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With the continuous progress of information acquisition technology, the volume of LiDAR point cloud data is also expanding rapidly, which greatly hinders the subsequent point cloud processing and engineering applications. In this study, we propose a point cloud simplification strategy utilizing probabilistic membership to address this challenge. The methodology initially develops a feature extraction scheme based on curvature to identify the set of feature points. Subsequently, a combination of k-means clustering and Possibilistic C-Means is employed to partition the point cloud into subsets, and to simultaneously acquire the probabilistic membership information of the point cloud. This information is then utilized to establish a rational and efficient simplification scheme. Finally, the simplification results of the feature point set and the remaining point set are merged to obtain the ultimate simplification outcome. This simplification method not only effectively preserves the features of the point cloud while maintaining uniformity in the simplified results but also offers flexibility in balancing feature retention and the degree of simplification. Through comprehensive comparative analysis across multiple point cloud models and benchmarking against various simplification methods, the proposed approach demonstrates superior performance. Finally, the proposed algorithm was critically discussed in light of the experimental results.

Clustering is a fundamental cornerstone in unsupervised learning, playing a pivotal role in various data mining techniques. The precise and efficient classification of data stands as a central focus for numerous researchers and practitioners alike. In this study, we design an effective soft partition classification method which refines and extends the prototype of the well-known Fuzzy C-Means clustering algorithm. Specifically, the developed scheme employs membership function to extend the prototypes into a series of granular prototypes, thus achieving a deeper revelation of the structure of the data. This process softly divides the data into core and extended parts. The core part can be succinctly encapsulated through several information granules, whereas the extended part lacks discernible geometry and requires formal descriptors (such as membership formulas). Our objective is to develop information granules that shape the core structure within the dataset, delineate their characteristics, and explore the interaction among these granules that result in their deformation. The granular prototypes become the main component of the information granules and provide an optimization space for traditional prototypes. Subsequently, we apply quantum-behaved particle swarm optimization to identify the optimal partition matrix for the data. This optimized matrix significantly enhances the partition performance of the data. Experimental results provide substantial evidence of the effectiveness of the proposed approach.

The identification of co-regulated genes and their Transcription-Factor Binding Sites (TFBSs) are the key steps toward understanding transcription regulation. In addition to effective laboratory assays, various bi-clustering algorithms for the detection of the co-expressed genes have been developed. Bi-clustering methods are used to discover subgroups of genes with similar expression patterns under to-be-identified subsets of experimental conditions when applied to gene expression data. By building two fuzzy partition matrices of the gene expression data with the Axiomatic Fuzzy Set (AFS) theory, this paper proposes a novel fuzzy bi-clustering algorithm for the identification of co-regulated genes. Specifically, the gene expression data are transformed into two fuzzy partition matrices via the sub-preference relations theory of AFS at first. One of the matrices considers the genes as the universe and the conditions as the concept, and the other one considers the genes as the concept and the conditions as the universe. The identification of the co-regulated genes (bi-clusters) is carried out on the two partition matrices at the same time. Then, a novel fuzzy-based similarity criterion is defined based on the partition matrices, and a cyclic optimization algorithm is designed to discover the significant bi-clusters at the expression level. The above procedures guarantee that the generated bi-clusters have more significant expression values than those extracted by the traditional bi-clustering methods. Finally, the performance of the proposed method is evaluated with the performance of the three well-known bi-clustering algorithms on publicly available real microarray datasets. The experimental results are in agreement with the theoretical analysis and show that the proposed algorithm can effectively detect the co-regulated genes without any prior knowledge of the gene expression data.

As an information granulation technology, clustering plays a pivotal role in unsupervised learning, serving as a fundamental cornerstone for various data mining techniques. The effective and accurate classification of data is a central focus for numerous researchers. For a dataset, we assert that the classification performance of a clustering method is significantly influenced by uncertain data, particularly those situated at the cluster boundaries. It is evident that uncertain data encapsulate richer information compared with others. Generally, the greater the uncertainty, the more information the data holds. Therefore, conducting a comprehensive analysis of this particular subset of data carries substantial significance. This study presents an approach to characterize data distribution properties using fuzzy clustering and defines the boundary and non-boundary characteristics (certainty and uncertainty) of the data. To improve the classification performance, the strategy focuses on reducing the uncertainty associated with boundary data. The proposed scheme involves inserting data points with the cloud computing technology based on the distribution characteristics of the membership functions to diminish the uncertainty of uncertain data. Building upon this, the contribution of boundary data is reassigned to the prototype in order to diminish the proportion of uncertain data. Subsequently, the classifier is optimized through data label (classification error) supervision. Ultimately, the objective is to leverage clustering algorithms for classification, thereby enhancing overall classification accuracy. Experimental results substantiate the effectiveness of the proposed scheme.

Passive location based on TDOA (time difference of arrival) and FDOA (frequency difference of arrival) is the mainstream method for target localization. This paper proposes a fast time–frequency difference positioning method to address issues such as low accuracy, large computational resource utilization, and limited suitability for real-time signal processing in the conventional CAF (cross-ambiguity function)-based approach, aiming to complete the processing of the target radiation source to obtain the target parameters within a short timeframe. In the mixing product operation step of the CAF, a frequency-domain approach replaces the time-domain convolution operation in PW-ZFFT (pre-weighted Zoom-FFT) to reduce the computational load of the CAF. Additionally, a quadratic surface fitting method is used to enhance the accuracy of TDOA and FDOA. The localization solution is obtained using Newton’s method, which can provide more accurate results compared to analytical methods. Next, a signal processing platform is designed with FPGA (field-programmable gate array) and multi-core DSP (digital signal processor), and works by dividing and mapping the algorithm functional modules according to the hardware’s characteristics. We analyze the architectural advantages of multi-core DSP and design methods to improve program performance, such as EDMA transfer optimization, inline function optimization, and cache optimization. Finally, this paper constructs simulation tests in typical positioning scenarios and compares them to hardware measurement results, thus confirming the correctness and real-time capability of the program.

Abstract Sparse frequency agile linear frequency modulation‐orthogonal frequency division multiplexing (LFM‐OFDM) radar can improve anti‐interference ability as well as reduce the sampling rate of the radar system. However, the frequency agility makes it difficult to realize coherent processing by using the fast Fourier transform. To overcome this problem, a signal processing scheme for the sparse frequency agile LFM‐OFDM radar is proposed to obtain the high range and Doppler resolution in this study. The subcarriers are synthesised to an LFM signal in the time domain at first. Then, within the framework of the compressed sensing theory, an improved orthogonal matching pursuit algorithm is proposed to obtain the high‐resolution range‐velocity profile while reducing the computation complexity. Finally, several numerical simulations are provided to verify the effectiveness of the proposed method for moving targets.

Abstract In this letter, the authors elaborate on a novel method to detect the number of targets in low SNR and small snapshots through using the Fuzzy C‐Means clustering based granulation‐degranulation mechanism. In the developed scheme, the eigenvalues of the array output correlation matrix are regarded as a time series, and granulated into a pair of ordered information granules. The information about the number of targets is included in the information granules. Subsequently, the degranulation mechanism is used to modify the cost function of the granulation mechanism to improve the quality of the information granules, which can increase the detection accuracy of the number of targets. Finally, the number of targets is determined by the prototype matrix and partition matrix generated by the enhanced version of the granulation. Explicit analysis and derivation of the proposed scheme are presented.

Bi-clustering refers to the task of finding sub-matrices (indexed by a group of columns and a group of rows) within a matrix of data such that the elements of each sub-matrix (data and features) are related in a particular way, for instance, that they are similar with respect to some metric. In this paper, after analyzing the well-known Cheng and Church bi-clustering algorithm which has been proved to be an effective tool for mining co-expressed genes. However, Cheng and Church bi-clustering algorithm and summarizing its limitations (such as interference of random numbers in the greedy strategy; ignoring overlapping bi-clusters), we propose a novel enhancement of the adaptive bi-clustering algorithm, where a shielding complex sub-matrix is constructed to shield the bi-clusters that have been obtained and to discover the overlapping bi-clusters. In the shielding complex sub-matrix, the imaginary and the real parts are used to shield and extend the new bi-clusters, respectively, and to form a series of optimal bi-clusters. To assure that the obtained bi-clusters have no effect on the bi-clusters already produced, a unit impulse signal is introduced to adaptively detect and shield the constructed bi-clusters. Meanwhile, to effectively shield the null data (zero-size data), another unit impulse signal is set for adaptive detecting and shielding. In addition, we add a shielding factor to adjust the mean squared residue score of the rows (or columns), which contains the shielded data of the sub-matrix, to decide whether to retain them or not. We offer a thorough analysis of the developed scheme. The experimental results are in agreement with the theoretical analysis. The results obtained on a publicly available real microarray dataset show the enhancement of the bi-clusters performance thanks to the proposed method.

Fusarium head blight (FHB) of wheat is an important disease worldwide, affecting the yield, end-use quality and threatening food safety. Genetic resources or stable loci for FHB resistance are still limited in breeding programs. A panel of 265 bread wheat accessions from China, CIMMYT-Mexico and other countries was screened for FHB resistance under 5 field experiments in Mexico and China, and a genome-wide association analysis was performed to identify QTLs associated with FHB resistance. The major locus Fhb1 was significantly associated with FHB severity and Deoxynivalenol content in grains. FHB screening experiments in multiple environments showed that Fhb1-harbouring accessions Sumai3, Sumai5, Ningmai9, Yangmai18 and Tokai66 had low FHB index, disease severity and DON content in grains in response to different Fusarium species and ecological conditions in Mexico and China. Accessions Klein Don Enrique, Chuko and Yumai34 did not have Fhb1 but still showed good FHB resistance and low mycotoxin accumulation. Sixteen loci associated with FHB resistance or DON content in grains were identified on chromosomes 1A, 1B, 2B, 3A, 3D, 4B, 4D, 5A, 5B, 7A, and 7B in multiple environments, explaining phenotypic variation of 4.43–10.49%. The sources with good FHB resistance reported here could be used in breeding programs for resistance improvement in Mexico and China, and the significant loci could be further studied and introgressed for resistance improvement against FHB and mycotoxin accumulation in grains.

Abstract Background An appropriate diet is an important determinant of kidney health. However, the association between vegetarian diets and renal function is unclear. We aimed to study the association between vegetarian diets and renal function in healthy adults. Methods A total of 269 vegetarians and 269 sex- and age-matched nonvegetarian omnivores were enrolled in this cross-sectional study. Basic characteristics and daily dietary intakes were assessed by face-to-face interviews. Blood samples were collected, and renal function was assessed by measuring blood urea nitrogen (BUN), serum creatinine (SCr), uric acid (UA) and the estimated glomerular filtration rate (eGFR). Blood pressure, fasting blood glucose and blood lipid profiles were also assessed. Results The average age of the vegetarians was 35.4 ± 8.6 years, 82.2% of whom were female. We evaluated the association between vegetarian diets and renal function using multivariate analysis. Compared with omnivores, vegetarians had lower BUN [β = − 0.63, 95% confidence interval (CI): (− 0.88, − 0.38)], SCr [β = − 2.04, 95% CI:(− 4.10, 0.02)], and UA levels [β = − 15.15, 95% CI: (− 27.81, − 2.50)] and higher eGFRs [β = 4.04, 95% CI: (0.30, 7.78)] after adjusting for sex, age, body mass index (BMI), physical activity, alcohol consumption, smoking status, low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), systolic pressure and fasting blood glucose. Further analysis of food composition and renal function showed that dietary fiber intake was significantly negatively associated with BUN [β = − 0.02, 95% CI: (− 0.03, 0.00)], SCr [β = − 0.14, 95% CI: (− 0.25, 0.04)], and UA levels [β = − 0.72, 95% CI: (− 1.36, 0.07)] and positively associated with the eGFR [β = 0.20, 95% CI: (0.00, 0.40)]. Conclusions Healthy adult vegetarians have better renal function than omnivores, and the higher dietary fiber intake associated with vegetarian diets may contribute to the protective effect on renal function.

Rotenone is a widely used botanical insecticide, which can inhibit the mitochondrial respiratory chain of various insect cells, while the mechanism of its toxicity to insect cells needs further investigation. The purpose of this study was to understand the toxicity level of low (0.2 µg/mL) and high (20 µg/mL) concentrations of rotenone in Spodoptera litura cells (SL-1) using trypan blue and Coomassie brilliant blue staining assays. Our study found that rotenone possessed cytotoxicity against SL-1 cells with varying effects of action between high and low concentrations. After low-concentration rotenone treatment, the SL-1 cells showed obvious aggregation time-dependently, with the fastest aggregation rate observed under the first 8 h of treatment time, but no such phenomenon was observed at high concentration. Furthermore, this aggregation phenomenon caused SL-1 cells to squeeze each other and led to the destruction of the cell membrane structure and function. Taken together, the results suggested that treatment with a low concentration of rotenone exhibited a chronic toxic effect that was significantly different from treatment with a high concentration of rotenone, which provides new insight into the cytotoxic mechanism of rotenone.

The health benefits of whole wheat grains are partially attributed to their phenolic acid composition, especially that of trans-ferulic acid (TFA), which is a powerful natural antioxidant. Breeders and producers are becoming interested in wheat with enhanced health-promoting effects. This study investigated the effects of different nitrogen (N) application rates (0, 42, 84, 126, and 168 N kg ha−1) on the phenolic acid composition of three wheat varieties in four locations for two years. The results indicate that the different N rates did not affect the TFA concentration but that they significantly affected the concentrations of para-coumaric acid, sinapic acid, and cis-ferulic acid in the wheat grains. A statistical analysis suggested that the wheat phenolic acid composition was predominantly determined by wheat variety, though there existed some interaction effect between the wheat variety and environments. The TFA concentration of the variety Jimai 22 was generally higher (with a mean value of 726.04 µg/g) but was easily affected by the environment, while the TFA concentration of the variety Zhongmai 578 (with a mean value of 618.01 µg/g) was more stable across the different environments. The results also suggest that it is possible to develop new wheat varieties with high yield potential, good end-use properties, and enhanced nutraceutical values.

Wheat blast (WB) caused by Magnaporthe oryzae pathotype Triticum (MoT) is an important fungal disease in tropical and subtropical wheat production regions. The disease was initially identified in Brazil in 1985, and it subsequently spread to some major wheat-producing areas of the country as well as several South American countries such as Bolivia, Paraguay, and Argentina. In recent years, WB has been introduced to Bangladesh and Zambia via international wheat trade, threatening wheat production in South Asia and Southern Africa with the possible further spreading in these two continents. Resistance source is mostly limited to 2NS carriers, which are being eroded by newly emerged MoT isolates, demonstrating an urgent need for identification and utilization of non-2NS resistance sources. Fungicides are also being heavily relied on to manage WB that resulted in increasing fungal resistance, which should be addressed by utilization of new fungicides or rotating different fungicides. Additionally, quarantine measures, cultural practices, non-fungicidal chemical treatment, disease forecasting, biocontrol etc., are also effective components of integrated WB management, which could be used in combination with varietal resistance and fungicides to obtain reasonable management of this disease.

Unmanned aerial vehicle (UAV) based remote sensing is a promising approach for non-destructive and high-throughput assessment of crop water and nitrogen (N) efficiencies. In this study, UAV was used to evaluate two field trials using four water (T0 = 0 mm, T1 = 80 mm, T2 = 120 mm, and T3 = 160 mm), and four N (T0 = 0, T1 = 120 kg ha–1, T2 = 180 kg ha–1, and T3 = 240 kg ha–1) treatments, respectively, conducted on three wheat genotypes at two locations. Ground-based destructive data of water and N indictors such as biomass and N contents were also measured to validate the aerial surveillance results. Multispectral traits including red normalized difference vegetation index (RNDVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), red-edge chlorophyll index (RECI) and normalized green red difference index (NGRDI) were recorded using UAV as reliable replacement of destructive measurements by showing high r values up to 0.90. NGRDI was identified as the most efficient non-destructive indicator through strong prediction values ranged from R2 = 0.69 to 0.89 for water use efficiencies (WUE) calculated from biomass (WUE.BM), and R2 = 0.80 to 0.86 from grain yield (WUE.GY). RNDVI was better in predicting the phenotypic variations for N use efficiency calculated from nitrogen contents of plant samples (NUE.NC) with high R2 values ranging from 0.72 to 0.94, while NDRE was consistent in predicting both NUE.NC and NUE.GY by 0.73 to 0.84 with low root mean square errors. UAV-based remote sensing demonstrates that treatment T2 in both water 120 mm and N 180 kg ha–1 supply trials was most appropriate dosages for optimum uptake of water and N with high GY. Among three cultivars, Zhongmai 895 was highly efficient in WUE and NUE across the water and N treatments. Conclusively, UAV can be used to predict time-series WUE and NUE across the season for selection of elite genotypes, and to monitor crop efficiency under varying N and water dosages.

This paper proposes an imaging algorithm for synthetic aperture radar (SAR) mounted on a high-speed maneuvering platform with squint terrain observation by progressive scan mode. To overcome the mismatch between range model and the signal after range walk correction, the range history is calculated in local polar format. The Doppler ambiguity is resolved by nonlinear derotation and zero-padding. The recovered signal is divided into several blocks in Doppler according to the angular division. Keystone transform is used to remove the space-variant range cell migration (RCM) components. Thus, the residual RCM terms can be compensated by a unified phase function. Frequency domain perturbation terms are introduced to correct the space-variant Doppler chirp rate term. The focusing parameters are calculated according to the scene center of each angular block and the signal of each block can be processed in parallel. The image of each block is focused in range-Doppler domain. After the geometric correction, the final focused image can be obtained by directly combined the images of all angular blocks. Simulated SAR data has verified the effectiveness of the proposed algorithm.

Spatial spectrum estimation, also known as direction of arrival (DOA) detection, is a popular issue in many fields, including remote sensing, radar, communication, sonar, seismic exploration, radio astronomy, and biomedical engineering. MUltiple SIgnal Classification (MUSIC) and Estimation Signal Parameter via Rotational Invariance Technique (ESPRIT), which are well-known for their high-resolution capability for detecting DOA, are two examples of an eigen-subspace algorithm. However, missed detection and estimation accuracy reduction often occur due to the low signal-to-noise ratio (SNR) and snapshot deficiency (small time-domain samples of the observed signal), especially for sources with different SNRs. To avoid the above problems, in this study, we develop a DOA detection approach through signal subspace reconstruction using Quantum-Behaved Particle Swarm Optimization (QPSO). In the developed scheme, according to received data, a noise subspace is established through performing an eigen-decomposition operation on a sampling covariance matrix. Then, a collection of angles randomly selected from the observation space are used to build a potential signal subspace on the basis of the steering matrix of the array. Afterwards, making use of the fact that the signal space is orthogonal to the noise subspace, a cost function, which contains the desired DOA information, is designed. Thus, the problem of capturing the DOA information can be transformed into the optimization of the already constructed cost function. In this respect, the DOA finding of multiple signal sources—that is, the multi-objective optimization problem—can be regarded as a single objective optimization problem, which can effectively reduce the probability of missed detection of the signals. Subsequently, the QPSO is employed to determine an optimal signal subspace by minimizing the orthogonality error so as to obtain the DOA. Ultimately, the performance of DOA detection is improved. An explicit analysis and derivation of the developed scheme are provided. The results of computer simulation show that the proposed scheme has superior estimation performance when detecting signals with very different SNR levels and small snapshots.

Abstract Switchgrass (Panicum virgatum L.; family Poaceae) is a warm-season C4 perennial grass. Tillering plays an important role in determining the morphology of aboveground parts and the final biomass yield of switchgrass. Auxin distribution in plants can affect a variety of important growth and developmental processes, including the regulation of shoot and root branching, plant resistance and biological yield. Auxin transport and gradients in plants are mediated by influx and efflux carriers. PvPIN1, a switchgrass PIN1-like gene that is involved in regulating polar transport, is a putative auxin efflux carrier. Neighbor-joining analysis using sequences deposited in NCBI databases showed that the PvPIN1gene belongs to the PIN1 family and is evolutionarily closer to the Oryza sativa japonica group. Tiller emergence and development was significantly promoted in plants subjected toPvPIN1 RNA interference (RNAi), which yielded a phenotype similar to that of wild-type plants treated with the auxin transport inhibitor TIBA (2,3,5-triiodobenzoic acid). A transgenic approach that inducedPvPIN1 gene overexpression or suppression altered tiller number and the shoot/root ratio. These data suggest that PvPIN1plays an important role in auxin-dependent adventitious root emergence and tillering.