scholarly journals Field Geometry and the Spatial and Temporal Generalization of Crop Classification Algorithms—A Randomized Approach to Compare Pixel Based and Convolution Based Methods

2021 ◽  
Vol 13 (4) ◽  
pp. 775
Author(s):  
Mario Gilcher ◽  
Thomas Udelhoven
Keyword(s):  
Recent Trend ◽  
Training Data ◽  
Large Contribution ◽  
Practical Applications ◽  
Temporal Validation ◽  
Segmentation Algorithms ◽  
Field Geometry ◽  
Temporal Generalization ◽  
One Year ◽  
Better Than

With the ongoing trend towards deep learning in the remote sensing community, classical pixel based algorithms are often outperformed by convolution based image segmentation algorithms. This performance was mostly validated spatially, by splitting training and validation pixels for a given year. Though generalizing models temporally is potentially more difficult, it has been a recent trend to transfer models from one year to another, and therefore to validate temporally. The study argues that it is always important to check both, in order to generate models that are useful beyond the scope of the training data. It shows that convolutional neural networks have potential to generalize better than pixel based models, since they do not rely on phenological development alone, but can also consider object geometry and texture. The UNET classifier was able to achieve the highest F1 scores, averaging 0.61 in temporal validation samples, and 0.77 in spatial validation samples. The theoretical potential for overfitting geometry and just memorizing the shape of fields that are maize has been shown to be insignificant in practical applications. In conclusion, kernel based convolutions can offer a large contribution in making agricultural classification models more transferable, both to other regions and to other years.

Resolution and measurement in the scanning electron microscope

1987 ◽  
Vol 45 ◽  
pp. 534-537 ◽  
Author(s):  
Michael T. Postek
Keyword(s):  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Engineering Design ◽  
Resolving Power ◽  
Practical Applications ◽  
Instrument Resolution ◽  
Accurate Definition ◽  
Definition Of ◽  
Scanning Electron ◽  
Better Than

The term ultimate resolution or resolving power is the very best performance that can be obtained from a scanning electron microscope (SEM) given the optimum instrumental conditions and sample. However, as it relates to SEM users, the conventional definitions of this figure are ambiguous. The numbers quoted for the resolution of an instrument are not only theoretically derived, but are also verified through the direct measurement of images on micrographs. However, the samples commonly used for this purpose are specifically optimized for the measurement of instrument resolution and are most often not typical of the sample used in practical applications.SEM RESOLUTION. Some instruments resolve better than others either due to engineering design or other reasons. There is no definitively accurate definition of how to quantify instrument resolution and its measurement in the SEM.

The Role of Chess in the Intellectual Development of Childrens from Primary School

2019 ◽  
pp. 58-62
Author(s):  
Vlad Stegariu ◽  
Simona Andreea Popușoi ◽  
Beatrice Abălașei ◽  
Nicolae Lucian Voinea ◽  
Ioan Stelescu ◽  
...  
Keyword(s):  
Cognitive Development ◽  
Primary School ◽  
Intellectual Development ◽  
Resources Allocation ◽  
Control Group ◽  
Cognitive Resources ◽  
One Year ◽  
Better Than ◽  
Chess Playing

Chess playing has a significant role in participants’ resources allocation, both at a psychological level, but mostly concerning the cognitive resources. The aim of the present study was to examine the effect of chess playing on the intellectual development of primary-class students. 67 children were tested using the Raven Standard Progressive Matrices and were distributed in three different groups according to their experience with chess, namely: the control group (formed by students with no experience with chess playing), the beginners group (students with less than one year in chess playing training) and the advanced group (children with more than two years experience with chess). Results indicated that chess playing had a significant effect on the SPM performance, indicating that those in the advanced group performed significantly better than those in the control or in the beginners group. Conclusions of this study tap into the benefits of playing chess with a focus on the children’s’ cognitive development.

scholarly journals Automated Ventricular System Segmentation in Paediatric Patients Treated for Hydrocephalus Using Deep Learning Methods

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Michał Klimont ◽  
Mateusz Flieger ◽  
Jacek Rzeszutek ◽  
Joanna Stachera ◽  
Aleksandra Zakrzewska ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Augmentation ◽  
Ct Images ◽  
Policy Transfer ◽  
Training Data ◽  
Intraobserver Variability ◽  
Practical Applications ◽  
Brain Scans ◽  
Rate Policy ◽  
Ct Brain

Hydrocephalus is a common neurological condition that can have traumatic ramifications and can be lethal without treatment. Nowadays, during therapy radiologists have to spend a vast amount of time assessing the volume of cerebrospinal fluid (CSF) by manual segmentation on Computed Tomography (CT) images. Further, some of the segmentations are prone to radiologist bias and high intraobserver variability. To improve this, researchers are exploring methods to automate the process, which would enable faster and more unbiased results. In this study, we propose the application of U-Net convolutional neural network in order to automatically segment CT brain scans for location of CSF. U-Net is a neural network that has proven to be successful for various interdisciplinary segmentation tasks. We optimised training using state of the art methods, including “1cycle” learning rate policy, transfer learning, generalized dice loss function, mixed float precision, self-attention, and data augmentation. Even though the study was performed using a limited amount of data (80 CT images), our experiment has shown near human-level performance. We managed to achieve a 0.917 mean dice score with 0.0352 standard deviation on cross validation across the training data and a 0.9506 mean dice score on a separate test set. To our knowledge, these results are better than any known method for CSF segmentation in hydrocephalic patients, and thus, it is promising for potential practical applications.

scholarly journals NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2503
Author(s):  
Taro Suzuki ◽  
Yoshiharu Amano
Keyword(s):  
Machine Learning ◽  
Satellite System ◽  
Training Data ◽  
Support Vector ◽  
Positioning Errors ◽  
Automated Method ◽  
Global Navigation Satellite ◽  
Better Than ◽  
Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

A Generic Method to Generate AS-Curve Profile in Commercial Motion Controller

2017 ◽  
Author(s):  
Youdun Bai ◽  
Xin Chen ◽  
Zhijun Yang
Keyword(s):  
Motion Controller ◽  
Residual Vibration ◽  
Practical Applications ◽  
Analytic Polynomial ◽  
Acceleration And Deceleration ◽  
Special Knowledge ◽  
Curve Profile ◽  
S Curve ◽  
Motion Profile ◽  
Better Than

It is well believed that S-curve motion profiles are able to reduce residual vibration, and are widely applied in the motion control fields. Recently, a new asymmetric S-curve (AS-curve) motion profile, which is able to effectively adjust the acceleration and deceleration periods, is proposed to enhance the performance of S-curve motion profile, and proved to be better than the traditional symmetric S-curve in many cases. However, most commercial motion controllers do not support the AS-curve motion profiles inherently. Special knowledge or expensive advanced controlling systems, such as dSPACE system, are required to generate the AS-curve motion command, which limits the applications of the AS-curve motion profile in many practical applications. In this paper, a generic method based on the Position-Velocity-Time (PVT) mode move supported by most commercial motion controllers is proposed to generate exact AS-curve motion command in real machines. The analytic polynomial functions of AS-curve motion profile are also derived to simplify the further application, and the effectiveness of the proposed method is verified by numerical simulation.

scholarly journals Generalization-Based Acquisition of Training Data for Motor Primitive Learning by Neural Networks

2021 ◽  
Vol 11 (3) ◽  
pp. 1013
Author(s):  
Zvezdan Lončarević ◽  
Rok Pahič ◽  
Aleš Ude ◽  
Andrej Gams
Keyword(s):  
Neural Networks ◽  
Dimensionality Reduction ◽  
Gaussian Process Regression ◽  
Search Space ◽  
Robot Learning ◽  
Training Data ◽  
Practical Applications ◽  
Latent Space ◽  
Real Robot ◽  
Low Dimensional

Autonomous robot learning in unstructured environments often faces the problem that the dimensionality of the search space is too large for practical applications. Dimensionality reduction techniques have been developed to address this problem and describe motor skills in low-dimensional latent spaces. Most of these techniques require the availability of a sufficiently large database of example task executions to compute the latent space. However, the generation of many example task executions on a real robot is tedious, and prone to errors and equipment failures. The main result of this paper is a new approach for efficient database gathering by performing a small number of task executions with a real robot and applying statistical generalization, e.g., Gaussian process regression, to generate more data. We have shown in our experiments that the data generated this way can be used for dimensionality reduction with autoencoder neural networks. The resulting latent spaces can be exploited to implement robot learning more efficiently. The proposed approach has been evaluated on the problem of robotic throwing at a target. Simulation and real-world results with a humanoid robot TALOS are provided. They confirm the effectiveness of generalization-based database acquisition and the efficiency of learning in a low-dimensional latent space.

A Therapeutic Trial of Fatty Acid Supplementation in Cystic Fibrosis

PEDIATRICS ◽  
1976 ◽  
Vol 57 (4) ◽  
pp. 474-479
Author(s):  
Robert B. Elliott
Keyword(s):  
Cystic Fibrosis ◽  
Fatty Acid ◽  
Clinical Course ◽  
Therapeutic Trial ◽  
Biochemical Abnormality ◽  
Fatty Acid Supplementation ◽  
Oil Emulsion ◽  
Soya Oil ◽  
One Year ◽  
Better Than

Seven children with cystic fibrosis (CF) have been treated for at least one year with intravenously administered soya oil emulsion. In all, an improvement of at least one biochemical abnormality in character with the disease appeared. The children's clinical course remains benign. This course is remarkably better than that of other children with CF treated without Intralipid in Auckland in the same period, though a placebo effect cannot be discounted. It is postulated that intravenous supplementation with essential fatty acid in CF may in turn partially correct an error of metabolism of prostaglandins present in the disease.

scholarly journals DeepIsoFun: a deep domain adaptation approach to predict isoform functions

2018 ◽  
Vol 35 (15) ◽  
pp. 2535-2544 ◽  
Author(s):  
Dipan Shaw ◽  
Hao Chen ◽  
Tao Jiang
Keyword(s):  
Network Architecture ◽  
Domain Adaptation ◽  
Multiple Instance Learning ◽  
Training Data ◽  
Supplementary Information ◽  
Operating Characteristics ◽  
Neural Network Architecture ◽  
Receiver Operating Characteristics Curve ◽  
Gene Functions ◽  
Better Than

AbstractMotivationIsoforms are mRNAs produced from the same gene locus by alternative splicing and may have different functions. Although gene functions have been studied extensively, little is known about the specific functions of isoforms. Recently, some computational approaches based on multiple instance learning have been proposed to predict isoform functions from annotated gene functions and expression data, but their performance is far from being desirable primarily due to the lack of labeled training data. To improve the performance on this problem, we propose a novel deep learning method, DeepIsoFun, that combines multiple instance learning with domain adaptation. The latter technique helps to transfer the knowledge of gene functions to the prediction of isoform functions and provides additional labeled training data. Our model is trained on a deep neural network architecture so that it can adapt to different expression distributions associated with different gene ontology terms.ResultsWe evaluated the performance of DeepIsoFun on three expression datasets of human and mouse collected from SRA studies at different times. On each dataset, DeepIsoFun performed significantly better than the existing methods. In terms of area under the receiver operating characteristics curve, our method acquired at least 26% improvement and in terms of area under the precision-recall curve, it acquired at least 10% improvement over the state-of-the-art methods. In addition, we also study the divergence of the functions predicted by our method for isoforms from the same gene and the overall correlation between expression similarity and the similarity of predicted functions.Availability and implementationhttps://github.com/dls03/DeepIsoFun/Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals The Influence of Rooting Substrate and Growth Regulators Indol butyric Acid and Naphthalene Acetic Acid in the Number and Length of Adventitious Roots to Hardwood Cuttings in Blueberry cv. ‘Bluecrop’ (Vaccinium corymbosum L.)

2016 ◽  
Vol 4 (4) ◽  
pp. 28
Author(s):  
Sabri Braha ◽  
Petrit Rama
Keyword(s):  
Growth Regulators ◽  
Growing Season ◽  
Vaccinium Corymbosum ◽  
Naphthalene Acetic Acid ◽  
Important Variation ◽  
Physiological Processes ◽  
One Year ◽  
The Impact ◽  
Better Than

The purpose of this research is to determine the impact of the turf-only substrate and turf–perlite in the ratio 2:1 and of growth regulators in the quality of adventive roots ( the number and length) of well lignified one-year old branches without fruit buds in the Bluecrop cultivar (Vaccinium corymbosum L.) taken at the end of the latent period before budding at the February 15 th during the -2015 growing season. In order to support the increase of the number of roots and their length the hardwood cuttings are treated with different IBA and NAA concentrations (1500, 3000, 4500 ppm), while a part of cuttings were untreated control. The number and the length of roots have increased in relation to the increase of concentration from 1500 to 3000 ppm followed by a decline of these values in concentrations over 3000 ppm. Respectively, the number of roots (8) and the higher values of root length (4.6 cm) are achieved in the turf–perlite substrate, IBA 3000 ppm (compared to the turf-only substrate). The presence of perlite helps the aeration of the substrate and supports biochemical and physiological processes which lead to the inducing of adventive roots. Regarding the number and length of roots an important variation for (p<0.05) was observed between different concentrations of IBA and NAA. In general the effect of IBA was a lot better than the effect of NAA.

scholarly journals AnnotatorJ: an ImageJ plugin to ease hand-annotation of cellular compartments

2020 ◽  
Author(s):  
Réka Hollandi ◽  
Ákos Diósdi ◽  
Gábor Hollandi ◽  
Nikita Moshkov ◽  
Péter Horváth
Keyword(s):  
Deep Learning ◽  
Training Data ◽  
Automatic Annotation ◽  
Cellular Analysis ◽  
Manual Labour ◽  
Cellular Compartments ◽  
Analysis Quality ◽  
Microscopy Images ◽  
Imagej Plugin ◽  
Better Than

AbstractAnnotatorJ combines single-cell identification with deep learning and manual annotation. Cellular analysis quality depends on accurate and reliable detection and segmentation of cells so that the subsequent steps of analyses e.g. expression measurements may be carried out precisely and without bias. Deep learning has recently become a popular way of segmenting cells, performing unimaginably better than conventional methods. However, such deep learning applications may be trained on a large amount of annotated data to be able to match the highest expectations. High-quality annotations are unfortunately expensive as they require field experts to create them, and often cannot be shared outside the lab due to medical regulations.We propose AnnotatorJ, an ImageJ plugin for the semi-automatic annotation of cells (or generally, objects of interest) on (not only) microscopy images in 2D that helps find the true contour of individual objects by applying U-Net-based pre-segmentation. The manual labour of hand-annotating cells can be significantly accelerated by using our tool. Thus, it enables users to create such datasets that could potentially increase the accuracy of state-of-the-art solutions, deep learning or otherwise, when used as training data.

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