Content Based Image Retrieval (CBIR) focuses on retrieving images from repositories based on visual features extracted from the images. Texture and colour are one of the popularly used feature ...combination in CBIR. A major challenge in colour image retrieval is the characterization of features of the constituent channels and their integration. The commonly adopted methodology include extraction of features of various channels followed by their concatenation. However, the resulting image feature vector is generally of high dimensionality. To address this problem, in this paper a texture-colour descriptor is proposed integrating the multi-channel features. For texture computation, a fixed sized local intensity based descriptor, Maximal Multi-channel Local Binary Pattern (MMLBP), which integrates the multi-channel local binary information through an adder-map followed by thresholding is introduced. The histogram of the obtained patterns is used for representing the image texture. Colour information is captured by quantizing the RGB colour space and is represented with histogram. The colour-texture descriptors are further fused to characterize the images. The efficacy of the descriptor is evaluated by carrying out retrieval on benchmarked datasets for image retrieval such as Wang’s 1 K, Corel 5 K, Corel 10 K, Coloured Brodatz Texture and Zubud, using precision and recall measures as evaluation metrics. It is observed that the proposed descriptor presents improved retrieval performance over the databases under consideration and outperforms other descriptors.
Quantitative determination of kerosene fraction present in diesel has been carried out based on excitation emission matrix fluorescence (EEMF) along with parallel factor analysis (PARAFAC) and
N-way ...partial least squares regression (
N-PLS). EEMF is a simple, sensitive and nondestructive method suitable for the analysis of multifluorophoric mixtures. Calibration models consisting of varying compositions of diesel and kerosene were constructed and their validation was carried out using leave-one-out cross validation method. The accuracy of the model was evaluated through the root mean square error of prediction (RMSEP) for the PARAFAC,
N-PLS and unfold PLS methods.
N-PLS was found to be a better method compared to PARAFAC and unfold PLS method because of its low RMSEP values.
This paper presents the distinctiveness of particulate matter (PM) mass concentrations (PM
10
, PM
2.5
, and PM
10-2.5
) and meteorological effect in Pune city during 2011–2012. The PM samples were ...collected using Mini-Vol TAS air sampler (Airmetrics Co. Inc., 5 l min
−1
flow rate). The meteorological parameters were also measured during the study period. The analysis of 24-h average PM
10
, PM
2.5
, and PM
10-2.5
concentrations showed the maximum during winter (267.2–67.2, 180.6–55.6, 138.9–11.7 μg m
−3
) followed by summer (236.1–55.5, 138.8–27.7, 125–13.8 μg m
−3
) and post-monsoon (153.3–82.3, 138.9–41.7, 41.7–14.4 μg m
−3
) and showed the lowest concentration during monsoon (98.9–27.8, 83.3–13.9, 40.0–6.0 μg m
−3
) seasons in the entire study. PM
10
comprised a vast fraction of PM
2.5
(61 % of PM
2.5
), while the estimated PM
2.5
/PM
10
ratios for monsoon, post-monsoon, winter, and summer seasons were ranged between 0.5 and 0.9, 0.51 and 0.91, 0.3 and 0.9, and 0.3 and 0.8, respectively. The 7-day back trajectories analysis for whole year shows that the air masses transported to Pune were mixed mainland-maritime such as from southwesterly, north, northwest. Chemometric analysis was applied as a tool to evaluate and predict the particulate mass concentration from available meteorological data. To achieve this, a calibration model was developed by partial least squares regression (PLSR) method and was further used to predict the PM concentrations based on meteorological data. On predicting the PM concentration from local meteorological data, the model performance and quality was found very good in case of PM
10
compared to PM
2.5
.
The present work compares the dissimilarity and covariance based unsupervised chemometric classification approaches by taking the total synchronous fluorescence spectroscopy data sets acquired for ...the cumin and non-cumin based herbal preparations. The conventional decomposition method involves eigenvalue-eigenvector analysis of the covariance of the data set and finds the factors that can explain the overall major sources of variation present in the data set. The conventional approach does this irrespective of the fact that the samples belong to intrinsically different groups and hence leads to poor class separation. The present work shows that classification of such samples can be optimized by performing the eigenvalue-eigenvector decomposition on the pair-wise dissimilarity matrix.
A multivariate calibration model for the simultaneous estimation of propranolol (PRO) and amiloride (AMI) using synchronous fluorescence spectroscopic data has been presented in this paper. Two ...multivariate techniques, PCR (Principal Component Regression) and PLSR (Partial Least Square Regression), have been successfully applied for the simultaneous determination of AMI and PRO in synthetic binary mixtures and pharmaceutical dosage forms. The SF spectra of AMI and PRO (calibration mixtures) were recorded at several concentrations within their linear range between wavelengths of 310 and 500 nm at an interval of 1 nm. Calibration models were constructed using 32 samples and validated by varying the concentrations of AMI and PRO in the calibration range. The results indicated that the model developed was very robust and able to efficiently analyze the mixtures with low RMSEP values.
Products of petroleum crude are multifluorophoric in nature due to the presence of a mixture of a variety polycyclic aromatic hydrocarbons (PAHs). The use of excitation–emission matrix fluorescence ...(EEMF) spectroscopy for the analysis of such multifluorophoric samples is gaining progressive acceptance. In this work, EEMF spectroscopic data is processed using chemometric multivariate methods to develop a reliable calibration model for the quantitative determination of kerosene fraction present in petrol. The application of the N-way partial least squares regression (N-PLS) method was found to be very efficient for the estimation of kerosene fraction. A very good degree of accuracy of prediction, expressed in terms of root mean square error of prediction (RMSEP), was achieved at a kerosene fraction of 2.05%.
Synchronous fluorescence spectroscopy (SFS) is a rapid, sensitive and nondestructive method suitable for the analysis of multifluorophoric mixtures. The present study demonstrates the use of SFS and ...multivariate methods for the analysis of petroleum products which is a complex mixture of multiple fluorophores. Two multivariate techniques principal component regression (PCR) and partial least square regression (PLSR) have been successfully applied for the classification of petrol–kerosene mixtures. Calibration models were constructed using 35 samples and their validation was carried out with varying composition of petrol and kerosene in the calibration range. The results showed that the method could be used for the estimation of kerosene in kerosene-mixed petrol. The model was found to be sensitive, detecting even 1% contamination of kerosene in petrol.
Content based image retrieval uses different feature descriptors for image search and retrieval. For image retrieval from huge image repositories, the query image features are extracted and compares ...these features with the contents of feature repository. The most matching image is found and retrieved from the database. This mapping is done based on the distance calculated between feature vector of query image and the extracted feature vectors of images in the database. There are various distance measures used for comparing image feature vectors. This paper compares a set of distance measures using a set of features used for CBIR. The city-block distance measure gives the best results for CBIR.
Abstract
A blockchain based platform can be decentralized, meaning that it is not controlled by a single entity or organization. This can help to cut down the risk of data manipulation or fraud, and ...can also make the platform more resilient to cyber-attacks or other security threats. By automating many of the processes involved in data collection and analysis, blockchain technology can assist farmers to decrease their costs and increase profitability. Architecture for blockchain powered IoT platform for autonomous drone operations in smart farming is proposed. It has three layers which include data acquistion and encryption in IoT layer and creation and management of ledger in the blockchain layer and service provider layer that include user interaction and farming software. Agriculture integrates many of the new automation technologies already in their routine. The proposed architecture enriches the functioning of farming without affecting the current framework. Overall, the blockchain powered IoT platform for autonomous drone operations in smart farming can help farmers improve their efficiency, increase transparency and security, reduce costs, and ultimately achieve more environment sustainability and profitable farming practices.
In the present work, four different spectrophotometric methods for simultaneous estimation of losartan potassium, amlodipine besilate and hydrochlorothiazide in raw materials and in formulations are ...described. Overlapped data was quantitatively resolved by using chemometric methods, classical least squares (CLS), multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS). Calibrations were constructed using the absorption data matrix corresponding to the concentration data matrix, with measurements in the range of 230.5-350.4 nm (Δλ = 0.1 nm) in their zero order spectra. The linearity range was found to be 8-40, 1-5 and 3-15 μg mL-1 for losartan potassium, amlodipine besilate and hydrochlorothiazide, respectively. The validity of the proposed methods was successfully assessed for analyses of drugs in the various prepared physical mixtures and in tablet formulations.
U radu su opisane četiri spektrofotometrijske metode za istodobno određivanje losartan kalija, amlodipin besilata i hidroklorotiazida u sirovinama i farmaceutskim pripravcima. Podaci koji su se preklapali kvantitativno su razlučeni kemometrijskim metodama, klasičnom metodom najmanjih kvadrata (CLS), multiplom linearnom regresijom (MLR), regresijom glavnih komponenata (PCR) te metodom parcijalnih najmanjih kvadrata (PLS). Kalibracije su provedene koristeći podatke o ovisnosti apsorpcije o koncentracijama, mjereći spektre nultog reda u rasponu 230,5-350,4 nm (Δλ = 0,1 nm). Linearnost za losartan kalij bila je 8-40, za amlodipin besilat 1-5, a za hidroklorotiazid 3-15 μg mL-1. Valjanost predloženih metoda uspješno je potvrđena analizom navedenih lijekova u različitim pripremljenim smjesama i tabletama.
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