Study on Identification Method of Foreign Fibers of Seed Cotton in Hyper-spectral Images Based on Minimum Noise Fraction

International audienceIn order to improve the recognition accuracy of seed cotton foreign fibers, a study on identification method in hyper-spectral images based on Minimum Noise Fraction (MNF) was proposed ,which was applied to feature extraction to reduce the dimension of hyper-spectral images. This method reduced the numbers of hyper-spectral data, lessened the images noise to the minimum , but also decreased the computational requirements for subsequent processing. The white foreign fibers and cotton which were in small discrimination were selected in this paper as the research object. The hyper-spectral images were displayed in software ENVI with 256 bands in the wavelenghth range of 871.60nm-1766.32nm. Afterwards, the images would be processed with the iteration threshold segmentation method, inflation and corrosion. Meanwhile, the correlation of template images and destination images were calculated to find the spectral peaks so that to make template matching to eliminate the images of the cotton seeds. Results of experiments show that the above methods is suitable for identifying foreign fibers of seed cotton which achieved 84.09% rate of recognition

Spectral LADAR: Active Range-Resolved Imaging Spectroscopy

Imaging spectroscopy using ambient or thermally generated optical sources is a well developed technique for capturing two dimensional images with high per-pixel spectral resolution. The per-pixel spectral data is often a sufficient sampling of a material's backscatter spectrum to infer chemical properties of the constituent material to aid in substance identification. Separately, conventional LADAR sensors use quasi-monochromatic laser radiation to create three dimensional images of objects at high angular resolution, compared to RADAR. Advances in dispersion engineered photonic crystal fibers in recent years have made high spectral radiance optical supercontinuum sources practical, enabling this study of Spectral LADAR, a continuous polychromatic spectrum augmentation of conventional LADAR. This imaging concept, which combines multi-spectral and 3D sensing at a physical level, is demonstrated with 25 independent and parallel LADAR channels and generates point cloud images with three spatial dimensions and one spectral dimension. The independence of spectral bands is a key characteristic of Spectral LADAR. Each spectral band maintains a separate time waveform record, from which target parameters are estimated. Accordingly, the spectrum computed for each backscatter reflection is independently and unambiguously range unmixed from multiple target reflections that may arise from transmission of a single panchromatic pulse. This dissertation presents the theoretical background of Spectral LADAR, a shortwave infrared laboratory demonstrator system constructed as a proof-of-concept prototype, and the experimental results obtained by the prototype when imaging scenes at stand off ranges of 45 meters. The resultant point cloud voxels are spectrally classified into a number of material categories which enhances object and feature recognition. Experimental results demonstrate the physical level combination of active backscatter spectroscopy and range resolved sensing to produce images with a level of complexity, detail, and accuracy that is not obtainable with data-level registration and fusion of conventional imaging spectroscopy and LADAR. The capabilities of Spectral LADAR are expected to be useful in a range of applications, such as biomedical imaging and agriculture, but particularly when applied as a sensor in unmanned ground vehicle navigation. Applications to autonomous mobile robotics are the principal motivators of this study, and are specifically addressed

MOLECULAR AND CHEMICAL DISSECTION OF CELLULOSE BIOSYNTHESIS IN PLANTS

Plant cell walls are complex structures that must not only constrain cellular turgor pressure but also allow for structural modification during the dynamic processes of cell division and anisotropic expansion. Cell walls are composed of highly glycosylated proteins and polysaccharides, including pectin, hemicellulose and cellulose. The primary cell wall polysaccharide is cellulose, a polymer composed of high molecular weight !- 1,4-glucan chains. Although cellulose is the most abundant biopolymer on Earth, there is still a lot to learn about its biosynthesis and regulation. This research began by applying a variety of analytical techniques in an attempt to understand differences in cell wall composition and cellulose structure within the plant body, between different plant species and as a result of acclimation by the plant to different environmental conditions. Next, a number of different Arabidopsis thaliana lines possessing mutations affecting cell wall biosynthesis were analyzed for changes in cellulose structure (crystallinity) and biomass saccharification efficiency. One of these mutants, isoxaben resistance1-2 (ixr1- 2), which contains a point mutation in the C-terminal transmembrane region (TMR) of cellulose synthase 3 (CESA3), exhibited a 34% lower biomass crystallinity index and a 151% improvement in saccharification efficiency relative to that of wild-type. The culmination of this research began with a chemical screen that identified the molecule quinoxyphen as a primary cell wall cellulose biosynthesis inhibitor. By forward genetics, a semi-dominant mutant showing strong resistance to quinoxyphen named aegeus was identified in A. thaliana and the resistance locus mapped to a point mutation in the TMR of CESA1. cesa1aegeus occurs in a similar location to that of cesa3ixr1-2, illustrating both subunit specificity and commonality of resistance locus. These drug resistant CESA TMR mutants are dwarfed and have aberrant cellulose deposition. High-resolution synchrotron X-ray diffraction and 13C solid-state nuclear magnetic resonance spectroscopy analysis of cellulose produced from cesa1aegeus, cesa3ixr1-2 and the double mutant shows a reduction in cellulose microfibril width and an increase in mobility of the interior glucan chains of the cellulose microfibril relative to wild-type. These data demonstrate the importance of the TMR region of CESA1 and CESA3 for the arrangement of glucan chains into a crystalline cellulose microfibril in primary cell walls

IMAGE PROCESSING, SEGMENTATION AND MACHINE LEARNING MODELS TO CLASSIFY AND DELINEATE TUMOR VOLUMES TO SUPPORT MEDICAL DECISION

Techniques for processing and analysing images and medical data have become the main’s translational applications and researches in clinical and pre-clinical environments. The advantages of these techniques are the improvement of diagnosis accuracy and the assessment of treatment response by means of quantitative biomarkers in an efficient way. In the era of the personalized medicine, an early and efficacy prediction of therapy response in patients is still a critical issue. In radiation therapy planning, Magnetic Resonance Imaging (MRI) provides high quality detailed images and excellent soft-tissue contrast, while Computerized Tomography (CT) images provides attenuation maps and very good hard-tissue contrast. In this context, Positron Emission Tomography (PET) is a non-invasive imaging technique which has the advantage, over morphological imaging techniques, of providing functional information about the patient’s disease. In the last few years, several criteria to assess therapy response in oncological patients have been proposed, ranging from anatomical to functional assessments. Changes in tumour size are not necessarily correlated with changes in tumour viability and outcome. In addition, morphological changes resulting from therapy occur slower than functional changes. Inclusion of PET images in radiotherapy protocols is desirable because it is predictive of treatment response and provides crucial information to accurately target the oncological lesion and to escalate the radiation dose without increasing normal tissue injury. For this reason, PET may be used for improving the Planning Treatment Volume (PTV). Nevertheless, due to the nature of PET images (low spatial resolution, high noise and weak boundary), metabolic image processing is a critical task. The aim of this Ph.D thesis is to develope smart methodologies applied to the medical imaging field to analyse different kind of problematic related to medical images and data analysis, working closely to radiologist physicians. Various issues in clinical environment have been addressed and a certain amount of improvements has been produced in various fields, such as organs and tissues segmentation and classification to delineate tumors volume using meshing learning techniques to support medical decision. In particular, the following topics have been object of this study: • Technique for Crohn’s Disease Classification using Kernel Support Vector Machine Based; • Automatic Multi-Seed Detection For MR Breast Image Segmentation; • Tissue Classification in PET Oncological Studies; • KSVM-Based System for the Definition, Validation and Identification of the Incisinal Hernia Reccurence Risk Factors; • A smart and operator independent system to delineate tumours in Positron Emission Tomography scans; 3 • Active Contour Algorithm with Discriminant Analysis for Delineating Tumors in Positron Emission Tomography; • K-Nearest Neighbor driving Active Contours to Delineate Biological Tumor Volumes; • Tissue Classification to Support Local Active Delineation of Brain Tumors; • A fully automatic system of Positron Emission Tomography Study segmentation. This work has been developed in collaboration with the medical staff and colleagues at the: • Dipartimento di Biopatologia e Biotecnologie Mediche e Forensi (DIBIMED), University of Palermo • Cannizzaro Hospital of Catania • Istituto di Bioimmagini e Fisiologia Molecolare (IBFM) Centro Nazionale delle Ricerche (CNR) of Cefalù • School of Electrical and Computer Engineering at Georgia Institute of Technology The proposed contributions have produced scientific publications in indexed computer science and medical journals and conferences. They are very useful in terms of PET and MRI image segmentation and may be used daily as a Medical Decision Support Systems to enhance the current methodology performed by healthcare operators in radiotherapy treatments. The future developments of this research concern the integration of data acquired by image analysis with the managing and processing of big data coming from a wide kind of heterogeneous sources

Sustainable Textile Marketing

We know that sustainability has become an important topic in every aspect of life. The textile, fashion, and material industries must also be sustainable, which could be imparted in their development, production, or even marketing. The textile industry has a huge market, as clothing is arguably the most important human need after food. Recently, this industry has been labeled as a polluting industry, a label that could be overcome by the proper development of textile goods and careful marketing strategies. There are specific roles that government, entrepreneurs, and even universities can play in properly educating people to make the textile industry cleaner and greener. Several journals focus only on one of the aspects of this key problem, i.e., the production of sustainable materials, textile education, or textile marketing. However, herein, we strive to bring different areas together on one platform to cover different aspects, i.e., production, policy, education, and marketing related to textile fashion and textile materials

Determination of Time Dependent Stress Distribution on Potato Tubers at Mechanical Collision

This study focuses on determining internal stress progression and the realistic representation of time dependent deformation behaviour of potato tubers under a sample mechanical collision case. A reverse engineering approach, physical material tests and finite element method (FEM)-based explicit dynamics simulations were utilised to investigate the collision based deformation characteristics of the potato tubers. Useful numerical data and deformation visuals were obtained from the simulation results. The numerical results are presented in a format that can be used for the determination of bruise susceptibility magnitude on solid-like agricultural products. The modulus of elasticity was calculated from experimental data as 3.12 [MPa] and simulation results showed that the maximum equivalent stress was 1.40 [MPa] and 3.13 [MPa] on the impacting and impacted tubers respectively. These stress values indicate that bruising is likely on the tubers. This study contributes to further research on the usage of numerical-methods-based nonlinear explicit dynamics simulation techniques in complicated deformation and bruising investigations and industrial applications related to solid-like agricultural products

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 259)

A bibliography containing 476 documents introduced into the NASA scientific and technical information system in May 1984 is presented. The primary subject categories included are: life sciences, aerospace medicine, behavioral sciences, man/system technology, life support, and planetary biology. Topics extensively represented were space flight stress, man machine systems, weightlessness, human performance, mental performance, and spacecraft environments. Abstracts for each citation are given