Integrated Sensing and Communications with Joint Beam Squint and Beam Split for Massive MIMO

Integrated sensing and communications (ISAC) has attracted tremendous attention for the future 6G wireless communication systems. To improve the transmission rates and sensing accuracy, massive multi-input multi-output (MIMO) technique is leveraged with large transmission bandwidth. However, the growing size of transmission bandwidth and antenna array results in the beam squint effect, which hampers the communications. Moreover, the time overhead of the traditional sensing algorithm is prohibitive for practical systems. In this paper, instead of alleviating the wideband beam squint effect, we take advantage of joint beam squint and beam split effect and propose a novel user directions sensing method integrated with massive MIMO orthogonal frequency division multiplexing (OFDM) systems. Specifically, with the beam squint effect, the BS utilizes the true-time-delay (TTD) lines to steer the beams of different OFDM subcarriers towards different directions simultaneously. The users feedback the subcarrier frequency with the maximum array gain to the BS. Then, the BS calculates the direction based on the subcarrier frequency feedback. Futhermore, the beam split effect introduced by enlarging the inter-antenna spacing is exploited to expand the sensing range. The proposed sensing method operates over frequency-domain, and the intended sensing range is covered by all the subcarriers simultaneously, which reduces the time overhead of the conventional sensing significantly. Simulation results have demonstrated the effectiveness as well as the superior performance of the proposed ISAC scheme.Comment: 13 pages, 11 figures, submitted to IEEE journa

Effect of symbiotic fungi-Armillaria gallica on the yield of Gastrodia elata Bl. and insight into the response of soil microbial community

Armillaria members play important roles in the nutrient supply and growth modulation of Gastrodia elata Bl., and they will undergo severe competition with native soil organisms before colonization and become symbiotic with G. elata. Unraveling the response of soil microbial organisms to symbiotic fungi will open up new avenues to illustrate the biological mechanisms driving G. elata’s benefit from Armillaria. For this purpose, Armillaria strains from four main G. elata production areas in China were collected, identified, and co-planted with G. elata in Guizhou Province. The result of the phylogenetic tree indicated that the four Armillaria strains shared the shortest clade with Armillaria gallica. The yields of G. elata were compared to uncover the potential role of these A. gallica strains. Soil microbial DNA was extracted and sequenced using Illumina sequencing of 16S and ITS rRNA gene amplicons to decipher the changes of soil bacterial and fungal communities arising from A. gallica strains. The yield of G. elata symbiosis with the YN strain (A. gallica collected from Yunnan) was four times higher than that of the GZ strain (A. gallica collected from Guizhou) and nearly two times higher than that of the AH and SX strains (A. gallica collected from Shanxi and Anhui). We found that the GZ strain induced changes in the bacterial community, while the YN strain mainly caused changes in the fungal community. Similar patterns were identified in non-metric multidimensional scaling analysis, in which the GZ strain greatly separated from others in bacterial structure, while the YN strain caused significant separation from other strains in fungal structure. This current study revealed the assembly and response of the soil microbial community to A. gallica strains and suggested that exotic strains of A. gallica might be helpful in improving the yield of G. elata by inducing changes in the soil fungal community

Reticulation is a Risk Factor of Progressive Subpleural non-Fibrotic Interstitial Lung Abnormalities

Rationale: Interstitial lung abnormalities (ILAs) are being increasingly identified in clinical practice. In particular for subpleural non-fibrotic ILAs, the risk of progression over time and the risk factors for progressive behavior are still largely unknown. Objectives: To determine the age band prevalence of ILAs and the risk of radiological progression of subpleural non-fibrotic ILAs over time in a large health check-up population, and to identify how reticulation contributes to the risk of radiological progression. Methods: Based on ILAs definition by the Fleischner Society, low-dose chest CT images from community-dwelling population undergone health check-up were evaluated for ILAs. Multivariable logistic regression was used to assess the risk of radiological progression. Measurements and Main Results: Among 155,539 individuals, 3,300 (2.1%) were confirmed to have ILAs: the vast majority (81.7%) were defined as subpleural non-fibrotic ILAs. The prevalence of ILAs increased linearly with age (P for trend<0.0001). Of 454 individuals with subpleural non-fibrotic ILAs, 198 (43.6%) had radiological progression over 4 years. The presence of reticulation on initial imaging was an independent predictor of radiological progression (OR 1.9; 95%CI 1.2-3.0, P=0.0040). No difference in radiological progression was identified between subpleural non-fibrotic ILAs with extensive reticulation and subpleural fibrotic ILAs (73.0% vs. 68.8%, P=0.7626). Conclusions: The prevalence of ILAs increases linearly with age. Nearly half of subpleural non-fibrotic ILAs progress radiologically over 4 years. The presence of reticulation is a risk factor for radiological progression. Subpleural non-fibrotic ILAs with extensive reticulation are likely to be a feature of subpleural fibrotic ILAs

Design and Experimental Research of Intelligent Inspection and Classification System for Yuba Skin Quality

At present, the surface quality of Yuba skin is determined by sensory methods. In order to realize the intelligent classification detection of Yuba skin quality, this study designed a system that automatically determines the quality of Yuba skin surfaces based on image processing and support vector machine (SVM) approaches. Specifically, the system uses image preprocessing to extract the grayscale eigenvalues, gray level co-occurrence matrix (GLCM) eigenvalues, and gray level run length matrix (GLRLM) eigenvalues of the sample image and uses them as input values for a quality grading system. Through model evaluation of three classification models, the SVM classification model was selected according to the evaluation results, and different kernel functions were used in the model for sample training. Based on Matlab, the quality grading software of Yuba skin was developed and designed. Intelligent detection and grading were realized through the radial basis kernel function support vector machine (RBF-SVM) grading model. The best penalty factor (c = 3.50) and kernel parameter value (g = 0.98) were obtained through cross-validation. The accuracy of the model was 95.31% and 94.16% for the training and test sets, respectively. The grading accuracy of the RBF-SVM grading system was 93.56%, and the error was less than 5% compared with the traditional sensory method of grading; thus, the quality classification method based on the SVM classification system for Yuba skin is feasible and can be used for quality detection