Simulation on probabilistic anti collision protocols of RFID using variable delay

In RFID System, it is important to avoid tag collision for identifying tag faster. In this paper, we proposed concept of variable delay for tag estimation & identification. The scheme is based on the Multi-level dynamic framed ALOHA protocol. Simulation results indicate that the time delay is added to each tag for avoiding collision. The main advantage of this is the delay is in microseconds which will not create problem of more time consumption

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

The Citizen, January 1966

https://egrove.olemiss.edu/citizens_news/1052/thumbnail.jp

Data Structures & Algorithm Analysis in C++

This is the textbook for CSIS 215 at Liberty University.https://digitalcommons.liberty.edu/textbooks/1005/thumbnail.jp

Small business innovation research. Abstracts of 1988 phase 1 awards

Non-proprietary proposal abstracts of Phase 1 Small Business Innovation Research (SBIR) projects supported by NASA are presented. Projects in the fields of aeronautical propulsion, aerodynamics, acoustics, aircraft systems, materials and structures, teleoperators and robots, computer sciences, information systems, data processing, spacecraft propulsion, bioastronautics, satellite communication, and space processing are covered

IENE 2020 International Conference LIFE LINES – Linear Infrastructure Networks with Ecological Solutions. Abstract Book

In the past 12 to 14 of January 2021, the University of Évora, in the framework of the LIFE LINES project, and the Infrastructure and Ecology Network Europe held the online event IENE International Conference, under the theme “LIFE LINES – Linear Infrastructure Networks with Ecological Solutions”. We had 293 attendees from 40 different countries representing the five continents. During three days, participants were able to assist to 115 full oral presentations, 36 lightning talks, 13 workshops and chat with 40 posters authors, representing studies and projects worldwide. The event counted with 50 thematic sessions, running in five parallel sessions mixing live and pre-recorded interventions

A survey of online data-driven proactive 5G network optimisation using machine learning

In the fifth-generation (5G) mobile networks, proactive network optimisation plays an important role in meeting the exponential traffic growth, more stringent service requirements, and to reduce capitaland operational expenditure. Proactive network optimisation is widely acknowledged as on e of the most promising ways to transform the 5G network based on big data analysis and cloud-fog-edge computing, but there are many challenges. Proactive algorithms will require accurate forecasting of highly contextualised traffic demand and quantifying the uncertainty to drive decision making with performance guarantees. Context in Cyber-Physical-Social Systems (CPSS) is often challenging to uncover, unfolds over time, and even more difficult to quantify and integrate into decision making. The first part of the review focuses on mining and inferring CPSS context from heterogeneous data sources, such as online user-generated-content. It will examine the state-of-the-art methods currently employed to infer location, social behaviour, and traffic demand through a cloud-edge computing framework; combining them to form the input to proactive algorithms. The second part of the review focuses on exploiting and integrating the demand knowledge for a range of proactive optimisation techniques, including the key aspects of load balancing, mobile edge caching, and interference management. In both parts, appropriate state-of-the-art machine learning techniques (including probabilistic uncertainty cascades in proactive optimisation), complexity-performance trade-offs, and demonstrative examples are presented to inspire readers. This survey couples the potential of online big data analytics, cloud-edge computing, statistical machine learning, and proactive network optimisation in a common cross-layer wireless framework. The wider impact of this survey includes better cross-fertilising the academic fields of data analytics, mobile edge computing, AI, CPSS, and wireless communications, as well as informing the industry of the promising potentials in this area