Effect of Ultrasonic Irradiation on Preparation and Properties of Ionogels

Silica-gel matrices containing ionic liquid (IL) 1-butyl-3-methylimidazolium hexafluorophosphate viz. ionogels have been synthesized using one-pot nonhydrolytic sol-gel method and taking tetraethyl orthosilicate (TEOS) as starting precursor. Effect of ultrasonic irradiation on pore parameters of ionogels and vibrational properties of the IL upon confinement in the porous matrix has been investigated. The synthesized gels have been characterized by BET, DSC, TGA, and FTIR. BET analysis shows some changes in the pore parameters due to ultrasonic irradiation. DSC results indicate shift in glass transition temperature upon confinement of the IL. The FTIR spectra show changes in vibrational bands on confinement, particularly, the bands related to the imidazolium ring; aliphatic chain and anion PF6 − of the IL are found to shift upon confinement in porous silica matrix obtained due to ultrasonic irradiation. Ultrasonic irradiation has been found to affect the gelation dynamics and kinetics and pore parameters

Automatic Bright Circular Type Oil Tank Detection Using Remote Sensing Images

Automatic target detection like oil tank from satellite based remote sensing imagery is one of the important domains in many civilian and military applications. This could be used for disaster monitoring, oil leakage, etc. We present an automatic approach for detection of circular shaped bright oil tanks with high accuracy. The image is first enhanced to emphasize the bright objects using a morphological approach. Then, the enhanced image is segmented using split-and-merge segmentation technique.  Here, we introduce a knowledge base strategy based on the region removal technique and spatial relationship operation for detection of possible oil tanks from the segmented image using minimal spanning tree. Lastly, we introduce a supervised classifier, for identification of oil tanks, based on the knowledge database of large amount data of oil tanks. The uniqueness of the proposed technique is that it is useful for detection bright oil tanks from high as well as low resolution images, but the technique is always better for high-resolution imagery. We have systematically evaluated the algorithm on different satellite images like IRS – 1C, IKONOS, QuickBird and CARTOSAT – 2A. The proposed technique is detected bright structures but unable to detect the dark structure. If the oil tank structures are bright relative to the background illumination in the image then the detection accuracy by the proposed technique for the high resolution image is more than 95 per cent.Defence Science Journal, 2013, 63(3), pp.298-304, DOI:http://dx.doi.org/10.14429/dsj.63.273

Segmentation Techniques through Machine Based Learning for Latent Fingerprint Indexing and Identification

201-208Latent fingerprints have become most important evidence in law enforcement department and forensic agencies worldwide. It is also very important evidence in forensic applications to identify criminals as it is mostly encountered in crime scenes. Segmentation is one of the solutions to extract quality features. Fingerprint indexing reduces the search space without compromising accuracy. In this paper, minutiae based rotational and translational features and a global matching approach in combination with local matching is used in order to boost the indexing efficiency. Also, a machine learning (ML) based segmentation model is designed as a binary classification model to classify local blocks into foreground and background. Average indexed time as well as accuracy for full as well as partial fingerprints is tabulated by varying the template sminutiae

Mucormycosis During Second Wave Of COVID-19 in India: Exploratory Analysis From Tertiary Care Centre Of Uttar Pradesh

Background: In April and May 2021, India faced massive second wave of COVID-19 pandemic and then became the hotspots of mucormycosis across globe. Hence, we planned the first analysis of mucormycosis in Uttar Pradesh. Aim and Objectives: coThe objectives were to explore the epidemiology, vaccination status, clinical features, microbiological findings, treatment of mucormycosis and outcome. Material and Methods: This was an observational study of mucormycosis at tertiary care centre of Agra, Uttar Pradesh, India from 20 May 2021 to 15 July 2021. Results: 98 patients’ data with mucormycosis was analyzed. Patients had median age of 55 years. COVID-19 infection was present in 69.4% of participants and diabetes mellitus was present as underlying co-morbid condition in more than 85% of patients. 91.8 % of cases were unvaccinated with steroid consumption in almost 60% of patients. R. arrhizus was among the most common agent while we found less commonly reported fungi like R.homothallicus, Apophysomyces, Cunninghamella. 41.8% of cases were successfully treated and 16.3% of cases expired. Conclusion: Diabetes mellitus was labeled as major predisposing factor and most patients were unvaccinated. Awareness about fungal infections, rigorous monitoring of chronic diseases, judicious use of drugs, early diagnosis and prompt treatment is decisive for better outcome

Bounded PCA based Multi Sensor Image Fusion Employing Curvelet Transform Coefficients

The fusion of thermal and visible images acts as an important device for target detection. The quality of the spectral content of the fused image improves with wavelet-based image fusion. However, compared to PCA-based fusion, most wavelet-based methods provide results with a lower spatial resolution. The outcome gets better when the two approaches are combined, but they may still be refined. Compared to wavelets, the curvelet transforms more accurately depict the edges in the image. Enhancing the edges is a smart way to improve spatial resolution and the edges are crucial for interpreting the images. The fusion technique that utilizes curvelets enables the provision of additional data in both spectral and spatial areas concurrently. In this paper, we employ an amalgamation of Curvelet Transform and a Bounded PCA (CTBPCA) method to fuse thermal and visible images. To evidence the enhanced efficiency of our proposed technique, multiple evaluation metrics and comparisons with existing image merging methods are employed. Our approach outperforms others in both qualitative and quantitative analysis, except for runtime performance. Future Enhancement-The study will be based on using the fused image for target recognition. Future work should also focus on this method’s continued improvement and optimization for real-time video processing

Multilevel Hashing based Access Control for Authentication and Security in Relational Database Management System

The relational database is very widely used in these days and so as the relational database security is very vital to protect from different kinds of threats and attacks. The security of relational database is very important because now these days all data and information are stored in database by some form of database objects including user’s personal information like credit/debit card details, username, passwords etc. as well as confidential data of business organization and companies. Many researches are done in order to protect the relational database from these vulnerabilities but the methodologies of relational database security are not able to protect the relational database from all security issues. Authentication of users is an important issue in the database that is needed to be addressed most because it can give full access to the database objects. In this paper we present various security issues that can cause degradation in relational database security and we also identify various problems in the current database security policies. In this paper we propose the use of Multilevel Hashing based Access control mechanism for authentication and security in Relational database

Cone Beam Computed Tomography Evaluation and Surgical Treatment of an Open Apex Case with Biodentine

Trauma to the facial region is a common event with injury of teeth. The treatment is aimed at the functional restoration of tooth along with complete biologic healing. Biodentine (Septodont, St Maurdes Fossés, France) introduced in the year 2011. It is a calcium silicate based material and is claimed by the manufacturers as repair of crown and root dentin repair treatment, perforations, resorptions, apexification, and root end fillings. This article presents a case report of conebeam computed tomography (CBCT) diagnosis and surgical management of an open apex case using biodentine as retrograde filling material

Nonsteroidal Management of Accidental Extrusion of Sodium Hypochlorite beyond Apex

Sodium hypochlorite is often used as an intracanal irrigant in root canal treatment for removal of organic debris. It is also used for its ability to dissolve pulpal tissues, removal of organic debris from smear layer. Despite its safe properties, serious complications can result from inadvertent use. The present case report discusses the inadvertent effects and management of accidental extrusion of concentrated NaOCl and nonsteroidal management. Extrusion of NaOCl beyond the root canal can occur when there is iatrogenic widening of the apical foramen, lateral perforation, or wedging of the irrigating needle. Injudicious use of the irrigant should be avoided to prevent the NaOCl accidents. This report demonstrates destructive effect of concentrated NaOCl solution on soft tissues and subsequent management of the case

Segmentation Techniques through Machine Based Learning for Latent Fingerprint Indexing and Identification

Latent fingerprints have become most important evidence in law enforcement department and forensic agencies worldwide. It is also very important evidence in forensic applications to identify criminals as it is mostly encountered in crime scenes. Segmentation is one of the solutions to extract quality features. Fingerprint indexing reduces the search space without compromising accuracy. In this paper, minutiae based rotational and translational features and a global matching approach in combination with local matching is used in order to boost the indexing efficiency. Also, a machine learning (ML) based segmentation model is designed as a binary classification model to classify local blocks into foreground and background. Average indexed time as well as accuracy for full as well as partial fingerprints is tabulated by varying the template sminutiae

Latent Fingerprint Indexing for Faster Retrieval from Dataset with Image Enhancement Technique

730-753Since decades fingerprints have been the prime source in identification of suspects latent fingerprints are compared and examined with rolled and plain fingerprints which are stored in the dataset. The common challenges which are faced while examining latent fingerprints are background noise, nonlinear distortions, poor ridge clarity and partial impression of the finger. As conventional methods of Segmentation doesn’t perform well on latent fingerprints. The current advancement in machine learning based segmentation approach has been showing good results in terms of segmentation accuracy but lacks to provide accurate result in terms of matching accuracy. As one of the problem faced in matching latent fingerprint is low clarity of ridge-valley pattern which results in detection of false minutiae and poor matching accuracy. A multilayer processing of artificial neural network based segmentation is proposed to minimize the detection of false minutiae and increase the matching accuracy. This approach is designed on binary classification model where the simulation will be carried out on IIIT-D latent fingerprint dataset. Segmentation will be divided into full and partial impression fingerprints which are then compared with minutiae with the database using local and global matching algorithm. An improvised result is received which is more accurate as compared to the previous algorithms