Morphology development in polyethylene/polystyrene blends: the influence of processing conditions and interfacial modification

The influence of processing conditions and interfacial modification on the morphology evolution and the composition range within which fully co‐continuous high density polyethylene/polystyrene blend structures can exist during blending in a single screw extruder was studied. Blends ranging from pure A to pure B component, with and without compatibilizer, were prepared under two different shear rates. It was found that high shear rates displaced the breakdown–coalescence balance of the dispersed nodules to the side of coalescence, narrowing the percolation domain and the critical composition for full co‐continuity decreased with increasing shear rates. The addition of a tri‐block compatibilizer induced the percolation threshold of the polystyrene phase to begin at lower percentages of polyethylene but the phase inversion point did not change. The experimental results are discussed in the light of various theoretical models. Copyright © 2005 Society of Chemical Industry     

A fast PNN design algorithm for entropy-constrained residual vectorquantization

A clustering algorithm based on the pairwise nearest-neighbor (PNN) algorithm developed by Equitz (1989), is introduced for the design of entropy-constrained residual vector quantizers. The algorithm designs residual vector quantization codebooks by merging the pair of stage clusters that minimizes the increase in overall distortion subject to a given decrease in entropy. Image coding experiments show that the clustering design algorithm typically results in more than a 200:1 reduction in design time relative to the standard iterative entropy-constrained residual vector quantization algorithm while introducing only small additional distortion. Multipath searching over the sequence of merges is also investigated and shown experimentally to slightly improve rate-distortion performance. The proposed algorithm can be used alone or can he followed by the iterative algorithm to improve the reproduction quality at the same bit rate.     

Multireference object pose retrieval with volume feedback

Pose retrieval of a rigid object from monocular video sequences or images is addressed. Initially, the object pose is estimated in each image assuming flat depth maps. Shape-from-silhouette is then applied to make a 3-D model (volume), which is used for a new round of pose estimations, this time by a model-based method that gives better estimates. Before repeating this process by building a new volume, pose estimates are adjusted to reduce error by maximizing a novel quality factor for shape-from-silhouette volume reconstruction. The feedback loop is terminated when pose estimates do not change much, as compared with those produced by the previous iteration. Based on a theoretical study of the proposed system, a test of convergence to a given set of poses is devised. Reliable performance of the system is also proved by several experiments on both synthetic and real image sequences. No model is assumed for the object and no feature point is detected or tracked as there is no problematic feature matching or correspondence. Our method can be used for 3-D object tracking in video, 3-D modeling, and volume reconstruction from video.     

Fast and high performance image subsampling using feedforwardneural networks

We introduce a fast and high performance image subsampling method using feedforward artificial neural networks (FANNs). Our method employs a pattern matching technique to extract local edge information within the image, in order to select the FANN desired output values during the supervised training stage. Subjective and objective evaluations of experimental results using still images and video frames show that our method, while less computationally intensive, outperforms the standard lowpass filtering and subsampling method.     

Generalized S transform

The generalized S transform (GST), a family of reversible integer-to-integer transforms inspired by the S transform, is proposed. This family of transforms is then studied in detail by considering topics such as GST parameter calculation, the effects of using different rounding operators in the GST, and the relationship between the GST and the lifting scheme. Some examples of specific transforms in the GST family are also given. In particular, a new transform in this family is introduced, and its practical utility is demonstrated.     

Reconstruction of baseline JPEG coded images in error proneenvironments

A two-stage method for the reconstruction of missing data in the transmission of baseline JPEG coded images in error prone environments is proposed. In the first stage, we estimate the values of the missing DC coefficients. As effects of errors in estimating the missing DC values will appear as a number of stripes across the image, a technique for removing such stripes is also developed. In the second stage, the data of missing blocks is reconstructed by exploiting the correlation between adjacent blocks. Simulation results intricate that our reconstruction method performs very well. The two key contributions of our method are that it does not assume nondifferential encoding of the DC coefficients, and that it performs well in the reconstruction of diagonal edges.     

Feedforward neural network design with tridiagonal symmetryconstraints

This paper introduces a pruning algorithm with tridiagonal symmetry constraints for feedforward neural network (FANN) design. The algorithm uses a reflection transform applied to the input-hidden weight matrix in order to reduce it to its tridiagonal form. The designed FANN structures obtained by applying the proposed algorithm are compact and symmetrical. Therefore, they are well suited for efficient hardware and software implementations. Moreover, the number of the FANN parameters is reduced without a significant loss in performance. We illustrate the complexity and performance of the proposed algorithm by applying it as a solution to a nonlinear regression problem. We also compare the results of our proposed algorithm with those of the optimal brain damage algorithm     

A computation-distortion optimized framework for efficientDCT-based video coding

The rapidly expanding field of multimedia communications has fueled significant research and development work in the area of real-time video encoding. Dedicated hardware solutions have reached maturity and cost-efficient hardware encoders are being developed by several manufacturers. However, software solutions based on a general purpose processor or a programmable digital signal processor (DSP) have significant merits. Toward this objective, we have developed a flexible framework for video encoding that yields very good computation-performance tradeoffs. The proposed framework consists of a set of optimized core components: motion estimation (ME), the discrete cosine transform (DCT), quantization, and mode selection. Each of the components can be configured to achieve a desired computation-performance tradeoff. The components can be assembled to obtain encoders with varying degrees of computational complexity. Computation control has been implemented within the proposed framework to allow the resulting algorithms to adapt to the available computational resources. The proposed framework was applied to MPEG-2 and H.263 encoding using Intel's Pentium/MMX desktop processor. Excellent speed-performance tradeoffs were obtained     

A concealment method for shape information in MPEG-4 coded videosequences

We propose a new method for error concealment of shape information in MPEG-4 video bit streams that are transmitted over error prone channels. The proposed method employs a MAP estimator with a Markov random field (MRF) as the image a priori model. The MRF is designed for binary shape information and its parameters are adapted based on the information of neighboring blocks. Our experimental results show that the proposed concealment method restores missing shape blocks with high accuracy. Compared to the median filtering method, our method restores 20% more missing shape data, with a much greater subjective improvement. The proposed algorithm requires a relatively small number of integer multiplications and additions and simple logic operations, making it suitable for real-time implementations     

Spatial and temporal variations of the coefficient of reflection to solar radiation of the surface ground in Tunisia measured from METEOSAT B2 images

GISTEL is a physical model based on the calculation of the brightness, which estimates the solar energy at the ground surface level using METEOSAT numerical images. In this paper, GISTEL is applied to B2 images in order to evaluate the reflection coefficient of the ground surface including both the geographical distribution and temporal variation over Tunisian continental areas. Apart from some seasonal fluctuations, that do not exceed 1%, the reflection coefficient shows a rather regular variation from the north to the south of Tunisia. The obtained results are in good agreement with other works, as well as the ground stations measurements made by the Tunisian National Institute of Meteorology. As the reflection coefficient stands for a characteristic parameter of the ground, it is shown to be very useful in many energetic, climatic and agronomic applications, which makes it decisive in the determination of the economic policy of the country. Its presentation in the form of maps makes it more accessible to users.