scholarly journals Use of a Portable Microscope Combined with a Smartphone to Determine the Authenticity of Brazilian Banknotes and National Driver’s Licenses

2022 ◽  
Author(s):  
Rayana Costa ◽  
Bruno Vittorazzi ◽  
Amanda Barbosa ◽  
Victória da Rocha ◽  
Jandira Brandão ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Low Cost ◽  
Image Acquisition ◽  
Region Of Interest ◽  
Principal Component ◽  
Component Analysis ◽  
The State ◽  
Technical Failure ◽  
The Republic ◽  
Chemometric Treatment

This study proposed using a portable microscope combined with a smartphone and application (PhotoMetrix® application) to evaluate the authenticity of Brazilian banknotes (R$50 and R$100) and national driver’s licenses (NDL) through image acquisition (Samsung S7) and chemometric treatment (principal component analysis (PCA)). Six regions of the banknote were analyzed: holographic band; lower and upper tactile regions containing the number referring to the value of the note; microprints above the effigy (obverse); and numbers and the surroundings of the animal formed by microprints (reverse). For NDLs, the regions were the following: the coat of arms of the republic; the state map with microprints; optical ink variation; distorted positive microletters with technical failure; negative guilloche; typographic numbering; micro letter wire; and region with line printing. For the chemometric study with Photometrix®, we selected a region of interest (ROI) of 32 × 32 and 64 × 64 pixels with autoscaled data using the channels red (R), green (G), blue (B), hue (H), saturation (S), value (V), lightness (L) and intensity (I). We obtained excellent results for differentiating banknotes and NDLs, both by visual and chemometric analyses (PCA). This study demonstrates the effectiveness of using a portable microscope and a smartphone as a portable forensic tool that is fast, robust, low-cost and reliable.

scholarly journals 33 Aplicação da análise exploratória de dados na discriminação geográfica do quiabo do Rio Grande do Norte e Pernambuco

2018 ◽  
Vol 34 (3) ◽  
pp. 33
Author(s):  
Francisco Dos Santos Panero ◽  
Maria de Fátima Pereira Vieira ◽  
Ângela Maria Paiva Cruz ◽  
Maria de Fátima Vitória De Moura ◽  
Henrique Eduardo Bezerra Da Silva
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Rio Grande ◽  
Component Analysis ◽  
The State ◽  
Data Treatment ◽  
Different Origins

Samples of okra from Caruaru and Vitória of Santo Antão, in the State of Pernambuco, and Ceará-Mirim, Macaíba and Extremoz in the State of Rio Grande do Norte have been analysed. Two different methods were applied in the data treatment allowing to geographically discriminate samples from different origins: Principal Component Analysis - PCA and Hierarquical Cluster Analysis - HCA.

scholarly journals Detection of Lethal Bronzing Disease in Cabbage Palms (Sabal palmetto) Using a Low-Cost Electronic Nose

Biosensors ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 188
Author(s):  
Martin J. Oates ◽  
Nawaf Abu-Khalaf ◽  
Carlos Molina-Cabrera ◽  
Antonio Ruiz-Canales ◽  
Jose Ramos ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Fourier Transform ◽  
Discrete Fourier Transform ◽  
Electronic Nose ◽  
Low Cost ◽  
Harmonic Component ◽  
Principal Component ◽  
Component Analysis ◽  
Sabal Palmetto ◽  
Cabbage Palm

Lethal Bronzing Disease (LB) is a disease of palms caused by the 16SrIV-D phytoplasma. A low-cost electronic nose (eNose) prototype was trialed for its detection. It includes an array of eight Taguchi-type (MQ) sensors (MQ135, MQ2, MQ3, MQ4, MQ5, MQ9, MQ7, and MQ8) controlled by an Arduino NANO® microcontroller, using heater voltages that vary sinusoidally over a 2.5 min cycle. Samples of uninfected, early symptomatic, moderate symptomatic, and late symptomatic infected palm leaves of the cabbage palm were processed and analyzed. MQ sensor responses were subjected to a 256 element discrete Fourier transform (DFT), and harmonic component amplitudes were reviewed by principal component analysis (PCA). The experiment was repeated three times, each showing clear evidence of differences in sensor responses between the samples of uninfected leaves and those in the early stages of infection. Within each experiment, four groups of responses were identified, demonstrating the ability of the unit to repeatedly distinguish healthy leaves from diseased ones; however, detection of the severity of infection has not been demonstrated. By selecting appropriate coefficients (here demonstrated with plots of MQ5 Cos1 vs. MQ8 Sin3), it should be possible to build a ruleset classifier to identify healthy and unhealthy samples.

scholarly journals Southwest monsoon rainfall in Assam: An application of principal component analysis for understanding of variability

MAUSAM ◽  
2021 ◽  
Vol 68 (2) ◽  
pp. 357-366
Author(s):  
PIJUSH BASAK
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Temporal Variability ◽  
Monsoon Rainfall ◽  
Principal Component ◽  
Component Analysis ◽  
Southwest Monsoon ◽  
The State ◽  
Spatial And Temporal Variability ◽  
Southwest Monsoon Rainfall

The principal component analysis (PCA) is applied to understand the spatial and temporal variability of monsoonal rainfall in the state Assam in India. The Southwest Monsoon (SWM) rainfall data over 12 widely spread stations located over the state has been analyzed for a period of 60 years for understanding variability. A statistically significant trend and a above/below transition signal has been observed for a few stations and the corresponding principal components (PCs). Coherent regions of Northern and Southern Assam have been identified through PCA to bring out the possible significant signals. It is observed that some of PCs for state-wise and coherent regions have positive or negative trend and significant above/below transition.    

Using a single sensor for bridge condition monitoring via moving embedded principal component analysis

2020 ◽  
pp. 147592172098051
Author(s):  
Zhenhua Nie ◽  
Zhaofeng Shen ◽  
Jun Li ◽  
Hong Hao ◽  
Yizhou Lin ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Time Delay ◽  
State Space ◽  
Structural Damage ◽  
Damage Index ◽  
Principal Component ◽  
Component Analysis ◽  
The State ◽  
Contribution Ratio ◽  
Recorded Data

This article presents a novel data-driven structural damage detection method named moving embedded principal component analysis to monitor the bridge condition and detect the damage occurrence using only one sensor. A fixed moving window is used to cut out the time series of the recorded data for the analysis. The data set inside the window is embedded to be a multidimensional state space using time delay method. The matrix of the state space is analyzed using the standard principal component analysis method, and a novel damage index Rj defined with the eigenvalue is proposed to identify structural damage occurrence. The window length is determined by a new approach through examining the convergent spectrum of the contribution ratio of the first principal component of the embedded state space. The time delay is determined by the autocorrelation function of the response, and the embedding dimension is obtained by the cumulative contribution ratio of the state space. The windowed damage index can be calculated continuously by moving the window along the recorded vibration data. To demonstrate the performance of the proposed method, responses of a beam bridge model subjected to stochastic loads obtained with numerical simulations and experimental tests are analyzed to monitor the structural conditions. The results demonstrate that the proposed method can accurately identify the occurrence of damage and the abnormal behavior of the structure. The recorded data on a large suspension bridge are also analyzed. The analysis successfully identified an incident on this bridge when it was slightly scraped by the mast of a sand ship. This further verifies the effectiveness of the proposed method.

Capsaicin determination and chili sauce discrimination using low-cost and portable electrochemical sensors based on all graphite pencil electrodes

2016 ◽  
Vol 8 (39) ◽  
pp. 7025-7029 ◽  
Author(s):  
Shixin Wu ◽  
Jiamin Zeng ◽  
Hong Xie ◽  
Sum Huan Ng
Keyword(s):  
Principal Component Analysis ◽  
Electrochemical Sensors ◽  
Low Cost ◽  
Principal Component ◽  
Component Analysis ◽  
Electrochemical Cells

Determination of capsaicin using home-made electrochemical cells with all graphite pencil electrodes (GPEs) and successful discrimination of chili sauces by principal component analysis (PCA) andk-means clustering were performed.

Shell morphometric ratios as a tool for taxonomic determination in gastropods: a case study in Nerita (Gastropoda, Neritidae)

2021 ◽  
pp. 1-7
Author(s):  
Leslie Nascimento Altomari ◽  
Brunno Henryco Borges Alves ◽  
Weverton John Pinheiro dos Santos ◽  
Mara Rúbia Ferreira Barros ◽  
Marko Herrmann ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Low Cost ◽  
Principal Component ◽  
Component Analysis ◽  
Shell Shape ◽  
Aperture Width ◽  
Taxonomic Studies ◽  
External Characteristics ◽  
Shape Morphometrics

Abstract In the study, we compare the shell shape morphometrics in four species of neritid gastropods (Nerita fulgurans, Nerita tessellata, Nerita peloronta and Nerita versicolor), collected in Accra Beach (Barbados Island). We tested the hypothesis that the morphometric ratios can be used as a tool in the taxonomic determination among these four species of neritids. For this we determine the morphometric ratios from the external (length, height, width) and internal (shell aperture length, shell aperture width) measures. A principal component analysis (PCA) was used to determine which ratios were significant, and subsequently the proposed hypothesis was tested using the Kruskal–Wallis test. The morphometric ratios AW/H and AL/L were decisive in distinguishing the four species of neritids studied. In this study, the hypothesis of the efficacy of the use of shell morphometric ratios as an instrument in taxonomic studies was corroborated for the four species. Due to its low cost, this methodology can be applied in the recognition of species that have lost their external characteristics such as operculum, spire or colour and also in the identification of fossil specimens.

scholarly journals Spatial Perspectives toward the Recommendation of Remote Sensing Images Using the INDEX Indicator, Based on Principal Component Analysis

2020 ◽  
Vol 12 (8) ◽  
pp. 1277
Author(s):  
Jung-Hong Hong ◽  
Zeal Li-Tse Su ◽  
Eric Hsueh-Chan Lu
Keyword(s):  
Remote Sensing ◽  
Principal Component Analysis ◽  
Hausdorff Distance ◽  
Image Acquisition ◽  
Principal Component ◽  
Component Analysis ◽  
Sensor Technology ◽  
Remote Sensing Images ◽  
Measuring Methods ◽  
Image Type

Progress in the development of sensor technology has increased the speed and convenience of remote sensing (RS) image acquisition. As the volume of RS images steadily increases, the challenge is no longer in producing and acquiring an RS image, but in finding a particular image from numerous RS images that precisely meets user application needs. Some spatial measuring methods specific to the recommendation of RS images have been proposed and could be used to score and sort RS images according to users’ requests. Our previous study introduced two measuring methods, namely, available space (AS) and image extension (IE), which have similar results but complementary effects for spatially ranking recommended images. The AS indicator could cover the inadequacies of the IE indicator in some cases and vice versa. The current study combines these two indicators using principal component analysis and produced a new indicator called INDEX, which we used in the RS image spatial recommendation. The ranking results were measured using a normalized discounted cumulative gain (NDCG) and several other statistic criteria. The results indicate that users are more satisfied with the recommendations of the INDEX indicator than those of AS, IE and Hausdorff distance for single RS image type selections which is the most common scenario for RS image applications. When dealing with hybrid RS image types, the INDEX indicator performs very closely to the dominant IE indicator, yet maintaining the characteristics of the AS indicator.

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