Abstract-Over recent years, mobile Internet devices such as laptops, PDAs, smart phones etc, have become extremely popular and widespread. Once on board of a vehicle, these devices can automatically connect to the vehicle processor and thus greatly amplify the communications and processing capabilities available to the owner in a "pedestrian mode." We envision that this "amplification" opportunity will be one of the drivers of car to car and car to curb communications. In fact, the car communications system will not be used exclusively for mobile Internet access, but also as a distributed platform for the "opportunistic" cooperation among people with shared interests/goals. Exchanging safety messages among vehicles is a compelling example. Stretching opportunistic cooperation well beyond safety messages, we discuss in this paper the concept of virtual "flea market" over VANET called FleaNet. In FleaNet, customers, either mobile (i.e., vehicles) or stationary (i.e., pedestrians, roadside shop owner), express their demands/offers, e.g., want to buy or sell an item, via radio queries. These queries are opportunistically disseminated exploiting in part the mobility of other customers in order to find the customer/vendor with matching needs/resources. In the paper we identify the key performance metrics, namely query resolution latency, scalability, and mobility. Based on the metrics, using models and simulation, we show that FleaNet can efficiently support a market place over vehicular networks.
Super-oscillating beams can be used to create light spots whose size is below the diffraction limit with a side ring of high intensity adjacent to them. Optical traps made of the super-oscillating part of such beams exhibit superior localization of submicron beads compared to regular optical traps. Here we focus on the effect of the ratio of particle size to trap size on the localization and stiffness of optical traps made of super-oscillating beams. We find a non-monotonic dependence of trapping stiffness on the ratio of particle size to beam size. Optimal trapping is achieved when the particle is larger than the beam waist of the super-oscillating feature but small enough not to overlap with the side ring. PACS numbers:In the early 70s, Artur Ashkin showed that a weakly focused laser beam can draw small particles with high refractive index towards its center and move them in the direction of light propagation [1]. A major breakthrough in this field happened in 1986 when Ashkin demonstrated the single beam optical gradient force traps [2], known nowadays as optical tweezers. Since then, optical trapping application has become a powerful tool used in physics and biology. However, the size of an optical trap is limited by the smallest spot which collimated light can be focused to using an annular aperture, as discussed in 1873 by Ernst Abbe [3] and later by Lord Rayleigh [4]. The diffraction limit of light determining this minimal beam size is given by w = 0.38λ/NA, where w is the beam waist defined as the full width at half maximum of the beam, λ is the wavelength of the beam, and NA is the numerical aperture of the focusing lens. In 1952 G. Toraldo di Francia suggested theoretically that by phase modulations one can achieve optical features below the diffraction limit [5]. In the 90's the concept of super oscillation (SO) was first introduce by Michel Berry for bandlimited functions that locally oscillate faster than their highest Fourier component [6]. In optics, the SO phenomena was used to generate optical beams with features smaller than the diffraction limit. Over the last 20 years SO beams were generated using different methods [7-9] and applied for super-resolution imaging [10,11].The effect of particle size, beam waist, and wavelength on the stiffness of optical trapping was studied theoretically for different scattering regimes [12][13][14]. Experimental verification of these predictions is challenging since neither beam size, wavelength, nor particle size can be changed continuously to provide a clean comparison [15][16][17][18]. Naturally, all previous measurements focused on diffraction-limited optical traps. Previously, we observed that a significant enhancement of optical trapping strength and localization occurred when a 490 nm particle was trapped in the SO part of a SO beam [19]. Here we study this effect in more detail. We use the unique feature of SO beams, namely, the ability to change continuously the beam waist and to focus the beam to below the diffraction limit, to measure the effect of p...
Abstract-Over recent years, mobile Internet devices such as laptops, PDAs, smart phones etc, have become extremely popular and widespread. Once on board of a vehicle, these devices can automatically connect to the vehicle processor and thus greatly amplify the communications and processing capabilities available to the owner in a "pedestrian mode." We envision that this "amplification" opportunity will be one of the drivers of car to car and car to curb communications. In fact, the car communications system will not be used exclusively for mobile Internet access, but also as a distributed platform for the "opportunistic" cooperation among people with shared interests/goals. Exchanging safety messages among vehicles is a compelling example. Stretching opportunistic cooperation well beyond safety messages, we discuss in this paper the concept of virtual "flea market" over VANET called FleaNet. In FleaNet, customers, either mobile (i.e., vehicles) or stationary (i.e., pedestrians, roadside shop owner), express their demands/offers, e.g., want to buy or sell an item, via radio queries. These queries are opportunistically disseminated exploiting in part the mobility of other customers in order to find the customer/vendor with matching needs/resources. In the paper we identify the key performance metrics, namely query resolution latency, scalability, and mobility. Based on the metrics, using models and simulation, we show that FleaNet can efficiently support a market place over vehicular networks.
Abstract. Current telecommunication network management systems rely extensively on human intervention. They are also prone to fundamental changes as the managed network evolves. These two attributes, combined with the growing complexity of networks and services, make the cost of network management very high. In recent years, we have witnessed the emergence of artificial intelligence applications. Some are aimed at the creation of autonomic network management systems. This paper offers a novel approach to the design of a network management system that incorporates intelligent agents. As a benchmark to this model, we use two approaches most widely in use in network management systems today. The focus of this paper is on synchronization issues, service discovery and policy enforcement.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.