Video coding mode decision as a classification problem

In this paper, we show that it is possible to reduce the complexity of Intra MB coding in H.264/AVC based on a novel chance constrained classifier. Using the pairs of simple mean-variances values, our technique is able to reduce the complexity of Intra MB coding process with a negligible loss in PSNR. We present an alternate approach to address the classification problem which is equivalent to machine learning. Implementation results show that the proposed method reduces encoding time to about 20% of the reference implementation with average loss of 0.05 dB in PSNR.

Scheduling expression trees for delayed-load architectures

In this paper we consider the problem of scheduling expression trees on delayed-load architectures. The problem tackled here takes root from the one considered in [Proceedings of the ACM SIGPLAN '91 Conf. on Programming Language Design and Implementation, 1991. p. 256] in which the leaves of the expression trees all refer to memory locations. A generalization of this involves the situation in which the trees may contain register variables, with the registers being used only at the leaves. Solutions to this generalization are given in [ACM Trans. Prog. Lang. Syst. 17 (1995) 740, Microproc. Microprog. 40 (1994) 577]. This paper considers the most general case in which the registers are reusable. This problem is tackled in [Comput. Lang, 21 (1995) 49] which gives an approximate solution to the problem under certain assumptions about the contiguity of the evaluation order: Here we propose an optimal solution (which may involve even a non-contiguous evaluation of the tree). The schedule generated by the algorithm given in this paper is optimal in the sense that it is an interlock-free schedule which uses the minimum number of registers required. An extension to the algorithm incorporates spilling. The problem as stated in this paper is an instruction scheduling problem. However, the problem could also be rephrased as an operations research problem with a difference in terminology. (C) 2002 Elsevier Science B.V. All rights reserved.

Manipulation of the Hydration Levels in Minerals of Sodium Cadmium Bisulfate toward the Design of Functional Materials

Several variants of hydrated sodium cadmium bisulfate, Na(2)Cd(2)(SO(4))(3) center dot 3H(2)O, Na(2)Cd(SO(4))(2) center dot 2H(2)O, and Na(2)Cd(SO(4))(2) center dot 4H(2)O have been synthesized, and their thermal properties followed by phase transitions have been invesigated. The formation of these phases depends on the stochiometry and the time taken for crystallization from water. Na(2)Cd(2)(SO(4))(3)center dot 3H(2)O, which crystallizes in the trigonal system, space group P3c, is grown from the aqueous solution in about four weeks. The krohnkite type mineral Na(2)Cd(SO(4))(2) center dot 2H(2)O and the mineral astrakhanite, also known as blodite, Na(2)Cd (SO(4))(2)center dot 4H(2)O, crystallize concomittantly in about 24 weeks. Both these minerals belong to the monoclinic system(space group P2(1)/c). Na(2)Cd(2)(SO(4))(3)center dot 3H(2)O loses water completely when heated to 250 degrees C and transforms to a dehydrated phase (cubic system, space group I (4) over bar 3d) whose structure has been established using ab initio powder diffration techniques. Na(2)Cd(SO(4))(2)center dot 2H(2)O transforms to alpha-Na(2)Cd(SO(4))(2) (space group C2/c) on heating to 150 degrees C which is a known high ionic conductor and remains intact over prolonged periods of exposure to moisture (over six months). However, when alpha-Na(2)Cd(SO(4))(2) is heated to 570 degrees C followed by sudden quenching in liquid nitrogen beta-Na(2)Cd(SO(4))(2) (P2(1)/c) is formed. beta-Na(2)Cd(SO(4))(2) takes up water from the atmosphere and gets converted completely to the krohnkite type mineral in about four weeks. Further, beta-Na(2)Cd(SO(4))(2) has a conductivity behavior comparable to the a-form up to 280 degrees C, the temperature required for the transformation of the beta- to alpha-form. These experiments demonstrate the possibility of utilizing the abundantly available mineral sources as precursors to design materials with special properties.

Surfactant-mediated Synthesis of Functional Metal Oxide Nanostructures via Microwave Irradiation-assisted Chemical Synthesis

Nanostructured materials have attracted considerable interest in recent years due to their properties which differ strongly from their bulk phase and potential applications in nanoscale electronic and optoelectronic devices. Metal oxide nanostructures can be synthesized by variety of different synthesis techniques developed in recent years such as thermal decomposition, sol-gel technique, chemical coprecipitation, hydrothermal process, solvothermal process, spray pyrolysis, polyol process etc. All the above processes go through a tedious synthesis procedure followed by prolonged heat treatment at elevated temperature and are time consuming. In the present work we describe a rapid microwave irradiation-assisted chemical synthesis technique for the growth of nanoparticles, nanorods, and nanotubes of a variety of metal oxides in the presence of an appropriate surfactant, without the use of any templates The method is simple, inexpensive, and helps one to prepare nanostructures in a very simple way, and in a very short time, measured in minutes. The synthesis procedure employs high quality metalorganic complexes (typically -diketonates) featuring a direct metal-to-oxygen bond in its molecular structure. The complex is dissolved in a suitable solvent, often with a surfactant added, and the solution then subjected to microwave irradiation in a domestic microwave oven operating at 2.45 GHz frequency with power varying from 160-800 W, from a few seconds to a few minutes, leading to the formation of corresponding metal oxides. This method has been used successfully to synthesize nanostructures of a variety of binary and ternary metal oxides such as ZnO, CdO, Fe2O3, CuO, Ga2O3, Gd2O3, ZnFe2O4, etc. There is an observed variation in the morphology of the nanostructures with the change of different parameters such as microwave power, irradiation time, appropriate solvent, surfactant type and concentration. Cationic, anionic, nonionic and polymeric surfactants have been used to generate a variety of nanostructures. Even so, to remove the surfactant, there is either no need of heat treatment or a very brief exposure to heat suffices, to yield highly pure and crystalline oxide materials as prepared. By adducting the metal complexes, the shape of the nanostructures can be controlled further. In this manner, very well formed, single-crystalline, hexagonal nanorods and nanotubes of ZnO have been formed. Adducting the zinc complex leads to the formation of tapered ZnO nanorods with a very fine tip, suitable for electron emission applications. Particle size and their monodispersity can be controlled by a suitable choice of a precursor complex, the surfactant, and its concentration. The resulting metal oxide nanostructures have been characterized by X-ray diffraction, field emission scanning electron microscopy, transmission electron microscopy, FTIR spectroscopy, photoluminescence, and electron emission measurements.

PocketAlign A Novel Algorithm for Aligning Binding Sites in Protein Structures

A fundamental task in bioinformatics involves a transfer of knowledge from one protein molecule onto another by way of recognizing similarities. Such similarities are obtained at different levels, that of sequence, whole fold, or important substructures. Comparison of binding sites is important to understand functional similarities among the proteins and also to understand drug cross-reactivities. Current methods in literature have their own merits and demerits, warranting exploration of newer concepts and algorithms, especially for large-scale comparisons and for obtaining accurate residue-wise mappings. Here, we report the development of a new algorithm, PocketAlign, for obtaining structural superpositions of binding sites. The software is available as a web-service at http://proline.physicslisc.emetin/pocketalign/. The algorithm encodes shape descriptors in the form of geometric perspectives, supplemented by chemical group classification. The shape descriptor considers several perspectives with each residue as the focus and captures relative distribution of residues around it in a given site. Residue-wise pairings are computed by comparing the set of perspectives of the first site with that of the second, followed by a greedy approach that incrementally combines residue pairings into a mapping. The mappings in different frames are then evaluated by different metrics encoding the extent of alignment of individual geometric perspectives. Different initial seed alignments are computed, each subsequently extended by detecting consequential atomic alignments in a three-dimensional grid, and the best 500 stored in a database. Alignments are then ranked, and the top scoring alignments reported, which are then streamed into Pymol for visualization and analyses. The method is validated for accuracy and sensitivity and benchmarked against existing methods. An advantage of PocketAlign, as compared to some of the existing tools available for binding site comparison in literature, is that it explores different schemes for identifying an alignment thus has a better potential to capture similarities in ligand recognition abilities. PocketAlign, by finding a detailed alignment of a pair of sites, provides insights as to why two sites are similar and which set of residues and atoms contribute to the similarity.

Solving a part classification problem using simulated annealing-like hybrid

Part classification and coding is still considered as laborious and time-consuming exercise. Keeping in view, the crucial role, which it plays, in developing automated CAPP systems, the attempts have been made in this article to automate a few elements of this exercise using a shape analysis model. In this study, a 24-vector directional template is contemplated to represent the feature elements of the parts (candidate and prototype). Various transformation processes such as deformation, straightening, bypassing, insertion and deletion are embedded in the proposed simulated annealing (SA)-like hybrid algorithm to match the candidate part with their prototype. For a candidate part, searching its matching prototype from the information data is computationally expensive and requires large search space. However, the proposed SA-like hybrid algorithm for solving the part classification problem considerably minimizes the search space and ensures early convergence of the solution. The application of the proposed approach is illustrated by an example part. The proposed approach is applied for the classification of 100 candidate parts and their prototypes to demonstrate the effectiveness of the algorithm. (C) 2003 Elsevier Science Ltd. All rights reserved.

Network Rhythms Influence the Relationship between Spike-Triggered Local Field Potential and Functional Connectivity

Characterizing the functional connectivity between neurons is key for understanding brain function. We recorded spikes and local field potentials (LFPs) from multielectrode arrays implanted in monkey visual cortex to test the hypotheses that spikes generated outward-traveling LFP waves and the strength of functional connectivity depended on stimulus contrast, as described recently. These hypotheses were proposed based on the observation that the latency of the peak negativity of the spike-triggered LFP average (STA) increased with distance between the spike and LFP electrodes, and the magnitude of the STA negativity and the distance over which it was observed decreased with increasing stimulus contrast. Detailed analysis of the shape of the STA, however, revealed contributions from two distinct sources-a transient negativity in the LFP locked to the spike (similar to 0 ms) that attenuated rapidly with distance, and a low-frequency rhythm with peak negativity similar to 25 ms after the spike that attenuated slowly with distance. The overall negative peak of the LFP, which combined both these components, shifted from similar to 0 to similar to 25 ms going from electrodes near the spike to electrodes far from the spike, giving an impression of a traveling wave, although the shift was fully explained by changing contributions from the two fixed components. The low-frequency rhythm was attenuated during stimulus presentations, decreasing the overall magnitude of the STA. These results highlight the importance of accounting for the network activity while using STAs to determine functional connectivity.

Soft classification based Sub-pixel allocation model

Land cover (LC) refers to what is actually present on the ground and provide insights into the underlying solution for improving the conditions of many issues, from water pollution to sustainable economic development. One of the greatest challenges of modeling LC changes using remotely sensed (RS) data is of scale-resolution mismatch: that the spatial resolution of detail is less than what is required, and that this sub-pixel level heterogeneity is important but not readily knowable. However, many pixels consist of a mixture of multiple classes. The solution to mixed pixel problem typically centers on soft classification techniques that are used to estimate the proportion of a certain class within each pixel. However, the spatial distribution of these class components within the pixel remains unknown. This study investigates Orthogonal Subspace Projection - an unmixing technique and uses pixel-swapping algorithm for predicting the spatial distribution of LC at sub-pixel resolution. Both the algorithms are applied on many simulated and actual satellite images for validation. The accuracy on the simulated images is ~100%, while IRS LISS-III and MODIS data show accuracy of 76.6% and 73.02% respectively. This demonstrates the relevance of these techniques for applications such as urban-nonurban, forest-nonforest classification studies etc.

A Unified Approach to Encoding and Classification Using Bimodal Projection-Based Features

In general the objective of accurately encoding the input data and the objective of extracting good features to facilitate classification are not consistent with each other. As a result, good encoding methods may not be effective mechanisms for classification. In this paper, an earlier proposed unsupervised feature extraction mechanism for pattern classification has been extended to obtain an invertible map. The method of bimodal projection-based features was inspired by the general class of methods called projection pursuit. The principle of projection pursuit concentrates on projections that discriminate between clusters and not faithful representations. The basic feature map obtained by the method of bimodal projections has been extended to overcome this. The extended feature map is an embedding of the input space in the feature space. As a result, the inverse map exists and hence the representation of the input space in the feature space is exact. This map can be naturally expressed as a feedforward neural network.

Structural Alignment based Kernels for Protein Structure Classification

Structural alignments are the most widely used tools for comparing proteins with low sequence similarity. The main contribution of this paper is to derive various kernels on proteins from structural alignments, which do not use sequence information. Central to the kernels is a novel alignment algorithm which matches substructures of fixed size using spectral graph matching techniques. We derive positive semi-definite kernels which capture the notion of similarity between substructures. Using these as base more sophisticated kernels on protein structures are proposed. To empirically evaluate the kernels we used a 40% sequence non-redundant structures from 15 different SCOP superfamilies. The kernels when used with SVMs show competitive performance with CE, a state of the art structure comparison program.