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A more accurate and efficient millimeter-wave imaging algorithm for a monostatic system is presented in this paper. Firstly, a more precise physical model is implemented, which considers the propagation loss of both the incident wave and scattered wave. However, this physical model cannot be processed using classical mathematical methods such as spherical wave decomposition and Weyl identity. Instead, the method of stationary phase (MSP) is applied to derive the imaging formula. As a result, the proposed algorithm achieves superior imaging results for multiple targets at different ranges. Additionally, the range stacking technique is introduced to avoid time-consuming interpolation, which also contributes to improving computational efficiency comparing to the algorithms employing interpolation. The simulation results show that the proposed algorithm reduces the computation time by half. The proposed algorithm has been further verified through real data collected with our in-house developed prototype. [ABSTRACT FROM AUTHOR]

An efficient and more accurate millimeter-wave imaging algorithm, applied to a close-range monostatic personnel screening system, with consideration of dual path propagation loss, is presented in this paper. The algorithm is developed in accordance with a more rigorous physical model for the monostatic system. The physical model treats incident waves and scattered waves as spherical waves with a more rigorous amplitude term as per electromagnetic theory. As a result, the proposed method can achieve a better focusing effect for multiple targets in different range planes. Since the mathematical methods in classical algorithms, such as spherical wave decomposition and Weyl identity, cannot handle the corresponding mathematical model, the proposed algorithm is derived through the method of stationary phase (MSP). The algorithm has been validated by numerical simulations and laboratory experiments. Good performance in terms of computational efficiency and accuracy has been observed. The synthetic reconstruction results show that the proposed algorithm has significant advantages compared with the classical algorithms, and the reconstruction by using full-wave data generated by FEKO further verifies the validity of the proposed algorithm. Finally, the proposed algorithm performs as expected over real data acquired by our laboratory prototype. [ABSTRACT FROM AUTHOR]

We present a general theory of the holographic image reconstruction with linear frequency modulation (LFM) radars. For the first time, the system limitations in terms of the object extent and distance are derived and explicitly related to the LFM radar frequency-modulation slope $\gamma $. The holographic inversion formula is improved to account for the spherical spread of the scattered wave. The theory and the generalized holographic inversion algorithm are validated by synthetic benchmark data as well as experimental data from an in-house LFM-radar prototype operating at 29.9-GHz central frequency and bandwidth of 5.8 GHz. Experiments confirm that the lateral spatial resolution is about 5 mm. For optimal performance, the system is calibrated using a simple but effective calibration approach based on a measurement with a metallic plate. Experiments, with a volunteer carrying metallic and nonmetallic objects, demonstrate very good performance in realistic scenarios. [ABSTRACT FROM AUTHOR]

In the smart grid and big data environment, accurate and large amount of power load data for users can be obtained with the wide application of non-intrusive load monitoring technology. In the research process of customers' information, information security protection of users' electricity data has become a research hotspot urgently. This article proposes a new type of load decomposition method for electric vehicle load information and compares it with hidden Markov model algorithm to verify its accuracy. On this basis, the elliptic curve encryption algorithm is used to encrypt the users' electricity data, and the function and effectiveness of the encryption algorithm are verified by comparing the load decomposition of the electric vehicle with the unencrypted data. [ABSTRACT FROM AUTHOR]

Projection adjustment is a technique that improves the rate of convergence, as measured by the number of iterations needed to achieve a given level of performance, of the Kaczmarz algorithm (KA) for iteratively solving a system of consistent linear equations, however at the cost of requiring additional time per iteration and increased storage. This hinders the applicability of the previously published Kaczmarz algorithm with projection adjustment (KAPA) to large-scale problems. An enhancement EKAPA of KAPA that uses projection adjustment only for a small subset of the equations is proposed for significantly reducing the time and storage requirements. An analysis of the behavior of EKAPA is provided. An illustration is given to show that EKAPA using a small subset of the equations for projection adjustment can achieve a speed-up over KA similar to that of KAPA in terms of the number of iterations, but requires much less computer time and storage; hence, it is more suitable for large-scale problems. [ABSTRACT FROM AUTHOR]

As the key technology for Internet of things, wireless sensor networks have received more attentions in recent years. Mobile localization is one of the significant topics in wireless sensor networks. In wireless sensor network, non-line-of-sight propagation is a common phenomenon leading to the growing non-line-of-sight error. It is a fatal impact for the localization accuracy of the mobile target. In this article, a novel method based on the nearest neighbor variable estimation is proposed to mitigate the non-line-of-sight error. First, the linear regression model of the extended Kalman filter is used to obtain the residual of the distance measurement value. After that, the residual analysis is used to complete the identification of the measurement value state. Then, by analyzing the statistical characteristics of the non-line-of-sight residual, the nearest neighbor variable estimation is proposed to estimate the probability density function of residual. Finally, the improved M-estimation is proposed to locate the mobile robot. Experiment results prove that the accuracy and robustness of the proposed algorithm are better than other methods in the mixed line-of-sight/non-line-of-sight environment. The proposed algorithm effectively inhibits the non-line-of-sight error. [ABSTRACT FROM AUTHOR]

For item selection in cognitive diagnostic computerized adaptive testing (CD-CAT), ideally, a single item selection index should be created to simultaneously regulate precision, exposure status, and attribute balancing. For this purpose, in this study, we first proposed an attribute-balanced item selection criterion, namely, the standardized weighted deviation global discrimination index (SWDGDI), and subsequently formulated the constrained progressive index (CP_SWDGDI) by casting the SWDGDI in a progressive algorithm. A simulation study revealed that the SWDGDI method was effective in balancing attribute coverage and the CP_SWDGDI method was able to simultaneously balance attribute coverage and item pool usage while maintaining acceptable estimation precision. This research also demonstrates the advantage of a relatively low number of attributes in CD-CAT applications. [ABSTRACT FROM AUTHOR]

Highlights • The p -center is a social equity flow-refueling model as opposed to a social efficiency model. • The p -center determines the lowest maximum deviation for the full-coverage model in p. • A link-based algorithm is developed to optimally solve this problem. • Multiple optimal solutions may exist, but they are associated with different total trip distances. • The maximum deviation may not always decrease as the vehicle range increases. Abstract The p -center flow-refueling facility location problem locates p refueling facilities to minimize the maximum percentage deviation of all drivers. It is a social equity resource allocation model as opposed to a social efficiency p -maximum coverage flow-refueling model. We propose a nonlinear integer program based on link formulation and analyze its relationship with p -maximum coverage and set covering location flow-refueling problems. We develop a link-based implicit enumeration algorithm with an embedded vehicle range-constrained shortest path subproblem to optimally solve the problem. The computational results show that multiple optimal solutions may exist but that they are associated with different total trip distances. In addition, the maximum deviation may not decrease as the vehicle range or the number of refueling facilities marginally increases. [ABSTRACT FROM AUTHOR]

A new adaptive critic autopilot design for bank-to. turn missiles is presented. In this paper, the architecture of adaptive critic learning scheme contains a fuzzy-basis-function-network based associative search element (ASE), which is employed to approximate nonlinear and complex functions of bank-to-turn missiles, and an adaptive critic element (ACE) generating the reinforcement signal to tune the associative search element. In the design of the adaptive critic autopilot, the control law receives signals from a fixed gain controller, an ASE and an adaptive robust element, which can eliminate approximation errors and disturbances. Traditional adaptive critic reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic environment, however, the proposed tuning algorithm can significantly shorten the learning time by on-line tuning all parameters of fuzzy basis functions and weights of ASE and ACE. Moreover, the weight updating law derived from the Lyapunov stability theory is capable of guaranteeing both tracking performance and stability. Computer simulation results confirm the effectiveness of the proposed adaptive critic autopilot. [ABSTRACT FROM AUTHOR]

This study proposed a model for highway accident detection that combines the You Only Look Once v3 (YOLOv3) object detection algorithm and Canny edge detection algorithm. It not only detects whether an accident has occurred in front of a vehicle, but further performs a preliminary classification of the accident to determine its severity. First, this study established a dataset consisting of around 4500 images mainly taken from the angle of view of dashcams from an open-source online platform. The dataset was named the Highway Dashcam Car Accident for Classification System (HDCA-CS) and was developed with the aim of conforming to the setting of this study. The HDCA-CS not only considers weather conditions (rainy days, foggy days, nighttime settings, and other low-visibility conditions), but also various types of accidents, thus increasing the diversity of the dataset. In addition, we proposed two types of accidents—accidents involving damaged cars and accidents involving overturned cars—and developed three different design methods for comparing vehicles involved in accidents involving damaged cars. Canny edge detection algorithm processed single high-resolution images of accidents were also added to compensate for the low volume of accident data, thereby addressing the problem of data imbalance for training purposes. Lastly, the results showed that the proposed model achieved a mean average precision (mAP) of 62.60% when applied to the HDCA-CS testing dataset. When comparing the proposed model with a benchmark model, two abovementioned accident types were combined to allow the proposed model to produce binary classification outputs (i.e., non-occurrence and occurrence of an accident). The HDCA-CS was then applied to the two models, and testing was conducted using single high-resolution images. At 76.42%, the mAP of the proposed model outperformed the benchmark model's 75.18%; and if we were to apply the proposed model to only test scenarios in which an accident has occurred, its performance would be even better relative to the benchmark. Therefore, our findings demonstrate that our proposed model is superior to other existing models. [ABSTRACT FROM AUTHOR]

An NO2 photoacoustic sensor system with a high reflective mirror based on a low power blue diode laser is developed in this work. The excitation power is enhanced by increasing the number of reflections. Comparing with a traditional photoacoustic system, the pool constant is improved from 300.24 (Pa⋅cm)/W to 1450.64 (Pa⋅cm)/W, and the signal sensitivity of the photoacoustic sensor is increased from 0.016 μV/ppb to 0.2562 μV/ppb. The characteristics of temperature and humidity of the new photoacoustic sensor are also obtained, and the algorithm is adjusted to provide a quantitative response and drift of the resonance frequency. The results of this research provide a new method and concept for further developing the NO2 photoacoustic sensors. [ABSTRACT FROM AUTHOR]