Dolores F. De Groff

Stochastical aspects of neuronal dynamics: Fokker-Planck approach

The Stochastical aspects of noise-perturbed neuronal dynamics are studied via the Fokker-Planck equation by considering the Langevin-type relaxational, nonlinear process associated with neuronal states. On the basis of a canonical, stochastically driven, dichotomous state modeling, the equilibrium conditions in the neuronal assembly are analyzed. The markovian structure of the random occurrence of action potentials due to the disturbances (noise) in the neuronal state is considered, and the corresponding solutions relevant to the colored noise spectrum of the disturbance effects are addressed. Stochastical instability (Lyapunov) considerations in solving discrete optimization problems via neural networks are discussed. The bounded estimate(s) of the Stochastical variates involved are presented, and the noise-induced perturbations on the saturated-state neuronal population are elucidated.

Neuro-Fuzzy Approach Towards Technoeconomic Forecasting

Proposed in this paper is a fuzzy inference engine (FIE) intended to ascertain ex ante forecast details on a dependent variable y, based on a set of ex post information gathered on y in technoeconomic contexts. The FIE constructed thereof conforms to an artificial neural network (ANN), and, the ANN outcome deduced yields the forecasting on the temporal evolution of y(t) in the ex ante time-frame (t) vis-a-vis a set of ex post data availed. The ex post data available is however, sparse and inadequate for robust forecasting. Therefore, its cardinality is first improved and sufficient number of such sets is obtained as pseudoreplicates via statistical bootstrapping. The test ANN then uses these pseudoreplicates as training inputs toward robust prediction/forecast schedules. Further, the pseudoreplicated sets are considered as overlapping and hence, fuzzy. Therefore, the test ANN adopted is relevant to a FIE realization. Real-world technoeconomic data set on ADSL sales-cum-facility details at a wire-center in a telecommunication company (telco) is used to test the efficacy of the FIE proposed and validate the forecasting method described.

Indoor RF-channel characterization of nano-through femto-cell ambient at millimeter-wave/THz frequencies in LTE contexts

RF channel characterization in forging conceivable short-range wireless links in nano-through femto-cells applications of WLAN/WPAN in long term evolution (LTE) context is considered. Relevant next-generation wireless-based indoor services are required to support multi-gigabit information transfer rates. As such, the associated electromagnetic (EM) spectral needs warrant accommodating almost unlimited wireless channels each shouldering enormous bandwidth. Relevant wireless transport requirements can be met with the span of EM spectra that currently remain unclaimed and unregulated. They exist as prospective resources in the frontiers of mm-wave range (spanning 30 GHz to terahertz band). Addressed in this study thereof is the feasibility of conceiving “inferential prototypes” of RF channel-models in the 30+ GHz through THz spectrum of indoor ambient by judiciously sharing the “similarity” of details pertinent to already existing (known) “models” of traditional, lower-side EM spectrum, (namely, VLF through micro-/mm-wave); and, an approach based on the principle of similitude due to Edgar Buckingham is invoked toward model-to-(inferential) prototype transformations. Examples on indoor path-loss estimation for line-of-sight (LoS) case is presented for the spectral range of interest and the efficacy of the proposal is outlined.

Coevolution of competitive market structures in the context of Mobile OS industry

Technoeconomics of modern telecommunication business depicts a competitive complex structure. Proposed in this paper is a stochastical growth model that describes the business performance of competing telecommunication companies (telcos) such as mobile platforms. The underlying competition is modelled as coevolving pre-predator system and hence, the growth/decay profile of the competitors is deduced. The model is applied to econometric forecasting via artificial neural network-based simulations. Example results pertinent to real-world data on the temporal dynamics of competing/co-evolving competitors known in Mobile OS industry, (like Android, Symbian and iPhone) are presented demonstrating the efficacy of forecast feasibility as well as the validity of the model proposed.