Hyperspectral Imaging and Their Applications in the Nondestructive Quality Assessment of Fruits and Vegetables

Over the past decade, hyperspectral imaging has been rapidly developing and widely used as an emerging scientific tool in nondestructive fruit and vegetable quality assessment. Hyperspectral imaging technique integrates both the imaging and spectroscopic techniques into one system, and it can acquire a set of monochromatic images at almost continuous hundreds of thousands of wavelengths. Many researches based on spatial image and/or spectral image processing and analysis have been published proposing the use of hyperspectral imaging technique in the field of quality assessment of fruits and vegetables. This chapter presents a detailed overview of the introduction, latest developments and applications of hyperspectral imaging in the nondestructive assessment of fruits and vegetables. Additionally, the principal components, basic theories, and corresponding processing and analytical methods are also reported in this chapter

Detection of Fusarium Head Blight in Wheat Grains Using Hyperspectral and RGB Imaging

In modern agriculture, it is imperative to ensure that crops are healthy and safe for consumption. Fusarium Head Blight (FHB) can cause significant damage to wheat grains by reducing essential components such as moisture, protein, and starch, while also introducing dangerous toxins. Therefore, accurately distinguishing between healthy and FHB-infected wheat grains is essential to guarantee stable and reliable wheat production while limiting financial losses and ensuring food safety. This thesis proposes effective methods to classify healthy and FHB infected wheat grains using Hyperspectral Imaging (HSI) and Red Green Blue (RGB) images. The approach includes a combination of Principal Component Analysis (PCA) with morphology, in addition to dark and white reference correction, to create masks for grains in each image. The classification for the hyperspectral images was achieved using a Partial Least Squares Discriminant Analysis (PLS-DA) model for hyperspectral images and a Convolutional Neural Network (CNN) model for RGB images. Both object-based and pixel-based approaches were compared for the PLS-DA model. The results indicated that the object-based approach outperformed the pixel-based approach and other well-known machine learning algorithms, including Random Forest (RF), linear Support Vector Machine (SVM), Stochastic Gradient Descent (SGD) calibrated one-vs-all and DecisionTree. The PLS-DA model using the object-based method yielded better results when tested on all wheat varieties, achieving an F1-score of 99.4%. Specific wavelengths were investigated based on a loading plot, and four effective wavelengths were identified, 953 nm, 1373 nm, 1923 nm and 2493 nm, with classification accuracy found to be similar to the full spectral range. Moreover, the moisture and water content in the grains were analyzed using hyperspectral images through an aquagram, which demonstrated that healthy grains exhibited higher absorbance values than infected grains for all Water Matrix Coordinates (WAMACS). Furthermore, the CNN model was trained on cropped individual grains, and the classification accuracy was similar to the PLS-DA model, with an F1- score of 98.1%. These findings suggest that HSI is suitable for identifying FHB-infected wheat grains, while RGB images may provide a cost-effective alternative to hyperspectral images for this specific classification task. Further research should consider to explore the potential benefits of HSI for deeper investigations into how water absorption affects spectral measurements and moisture content in grains, in addition to user-friendly interfaces for deep learning based image classification

Discrimination Analysis for Predicting Defect-Prone Software Modules

Software defect prediction studies usually build models without analyzing the data used in the procedure. As a result, the same approach has different performances on different data sets. In this paper, we introduce discrimination analysis for providing a good method to give insight into the inherent property of the software data. Based on the analysis, we find that the data sets used in this field have nonlinearly separable and class-imbalanced problems. Unlike the prior works, we try to exploit the kernel method to nonlinearly map the data into a high-dimensional feature space. By combating these two problems, we propose an algorithm based on kernel discrimination analysis called KDC to build more effective prediction model. Experimental results on the data sets from different organizations indicate that KDC is more accurate in terms of F-measure than the state-of-the-art methods. We are optimistic that our discrimination analysis method can guide more studies on data structure, which may derive useful knowledge from data science for building more accurate prediction models

Applied Mathematics and Computational Physics

As faster and more efficient numerical algorithms become available, the understanding of the physics and the mathematical foundation behind these new methods will play an increasingly important role. This Special Issue provides a platform for researchers from both academia and industry to present their novel computational methods that have engineering and physics applications

ADVANCES IN SYSTEM RELIABILITY-BASED DESIGN AND PROGNOSTICS AND HEALTH MANAGEMENT (PHM) FOR SYSTEM RESILIENCE ANALYSIS AND DESIGN

Failures of engineered systems can lead to significant economic and societal losses. Despite tremendous efforts (e.g., $200 billion annually) denoted to reliability and maintenance, unexpected catastrophic failures still occurs. To minimize the losses, reliability of engineered systems must be ensured throughout their life-cycle amidst uncertain operational condition and manufacturing variability. In most engineered systems, the required system reliability level under adverse events is achieved by adding system redundancies and/or conducting system reliability-based design optimization (RBDO). However, a high level of system redundancy increases a system's life-cycle cost (LCC) and system RBDO cannot ensure the system reliability when unexpected loading/environmental conditions are applied and unexpected system failures are developed. In contrast, a new design paradigm, referred to as resilience-driven system design, can ensure highly reliable system designs under any loading/environmental conditions and system failures while considerably reducing systems' LCC. In order to facilitate the development of formal methodologies for this design paradigm, this research aims at advancing two essential and co-related research areas: Research Thrust 1 - system RBDO and Research Thrust 2 - system prognostics and health management (PHM). In Research Thrust 1, reliability analyses under uncertainty will be carried out in both component and system levels against critical failure mechanisms. In Research Thrust 2, highly accurate and robust PHM systems will be designed for engineered systems with a single or multiple time-scale(s). To demonstrate the effectiveness of the proposed system RBDO and PHM techniques, multiple engineering case studies will be presented and discussed. Following the development of Research Thrusts 1 and 2, Research Thrust 3 - resilience-driven system design will establish a theoretical basis and design framework of engineering resilience in a mathematical and statistical context, where engineering resilience will be formulated in terms of system reliability and restoration and the proposed design framework will be demonstrated with a simplified aircraft control actuator design problem

Tube and Sheet Metal Forming Processes and Applications

At present, the manufacturing industry is focused on the production of lighter weight components with better mechanical properties and always fulfilling all the environmental requirements. These challenges have caused a need for developing manufacturing processes in general, including obviously those devoted in particular to the development of thin-walled metallic shapes, as is the case with tubular and sheet metal parts and devices.This Special Issue is thus devoted to research in the fields of sheet metal forming and tube forming, and their applications, including both experimental and numerical approaches and using a variety of scientific and technological tools, such as forming limit diagrams (FLDs), analysis on formability and failure, strain analysis based on circle grids or digital image correlation (DIC), and finite element analysis (FEA), among others.In this context, we are pleased to present this Special Issue dealing with recent studies in the field of tube and sheet metal forming processes and their main applications within different high-tech industries, such as the aerospace, automotive, or medical sectors, among others

Signal and data processing for machine olfaction and chemical sensing: A review

Signal and data processing are essential elements in electronic noses as well as in most chemical sensing instruments. The multivariate responses obtained by chemical sensor arrays require signal and data processing to carry out the fundamental tasks of odor identification (classification), concentration estimation (regression), and grouping of similar odors (clustering). In the last decade, important advances have shown that proper processing can improve the robustness of the instruments against diverse perturbations, namely, environmental variables, background changes, drift, etc. This article reviews the advances made in recent years in signal and data processing for machine olfaction and chemical sensing