Improving acoustic vehicle classification by information fusion

We present an information fusion approach for ground vehicle classification based on the emitted acoustic signal. Many acoustic factors can contribute to the classification accuracy of working ground vehicles. Classification relying on a single feature set may lose some useful information if its underlying sound production model is not comprehensive. To improve classification accuracy, we consider an information fusion diagram, in which various aspects of an acoustic signature are taken into account and emphasized separately by two different feature extraction methods. The first set of features aims to represent internal sound production, and a number of harmonic components are extracted to characterize the factors related to the vehicle’s resonance. The second set of features is extracted based on a computationally effective discriminatory analysis, and a group of key frequency components are selected by mutual information, accounting for the sound production from the vehicle’s exterior parts. In correspondence with this structure, we further put forward a modifiedBayesian fusion algorithm, which takes advantage of matching each specific feature set with its favored classifier. To assess the proposed approach, experiments are carried out based on a data set containing acoustic signals from different types of vehicles. Results indicate that the fusion approach can effectively increase classification accuracy compared to that achieved using each individual features set alone. The Bayesian-based decision level fusion is found fusion is found to be improved than a feature level fusion approac

Customizing kernel functions for SVM-based hyperspectral image classification

Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available algorithms. However, few efforts have been made to extend SVMs to cover the specific requirements of hyperspectral image classification, for example, by building tailor-made kernels. Observation of real-life spectral imagery from the AVIRIS hyperspectral sensor shows that the useful information for classification is not equally distributed across bands, which provides potential to enhance the SVM's performance through exploring different kernel functions. Spectrally weighted kernels are, therefore, proposed, and a set of particular weights is chosen by either optimizing an estimate of generalization error or evaluating each band's utility level. To assess the effectiveness of the proposed method, experiments are carried out on the publicly available 92AV3C dataset collected from the 220-dimensional AVIRIS hyperspectral sensor. Results indicate that the method is generally effective in improving performance: spectral weighting based on learning weights by gradient descent is found to be slightly better than an alternative method based on estimating ";relevance"; between band information and ground trut

Linking supply chain quality integration with mass customization and product modularity

Supply chain quality management has received increasing attention from researchers and practitioners in recent years. However, the knowledge about the effects of a manufacturer's design and production capabilities on supply chain quality management is limited. In this study, we propose a model to investigate the effects of mass customization and product modularity on supply chain quality integration (i.e. internal, supplier, and customer quality integration) and the impact of supply chain quality integration on competitive performance. We use data collected from 317 global manufacturers to empirically test the conceptual model. The results show that mass customization and product modularity directly improve internal quality integration, and product modularity also improves internal quality integration indirectly through mass customization. Product modularity improves supplier quality integration directly, and both mass customization and product modularity improve supplier quality integration indirectly through internal quality integration. Mass customization improves customer quality integration both directly and indirectly through internal quality integration, and product modularity improves customer quality integration indirectly through mass customization and internal quality integration. We also find that supplier quality integration directly enhances competitive performance, and internal quality integration enhances competitive performance both directly and indirectly through supplier quality integration. Our findings contribute to production and quality management literature and practices

A Method of Decreasing Time Delay for A Tele-surgery System

Abstract -The haptics-based master-slave system for Minimally Invasive Surgery is a promising way to protect surgeons from long time radiation and to train novice doctors to learn basic wire or catheter handling skills. However, the time delay of transmission of visual video and the time difference between image information and force signals restrict the application of this technology in some extent. In this paper, we proposed a new method to reduce time delay effectively. At the slave side, the tip of the active catheter is tracked in real time to provide information on the location of the catheter in the blood vessel model. And then transmitted the coordinate values to the master site. At the master site, the location of the catheter was reappeared in the navigation chart which is the same structure with the blood vessels at master side according to the coordinate values received from the slave side. Therefore the transmission time of image information is decreased. Experimental results are given to illustrate the accuracy of our method

Aerial Visual Perception in Smart Farming: Field Study of Wheat Yellow Rust Monitoring

Agriculture is facing severe challenges from crop stresses, threatening its sustainable development and food security. This work exploits aerial visual perception for yellow rust disease monitoring, which seamlessly integrates state-of-the-art techniques and algorithms including UAV sensing, multispectral imaging, vegetation segmentation and deep learning U-Net. A field experiment is designed by infecting winter wheat with yellow rust inoculum, on top of which multispectral aerial images are captured by DJI Matrice 100 equipped with RedEdge camera. After image calibration and stitching, multispectral orthomosaic is labelled for system evaluation by inspecting high-resolution RGB images taken by Parrot Anafi Drone. The merits of the developed framework drawing spectral-spatial information concurrently are demonstrated by showing improved performance over purely spectral based classifier by the classical random forest algorithm. Moreover, various network input band combinations are tested including three RGB bands and five selected spectral vegetation indices by Sequential Forward Selection strategy of Wrapper algorithm

A signal theory approach to support vector classification: the sinc kernel

Fourier-based regularisation is considered for the support vector machine classification problem over absolutely integrable loss functions. By invoking the modest assumption that the decision function belongs to a Paley–Wiener space, it is shown that the classification problem can be developed in the context of signal theory. Furthermore, by employing the Paley–Wiener reproducing kernel, namely the sinc function, it is shown that a principled and finite kernel hyper-parameter search space can be discerned, a priori. Subsequent simulations performed on a commonly-available hyperspectral image data set reveal that the approach yields results that surpass state-of-the-art benchmarks

Involvement of Glutamate Transporter-1 in Neuroprotection against Global Brain Ischemia-Reperfusion Injury Induced by Postconditioning in Rats

Ischemic postconditioning refers to several transient reperfusion and ischemia cycles after an ischemic event and before a long duration of reperfusion. The procedure produces neuroprotective effects. The mechanisms underlying these neuroprotective effects are poorly understood. In this study, we found that most neurons in the CA1 region died after 10 minutes of ischemia and is followed by 72 hours of reperfusion. However, brain ischemic postconditioning (six cycles of 10 s/10 s reperfusion/re-occlusion) significantly reduced neuronal death. Significant up-regulation of Glutamate transporter-1 was found after 3, 6, 24, 72 hours of reperfusion. The present study showed that ischemic postconditioning decreases cell death and that upregulation of GLT-1 expression may play an important role on this effect

The Impact of Variational Primary Collaterals on Cerebral Autoregulation

The influence of the anterior and posterior communicating artery (ACoA and PCoA) on dynamic cerebral autoregulation (dCA) is largely unknown. In this study, we aimed to test whether substantial differences in collateral anatomy were associated with differences in dCA in two common types of stenosis according to digital subtraction angiography (DSA): either isolated basal artery and/or bilateral vertebral arteries severe stenosis/occlusion (group 1; group 1A: with bilateral PCoAs; and group 1B: without bilateral PCoAs), or isolated unilateral internal carotid artery severe stenosis/occlusion (group 2; group 2A: without ACoA and with PCoA; group 2B: with ACoA and without PCoAs; and group 2C: without both ACoA and PCoA). The dCA was calculated by transfer function analysis (a mathematical model), and was evaluated in middle cerebral artery (MCA) and/or posterior cerebral artery (PCA). Of a total of 231 non-acute phase ischemic stroke patients who received both dCA assessment and DSA in our lab between 2014 and 2017, 51 patients met inclusion criteria based on the presence or absence of ACoA or PCoA, including 21 patients in the group 1, and 30 patients in the group 2. There were no significant differences in gender, age, and mean blood pressure between group 1A and group 1B, and among group 2A, group 2B, and group 2C. In group 1, the PCA phase difference values (autoregulatory parameter) were significantly higher in the subgroup with patent PCoAs, compared to those without. In group 2, the MCA phase difference values were higher in the subgroup with patent ACoA, compared to those without. This pilot study found that the cross-flow of the ACoA/PCoA to the affected area compensates for compromised dCA in the affected area, which suggests an important role of the ACoA/PCoA in stabilizing cerebral blood flow

Gait feature subset selection by mutual information

Feature subset selection is an important preprocessing step for pattern recognition, to discard irrelevant and redundant information, as well as to identify the most important attributes. In this paper, we investigate a computationally efficient solution to select the most important features for gait recognition. The specific technique applied is based on mutual information (MI), which evaluates the statistical dependence between two random variables and has an established relation with the Bayes classification error. Extending our earlier research, we show that a sequential selection method based on MI can provide an effective solution for high-dimensional human gait data. To assess the performance of the approach, experiments are carried out based on a 73-dimensional model-based gait features set and on a 64 by 64 pixels model-free gait symmetry map on the Southampton HiD Gait database. The experimental results confirm the effectiveness of the method, removing about 50% of the model-based features and 95% of the symmetry map’s pixels without significant loss in recognition capability, which outperforms correlation and analysis-of-variance-based method