Image enhancement in embedded devices for internet of things

This paper proposes a new color interpolation method which can be used in embedded devices for IoT system. In this work, we use regression approach for generating and designing filters to restore color image. The filters are designed with four sizes, 5x5 training filter, 7x7 training filter, 9x9 training filter, and 11x11 training filter. The obtained filters are tested in 25 LC dataset to assess the performance. Experimental results inform that the proposed filters provide outstanding performance when they are compared with conventional methods. As compared with the other methods, the proposed filters produce the best average interpolation performance both objectively and visually

Active Contour Based Segmentation Techniques for Medical Image Analysis

Image processing is a technique which is used to derive information from the images. Segmentation is a section of image processing for the separation or segregation of information from the required target region of the image. There are different techniques used for segmentation of pixels of interest from the image. Active contour is one of the active models in segmentation techniques, which makes use of the energy constraints and forces in the image for separation of region of interest. Active contour defines a separate boundary or curvature for the regions of target object for segmentation. The contour depends on various constraints based on which they are classified into different types such as gradient vector flow, balloon and geometric models. Active contour models are used in various image processing applications specifically in medical image processing. In medical imaging, active contours are used in segmentation of regions from different medical images such as brain CT images, MRI images of different organs, cardiac images and different images of regions in the human body. Active contours can also be used in motion tracking and stereo tracking. Thus, the active contour segmentation is used for the separation of pixels of interest for different image processing

Proceedings Of The 18th Annual Meeting Of The Asia Oceania Geosciences Society (Aogs 2021)

The 18th Annual Meeting of the Asia Oceania Geosciences Society (AOGS 2021) was held from 1st to 6th August 2021. This proceedings volume includes selected extended abstracts from a challenging array of presentations at this conference. The AOGS Annual Meeting is a leading venue for professional interaction among researchers and practitioners, covering diverse disciplines of geosciences

Earth Resources: A continuing bibliography with indexes, issue 36

This bibliography lists 576 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System between October 1 and December 31, 1982. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

An interval type-2 fuzzy active contour model for auroral oval segmentation

Aurora is a recurrent feature of the atmosphere, acting as a mirror of otherwise invisible coupling between different atmospheric layers. Advanced processing of auroral images has proven essential to investigate some key physical processes in near-Earth space; in particular, auroral images carry important information for research on power networks, communication systems, meteorology, and complex biological systems. Segmenting aurora images to detect auroral regions is an important step of this study. Classical image segmentation approaches fail to effectively detect auroral regions when the auroral oval is not distinct from its background in terms of pixel intensity. To reduce the negative influence of intensity inhomogeneity in auroral oval images, we design a novel active contour model which employs interval type-2 fuzzy sets for auroral oval image segmentation. The proposed method can robustly segment auroral oval images even in the presence of high intensity variations. Experimental results on Ultraviolet Imager (UVI) auroral oval images acquired from an online database including data collected by NASA Polar satellite\u2019s UVI demonstrate the advantages of our method in terms of human visual perception and segmentation accuracy

Dependency Encoding for Relation Extraction

The surge in information in the form of textual data demands automated systems to extract structured information from unstructured data. Relation extraction plays a key role in the process, with the aim of extracting semantic relations between entities in a text. Since dependency parse trees are capable of capturing the grammatical structure of sentences, this thesis experiments with different encodings of the dependency parse tree to distinguish different semantic relationships. Experiments are conducted on three different data sets that vary in domain and complexity and experimented with varying encoding schemas that can be grouped into two. The first group focuses on encoding the structure of the dependency parse tree with a Deep Graph Convolution Neural Network (DGCNN). The second group focuses on encoding the linguistic features obtained from the dependency parse tree with classical machine learning models such as Random Forest, Support Vector Machine, and Feed-Forward Network, and deep models such as BERT and Transformer encoder stack. The objective of this thesis is not to achieve state-of-the-art (SOTA) performance, rather to evaluate how dependency parse tree based linguistic features perform on different encoding schemas, including deep transformer-based models, on the relation extraction task. The results of the experiments show that these features on certain data sets being less computationally demanding are competitive for complex language models such as BERT, and incorporating them externally to BERT improves the performance rather than confounding

Particle Physics Reference Library

This second open access volume of the handbook series deals with detectors, large experimental facilities and data handling, both for accelerator and non-accelerator based experiments. It also covers applications in medicine and life sciences. A joint CERN-Springer initiative, the “Particle Physics Reference Library” provides revised and updated contributions based on previously published material in the well-known Landolt-Boernstein series on particle physics, accelerators and detectors (volumes 21A,B1,B2,C), which took stock of the field approximately one decade ago. Central to this new initiative is publication under full open access

Space station systems: A bibliography with indexes (supplement 9)

This bibliography lists 1,313 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1, 1989 and June 30, 1989. Its purpose is to provide helpful information to researchers, designers and managers engaged in Space Station technology development and mission design. Coverage includes documents that define major systems and subsystems related to structures and dynamic control, electronics and power supplies, propulsion, and payload integration. In addition, orbital construction methods, servicing and support requirements, procedures and operations, and missions for the current and future Space Station are included