Miracle-dz/UniFAR-FTUs huggingface dataset:
Person recognition across multi-session and multi-exemplar images using ensemble of classifiers

Person recognition over time is a bit challenging task as compared to re-identification in multi-camera environment. Usually, people appear after certain time period at public places like airports, carrying accessories and changing of clothes etc. In this paper, we proposed a newer recognition framework using two types of images i.e. whole and upper body silhouette. A customized version of DeepLabv3 is used for human body semantic segmentation. The Generic Fourier Descriptor (GFD) based feature set is fed to One-Vs-Rest schema in ensemble of K-Nearest Neighbor (KNN) and Random Forest (RF) classifiers. The experiments are carried out on Front-View Gait (FVG) dataset recorded in year 2017 and 2018 respectively. An overall recognition accuracy of more than 93% is noted using both classifiers on whole body human silhouette images. On the other hand, upper half human silhouette obtained recognition accuracy of more than 91% and 88% using RF and KNN respectively. Code is available at https://git.io/JtfMY
Image fusion method for of long-wave infrared sensor and low illumination CMOS

Image fusion method is an algorithm that synthesizes images from different sources into a new image. The advantage of image fusion technology is that different image information presented by multi-source sensors can complement and improve each other to extract the effective information in the source image. It can also obtain a more effective and richer image with description information. According to the technical methods of image fusion, an improved image fusion method for fast non-downsampled contourlet transform (NSCT) was selected for handheld. The source infrared image and source low-light image collected by the observer were image fusion. In this method, the scale filter and the directional filter were convolved respectively in the composition of image decomposition and reconstruction to reduce the number of iterations of image decomposition and reconstruction, improve the efficiency of algorithm operation, and ensure that the system worked in real time.
Osmium and Ruthenium Catalysts for Dehydrogenation of Alcohols

A series of pincer-type complexes of Os and Ru have been synthesized and investigated in catalytic alcohol dehydrogenation. The hydrides OsHCl(CO)[HN(C2H4PiPr2)2] and OsH2(CO)[HN(C2H4PiPr2)2] possess good air, moisture, and thermal stability and are outstanding versatile dehydrogenation catalysts for primary alcohols for reactions of transfer hydrogenation, dehydrogenative coupling, and amine alkylation.
Binocular monitoring method based on fisheye lens camera

In the field of video surveillance, fisheye lens cameras have a larger field of view than traditional cameras. However, fisheye lens cameras have larger image distortion and lower image resolution, making it difficult to observe the detailed information of the target. A binocular monitoring method based on a fisheye lens camera is introduced. The system uses a fisheye lens camera as a guidance system for panoramic monitoring, and uses SuBSENSE moving target detection algorithm to detect moving targets.The traditional binocular camera is used as the tracking system for target tracking and ranging, and ultra-wide-angle video surveillance is realized. The experimental results show that the method can accurately detect the moving target in the fisheye image, guide the binocular camera to accurately track the target and measure the target distance, which provides a detection method for video surveillance technology.
A road segmentation method based on reorganized LiDAR points and line scanning

Road segmentation is an important part of autonomous driving vehicles. Reliable road segmentation results are a prerequisite for autonomous driving tasks, e.g., path planning. In this paper, we propose a road segmentation method based on 3D point cloud organization and line scanning of LiDAR data. Our model assumes that road areas are always flatter than non-road areas. First, we propose an adjacent-line-difference (ALD) feature to define the flatness of the point cloud. Using this feature, the approximate road area can be estimated. Then, we proposed a horizontal and vertical scanning strategy to obtain a more accurate road area. In order to prove the effectiveness of our method, experiments are conducted on the public KITTI-Road benchmark. The experimental results prove that the proposed approach can achieve one of the best performances among all LiDAR-based methods.
Fabrication of high-power hollow-core fiber end-cap

Hollow-core fibers (HCFs) provide an ideal environment for the interaction of light and gases, which gives birth to a novel kind of lasers, namely fiber gas lasers (FGLs). Although there is a rapid development of FGLs in the past years, highly efficient and stable coupling of the pump light is still a key limit for their applications in the future. Here, we propose and fabricate the HCF end-cap for the first time. The measured results show that the HCF end-cap can bear several hundreds of watt laser power, and the coupling efficiency is around 70%. This work opens new opportunity for the development of high-power FGLs.
Machine learning aided profile measurement in high-aspect-ratio nanostructures

The trend to produce semiconductor devices having more complex nanostructures results in the increasing importance of exquisite systems measuring multiple Critical Dimensions (CDs) of nanostructures. However, from a practical point of view, it is difficult to apply conventional methodologies to mass production because of cost and complexity issues. In this study, we propose an application of machine learning techniques utilizing optical information to measure nanoscale profiles of channel holes in High-Aspect-Ratio (HAR) structure of vertical NAND flash, which is applicable to mass production. By combining the conventional methodologies, the proposed method yields data pairs for supervised learning which include optical spectra obtained with Rotating Polarizer Ellipsometer (RPE) and images obtained with Scanning Electron Microscopy (SEM). Several preprocessing steps and machine learning techniques are introduced to train a model with sufficient performance to be applicable to mass production. In experiments, we obtained a model with coefficient of determination (R2) of 0.8 and Root Mean Square Error (RMSE) of 1.3 nm when predicting hundreds of nanoscale profiles of the channel holes which are measured with SEM. Furthermore, we confirmed that only 500 samples of data are sufficient to achieve the model performance with R2 greater than 0.7 and RMSE less than 1.5 nm. The proposed method is capable of replacing the conventional methods of profile measurement in the mass production stage by reducing the cost of destructive methods and accurately measuring the profiles of complex nanostructures without theoretical modeling.
Ensemble cross-correlation for image retrieval from the intensity signal recorded by a single pixel

Exploiting the optical memory effect and the equivalency between the spatial and the ensemble cross-correlation of speckle patterns, we developed a new single-pixel camera concept. It allows lensless microscopic spectrally and depth resolved imaging with spatial light modulators displaying pseudorandom scattering arrays. Since the image retrieval is based on correlation calculations, the concept proves to be quite robust to noise.
CNN-HKNN for osteoporosis magnetic resonance imaging classification with data augmentation

Osteoporosis is a systemic bone disease that characterized by an increase in bone fragility due to bone microstructure damage. Currently, osteoporosis is diagnosed clinically and confirmed by Dual-energy X-ray absorptiometry (DXA), which mainly depends on bone density and somehow being subjective. This study aimed to develop a deep learning method combined with bone tissue microstructure for the early diagnosis of osteoporosis. First, we applied Gabor filters to preprocess the raw osteoporotic MRI images in three scales and three directions for data augmentation. Second, we proposed a novel hybrid CNN-HKNN system which combines convolutional neural network (CNN) with k-local hyperplane distance nearest neighbour algorithm (HKNN) for osteoporotic MRI classification. Third, we introduced a transfer learning technique by pre-training the CNN model with the augmented dataset to improve the robustness of the proposed model. Experiments under 10-fold cross-validation showed accuracy of the system is 0.963, and the area under the receiver operating characteristic curve (AUC) was 0.980. In conclusion, the proposed method has an excellent ability to diagnose osteoporosis, which has certain clinical application prospects.
Efficiency calibration of NaI (Tl) detector for disc source using point source experimental technique and Monte Carlo simulations

The detection efficiency of a gamma ray detector should be calibrated prior to the measurement of activity in samples. The aim of this research is to develop a simple experimental technique to calibrate the efficiency of detectors for geometrical sources by replicating their responses from virtually equivalent sources (VES) constructed from a collection of Standard Point Source (SPS) measurements. Experiments were performed to replicate the response of a disk source of 5.2 cm radius by equivalent disk sources (EDS) reconstructed from a collection of 19, 91 and 169 SPS measurements to optimize the number of measurements, response of detector for radially symmetric SPS locations was verified through Absolute Peak Efficiency (APE) calculations. APE was calculated experimentally for the actual disk source and EDS as well as using EGS5 simulations. The difference between experimentally measured and simulated APE for 5.2 cm EDS was observed to be about 7.8 %, 5.2 % and 5.4 % for 137Cs and about -1.3 %, 1.3 % and 2.7 % for 22Na for the EDS constructed from 19, 91- and 169 SPS measurements respectively
Focusing of vector beams with fractional-order azimutal polarization

By simulating sharp focusing of vector beams with azimuthal polarization of fractional order 0 < m <1 (m = 1 is azimuthal polarization, m = 0 is linear polarization), it was shown that the shape of the intensity distribution in the focal spot changes from annular (m = 1) to round (m = 0.5) and then to elliptical ring (m = 0). The opposite changes we can see for the shape of the distribution of the longitudinal component of the Poynting vector (energy flux) in the focal spot: from annular (m = 1) to elliptical (m = 0.5) and then to circular ring (m = 0). The diameter of the focal spot at full width at half maximum for a beam with azimuthal polarization (m = 1) with an optical vortex of the first order for a numerical aperture NA = 0.95 is 0.46 of the wavelength, and the diameter of the axial energy flux for linearly polarized light (m = 0) is 0.45 of the wavelength. Because of this, the answers to the questions: Whether the focal spot is round or elliptical and whether the focal spot is minimal: with azimuthal polarization with a vortex or with linear polarization without a vortex, depend on whether we are considering the intensity at the focus or the energy flow.
Automated visual inspection of fabric image using deep learning approach for defect detection

As a popular topic in automation, fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. The main challenge for automatically detecting fabric damage, in most cases, is the complex structure of the textile. This article presents a two-stage approach, combining novel and traditional algorithms to enhance image enhancement and defect detection. The first stage is a new combined local and global transform domain-based image enhancement algorithm using block-based alpha-rooting. In the second stage, we construct a neural network based on the modern architecture to detect fabric damage accurately. This solution allows localizing defects with higher accuracy than traditional methods of machine learning and modern methods of deep learning. All experiments were carried out using a public database with examples of damage to the TILDA fabric dataset.
Fabrication of computer-generated holograms on silicon by femtosecond laser-assisted with chemical etching

We proposed a simple and flexible method for fabricating computer-generated holograms (CGHs) on crystalline silicon via femtosecond laser-assisted with chemical etching. Femtosecond laser irradiation led to the transformation of crystalline silicon into amorphous silicon, and its chemical activity is significantly reduced, so it can be used as an etching mask in alkaline solution. The amorphous silicon mask led to the formation of nanopillar structure after wet etching. The height of the nanopillars could be flexibly adjusted by controlling the etching time. By means of laser dynamic scanning, we realized the fabrication of holographic array on silicon. The diffraction pattern of the CGHs on silicon becomes clearer with the increase of the height of the nanopillar.
Identification and measurement method of atmospheric pollution sources

Global environment and climate change are the focus and frontier subjects of ecology, biogeochemistry and environmental science. Simultaneous in-situ monitoring of multiple atmospheric pollution components base on laser absorption spectroscopy technology has become an effective way for in-depth analysis and accurate identification of atmospheric pollution sources by analyzing the correlation of their concentration data. In view of the wide variety of actual atmospheric pollutants, the source, transformation mechanism, and transportation process of each component are extremely complex and overlap phenomenon, the in-depth analysis of atmospheric pollution sources is an important challenge. The depth analysis of air pollution sources is the key basis for scientific control of the air pollution. It is of great significance to carry out in situ monitoring techniques and analytical algorithms for various pollutants. In this paper, carbon monoxide (CO), a typical pollutant in the atmosphere, is firstly selected and analyzed by using HYSPLIT backward trajectory model, and the reliability of the proposed algorithm is verified. In addition, with PM2.5 as the analysis target, the backward trajectory of air mass during 72 h of pollution was simulated by HYSPLIT model for Beijing's heavy pollution during January 26-28, 2020. The potential transport channels and pollution contribution sources of PM2.5 in different areas to Beijing were analyzed by daily trajectory clustering analysis.
Modulating linearly polarized light into a full Poincaré beam by stress-engineered optical element

Aiming at the requirement of illumination uniformity and backscattering suppression in laser-driven inertial confinement fusion (ICF) facilities, we propose a polarization smoothing (PS) scheme based on stress-engineered optical element that can modulate linearly polarized light into a full Poincaré beam. The unique law of modulation of the polarization state by glass window under symmetrical loads is revealed by numerical simulations, the results indicate that the glass window exhibits a fast axis orientation that rotates with the polar angle and a phase retardance that increases with the radius under more than two symmetrical pressure loads. Further optical calculations show that when the pressure of the loads and the thickness of the glass are sufficient, the window can modulate the linearly polarized light into a full Poincarébeam.
Angstrom-accuracy multilayer thickness determination using optical metrology and machine learning

The era of big data and cloud computing services has driven the demand for higher capacity and more compact semiconductor devices. As a result, semiconductor devices are moving from 2-D to 3-D. Most notably, threedimensional (3D) NAND flash memory is the most successful 3D semiconductor device today. 3D NAND overcomes the spatial limitation of conventional planar NAND by stacking memory cells vertically. Since hundreds of vertically stacked semiconductor materials become the channel length in the final product, accurate thickness characterization is critical. In this paper, we propose a non-destructive multilayer thickness characterization method using optical measurements and machine learning. For a silicon oxide/nitride multilayer stack of <200 layers, we could predict the thickness of each layer with an average root-mean-square error (RMSE) of 1.6 Å . In addition, we could successfully classify normal and outlier devices using simulated data. We expect this method to be highly suitable for semiconductor fabrication processes.
Brain tumor segmentation based on multi-scale superpixel and kernel low-rank representation

A segmentation method for brain tumor MR images based on multi-scale superpixel and kernel low-rank representation (KLRR) is proposed. First, homogeneous regions of the image are generated by the multi-scale superpixel segmentation, from which the spatial features are extracted to construct multi-scale superpixel kernels. Then, KLRR is adopted to model the high-dimensional feature space of the brain tumor image, and the representation coefficients in the model are solved by introducing the constructed multi-scale superpixel kernels. Finally, the optimal classification of samples is obtained by voting strategy, so as to extract necrosis, enhanced tumor and edema, respectively. Compared with a square window, the spatial features extracted based on multi-scale superpixel regions not only conform to the structural characteristics of brain tissues and tumors so as to maintain the boundaries better, but also can give more accurate descriptions of brain tissues and tumors of different sizes. In addition, KLRR combines the linear separability of the high-dimensional feature space induced by the kernel function with the advantages of low-rank representation (LRR) for describing the global structure, which improves the accuracy of the image representation. The experimental results on the BraTS data set show that, in addition to lower requirements for the size of the training samples, the segmentation accuracy of the proposed method under different indicators is better than that of the existing methods.
Genetic Dissection of Quantitative Resistance to Common Rust (Puccinia sorghi) in Tropical Maize (Zea mays L.) by Combined Genome-Wide Association Study, Linkage Mapping, and Genomic Prediction

Common rust is one of the major foliar diseases in maize, leading to significant grain yield losses and poor grain quality. To dissect the genetic architecture of common rust resistance, a genome-wide association study (GWAS) panel and a bi-parental doubled haploid (DH) population, DH1, were used to perform GWAS and linkage mapping analyses. The GWAS results revealed six single-nucleotide polymorphisms (SNPs) significantly associated with quantitative resistance of common rust at a very stringent threshold of P-value 3.70 × 10–6 at bins 1.05, 1.10, 3.04, 3.05, 4.08, and 10.04. Linkage mapping identified five quantitative trait loci (QTL) at bins 1.03, 2.06, 4.08, 7.03, and 9.00. The phenotypic variation explained (PVE) value of each QTL ranged from 5.40 to 12.45%, accounting for the total PVE value of 40.67%. Joint GWAS and linkage mapping analyses identified a stable genomic region located at bin 4.08. Five significant SNPs were only identified by GWAS, and four QTL were only detected by linkage mapping. The significantly associated SNP of S10_95231291 detected in the GWAS analysis was first reported. The linkage mapping analysis detected two new QTL on chromosomes 7 and 10. The major QTL on chromosome 7 in the region between 144,567,253 and 149,717,562 bp had the largest PVE value of 12.45%. Four candidate genes of GRMZM2G328500, GRMZM2G162250, GRMZM2G114893, and GRMZM2G138949 were identified, which played important roles in the response of stress resilience and the regulation of plant growth and development. Genomic prediction (GP) accuracies observed in the GWAS panel and DH1 population were 0.61 and 0.51, respectively. This study provided new insight into the genetic architecture of quantitative resistance of common rust. In tropical maize, common rust could be improved by pyramiding the new sources of quantitative resistance through marker-assisted selection (MAS) or genomic selection (GS), rather than the implementation of MAS for the single dominant race-specific resistance gene.
Acquisition of Tigecycline Resistance by Carbapenem-Resistant Klebsiella pneumoniae Confers Collateral Hypersensitivity to Aminoglycosides

Tigecycline is a last-resort antibiotic for infections caused by carbapenem-resistant Klebsiella pneumoniae (CRKP). This study aimed to broaden our understanding of the acquisition of collateral hypersensitivity by CRKP, as an evolutionary trade-off of developing resistance to tigecycline. Experimental induction of tigecycline resistance was conducted with tigecycline-sensitive CRKP clinical isolates. Antimicrobial susceptibility testing, microbial fitness assessment, genotypic analysis and full-genome sequencing were carried out for these clinical isolates and their resistance-induced descendants. We found that tigecycline resistance was successfully induced after exposing CRKP clinical isolates to tigecycline at gradually increased concentrations, at a minor fitness cost of bacterial cells. Quantitative reverse transcription-polymerase chain reaction (RT-PCR) found higher expression of the efflux pump gene acrB (5.3–64.5-fold) and its regulatory gene ramA (7.4–65.8-fold) in resistance-induced strains compared to that in the tigecycline-sensitive clinical isolates. Stable hypersensitivities to aminoglycosides and other antibiotics were noticed in resistance-induced strains, showing significantly lowered MICs (X 4 – >500 times). Full genome sequencing and plasmid analysis suggested the induced collateral hypersensitivity might be multifaceted, with the loss of an antimicrobial resistance (AMR) plasmid being a possible major player. This study rationalized the sequential combination of tigecycline with aminoglycosides for the treatment of CRKP infections.
Effective Nondestructive Purification of Single-Walled Carbon Nanotubes Based on High-Speed Centrifugation with a Photochemically Removable Dispersant

A purification method for raw single-walled carbon nanotubes (SWCNTs) without damage to their intrinsic structures has been desired in many applications. We investigated the purification of SWCNTs based on high-speed centrifugation of water-dispersed SWCNTs using the photoreactive dispersant we previously investigated. SWCNTs wrapped with the dispersant were separated from impurities, such as an amorphous carbon and metal particles by centrifugation, similarly to conventional physical purification using surfactants. In contrast to general surfactants that form micelles to disperse SWCNTs in aqueous solutions, the photoreactive dispersant did not form micelles. Therefore, an excess amount of the dispersant, which did not adsorb onto the SWCNT surfaces, was removable by dialysis of the supernatant. Since the amount of the dispersant was minimized by dialysis, we tuned the UV-irradiation time to eliminate the dispersibility of SWCNTs in water to as low a value as ∼2 h. The SWCNT precipitates were collected, .