8,346 research outputs found

    Recognizing Scoring in Basketball Game from AER Sequence by Spiking Neural Networks

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    The automatic score detection and recognition in basketball game has important application potentials, for examples, basketball technique analysis and 24 second control in the game. Although existing studies have been conducted on broadcast videos, most of them usually learned a machine learning algorithm on long videos recorded by traditional cameras. Address Event Representation (AER) sensor provides a possibility to deal with the problem by a human sensing manner. It represents the visual information as a series of spike-based events and records event sequences. Compared to traditional videos, AER events can fully utilize their addresses and timestamp information, forming precise spatio-temporal features with significantly less storage cost. More importantly, it issues spikes which can be naturally processed by human-style spiking neural networks (SNNs). In this paper, we propose to recognize scoring in basketball game from AER sequences. A new model is designed to extract dynamic features and discriminate different event streams using SNN. To handle the imbalance problem between positive and negative samples, we use an imbalanced Tempotron algorithm in our SNN model. Meanwhile, an AER sequence dataset of basketball games is collected. The experimental results demonstrate that our method achieves better performance compared with existing models

    HybridSNN: Combining Bio-Machine Strengths by Boosting Adaptive Spiking Neural Networks

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    Spiking neural networks (SNNs), inspired by the neuronal network in the brain, provide biologically relevant and low-power consuming models for information processing. Existing studies either mimic the learning mechanism of brain neural networks as closely as possible, for example, the temporally local learning rule of spike-timing-dependent plasticity (STDP), or apply the gradient descent rule to optimize a multilayer SNN with fixed structure. However, the learning rule used in the former is local and how the real brain might do the global-scale credit assignment is still not clear, which means that those shallow SNNs are robust but deep SNNs are difficult to be trained globally and could not work so well. For the latter, the nondifferentiable problem caused by the discrete spike trains leads to inaccuracy in gradient computing and difficulties in effective deep SNNs. Hence, a hybrid solution is interesting to combine shallow SNNs with an appropriate machine learning (ML) technique not requiring the gradient computing, which is able to provide both energy-saving and high-performance advantages. In this article, we propose a HybridSNN, a deep and strong SNN composed of multiple simple SNNs, in which data-driven greedy optimization is used to build powerful classifiers, avoiding the derivative problem in gradient descent. During the training process, the output features (spikes) of selected weak classifiers are fed back to the pool for the subsequent weak SNN training and selection. This guarantees HybridSNN not only represents the linear combination of simple SNNs, as what regular AdaBoost algorithm generates, but also contains neuron connection information, thus closely resembling the neural networks of a brain. HybridSNN has the benefits of both low power consumption in weak units and overall data-driven optimizing strength. The network structure in HybridSNN is learned from training samples, which is more flexible and effective compared with existing fixed multilayer SNNs. Moreover, the topological tree of HybridSNN resembles the neural system in the brain, where pyramidal neurons receive thousands of synaptic input signals through their dendrites. Experimental results show that the proposed HybridSNN is highly competitive among the state-of-the-art SNNs

    Influence of severe plastic deformation on the precipitation hardening of a FeSiTi steel

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    The combined strengthening effects of grain refinement and high precipitated volume fraction (~6at.%) on the mechanical properties of FeSiTi alloy subjected to SPD processing prior to aging treatment were investigated by atom probe tomography and scanning transmission electron microscopy. It was shown that the refinement of the microstructure affects the precipitation kinetics and the spatial distribution of the secondary hardening intermetallic phase, which was observed to nucleate heterogeneously on dislocations and sub-grain boundaries. It was revealed that alloys successively subjected to these two strengthening mechanisms exhibit a lower increase in mechanical strength than a simple estimation based on the summation of the two individual strengthening mechanisms

    Solidly Mounted Resonators with Carbon Nanotube Electrodes for Biosensing Applications

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    The work reported here shows a direct experimental comparison of the sensitivities of AlN solidly mounted resonators (SMR)-based biosensors fabricated with standard metal electrodes and with carbon nanotube electrodes. SMRs resonating at frequencies around 1.75 GHz have been fabricated, some devices using a thin film of multi-wall carbon nanotubes (CNTs) as the top electrode material and some identical devices using a chromium/gold electrode. Protein solutions with different concentrations were loaded on the top of the resonators and their responses to mass-load from physically adsorbed coatings were investigated. Results show that resonators using CNTs as the top electrode material exhibited higher frequency change for a given load due to the higher active surface area of a thin film of interconnecting CNTs compared to that of a metal thin film electrode and hence exhibited greater mass loading sensitivity. It is therefore concluded that the use of CNT electrodes on resonators for their use as gravimetric biosensors is viable and worthwhile

    CAVASS: A Computer-Assisted Visualization and Analysis Software System

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    The Medical Image Processing Group at the University of Pennsylvania has been developing (and distributing with source code) medical image analysis and visualization software systems for a long period of time. Our most recent system, 3DVIEWNIX, was first released in 1993. Since that time, a number of significant advancements have taken place with regard to computer platforms and operating systems, networking capability, the rise of parallel processing standards, and the development of open-source toolkits. The development of CAVASS by our group is the next generation of 3DVIEWNIX. CAVASS will be freely available and open source, and it is integrated with toolkits such as Insight Toolkit and Visualization Toolkit. CAVASS runs on Windows, Unix, Linux, and Mac but shares a single code base. Rather than requiring expensive multiprocessor systems, it seamlessly provides for parallel processing via inexpensive clusters of work stations for more time-consuming algorithms. Most importantly, CAVASS is directed at the visualization, processing, and analysis of 3-dimensional and higher-dimensional medical imagery, so support for digital imaging and communication in medicine data and the efficient implementation of algorithms is given paramount importance

    AlN-based BAW resonators with CNT electrodes for gravimetric biosensing

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    Solidly mounted resonators (SMRs) with a top carbon nanotubes (CNTs) surface coating that doubles as an electrode and as a sensing layer have been fabricated. The influence of the CNTs on the frequency response of the resonators was studied by direct comparison to identical devices with a top metallic electrode. It was found that the CNTs introduced significantly less mass load on the resonators and these devices exhibited a greater quality factor, Q (>2000, compared to ∼1000 for devices with metal electrodes), which increases the gravimetric sensitivity of the devices by allowing the tracking of smaller frequency shifts. Protein solutions with different concentrations were loaded on the top of the resonators and their responses to mass-load from physically adsorbed coatings were investigated. Results show that resonators using CNTs as the top electrode exhibited a higher frequency change for a given load (∼0.25 MHz cm2 ng−1) compared to that of a metal thin film electrode (∼0.14 MHz cm2 ng−1), due to the lower mass of the CNT electrodes and their higher active surface area compared to that of a thin film metal electrode. It is therefore concluded that the use of CNT electrodes on resonators for their use as gravimetric biosensors is a significant improvement over metallic electrodes that are normally employed

    UNCLES: Method for the identification of genes differentially consistently co-expressed in a specific subset of datasets

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    Background: Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. Results: Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. Conclusions: The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.The National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number RP-PG-0310-1004)

    Local antiferromagnetic exchange and collaborative Fermi surface as key ingredients of high temperature superconductors

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    Cuprates, ferropnictides and ferrochalcogenides are three classes of unconventional high-temperature superconductors, who share similar phase diagrams in which superconductivity develops after a magnetic order is suppressed, suggesting a strong interplay between superconductivity and magnetism, although the exact picture of this interplay remains elusive. Here we show that there is a direct bridge connecting antiferromagnetic exchange interactions determined in the parent compounds of these materials to the superconducting gap functions observed in the corresponding superconducting materials. High superconducting transition temperature is achieved when the Fermi surface topology matches the form factor of the pairing symmetry favored by local magnetic exchange interactions. Our result offers a principle guide to search for new high temperature superconductors.Comment: 12 pages, 5 figures, 1 table, 1 supplementary materia

    Material quality characterization of CdZnTe substrates for HgCdTe epitaxy

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    Cd1-xZnxTe (CZT) substrates were studied to investigate their bulk and surface properties. Imperfections in CZT substrates affect the quality of Hg1-xCdxTe (MCT) epilayers deposited on them and play a role in limiting the performance of infrared (IR) focal plane arrays. CZT wafers were studied to investigate their bulk and surface properties. Transmission and surface x-ray diffraction techniques, utilizing both a conventional closed-tube x-ray source as well as a synchrotron radiation source, and IR transmission microspectroscopy, were used for bulk and surface investigation. Synchrotron radiation offers the capability to combine good spatial resolution and shorter exposure times than conventional x-ray sources, which allows for high-resolution mapping of relatively large areas in an acceptable amount of time. Information on the location of grain boundaries and precipitates was also obtained. The ultimate goal of this work is to understand the defects in CZT substrates and their effects on the performance and uniformity of MCT epilayers and then to apply this understanding to produce better infrared detectors
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