To address the growing security issues faced by ARM-based mobile devices today, TrustZone was adopted to provide a trusted execution environment (TEE) to protect sensitive data. Such TrustZone-based models have been proven to be effective, but they target CPU architectures and do not work for the security of widely used heterogeneous computing platforms such as FPGAs. To solve this issue, we propose a comprehensive SoC-FPGA security framework, FPGA-TrustZone, to support FPGA TEE by extending the security of ARM TrustZone. Experiments on real SoC-FPGA hardware development boards show that FPGA-TrustZone provides high security with low performance overhead.

A deep understanding of the Channel Impulse Response (CIR) is crucial for the design and optimization of modern communication systems. CIR not only provides a comprehensive description of channel characteristics but also plays a key role in various aspects, including channel estimation, system design, and Multiple-Input Multiple-Output (MIMO) system development. Traditional channel modeling and measurement methods for obtaining regional CIR typically require point-by-point scanning of the region, which significantly increases time and equipment overhead. In this paper, we propose a three-dimensional (3-D) Ray Inversion (RI) method based on the Image Method (IM) in Ray Tracing (RT) method. The RI method does not require knowledge of the geometric information of the surrounding environment and can reconstruct regional CIR based on a limited number of observed CIR. The RI method discretizes the spatial domain into tetrahedral elements, where the CIR at the nodal points is the observed data. The multipath components of the observed CIR are classified first, and then the location of the secondary wave sources are determined from the ray propagation paths, thereby reconstructing the regional CIR within the tetrahedrons. To optimize computational efficiency while maintaining solution accuracy, an adaptive refinement algorithm based on the longest-edge bisection criterion is implemented to ensure the generated subtetrahedra have small and uniform edge lengths. Experimental results show that the RI method maintains high accuracy in both indoor and outdoor scenarios, and the computational efficiency is improved by more than 20 times compared to the IM method, and the efficiency gains are greater as the sampling distance decreases.

In modern wireless communication systems, a profound grasp of the channel impulse response (CIR) is pivotal for optimizing the design and functionality of algorithms and systems, especially for multiple-input-multiple-output (MIMO) technology. Conventional methods for gauging and modeling the channels rely on evaluating spatial points sampled discretely, resulting in limitations in acquiring pertinent channel information across a broad region. To overcome these limitations, this study embeds the physical principles of electromagnetic wave propagation into data-driven deep learning models, achieving second-level regional CIR computing efficiency that is hundreds of times faster. The proposed physics-informed deep ray tracing network (PIDRTN) integrates multiple U-shaped network (U-Net) encoder-decoder blocks, capturing radio wave propagation characteristics within a specific region surrounded by buildings, including two equivalent signal propagation directions in a two-dimensional space and a signal intensity correction term. Then, the network employs a parameter-free nonlinear signal transmission module to emulate the physical principles of signal propagation and obtain accurate CIRs from limited anchor locations, which will iteratively generate CIRs for various times within a specified region subjected to enhancement and denoising operations. Furthermore, the PIDRTN-A model, which utilizes anchor data to improve model accuracy, is proposed. A dataset encompassing diverse fading scenarios is constructed using the ray tracing (RT) method. Extensive experiments demonstrate that the proposed models effectively capture directional and reflective properties of signals; using the RT model as a benchmark, normalized root mean squared errors (NRMSEs) of 0.1226 and 0.0969 are obtained for the PIDRTN and PIDRTN-A models, respectively.

With the application and development of the fifth-generation (5G) communications, it is essential to gain insight understanding of the multi-antenna wireless channel characterizations. In particular, their relevant Channel Impulse Response (CIR) so to ensure the effective design of algorithms and systems. However, both the traditional channel measurement and modelling are essentially based on assessment at discretely sampled spatial points without the capability to obtain the relevant channel information over a given surrounding area. To overcome this limitation, this paper proposes to re-assemble the discretely sampled CIRs into equalized video streams. With this basis, a deep learning based video super-resolution method, namely, the Time Decomposition Video Super-Resolution (TDVSR), has been proposed to restore the area channel information for the first time. Moreover, a time decomposition module based on Bidirectional Long Short-Term Memory (BiLSTM) has been designed to decompose the re-assembled CIRs into video form in the time dimension. A retrained video super-resolution model will then process the composited data and output high-resolution frames, which will be reversed to the CIRs at the dense density target area. A data set with various typical fading scenarios has been constructed by Ray Tracing (RT) method. Extensive experiments demonstrate that the proposed TDVSR model successfully learned the nonlinear propagation laws through the data-driven method, which shows satisfied restoration accuracy with significantly increased computation efficiency.

Three-dimensional fluorescence spectroscopy is a fast, nondestructive analysis method with good selectivity and high precision, which provides a foundation for the development of the current smart agriculture system. In modern agriculture, where agricultural information is fully perceived, it is still very difficult to quickly and destructively detect the internal chemical composition of soil, crops and agricultural products. Accurate determination of oil pollutants in water by using three-dimensional fluorescence spectroscopy technology can provide a basis for crop irrigation and is of great significance for improving agricultural benefits. The fluorescence spectrum analysis method is adopted to distinguish three kinds of mineral oil-gasoline, kerosene and diesel. In order to make the distinguishment more intuitive and convenient, a new identification method for mineral oil is proposed. The three-dimensional fluorescence spectra of the experimental dimension are reduced into two-dimensional fluorescence spectra. The concrete operations are as follows: adopting the method of end-to-end data matrix to constitute a large Ex image, and then figuring out the envelope curve, processing and analyzing the envelope image. Four factors, such as the ranges of excitation wavelength when the relative fluorescence intensity is greater than 0.5, the optimal excitation wavelengths, their kurtosis coefficients and skewness coefficients, are to be selected as the distinguishing feature parameters of mineral oil, and thus different kinds of mineral oil can be distinguished directly according to the feature parameters. The experimental results show that the proposed method has a high resolution for different kinds of mineral oil. Accurate and fast spectral data analysis methods can make up for the deficiencies of other agricultural information perception methods, provide a basis for the application of smart agriculture in many aspects and have a positive significance for promoting the comprehensive intelligent development of agriculture.

Ship detection from remote sensing images plays an important role in military and civilian fields. However, since the small size of ship targets and the interference of cloud cover, this task still suffers from great missed detection and false-alarm. To tackle these problems, a Saliency Adjusted YOLO (SA-YOLO) for optical satellite image ship detection is developed. First, due to the fact that the ship in low resolution imagery can be regarded as a salient object, we designed a saliency guided dense sampling layer (SDSL) to improve the spatial sampling of small ship targets. Secondly, the saliency region-aware convolution (SAConv) strategy is designed to improve the representation capability of salient regions and increase the attention of network to these regions. We validated the proposed method using more than 2000 remote sensing images from GF-1 satellite. The experimental results demonstrated that the proposed method obtained a better detection performance than the state-of-the-art methods.