893 resultados para feature based cost


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In this work, we propose an algorithm for optical flow estimation using Approximate Nearest Neighbor Fields (ANNF). Proposed optical flow estimation algorithm consists of two steps, flow initialization using ANNF maps and cost filtering. Flow initialization is done by computing the ANNF map using FeatureMatch between two consecutive frames. The ANNF map obtained represents a noisy optical flow, which is refined by making use of superpixels. The best flow associated with each superpixel is computed by optimizing a cost function. The proposed approach is evaluated on Middlebury and MPI-Sintel optical flow dataset and is found to be comparable with the state of the art methods for optical flow estimation.

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Representing images and videos in the form of compact codes has emerged as an important research interest in the vision community, in the context of web scale image/video search. Recently proposed Vector of Locally Aggregated Descriptors (VLAD), has been shown to outperform the existing retrieval techniques, while giving a desired compact representation. VLAD aggregates the local features of an image in the feature space. In this paper, we propose to represent the local features extracted from an image, as sparse codes over an over-complete dictionary, which is obtained by K-SVD based dictionary training algorithm. The proposed VLAD aggregates the residuals in the space of these sparse codes, to obtain a compact representation for the image. Experiments are performed over the `Holidays' database using SIFT features. The performance of the proposed method is compared with the original VLAD. The 4% increment in the mean average precision (mAP) indicates the better retrieval performance of the proposed sparse coding based VLAD.

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This paper reports on the effect of multiwall carbon nanotubes (CNTs) without and with chemical functionalization on the mechanical properties of Bisphenol E cyanate ester resin (BECy) based carbon fibre reinforced plastic (CFRP) laminated composites. BECy with its low viscosity, low moisture uptake and superior mechanical properties is selected for its application in CFRP laminates through the cost-effective Vacuum Assisted Resin Transfer Moulding (VARTM) process. However, unlike CNT-epoxy-CFRP composites, processing and performance issues such as dispersion of CNTs, chemical bonding with resin, functionalization effects, effects on mechanical properties, etc. for BECy-CNT-CFRP composite system are not well reported. The objective of this study is to improve the mechanical properties of BECy resin with small additions of CNTs and functionalized CNTs in CFRP laminates. CNTs and fCNTs are infused into BECy using ultrasonication and standard mixing methods. Improvements in Young's modulus and strength in tension, compression, shear, flexure and interlaminar shear strength are analysed. It is observed that addition of 0.5wt% CNTs effected for maximum mechanical properties of the resin and 1wt% CNTs for the mechanical properties of CNT-CFRP nanocomposite. Further, improvements obtained with fCNTs are marginal. Dispersion behaviour and effect of CNTs/fCNTs in load transfer corroborated with SEM pictures are presented. The enhanced mechanical properties realized in VARTM processing of BECy-CFRP laminate indicate clear advantage of CNT based modification of the process.

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Reduced graphene oxide (RGO) is prepared by thermal exfoliation of graphite oxide in air. Symmetric RGO/RGO supercapacitors are constructed in a non-aqueous electrolyte and characterized. The values of energy density are 44 Wh kg(-1) and 15 Wh kg(-1), respectively at 0.15 and 8.0 kW kg(-1). The symmetric supercapacitor exhibits stable charge/discharge cycling tested up to 3000 cycles. The low-temperature thermal exfoliation approach is convenient for mass production of RGO at low cost and it can be used as electrode material for energy storage applications. (c) The Author(s) 2015. Published by ECS. All rights reserved.

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In this paper, we propose a new state transition based embedding (STBE) technique for audio watermarking with high fidelity. Furthermore, we propose a new correlation based encoding (CBE) scheme for binary logo image in order to enhance the payload capacity. The result of CBE is also compared with standard run-length encoding (RLE) compression and Huffman schemes. Most of the watermarking algorithms are based on modulating selected transform domain feature of an audio segment in order to embed given watermark bit. In the proposed STBE method instead of modulating feature of each and every segment to embed data, our aim is to retain the default value of this feature for most of the segments. Thus, a high quality of watermarked audio is maintained. Here, the difference between the mean values (Mdiff) of insignificant complex cepstrum transform (CCT) coefficients of down-sampled subsets is selected as a robust feature for embedding. Mdiff values of the frames are changed only when certain conditions are met. Hence, almost 50% of the times, segments are not changed and still STBE can convey watermark information at receiver side. STBE also exhibits a partial restoration feature by which the watermarked audio can be restored partially after extraction of the watermark at detector side. The psychoacoustic model analysis showed that the noise-masking ratio (NMR) of our system is less than -10dB. As amplitude scaling in time domain does not affect selected insignificant CCT coefficients, strong invariance towards amplitude scaling attacks is also proved theoretically. Experimental results reveal that the proposed watermarking scheme maintains high audio quality and are simultaneously robust to general attacks like MP3 compression, amplitude scaling, additive noise, re-quantization, etc.

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Clinical microscopy is a versatile and robust tool used for the diagnosis of a plethora of diseases. However, due to various reasons, it remains inaccessible in resource limited settings. In this paper, we present an automated and cost-effective alternative to microscopy for use in clinical diagnostics. With the use of custom optics and microfluidics, we demonstrate a field-portable imaging flow cytometry system. Using the presented system, we have been able to image 586 cells per second. We demonstrate the clinical relevance of the proposed system by differentiating between suspensions of healthy and sphered RBCs based on high-throughput morphometric analysis. The instrument presented here is a major advancement in the domain of field portable diagnostics as it enables fast and robust quantitative diagnostic testing at the point-of-care.

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Wireless Sensor Networks have gained popularity due to their real time applications and low-cost nature. These networks provide solutions to scenarios that are critical, complicated and sensitive like military fields, habitat monitoring, and disaster management. The nodes in wireless sensor networks are highly resource constrained. Routing protocols are designed to make efficient utilization of the available resources in communicating a message from source to destination. In addition to the resource management, the trustworthiness of neighboring nodes or forwarding nodes and the energy level of the nodes to keep the network alive for longer duration is to be considered. This paper proposes a QoS Aware Trust Metric based Framework for Wireless Sensor Networks. The proposed framework safeguards a wireless sensor network from intruders by considering the trustworthiness of the forwarder node at every stage of multi-hop routing. Increases network lifetime by considering the energy level of the node, prevents the adversary from tracing the route from source to destination by providing path variation. The framework is built on NS2 Simulator. Experimental results show that the framework provides energy balance through establishment of trustworthy paths from the source to the destination. (C) 2015 The Authors. Published by Elsevier B.V.

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Mobile Ad hoc Networks (MANETs) are self-organized, infrastructureless, decentralized wireless networks consist of a group of heterogeneous mobile devices. Due to the inherent characteristics of MANE -Ts, such as frequent change of topology, nodes mobility, resource scarcity, lack of central control, etc., makes QoS routing is the hardest task. QoS routing is the task of routing data packets from source to destination depending upon the QoS resource constraints, such as bandwidth, delay, packet loss rate, cost, etc. In this paper, we proposed a novel scheme of providing QoS routing in MANETs by using Emergent Intelligence (El). The El is a group intelligence, which is derived from the periodical interaction among a group of agents and nodes. We logically divide MANET into clusters by centrally located static agent, and in each cluster a mobile agent is deployed. The mobile agent interacts with the nodes, neighboring mobile agents and static agent for collection of QoS resource information, negotiations, finding secure and reliable nodes and finding an optimal QoS path from source to destination. Simulation and analytical results show that the effectiveness of the scheme. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.ore/licenscs/by-nc-nd/4.0/). Peer-review under responsibility of the Conference Program Chairs

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A benzil-based semi-rigid dinuclear organometallic acceptor 4,4'-bistrans-Pt(PEt3)(2)(NO3)(ethynyl)]benzil (bisPt-NO3) containing a Pt-ethynyl functionality was synthesized in good yield and characterized by multinuclear NMR (H-1, P-31, and C-13), electrospray ionization mass spectrometry (ESI-MS), and single-crystal X-ray diffraction analysis of the iodide analogue bisPt-I. The stoichiometric (1:1) combination of the acceptor bisPt-NO3 separately with four different ditopic donors (L-1-L-4; L-1 = 9-ethyl-3,6-di(1H-imidazol-1-yl)-9H-carbazole, L-2 = 1,4-bis((1H-imidazol-1-yl)methyl)benzene, L-3 = 1,3-bis((1H-imidazol-1-yl)methyl)benzene and L-4 = 9,10-bis((1H-imidazol-1-yl) methyl)anthracene) yielded four 2 + 2] self-assembled metallacycles M-1-M-4 in quantitative yields, respectively. All these newly synthesized assemblies were characterized by various spectroscopic techniques (NMR, IR, ESI-MS) and their sizes/shapes were predicted through geometry optimization employing the PM6 semi-empirical method. The benzil moiety was introduced in the backbone of the acceptor bisPt-NO3 due to the interesting structural feature of long carbonyl C-C bond (similar to 1.54 angstrom), which enabled us to probe the role of conformational flexibility on size and shapes of the resulting coordination ensembles.

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High-kappa TiO2 thin films have been fabricated using cost effective sol-gel and spin-coating technique on p-Si (100) wafer. Plasma activation process was used for better adhesion between TiO2 films and Si. The influence of annealing temperature on the structure-electrical properties of titania films were investigated in detail. Both XRD and Raman studies indicate that the anatase phase crystallizes at 400 degrees C, retaining its structural integrity up to 1000 degrees C. The thickness of the deposited films did not vary significantly with the annealing temperature, although the refractive index and the RMS roughness enhanced considerably, accompanied by a decrease in porosity. For electrical measurements, the films were integrated in metal-oxide-semiconductor (MOS) structure. The electrical measurements evoke a temperature dependent dielectric constant with low leakage current density. The Capacitance-voltage (C-V) characteristics of the films annealed at 400 degrees C exhibited a high value of dielectric constant (similar to 34). Further, frequency dependent C-V measurements showed a huge dispersion in accumulation capacitance due to the presence of TiO2/Si interface states and dielectric polarization, was found to follow power law dependence on frequency (with exponent `s'=0.85). A low leakage current density of 3.6 x 10(-7) A/cm(2) at 1 V was observed for the films annealed at 600 degrees C. The results of structure-electrical properties suggest that the deposition of titania by wet chemical method is more attractive and cost-effective for production of high-kappa materials compared to other advanced deposition techniques such as sputtering, MBE, MOCVD and AID. The results also suggest that the high value of dielectric constant kappa obtained at low processing temperature expands its scope as a potential dielectric layer in MOS device technology. (C) 2015 Elsevier Ltd. All rights reserved.

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In this paper, we have proposed an anomaly detection algorithm based on Histogram of Oriented Motion Vectors (HOMV) 1] in sparse representation framework. Usual behavior is learned at each location by sparsely representing the HOMVs over learnt normal feature bases obtained using an online dictionary learning algorithm. In the end, anomaly is detected based on the likelihood of the occurrence of sparse coefficients at that location. The proposed approach is found to be robust compared to existing methods as demonstrated in the experiments on UCSD Ped1 and UCSD Ped2 datasets.

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In this paper, we study two multi-dimensional Goodness-of-Fit tests for spectrum sensing in cognitive radios. The multi-dimensional scenario refers to multiple CR nodes, each with multiple antennas, that record multiple observations from multiple primary users for spectrum sensing. These tests, viz., the Interpoint Distance (ID) based test and the h, f distance based tests are constructed based on the properties of stochastic distances. The ID test is studied in detail for a single CR node case, and a possible extension to handle multiple nodes is discussed. On the other hand, the h, f test is applicable in a multi-node setup. A robustness feature of the KL distance based test is discussed, which has connections with Middleton's class A model. Through Monte-Carlo simulations, the proposed tests are shown to outperform the existing techniques such as the eigenvalue ratio based test, John's test, and the sphericity test, in several scenarios.

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We propose apractical, feature-level and score-level fusion approach by combining acoustic and estimated articulatory information for both text independent and text dependent speaker verification. From a practical point of view, we study how to improve speaker verification performance by combining dynamic articulatory information with the conventional acoustic features. On text independent speaker verification, we find that concatenating articulatory features obtained from measured speech production data with conventional Mel-frequency cepstral coefficients (MFCCs) improves the performance dramatically. However, since directly measuring articulatory data is not feasible in many real world applications, we also experiment with estimated articulatory features obtained through acoustic-to-articulatory inversion. We explore both feature level and score level fusion methods and find that the overall system performance is significantly enhanced even with estimated articulatory features. Such a performance boost could be due to the inter-speaker variation information embedded in the estimated articulatory features. Since the dynamics of articulation contain important information, we included inverted articulatory trajectories in text dependent speaker verification. We demonstrate that the articulatory constraints introduced by inverted articulatory features help to reject wrong password trials and improve the performance after score level fusion. We evaluate the proposed methods on the X-ray Microbeam database and the RSR 2015 database, respectively, for the aforementioned two tasks. Experimental results show that we achieve more than 15% relative equal error rate reduction for both speaker verification tasks. (C) 2015 Elsevier Ltd. All rights reserved.

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This work intends to demonstrate the effect of geometrically non-linear cross-sectional analysis of certain composite beam-based four-bar mechanisms in predicting the three-dimensional warping of the cross-section. The only restriction in the present analysis is that the strains within each elastic body remain small (i.e., this work does not deal with materials exhibiting non-linear constitutive laws at the 3-D level). Here, all component bars of the mechanism are made of fiber-reinforced laminates. They could, in general, be pre-twisted and/or possess initial curvature, either by design or by defect. Each component of the mechanism is modeled as a beam based on geometrically non-linear 3-D elasticity theory. The component problems are thus split into 2-D analyses of reference beam cross-sections and non-linear 1-D analyses along the three beam reference curves. The splitting of the three-dimensional beam problem into two- and one-dimensional parts, called dimensional reduction, results in a tremendous savings of computational effort relative to the cost of three-dimensional finite element analysis, the only alternative for realistic beams. The analysis of beam-like structures made of laminated composite materials requires a much more complicated methodology. Hence, the analysis procedure based on Variational Asymptotic Method (VAM), a tool to carry out the dimensional reduction, is used here. The representative cross-sections of all component bars are analyzed using two different approaches: (1) Numerical Model and (2) Analytical Model. Four-bar mechanisms are analyzed using the above two approaches for Omega = 20 rad/s and Omega = pi rad/s and observed the same behavior in both cases. The noticeable snap-shots of the deformation shapes of the mechanism about 1000 frames are also reported using commercial software (I-DEAS + NASTRAN + ADAMS). The maximum out-of-plane warping of the cross-section is observed at the mid-span of bar-1, bar-2 and bar-3 are 1.5 mm, 250 mm and 1.0 mm, respectively, for t = 0:5 s. (C) 2015 Elsevier Ltd. All rights reserved.

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In this article, a Field Programmable Gate Array (FPGA)-based hardware accelerator for 3D electromagnetic extraction, using Method of Moments (MoM) is presented. As the number of nets or ports in a system increases, leading to a corresponding increase in the number of right-hand-side (RHS) vectors, the computational cost for multiple matrix-vector products presents a time bottleneck in a linear-complexity fast solver framework. In this work, an FPGA-based hardware implementation is proposed toward a two-level parallelization scheme: (i) matrix level parallelization for single RHS and (ii) pipelining for multiple-RHS. The method is applied to accelerate electrostatic parasitic capacitance extraction of multiple nets in a Ball Grid Array (BGA) package. The acceleration is shown to be linearly scalable with FPGA resources and speed-ups over 10x against equivalent software implementation on a 2.4GHz Intel Core i5 processor is achieved using a Virtex-6 XC6VLX240T FPGA on Xilinx's ML605 board with the implemented design operating at 200MHz clock frequency. (c) 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:776-783, 2016