Feature Matching in Iris Recognition System using MATLAB

Iris recognition system is a secure human authentication in biometric technology. Iris recognition system consists of five stages. They are Feature matching, Feature encoding, Iris Normalization, Iris Segmentation and Image acquisition. In Image acquisition, the eye Image is captured from the CASIA database, the Image must have good quality with high resolution to process next steps. In Iris Segmentation, the Iris part is detected by using Hough transform technique and Canny Edge detection technique. Iris from an eye Image segmented. In normalization, the Iris region is converted from the circular region into a rectangular region by using polar transform technique. In feature encoding, the normalized Iris can be encoded in the form of binary bit format by using Gabor filter techniques.  In feature matching, the encoded Iris template is compared with database eye Image of Iris template and generated the matching score by using Hamming distance technique and Euclidean distance technique. Based on the matching score, we get the result. This project is developed using Image processing toolbox of Matlab software

A Study on Sanctuary and Seclusion Issues in Internet-of-Things

Internet-of-Things (IoT) are everywhere in our daily life. They are used in our homes, in hospitals, deployed outside to control and report the changes in environment, prevent fires, and many more beneficial functionality. However, all those benefits can come of huge risks of seclusion loss and sanctuary issues. To secure the IoT devices, many research works have been con-ducted to countermeasure those problems and find a better way to eliminate those risks, or at least minimize their effects on the user�s seclusion and sanctuary requirements. The study consists of four segments. The first segment will explore the most relevant limitations of IoT devices and their solutions. The second one will present the classification of IoT attacks. The next segment will focus on the mechanisms and architectures for authentication and access control. The last segment will analyze the sanctuary issues in different layers

Edge Intelligence with Light Weight CNN Model for Surface Defect Detection in Manufacturing Industry

Surface defect identification is essential for maintaining and improving the quality of industrial products. However, numerous environmental factors, including reflection, radiance, light, and material, affect the defect detection process, considerably increasing the difficulty of detecting surface defects. Deep Learning, a part of Artificial intelligence, can detect surface defects in the industrial sector. However, conventional deep learning techniques are heavy in terms of expensive GPU requirements to support massive computations during the defect detection process.CondenseNetV2, a Lightweight CNN-based model, which performs well on microscopic defect inspection, and can be operated on low-frequency edge devices, was proposed in this research. It provides sufficient feature extractions with little computational overhead by reusing a set of the existing Sparse Feature Reactivation module. The training data are subjected to data augmentation techniques, and the hyper-parameters of the proposed model are fine-tuned with transfer learning. The model was tested extensively with two real datasets while running on an edge device (NVIDIA Jetson Xavier Nx SOM). The experiment results confirm that the projected model can efficiently detect the faults in the real-world environment while reliably and robustly diagnosing them

Effect of post harvest treatments and harvesting stage on vase life and flower quality of cut Oriental lily

An investigation was carried out to study the effect of post harvest treatments and harvesting stage on vase life and flower quality of cut Oriental lily cv. Avocado. The results showed that highest vase life (15.83 days) and vase solution uptake (49.17 ml) was recorded with sucrose (2%) + 5-SSA (200ppm), whereas maximum flower diameter (15.17 cm) was recorded in vase solution containing sucrose (2%) + 5-SSA (100ppm). Earliest opening of florets (4.42 days) reported under sucrose (2%) + 5-SSA (200ppm). Effect of treatments was found non-significant in respect to opening of florets. Harvesting at green bud stage exhibited extended vase life (14.33 days) and higher vase solution uptake (40.43 ml), whereas maximum flower diameter (14.25 cm) recorded at 75% colour development stage. Based on the results it is concluded that 5-SSA could be an inexpensive and potential chemical for delaying senescence and for extending the keeping quality of cut liliums commercially

Gesture Recognition for Enhancing Human Computer Interaction

Gesture recognition is critical in human-computer communication. As observed, a plethora of current technological developments are in the works, including biometric authentication, which we see all the time in our smartphones. Hand gesture focus, a frequent human-computer interface in which we manage our devices by presenting our hands in front of a webcam, can benefit people of different backgrounds. Some of the efforts in human-computer interface include voice assistance and virtual mouse implementation with voice commands, fingertip recognition and hand motion tracking based on an image in a live video. Human Computer Interaction (HCI), particularly vision-based gesture and object recognition, is becoming increasingly important. Hence, we focused to design and develop a system for monitoring fingers using extreme learning-based hand gesture recognition techniques. Extreme learning helps in quickly interpreting the hand gestures with improved accuracy which would be a highly useful in the domains like healthcare, financial transactions and global busines

Object Sub-Categorization and Common Framework Method using Iterative AdaBoost for Rapid Detection of Multiple Objects

Object detection and tracking in real time has numerous applications and benefits in various fields like survey, crime detection etc. The idea of gaining useful information from real time scenes on the roads is called as Traffic Scene Perception (TSP). TSP actually consists of three subtasks namely, detecting things of interest, recognizing the discovered objects and tracking of the moving objects. Normally the results obtained could be of value in object recognition and tracking, however the detection of a particular object of interest is of higher value in any real time scenario. The prevalent systems focus on developing unique detectors for each of the above-mentioned subtasks and they work upon utilizing different features. This obviously is time consuming and involves multiple redundant operations. Hence in this paper a common framework using the enhanced AdaBoost algorithm is proposed which will examine all dense characteristics only once thereby increasing the detection speed substantially. An object sub-categorization strategy is proposed to capture the intra-class variance of objects in order to boost generalisation performance even more. We use three detection applications to demonstrate the efficiency of the proposed framework: traffic sign detection, car detection, and bike detection. On numerous benchmark data sets, the proposed framework delivers competitive performance using state-of-the-art techniques

Fabrication of α‑Fe2O3 Nanostructures: synthesis, characterization, and their promising application in the treatment of Carcinoma A549 Lung Cancer Cells

In the present work, iron nanoparticles were synthesized in the α-Fe2O3 phase with the reduction of potassium hexachloroferrate(III) by using l-ascorbic acid as a reducing agent in the presence of an amphiphilic non-ionic polyethylene glycol surfactant in an aqueous solution. The synthesized α-Fe2O3 NPs were characterized by powder X-ray diffraction, field emission scanning electron microscopy, transmission electron microscopy, atomic force microscopy, dynamic light scattering, energy dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy, and ultraviolet–visible spectrophotometry. The powder X-ray diffraction analysis result confirmed the formation of α-Fe2O3 NPs, and the average crystallite size was found to be 45 nm. The other morphological studies suggested that α-Fe2O3 NPs were predominantly spherical in shape with a diameter ranges from 40 to 60 nm. The dynamic light scattering analysis revealed the zeta potential of α-Fe2O3 NPs as −28 ± 18 mV at maximum stability. The ultraviolet–visible spectrophotometry analysis shows an absorption peak at 394 nm, which is attributed to their surface plasmon vibration. The cytotoxicity test of synthesized α-Fe2O3 NPs was investigated against human carcinoma A549 lung cancer cells, and the biological adaptability exhibited by α-Fe2O3 NPs has opened a pathway to biomedical applications in the drug delivery system. Our investigation confirmed that l-ascorbic acid-coated α-Fe2O3 NPs with calculated IC50 ≤ 30 μg/mL are the best suited as an anticancer agent, showing the promising application in the treatment of carcinoma A549 lung cancer cells

Discriminating neutrino mass models using Type II seesaw formula

In this paper we propose a kind of natural selection which can discriminate the three possible neutrino mass models, namely the degenerate, inverted hierarchical and normal hierarchical models, using the framework of Type II seesaw formula. We arrive at a conclusion that the inverted hierarchical model appears to be most favourable whereas the normal hierarchical model follows next to it. The degenerate model is found to be most unfavourable. We use the hypothesis that those neutrino mass models in which Type I seesaw term dominates over the Type II left-handed Higgs triplet term are favoured to survive in nature.Comment: No change in the results, a few references added, some changes in Type[IIB] calculation