Vibration and noise characteristics of hook type olive harvesters

The objective of this study was to obtain and evaluate the vibration and noise characteristics of portable hook type mechanical olive harvesters. Experiments included five hook type olive harvesters. In this study, the vibration and sound pressure levels of different harvesters were measured at idling and full load condition. The vibration levels on the handle grip of harvesters were measured and analyzed for both operator’s right and left hand, respectively. The sound pressure level was measured at ear level of the operator. The frequency weighting acceleration was calculated. The vibration total value was expressed as the root-mean-square (rms) of three component values. The acceleration values vary between 5.52 and 39.15 ms-2 for right hand and 4.18 and 61.01 ms-2 for left hand. The equivalent noise pressure levels of the harvesters were measured between 91 and 103 dB (A) in the full loading conditions and between 67 and 80 dB (A) idling working conditions.Key words: Olive harvester, vibration, noise, ha

Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution

In this work, we investigate the value of uncertainty modeling in 3D super-resolution with convolutional neural networks (CNNs). Deep learning has shown success in a plethora of medical image transformation problems, such as super-resolution (SR) and image synthesis. However, the highly ill-posed nature of such problems results in inevitable ambiguity in the learning of networks. We propose to account for intrinsic uncertainty through a per-patch heteroscedastic noise model and for parameter uncertainty through approximate Bayesian inference in the form of variational dropout. We show that the combined benefits of both lead to the state-of-the-art performance SR of diffusion MR brain images in terms of errors compared to ground truth. We further show that the reduced error scores produce tangible benefits in downstream tractography. In addition, the probabilistic nature of the methods naturally confers a mechanism to quantify uncertainty over the super-resolved output. We demonstrate through experiments on both healthy and pathological brains the potential utility of such an uncertainty measure in the risk assessment of the super-resolved images for subsequent clinical use.Comment: Accepted paper at MICCAI 201

Finsler geometry on higher order tensor fields and applications to high angular resolution diffusion imaging.

We study 3D-multidirectional images, using Finsler geometry. The application considered here is in medical image analysis, specifically in High Angular Resolution Diffusion Imaging (HARDI) (Tuch et al. in Magn. Reson. Med. 48(6):1358–1372, 2004) of the brain. The goal is to reveal the architecture of the neural fibers in brain white matter. To the variety of existing techniques, we wish to add novel approaches that exploit differential geometry and tensor calculus. In Diffusion Tensor Imaging (DTI), the diffusion of water is modeled by a symmetric positive definite second order tensor, leading naturally to a Riemannian geometric framework. A limitation is that it is based on the assumption that there exists a single dominant direction of fibers restricting the thermal motion of water molecules. Using HARDI data and higher order tensor models, we can extract multiple relevant directions, and Finsler geometry provides the natural geometric generalization appropriate for multi-fiber analysis. In this paper we provide an exact criterion to determine whether a spherical function satisfies the strong convexity criterion essential for a Finsler norm. We also show a novel fiber tracking method in Finsler setting. Our model incorporates a scale parameter, which can be beneficial in view of the noisy nature of the data. We demonstrate our methods on analytic as well as simulated and real HARDI data

Investigation of branch breaking resistances in “Sari Zeybek” fig cultivar

The aim of this study was to examine the branch breaking characteristics of the 4 to 5 years old branches mechanically by a determination of the breaking resistance in Sari Zeybek fig cultivars. In this respect, the study was conducted with 10 different ages for trees belonging to Sarilop cultivar, known with a characteristic of drying and Sari Zeybek cultivar, known with equal or even better characteristics. The sclerenchyma tissues of the 1 to 5 aged branches of Sari Zeybek cultivar were weaker in degree than some other fruit tree species, especially the Sarilop cultivar. Furthermore, it was brought out that the breakages were related to the flexibility of tissues even with the excess forced application (150 kg.) in Sarilop. In Sari Zeybek cultivar, where there is an existence of the branch breakage problem, the length and diameters of branches increased with the increasing branch ages. In particular, the forces at the branch collar were increased due to the fruit weight increase in trees, starting with the 4 to 5 year aged branches. According to the approach of encouragement of branch breakages due to the aforementioned reason, it was taken seriously that shape prunings need to be made more carefully, and not allowed in over critical levels branch lengths and angles within 1 to 4 years growth in Sari Zeybek cultivar.Key words: Sari Zeybek, fig, branch breakage

Learning-based Ensemble Average Propagator Estimation

By capturing the anisotropic water diffusion in tissue, diffusion magnetic resonance imaging (dMRI) provides a unique tool for noninvasively probing the tissue microstructure and orientation in the human brain. The diffusion profile can be described by the ensemble average propagator (EAP), which is inferred from observed diffusion signals. However, accurate EAP estimation using the number of diffusion gradients that is clinically practical can be challenging. In this work, we propose a deep learning algorithm for EAP estimation, which is named learning-based ensemble average propagator estimation (LEAPE). The EAP is commonly represented by a basis and its associated coefficients, and here we choose the SHORE basis and design a deep network to estimate the coefficients. The network comprises two cascaded components. The first component is a multiple layer perceptron (MLP) that simultaneously predicts the unknown coefficients. However, typical training loss functions, such as mean squared errors, may not properly represent the geometry of the possibly non-Euclidean space of the coefficients, which in particular causes problems for the extraction of directional information from the EAP. Therefore, to regularize the training, in the second component we compute an auxiliary output of approximated fiber orientation (FO) errors with the aid of a second MLP that is trained separately. We performed experiments using dMRI data that resemble clinically achievable $q$-space sampling, and observed promising results compared with the conventional EAP estimation method.Comment: Accepted by MICCAI 201

Correlation between live weight and body measurements in certain dog breeds

The purpose of this study was to determine the correlation between live weight and body measurements in Zagar, Zerdava, and Catalburun dogs. Animal materials were obtained from various regions of Turkey. A total of 304 dogs from three breeds were used: Zagar (45 females, 59 males), Zerdava (50 females, 50 males), and Catalburun (62 females, 38 males). Live weights and certain body measurements were determined. A linear regression model was created using the parameters obtained in this study. The bodyweights calculated with the body measurements were found to be at a high or acceptable level in the Zagar, Zerdava, and Catalburun genotypes (R-2 = 0.902, 0.467, and 0.697, respectively).Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)The authors would like to thank Scientific and Technological Research Council of Turkey (TUBITAK) and the owners of the Zagar, Zerdava and Catalburun dogs for their support to the project

Efficient Computation of PDF-Based Characteristics from Diffusion MR Signal

International audienceWe present a general method for the computation of PDF-based characteristics of the tissue micro-architecture in MR imaging. The approach relies on the approximation of the MR signal by a series expansion based on Spherical Harmonics and Laguerre-Gaussian functions, followed by a simple projection step that is efficiently done in a finite dimensional space. The resulting algorithm is generic, flexible and is able to compute a large set of useful characteristics of the local tissues structure. We illustrate the effectiveness of this approach by showing results on synthetic and real MR datasets acquired in a clinical time-frame

Image quality transfer and applications in diffusion MRI

This paper introduces a new computational imaging technique called image quality transfer (IQT). IQT uses machine learning to transfer the rich information available from one-off experimental medical imaging devices to the abundant but lower-quality data from routine acquisitions. The procedure uses matched pairs to learn mappings from low-quality to corresponding high-quality images. Once learned, these mappings then augment unseen low quality images, for example by enhancing image resolution or information content. Here, we demonstrate IQT using a simple patch-regression implementation and the uniquely rich diffusion MRI data set from the human connectome project (HCP). Results highlight potential benefits of IQT in both brain connectivity mapping and microstructure imaging. In brain connectivity mapping, IQT reveals, from standard data sets, thin connection pathways that tractography normally requires specialised data to reconstruct. In microstructure imaging, IQT shows potential in estimating, from standard “single-shell” data (one non-zero b-value), maps of microstructural parameters that normally require specialised multi-shell data. Further experiments show strong generalisability, highlighting IQT's benefits even when the training set does not directly represent the application domain. The concept extends naturally to many other imaging modalities and reconstruction problems