Iete Technical Review
Taylor & Francis
About: Iete Technical Review is an academic journal published by Taylor & Francis. The journal publishes majorly in the area(s): Computer science & Wireless. It has an ISSN identifier of 0256-4602. Over the lifetime, 1889 publications have been published receiving 15207 citations. The journal is also known as: Institution of Electronics and Telecommunication Engineers Technical Review & Technical Review - Institution of Electronics and Telecommunication Engineers..
Papers published on a yearly basis
TL;DR: For the first time, memristor-based chaotic circuits have been derived from the canonical Chua’s circuit, and these circuits present opportunities for developing applications under the constraints of scalability and low power.
Abstract: Ever since its physical fabrication in 2008, the memristor has been promising in the fields of nanoelectronics, computer logic and neuromorphic computers. Taking advantage of the circuit properties of the memristor, this paper proposes memristor-based chaotic circuits. For the first time, memristor-based chaotic circuits have been derived from the canonical Chua’s circuit. These circuits present opportunities for developing applications under the constraints of scalability and low power. They also provide a memristor-based framework for secure communications with chaos.
TL;DR: A novel framework has been proposed, combining both the concepts of decision fusion and feature fusion to increase the performance of classification, and experiments have been done to prove the robustness of combining feature fusion and decision fusion techniques.
Abstract: For any pattern classification task, an increase in data size, number of classes, dimension of the feature space, and interclass separability affect the performance of any classifier. A single classifier is generally unable to handle the wide variability and scalability of the data in any problem domain. Most modern techniques of pattern classification use a combination of classifiers and fuse the decisions provided by the same, often using only a selected set of appropriate features for the task. The problem of selection of a useful set of features and discarding the ones which do not provide class separability are addressed in feature selection and fusion tasks. This paper presents a review of the different techniques and algorithms used in decision fusion and feature fusion strategies, for the task of pattern classification. A survey of the prominent techniques used for decision fusion, feature selection, and fusion techniques has been discussed separately. The different techniques used for fus...
TL;DR: The latest segmentation methods applied in medical image analysis are described and the advantages and disadvantages of each method are described besides examination of each algorithm with its application in Magnetic Resonance Imaging and Computed Tomography image analysis.
Abstract: Medical images have made a great impact on medicine, diagnosis, and treatment. The most important part of image processing is image segmentation. Many image segmentation methods for medical image analysis have been presented in this paper. In this paper, we have described the latest segmentation methods applied in medical image analysis. The advantages and disadvantages of each method are described besides examination of each algorithm with its application in Magnetic Resonance Imaging and Computed Tomography image analysis. Each algorithm is explained separately with its ability and features for the analysis of grey-level images. In order to evaluate the segmentation results, some popular benchmark measurements are presented in the final section.
TL;DR: This work proposes server consolidation algorithm - Sercon, which not only minimizes the overall number of used servers, but also minimized the number of migrations, and verified the feasibility of the algorithm along with showing its scalability by conducting experiments with eight different test cases.
Abstract: Virtualization technologies changed the way data centers of enterprises utilize their server resources. Instead of using dedicated servers for each type of application, virtualization allows viewing resources as a pool of unified resources, thereby reducing complexity and easing manageability. Server consolidation technique, which deals with reducing the number of servers used by consolidating applications, is one of the main applications of virtualization in data centers. The latter technique helps to use computing resources more effectively and has many benefits, such as reducing costs of power, cooling and, hence, contributes to the Green IT initiative. In a dynamic data center environment, where applications encapsulated as virtual machines are mapped to and released from the nodes frequently, reducing the number of server nodes used can be achieved by migrating applications without stopping their services, the technology known as live migration. However, live migration is a costly operation; ...
TL;DR: The challenges for Database Management in the Internet of Things are considered, in particular, the areas of querying, indexing, process modeling, transaction handling, and integration of heterogeneous systems.
Abstract: This article discusses the challenges for Database Management in the Internet of Things. We provide scenarios to illustrate the new world that will be produced by the Internet of Things, where physical objects are fully integrated into the information highway. We discuss the different types of data that will be part of the Internet of Things. These include identification, positional, environmental, historical, and descriptive data. We consider the challenges brought by the need to manage vast quantities of data across heterogeneous systems. In particular, we consider the areas of querying, indexing, process modeling, transaction handling, and integration of heterogeneous systems. We refer to the earlier work that might provide solutions for these challenges. Finally we discuss a road map for the Internet of Things and respective technical priorities.