We present a mean-field theory to describe volume phase transitions of side-chain liquid crystalline (LC) gels, accompanied by isotropic-nematic-smectic-A phase transitions. Three different uniaxial nematic phases (N1...
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We present a mean-field theory to describe volume phase transitions of side-chain liquid crystalline (LC) gels, accompanied by isotropic-nematic-smectic-A phase transitions. Three different uniaxial nematic phases (N1, N2, and N3) and smectic-A phases (S1, S2, and S3) are defined by using orientational order parameter Sm of side-chain liquid crystals (mesogens), Sb of semiflexible backbone chains, and a translational order parameter σ for a smectic-A phase. We derive the free energy for smectic-A phases of side-chain LC gels dissolved in an isotropic solvent and examine the swelling curve of the LC gel, the orientational order parameters, and the deformation of the LC gel as a function of temperature. We find that the LC gel discontinuously changes the volume at an isotropic-nematic, an isotropic-smectic-A, and a nematic-smectic-A phase transition.
The home network of the near future will be a heterogeneous broadband network supporting the use of wired as well as wireless transmission technologies. The variety of services will range from HDTV via gaming to emerg...
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The linear fractional differential equation is solved using the enhanced homotopy perturbation method (EHPM). In this method, the convergence has been provided by selecting a stabilizing linear part. The most signific...
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Client/Server-based DVE approaches incur scalability and load balancing problems while addressing massive number of users. In order to mitigate these problems P2P overlays are proposed for content provision and manage...
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Bit-serial architectures offer a number of attractive features over their bit-parallel counterparts such as smaller area cost, lower density interconnection, a reduced number of pins, higher clock frequency, simpler r...
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Bit-serial architectures offer a number of attractive features over their bit-parallel counterparts such as smaller area cost, lower density interconnection, a reduced number of pins, higher clock frequency, simpler routing and etc. These attractive features make them suitable for using in VLSI design and reduce overall production cost. In this paper, we propose the first least significant bit (LSB) bit-serial sum of absolute difference (SAD) hardware accelerator for integer variable block size motion estimation (VBSME) of H.264. This hardware accelerator is based on a previous state-of-art bit-parallel architecture namely propagate partial SAD. In order to reduce area cost and improve throughput, pixel truncation technique is adopted. Due to the bit-serial pipeline architecture and using small processing elements, our architecture works at much higher clock frequency (at least 4 times) and reduces area cost about 32% compared with its bit-parallel counterpart. The proposed hardware accelerator can be used in different disciplines from low bit rate to high bit rate by making a tradeoff between the degree of parallelism or using fast algorithm or a combination of both.
Microdevices have recently been developed by various micromachining technologies such as laser cutting, lithography, etc. PTFE is widely used as a microwave and millimeter-wave material. However, it is known that PTFE...
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Insolvency of insurance companies has been a concern to the community due to the need to protect the general public from the aftermath of insurer insolvency and to try to minimize the costs associated to this difficul...
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Insolvency of insurance companies has been a concern to the community due to the need to protect the general public from the aftermath of insurer insolvency and to try to minimize the costs associated to this difficulty such as the insurance guaranty funds. The artificial neural network is utilized in this study to create an insolvency predictive model that could predict any future failure of general insurance company in Malaysia. The neural networks results show high predictability, suggesting the usefulness of this method for predicting future insurer insolvency in Malaysia.
Web caching is a well-known strategy for improving performance of Web-based system by keeping Web objects that are likely to be used in the near future closer to the client. Although most researchers focused on design...
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Web caching is a well-known strategy for improving performance of Web-based system by keeping Web objects that are likely to be used in the near future closer to the client. Although most researchers focused on designing efficient caching with proxy and origin servers, the potential gain of exploiting client-side caching based on neuro-fuzzy system is not yet being investigated. Hence, this paper proposes a splitting Web client-side cache to two caches, short-term cache and long-term cache. Initially, a Web object is stored in short-term cache. The Web objects that are visited more than the pre-specified threshold value will be moved to long-term cache and other objects in short-term cache are removed with time. Thus, we ensure that the preferred Web objects are cached in long-term cache for longer time. In this study, neuro-fuzzy is employed to determine which Web objects should be removed in order to create more spaces for the new Web objects. By implementing this mechanism, the cache space is used properly. Experimental results have shown that the proposed approach has better performance compared to the most common caching policies and has improved the performance of client-side caching substantially.
In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. This study proposes h...
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In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. This study proposes hybrid learning of RBF Network with Particle Swarm Optimization (PSO) for better convergence, error rates and classification results. The hybrid learning of RBF Network involves two phases. The first phase is a structure identification, in which unsupervised learning is exploited to determine the RBF centers and widths. This is done by executing different algorithms such as k-mean clustering and standard derivation respectively. The second phase is parameters estimation, in which supervised learning is implemented to establish the connections weights between the hidden layer and the output layer. This is done by performing different algorithms such as Least Mean Squares (LMS) and gradient based methods. The incorporation of PSO in hybrid learning of RBF Network is accomplished by optimizing the centers, the widths and the weights of RBF Network. The results for training, testing and validation of five datasets (XOR, Balloon, Cancer, Iris and Ionosphere) illustrate the effectiveness of PSO in enhancing RBF Network learning compared to conventional Backpropogation.
The enhanced homotopy perturbation method (EHPM) is applied for finding improved approximate solutions of the well-known Bagley-Torvik equation for three different cases. The main characteristic of the EHPM is using a...
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