Big data analytics helps in analyzing a huge set of data whereas IoT is about data, devices and connectivity. Internet of Things (IoT) involves connecting physical objects to the Internet to build smart systems and universal mobile accessibility advanced technologies like Intelligent Transportation System (ITS). IoT solutions are playing a major role in driving the global IoT in Intelligent Transportation System. Communication between vehicles using IoT will be a new era of communication that leads to ITS. IoT is a combination of storing and processing sensor data and computing using data analytics to achieve and assist in managing the Traffic system effectively. IoT based Intelligent transportation system (IoT-ITS) helps in automating railways, roadways, airways and marine which enhance customer experience about the way goods are transported, tracked and delivered. A case study on Intelligent Traffic Management System based on IoT and big data, which will be a part of, smart traffic solutions for smarter cities. The ITS-IoT system itself forms an eco-system comprising of sensor systems, monitoring system and display system. There are several techniques and algorithms involved in full functioning of IoT-ITS. The proposed case study will examine and explain a complete design and implementation of a typical IoT-ITS system for a smart city scenario set on typical Indian subcontinent. This case study will also explain about several hardware and software components associated with the system. How concepts like Multiple regression analysis, Multiple discriminant analysis and logistic regression, Cojoint analysis, Cluster analysis and other big data analytics techniques will merge with IoT and help to build IoT-ITS will also be emphasized. The case study will also display some big data analytics results and how the results are utilized in smart transportation systems.

IoT Based Intelligent Transportation System (IoT-ITS) for Global Perspective: A Case Study

In recent works on the Internet of Vehicles (IoV), “intelligent” and “sustainable” have been the buzzwords in the context of transportation. Maintaining sustainability in IoV is always a challenge. Sustainability in IoV can be achieved not only by the use of pollution-free vehicular systems, but also by maintenance of road traffic safety or prevention of accidents or collisions. With the aim of establishing an effective sustainable transportation planning system, this study performs a short analysis of existing sustainable transportation methods in the IoV. This study also analyzes various characteristics of sustainability and the advantages and disadvantages of existing transportation systems. Toward the end, this study provides a clear suggestion for effective sustainable transportation planning aimed at the maintenance of an eco-friendly environment and road traffic safety, which, in turn, would lead to a sustainable transportation system.

Comparative Analysis of Intelligent Transportation Systems for Sustainable Environment in Smart Cities

In the digital world, watermarking technology is a solution for data hiding and completely essential for management and secure communications of digital data propagated over the internet-based platforms. Reversible watermarking is a quality-aware type of watermarking which has been applied in managing digital contents such as digital images, texts, audios and videos. Reversible watermarking is also known as lossless watermarking due to its preservation of all details of host and hidden data. One of the important uses of this kind of watermarking is to manage medical data regarding DICOM images. In the recent years, a new type of reversible watermarking technology entitled interpolation-based reversible watermarking has been introduced, and we are going to enhance it for DICOM images by using a hybrid approach based on computing error histogram and by applying an image interpolation with greedy weights (adaptive weighting). In practice, simulation results clearly show better performance of the proposed scheme compared to the previous techniques using interpolation-based reversible watermarking on different DICOM images.

A lossless data hiding scheme for medical images using a hybrid solution based on IBRW error histogram computation and quartered interpolation with greedy weights

Towards genre-specific frameworks for video summarisation: A survey

This paper proposes a novel algorithm for extracting key frames to represent video shots. Regarding whether, or how well, a key frame represents a shot, different interpretations have been suggested. We develop our algorithm on the assumption that more important content may demand more attention and may last relatively more frames. Unsupervised clustering is used to divide the frames into clusters within a shot, and then a key frame is selected from each candidate cluster. To make the algorithm independent of video sequences, we employ a statistical model to calculate the clustering threshold. The proposed algorithm can capture the important yet salient content as the key frame. Its robustness and adaptability are validated by experiments with various kinds of video sequences.

Key Frame Extraction Using Unsupervised Clustering Based on a Statistical Model

Key-Frame Extraction Techniques: A Review

Coral reefs, of all marine ecosystems, are the most diverse. They teem with life with almost a quarter of all ocean species depending upon reefs for food and shelter. Because of the immense diversity of coral reefs, they are often referred to as the ‘Rain-Forests of the Sea’. But due to the lack of consistency and objectivity in human labeling, the manual annotations of coral reefs are not conceivable. Multiple species of coral have coinciding characteristics and this makes automatic annotation, a challenging task. This research work is based on developing machine learning algorithms to employ Aura Matrix based on gradient based Cumulative Relative Difference which exploits the neighborhood relationship between pixels by comparing the relative pixel strength with respect to the center pixel. The objective of this paper is to show improvement of coral reef classification accuracy and that the proposed work outperforms the state-of-the art CNN classifiers on four coral datasets namely EILAT, EILAT 2, MLC 2010 and RSMAS.

Gradient Based Aura Feature Extraction for Coral Reef Classification

In the realm of visual cryptography, secret sharing is the predominant method of transmission and reception of secure data. Most of the n, n secret sharing schemes suffer from one common flaw-locking of information when the all-n number of receivers are not available for some reason. This paper proposes a new method of compartmented secret sharing scheme where some threshold number of equally privileged from each compartment can retrieve data. This scheme rules out regeneration of secret image at the single compartment thereby eliminating the danger of misusing secret image. The key features of this scheme are: better visual quality of the recovered image with no pixel expansion; non-requirement of half toning of color images; less computational complexity by reconstructing secret through XORing and simple addition of all share images. This scheme is highly beneficial in applications where data has to be stored securely in a database and in cloud computing to synchronize information passed to different groups or clusters from a single host.

P. Arockia Jansi Rani

Papers

Key-Frame Extraction Techniques: A Review

Gradient Based Aura Feature Extraction for Coral Reef Classification

Threshold Secret Sharing Scheme for Compartmented Access Structures

Mitigation of black hole attacks using firefly and artificial neural network

Static and Multiresolution Feature Extraction for Video Summarization