80 resultados para PARTIAL ANHYSTERETIC REMANENCE


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We consider a server serving a time-slotted queued system of multiple packet-based flows, where not more than one flow can be serviced in a single time slot. The flows have exogenous packet arrivals and time-varying service rates. At each time, the server can observe instantaneous service rates for only a subset of flows ( selected from a fixed collection of observable subsets) before scheduling a flow in the subset for service. We are interested in queue length aware scheduling to keep the queues short. The limited availability of instantaneous service rate information requires the scheduler to make a careful choice of which subset of service rates to sample. We develop scheduling algorithms that use only partial service rate information from subsets of channels, and that minimize the likelihood of queue overflow in the system. Specifically, we present a new joint subset-sampling and scheduling algorithm called Max-Exp that uses only the current queue lengths to pick a subset of flows, and subsequently schedules a flow using the Exponential rule. When the collection of observable subsets is disjoint, we show that Max-Exp achieves the best exponential decay rate, among all scheduling algorithms that base their decision on the current ( or any finite past history of) system state, of the tail of the longest queue. To accomplish this, we employ novel analytical techniques for studying the performance of scheduling algorithms using partial state, which may be of independent interest. These include new sample-path large deviations results for processes obtained by non-random, predictable sampling of sequences of independent and identically distributed random variables. A consequence of these results is that scheduling with partial state information yields a rate function significantly different from scheduling with full channel information. In the special case when the observable subsets are singleton flows, i.e., when there is effectively no a priori channel state information, Max-Exp reduces to simply serving the flow with the longest queue; thus, our results show that to always serve the longest queue in the absence of any channel state information is large deviations optimal.

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Two-dimensional magnetic recording (2-D TDMR) is an emerging technology that aims to achieve areal densities as high as 10 Tb/in(2) using sophisticated 2-D signal-processing algorithms. High areal densities are achieved by reducing the size of a bit to the order of the size of magnetic grains, resulting in severe 2-D intersymbol interference (ISI). Jitter noise due to irregular grain positions on the magnetic medium is more pronounced at these areal densities. Therefore, a viable read-channel architecture for TDMR requires 2-D signal-detection algorithms that can mitigate 2-D ISI and combat noise comprising jitter and electronic components. Partial response maximum likelihood (PRML) detection scheme allows controlled ISI as seen by the detector. With the controlled and reduced span of 2-D ISI, the PRML scheme overcomes practical difficulties such as Nyquist rate signaling required for full response 2-D equalization. As in the case of 1-D magnetic recording, jitter noise can be handled using a data-dependent noise-prediction (DDNP) filter bank within a 2-D signal-detection engine. The contributions of this paper are threefold: 1) we empirically study the jitter noise characteristics in TDMR as a function of grain density using a Voronoi-based granular media model; 2) we develop a 2-D DDNP algorithm to handle the media noise seen in TDMR; and 3) we also develop techniques to design 2-D separable and nonseparable targets for generalized partial response equalization for TDMR. This can be used along with a 2-D signal-detection algorithm. The DDNP algorithm is observed to give a 2.5 dB gain in SNR over uncoded data compared with the noise predictive maximum likelihood detection for the same choice of channel model parameters to achieve a channel bit density of 1.3 Tb/in(2) with media grain center-to-center distance of 10 nm. The DDNP algorithm is observed to give similar to 10% gain in areal density near 5 grains/bit. The proposed signal-processing framework can broadly scale to various TDMR realizations and areal density points.

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Mesophase organization of molecules built with thiophene at the center and linked via flexible spacers to rigid side arm core units and terminal alkoxy chains has been investigated. Thirty homologues realized by varying the span of the spacers as well as the length of the terminal chains have been studied. In addition to the enantiotropic nematic phase observed for all the mesogens, the increase of the spacer as well as the terminal chain lengths resulted in the smectic C phase. The molecular organization in the smectic phase as investigated by temperature dependent X-ray diffraction measurements revealed an interesting behavior that depended on the length of the spacer vis-a-vis the length of the terminal chain. Thus, a tilted interdigitated partial bilayer organization was observed for molecules with a shorter spacer length, while a tilted monolayer arrangement was observed for those with a longer spacer length. High-resolution solid state C-13 NMR studies carried out for representative mesogens indicated a U-shape for all the molecules, indicating that intermolecular interactions and molecular dynamics rather than molecular shape are responsible for the observed behavior. Models for the mesophase organization have been considered and the results understood in terms of segregation of incompatible parts of the mesogens combined with steric frustration leading to the observed lamellar order.

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A hitherto unseen rotation of the isopropyl group in the solid state, predicted to be forbidden based on theoretical investigations, is reported. This C-C rotation observed during the temperature dependent single-crystal-to-single-crystal transformation is attributed to the concomitant changes in molecular structure and intermolecular packing.

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Achieving control on the formation of different organization states of magnetic nanoparticles is crucial to harness their organization dependent physical properties in desired ways. In this study, three organization states of iron oxide nanoparticles (gamma-Fe2O3), defining as (i) assembly (ii) network aggregate and (iii) cluster, have been developed by simply changing the solvent evaporation conditions. All three systems have retained the same phase and polydispersity of primary particles. Magnetic measurements show that the partial alignment of the easy axes of the particles in the network system due to the stacking aggregation morphology can result in significant enhancement of the coercivity and remanence values, while the opposite is obtained for the cluster system due to the random orientation of easy axes. Partial alignment in the aggregate system also results in noticeable non -monotonic field dependence of ZFC peak temperature (TpeaB). The lowest value of the blocking temperature (TB) for the cluster system is related to the lowering of the effective anisotropy due to the strongest demagnetizing effect. FC (Field cooled) memory effect was observed to be decreasing with the increasing strength of dipolar interaction of organization states. Therefore, the stacking aggregation and the cluster formation are two interesting ways of magnetic nanoparticles organization for modulating collective magnetic properties significantly, which can have renewed application potentials from recording devices to biomedicine. (C) 2016 Elsevier B.V. All rights reserved.