Investigating properties of the cardiovascular system using innovative analysis algorithms based on ensemble empirical mode decomposition

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited - Copyright @ 2012 Jia-Rong Yeh et al.Cardiovascular system is known to be nonlinear and nonstationary. Traditional linear assessments algorithms of arterial stiffness and systemic resistance of cardiac system accompany the problem of nonstationary or inconvenience in practical applications. In this pilot study, two new assessment methods were developed: the first is ensemble empirical mode decomposition based reflection index (EEMD-RI) while the second is based on the phase shift between ECG and BP on cardiac oscillation. Both methods utilise the EEMD algorithm which is suitable for nonlinear and nonstationary systems. These methods were used to investigate the properties of arterial stiffness and systemic resistance for a pig's cardiovascular system via ECG and blood pressure (BP). This experiment simulated a sequence of continuous changes of blood pressure arising from steady condition to high blood pressure by clamping the artery and an inverse by relaxing the artery. As a hypothesis, the arterial stiffness and systemic resistance should vary with the blood pressure due to clamping and relaxing the artery. The results show statistically significant correlations between BP, EEMD-based RI, and the phase shift between ECG and BP on cardiac oscillation. The two assessments results demonstrate the merits of the EEMD for signal analysis.This work is supported by the National Science Council (NSC) of Taiwan (Grant number NSC 99-2221-E-155-046-MY3), Centre for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan which is sponsored by National Science Council (Grant number: NSC 100–2911-I-008-001) and the Chung-Shan Institute of Science & Technology in Taiwan (Grant numbers: CSIST-095-V101 and CSIST-095-V102)

An experimental investigation of structured roughness on heat transfer during single-phase liquid flow at microscale

This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the Makedonia Palace Hotel, Thessaloniki in Greece. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, Aristotle University of Thessaloniki, University of Thessaly, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute.The effect of structured roughness on the heat transfer of water flowing through minichannels was experimentally investigated in this study. The test channels were formed by two stainless steel plates, 4 mm thick, 12.7 mm tall, and 94.6 mm in length. The surfaces of the plates forming the channel walls were machined with structured roughness elements with height ranging from 18 μm to 96 μm, and pitch ranging from 250 μm to 400 μm. The hydraulic diameter of the channels range from 0.71 mm to 1.87 mm. After accounting for the heat loss from the edges and end sections, the heat transfer coefficient for smooth channels was calculated. The coefficient was found to be in good agreement with the conventional correlations in the laminar entry region and laminar fully developed region. Convective heat transfer was found to be enhanced by the roughness. In the ranges of tested parameters, the roughness element pitch was found to have almost no effect, while the heat transfer coefficient was significantly enhanced by increasing the roughness element height. An earlier transition from laminar to turbulent flow was observed with increasing relative roughness. Comparing with inserts, the highest relative roughness element provided the highest thermal performance factor in the Reynolds number in the range from about 400 to 2800

DeepKSPD: Learning Kernel-matrix-based SPD Representation for Fine-grained Image Recognition

Being symmetric positive-definite (SPD), covariance matrix has traditionally been used to represent a set of local descriptors in visual recognition. Recent study shows that kernel matrix can give considerably better representation by modelling the nonlinearity in the local descriptor set. Nevertheless, neither the descriptors nor the kernel matrix is deeply learned. Worse, they are considered separately, hindering the pursuit of an optimal SPD representation. This work proposes a deep network that jointly learns local descriptors, kernel-matrix-based SPD representation, and the classifier via an end-to-end training process. We derive the derivatives for the mapping from a local descriptor set to the SPD representation to carry out backpropagation. Also, we exploit the Daleckii-Krein formula in operator theory to give a concise and unified result on differentiating SPD matrix functions, including the matrix logarithm to handle the Riemannian geometry of kernel matrix. Experiments not only show the superiority of kernel-matrix-based SPD representation with deep local descriptors, but also verify the advantage of the proposed deep network in pursuing better SPD representations for fine-grained image recognition tasks

Retrieval of interatomic separations of molecules from laser-induced high-order harmonic spectra

We illustrate an iterative method for retrieving the internuclear separations of N$_2$, O$_2$ and CO$_2$ molecules using the high-order harmonics generated from these molecules by intense infrared laser pulses. We show that accurate results can be retrieved with a small set of harmonics and with one or few alignment angles of the molecules. For linear molecules the internuclear separations can also be retrieved from harmonics generated using isotropically distributed molecules. By extracting the transition dipole moment from the high-order harmonic spectra, we further demonstrated that it is preferable to retrieve the interatomic separation iteratively by fitting the extracted dipole moment. Our results show that time-resolved chemical imaging of molecules using infrared laser pulses with femtosecond temporal resolutions is possible.Comment: 14 pages, 9 figure

An experimental investigation on friction characteristics of air flow in microtube with structured surface roughness

This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the Makedonia Palace Hotel, Thessaloniki in Greece. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, Aristotle University of Thessaloniki, University of Thessaly, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute.Experiments were conducted in this research to investigate roughness effect to flow characteristics and heat transfer coefficient of air and CO2 flow in circular micro-tubes. The internal surface of tested tube included smooth, structure helical fin surfaces and random roughness surfaces. Smooth tube is a commercial S. S. 304 tube with internal diameter of 962 μm and average roughness Ra=0.8 μm, while rough circular tubes were lab made Nickel tube with diameters ranging from 926 μm to 977 μm and roughness elements from 5.3 μm to 44.6 μm in height. The experimental results indicated that f and Nu in smooth tube was predicted very well by conventional correlations both for air and CO2. In rough tubes the friction factor was significant higher than the prediction of conventional correlations both in laminar and turbulent flow. Heat transfer enhancement in laminar flow is slightly, nevertheless, in turbulent flow the heat transfer enhancement was significant and the enhancement increases with the increasing of Re. The random rough tubes revealed a higher heat transfer enhancement than the structured helical fin tubes

ADVISE: Symbolism and External Knowledge for Decoding Advertisements

In order to convey the most content in their limited space, advertisements embed references to outside knowledge via symbolism. For example, a motorcycle stands for adventure (a positive property the ad wants associated with the product being sold), and a gun stands for danger (a negative property to dissuade viewers from undesirable behaviors). We show how to use symbolic references to better understand the meaning of an ad. We further show how anchoring ad understanding in general-purpose object recognition and image captioning improves results. We formulate the ad understanding task as matching the ad image to human-generated statements that describe the action that the ad prompts, and the rationale it provides for taking this action. Our proposed method outperforms the state of the art on this task, and on an alternative formulation of question-answering on ads. We show additional applications of our learned representations for matching ads to slogans, and clustering ads according to their topic, without extra training.Comment: To appear, Proceedings of the European Conference on Computer Vision (ECCV

Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery

Automatic multi-class object detection in remote sensing images in unconstrained scenarios is of high interest for several applications including traffic monitoring and disaster management. The huge variation in object scale, orientation, category, and complex backgrounds, as well as the different camera sensors pose great challenges for current algorithms. In this work, we propose a new method consisting of a novel joint image cascade and feature pyramid network with multi-size convolution kernels to extract multi-scale strong and weak semantic features. These features are fed into rotation-based region proposal and region of interest networks to produce object detections. Finally, rotational non-maximum suppression is applied to remove redundant detections. During training, we minimize joint horizontal and oriented bounding box loss functions, as well as a novel loss that enforces oriented boxes to be rectangular. Our method achieves 68.16% mAP on horizontal and 72.45% mAP on oriented bounding box detection tasks on the challenging DOTA dataset, outperforming all published methods by a large margin (+6% and +12% absolute improvement, respectively). Furthermore, it generalizes to two other datasets, NWPU VHR-10 and UCAS-AOD, and achieves competitive results with the baselines even when trained on DOTA. Our method can be deployed in multi-class object detection applications, regardless of the image and object scales and orientations, making it a great choice for unconstrained aerial and satellite imagery.Comment: ACCV 201

ShapeCodes: Self-Supervised Feature Learning by Lifting Views to Viewgrids

We introduce an unsupervised feature learning approach that embeds 3D shape information into a single-view image representation. The main idea is a self-supervised training objective that, given only a single 2D image, requires all unseen views of the object to be predictable from learned features. We implement this idea as an encoder-decoder convolutional neural network. The network maps an input image of an unknown category and unknown viewpoint to a latent space, from which a deconvolutional decoder can best "lift" the image to its complete viewgrid showing the object from all viewing angles. Our class-agnostic training procedure encourages the representation to capture fundamental shape primitives and semantic regularities in a data-driven manner---without manual semantic labels. Our results on two widely-used shape datasets show 1) our approach successfully learns to perform "mental rotation" even for objects unseen during training, and 2) the learned latent space is a powerful representation for object recognition, outperforming several existing unsupervised feature learning methods.Comment: To appear at ECCV 201

Self-repairing symmetry in jellyfish through mechanically driven reorganization

What happens when an animal is injured and loses important structures? Some animals simply heal the wound, whereas others are able to regenerate lost parts. In this study, we report a previously unidentified strategy of self-repair, where moon jellyfish respond to injuries by reorganizing existing parts, and rebuilding essential body symmetry, without regenerating what is lost. Specifically, in response to arm amputation, the young jellyfish of Aurelia aurita rearrange their remaining arms, recenter their manubria, and rebuild their muscular networks, all completed within 12 hours to 4 days. We call this process symmetrization. We find that symmetrization is not driven by external cues, cell proliferation, cell death, and proceeded even when foreign arms were grafted on. Instead, we find that forces generated by the muscular network are essential. Inhibiting pulsation using muscle relaxants completely, and reversibly, blocked symmetrization. Furthermore, we observed that decreasing pulse frequency using muscle relaxants slowed symmetrization, whereas increasing pulse frequency by lowering the magnesium concentration in seawater accelerated symmetrization. A mathematical model that describes the compressive forces from the muscle contraction, within the context of the elastic response from the mesoglea and the ephyra geometry, can recapitulate the recovery of global symmetry. Thus, self-repair in Aurelia proceeds through the reorganization of existing parts, and is driven by forces generated by its own propulsion machinery. We find evidence for symmetrization across species of jellyfish (Chrysaora pacifica, Mastigias sp., and Cotylorhiza tuberculata)