scholarly journals W-Band Multi-Aspect High Resolution Range Profile Radar Target Classification Using Support Vector Machines

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2385
Author(s):  
Tomasz Jasinski ◽  
Graham Brooker ◽  
Irina Antipov
Keyword(s):  
High Resolution ◽  
Predictive Value ◽  
Fourier Transforms ◽  
Unmanned Aircraft ◽  
Support Vector ◽  
Resource Limited ◽  
W Band ◽  
Kolmogorov Smirnov ◽  
Test For Dimensionality ◽  
Range Alignment

Millimeter-wave (W-band) radar measurements were taken for two maritime targets instrumented with attitude and heading reference systems (AHRSs) in a littoral environment with the aim of developing a multiaspect classifier. The focus was on resource-limited implementations such as short-range, tactical, unmanned aircraft systems (UASs) and dealing with limited and imbalanced datasets. Radar imaging and preprocessing consisted of recording high-resolution range profiles (HRRPs) and performing range alignment using peak detection and fast Fourier transforms (FFTs). HRRPs were used because of their simplicity, reliability, and speed. The features used were fixed-length, frequency domain range profiles. Two linear support vector machine (SVM)-based classifiers were developed which both yielded excellent results in their general forms and were simple to implement. The first approach utilized the positive predictive value (PPV) and negative predictive value (NPV) statistics of the SVM directly to generate target probabilities and consequently determine the optimal aspect transitions for classification. The second approach used the Kolmogorov–Smirnov test for dimensionality reduction, followed by concatenating feature vectors across several aspects. The latter approach is particularly well-suited to resource-constrained scenarios, potentially allowing for retraining and updating in the field.

scholarly journals Use of High Resolution Unmanned Aerial Systems Imagery and Machine Learning to Evaluate Grain Sorghum Tolerance to Mesotrione

2020 ◽  
Author(s):  
Isaac Barnhart ◽  
Sushila Chauhaudri ◽  
Balaji Aravindhan Pandian ◽  
P.V. Vara Prasad ◽  
Ignacio A. Ciampitti ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Data Extraction ◽  
Block Design ◽  
Grain Sorghum ◽  
Unmanned Aircraft ◽  
Support Vector ◽  
Unmanned Aerial Systems ◽  
Machine Learning Classification ◽  
Vegetative Indices

Manual evaluation of crop injury to herbicides is time-consuming. Unmanned aircraft systems (UAS) and high-resolution multispectral sensors and machine learning classification techniques have the potential to save time and improve precision in the evaluation of herbicide injury in crops, including grain sorghum (Sorghum bicolor L. Moench). The objectives of this research were to (1) evaluate three supervised classification algorithms (support vector machine, maximum likelihood, and random forest) for categorizing high-resolution UAS imagery to aid in data extraction and (2) evaluate the use of vegetative indices (VIs) collected from UAV imagery as an alternative to traditional methods of visual herbicide injury assessment in mesotrione-tolerant grain sorghum breeding trials. An experiment was conducted in a randomized complete block design using a factorial treatment arrangement of three genotypes by four mesotrione doses. Herbicide injury was rated visually on a scale of 0 (no injury) to 100 (complete plant mortality). The UAS flights were flown at 9, 15, 21, 27, and 35 days after treatment. Results show the SVM algorithm to be the most consistently accurate, and high correlations (r = -0.83 to -0.94; p < 0.0001) were observed between the normalized difference vegetative index (NDVI) and ground-measured herbicide injury. Therefore we conclude that VIs collected with UAS coupled with machine learning image classification, has the potential to be an effective method of evaluating mesotrione injury in grain sorghum.

Computer processing of high-resolution images of periodic specimens

1986 ◽  
Vol 44 ◽  
pp. 2-5
Author(s):  
W. Chiu ◽  
M.F. Schmid ◽  
T.-W. Jeng
Keyword(s):  
High Resolution ◽  
Fourier Transforms ◽  
Complex Structure ◽  
Distribution Functions ◽  
Multiple Image ◽  
Cryo Electron Microscopy ◽  
Structure Factors ◽  
Unit Cells ◽  
High Resolution Images ◽  
Phase Probability Distribution

Cryo-electron microscopy has been developed to the point where one can image thin protein crystals to 3.5 Å resolution. In our study of the crotoxin complex crystal, we can confirm this structural resolution from optical diffractograms of the low dose images. To retrieve high resolution phases from images, we have to include as many unit cells as possible in order to detect the weak signals in the Fourier transforms of the image. Hayward and Stroud proposed to superimpose multiple image areas by combining phase probability distribution functions for each reflection. The reliability of their phase determination was evaluated in terms of a crystallographic “figure of merit”. Grant and co-workers used a different procedure to enhance the signals from multiple image areas by vector summation of the complex structure factors in reciprocal space.

scholarly journals Drill-Core Mineral Abundance Estimation Using Hyperspectral and High-Resolution Mineralogical Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1218
Author(s):  
Laura Tuşa ◽  
Mahdi Khodadadzadeh ◽  
Cecilia Contreras ◽  
Kasra Rafiezadeh Shahi ◽  
Margret Fuchs ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Ore Deposits ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Drill Core ◽  
Data Types ◽  
Mineralogical Characterization ◽  
Core Samples

Due to the extensive drilling performed every year in exploration campaigns for the discovery and evaluation of ore deposits, drill-core mapping is becoming an essential step. While valuable mineralogical information is extracted during core logging by on-site geologists, the process is time consuming and dependent on the observer and individual background. Hyperspectral short-wave infrared (SWIR) data is used in the mining industry as a tool to complement traditional logging techniques and to provide a rapid and non-invasive analytical method for mineralogical characterization. Additionally, Scanning Electron Microscopy-based image analyses using a Mineral Liberation Analyser (SEM-MLA) provide exhaustive high-resolution mineralogical maps, but can only be performed on small areas of the drill-cores. We propose to use machine learning algorithms to combine the two data types and upscale the quantitative SEM-MLA mineralogical data to drill-core scale. This way, quasi-quantitative maps over entire drill-core samples are obtained. Our upscaling approach increases result transparency and reproducibility by employing physical-based data acquisition (hyperspectral imaging) combined with mathematical models (machine learning). The procedure is tested on 5 drill-core samples with varying training data using random forests, support vector machines and neural network regression models. The obtained mineral abundance maps are further used for the extraction of mineralogical parameters such as mineral association.

Diagnostic Accuracy of HRCT Chest in Detection of Covid-19 Infection Taking PCR as Gold Standard

2021 ◽  
Vol 15 (6) ◽  
pp. 1679-1681
Author(s):  
Afaque Ali ◽  
Majid Shaikh ◽  
Ahsanullah . ◽  
Adeel Ahmed ◽  
Abid Ali Sahito ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Diagnostic Accuracy ◽  
Predictive Value ◽  
Gold Standard ◽  
Cross Sectional Study ◽  
Radiology Department ◽  
High Resolution Computed Tomography ◽  
Cross Sectional ◽  
Head Of Department

Objective: To determine the diagnostic accuracy of High-resolution computed tomography (HRCT) chest in detection of covid-19 infection taking PCR as gold standard. Study Design: Cross-sectional study Setting: Radiology department of Tabba Hospital, Karachi. Duration: From March 2019 to September 2020 Material and Methods: All the clinically suspected patients of covid-19, of any age, both genders and those referred to radiology for High-resolution computed tomography (HRCT) chest to detect the covid-19 infection were included. After two days, patients’ PCR reports were collected from the ward, after taking informed consent and permission from head of department. The diagnostic accuracy of HRCT was established with respect to sensitivity, PPV, NPV, and specificity by taking PCR as gold standard. All the information was collected via study proforma. Results: Total 70 patients suspected for COVID-19 were studied, and the patients’ mean age was 58.23±9.52 years. Males were in majority 54(77.1%). As per HRCT findings, COVID-19 infection was positive in 46 patients, however, 48 patients were detected positive for COVID-19 infection as per PCR findings. In the detection of COVID-19 infection, HRCT chest showed sensitivity of 91%, specificity of 90%, PPV of 83%, NPV of 84% and diagnostic accuracy of 94%; by taking PCR as gold standard. Conclusion: High-resolution computed tomography (HRCT) is a reliable diagnostic approach in promptly detecting the COVID-19; with 91% sensitivity, 90% specificity, 83% positive predictive value, 84% negative predictive value and 94% diagnostic accuracy. Keywords: Accuracy, HRCT, COVID-19

scholarly journals Two-Step Urban Water Index (TSUWI): A New Technique for High-Resolution Mapping of Urban Surface Water

2018 ◽  
Vol 10 (11) ◽  
pp. 1704 ◽  
Author(s):  
Wei Wu ◽  
Qiangzi Li ◽  
Yuan Zhang ◽  
Xin Du ◽  
Hongyan Wang
Keyword(s):  
Surface Water ◽  
High Resolution ◽  
Urban Surface ◽  
Mapping Technique ◽  
Support Vector ◽  
Svm Classifier ◽  
P Value ◽  
Urban Water ◽  
Water Index ◽  
Water Mapping

Urban surface water mapping is essential for studying its role in urban ecosystems and local microclimates. However, fast and accurate extraction of urban water remains a great challenge due to the limitations of conventional water indexes and the presence of shadows. Therefore, we proposed a new urban water mapping technique named the Two-Step Urban Water Index (TSUWI), which combines an Urban Water Index (UWI) and an Urban Shadow Index (USI). These two subindexes were established based on spectral analysis and linear Support Vector Machine (SVM) training of pure pixels from eight training sites across China. The performance of the TSUWI was compared with that of the Normalized Difference Water Index (NDWI), High Resolution Water Index (HRWI) and SVM classifier at twelve test sites. The results showed that this method consistently achieved good performance with a mean Kappa Coefficient (KC) of 0.97 and a mean total error (TE) of 2.28%. Overall, classification accuracy of TSUWI was significantly higher than that of the NDWI, HRWI, and SVM (p-value < 0.01). At most test sites, TSUWI improved accuracy by decreasing the TEs by more than 45% compared to NDWI and HRWI, and by more than 15% compared to SVM. In addition, both UWI and USI were shown to have more stable optimal thresholds that are close to 0 and maintain better performance near their optimum thresholds. Therefore, TSUWI can be used as a simple yet robust method for urban water mapping with high accuracy.

scholarly journals Novel Genetic Variants of Hepatitis B Virus in Fulminant Hepatitis

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Jack Bee Chook ◽  
Yun Fong Ngeow ◽  
Kok Keng Tee ◽  
Suat Cheng Peh ◽  
Rosmawati Mohamed
Keyword(s):  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Predictive Value ◽  
Fulminant Hepatitis ◽  
Predictive Accuracy ◽  
Stop Codon ◽  
Acute Infection ◽  
Support Vector ◽  
Svm Model ◽  
B Virus

Fulminant hepatitis (FH) is a life-threatening liver disease characterised by intense immune attack and massive liver cell death. The common precore stop codon mutation of hepatitis B virus (HBV), A1896, is frequently associated with FH, but lacks specificity. This study attempts to uncover all possible viral nucleotides that are specifically associated with FH through a compiled sequence analysis of FH and non-FH cases from acute infection. We retrieved 67 FH and 280 acute non-FH cases of hepatitis B from GenBank and applied support vector machine (SVM) model to seek candidate nucleotides highly predictive of FH. Six best candidates with top predictive accuracy, 92.5%, were used to build a SVM model; they are C2129 (85.3%), T720 (83.0%), Y2131 (82.4%), T2013 (82.1%), K2048 (82.1%), and A2512 (82.1%). This model gave a high specificity (99.3%), positive predictive value (95.6%), and negative predictive value (92.1%), but only moderate sensitivity (64.2%). We successfully built a SVM model comprising six variants that are highly predictive and specific for FH: four in the core region and one each in the polymerase and the surface regions. These variants indicate that intracellular virion/core retention could play an important role in the progression to FH.

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