A survey of face detection, extraction and recognition

The goal of this paper is to present a critical survey of existing literatures on human face recognition over the last 4-5 years. Interest and research activities in face recognition have increased significantly over the past few years, especially after the American airliner tragedy on September 11 in 2001. While this growth largely is driven by growing application demands, such as static matching of controlled photographs as in mug shots matching, credit card verification to surveillance video images, identification for law enforcement and authentication for banking and security system access, advances in signal analysis techniques, such as wavelets and neural networks, are also important catalysts. As the number of proposed techniques increases, survey and evaluation becomes important

Multilingual Text Detection with Nonlinear Neural Network

Multilingual text detection in natural scenes is still a challenging task in computer vision. In this paper, we apply an unsupervised learning algorithm to learn language-independent stroke feature and combine unsupervised stroke feature learning and automatically multilayer feature extraction to improve the representational power of text feature. We also develop a novel nonlinear network based on traditional Convolutional Neural Network that is able to detect multilingual text regions in the images. The proposed method is evaluated on standard benchmarks and multilingual dataset and demonstrates improvement over the previous work

The effects of new urbanization pilot city policies on urban innovation: Evidence from China

The new urbanization city pilot policy is China’s most recent policy on urban urbanization. This paper uses new urbanization pilot policies as a quasi-natural experiment to empirically test the impact of new urbanization pilot policies on urban innovation through the difference-in-differences (DID) method using panel data from 199 cities in China from 2011 to 2019. The results show that: (1) The new urbanization city pilot policy has significantly enhanced urban innovation. (2) The theoretical mechanism test shows that the pilot policy of new urbanization promotes urban innovation through the level of human capital. (3) The results of the heterogeneity analysis show that the new urbanization pilot policies have obvious city-level heterogeneity and regional heterogeneity on the improvement of urban innovation levels. The impact effect of new urbanization pilot policies is higher in first-tier and second-tier cities than in fourth-tier and fifth-tier cities; the effect of new urbanization pilot policies is higher in western regions than in eastern and middle regions.National Natural Science Foundation of Chin

CBS: constraint-based approach for scheduling in bluetooth networks

In Bluetooth networks, devices are organized into small piconets and large scatternets, and each node acts as the role of master, slave or gateway. Due to dynamic topology changes, different bandwidth available and unpredictable interference of media in Bluetooth networks, the congestion of data flow will inevitably emerges on the link, and the gateway has to switch between piconets on a time division basis, so its presence in the different piconet has to be controlled by scheduling mechanism such as inter- and intra -piconet scheduling. However, the time division in gateways will limit the network capacity and introduce bottleneck points in the network, and the switch between piconets will prevent the packet from transmitting smoothly and efficiently. Most of the published work on Bluetooth scheduling has focused on the polling scheme between master and slaves. In this paper, we put our approach on the inner constraints of Bluetooth networks and present a constraint-based scheduler (CBS), to adaptively cater to the changing role of each node throughout Bluetooth ad hoc networks, thereby it will save time and definitely enhance fairness and efficiency on packet scheduling in Bluetooth environment.Facultad de Informátic

Improvement Schemes for Indoor Mobile Location Estimation: A Survey

Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research

Adaptive Initialization Method Based on Spatial Local Information for k

k-means algorithm is a widely used clustering algorithm in data mining and machine learning community. However, the initial guess of cluster centers affects the clustering result seriously, which means that improper initialization cannot lead to a desirous clustering result. How to choose suitable initial centers is an important research issue for k-means algorithm. In this paper, we propose an adaptive initialization framework based on spatial local information (AIF-SLI), which takes advantage of local density of data distribution. As it is difficult to estimate density correctly, we develop two approximate estimations: density by t-nearest neighborhoods (t-NN) and density by ϵ-neighborhoods (ϵ-Ball), leading to two implements of the proposed framework. Our empirical study on more than 20 datasets shows promising performance of the proposed framework and denotes that it has several advantages: (1) can find the reasonable candidates of initial centers effectively; (2) it can reduce the iterations of k-means’ methods significantly; (3) it is robust to outliers; and (4) it is easy to implement

Application of symmetric orthogonal multiwavelets and prefilter technique for image compression

Multiwavelets are new addition to the body of wavelet theory. There are many types of symmetric multiwavelets such as Geronimo-Hardin-Massopust (GHM) and Chui-Lian (CL) multiwavelets. However, the matrix filter generating the GHM system multiwavelets does not satisfy the symmetric property. For this reason, this paper presents a new method to construct the symmetric orthogonal matrix filter, which leads to the symmetric orthogonal multiwavelets (SOM). Moreover, we analyze the prefilter technique, corresponding to the symmetric orthogonal matrix filter, to get a good combining frequency response. To prove the good property of SOM in image compression application, we compared the compression effect with other writers' work, which was in published literature.Facultad de Informátic

Disease burden of urinary tract infections among type 2 diabetes mellitus patients in the U.S.

AbstractAimsType 2 diabetes is a reported risk factor for more frequent and severe urinary tract infections (UTI). We sought to quantify the annual healthcare cost burden of UTI in type 2 diabetic patients.MethodsAdult patients diagnosed with type 2 diabetes were identified in MarketScan administrative claims data. UTI occurrence and costs were assessed during a 1-year period. We examined UTI-related visit and antibiotic costs among patients diagnosed with UTI, comparing those with versus without a history of UTI in the previous year (prevalent vs. incident UTI cases). We estimated the total incremental cost of UTI by comparing all-cause healthcare costs in patients with versus without UTI, using propensity score-matched samples.ResultsWithin the year, 8.2% (6,014/73,151) of subjects had ≥1 UTI, of whom 33.8% had a history of UTI. UTI-related costs among prevalent versus incident cases were, respectively, $603 versus$447 (p=0.033) for outpatient services, $1,607 versus$1,819 (p=NS) for hospitalizations, and $61 versus$35 (p<0.0001) for antibiotics. UTI was associated with a total all-cause incremental cost of $7,045 (95% CI: 4,130, 13,051) per patient with UTI per year.ConclusionsUTI is common and may impose a substantial direct medical cost burden among patients with type 2 diabetes

Associations between microstructural tissue changes, white matter hyperintensity severity, and cognitive impairment: an intravoxel incoherent motion imaging study

IntroductionWhite matter hyperintensities (WMHs) are a common age- and vascular risk factor-related disease and have been recognized to play an important role in cognitive impairment. However, it is still unclear what the mechanism of this effect is. In this study, intravoxel incoherent motion (IVIM) was employed to assess the microvasculature and parenchymal microstructure changes of WMHs and explore their relationship with cognitive function.MethodsForty-nine WMH patients and thirty-one healthy controls underwent IVIM imaging, a diffusion technique that provides parenchymal diffusivity D, intravascular diffusivity D*, and perfusion fraction f . The IVIM dual exponential model parameters were obtained in specific regions of interest, including deep white matter hyperintensities (DWMHs), periventricular white matter hyperintensities (PWMHs), and normal-appearing white matter (NAWM). The independent-sample t-test or Mann–Whitney U-test was utilized to compare IVIM parameters between patients and controls. The Kruskal–Wallis test or one-way analysis of variance was used to compare IVIM parameters among DWMH, PWMH, and NAWM for patients. The Wilcoxon two-sample test or independent-sample t-test was used to assess the differences in IVIM parameters based on the severity of WMH. The multivariate linear regression analysis was conducted to explore the factors influencing cognitive scores.ResultsWMH patients exhibited significantly higher parenchymal diffusivity D than controls in DWMH, PWMH, and NAWM (all p &lt; 0.05). IVIM parameters in the three groups (DWMH, PWMH, and NAWM) were significantly different for patients (all p &lt; 0.001). The severe WMH group had a significantly higher parenchymal diffusivity D (DWMH and PWMH) than mild WMH (both p &lt; 0.05). The multiple linear regression analysis identified D in DWMH and PWMH as influencing cognitive function scores (all p &lt; 0.05).ConclusionIVIM has the potential to provide a quantitative marker of parenchymal diffusivity for assessing the severity of WMH and may serve as a quantitative marker of cognitive dysfunction in WMH patients