Mobile ad-hoc network (MANET) is a collection of mobile
terminals forming an infrastructure less and quick deployable network,
which can communicate to each other via multiple hops or single hop.
Such ad-hoc networks have always been important for various applications like defence applications especially for countries like India having
boundaries and regions with large geographical diversity. Mobility attribute is a notable one in MANETs, as this leads to frequent topology
changes which are the primary cause of route failure. A route is an ordered set of links, hence for predicting future availability of any particular
route, it is important to estimate the availability of its currently available constituent links. This paper explores various link availability prediction model and proposes a least square polynomial regression-based
statistical approach to predict the availability of link. Proposed approach
assumes that movement of nodes are based on column mobility model i.e
each node in the network is linearly moving with constant speed. Each
node in the network periodically broadcasts hello packets to its neighbours to inform it’s availability in the network. Neighbour node receives
hello packet and uses its signal strength to estimate distance between
sender and receiver of hello packet. A monotonically decreasing signal
strength of hello packets at receiver node indicates that nodes are moving away from each other and link between them may break in future so
it starts link residual time prediction algorithm to predict the time when
the distance between them will exceed the pre-defined threshold value.
The proposed algorithm is simulated using NS 2.35. The performance
of the algorithm has been analyzed for identified parameters. The results are also been compared by simulating other existing link prediction
approaches based on interpolation.
We compare major factor models and find that the Stambaugh and Yuan (2016) four-factor model is the overall winner in the time-series domain. The Hou, Xue, and Zhang (2015) q-factor model takes second place and the Fama and French (2015) five-factor model and the Barillas and Shanken (2018) six-factor model jointly take third place. But the pairwise cross-sectional R2 and the multiple model comparison tests show that the Hou, Xue, and Zhang (2015) q-factor model, the Fama and French (2015) five-factor and four-factor models, and the Barillas and Shanken (2018) six-factor model take equal first place in the horse race.
Com a evolução constante da eletrônica, a necessidade de produzir protótipos em placa de circuito impresso é cada vez mais
cobrada e importante para se elaborar tecnologia de forma rápida, mas sem deixar a qualidade de produção baixa. Pensando
nisso, este artigo propõe o desenvolvimento de uma fresadora CNC com base no comando numérico computadorizado para a
confecção de trilhas na placa de circuito impresso de forma otimizada. Deste modo, são mostrados passo a passo os pilares
teóricos que compõem a base de conhecimento para que se possa entender e desenvolver a ferramenta que irá usinar e por
sua vez produzir o protótipo de forma eficaz. Os resultados obtidos em relação à montagem da ferramenta e o material
usinado foi classificado com satisfatório, já que a máquina CNC conseguiu atingir seus objetivos, perfurando, cortando a
placa e isolando as trilhas formando assim o circuito.
José Gleury Galvino Pereira e Adriana Maria Rebouças do Nascimento