Authors reply to "On uniqueness, transfer and combination of acoustic sources in one-dimensional systems"

The use of electroacoustic analogies suggests that a source of acoustical energy (such as an engine, compressor, blower, turbine, loudspeaker, etc.) can be characterized by an acoustic source pressure ps and internal source impedance Zs, analogous to the open-circuit voltage and internal impedance of an electrical source. The present paper shows analytically that the source characteristics evaluated by means of the indirect methods are independent of the loads selected; that is, the evaluated values of ps and Zs are unique, and that the results of the different methods (including the direct method) are identical. In addition, general relations have been derived here for the transfer of source characteristics from one station to another station across one or more acoustical elements, and also for combining several sources into a single equivalent source. Finally, all the conclusions are extended to the case of a uniformly moving medium, incorporating the convective as well as dissipative effects of the mean flow.

A PPCA based non-parametric modeling and retrieval of PD signal buried in excessive noise

The problem of on-line recognition and retrieval of relatively weak industrial signals such as partial discharges (PD), buried in excessive noise, has been addressed in this paper. The major bottleneck being the recognition and suppression of stochastic pulsive interference (PI) due to the overlapping broad band frequency spectrum of PI and PD pulses. Therefore, on-line, onsite, PD measurement is hardly possible in conventional frequency based DSP techniques. The observed PD signal is modeled as a linear combination of systematic and random components employing probabilistic principal component analysis (PPCA) and the pdf of the underlying stochastic process is obtained. The PD/PI pulses are assumed as the mean of the process and modeled instituting non-parametric methods, based on smooth FIR filters, and a maximum aposteriori probability (MAP) procedure employed therein, to estimate the filter coefficients. The classification of the pulses is undertaken using a simple PCA classifier. The methods proposed by the authors were found to be effective in automatic retrieval of PD pulses completely rejecting PI.

Comparison of HMM and SDTW for Tamil handwritten character recognition

In this paper, we compare the experimental results for Tamil online handwritten character recognition using HMM and Statistical Dynamic Time Warping (SDTW) as classifiers. HMM was used for a 156-class problem. Different feature sets and values for the HMM states & mixtures were tried and the best combination was found to be 16 states & 14 mixtures, giving an accuracy of 85%. The features used in this combination were retained and a SDTW model with 20 states and single Gaussian was used as classifier. Also, the symbol set was increased to include numerals, punctuation marks and special symbols like $, & and #, taking the number of classes to 188. It was found that, with a small addition to the feature set, this simple SDTW classifier performed on par with the more complicated HMM model, giving an accuracy of 84%. Mixture density estimation computations was reduced by 11 times. The recognition is writer independent, as the dataset used is quite large, with a variety of handwriting styles.

Hybrid Bayesian Classifier for Improved Classification Accuracy

The widely used Bayesian classifier is based on the assumption of equal prior probabilities for all the classes. However, inclusion of equal prior probabilities may not guarantee high classification accuracy for the individual classes. Here, we propose a novel technique-Hybrid Bayesian Classifier (HBC)-where the class prior probabilities are determined by unmixing a supplemental low spatial-high spectral resolution multispectral (MS) data that are assigned to every pixel in a high spatial-low spectral resolution MS data in Bayesian classification. This is demonstrated with two separate experiments-first, class abundances are estimated per pixel by unmixing Moderate Resolution Imaging Spectroradiometer data to be used as prior probabilities, while posterior probabilities are determined from the training data obtained from ground. These have been used for classifying the Indian Remote Sensing Satellite LISS-III MS data through Bayesian classifier. In the second experiment, abundances obtained by unmixing Landsat Enhanced Thematic Mapper Plus are used as priors, and posterior probabilities are determined from the ground data to classify IKONOS MS images through Bayesian classifier. The results indicated that HBC systematically exploited the information from two image sources, improving the overall accuracy of LISS-III MS classification by 6% and IKONOS MS classification by 9%. Inclusion of prior probabilities increased the average producer's and user's accuracies by 5.5% and 6.5% in case of LISS-III MS with six classes and 12.5% and 5.4% in IKONOS MS for five classes considered.

Curcumin-Arteether Combination Therapy of Plasmodium berghei-Infected Mice Prevents Recrudescence Through Immunomodulation

Earlier studies in this laboratory have shown the potential of artemisinin-curcumin combination therapy in experimental malaria. In a parasite recrudescence model in mice infected with Plasmodium berghei (ANKA), a single dose of alpha, beta-arteether (ART) with three oral doses of curcumin prevented recrudescence, providing almost 95% protection. The parasites were completely cleared in blood with ART-alone (AE) or ART+curcumin (AC) treatments in the short-term, although the clearance was faster in the latter case involving increased ROS generation. But, parasites in liver and spleen were not cleared in AE or AC treatments, perhaps, serving as a reservoir for recrudescence. Parasitemia in blood reached up to 60% in AE-treated mice during the recrudescence phase, leading to death of animals. A transient increase of up to 2-3% parasitemia was observed in AC-treatment, leading to protection and reversal of splenomegaly. A striking increase in spleen mRNA levels for TLR2, IL-10 and IgG-subclass antibodies but a decrease in those for INF gamma and IL-12 was observed in AC-treatment. There was a striking increase in IL-10 and IgG subclass antibody levels but a decrease in INF gamma levels in sera leading to protection against recrudescence. AC-treatment failed to protect against recrudescence in TLR2(-/-) and IL-10(-/-) animals. IL-10 injection to AE-treated wild type mice and AC-treated TLR22/2 mice was able to prolong survival. Blood from the recrudescence phase in AE-treatment, but not from AC-treatment, was able to reinfect and kill naive animals. Sera from the recrudescence phase of AC-treated animals reacted with several parasite proteins compared to that from AE-treated animals. It is proposed that activation of TLR2-mediated innate immune response leading to enhanced IL-10 production and generation of anti-parasite antibodies contribute to protective immunity in AC-treated mice. These results indicate a potential for curcumin-based combination therapy to be tested for prevention of recrudescence in falciparum and relapse in vivax malaria.

Artemisinin-based combination with curcumin adds a new dimension to malaria therapy

Malaria afflicts 300 million people worldwide, with over a million deaths every year. With no immediate prospect of a vaccine against the disease, drugs are the only choice to treat it. Unfortunately, the parasite has become resistant to most antimalarials, restricting the option to use artemisinins (ARTs) for effective cure. With the use of ARTs as the front-line antimalarials, reports are already available on the possible resistance development to these drugs as well. Therefore, it has become necessary to use ART-based combination therapies to delay emergence of resistance. It is also necessary to discover new pharmacophores to eventually replace ART. Studies in our laboratory have shown that curcumin not only synergizes with ART as an antimalarial to kill the parasite, but is also uniquely able to prime the immune system to protect against parasite recrudescence in the animal model. The results indicate a potential for the use of ART curcumin combination against recrudescence/relapse in falciparum and vivax malaria. In addition, studies have also suggested the use of curcumin as an adjunct therapy against cerebral malaria. In this review we have attempted to highlight these aspects as well as the studies directed to discover new pharmacophores as potential replacements for ART.

Human action recognition using a fast learning fully complex-valued classifier

In this paper, we use optical flow based complex-valued features extracted from video sequences to recognize human actions. The optical flow features between two image planes can be appropriately represented in the Complex plane. Therefore, we argue that motion information that is used to model the human actions should be represented as complex-valued features and propose a fast learning fully complex-valued neural classifier to solve the action recognition task. The classifier, termed as, ``fast learning fully complex-valued neural (FLFCN) classifier'' is a single hidden layer fully complex-valued neural network. The neurons in the hidden layer employ the fully complex-valued activation function of the type of a hyperbolic secant function. The parameters of the hidden layer are chosen randomly and the output weights are estimated as the minimum norm least square solution to a set of linear equations. The results indicate the superior performance of FLFCN classifier in recognizing the actions compared to real-valued support vector machines and other existing results in the literature. Complex valued representation of 2D motion and orthogonal decision boundaries boost the classification performance of FLFCN classifier. (c) 2012 Elsevier B.V. All rights reserved.

Structure and mechanical properties of Ti-C films deposited using combination of pulsed DC and normal DC magnetron co-sputtering

Titanium-carbon (Ti-C) thin films of different compositions were prepared by a combination of pulsed DC (for Ti target) and normal DC (for graphite target) magnetron co-sputtering on oxidized silicon and fused quartz substrates. At 33.7 at.% of C content, pure hcp Ti transforms into fcc-TiC with a preferential orientation of (2 2 0) along with (1 1 1) and (2 0 0). A clear transformation in the preferential orientation from (2 2 0) to (1 1 1) has been observed when the C content was increased to 56 at.%. At 62.5 at.% of C, TiC precipitates in an amorphous carbon matrix whereas further increase in C leads to X-ray amorphous films. The cross-sectional scanning electron microscope images reveal that the films with low carbon content consists of columnar grains, whereas, randomly oriented grains are in an amorphous carbon matrix at higher carbon content. A dramatic variation was observed in the mechanical properties such as hardness, H, from 30 to 1 GPa and in modulus, E, from 255 to 25 GPa with varying carbon content in the films. Resistance to plastic deformation parameter was observed as 0.417 for films containing 62.5 at.% of C. Nanoscratch test reveals that the films are highly scratch resistant with a coefficient of friction ranging from 0.15 to 0.04. (C) 2012 Elsevier B.V. All rights reserved.

Silicon surface texturing with a combination of potassium hydroxide and tetra-methyl ammonium hydroxide etching

A two step silicon surface texturing, consisting of potassium hydroxide (KOH) etching followed by tetra-methyl ammonium hydroxide etching is presented. This combined texturing results in 13.8% reflectivity at 600 nm compared to 16.1% reflectivity for KOH etching due to the modification of microstructure of etched pyramids. This combined etching also results in significantly lower flat-band voltage (V-FB) (-0.19V compared to -1.3 V) and interface trap density (D-it) (2.13 x 10(12) cm(-2) eV(-1) compared to 3.2 x 10(12) cm(-2) eV(-1)). (C) 2013 American Institute of Physics. http://dx.doi.org/10.1063/1.4776733]

On improving the generalization of SVM classifier

The generalization performance of the SVM classifier depends mainly on the VC dimension and the dimensionality of the data. By reducing the VC dimension of the SVM classifier, its generalization performance is expected to increase. In the present paper, we argue that the VC dimension of SVM classifier can be reduced by applying bootstrapping and dimensionality reduction techniques. Experimental results showed that bootstrapping the original data and bootstrapping the projected (dimensionally reduced) data improved the performance of the SVM classifier.

Combination of similarity measures for time series classification using genetic algorithms

Time series classification deals with the problem of classification of data that is multivariate in nature. This means that one or more of the attributes is in the form of a sequence. The notion of similarity or distance, used in time series data, is significant and affects the accuracy, time, and space complexity of the classification algorithm. There exist numerous similarity measures for time series data, but each of them has its own disadvantages. Instead of relying upon a single similarity measure, our aim is to find the near optimal solution to the classification problem by combining different similarity measures. In this work, we use genetic algorithms to combine the similarity measures so as to get the best performance. The weightage given to different similarity measures evolves over a number of generations so as to get the best combination. We test our approach on a number of benchmark time series datasets and present promising results.

Multi-script and multi-oriented text localization from scene images

This paper describes a new method of color text localization from generic scene images containing text of different scripts and with arbitrary orientations. A representative set of colors is first identified using the edge information to initiate an unsupervised clustering algorithm. Text components are identified from each color layer using a combination of a support vector machine and a neural network classifier trained on a set of low-level features derived from the geometric, boundary, stroke and gradient information. Experiments on camera-captured images that contain variable fonts, size, color, irregular layout, non-uniform illumination and multiple scripts illustrate the robustness of the method. The proposed method yields precision and recall of 0.8 and 0.86 respectively on a database of 100 images. The method is also compared with others in the literature using the ICDAR 2003 robust reading competition dataset.

Combination of single walled carbon nanotubes/graphene oxide with paclitaxel: a reactive oxygen species mediated synergism for treatment of lung cancer

Heterogeneity in tumors has led to the development of combination therapies that enable enhanced cell death. Previously explored combination therapies mostly involved the use of bioactive molecules. In this work, we explored a non-conventional strategy of using carbon nanostructures (CNs) single walled carbon nanotube (SWNT) and graphene oxide (GO)] for potentiating the efficacy of a bioactive molecule paclitaxel (Tx)] for the treatment of lung cancer. The results demonstrated enhanced cell death following combination treatment of SWNT/GO and Tx indicating a synergistic effect. In addition, synergism was abrogated in the presence of an anti-oxidant, N-acetyl cysteine (NAC), and was therefore shown to be reactive oxygen species (ROS) dependent. It was further demonstrated using bromodeoxyuridine (BrdU) incorporation assay that treatment with CNs was associated with enhanced mitogen associated protein kinase (MAPK) activation that was ROS mediated. Hence, these results for the first time demonstrated the potential of SWNT/GO as co-therapeutic agents with Tx for the treatment of lung cancer.

A regularized linear classifier for effective text classification

In document community support vector machines and naïve bayes classifier are known for their simplistic yet excellent performance. Normally the feature subsets used by these two approaches complement each other, however a little has been done to combine them. The essence of this paper is a linear classifier, very similar to these two. We propose a novel way of combining these two approaches, which synthesizes best of them into a hybrid model. We evaluate the proposed approach using 20ng dataset, and compare it with its counterparts. The efficacy of our results strongly corroborate the effectiveness of our approach.