Structural and electrical properties of TlLiWO4 ceramics

Abstracts are not published in this journal     

Delay analysis of selective-repeat ARQ with applications to link adaptation in wireless packet data systems

Radio link control (RLC) protocols are typically employed for reliable in-sequence delivery of service data units (SDUs) in wireless packet data systems. The RLC layer segments packets obtained from the upper layer (referred to as SDUs) into smaller RLC transmission units (or blocks) and uses selective-repeat automatic repeat request (SR-ARQ) for error recovery of RLC blocks. In earlier work, SR-ARQ performance is typically characterized in terms of the long-term throughput or in-sequence delivery delay of RLC blocks. The SDU delivery delay which is a more meaningful measure of RLC performance (in terms of the service provided to a higher layer, e.g., transmission control protocol) has not been quantified. In this paper, we analyze the SDU delivery delay of SR-ARQ as a function of the SDU size and the channel coding scheme employed. Closed-form delay expressions as well as approximations are provided. The analysis is verified through enhanced general packet radio service RLC simulations. Based on the analysis, we propose that link adaptation be backlog dependent in order to reduce the SDU delivery delay at the RLC layer.     

SpecSyn: an environment supporting the specify-explore-refineparadigm for hardware/software system design

System-level design issues are gaining increasing attention, as behavioral synthesis tools and methodologies mature. We present the SpecSyn system-level design environment, which supports the new specify-explore-refine (SER) design paradigm. This three-step approach to design includes precise specification of system functionality, rapid exploration of numerous system-level design options, and refinement of the specification into one reflecting the chosen option. A system-level design option consists of an allocation of system components, such as standard and custom processors, memories, and buses, and a partitioning of functionality among those components. After refinement, the functionality assigned to each component can then he synthesized to hardware or compiled to software. We describe the issues and approaches for each part of the SpecSyn environment. The new paradigm and environment are expected to lead to a more than ten times reduction in design time, and our experiments support this expectation     

Modified neo-fuzzy neuron-based approach for economic and environmental optimal power dispatch

At the central energy management center in a power system, the real time controls continuously track the load changes and endeavor to match the total power demand with total generation in such a manner that the operating cost is minimized while all the operating constraints are satisfied. However, due to the strict government regulations on environmental protection, operation at minimum cost is no longer the only criterion for dispatching electrical power. The idea behind the environmentally constrained economic dispatch formulation is to estimate the optimal generation schedule of generating units in such a manner that fuel cost and harmful emission levels are both simultaneously minimized for a given load demand. Conventional optimization techniques become very time consuming and computationally extensive for such complex optimization tasks. These methods are hence not suitable for on-line use. Neural networks and fuzzy systems can be trained to generate accurate relations among variables in complex non-linear dynamical environment, as both are model-free estimators. The existing synergy between these two fields has been exploited in this paper for solving the economic and environmental dispatch problem on-line. A multi-output modified neo-fuzzy neuron (NFN), capable of real time training is proposed for economic and environmental power generation allocation.This model is found to achieve accurate results and the training is observed to be faster than other popular neural networks. The proposed method has been tested on medium-sized sample power systems with three and six generating units and found to be suitable for on-line combined environmental economic dispatch (CEED).     

Adenosine A1 receptor stimulation antagonizes the negative inotropic effects of the PKC activator dioctanoylglycerol

It has been suggested that adenosine cardioprotection occurs via adenosine A1 receptor-mediated activation of protein kinase C (PKC). However, adenosine has well-known vasodilatory effects in the myocardium, whereas PKC is a vasoconstrictor. This study examined whether adenosine A1 receptor activation alters the effects of the PKC activator. 1,2-dioctanoyl-s,n-glycerol (DOG) in isolated perfused rat hearts (left-ventricular developed pressure) and rat ventricular myocytes ([Ca2+]i and cell shortening). Exposure to DOG decreased left-ventricular developed pressure by 30%, an effect that was completely reversible. Pretreatment of isolated hearts with either the PKC inhibitor chelerythrine or the adenosine A1 agonist 2-chloro-N6-cyclo-cyclo-isolated pentlyadenosine (CCPA) attenuated the negative inotropic effects of DOG. In the isolated myocytes, DOG decreased [Ca2+]i and cell shortening by 25 and 28%, respectively, effects that were attenuated by both chelerythrine and CCPA. The CCPA attenuation of the DOG-induced decrease in [Ca2+]i and cell shortening was blocked by pretreating the myocytes with the adenosine A1 antagonist, 8-cyclopentyl-1,3-dipropylxanthine (DPCPX). These results indicate that in rat ventricular myocardium, adenosine A1 receptor activation attenuates the apparent PKC-dependent negative inotropic effects of DOG via preservation of [Ca2+]i levels.     

Role of self-assembled gold nanodots in improving the electrical and optical characteristics of zinc oxide films

We have studied the effect of embedding nanocrystalline Au particles on the electrical and optical characteristics of ZnO films. Au-embedded epitaxial ZnO films were deposited on (0001) sapphire substrates with a pulsed laser deposition technique. The crystalline quality of both the ZnO matrix and Au nanoparticles was investigated by X-ray diffraction and transmission electron microscopy. Composite films were characterized by photoluminescence, optical absorption, and low-temperature electrical resistivity measurements. Photoluminescence spectra of theses films showed a sharp excitonic peak at 3.22 +/- 0.05 eV without any signature of green band emission. Electrical resistivity measurements showed these films to be highly conducting, with a room-temperature resistivity of 3.4 +/- 0.2 m omega-cm.     

Realization of a temperature sensor using both two- and three-dimensional photonic structures through a machine learning technique

A temperature sensor based on photonic crystal structures with two- and three-dimensional geometries is proposed, and its measurement performance is estimated using a machine learning technique. The temperature characteristics of the photonic crystal structures are studied by mathematical modeling. The physics of the structure is investigated based on the effective electrical permittivity of the substrate (silicon) and column (air) materials for a signal at 1200 nm, whereas the mathematical principle of its operation is studied using the plane-wave expansion method. Moreover, the intrinsic characteristics are investigated based on the absorption and reflection losses as frequently considered for such photonic structures. The output signal (transmitted energy) passing through the structures determines the magnitude of the corresponding temperature variation. Furthermore, the numerical interpretation indicates that the output signal varies nonlinearly with temperature for both the two- and three-dimensional photonic structures. The relation between the transmitted energy and the temperature is found through polynomial-regression-based machine learning techniques. Moreover, rigorous mathematical computations indicate that a second-order polynomial regression could be an appropriate candidate to establish this relation. Polynomial regression is implemented using the Numpy and Scikit-learn library on the Google Colab platform.

     

A powerful algorithm for power system planning amenable to small computers

This paper presents the development of a highly effective and reliable piecewise fast decoupled load flow algorithm. The algorithm requires minimal storage, independent of the system size, and can be used effectively for system planning on small computers.