3D surface reconstruction for lower limb prosthetic model using modified radon transform

Computer vision has received increased attention for the research and innovation on three-dimensional surface reconstruction with aim to obtain accurate results. Although many researchers have come up with various novel solutions and feasibility of the findings, most require the use of sophisticated devices which is computationally expensive. Thus, a proper countermeasure is needed to resolve the reconstruction constraints and create an algorithm that is able to do considerably fast reconstruction by giving attention to devices equipped with appropriate specification, performance and practical affordability. This thesis describes the idea to realize three-dimensional surface of the residual limb models by adopting the technique of tomographic imaging coupled with the strategy based on multiple-views from a digital camera and a turntable. The surface of an object is reconstructed from uncalibrated two-dimensional image sequences of thirty-six different projections with the aid of Radon transform algorithm and shape-from-silhouette. The results show that the main objective to reconstruct three-dimensional surface of lower limb model has been successfully achieved with reasonable accuracy as the starting point to reconstruct three-dimensional surface and extract digital reading of an amputated lower limb model where the maximum percent error obtained from the computation is approximately 3.3 % for the height whilst 7.4%, 7.9% and 8.1% for the diameters at three specific heights of the objects. It can be concluded that the reconstruction of three-dimensional surface for the developed method is particularly dependent to the effects the silhouette generated where high contrast two-dimensional images contribute to higher accuracy of the silhouette extraction. The advantage of the concept presented in this thesis is that it can be done with simple experimental setup and the reconstruction of three-dimensional model neither involves expensive equipment nor require any service by an expert to handle sophisticated mechanical scanning system

Is Internet privacy dead? Recovering Internet privacy in an increasingly surveillant society.

Surveillance on the Internet is a new battleground which attracts attention from all walks of life in our society. Since the 2013 Snowden revelations, the practice of Internet surveillance has become common knowledge. This research critically examines whether or not Internet privacy is dead, with a specific focus on the technical aspects of the Internet in order to express how technology is used to enhance and to invade privacy. This sets it apart from the existing literature in the field. In this research, three jurisdictions are chosen as case studies: the US and the UK as western jurisdictions with different legal systems, and China which has extensive surveillance and limited Internet privacy. The research explores the meaning of privacy in the information society and investigates the ways in which Internet privacy is integrated in the three chosen jurisdictions are critically analysed and discussed. The research findings reveal that Internet privacy is being taken away in both the US and the UK and it is hard to be optimistic for the future in the light of the 2013 Snowden revelations and ongoing changes to legislation, particularly the Investigatory Powers Bill in the UK. Through the examination of the evolution of the Internet in China and its nascent and evolving laws relating to data protection and privacy, the research findings demonstrate that China holds a great deal of control over its Internet and has implemented technical measures of surveillance, effectively meaning that Internet privacy in China is dead. Most importantly, through the examination of these three jurisdictions, there is strong evidence to suggest that these nation states are not so different when it comes to the invasion of Internet privacy. Despite these, there is still hope and the research concludes by examining possible ways to prevent the demise of Internet privacy

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

Transcriptome and Genome Analyses Applied to Aquaculture Research

Aquaculture is an important economic activity for food production all around the world that has experienced an exponential growth during the last few decades. However, several weaknesses and bottlenecks still need to be addressed in order to improve the aquaculture productive system. The recent fast development of the omics technologies has provided scientists with meaningful tools to elucidate the molecular basis of their research interests. This reprint compiles different works about the use of transcriptomics and genomics technologies in different aspects of the aquaculture research, such as immunity, stress response, development, sexual dimorphism, among others, in a variety of fish and shellfish, and even in turtles. Different transcriptome (mRNAs and non-coding RNAs (ncRNAs)), genome (Single Nucleotide Polymorphisms (SNPs)), and metatranscriptome analyses were conducted to unravel those different aspects of interest