Design and Development of a User Specific Dynamic E-Magazine

Internet and electronic media gaining more popularity due to ease and speed, the count of Internet users has increased tremendously. The world is moving faster each day with several events taking place at once and the Internet is flooded with information in every field. There are categories of information ranging from most relevant to user, to the information totally irrelevant or less relevant to specific users. In such a scenario getting the information which is most relevant to the user is indispensable to save time. The motivation of our solution is based on the idea of optimizing the search for information automatically. This information is delivered to user in the form of an interactive GUI. The optimization of the contents or information served to him is based on his social networking profiles and on his reading habits on the proposed solution. The aim is to get the user's profile information based on his social networking profile considering that almost every Internet user has one. This helps us personalize the contents delivered to the user in order to produce what is most relevant to him, in the form of a personalized e-magazine. Further the proposed solution learns user's reading habits for example the news he saves or clicks the most and makes a decision to provide him with the best contents.Comment: 19 pages, 6 figure

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Active networks: an evolution of the internet

Active Networks can be seen as an evolution of the classical model of packet-switched networks. The traditional and ”passive” network model is based on a static definition of the network node behaviour. Active Networks propose an “active” model where the intermediate nodes (switches and routers) can load and execute user code contained in the data units (packets). Active Networks are a programmable network model, where bandwidth and computation are both considered shared network resources. This approach opens up new interesting research fields. This paper gives a short introduction of Active Networks, discusses the advantages they introduce and presents the research advances in this field

Operator Performance Support System (OPSS)

In the complex and fast reaction world of military operations, present technologies, combined with tactical situations, have flooded the operator with assorted information that he is expected to process instantly. As technologies progress, this flow of data and information have both guided and overwhelmed the operator. However, the technologies that have confounded many operators today can be used to assist him -- thus the Operator Performance Support Team. In this paper we propose an operator support station that incorporates the elements of Video and Image Databases, productivity Software, Interactive Computer Based Training, Hypertext/Hypermedia Databases, Expert Programs, and Human Factors Engineering. The Operator Performance Support System will provide the operator with an integrating on-line information/knowledge system that will guide expert or novice to correct systems operations. Although the OPSS is being developed for the Navy, the performance of the workforce in today's competitive industry is of major concern. The concepts presented in this paper which address ASW systems software design issues are also directly applicable to industry. the OPSS will propose practical applications in how to more closely align the relationships between technical knowledge and equipment operator performance