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Carrier-free ultra-wideband sensors have high penetrability anti-jamming solid ability, which is not easily affected by the external environment, such as weather. Also, it has good performance in the complex jungle environment. In this paper, we propose a jungle vehicle identification system based on a carrier-free ultra-wideband sensor. Firstly, a composite jungle environment with the target vehicle is modeled. From this model, the simulation obtains time-domain echoes under the excitation of carrier-free ultra-wideband sensor signals in different orientations. Secondly, the time-domain signals are transformed into MTF images through the Markov transfer field to show the statistical characteristics of the time-domain echoes. At the same time, we propose an improved RepVGG network. The structure of the RepVGG network contains five stages, which consist of several RepVGG Blocks. Each RepVGG Block is created by combining convolutional kernels of different sizes using a weighted sum. We add the self-attention module to the output of stage 0 to improve the ability to extract the features of the MTF map and better capture the complex relationship between characteristics during training. In addition, a self-attention module is added before the linear layer classification output in stage 4 to improve the classification accuracy of the network. Moreover, a combined cross-entropy loss and sparsity penalty loss function helps enhance the performance and accuracy of the network. The experimental results show that the system can recognize jungle vehicle targets well. [ABSTRACT FROM AUTHOR]

Foods are susceptible to deterioration and sour due to external environmental influences during production and storage. Coating can form a layer of physical barrier on the surface of foods to achieve the purpose of food preservation. Because of its good barrier properties and biocompatibility, prolamin-based film has been valued as a new green and environment-friendly material in the application of food preservation. Single prolamin-based film has weaknesses of poor toughness and stability, and it is necessary to select appropriate modification methods to improve the performance of film according to the application requirements. The practical application effect of film is not only affected by the raw materials and the properties of the film itself, but also affected by the selection of preparation methods and processing techniques of film-forming liquid. In this review, the properties and selection of prolamins, the forming mechanisms and processes of prolamin-based coatings, the coating techniques, and the modifications of prolamin-based coatings were systematically introduced from the perspective of food coating applications. Moreover, the defects and deficiencies in the research and development of prolamin-based coatings were also reviewed in order to provide a reference for the follow-up research on the application of prolamin-based coatings in food preservation. [ABSTRACT FROM AUTHOR]

When no-wait constraint holds in job shops, a job has to be processed with no waiting time from the first to the last operation, and the start time of a job is greatly restricted. Using key elements of the iterated greedy algorithm, this paper proposes a population-based iterated greedy (PBIG) algorithm for finding high-quality schedules in no-wait job shops. Firstly, the Nawaz–Enscore–Ham (NEH) heuristic used for flow shop is extended in no-wait job shops, and an initialization scheme based on the NEH heuristic is developed to generate start solutions with a certain quality and diversity. Secondly, the iterated greedy procedure is introduced based on the destruction and construction perturbator and the insert-based local search. Furthermore, a population-based co-evolutionary scheme is presented by imposing the iterated greedy procedure in parallel and hybridizing both the left timetabling and inverse left timetabling methods. Computational results based on well-known benchmark instances show that the proposed algorithm outperforms two existing metaheuristics by a significant margin. [ABSTRACT FROM AUTHOR]

This paper investigates the airborne synthetic aperture radar via outfield experiments based on carrier-free ultra-wideband signals with fifth-order Gaussian pulses with imaging using the backward projection algorithm. Typical blanket jamming is selected in the study, and NAM, NFM, and SFM are used as jamming signals to investigate the backward projection BP imaging effect of airborne synthetic aperture radar when subjected to blanket jamming in a real-world environment. Subsequently, a variational modal decomposition algorithm is proposed, aiming at the anti-jamming processing of airborne synthetic aperture radar. Further, the variational modal decomposition algorithms are compared with the empirical modal decomposition for the anti-jamming effect, which verified the effectiveness and high efficiency of the method. This study provides a new solution for the imaging problem of airborne synthetic aperture radar when subjected to blanket jamming, and a helpful reference is provided for the selection of anti-jamming processing methods. [ABSTRACT FROM AUTHOR]

In recent years, the interest in radar automatic target recognition (RATR) based on the carrier-free ultra-wideband (UWB) radar has been increasing. Compared with narrow-band and other bandwidth radars, the echo signal of the carrier-free UWB radar includes more comprehensive and detailed information with respect to the targeted object. In this paper, we first utilized 3ds Max to acquire accurate geometric models and applied a time-domain integral equation (TDIE) for echo signal acquisition under the condition that the transmitted signals had an extremely short duration period. By comparing the simulated waveform with the actual one, the accuracy of the electromagnetic modeling is verified. Furthermore, given that the actual environment is full of noise and clutter, we propose an improved two-dimensional variational mode decomposition (2D-IVMD), and an algorithm is proposed to eliminate noise and extract edge features preliminarily, which lays a foundation for further in-depth feature extraction. Then, the deep conventional neural network (DCNN) is introduced for the final recognition. The results show that the proposed methods achieve promising classification performance under the condition of low signal-to-noise ratio (SNR) values. [ABSTRACT FROM AUTHOR]

Automatically recognizing the modulation of radar signals is a necessary survival technique in electronic intelligence systems. In order to avoid the complex process of the feature extracting and realize the intelligent modulation recognition of various radar signals under low signal-to-noise ratios (SNRs), this paper proposes a method based on intrapulse signatures of radar signals using adaptive singular value reconstruction (ASVR) and deep residual learning. Firstly, the time-frequency spectrums of radar signals under low SNRs are improved after ASVR denoising processing. Secondly, a series of image processing techniques, including binarizing and morphologic filtering, are applied to suppress the background noise in the time-frequency distribution images (TFDIs). Thirdly, the training process of the residual network is achieved using TFDIs, and classification under various conditions is realized using the new-trained network. Simulation results show that, for eight kinds of modulation signals, the proposed approach still achieves an overall probability of successful recognition of 94.1% when the SNR is only −8 dB. Outstanding performance proves the superiority and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Water vapor is a crucial component of the atmosphere. Its absorption significantly influences remote sensing by impacting radiation signals transmitted through the atmosphere. Determining columnar water vapor (CWV) from hyperspectral remote sensing data is essential during the imagery atmospheric correction process. Over the past 40 years, numerous CWV inversion algorithms have been developed, with refinements to enhance retrieval accuracy and reliability. In this study, we proposed an enhanced atmospheric pre-corrected differential absorption (APDA) algorithm. This enhancement was achieved by thoroughly analyzing water vapor absorption in relation to elevation and aerosol optical depth and extending look up tables (LUTs). The enhanced method utilizes a pre-built MODTRAN lookup table and is applied to ZY1-02D hyperspectral data from a satellite launched in 2020. We compared the inversion results of 10 ZY1-02D scenes obtained using the improved method with AERONET measurements and inversion results from commonly used atmospheric correction software, namely, FLAASH and ATCOR. The updated algorithm demonstrated a lower average error (0.0568 g·cm−2) and relative average error (10.49%) compared to the ATCOR software (0.17 g·cm−2 and 40.78%, respectively) and the FLAASH module (0.13 g·cm−2 and 30.82%, respectively). Consequently, the enhanced method outperforms traditional CWV inversion algorithms, especially at high altitudes. [ABSTRACT FROM AUTHOR]

Land surface reflectance (LSR) data form the basis of quantitatively remotely sensed applications. For accurate LSR retrieval, atmospheric correction has been investigated by many researchers and implemented in typical processing systems, including common atmospheric correction software for various types of datasets and automatic operating systems for application to certain individual data sources. In recent years, China has launched multiple medium–high-resolution satellites but has not provided standard LSR products partly because of the lack of an appropriate operational system. In this paper, a multi-source remote sensing LSR product system for medium- and high-resolution data is proposed, called the "Operational Atmospheric Correction Framework for multi-source Medium-high-resolution Remote Sensing data of China" (ACFrC). The AC algorithm, processing flow, and design of the multi-source LSR system were described in detail. A practical atmospheric correction algorithm was proposed specially for data in only the visible and near-infrared (VNIR) bands. The entire processing chain was divided into modules for multi-source data ingestion, apparent reflectance calculation, cloud and water identification, atmospheric correction, and standard LSR product generation. To date, most types of multi-source data have been tested using the ACFrC system, with reasonable results being obtained. From the preliminary results, the 313 scenes of LSR products from the GaoFen-2 (GF-2) satellite over China for the period from 2015 to 2018 were cross-compared with Landsat-8 LSR acquired on the same day, showing an overall uncertainty less than 0.112 × LSR + 0.0112. Further, the ACFrC data processing efficiency was found to be suitable for automatic operation. System improvement is ongoing and future refinements will include online cloud parallel computing functionality and services, more robust algorithms, and other radiometric processing functions. [ABSTRACT FROM AUTHOR]

Impurity removal and modification of rare earth concentrate powder were conducted by roasting weak acid-weak alkali leaching to obtain the active components of denitrification catalysts. NH3 selective catalytic reduction catalyst samples were prepared by mixing and kneading with pseudo-γ-Al2O3 boehmite as carrier. The results showed that the Ce7O12 content in the active component samples increased and dispersed more evenly. The grain size of the samples was refined, the specific surface area increased, and the active sites exposed more. Ce coexists in the form of Ce3+ and Ce4+. Fe coexists in the form of Fe3+ and Fe2+, but Fe3+ is abundant. Some Ce, La, Nd, Pr, Fe, Mn, and other components formed solid melts during preparation, which increased the synergistic catalytic effect. The denitrification efficiency of the catalyst sample was 92.8% under the conditions of reaction temperature 400 °C, NO content was 600 ppm, NH3/NO ratio was 1.5, and O2 concentration was 4%. [ABSTRACT FROM AUTHOR]