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Internet of Things is a physical device and objects that collect data, store, and analyze the data. In-home appliances manually operated activities and functions. The IoT uses technical product elements, in-home appliances rapid changes in society. The IoT system design development, control and monitoring in various applications such as health, transport, agriculture, home appliances, etc. We propose a framework model future smart home appliances using IoT which helps the developer to build infrastructure home automation applications accordingly to the user specifications and the requirements. The proposed model is the best solution to use smart home applications with the use of sensors, communication, smart home operations, controlling with the use of mobile apps and Arduino. The system will provide security, smart home automation. In future, it will be extended to develop intelligent smart-based application with an integrated environment and reporting applications.

Quick sort is one of the most sought after algorithms, because if implemented properly quick sort could be much faster than its counterparts, merge sort and heapsort. The main crux of quick sort algorithm is the implementation of the partitioning operation. Nico Lomuto and C. A. R Hoare have put forth partitioning algorithms that have gained prominent significance. Despite this, one can always shed more light on this partially understood operation of partition. Sorting algorithms have been further developed to enhance its performance in terms of computational complexity, memory and other factors. The proposed method is the use of randomized pivot in the implementation of Lomuto partition and Hoare partition algorithms, respectively. It is analysed with and without a randomized pivot element. This paper presents a comparison of the performance of a quick sort algorithm in different cases using contrasting partition approaches with and without randomized pivot. The results provide a theoretical explanation for the observed behaviour and give new insights on behaviour of quick sort algorithm for different cases. The Hoare partition approach with randomized pivot gives best time complexity in comparison to the other cases.

The automatic use of physiological or behavioral characteristics to determine or verify identity of individual's is regarded as biometrics. Fingerprints, Iris, Voice, Face, and palmprints are considered as physiological biometrics whereas voice and signature are behavioral biometrics. Palmprint recognition is one of the popular methods which have been investigated over last fifteen years. Palmprint have very large internal surface and contain several unique stable characteristic features used to identify individuals. Several palmprint recognition methods have been extensively studied. This chapter is an attempt to review current palmprint research, describing image acquisition, preprocessing palmprint feature extraction and matching, palmprint related fusion and techniques used for real time palmprint identification in large databases. Various palmprint recognition methods are compared.

In this paper, a new Palmprint recognition technique is introduced based on the gray-level co-occurrence matrix (GLCM). GLCM represents the distributions of the intensities and the information about relative positions of neighboring pixels of an image. GLCM matrices are calculated corresponding to different orientation (0, 45, 90, 135) with four different offset values. After the calculation of GLCMs, each GLCM is divided into 32 × 32 sub-matrices. For each such sub-matrix four Haralick features are calculated. The performance of the proposed identification system based on Haralick features is determined using False Acceptance Rate (FAR) and Genuine Acceptance Rate (GAR).

Palmprint technology is a new branch of biometrics used to identify an individual. Palmprint has rich set of features like palm lines, wrinkles, minutiae points, texture, ridges, etc. Several line and texture extraction techniques for palmprint have been extensively studied. This paper presents an intramodal authentication system based on texture information extracted from the palmprint using the Haralick features, 2D-Gabor and 2D-log Gabor filters. An individual feature vector is computed for a palmprint using the extracted texture information of each filter type. Performance of the system using three feature types is evaluated individually. Finally, we combine the three feature types using feature level fusion to develop an intramodal palmprint recognition system. The experiments are evaluated on a standard benchmark database (PolyU Database), and the results shows that significant improvement in terms of recognition accuracy and error rates with the proposed intramodal recognition system compared to individual representations.

Palm print authentication is a biometric technology to identify a person's identity. In this paper, phase congruency method is used to extract features from palm print ROI images. The phase congruency is an efficient method to extract features at varying illumination condition and the image is invariant to contrast. By applying this method, local phase congruency (LPC), local orientation (LO) and local phase (LP) are extracted individually and fused using score level fusion. To reduce false acceptance rate (FAR), Min-Max threshold range is employed and the proposed method is tested on PolyU database of 7480 images from 374 individuals, with 20 image samples per individual. The proposed system achieves genuine acceptance rate (GAR) of 100% and FAR of 0.65%.