Summary form only given. The complete presentation was not made available for publication as part of the conference proceedings. Internet of Things (IoT) is the network of physical objects or “things” embedded with electronics, software, sensors, and network connectivity. It enables the objects to collect, share, and analyze data. The IoT has become an integral part of our daily lives through applications such as public safety, intelligent tracking in transportation, industrial wireless automation, personal health monitoring, and health care for the aged community. IoT is one of the latest technologies that will change our lifestyle in the coming years. Experts estimate that as of now, there are 23 billion connected devices, and by 2020 it would reach 30 billion devices. This tutorial aims to introduce the design and implementation of IoT systems. The foundations of IoT will be discussed throughout real applications. Challenges and constraints for future research in IoT will be discussed. In addition, research opportunities and collaboration will be offered for the attendees.
In modern communication network unwanted failures occurred. A design of robust network is required to handle the failures, restore the failed part and maintain the traffic flow of the network within a permissible time frame, at the lowest cost. The next-generation (5G) communication and computing networks, software-defined networks and internet-of-things networks assures to provide high speeds, impressive data rates and remarkable reliability. We need to design complex and robust next generation networks that must be built with minimum failure possibilities, quick failure recovery capabilities and high network restoration capacity. Therefore, improved network restoration models are needs to be developed and incorporated. In this paper, a comprehensive study on network restoration mechanisms that are being developed for addressing network failures in current and next generation (5G) networks is carried out. Open-ended problems are identified, while invaluable ideas for better adaptation of network restoration to evolving 5G communication and computing models are discussed.
This survey aims at finding suitable algorithms for each phase for recognizing users based on locations in secured areas. Now-a days the security system is keep on updating its features every accepts. Here, in this project we have mainly focused on developing a multimodal system for government organization. The multimodal system contains the character recognition and face recognition. The main objective of our project is to develop an automated multimodal security system which recognize and alerts the security officer when an unauthorized person enters the restricted area in governmental organization. In this paper we have done a detail study about the character recognition, the face recognition and how these both can be linked to develop a security system. The performance metrics has been identified for testing the system in future.
Energy efficiency and adaptability in the utilization of radio spectrum are two major research challenges for the advancement of future wireless communications technologies. To address these difficulties, we enhance the energy efficiency by utilizing spectrum opportunity forecasting (SOF) and optimal scheduling for sensor node activation to decrease the required number of SS events. The application of SOF to cooperative spectrum sensing (SS) is one of the exceptional commitments of this paper. Results demonstrated that this combination could significantly diminish sensor node energy utilization and in this manner increase cognitive radio network (CRN) lifetime. A positive direct relationship was found between forecast length and CRN lifetime. A vast change in the collective lifetime of the sensor system was observed when SOF was utilized for a forecast length of ten SS events, utilizing implicit enumeration (IE) under the uniform sensor node distribution.
Investigation of a coplanar pentagonal antenna is presented in this paper. The antenna offers a bandwidth of 9.48 % at 2.53 GHz with a gain of 4.39 dBi. The antenna has an omnidirectional radiation pattern. The advantage of coplanar antenna is that active circuitry can be mounted easily making it applicable for Monolithic Microwave Integrated Circuit applications (MMIC). The results are compared with a Bow-Tie antenna with the same substrate at same frequency.
This paper presents a multisensor fusion framework for video activities recognition based on statistical reasoning and D-S evidence theory. Precisely, the framework consists in the combination of the events’ uncertainty computation with the trained database and the fusion method based on the conflict management of evidences. Our framework aims to build Multisensor fusion architecture for event recognition by combining sensors, dealing with conflicting recognition, and improving their performance. According to a complex event’s hierarchy, Primitive state is chosen as our target event in the framework. A RGB camera and a RGB-D camera are used to recognise a person’s basic activities in the scene. The main convenience of the proposed framework is that it firstly allows adding easily more possible events into the system with a complete structure for handling uncertainty. And secondly, the inference of DempsterShafer theory resembles human perception and fits for uncertainty and conflict management with incomplete information. The cross-validation of real-world data (10 persons) is carried out using the proposed framework, and the evaluation shows promising results that the fusion approach has an average sensitivity of 93.31% and an average precision of 86.7%. These results are better than the ones when only one camera is used, encouraging further research focusing on the combination of more sensors with more events, as well as the optimization of the parameters in the framework for improvements.
This paper presents a survey with the intent to address a series of issues of the Lean-Kanban approach in the software development, and specifically the guidelines and tools used to set-up a Kanban board. Following the Lean principles, a software process can be broken down into steps and managed with a Kanban approach. Despite the recent increase of interest on the subject, there is no standard definition of Kanban system for software development, and the specific practices of Kanban have not yet been rigorously defined. The purpose of this work is a rigorous analysis of the available information, through research questions and answers, to show the state-of the-art about how Kanban approach is presented and used, in particular those related to the Kanban board management, and to study how they are addressed in practice. We used the methods of Evidence-based software engineering, performing a systematic review of the available information. In our opinion, the information gathered might be very useful to people considering Kanban board adoption, and to the whole community of agile developers practicing Lean-Kanban system approach.
This paper presents a simplified and very adaptable result of the equivalent circuit for the RF microstrip disk resonator. This equivalent circuit is obtained by connecting a parallel transmission line with parallel inductors and connecting in series with 50 O transmission line. To evaluate this structure, we present a disk resonator with 50mm of diameter in order to compare the resonant frequency responses between these two circuits, disk resonator in EM-simulation and its equivalent circuit in Circuit-simulation. The S-parameter performance of Circuit-simulation of the equivalent circuit is seen to be in excellent agreement with EMsimulation of disk resonator. This new equivalent circuit model can be a very useful benefit to design different kind of circuits which disk resonator is used, especially for bandpass filter.
Data Matrix Codes (DMC) are established by these days in many areas of industrial manufacturing thanks to their concentration of information on small spaces. In today's usually order-related industry, where increased tracing requirements prevail, they offer further advantages over other identification systems. This underlines in an impressive way the necessity of a robust code reading system for detecting DMC on the components in factories. This paper provides a solution for the Data Matrix Codes recognition. Using the Mean Shift Algorithm, the main information of the DMC pattern are computed and based on these, a virtual identification pattern is constructed. Seeking and matching over the image, the Data Matrix Code is located. We concentrate on Data Matrix Codes in industrial environment, punched, milled, lasered or etched on different materials in arbitrary orientation.
In this paper we provide a method to localize the modules of the industrial Data Matrix Code (DMC) marked on curved surfaces. An imaginary grid of points is constructed which has the same orientation as the surface. The imaginary grid is projected on to the image and according to this grid the modules of the code are scanned. If the surface considered is not a perfect sphere then the results are not accurate. To overcome this using an error correction based on the assumed centers and the computed centers a better approximation is obtained. Using this method we can localize the modules irrespective of the camera view or orientation of the DMC.
This paper describes an effective location method to minimize prediction error of observation and estimation with a neural system. This neural system differs from conventional learning methods. The proposed scheme is applied to solve location problem of random source generation of time and position. Four sensors are settled crossed on two axes. The number of sound sources is 16.on 2D plane. This sensor location gives to simplify the calculation of the hyperbolic method compared to arbitrary position of sensors used in the prior papers of the authors.
An increasing number of smart mobile devices offering the ability to perform various types of ubiquitous computation are emerging as large computer networks with an unprecedented scale. Large Scale Mobile Ad Hoc Networks (MANETs) place strong challenges on many aspects of network modeling, deployment, protocols, and resource discovery. Existing policies and techniques in MANETs need to be scaled efficiently as the average deployable network size increases. In this paper, we propose a new resource discovery scheme based on adaptive multi-hop clustering algorithm that divides the large network into several non-overlapping localities. Each cluster has members that are on average d-hops away from their clusterhead. The proposed resource discovery algorithm is a weight-based clusterhead election process that takes into consideration the dynamic topology changes of MANET due to nodes mobility and/or energy depletion. A comparative study is conducted by simulation to demonstrate the superiority of our scheme compared to other proposed techniques in the literature in terms of number of clusters, cluster size, cluster stability and nodes reaffiliation as measures to the algorithm efficiency.
The wireless networks have been the object of many studies and analyzes of current technology industry, providing not only communication with mobility to end users, but also incorporating new applications. One such technology is known as Wireless Fidelity (Wi-Fi) 802.11. This paper aims to present the behavior of the propagation of electromagnetic waves radiated from an access point Wi-Fi (AP), with different positions of the antenna. These analyses were based on measurements taken in an environment considering line of sight (LOS) at different distances from the AP, but in a confined environment. Consequently, although there were no obstacles (LOS) between transmitter and receiver, there were conditions of confinement on the propagated signal, given the characteristics of this environment (such as the height of the ceiling and the walls themselves). Thus, measurements were made by switching the antenna positioning of the access point, vertically and horizontally. From the analysis of these measurements, it was possible to verify the positioning of the antenna in the access point that generated improved signal coverage, even moderately. In addition, from the measured data (statistically based) the technique of linear regression was used in order to generate mathematical models for each specific situation measured. These proposed models were compared to the Friis model plus correction factors, and were used both for validating the measurements, as a basis for installation of new access points in similar environments.
The current methods of communication using steganography techniques involve the use of carrier files including network protocols. The current study presents a new method of communication using steganography techniques available today, the carrier being a ping. The study will also present the most effective ways of prevention against this form of secret communication.
This paper describes a synchronized neural system to minimize errors of prediction between observation and estimation. This scheme is different to the principle of conventional learning methods. The proposed scheme is applied to the problem of time-space analysis of multiple sound source locations in 3D space. The number of sources is assumed 16 sounds with a time-frame of 15 sec, and number and size of sensors are 6 and 0.2 meter cubic. The proposed scheme is confirmed with well coincidence of generation times and locations.
This paper proposes a method to customize a wavelet function for the analysis of pupil diameter fluctuation in the detection of drowsiness states under a driving simulation. The methodology relies on a genetic algorithm-based optimization and lifting schemes, which are a flexible and fast implementation of the discrete wavelet transform. To customize the wavelet function a clustering separability metric is employed as a fitness function so that the feature space created by the wavelet analysis exhibits the maximum class separability favorable for classification. Therefore, a completely new wavelet function is created, having unique characteristics customized to pupil diameter fluctuation analysis. It is demonstrated that the customized wavelet function own distinguished frequency and temporal responses suitable specifically for pupil diameter fluctuation analysis (namely, application-dependent), and in the classification they outperform classical wavelet families including Daubechies, Coiflet and Symlet, which are assumed to be application-independent. Thus the proposed method is useful for analysis of pupil fluctuation in evaluating sleepiness levels, as has been demonstrated in other applications.
