Due to the low altitude, slow speed and small radar reflection area of LSST, radar has low observability for low and slow targets, that is difficult to meet the requirements. ASP can be an important way to fill the blindness of low altitude targets, which can give the information of target azimuth, height angle and flight status to improve the detection efficiency. In this paper, the problem of radar blind area determination and optimal deployment of ASP is studied for the detection of LSST in complex electromagnetic environment. Using data fusion, improved Xdraw algorithm, and k-means clustering algorithm, the optimal deployment scheme of ASP for radar blind area is constructed, and the effectiveness of the method and results are verified by simulation analysis.

Research of Optimized Deployment of LSST Detection Based on Improved Xdraw Algorithm

The detection power analysis of radar network is an essential but time-consuming problem in the domain of situation awareness. In this paper, we propose a novel GPU based method more efficient than existing solutions. The proposed approach is based on the field model, and the calculation of detection power is regarded as the construction and information handling process of the field model. Furthermore, it's a computation and visualization combined method. It directly produces the geotagged image, which is needed in the final display terminal. Finally, it fully utilizes the GPU hardware through various techniques, such as compressing solution space, optimizing memory usage, and instruction usage optimization. The result of the comparative experiment has shown that this novel GPU based algorithm is scalable to various conditions such as different cell resolution, detection possibility, and height. The optimized GPU version can reap the processing efficiency to about 93 times than the serial version.

A novel approach for radar network’s detection power analysis based on GPU

In recent years, the three-dimensional (3D) radar detection range has played an essential role in the layout of devices such as aircraft and drones. To compensate for the shortcomings of three-dimensional calculations for radar terrain masking, a new calculation method is proposed for assessing the terrain occlusion of radar detection range. First, the high-dimensional electromagnetic data after discretization are modeled based on the tensor data structure, and the tensor grid dilation operator is constructed. Then, the dilation process begins from the overlapping section of the radar detection range and terrain, and it is adjusted by the terrain occlusion judgment factor and the dilation judgment factor to obtain the obstructed part due to the terrain. Finally, the actual radar detection range under terrain occlusion is obtained. The simulation results show that the method proposed in this paper can adapt to different grid sizes and terrain shapes, significantly enhancing computational efficiency while maintaining internal features.

Calculation Model of Radar Terrain Masking Based on Tensor Grid Dilation Operator

Calculation of radar network detection power under terrain masking

Accurate determination of the moisture content in maize kernels conduces to screen maize germplasm materials with efficient dehydration. Low-field nuclear magnetic resonance (LF-NMR) single-kernel non-destructive testing technology was used to determine the moisture content at different phase states in the kernels for selected types of maize. The T2 relaxation inversion spectrum was monitored in maize kernels to determine the variation in the moisture content in different phase states with time. The total water and free water peaked at the filling stage of the maize kernels and then declined to a minimum at physiological maturity. The semi-bound water generally increased to a long-lasting peak in the dough stage and then declined. The bound water increased from kernel formation to maturity and then remained stable. The contents of total water, free water, semi-bound water, and bound water had significant differences among kernel types but not among varieties of the same type. The contents of semi-bound water and free water were linearly correlated with the dehydration rates of the kernels. The results of this study can provide a means for creating new germplasm materials. Keywords: Maize (Zea mays L.), LF-NMR, moisture phase state, kernel type, dehydration rate DOI: 10.25165/j.ijabe.20231601.7898 Citation: Li N, Xu T Y, Hao N. Phase states of moisture content in different maize kernel types. Int J Agric & Biol Eng, 2023; 16(1): 250–259. 

Phase states of moisture content in different maize kernel types

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Nan Hao

Author Tools

Chat about Author

Papers

Calculation of radar network detection power under terrain masking

Phase states of moisture content in different maize kernel types