Isotropic Constitutive Model for Sintering Particle Packings

This paper presents an isotropic model for the effective viscosities of sintering particle packings. The relationship between the macroscopic stress and deformation rate is based on the behavior of individual interparticle contacts, represented by contact viscosities, and the statistics of the packing. The contact viscosities depend on the contact area, which is the primary variable used to describe the state of the packing. A direct consequence of this choice of state variable is that the effective viscosities are identically zero for an undeformed and unsintered packing. Specific results are presented for the case of Newtonian viscous materials. The model is compared with existing models for porous Newtonian viscous materials. Experiments conducted with spherical glass powder packings show good agreement with the model. Finite-element simulations using the model have been used to study forging of a pellet.     

Viscosity of Porous Glasses

Viscosity of porous glasses has been derived from the elastic stress analysis, using the viscous analogy. Viscosity as a function of porosity has been estimated for spherical as well as for arbitrary pore geometry. Since the pore geometry changes during sintering, a shape factor that varies with pore geometry has been considered to predict the viscosity–porosity relationship. Viscosity as a function of porosity was measured on cordierite-type glass by isothermal sinter-forging experiments and data showed good agreement with the analysis. Experimental data from literature on viscosity as a function of porosity on two other glasses also show good agreement with the analysis.     

A Workflow Scheduling Method for Cloud Computing Platform

Wireless Personal Communications - Scheduling in computing environments such as homogeneous and heterogonous is very challenging and faces various difficulties computationally. This computing needs...     

Effect of linker used in D–A–π–A metal free dyes with different π-spacers for dye sensitized solar cells

Two novel D–A–π–A metal free dyes with triphenylamine as donor, dithiophene-diketo-pyrrolo-pyrrole as acceptor unit, thiophene and phenyl π-conjugated bridges and a cyanoacetic acid as electron acceptor (TDPP1 and TDPP2 were denoted for thiophene and phenyl π-conjugated bridge, respectively) have been designed and used as sensitizers for DSSCs. Incorporation of dithiophene-diketo-pyrrolo-pyrrole, reduces the band gap significantly. The influence of π-conjugated bridge on optical and electrochemical properties were investigated. Results demonstrated that the absorption band of TDPP with thiophene π-conjugated bridge has red shifted due to the enhancement of electron donating ability of π-conjugated bridge. The DSSC based on TDPP1 shows prominent power conversion efficiency about 4.81%, which is higher that for TDPP2 (3.42%). The electrochemical impedance spectroscopy analysis reveal that the charge recombination resistance at the TiO₂/dye/electrolyte interface for the DSSC based on TDPP1 is higher than that for TDPP2, which improves both J_sc and V_oc. The PCE of the DSSC based on TDPP1 is further improved up to 6.34%, when deoxycholic acid (DCA) was employed as coadsorbant.     

PSO-based learning of sub-band adaptive thresholding function for image denoising

In this paper, a new computationally efficient approach has been proposed for denoising the images which are corrupted by Gaussian noise. In this approach, relatively recent category of stochastic global optimization technique i.e., particle swarm optimization (PSO) technique have been proposed for learning the parameters of adaptive thresholding function required for optimum performance. The proposed PSO-based denoising approach not only speeds up the optimization but also improves the performance in comparison with wavelet transform-based thresholding neural network (WT-TNN) approach. The results obtained shows better edge preservation performance with bior6.8 wavelet filter when compared to db8 wavelet filter. Further, problem of dependency of learning time on initial value of thresholding parameters and noise level in the image have been sorted out in the proposed approach.     

Optical GFDM: an improved alternative candidate for indoor visible light communication

Photonic Network Communications - This paper proposes generalized frequency division multiplexing (GFDM) for indoor visible light communication (VLC). GFDM is a flexible multi-carrier scheme that...     

Continuous error detection (CED) for reliable communication

Block cyclic redundancy check (CRC) codes represent a popular and powerful class of error detection techniques used almost exclusively in modern data communication systems. Though efficient, CRCs can detect errors only after an entire block of data has been received and processed. In this work, we exploit the “continuous” nature of error detection that results from using arithmetic codes for error detection, which provides a novel tradeoff between the amount of added redundancy and the amount of time needed to detect an error once it occurs. We demonstrate how this continuous error detection framework improves the overall performance of communication systems, and show how considerable performance gains can be attained. We focus on several important scenarios: 1) automatic repeat request (ARQ) based transmission; 2) forward error correction (FEC frameworks based on (serially) concatenated coding systems involving an inner error-correction code and an outer error-detection code; and 3) reduced state sequence estimation (RSSE) for channels with memory. We demonstrate that the proposed CED framework improves the throughput of ARQ systems by up to 15% and reduces the computational/storage complexity of FEC and RSSE by a factor of two in the comparisons that we make against state-of-the-art systems     

Cooperative Communication Enabled Cognitive Radio in a Home-Care Application

Wireless Personal Communications - In this paper, cooperative communication (CC) assisted cognitive wireless sensor network (CWSN) is presented for monitoring health and activity of an end-user in...     

Optimal provisioning for virtual network request in cloud-based data centers

Today applications and services are migrating to a cloud-computing-based paradigm in which the users access the applications and services hosted in data centers, by using thin-clients on the user terminal device. These applications/services are typically hosted and run on virtual machines in interconnected data centers. Different applications from the same user may need to access and change shared data or information. Thus, we may abstract the applications from same user as a virtual network (VN). For better performance and efficiency, it is critical that the VN request be accommodated with optimal provisioning under the current resource state of data centers. In this paper, for addressing the issue of how to design an optimal provisioning scheme for the VN request such that the total revenue of is maximized, we first develop a framework for the optimal provisioning of VN request by using mixed integer programming. Since the optimal provisioning problem is NP-hard, we also propose a genetic algorithm?Cbased heuristic algorithm for addressing the problem of optimal provisioning for VN with unsplittable flow and optimal provisioning for VN with splittable flow problems. We demonstrate the effectiveness of our approach in improving the total revenue by conducting extensive simulations on different networks.