scholarly journals MICROVEGA (MICRO VESSEL FOR GEODETICS APPLICATION): A MARINE DRONE FOR THE ACQUISITION OF BATHYMETRIC DATA FOR GIS APPLICATIONS

2015 ◽  
Vol XL-5/W5 ◽  
pp. 123-130 ◽  
Author(s):  
F. Giordano ◽  
G. Mattei ◽  
C. Parente ◽  
F. Peluso ◽  
R. Santamaria
Keyword(s):  
3D Models ◽  
Precision Data ◽  
Typical Application ◽  
Bathymetric Data ◽  
Research Fields ◽  
Gis Applications ◽  
First Case ◽  
High Precision Data ◽  
Unmanned Surface Vessel

Bathymetric data are fundamental to produce navigational chart and sea-floor 3D models. They can be collected using different techniques and sensors on board of a variety of platforms, such as satellite, aircraft, ship and drone. The MicroVEGA drone is an Open Prototype of Autonomous Unmanned Surface Vessel (AUSV) conceived, designed and built to operate in the coastal areas (0-20 meters of depth), where a traditional boat is poorly manoeuvrable. It is equipped with a series of sensors to acquire the morpho-bathymetric high precision data. In this paper we presents the result of the first case study, a bathymetric survey carried out at Sorrento Marina Grande. This survey is a typical application case of this technology; the Open Prototype MicroVega has an interdisciplinary breath and it is going to be applied to various research fields. In future, it will expect to do new knowledge, new survey strategies and an industrial prototype in fiberglass.

scholarly journals Study of the Natural Heritage Condition of the Kursiu Nerija National Park Using Lidar Technology (Case Study of Agila Dune)

2019 ◽  
Vol 10 ◽  
pp. 36-42
Author(s):  
Deimante Pankauskyte ◽  
Jolanta Valciukiene ◽  
Indrius Kuklys ◽  
Lina Kukliene
Keyword(s):  
Cross Section ◽  
Cross Sections ◽  
National Park ◽  
Large Scale ◽  
Precision Data ◽  
Protective Measures ◽  
Current State ◽  
The Status ◽  
High Precision Data

Analysis of the condition of the Agila dune is presented in this Article. The analysis is based on data collected during accurate geodetic measurements using LIDAR technology. The current state of the Agila dune was compared to the data of the previous year's LIDAR points in order to ensure the reliability and value of the research. In the course of the study, eleven cross sections were compared by height differences with previous year‘s measurements. The condition of the Agila dune was found to be the worst in three cross sections. First cross section‘s erosion measured at 13,98 meters, erosion in the fifth cross section – 9.90 meters, and erosion in the eighth cross section - 11.34 meters. The main reasons for the deterioration of the natural values of the Kursiu Nerija National Park are climate, wind, high visitor flows and the persistent failure to carry out comprehensive research. Therefore, in order to preserve these unique natural values, it is important to collect large-scale and high-precision data on the status of these values, to systematize, analyze and take appropriate protective measures.

scholarly journals Estimate a Flexible Link’s Shape by the Use of Strain Gauge Sensors

ISRN Robotics ◽  
2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
M. H. Korayem ◽  
A. M. Shafei ◽  
F. Absalan
Keyword(s):  
Wheatstone Bridge ◽  
Curvature Function ◽  
Flexible Link ◽  
Data Logger ◽  
Precision Data ◽  
Interpolation Technique ◽  
Finite Set ◽  
High Precision Data ◽  
Continuous Curvature

This paper presents a method for estimating the flexible link’s shape by finite number of sensors. The position and orientation of flexible link are expressed as a function of curvature of the link. An interpolation technique gives this continuous curvature function from a finite set of the Wheatstone bridge made with strain gauges. For interpolation we can use different functions to find better way for estimation of link’s shape. Comparison between different types of function can show us best corresponding with nature of the link. Our case study is a single flexible link robot. A high-precision data logger is used as data acquisition instrument.

scholarly journals AN OPTIMISED WORKFLOW FOR THE INTERACTIVE EXPERIENCE WITH CULTURAL HERITAGE THROUGH REALITY-BASED 3D MODELS: CASES STUDY IN ARCHAEOLOGICAL AND URBAN COMPLEXES

2019 ◽  
Vol XLII-2/W11 ◽  
pp. 427-434 ◽  
Author(s):  
L. Cipriani ◽  
S. Bertacchi ◽  
G. Bertacchi
Keyword(s):  
Cultural Heritage ◽  
3D Models ◽  
First Case ◽  
Digital Contents ◽  
Game Engines ◽  
Pros And Cons ◽  
3D Documentation ◽  
Interactive Experience ◽  
Urban Sites

<p><strong>Abstract.</strong> The paper compares two workflows for the achievement of 3D models aimed at in-depth studies on the geometric features of Cultural Heritage artefacts and their dissemination. The purpose is the outlining of pros and cons of different techniques coming from entertainment and video games industry, starting from highly reliable 3D documentation of cultural assets, i.e. architectural/archaeological/urban sites. Two different possible applications are described: (i) procedural modelling used for understanding and visualising reconstruction hypotheses of the vaulted pavilions at Hadrian’s Villa, Tivoli, Rome; (ii) optimisation of 3D high-detailed models, as input files, turned into visual reliable and highly portable assets for game-engines. The first case study is focussed on creating a flexible model for evalueting reconstruction hypotheses and supplying restorers with useful hints for shape completion of ruined pavilions. The second case study makes available detailed digital contents for storytelling historical and cultural events in an attractive way, as in the case of the urban explorative model of Chiuro, a small town in northern Italy.</p>

Scales and Units

2018 ◽  
Author(s):  
Kathryn M. de Luna
Keyword(s):  
Language Change ◽  
Historical Linguistics ◽  
Bantu Languages ◽  
The North ◽  
First Case ◽  
Linguistic Evidence ◽  
River Region ◽  
History Of ◽  
Alternative Approaches

This chapter uses two case studies to explore how historians study language movement and change through comparative historical linguistics. The first case study stands as a short chapter in the larger history of the expansion of Bantu languages across eastern, central, and southern Africa. It focuses on the expansion of proto-Kafue, ca. 950–1250, from a linguistic homeland in the middle Kafue River region to lands beyond the Lukanga swamps to the north and the Zambezi River to the south. This expansion was made possible by a dramatic reconfiguration of ties of kinship. The second case study explores linguistic evidence for ridicule along the Lozi-Botatwe frontier in the mid- to late 19th century. Significantly, the units and scales of language movement and change in precolonial periods rendered visible through comparative historical linguistics bring to our attention alternative approaches to language change and movement in contemporary Africa.

Applications of random graphs

2017 ◽  
Author(s):  
A.C.C. Coolen ◽  
A. Annibale ◽  
E.S. Roberts
Keyword(s):  
Social Networks ◽  
Random Graphs ◽  
Interaction Network ◽  
Power Grids ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
First Case ◽  
The Stability ◽  
Protein Protein Interaction Network

This chapter reviews graph generation techniques in the context of applications. The first case study is power grids, where proposed strategies to prevent blackouts have been tested on tailored random graphs. The second case study is in social networks. Applications of random graphs to social networks are extremely wide ranging – the particular aspect looked at here is modelling the spread of disease on a social network – and how a particular construction based on projecting from a bipartite graph successfully captures some of the clustering observed in real social networks. The third case study is on null models of food webs, discussing the specific constraints relevant to this application, and the topological features which may contribute to the stability of an ecosystem. The final case study is taken from molecular biology, discussing the importance of unbiased graph sampling when considering if motifs are over-represented in a protein–protein interaction network.

Investigating the potential of redundant manipulators in narrow channels

2020 ◽  
pp. 095440622096451
Author(s):  
Ashish Singla ◽  
Jyotindra Narayan ◽  
Himanshu Arora
Keyword(s):  
Case Studies ◽  
Fuzzy Inference ◽  
Redundant Manipulators ◽  
Redundancy Resolution ◽  
Inference System ◽  
Narrow Channels ◽  
First Case ◽  
Task Priority ◽  
Patient Side

In this paper, an attempt has been made to investigate the potential of redundant manipulators, while tracking trajectories in narrow channels. The behavior of redundant manipulators is important in many challenging applications like under-water welding in narrow tanks, checking the blockage in sewerage pipes, performing a laparoscopy operation etc. To demonstrate this snake-like behavior, redundancy resolution scheme is utilized using two different approaches. The first approach is based on the concept of task priority, where a given task is split and prioritize into several subtasks like singularity avoidance, obstacle avoidance, torque minimization, and position preference over orientation etc. The second approach is based on Adaptive Neuro Fuzzy Inference System (ANFIS), where the training is provided through given datasets and the results are back-propagated using augmentation of neural networks with fuzzy logics. Three case studies are considered in this work to demonstrate the redundancy resolution of serial manipulators. The first case study of 3-link manipulator is attempted with both the approaches, where the objective is to track the desired trajectory while avoiding multiple obstacles. The second case study of 7-link manipulator, tracking trajectory in a narrow channel, is investigated using the concept of task priority. The realistic application of minimum-invasive surgery (MIS) based trajectory tracking is considered as the third case study, which is attempted using ANFIS approach. The 5-link spatial redundant manipulator, also known as a patient-side manipulator being developed at CSIR-CSIO, Chandigarh is used to track the desired surgical cuts. Through the three case studies, it is well demonstrated that both the approaches are giving satisfactory results.

scholarly journals Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Markus J. Ankenbrand ◽  
Liliia Shainberg ◽  
Michael Hock ◽  
David Lohr ◽  
Laura M. Schreiber
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Cardiac Mri ◽  
Magnetic Resonance Images ◽  
Model Interpretation ◽  
First Case ◽  
Segmentation Models

Abstract Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

scholarly journals Novel filter bank-based cooperative spectrum sensing under practical challenges for beyond 5G cognitive radios

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Sener Dikmese ◽  
Kishor Lamichhane ◽  
Markku Renfors
Keyword(s):  
Spectrum Sensing ◽  
Filter Bank ◽  
Cooperative Spectrum Sensing ◽  
Minimum Energy ◽  
Cognitive Radios ◽  
Spectrum Management ◽  
Wireless Channel ◽  
First Case ◽  
Channel Effects

AbstractCognitive radio (CR) technology with dynamic spectrum management capabilities is widely advocated for utilizing effectively the unused spectrum resources. The main idea behind CR technology is to trigger secondary communications to utilize the unused spectral resources. However, CR technology heavily relies on spectrum sensing techniques which are applied to estimate the presence of primary user (PU) signals. This paper firstly focuses on novel analysis filter bank (AFB) and FFT-based cooperative spectrum sensing (CSS) techniques as conceptually and computationally simplified CSS methods based on subband energies to detect the spectral holes in the interesting part of the radio spectrum. To counteract the practical wireless channel effects, collaborative subband-based approaches of PU signal sensing are studied. CSS has the capability to relax the problems of both hidden nodes and fading multipath channels. FFT- and AFB-based receiver side sensing methods are applied for OFDM waveform and filter bank-based multicarrier (FBMC) waveform, respectively, the latter one as a candidate beyond-OFDM/beyond-5G scheme. Subband energies are then applied for enhanced energy detection (ED)-based CSS methods that are proposed in the context of wideband, multimode sensing. Our first case study focuses on sensing potential spectral gaps close to relatively strong primary users, considering also the effects of spectral regrowth due to power amplifier nonlinearities. The study shows that AFB-based CSS with FBMC waveform is able to improve the performance significantly. Our second case study considers a novel maximum–minimum energy detector (Max–Min ED)-based CSS. The proposed method is expected to effectively overcome the issue of noise uncertainty (NU) with remarkably lower implementation complexity compared to the existing methods. The developed algorithm with reduced complexity, enhanced detection performance, and improved reliability is presented as an attractive solution to counteract the practical wireless channel effects under low SNR. Closed-form analytic expressions are derived for the threshold and false alarm and detection probabilities considering frequency selective scenarios under NU. The validity of the novel expressions is justified through comparisons with respective results from computer simulations.

scholarly journals Geospatial Serverless Computing: Architectures, Tools and Future Directions

2020 ◽  
Vol 9 (5) ◽  
pp. 311 ◽  
Author(s):  
Sujit Bebortta ◽  
Saneev Kumar Das ◽  
Meenakshi Kandpal ◽  
Rabindra Kumar Barik ◽  
Harishchandra Dubey
Keyword(s):  
Big Data ◽  
Forest Cover ◽  
Mineral Resources ◽  
Hyperspectral Data ◽  
Real Time Analysis ◽  
First Case ◽  
Amazon Web Services ◽  
Aws Lambda ◽  
Physical Features

Several real-world applications involve the aggregation of physical features corresponding to different geographic and topographic phenomena. This information plays a crucial role in analyzing and predicting several events. The application areas, which often require a real-time analysis, include traffic flow, forest cover, disease monitoring and so on. Thus, most of the existing systems portray some limitations at various levels of processing and implementation. Some of the most commonly observed factors involve lack of reliability, scalability and exceeding computational costs. In this paper, we address different well-known scalable serverless frameworks i.e., Amazon Web Services (AWS) Lambda, Google Cloud Functions and Microsoft Azure Functions for the management of geospatial big data. We discuss some of the existing approaches that are popularly used in analyzing geospatial big data and indicate their limitations. We report the applicability of our proposed framework in context of Cloud Geographic Information System (GIS) platform. An account of some state-of-the-art technologies and tools relevant to our problem domain are discussed. We also visualize performance of the proposed framework in terms of reliability, scalability, speed and security parameters. Furthermore, we present the map overlay analysis, point-cluster analysis, the generated heatmap and clustering analysis. Some relevant statistical plots are also visualized. In this paper, we consider two application case-studies. The first case study was explored using the Mineral Resources Data System (MRDS) dataset, which refers to worldwide density of mineral resources in a country-wise fashion. The second case study was performed using the Fairfax Forecast Households dataset, which signifies the parcel-level household prediction for 30 consecutive years. The proposed model integrates a serverless framework to reduce timing constraints and it also improves the performance associated to geospatial data processing for high-dimensional hyperspectral data.

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