Current Issue : January - March Volume : 2019 Issue Number : 1 Articles : 5 Articles
Automatic gesture recognition is an important field in the area of human-computer interaction. Until recently, the main approach\nto gesture recognition was based mainly on real time video processing. The objective of this work is to propose the utilization of\ncommodity smartwatches for such purpose. Smartwatches embed accelerometer sensors, and they are endowed with wireless\ncommunication capabilities (primarily Bluetooth), so as to connect with mobile phones on which gesture recognition algorithms\nmay be executed. The algorithmic approach proposed in this paper accepts as the input readings from the smartwatch accelerometer\nsensors and processes them on the mobile phone. As a case study, the gesture recognition application was developed for\nAndroid devices and the Pebble smartwatch. This application allows the user to define the set of gestures and to train the system to\nrecognize them. Three alternative methodologies were implemented and evaluated using a set of six 3-D natural gestures. All the\nreported results are quite satisfactory, while the method based on SAX (Symbolic Aggregate approXimation) was proven the\nmost efficient....
Time evolving Random Network Models are presented as a mathematical framework for\nmodelling and analyzing the evolution of complex networks. This framework allows the analysis\nover time of several network characterizing features such as link density, clustering coefficient, degree\ndistribution, as well as entropy-based complexity measures, providing new insight on the evolution\nof random networks. First, some simple dynamic network models, based only on edge density, are\nanalyzed to serve as a baseline reference for assessing more complex models. Then, a model that\ndepends on network structure with the aim of reflecting some characteristics of real networks is also\nanalyzed. Such model shows a more sophisticated behavior with two different regimes, one of them\nleading to the generation of high clustering coefficient/link density ratio values when compared\nwith the baseline values, as it happens in many real networks. Simulation examples are discussed to\nillustrate the behavior of the proposed models....
In cache-enabled device-to-device (D2D) -aided cellular networks, the technique of caching\ncontents in the cooperative crossing between base stations (BSs) and devices can significantly reduce\ncore traffic and enhance network capacity. In this paper, we propose a scheme that establishes\ndevice availability, which indicates whether a cache-enabled device can handle the transmission\nof the desired content within the required sending time, called the delay, while achieving optimal\nprobabilistic caching. We also investigate the impact of transmission device availability on the\neffectiveness of a scenario of cooperative crossing cache placement, where content delivery traffic can\nbe offloaded from the local cache, a D2D transmitterâ??s cache via a D2D link, or else directly from a BS\nvia a cellular link, in order to maximize the offloading probability. Further, we derive the cooperation\ncontent offloading strategy while considering successful content transmission by D2D transmitters or\nBSs to guarantee the delay, even though reducing the delay is not the focus of this study. Finally, the\nproposed problem is formulated. Owing to the non-convexity of the optimization problem, it can be\nrewritten as a minimization of the difference between the convex functions; thus, it can be solved\nby difference of convex (DC) programming using a low-complexity algorithm. Simulation results\nshow that the proposed cache placement scheme improves the offloading probability by 13.5% and\n23% compared to Most Popular Content (MPC) scheme, in which both BSs and devices cache the\nmost popular content and Coop. BS/D2D caching scheme, in which each BS tier and user tier applies\ncooperative content caching separately....
Cognitive radio communications depend on methods for sensing the spectrum as well as adapting transmission parameters\nto available resources. In this context, this work proposes a novel system that makes use of prediction to dynamically allocate\nsubcarriers to different transmissions in an orthogonal frequency division multiplexing (OFDM) system. To this end, the proposal\nis comprised of a predictive componentwhichmakes use of a neural network andmultiresolution analysis and a second component,\nwhich uses wavelet analysis and cognitive radio functions to carry out a dynamic allocation of subcarriers in an OFDM system.\nThe use of wavelets allows the system to split the data stream in blocks of information to be transmitted over multiple orthogonal\nsubcarriers. This proposed system makes use of the decision-making functions of a cognitive radio device to select the number\nand position of the subcarriers used for communications without interference. Although there exist other OFDM systems using\nwavelets, they are not used in combination with the decision-making functions implemented in cognitive radio devices. In contrast,\nthe proposed OFDM system operates using some of these functions, thus being able to better adapt its operational parameters. The\nuse of wavelets combined with a neural network model improves the prediction of the bandwidth utilization as shown in this\nwork. It is concluded that the proposed system improves spectral efficiency and data rate by using the decision-making functions\nof cognitive radios to select the appropriate OFDM subcarriers to be used during the data transmissions....
The trend of 5G mobile networks is increasing with the number of users and the transmission rate. Many operators are turning\nto small cell and indoor coverage of telecom network service. With the emerging Software Defined Networking and Network\nFunction Virtualization technologies, Internet Service Provider is able to deploy their networks more flexibly and dynamically.\nIn addition to the change of the wireless mobile network deployment model, it also drives the development trend of the Micro\nOperator...Telecom operators can provide regional network services through public buildings, shopping malls, or industrial\nsites. In addition, localized network services are provided and bandwidth consumption is reduced.The distributed architecture of...tackles computing requirements for applications, data, and services from cloud data center to edge network devices or to the\nmicro data center of...The service model of...is capable of reducing network latency in response to the low-latency applications\nfor future 5G edge computing environment. This paper addresses the design pattern of 5G micro operator and proposes a Decision\nTree Based Flow Redirection (DTBFR) mechanism to redirect the traffic flows to neighbor service nodes. The DTBFR mechanism\nallows different...to share network resources and speed up the development of edge computing in the future....
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