Imagery classification for autonomous ground vehicle mobility in cold weather environments

2021 ◽  
Author(s):  
Jason Olivier ◽  
Sally Shoop
Keyword(s):  
Image Data ◽  
Short Wave ◽  
Machine Learning Algorithms ◽  
Cold Weather ◽  
Vehicle Performance ◽  
Ground Vehicle ◽  
Swir Band ◽  
Military Applications ◽  
Vehicle Mobility

Autonomous ground vehicle (AGV) research for military applications is important for developing ways to remove soldiers from harm’s way. Current AGV research tends toward operations in warm climates and this leaves the vehicle at risk of failing in cold climates. To ensure AGVs can fulfill a military vehicle’s role of being able to operate on- or off-road in all conditions, consideration needs to be given to terrain of all types to inform the on-board machine learning algorithms. This research aims to correlate real-time vehicle performance data with snow and ice surfaces derived from multispectral imagery with the goal of aiding in the development of a truly all-terrain AGV. Using the image data that correlated most closely to vehicle performance the images were classified into terrain units of most interest to mobility. The best image classification results were obtained when using Short Wave InfraRed (SWIR) band values and a supervised classification scheme, resulting in over 95% accuracy.

scholarly journals IDENTIFICATION OF MANGROVE FORESTS USING MULTISPECTRAL SATELLITE IMAGERIES

2019 ◽  
Vol 16 (1) ◽  
pp. 63
Author(s):  
Anang Dwi Purwanto ◽  
Wikanti Asriningrum
Keyword(s):  
Mangrove Forest ◽  
Near Infrared ◽  
Image Data ◽  
Short Wave ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Visual Identification ◽  
Red Edge ◽  
Swir Band ◽  
Sentinel 2A

The visual identification of mangrove forests is greatly constrained by combinations of RGB composite. This research aims to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using the Optimum Index Factor (OIF) method. The OIF method uses the standard deviation value and correlation coefficient from a combination of three image bands. The image data comprise Landsat 8 imagery acquired on 30 May 2013, Sentinel 2A imagery acquired on 18 March 2018 and images from SPOT 6 acquired on 10 January 2015. The results show that the band composites of 564 (NIR+SWIR+Red) from Landsat 8 and 8a114 (Vegetation Red Edge+SWIR+Red) from Sentinel 2A are the best RGB composites for identifying mangrove forest, in addition to those of 341 (Red+NIR+Blue) from SPOT 6. The near-infrared (NIR) and short-wave infrared (SWIR) bands play an important role in determining mangrove forests. The properties of vegetation are reflected strongly at the NIR wavelength and the SWIR band is very sensitive to evaporation and the identification of wetlands.

scholarly journals Integration of Terrestrial and Drone-Borne Hyperspectral and Photogrammetric Sensing Methods for Exploration Mapping and Mining Monitoring

2018 ◽  
Vol 10 (9) ◽  
pp. 1366 ◽  
Author(s):  
Moritz Kirsch ◽  
Sandra Lorenz ◽  
Robert Zimmermann ◽  
Laura Tusa ◽  
Robert Möckel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Remote Sensing Data ◽  
Image Data ◽  
Sampling Strategy ◽  
Short Wave ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Mining District ◽  
Mining Areas

Mapping lithology and geological structures accurately remains a challenge in difficult terrain or in active mining areas. We demonstrate that the integration of terrestrial and drone-borne multi-sensor remote sensing techniques significantly improves the reliability, safety, and efficiency of geological activities during exploration and mining monitoring. We describe an integrated workflow to produce a geometrically and spectrally accurate combination of a Structure-from-Motion Multi-View Stereo point cloud and hyperspectral data cubes in the visible to near-infrared (VNIR) and short-wave infrared (SWIR), as well as long-wave infrared (LWIR) ranges acquired by terrestrial and drone-borne imaging sensors. Vertical outcrops in a quarry in the Freiberg mining district, Saxony (Germany), featuring sulfide-rich hydrothermal zones in a granitoid host, are used to showcase the versatility of our approach. The image data are processed using spectroscopic and machine learning algorithms to generate meaningful 2.5D (i.e., surface) maps that are available to geologists on the ground just shortly after data acquisition. We validate the remote sensing data with thin section analysis and laboratory X-ray diffraction, as well as point spectroscopic data. The combination of ground- and drone-based photogrammetric and hyperspectral VNIR, SWIR, and LWIR imaging allows for safer and more efficient ground surveys, as well as a better, statistically sound sampling strategy for further structural, geochemical, and petrological investigations.

Role of machine learning algorithms over heart diseases prediction

2020 ◽  
Author(s):  
Siva Kumar Jonnavithula ◽  
Abhilash Kumar Jha ◽  
Modepalli Kavitha ◽  
Singaraju Srinivasulu
Keyword(s):  
Machine Learning ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroto Yamashita ◽  
Rei Sonobe ◽  
Yuhei Hirono ◽  
Akio Morita ◽  
Takashi Ikka
Keyword(s):  
Short Wave ◽  
Machine Learning Algorithms ◽  
Estimation Methods ◽  
Chemical Information ◽  
Tea Quality ◽  
The Mean ◽  
Processing Techniques ◽  
Spectral Patterns ◽  
Physical And Chemical ◽  
Non Destructive

AbstractSpectroscopic sensing provides physical and chemical information in a non-destructive and rapid manner. To develop non-destructive estimation methods of tea quality-related metabolites in fresh leaves, we estimated the contents of free amino acids, catechins, and caffeine in fresh tea leaves using visible to short-wave infrared hyperspectral reflectance data and machine learning algorithms. We acquired these data from approximately 200 new leaves with various status and then constructed the regression model in the combination of six spectral patterns with pre-processing and five algorithms. In most phenotypes, the combination of de-trending pre-processing and Cubist algorithms was robustly selected as the best combination in each round over 100 repetitions that were evaluated based on the ratio of performance to deviation (RPD) values. The mean RPD values were ranged from 1.1 to 2.7 and most of them were above the acceptable or accurate threshold (RPD = 1.4 or 2.0, respectively). Data-based sensitivity analysis identified the important hyperspectral regions around 1500 and 2000 nm. Present spectroscopic approaches indicate that most tea quality-related metabolites can be estimated non-destructively, and pre-processing techniques help to improve its accuracy.

scholarly journals Harnessing SmartPhones to Personalize Nutrition in a Time of Global Pandemic

Nutrients ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 422
Author(s):  
Niv Zmora ◽  
Eran Elinav
Keyword(s):  
Modern Technology ◽  
Machine Learning Algorithms ◽  
Medical Attention ◽  
Nutritional Interventions ◽  
Healthcare Facilities ◽  
Global Pandemic ◽  
Healthy Nutrition ◽  
Food Diaries ◽  
Physical Interactions

The soar in COVID-19 cases around the globe has forced many to adapt to social distancing and self-isolation. In order to reduce contact with healthcare facilities and other patients, the CDC has advocated the use of telemedicine, i.e., electronic information and telecommunication technology. While these changes may disrupt normal behaviors and routines and induce anxiety, resulting in decreased vigilance to healthy diet and physical activity and reluctance to seek medical attention, they may just as well be circumvented using modern technology. Indeed, as the beginning of the pandemic a plethora of alternatives to conventional physical interactions were introduced. In this Perspective, we portray the role of SmartPhone applications (apps) in monitoring healthy nutrition, from their basic functionality as food diaries required for simple decision-making and nutritional interventions, through more advanced purposes, such as multi-dimensional data-mining and development of machine learning algorithms. Finally, we will delineate the emerging field of personalized nutrition and introduce pioneering technologies and concepts yet to be incorporated in SmartPhone-based dietary surveillance.

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

scholarly journals Is the ultraviolet waveband a special communication channel in avian mate choice?

2001 ◽  
Vol 204 (14) ◽  
pp. 2499-2507 ◽  
Author(s):  
Sarah Hunt ◽  
Innes C. Cuthill ◽  
Andrew T. D. Bennett ◽  
Stuart C. Church ◽  
Julian C. Partridge
Keyword(s):  
Mate Choice ◽  
Communication Channel ◽  
Visible Spectrum ◽  
Short Wave ◽  
Zebra Finches ◽  
Long Wave ◽  
Single Cone ◽  
Coloured Filters ◽  
Uv Reflection

SUMMARYThere is growing evidence that ultraviolet (UV) wavelengths play an important role in avian mate choice. One of the first experiments to support this idea showed that female zebra finches (Taeniopygia guttata) prefer UV-reflecting males to males whose ultraviolet reflection has been removed. The effect was very strong despite little or no UV reflection from several plumage areas. However, it is not clear how the importance of the UV waveband compares to other regions of the bird-visible spectrum. We tested whether the response of female zebra finches to the removal of male UV reflection is greater than to the removal of other wavebands. We presented females with a choice of males whose appearance was manipulated using coloured filters. The filters removed single blocks of the avian visible spectrum corresponding closely to the spectral sensitivities of each of the zebra finch’s single cone classes. This resulted in males that effectively had no UV (UV−), no short-wave (SW−), no medium-wave (MW−) or no long-wave (LW−) plumage reflection. Females preferred UV− and SW− males. LW− and MW− males were least preferred, suggesting that female zebra finches show the greatest response to the removal of longer wavelengths. Quantal catches of the single cone types viewing body areas of the male zebra finch are presented for each treatment. Our study suggests it is important to consider the role of the UV waveband in avian mate choice in conjunction with the rest of the avian visible spectrum.

scholarly journals Analysis Technology of Tennis Sports Match Based on Data Mining and Image Feature Retrieval

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Hong Huang ◽  
Risheng Deng
Keyword(s):  
Data Mining ◽  
Feature Recognition ◽  
Image Data ◽  
Technical Analysis ◽  
Machine Learning Algorithms ◽  
Technical Training ◽  
Image Feature ◽  
Mining Technology ◽  
Movement Characteristics ◽  
Real Time Tracking

Tennis game technical analysis is affected by factors such as complex background and on-site noise, which will lead to certain deviations in the results, and it is difficult to obtain scientific and effective tennis technical training strategies through a few game videos. In order to improve the performance of tennis game technical analysis, based on machine learning algorithms, this paper combines image analysis to identify athletes’ movement characteristics and image feature recognition processing with image recognition technology, realizes real-time tracking of athletes’ dynamic characteristics, and records technical characteristics. Moreover, this paper combines data mining technology to obtain effective data from massive video and image data, uses mathematical statistics and data mining technology for data processing, and scientifically analyzes tennis game technology with the support of ergonomics. In addition, this paper designs a controlled experiment to verify the technical analysis effect of the tennis match and the performance of the model itself. The research results show that the model constructed in this paper has certain practical effects and can be applied to actual competitions.

The Model Selection Curse

2019 ◽  
Vol 1 (2) ◽  
pp. 127-140 ◽  
Author(s):  
Kfir Eliaz ◽  
Ran Spiegler
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Learning Algorithms ◽  
Personal Data ◽  
Estimation Procedure ◽  
Machine Learning Algorithms ◽  
Human Interaction ◽  
Truth Telling ◽  
Agent Based

A statistician takes an action on behalf of an agent, based on the agent’s self-reported personal data and a sample involving other people. The action that he takes is an estimated function of the agent’s report. The estimation procedure involves model selection. We ask the following question: Is truth-telling optimal for the agent given the statistician’s procedure? We analyze this question in the context of a simple example that highlights the role of model selection. We suggest that our simple exercise may have implications for the broader issue of human interaction with machine learning algorithms. (JEL C52)

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