Araujo G.M.D.,Federal University of Santa Catarina |
Pinto A.R.,Sao Paulo State University |
Kaiser J.,Otto Von Guericke University of Magdeburg |
Kaiser J.,Universitatsplatz |
Becker L.B.,Federal University of Santa Catarina
Studies in Computational Intelligence | Year: 2014
Establishing adequate RF (Radio Frequency) connectivity is the basic requirement for the proper operation of any wireless network. In a mobile wireless network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time can avoid unnecessary or even unuseful control/data messages transmissions. The current paper presents the so-called Genetic Machine Learning Approach for Link Quality Prediction, or simply GMLA, which is a solution to forecast the remainder RF connectivity time in mobile environments. Differently from all related works, GMLA allows building connectivity knowledge to estimate the RF link duration without the need of a pre-runtime phase. This allows to apply GMLA at unknown environments and mobility patterns. Its structure combines a Classifier System with a Markov chain model of the RF link quality. As the Markov model parameters are discovered on-the-fly, there is no need of a previous history to feed the Markov model. Obtained simulation results show that GMLA is a very suitable solution, as it outperforms approaches that use geographical positioning systems (GPS) and also approaches that use link-quality prediction, such as BD and MTCP. GMLA is generic enough to be applied to any layer of the communication protocol stack, especially in the link and network layers. © 2014 Springer-Verlag Berlin Heidelberg.
Saldeitis K.,Leibniz Institute for Neurobiology |
Happel M.F.K.,Leibniz Institute for Neurobiology |
Happel M.F.K.,Otto Von Guericke University of Magdeburg |
Ohl F.W.,Leibniz Institute for Neurobiology |
And 7 more authors.
Journal of Comparative Neurology | Year: 2014
Knowledge of the anatomical organization of the auditory thalamocortical (TC) system is fundamental for the understanding of auditory information processing in the brain. In the Mongolian gerbil (Meriones unguiculatus), a valuable model species in auditory research, the detailed anatomy of this system has not yet been worked out in detail. Here, we investigated the projections from the three subnuclei of the medial geniculate body (MGB), namely, its ventral (MGv), dorsal (MGd), and medial (MGm) divisions, as well as from several of their subdivisions (MGv: pars lateralis [LV], pars ovoidea [OV], rostral pole [RP]; MGd: deep dorsal nucleus [DD]), to the auditory cortex (AC) by stereotaxic pressure injections and electrophysiologically guided iontophoretic injections of the anterograde tract tracer biocytin. Our data reveal highly specific features of the TC connections regarding their nuclear origin in the subdivisions of the MGB and their termination patterns in the auditory cortical fields and layers. In addition to tonotopically organized projections, primarily of the LV, OV, and DD to the AC, a large number of axons diverge across the tonotopic gradient. These originate mainly from the RP, MGd (proper), and MGm. In particular, neurons of the MGm project in a columnar fashion to several auditory fields, forming small- and medium-sized boutons, and also hitherto unknown giant terminals. The distinctive layer-specific distribution of axonal endings within the AC indicates that each of the TC connectivity systems has a specific function in auditory cortical processing. J. Comp. Neurol. 522:2397-2430, 2014. © 2014 Wiley Periodicals, Inc.