safety of high-voltage (HV) power equipment, while wideband high sensitivity PD detection is extremely imperative since most of the PD events reflecting potential insulation defects have low strength (<1 pC) with wideband features. However, conventional wideband detection methods such as high-frequency (HF) antennas often suffer from the trade-off between low sensitivity, increased noise and the bandwidth requirements due to their low-Q features in the wideband sensing, and the vulnerability to in-band narrowband interference, especially in complex industrial environments. To address these challenges, this paper proposes a novel high sensitivity super-wideband PD detection system for Industrial Internet of Things (IIoT) applications integrating a reconfigurable high-frequency antenna sensor (RHAS), a bandwidth-reconfigurable low-noise amplifier (BRLNA), and a sub-band synthesis algorithm with built-in narrowband interference rejection (NIR) function. The RHAS captures weak PD signals across 48 high-Q sub-bands, which are individually amplified and digitally synthesized to reconstruct the wideband signal with enhanced sensitivity. The proposed system enables remote, clamp-free PD detection with strong NIR capability near grounded conductors, significantly outperforming the traditional sensors such as High-Frequency Current Transformers (HFCTs). Experimental results demonstrate a detection bandwidth from 1.4 MHz to 98.2 MHz (194.38% relative bandwidth) and a minimum detectable PD level of 0.3 pC. Compared with HFCT based systems, the proposed method achieves a 0.4–4.2 dB improvement in PD signal-to-noise ratio (SNR) and a 6.1–8.4 dB increase in total system gain. These results validate the effectiveness and robustness of the proposed approach for high-sensitivity wideband PD monitoring.

Visible light communication (VLC) is a vital solution for information transmission in uncrewed aerial vehicles (UAVs), yet its effectiveness is highly dependent on the accuracy of UAV positioning. A route planning method has been developed to assist an UAV in accurately identifying the communication light source within an Internet of Things (IoT) environment. This method is designed to guide the UAV selecting an appropriate path, executing its flight, and adjusting its flight path by contentiously sensing the intensity of specific light. As the UAV progressively narrows down the search area, it can ultimately identify the effective lighting area under the lighting source. A method for success rate assessment has been proposed, evaluating potential search routes using diverse geometric shapes: triangles, quadrangles, pentagons, hexagons, and circles. Factors that could significantly impact the search success rate and search distance are taken into account, including the lighting radius of the light source, the spatial light distribution based on Lambert coefficient, the geometry of the search path, number of search attempts, and the side length of polygons. Computational results show that the method achieves nearly 100% success when the light source has an emission angle of 37.5°. Although the proposed method requires further consideration of challenges, such as algorithm integration, transmission distance, and diverse application scenarios, it offers a route search method that is both simple and highly successful, effectively mitigating the impact of inaccurate UAV positioning in a VLC-based IoT system.

The timely detection of partial discharge (PD) of high-voltage (HV) power equipment is crucial to mitigate serious consequences such as the degradation of insulation and equipment failure. The ultra-high frequency (UHF) detection method stands out for its efficacy in this regard. In this study, a novel UHF antenna sensor is proposed for PD detection. The antenna's offset structure enables it to detect PD events near a conducting ground wire without being clamped to the wire like the typical high-frequency current transformer (HFCT). Meanwhile, its equivalent circuit is modeled as a ladder-structure band-pass filter (within the consideration of mutual inductances) to realize its wideband properties. Fabricated on a substrate and integrated with a low-noise amplifier, the antenna sensor exhibits a broad impedance bandwidth between 1 MHz and 108 MHz, as demonstrated through measurements in an anechoic chamber. In-lab and on-site systematic experiments affirm the efficiency of the proposed antenna sensor in PD detection. Notably, the Phase-Resolved Partial Discharge (PRPD) pattern is distinctly observable at the backend through Internet connectivity, further confirming the overall monitoring capabilities.

An ultrawideband multiple-input-multiple-output (MIMO) circularly polarized (CP) cube antenna is proposed for wireless communication and sensing of Internet of Things. This antenna comprises an ultrawideband grounded criss-cross radiator, four square parasitic patches, and a four-port feeding network. Following the inverted-F radiation principle, a criss-cross patch is incorporated with its center grounded. Surrounding it, four small exciting patches are strategically positioned to drive via electromagnetic couplings. The ultrawideband radiation characteristics are analyzed based on the mode behaviors predicted by characteristic mode analysis (CMA). The introduction of four square parasitic patches between the radiator and the ground plane leads to an improvement in impedance matching. To create a single-port CP antenna element, a four-port feeding network with equal magnitude, but spaced 90° apart in phase, is connected to the radiator. The MIMO diversity performance is assessed through metrics like envelope correlation coefficient (ECC), diversity gain (DG), mean effective gain (MEG), and channel capacity loss (CCL). To validate the design concept, a prototype of the cube antenna is fabricated and assembled, demonstrating the ability to radiate right-hand circularly polarized (RHCP) waves. The measured results indicate impressive performance metrics: a −10-dB impedance bandwidth of 2.2–7.8 GHz (112%), a 3-dB axial ratio bandwidth of 2.1–7.3 GHz (110.6%), a realized gain ranging from 5.6 to 9.5 dBic, and a radiation efficiency between 65% and 88.5%. Additionally, the port isolation exceeds 30 dB, and the ECCs are below 0.009.

An innovative artificial magnetic conductor (AMC)-backed quad-band antenna is proposed with gain enhancement, high out-of-band (OoB) rejection, forward radiation improvement, and backward radiation reduction for intelligent Internet of Vehicle (IIoV)-based sensing and communication. It includes a quad-band microstrip radiator and a tetracyclic nested AMC as a reflector. The radiator has a radiating pattern of slotted octagonal patch connected to a rectangular resonant loop, and a meshed defected ground structure (MDGS). The mechanism of resonant frequency generation is derived and analyzed. Then, an AMC reflector with four zero-phases in the reflection coefficient is designed and its equivalent circuit model is established. The AMC unit cell consists of four nested rings and two lumped capacitors. It generates four in-phase reflection bands, which are coincident with the four frequency bands of the radiator. The quad-band antenna gains are enhanced while its profile maintains low due to the in-phase reflection characteristics of the multi-band AMC reflector. To verify the design concept, a prototype with a total size of 95 mm × 101 mm × 21.2 mm is fabricated and measured. The measured 10-dB impedance bandwidths are 2.19-2.54 (14.8%), 3.06-5.25 (52.7%), 6.43-6.96 (7.9%), and 7.71-8.29 (7.3%) GHz, respectively. High OoB rejections between each band reduce electromagnetic interference to feed into radiofrequency circuits. The measured gains and radiation efficiencies range from 3.6 to 8 dBi and from 57.8% to 92%, respectively.

A wearable dual-band and dual-circularly polarized (DBDCP) antenna using a dodecagonal truncate pyramid-shaped artificial magnetic conductor (AMC) reflector for gain enhancement is proposed in this paper. Firstly, a compact deformed quadruple inverted-F antenna (QIFA) with meander-line-shaped radiation patches has been developed as the radiator. Then, to make this QIFA generate different circular polarization (CP) radiation characteristics in two frequency bands, two feeding networks are adopted for realizing lefthand and righthand CP properties simultaneously. Lastly, a novel AMC reflector is employed to improve antenna performance. The presented DBDCP antenna was fabricated to realize lefthand CP (LHCP) in the frequency band of 3.5-4.0 GHz (13.3%) and righthand CP (RHCP) in 5.4-5.9 GHz (8.8%). Due to installation of the AMC reflector, the gains of the antenna are enhanced by about 3-5.3 dB in the lower CP band. The achieved peak gains are about 11.9 dBic and 10.5 dBic at 3.5 GHz (LHCP) and 5.8 GHz (RHCP), respectively. Meanwhile, the antenna’s specific absorption rate (SAR) has been greatly reduced, which meets well the IEEE wearable device standards. It is found that the proposed DBDCP antenna is a promising candidate for the applications of 5G, industrial scientific medical (ISM), WLAN (5.8-GHz), and WiMAX (3.5-GHz) systems in industrial IoT scenarios.

Developing intelligent methods for partial discharge (PD) diagnosis, capable of handling various types of insulation defects in switchgear, has garnered significant attention in recent years. Certain PD signals exhibit similar characteristics, often leading to their confusion with noisy signals during data acquisition. To mitigate noise interference and enhance the precision of PD recognition, this article introduces a novel framework for separating PD signals from noise and acquiring discriminative features for identifying different types of PDs. Specifically, the proposed approach incorporates an adaptive frequency sampling strategy to extract effective and efficient features for the separation of PD signals and noise, followed by the clustering of the captured signals. Phase Resolved PD (PRPD) patterns are then generated for each clustered signal group, forming the PRPD pattern database. In order to identify the informative region within the PRPD patterns, we introduce spatial correlation attention and discriminative feature learning modules. These modules aim to reduce intraclass variance and increase interclass differences in the PRPD patterns. To evaluate the effectiveness of the proposed method in separating PD signals from noise and recognizing different PD patterns, we constructed a PD recognition dataset that encompasses noise as well as three types of PDs: 1) corona, 2) internal, and 3) surface. By conducting experiments and comparing the results with state-of-the-art methods, we demonstrate the performance of our method in achieving accurate PD recognition with a notable improvement of 1.9% on the constructed PD dataset.

Rails play an essential role in railway transportation, supporting the movement of various types of trains. Due to the high-frequency and high-intensity loads, as well as harsh operating environment, rails are susceptible to cracking or even fracturing. Among existing rail structural health monitoring (RSHM) methods, the advanced ones often rely on signal-driven deep learning algorithms, necessitating substantial computational time and extensive preliminary information for effective model training. Moreover, crack-related signals with low amplitudes are easily submerged in the complex interference noise environments. Although some noise reduction solutions have been reported, the RSHM results often obtain certain discrepancies from the actual outcomes. To address above issues, this paper presents an alternative RSHM method based on electromagnetic acoustic emission (EMAE) technology. The proposed method uses a double-layer signal processing (DLSP) algorithm and a novel health monitoring index, called spectrum information entropy (SIE). It can monitor the degradation state of rails accurately and quantitatively. In this method, the DLSP algorithm is utilized to identify EMAE signals from the original dataset, which contains complex and diverse interference noise signals. In addition, the SIE is extracted from the obtained EMAE signals to perform the RSHM. Experimental results validate the accuracy and simplicity of the proposed method.

Partial discharge (PD) is an essential indication of insulation degradation in high-voltage power equipment like gas-insulated switchgears (GIS). However, in certain applications that are exposed to intense external noise, traditional PD detection methods often encounter numerous challenges due to their vulnerability to noise and interference. This paper proposes a low-noise ultra-high frequency (UHF) sensing system for PD detection and classification. In analog front end, the noise performance is optimized using a broadband noise-shaping network (BNSN) integrated with a wideband printed monopole antenna (PMA). In digital back end, the combination of noise cancellation, wavelet time scattering (WTS) based features extraction and a support vector machine (SVM), yields 95% correct classification. Simulation and Comparative experimental results validate superior noise performance, effectiveness and accuracy of this UHF sensing system.

Landslide susceptibility prediction is critical in open pit mines and geotechnical fields. Prediction accuracy is very essential to reduce the risk of slope instability. Traditional statistical learning methods have been widely used in early warning systems, but they cannot thoroughly explore the coupling effect among related factors, which often results in low prediction accuracy. This paper establishes an ensemble learning prediction model optimized by a genetic algorithm (GA) to determine landslide susceptibility more quickly and efficiently. The model is based on 290 sets of slope cases containing height (H), slope angle (α), unit weight (γ), cohesion (c), friction angle (φ), and pore water pressure (ru). Two common algorithms are incorporated into the ensemble learning model: Xgboost and gradient boosting decision tree (GBDT). The area under the curve (AUC) of GA‐GBDT and GA‐Xgboost were found to be 0.928 and 0.933, respectively, both of which could predict landslide susceptibility better. Compared with multiple logistic regression and other machine learning algorithms, both GA‐GBDT and GA‐Xgboost models perform better in terms of accuracy and applicability. The study results demonstrate that the developed optimized machine learning model can accurately predict landslide susceptibility and that the parameters should be optimized on a case‐by‐case basis to achieve more accurate results after building a suitable machine model. The optimization model proposed in this paper can be an effective new method for the intelligent prediction of landslide susceptibility.

With the rapid development of the techniques of semiconductors, Internet of Everything (IoE), industry 4.0 and smart power are going to be realized, agile and accurate edge detection on pulse signals becomes essential for supporting versatile sensing applications targeting ubiquitous IoE monitoring. To ensure accurate pulse signal sensing at the edge with low power, a novel silicon-integrated mixer-enabled adaptive sensing platform is proposed. Based on the innovative chip architecture composed of the low-power ring voltage-controlled oscillator (VCO) and current-bleeding mixer on-chip, the wideband pulse signal would be mixed with the sinusoidal LO signal generated by VCO and detected by low-pass filtering and further digital processing. The frequency of the VCO can be configured flexibly by the FPGA to cover different pulse detection scenarios. Moreover, the VGA and the LPF are flexibly configurable to meet the link budget requirements and ensure accurate and adaptable detection covering various scenarios. Based on the systematic theoretical evaluation of the novel flexible sensing chip architecture, target pulse signals can be detected, exploring the capability of the efficient chip-based edge pulse detection system to be deployed for applications such as sustainable partial discharge detection for power electronics monitoring, ultrasound sensing, and so on.

With the rapid development of the internet of things (IoT), industry 4.0, smart manufacturing, intelligent building techniques, robots are becoming more and more demanding for emerging new applications. For robots used in complex environments, precise sensing of its surroundings is essential to enable safe and robust operation. Comprehensive and cognitive perception capability is needed to recognize human subjects in a complex and dynamic environment to avoid the possible collisions. Moreover, comprehensive sensing is required to enable accurate simultaneous localization and mapping (SLAM) under scenarios with clutters and moving human subjects. To achieve the goal, a CMOS-integrated radarassisted robot sensing platform is proposed. By leveraging the phased-array radar techniques and time-phase processing techniques, high accuracy can be achieved for localization and recognition of human subjects. Fabricated in a 65-nm CMOS process, the chip-scale radar sensing platform is with compact size. A series of experiments have been carried out on verifying the capabilities of the radar platform for ranging and human recognition based on vital signs, exploring the potential for seamless human-robot interaction applications.

An ultra-lower power (ULP) MEMS-based FSK transmitter is implemented in 65 nm CMOS. The transmitter operates in ISM-915 MHz band using a binary FSK modulator employing the high-Q dual microelectromechanical system (MEMS) resonators. The transmitter using an adaptive fast switching binary FSK modulator generates FSK signals in 915 MHz band with -8.2 dBm output power, supporting up to 10 Mb/s data rate. The phase noise (PN) of the FSK modulator was measured for each of the two different operation frequencies, achieving -139.4dBc/Hz and -138.7dBc/Hz at a 1-MHz offset at 911.9 MHz and 923.1 MHz, respectively. At 10 Mb/s, the transmitter consumes 753 pW, which translates to energy-efficiency of 75.3 pJ/b.

For wearable biomedical devices, it is essential to detect target signals under high noise and strong interferences. Moreover, it is critical to realize the system with compact size and easy implementation. To address both goals, this paper presents a chip-based photoacoustic (PA) sensor called QuACL. By leveraging the adaptive coherent lock-in technique, the high sensitivity and specificity can be achieved for detecting target signals. In-phase and quadrature PA templates are specifically designed based on the profile of the target signal and are generated by the FPGA board and the DAC board. The received signal is correlated with the templates to capture, track, and recover the target PA signal. Fabricated in 65-nm CMOS technology, the chip-based sensor system occupies only 0.6 mm2area. The robustness and versatility of the QuACL sensor system are verified by the experiments on discerning and recovering weak PA signals accurately.

Vehicular networks are becoming a prominent research field in the intelligent transportation system (ITS) due to the nature and characteristics of providing high-level road safety and optimized traffic management. Vehicles are equipped with the heavy communication equipment which requires a high power supply, on-board computing device, and data storage devices. Many wireless communication technologies are deployed to maintain and enhance the traffic management system. The ITS is capable of providing services to the traffic authorities and precautionary measures to the drivers and passengers. Several methods have been proposed for discussing the security and privacy issues for the vehicular ad hoc networks (VANETs) and vehicular cloud computing (VCC). They receive a great deal of attention from researchers around the world since they are new technologies, and they can improve road safety and enhance traffic flow by utilizing the vehicles resources and communication system. Firstly, the VANETs are presented, including the basic overview, characteristics, threats, and attacks. The location privacy methodologies are elaborated, which can protect the confidential information of the vehicle, such as the location detail and driver information. Secondly, the trust management models in the VANETs are comprehensively discussed, followed by the comparison of the cryptography and trust models in terms of different kinds of attacks. Then, the simulation tools and applications of the VANETs are discussed, and the evolution is presented from the VANETs to VCC in the vehicular network. Thirdly, the VCC is discussed from its architecture and the security and privacy issues. Finally, several research challenges on the VANETs and VCC are presented. In sum, this survey comprehensively covers the location privacy and trust management models of the VANETs and discusses the security and privacy issues in the VCC, which fills the gap of existing surveys. Also, it indicates the research challenges in the VANETs and VCC.

A novel coil-based electromagnetic-induced thermo-acoustic system is presented for detecting the flaws inside a rail. The fundamental is derived and the overall energy density distribution is simulated using finite element method. This paper gives an overview of the system architecture and describes the design process in detail. A mixed numerical experimental methodology is employed to extract the lumped parameters of a planar coil with the ferrite plate for designing the matching network, and then the coil and rail are co-simulated to observe the current density distributions and directions. Through the relationship of energy density and depth in the rail, it is found that the thermal energy mainly concentrates at the surface local area. From the interaction between the coil and rail, the inductive power transfer topology is illustrated and the simplified equivalent circuit model is further obtained. By analyzing the simulated and measured data, the changes in the resistance and inductance are shown with the frequency increasing. The induced ultrasonic wave propagation is simulated inside the rail with flaws, where the wavefronts and reflected signals are observed. Finally, the experimental results demonstrate that the proposed design is feasible and a crack with a diameter of 8 mm can be detected in the rail.

The loop inductances of a silicon carbide (SiC) half-bridge power module (HBPM) have a direct impact on its performance. This paper describes an improved two-port network method to measure and extract the loop inductances of a SiC HBPM with good accuracy.