UAV swarm based radar signal sorting via multi-source data fusion: A deep transfer learning framework

With the overlapping of signal parameters, signal sorting faces great challenges. K-means clustering algorithm and self-organizing Feature Mapping (SOFM) neural network algorithm are widely used in radar signal sorting. However, the cluster number of k-means algorithm needs to be determined in advance, and the initial cluster center also needs to be randomly selected, so it is easy to fall into local optimal. The accuracy of SOFM neural network sorting results is greatly affected by the preset structure. Aiming at the above two problems, this paper introduces the density dynamic clustering into the traditional k-means clustering algorithm and combines it with SOFM neural network to put forward an unsupervised structural adaptive radar signal sorting algorithm. The simulation results show that the algorithm can effectively solve the problem of signal sorting in the case of parameter space overlap and the computation is small.

Unsupervised k-means combined with SOFM structure adaptive radar signal sorting algorithm

In this paper, a robust and adaptive radar point cloud clustering algorithm, named radar elliptical density-based spatial clustering of applications with noise (REDBSCAN), is presented. The proposed algorithm shows better clustering results for adapting to the arbitrary shape of targets as well as any number of targets comparing with traditional clustering methods. The algorithm is presented and is implemented in experiments using the state-of-art mmWave radar sensor with multiple-input multiple-output (MIMO) antennas. The related signal processing chain and the clustering outcomes are also discussed.

Robust and Adaptive Radar Elliptical Density-Based Spatial Clustering and labeling for mmWave Radar Point Cloud Data

As an important part of electronic intelligence (ELINT) and electronic support measurement (ESM) systems, radar signal sorting directly affects the performance of electronic reconnaissance equipment and is a key technology for decision making. This paper discusses several clustering methods which could be used for radar signal sorting. The discussion includes artificial neural networks, classical clustering algorithm and its improvement, and support vector clustering.

The Latest Research on Clustering Algorithms Used for Radar Signal Sorting

The acceptance of new crop varieties by consumers is contingent on the presence of consumer-preferred traits, which include sensory attributes, nutritional value, industrial products and bioactive compounds production. Recent developments in genome editing technologies provide novel insight to identify gene functions and improve the various qualitative and quantitative traits of commercial importance in plants. Various conventional as well as advanced gene-mutagenesis techniques such as physical and chemical mutagenesis, CRISPR-Cas9, Cas12 and base editors are used for the trait improvement in crops. To meet consumer demand, breakthrough biotechnologies, especially CRISPR-Cas have received a fair share of scientific and industrial interest, particularly in plant genome editing. CRISPR-Cas is a versatile tool that can be used to knock out, replace and knock-in the desired gene fragments at targeted locations in the genome, resulting in heritable mutations of interest. This review highlights the existing literature and recent developments in CRISPR-Cas technologies (base editing, prime editing, multiplex gene editing, epigenome editing, gene delivery methods) for reliable and precise gene editing in plants. This review also discusses the potential of gene editing exhibited in crops for the improvement of consumer-demanded traits such as higher nutritional value, colour, texture, aroma/flavour, and production of industrial products such as biofuel, fibre, rubber and pharmaceuticals. In addition, the bottlenecks and challenges associated with gene editing system, such as off targeting, ploidy level and the ability to edit organelle genome have also been discussed.

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CRISPR-Cas: A robust technology for enhancing consumer-preferred commercial traits in crops

Radar signal sorting is one of the essential technologies in radar countermeasures reconnaissance system. Non-cooperative radar signal sorting without prior information has been a great challenge for radar countermeasures. This paper presents a k-means clustering radar signal sorting algorithm based on data field by introducing data field theory. Firstly potential value of all the data samples is calculated with the algorithm based on data field theory, and noise is eliminated by comparison of the calculated values, to find local maximum potential value. Then the sample data of the closest value to maximum is selected as the initial cluster center and the number of local maximum potential value as cluster number. Finally, the conventional k-means clustering algorithm is applied for clustering. The proposed method can eliminate noise points and automatically obtain initial cluster center and cluster number, which is suitable for non-cooperative radar without prior information. Simulation results verify the feasibility and effectiveness of the method.

Radar signal sorting algorithm of k-means clustering based on data field

A single-base insertion in BoDFR1 results in loss of anthocyanins in green-leaved ornamental kale

Leaf color is a crucial agronomic trait in ornamental kale. However, the molecular mechanism regulating leaf pigmentation patterns in green and white ornamental kale is not completely understood. To address this, we performed transcriptome and pigment content analyses of green and white kale leaf tissues. A total of 5,404 and 3,605 different expressed genes (DEGs) were identified in the green vs. white leaf and the green margin vs. white center samples. Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis showed that 24 and 15 common DEGs in two pairwise comparisons were involved in chlorophyll metabolism and carotenoid biosynthesis, respectively. Seventeen genes related to chlorophyll biosynthesis were significantly upregulated in green leaf tissue, especially chlH and por. Of the 15 carotenoid biosynthesis genes, all except CYP707A and BG1 were lower expressed in white leaf tissue. Green leaf tissue exhibited higher levels of chlorophyll and carotenoids than white leaf tissue. In addition, the DEGs involved in photosystem and chlorophyll-binding proteins had higher expression in green leaf tissue. The PSBQ, LHCB1.3, LHCB2.4, and HSP70 may be key genes of photosynthesis and chloroplast formation. These results demonstrated that green and white coloration in ornamental kale leaves was caused by the combined effects of chlorophyll and carotenoid biosynthesis, chloroplast development, as well as photosynthesis. These findings enhance our understanding of the molecular mechanisms underlying leaf color development in ornamental kale.

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Yang Liu

Papers

Radar signal sorting algorithm of k-means clustering based on data field

A single-base insertion in BoDFR1 results in loss of anthocyanins in green-leaved ornamental kale

Transcriptome Analysis of Green and White Leaf Ornamental Kale Reveals Coloration-Related Genes and Pathways

A novel methodology based on the reflected L(0,1) guided wave for quantitative detection of corrosion-induced wall thickness loss in continuous pipes