An Optical How Feature and McFIS Based Approach for 3-Dimensional Human Action Recognition

We propose to develop a 3-D optical flow features based human action recognition system. Optical flow based features are employed here since they can capture the apparent movement in object, by design. Moreover, they can represent information hierarchically from local pixel level to global object level. In this work, 3-D optical flow based features a re extracted by combining the 2-1) optical flow based features with the depth flow features obtained from depth camera. In order to develop an action recognition system, we employ a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). The m of McFIS is to find the decision boundary separating different classes based on their respective optical flow based features. McFIS consists of a neuro-fuzzy inference system (cognitive component) and a self-regulatory learning mechanism (meta-cognitive component). During the supervised learning, self-regulatory learning mechanism monitors the knowledge of the current sample with respect to the existing knowledge in the network and controls the learning by deciding on sample deletion, sample learning or sample reserve strategies. The performance of the proposed action recognition system was evaluated on a proprietary data set consisting of eight subjects. The performance evaluation with standard support vector machine classifier and extreme learning machine indicates improved performance of McFIS is recognizing actions based of 3-D optical flow based features.

Mechanism of action of selenious acid in the electrodeposition of manganese

The effect of selenious acid as an addition agent in the electrodeposition of manganese was studied by analysing the current-potential curves for manganese deposition. The mechanism of action of this addition agent was found to be essentially similar to that proposed for sulphur dioxide, namely to affect the manganese deposition indirectly by influencing the hydrogen evolution reaction which is a parallel reaction at the electrode surface.

Ultimate Strength of RC Wall Panels in One-Way In-Plane Action

Test results of 24 reinforced concrete wall panels in one-way in-plane action are presented. The panels were loaded at a small eccentricity to reflect possible eccentric loading in practice. Influences of slenderness ratio, aspect ratio, vertical steel, and horizontal steel on the ultimate load are studied. An empirical equation modifying two existing methods is proposed for the prediction of ultimate load. The modified equation includes the effects of slenderness ratio, amount of vertical steel, and aspect ratio. The results predicted by the proposed modified method and five other available equations are compared with 48 test data. The proposed modified equation is found to be satisfactory and, additionally, includes the effect of aspect ratio which is not present in other methods.

Computer modelling studies on the mechanism of action of ribonuclease T1

The mechanism of action of ribonuclease (RNase) T1 is still a matter of considerable debate as the results of x-ray, 2-D nmr and site-directed mutagenesis studies disagree regarding the role of the catalytically important residues. Hence computer modelling studies were carried out by energy minimisation of the complexes of RNase T1 and some of its mutants (His40Ala, His40Lys, and Glu58Ala) with the substrate guanyl cytosine (GpC), and of native RNase T1 with the reaction intermediate guanosine 2',3'-cyclic phosphate (G greater than p). The puckering of the guanosine ribose moiety in the minimum energy conformer of the RNase T1-GpC (substrate) complex was found to be O4'-endo and not C3'-endo as in the RNase T1-3'-guanylic acid (inhibitor/product) complex. A possible scheme for the mechanism of action of RNase T1 has been proposed on the basis of the arrangement of the catalytically important amino acid residues His40, Glu58, Arg77, and His92 around the guanosine ribose and the phosphate moiety in the RNase T1-GpC and RNase T1-G greater than p complexes. In this scheme, Glu58 serves as the general base group and His92 as the general acid group in the transphosphorylation step. His40 may be essential for stabilising the negatively charged phosphate moiety in the enzyme-transition state complex.

Ultimate Strength of R.C. Wall Panels in Two-Way In-Plane Action

Test results of 24 reinforced concrete wall panels in two-way action (i.e., supported on all the four sides) and subjected to in-plane vertical load are presented. The load is applied at an eccentricity to represent possible accidental eccentricity that occurs in practice due to constructional imperfections. Influences of aspect ratio, thinness ratio, slendemess ratio, vertical steel, and horizontal steel on the ultimate load are studied. Two equations are proposed to predict the ultimate load carried by the panels. The first equation is empirical and is arrived at from trial and error fitting with test data. The second equation is semi-empirical and is developed from a modification of the buckling strength of thin rectangular plates. Both the equations are formulated so as to give a safe prediction of a large portion of ultimate strength test results. Also, ultimate load cracking load and lateral deflections of identical panels in two-way action (all four sides supported) and oneway action (top and bottom sides only supported) are compared.

Effective action and adiabatic expansions for the 1-D and 2-D hubbard models at half-filling

We analyze here the occurrence of antiferromagnetic (AFM) correlations in the half-filled Hubbard model in one and two space dimensions using a natural fermionic representation of the model and a newly proposed way of implementing the half-filling constraint. We find that our way of implementing the constraint is capable of enforcing it exactly already at the lowest levels of approximation. We discuss how to develop a systematic adiabatic expansion for the model and how Berry's phase contributions arise quite naturally from the adiabatic expansion. At low temperatures and in the continuum limit the model gets mapped onto an O(3) nonlinear sigma model (NLsigma). A topological, Wess-Zumino term is present in the effective action of the ID NLsigma as expected, while no topological terms are present in 2D. Some specific difficulties that arise in connection with the implementation of an adiabatic expansion scheme within a thermodynamic context are also discussed, and we hint at possible solutions.

Fully complex-valued ELM classifiers for human action recognition

In this paper, we present a fast learning neural network classifier for human action recognition. The proposed classifier is a fully complex-valued neural network with a single hidden layer. The neurons in the hidden layer employ the fully complex-valued hyperbolic secant as an activation function. The parameters of the hidden layer are chosen randomly and the output weights are estimated analytically as a minimum norm least square solution to a set of linear equations. The fast leaning fully complex-valued neural classifier is used for recognizing human actions accurately. Optical flow-based features extracted from the video sequences are utilized to recognize 10 different human actions. The feature vectors are computationally simple first order statistics of the optical flow vectors, obtained from coarse to fine rectangular patches centered around the object. The results indicate the superior performance of the complex-valued neural classifier for action recognition. The superior performance of the complex neural network for action recognition stems from the fact that motion, by nature, consists of two components, one along each of the axes.

Subject independent human action recognition using spatio-depth information and meta-cognitive RBF network

In this paper, we present a machine learning approach for subject independent human action recognition using depth camera, emphasizing the importance of depth in recognition of actions. The proposed approach uses the flow information of all 3 dimensions to classify an action. In our approach, we have obtained the 2-D optical flow and used it along with the depth image to obtain the depth flow (Z motion vectors). The obtained flow captures the dynamics of the actions in space time. Feature vectors are obtained by averaging the 3-D motion over a grid laid over the silhouette in a hierarchical fashion. These hierarchical fine to coarse windows capture the motion dynamics of the object at various scales. The extracted features are used to train a Meta-cognitive Radial Basis Function Network (McRBFN) that uses a Projection Based Learning (PBL) algorithm, referred to as PBL-McRBFN, henceforth. PBL-McRBFN begins with zero hidden neurons and builds the network based on the best human learning strategy, namely, self-regulated learning in a meta-cognitive environment. When a sample is used for learning, PBLMcRBFN uses the sample overlapping conditions, and a projection based learning algorithm to estimate the parameters of the network. The performance of PBL-McRBFN is compared to that of a Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers with representation of every person and action in the training and testing datasets. Performance study shows that PBL-McRBFN outperforms these classifiers in recognizing actions in 3-D. Further, a subject-independent study is conducted by leave-one-subject-out strategy and its generalization performance is tested. It is observed from the subject-independent study that McRBFN is capable of generalizing actions accurately. The performance of the proposed approach is benchmarked with Video Analytics Lab (VAL) dataset and Berkeley Multimodal Human Action Database (MHAD). (C) 2013 Elsevier Ltd. All rights reserved.

RAPID HUMAN ACTION RECOGNITION IN H.264/AVC COMPRESSED DOMAIN FOR VIDEO SURVEILLANCE

This paper discusses a novel high-speed approach for human action recognition in H. 264/AVC compressed domain. The proposed algorithm utilizes cues from quantization parameters and motion vectors extracted from the compressed video sequence for feature extraction and further classification using Support Vector Machines (SVM). The ultimate goal of our work is to portray a much faster algorithm than pixel domain counterparts, with comparable accuracy, utilizing only the sparse information from compressed video. Partial decoding rules out the complexity of full decoding, and minimizes computational load and memory usage, which can effect in reduced hardware utilization and fast recognition results. The proposed approach can handle illumination changes, scale, and appearance variations, and is robust in outdoor as well as indoor testing scenarios. We have tested our method on two benchmark action datasets and achieved more than 85% accuracy. The proposed algorithm classifies actions with speed (>2000 fps) approximately 100 times more than existing state-of-the-art pixel-domain algorithms.

Human Action Recognition in Compressed Domain using PBL-McRBFN Approach

Large variations in human actions lead to major challenges in computer vision research. Several algorithms are designed to solve the challenges. Algorithms that stand apart, help in solving the challenge in addition to performing faster and efficient manner. In this paper, we propose a human cognition inspired projection based learning for person-independent human action recognition in the H.264/AVC compressed domain and demonstrate a PBL-McRBEN based approach to help take the machine learning algorithms to the next level. Here, we use gradient image based feature extraction process where the motion vectors and quantization parameters are extracted and these are studied temporally to form several Group of Pictures (GoP). The GoP is then considered individually for two different bench mark data sets and the results are classified using person independent human action recognition. The functional relationship is studied using Projection Based Learning algorithm of the Meta-cognitive Radial Basis Function Network (PBL-McRBFN) which has a cognitive and meta-cognitive component. The cognitive component is a radial basis function network while the Meta-Cognitive Component(MCC) employs self regulation. The McC emulates human cognition like learning to achieve better performance. Performance of the proposed approach can handle sparse information in compressed video domain and provides more accuracy than other pixel domain counterparts. Performance of the feature extraction process achieved more than 90% accuracy using the PTIL-McRBFN which catalyzes the speed of the proposed high speed action recognition algorithm. We have conducted twenty random trials to find the performance in GoP. The results are also compared with other well known classifiers in machine learning literature.

Human action recognition in H.264/AVC compressed domain using meta-cognitive radial basis function network

In this paper, we propose a H.264/AVC compressed domain human action recognition system with projection based metacognitive learning classifier (PBL-McRBFN). The features are extracted from the quantization parameters and the motion vectors of the compressed video stream for a time window and used as input to the classifier. Since compressed domain analysis is done with noisy, sparse compression parameters, it is a huge challenge to achieve performance comparable to pixel domain analysis. On the positive side, compressed domain allows rapid analysis of videos compared to pixel level analysis. The classification results are analyzed for different values of Group of Pictures (GOP) parameter, time window including full videos. The functional relationship between the features and action labels are established using PBL-McRBFN with a cognitive and meta-cognitive component. The cognitive component is a radial basis function, while the meta-cognitive component employs self-regulation to achieve better performance in subject independent action recognition task. The proposed approach is faster and shows comparable performance with respect to the state-of-the-art pixel domain counterparts. It employs partial decoding, which rules out the complexity of full decoding, and minimizes computational load and memory usage. This results in reduced hardware utilization and increased speed of classification. The results are compared with two benchmark datasets and show more than 90% accuracy using the PBL-McRBFN. The performance for various GOP parameters and group of frames are obtained with twenty random trials and compared with other well-known classifiers in machine learning literature. (C) 2015 Elsevier B.V. All rights reserved.

Compressed domain human action recognition in H.264/AVC video streams

This paper discusses a novel high-speed approach for human action recognition in H.264/AVC compressed domain. The proposed algorithm utilizes cues from quantization parameters and motion vectors extracted from the compressed video sequence for feature extraction and further classification using Support Vector Machines (SVM). The ultimate goal of the proposed work is to portray a much faster algorithm than pixel domain counterparts, with comparable accuracy, utilizing only the sparse information from compressed video. Partial decoding rules out the complexity of full decoding, and minimizes computational load and memory usage, which can result in reduced hardware utilization and faster recognition results. The proposed approach can handle illumination changes, scale, and appearance variations, and is robust to outdoor as well as indoor testing scenarios. We have evaluated the performance of the proposed method on two benchmark action datasets and achieved more than 85 % accuracy. The proposed algorithm classifies actions with speed (> 2,000 fps) approximately 100 times faster than existing state-of-the-art pixel-domain algorithms.

Elastic Properties Of Sodium Borovanadate Glasses

The elastic properties of sodium borovanadate glasses have been studied over a wide range of composition using ultrasonic measurements. It is found that variation of different elastic moduli is very similar in any given series of composition. The bulk and shear moduli show a monotonic variation with the covalent bond energy densities calculated from the proposed structural model for these glasses. The bulk moduli also vary as a negative power function of the mean atomic volume. The Debye temperature varies linearly with the glass transition temperature. The implications of the observed behavior have been discussed.

The role of lysine-41 in RNase A catalysis - A quantum chemical study on the active site ligand complex

Several mechanisms have been proposed to explain the action of enzymes at the atomic level. Among them, the recent proposals involving short hydrogen bonds as a step in catalysis by Gerlt and Gassman [1] and proton transfer through low barrier hydrogen bonds (LBHBs) [2, 3] have attracted attention. There are several limitations to experimentally testing such hypotheses, Recent developments in computational methods facilitate the study of active site-ligand complexes to high levels of accuracy, Our previous studies, which involved the docking of the dinucleotide substrate UpA to the active site of RNase A [4, 5], enabled us to obtain a realistic model of the ligand-bound active site of RNase A. From these studies, based on empirical potential functions, we were able to obtain the molecular dynamics averaged coordinates of RNase A, bound to the ligand UpA. A quantum mechanical study is required to investigate the catalytic process which involves the cleavage and formation of covalent bonds. In the present study, we have investigated the strengths of some of the hydrogen bonds between the active site residues of RNase A and UpA at the ab initio quantum chemical level using the molecular dynamics averaged coordinates as the starting point. The 49 atom system and other model systems were optimized at the 3-21G level and the energies of the optimized systems were obtained at the 6-31G* level. The results clearly indicate the strengthening of hydrogen bonds between neutral residues due to the presence of charged species at appropriate positions. Such a strengthening manifests itself in the form of short hydrogen bonds and a low barrier for proton transfer. In the present study, the proton transfer between the 2'-OH of ribose (from the substrate) and the imidazole group from the H12 of RNase A is influenced by K41, which plays a crucial role in strengthening the neutral hydrogen bond, reducing the barrier for proton transfer.

Mycobacterium tuberculosis pantothenate kinase: possible changes in location of ligands during enzyme action

The crystal structures of complexes of Mycobacterium tuberculosis pantothenate kinase with the following ligands have been determined: (i) citrate; (ii) the nonhydrolysable ATP analogue AMPPCP and pantothenate (the initiation complex); (iii) ADP and phosphopantothenate resulting from phosphorylation of pantothenate by ATP in the crystal (the end complex); (iv) ATP and ADP, each with half occupancy, resulting from a quick soak of crystals in ATP (the intermediate complex); (v) CoA; (vi) ADP prepared by soaking and cocrystallization, which turned out to have identical structures, and (vii) ADP and pantothenate. Solution studies on CoA binding and catalytic activity have also been carried out. Unlike in the case of the homologous Escherichia coli enzyme, AMPPCP and ADP occupy different, though overlapping, locations in the respective complexes; the same is true of pantothenate in the initiation complex and phosphopantothenate in the end complex. The binding site of MtPanK is substantially preformed, while that of EcPanK exhibits considerabl plasticity. The difference in the behaviour of the E. coli and M. tuberculosis enzymes could be explained in terms of changes in local structure resulting from substitutions. It is unusual for two homologous enzymes to exhibit such striking differences in action. Therefore, the results have to be treated with caution. However, the changes in the locations of ligands exhibited by M. tuberculosis pantothenate kinase are remarkable and novel.