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The authors consider the construction of a nonlinear multiple regression model, its confidence and prediction intervals to evaluate the efforts of mobile application development in the planning phase based on the multivariate normalizing transformation and outlier detection. The constructed model is compared to the linear regression model and nonlinear regression models based on the univariate transformations, such as the decimal logarithm, Box–Cox, and Johnson transformation. This model, in comparison with other regression models, has better prediction accuracy.

Background Cytokeratin-positive interstitial reticulum cells (CIRCs), which are a subgroup of fibroblastic reticular cells (FRCs), are known to be present in the lymph nodes. There have been only a few cases of tumors derived from CIRCs. Case presentation We have reported a new case involving a CIRC tumor in a 75-year-old man and reviewed the literature. The resected mediastinal lymph nodes showed epithelial-like proliferation of large atypical round and polygonal epithelioid cells. The tumor cells expressed CK8, CK18, CAM5.2, AE1/AE3, epithelial membrane antigen, vimentin, fascin, and some FRC markers, which is consistent with the diagnosis of a CIRC tumor. Following chemotherapy, the CIRC tumor was observed to have responded very well and became difficult to confirm on imaging, but a small cell lung carcinoma developed 12 months later. Chemoradiotherapy was performed, but the patient passed away 29 months after the initial diagnosis. The autopsy revealed the recurrence of the CIRC tumor, residual small cell lung carcinoma, and a very small latent carcinoma of the prostate. The relapsed CIRC tumor cells had a spindle shape; they were highly pleomorphic and had invaded the superior vena cava. Conclusion We first reported autopsy findings of CIRC tumors and demonstrated the transformation of the tumor from the epithelioid cell type to the spindle cell type.

The use of primary care electronic health records for research is abundant. The benefits gained from utilising such records lies in their size, longitudinal data collection and data quality. However, the use of such data to undertake high quality epidemiological studies, can lead to significant challenges particularly in dealing with misclassification, variation in coding and the significant effort required to pre-process the data in a meaningful format for statistical analysis. In this paper, we describe a methodology to aid with the extraction and processing of such databases, delivered by a novel software programme; the “Data extraction for epidemiological research” (DExtER). The basis of DExtER relies on principles of extract, transform and load processes. The tool initially provides the ability for the healthcare dataset to be extracted, then transformed in a format whereby data is normalised, converted and reformatted. DExtER has a user interface designed to obtain data extracts specific to each research question and observational study design. There are facilities to input the requirements for; eligible study period, definition of exposed and unexposed groups, outcome measures and important baseline covariates. To date the tool has been utilised and validated in a multitude of settings. There have been over 35 peer-reviewed publications using the tool, and DExtER has been implemented as a validated public health surveillance tool for obtaining accurate statistics on epidemiology of key morbidities. Future direction of this work will be the application of the framework to linked as well as international datasets and the development of standardised methods for conducting electronic pre-processing and extraction from datasets for research purposes. Electronic supplementary material The online version of this article (10.1007/s10654-020-00677-6) contains supplementary material, which is available to authorized users.

The modern process of the mediatisation of all spheres of public life affects both the volume of speech creation and the media texts themselves. The mass character and the multi-language openness of media discourse allow us to consider winged units as a means for representing and imple- menting phraseological innovations. The role of mass media in the formation of new sustainable turns is being actively studied, which speaks of their significance in this process, since it is the media discourse that demonstrates most clearly the current usage and viability of phraseological innovations in language and speech. The media discourse has a large number of ways of influencing the mass addressee, and, as recent studies of Russian media texts show, the fund of winged units as an integral part of the phraseological corpus of the national language is widely used in it. Various phrase resources (idioms, winged expressions, paremias, aphorisms, etc.) are often used both in a strong text position - the title or ending of the publication, and in the creation of media texts connecting the verbal and visual parts in which their transformational and creative potential is most clearly realized, multiplying their linguo-pragmatic effect on the addressee. The analysis of the media content of the site “Orthodox Laughs” revealed the tendency of creative use of folklorism “Баю-баюшки-баю, не ложися на краю. Придет серенький волчок - И укусит за бочок!”, which has not yet been fixed by lexicographical practice, as well as its variants that are the result of various methods of transformation language units. Structural and semantic transformations of various categories of phraseological units are an effective means of implementing specific expression in the media text. Previously, fiction was considered the main source of winged units, today the situation is changing, and the leadership belongs to synthetic art forms (cinema, television, pop, etc.). The analyzed potential winged unit has not one source of appearance: along with Russian and German folklore, this is German literature, but the expression in the Soviet animated film “The Bremen Town Musicians” was actualized. In analyzing the publications of this site, interdisciplinary methods of studying media content were used: discursive, descriptive-analytical, linguocultural, and media linguistic. The conducted research has shown that the functioning of the considered potential winged unit and its transforms confirms the thesis that these language units are in demand and widely used in modern media texts, since they have productive mechanisms of meaning formation.

Lineshape analysis is a recurrent and often computationally intensive task in optics, even more so for multiple peaks in the presence of noise. We demonstrate an algorithm which takes advantage of peak multiplicity (N) to retrieve line shape information. The method is exemplified via analysis of Lorentzian and Gaussian contributions to individual lineshapes for a practical spectroscopic measurement and benefits from a linear increase in sensitivity with the number N. The robustness of the method and its benefits in terms of noise reduction and order of magnitude improvement in run-time performance are discussed., 12 pages, 6 figures

Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach. Manual inspection of EEG brain signals is a time-consuming and laborious process, which puts heavy burden on neurologists and affects their performance. Several automatic techniques have been proposed using traditional approaches to assist neurologists in detecting binary epilepsy scenarios e.g. seizure vs. non-seizure or normal vs. ictal. These methods do not perform well when classifying ternary case e.g. ictal vs. normal vs. inter-ictal; the maximum accuracy for this case by the state-of-the-art-methods is 97+-1%. To overcome this problem, we propose a system based on deep learning, which is an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models. In a CNN model, the bottleneck is the large number of learnable parameters. P-1D-CNN works on the concept of refinement approach and it results in 60% fewer parameters compared to traditional CNN models. Further to overcome the limitations of small amount of data, we proposed augmentation schemes for learning P-1D-CNN model. In almost all the cases concerning epilepsy detection, the proposed system gives an accuracy of 99.1+-0.9% on the University of Bonn dataset., 18 pages

Background: Stabilization of the vital components of neonates before the transform significantly affects their response to treatment. This study aims at evaluating the effects of electronic learning on neonatal stabilization before transform on nurses’ knowledge. Methods: The current quasi-experimental, case-control study of e-learning of neonatal stabilization before the transform was conducted in 10 weeks in 2015 on nurses and midwives taking care of newborn infants. Three teaching hospitals in Tehran, Iran, were randomly selected and samples were selected using the convenience sampling method. The samples were allocated to two groups, intervention (n = 40) and control (n = 41). Data collection was conducted using two questionnaires on demographic information and a researcher-made multiple-choice questionnaire to assess the level of knowledge. Collected data were analyzed with SPSS version 18 using paired t and Fisher exact tests, P < 0.05 was considered as the level of significance. Results: In the current study, both the intervention and control groups were homogeneous in terms of demographic characteristics. A significant difference was also observed in the knowledge level of the intervention group between the pre- and post-intervention measures (P < 0.001), but the same difference in the control group was insignificant (P = 0.16). There was also a significant difference between the study groups in terms of pre- and post-intervention comparisons (P < 0.001). Conclusion: E-learning of neonatal stabilization before transform improved the knowledge of participants in the intervention group. It seems that implementation of the same training courses at large scales can improve the therapeutic results of transformed neonates.

We here harmonise two significant contributions to the field of wavelet analysis in the past two decades, namely the locally stationary wavelet process and the family of dual-tree complex wavelets. By combining these two components, we furnish a statistical model that can simultaneously access benefits from these two constructions. On the one hand, our model borrows the debiased spectrum and auto-covariance estimator from the locally stationary wavelet model. On the other hand, the enhanced directional selectivity is obtained from the dual-tree complex wavelets over the regular lattice. The resulting model allows for the description and identification of wavelet fields with significantly more directional fidelity than was previously possible. The corresponding estimation theory is established for the new model, and some stationarity detection experiments illustrate its practicality.

Local flaps are widely used in soft tissue reconstruction because they result in good colour and texture match. They can be quickly placed without donor site morbidity. However, it is not always easy to preoperatively design a feasible procedure, predict the final scar line or image the tension that may occur on and around the flap, especially for inexperienced surgeons. Because the elasticity of the skin varies with anatomical location and among individuals, unpredictable intraoperative design changes are sometimes required. Therefore, a preoperative back-up plan would be helpful. We evaluated the possibilities and advantages of an intraoperative switch from a hatchet flap (HF) to a reading man flap (RMF) procedure. Using a previously reported computer simulation with the finite element method (FEM) analysis, we developed an intraoperative local flap transformation (iLoFT) model.

In this paper, we design time-frequency localized three-band biorthogonal linear phase wavelet filter bank for epileptic seizure electroencephalograph (EEG) signal classification. Time-frequency localized analysis and synthesis low-pass filters (LPF) are designed using convex semidefinite programming (SDP) by transforming a nonconvex problem into a convex SDP using semidefinite relaxation technique. Three band parameterized lattice biorthogonal linear phase perfect reconstruction filter bank (BOLPPRFB) is chosen and nonlinear least squares algorithm is used to determine its parameters values that generate the designed analysis and synthesis LPF such that the band-pass and high-pass filters are also well localized in time and frequency domain. The designed analysis and synthesis three-band wavelet filter banks are compared with the standard two-band filter banks like Daubechies maximally regular filter banks, Cohen-Daubechies-Feauveau (CDF) biorthogonal filter banks and orthogonal time-frequency localized filter banks. Kruskal-Wallis statistical test is employed to measure the statistical significance of the subband features obtained from the various two and three-band filter banks for epileptic seizure EEG signal classification. The results show that the designed three-band analysis and synthesis filter banks both outperform two-band filter banks in the classification of seizure and seizure-free EEG signals. The designed three-band filter banks and multi-layer perceptron neural network (MLPNN) are further used together to implement a signal classifier that provides classification accuracy better than the recently reported results for epileptic seizure EEG signal classification. (C) 2016 Elsevier Inc. All rights reserved.

Psychotic experiences have been proposed to lie on a spectrum, ranging from subclinical experiences to treatment-resistant schizophrenia. We aimed to characterize functional connectivity and brain network characteristics in relation to the schizophrenia spectrum and bipolar disorder with psychosis to disentangle neural correlates to psychosis. Additionally, we studied antipsychotic medication and lithium effects on network characteristics. We analyzed functional connectivity strength and network topology in 487 resting-state functional MRI scans of individuals with schizophrenia spectrum disorder (SCZ), bipolar disorder with a history of psychotic experiences (BD), treatment-naïve subclinical psychosis (SCP), and healthy controls (HC). Since differences in connectivity strength may confound group comparisons of brain network topology, we analyzed characteristics of the minimum spanning tree (MST), a relatively unbiased backbone of the network. SCZ and SCP subjects had a lower connectivity strength than BD and HC individuals but showed no differences in network topology. In contrast, BD patients showed a less integrated network topology but no disturbances in connectivity strength. No differences in outcome measures were found between SCP and SCZ, or between BD patients that used antipsychotic medication or lithium and those that did not. We conclude that functional networks in patients prone to psychosis have different signatures for chronic SCZ patients and SCP compared to euthymic BD patients, with a limited role for medication. Connectivity strength effects may have confounded previous studies, as no functional network alterations were found in SCZ after strict correction for connectivity strength.

The recent advent of 3D in electron microscopy (EM) has allowed for detection of nanometer resolution structures. This has caused an explosion in dataset size, necessitating the development of automated workflows. Moreover, large 3D EM datasets typically require hours to days to be acquired and accelerated imaging typically results in noisy data. Advanced denoising techniques can alleviate this, but tend to be less accessible to the community due to low-level programming environments, complex parameter tuning or a computational bottleneck. We present DenoisEM: an interactive and GPU accelerated denoising plugin for ImageJ that ensures fast parameter tuning and processing through parallel computing. Experimental results show that DenoisEM is one order of magnitude faster than related software and can accelerate data acquisition by a factor of 4 without significantly affecting data quality. Lastly, we show that image denoising benefits visualization and (semi-)automated segmentation and analysis of ultrastructure in various volume EM datasets., Large 3D electron microscopy data sets frequently contain noisy data due to accelerated imaging, and denoising techniques require specialised skill sets. Here the authors introduce DenoisEM, an ImageJ plugin that democratises denoising EM data sets, enabling fast parameter tuning and processing through parallel computing.

This article investigates the impact of alternative data smoothing and traffic prediction methods on the accuracy of the performance of a two-stage short-term urban travel time prediction framework. Using this framework, we test the influence of the combination of two different data smoothing and four different prediction methods using travel time data from two substantially different urban traffic environments and under both normal and abnormal conditions. This constitutes the most comprehensive empirical evaluation of the joint influence of smoothing and predictor choice to date. The results indicate that the use of data smoothing improves prediction accuracy regardless of the prediction method used and that this is true in different traffic environments and during both normal and abnormal (incident) conditions. Moreover, the use of data smoothing in general has a much greater influence on prediction performance than the choice of specific prediction method, and this is independent of the specific smoothing method used. In normal traffic conditions, the different prediction methods produce broadly similar results but under abnormal conditions, lazy learning methods emerge as superior.

We present methods for computing refined features from 3D medial-surface point clouds.Features include: medial point classification, medial surface decomposition into sheets, Y-network extraction, and robust regularization.We compute our refined features efficiently and robustly using as input only a 3D unstructured medial cloud.We illustrate the use of our features in shape classification and patch-based and part-based shape segmentation. Medial representations have been widely used for many shape analysis and processing tasks. Large and complex 3D shapes are, in this context, a challenging case. Recently, several methods have been proposed that extract point-based medial surfaces with high accuracy and computational scalability. However, the resulting medial clouds are of limited use for shape processing due to the difficulty of computing refined medial features from such clouds. In this paper, we show how to bridge the gap between having a raw medial cloud and enriching this cloud with feature points, medial-point classification, medial axis decomposition into sheets, robust regularization, and Y-network extraction. We further show how such properties can be used to support several shape processing sample applications including edge detection and shape segmentation, for a wide range of complex 3D shapes.

Recently, new programming paradigms have emerged that combine parallelism and numerical computations with algorithmic differentiation. This approach allows for the hybridization of neural network techniques for inverse imaging problems with more traditional methods such as wavelet-based sparsity modelling techniques. The benefits are twofold: on the one hand traditional methods with well-known properties can be integrated in neural networks, either as separate layers or tightly integrated in the network, on the other hand, parameters in traditional methods can be trained end-to-end from datasets in a neural network "fashion" (e.g., using Adagrad or Adam optimizers). In this paper, we explore these hybrid neural networks in the context of shearlet-based regularization for the purpose of biomedical image restoration. Due to the reduced number of parameters, this approach seems a promising strategy especially when dealing with small training data sets.

Scientific research requires access, analysis, and sharing of data that is distributed across various heterogeneous data sources at the scale of the Internet. An eager ETL process constructs an integrated data repository as its first step, integrating and loading data in its entirety from the data sources. The bootstrapping of this process is not efficient for scientific research that requires access to data from very large and typically numerous distributed data sources. a lazy ETL process loads only the metadata, but still eagerly. Lazy ETL is faster in bootstrapping. However, queries on the integrated data repository of eager ETL perform faster, due to the availability of the entire data beforehand. In this paper, we propose a novel ETL approach for scientific data integration, as a hybrid of eager and lazy ETL approaches, and applied both to data as well as metadata. This way, Hybrid ETL supports incremental integration and loading of metadata and data from the data sources. We incorporate a human-in-the-loop approach, to enhance the hybrid ETL, with selective data integration driven by the user queries and sharing of integrated data between users. We implement our hybrid ETL approach in a prototype platform, Obidos, and evaluate it in the context of data sharing for medical research. Obidos outperforms both the eager ETL and lazy ETL approaches, for scientific research data integration and sharing, through its selective loading of data and metadata, while storing the integrated data in a scalable integrated data repository., Comment: Pre-print Submitted to the DMAH Special Issue of the Springer DAPD Journal

© 2018 American Scientific Publishers. All rights reserved. Heartbeat classification using electrocardiogram (ECG) data is an essential feature of modern day wearable devices. State-of-the-art machine learning-based heartbeat classifiers are designed using convolutional neural networks (CNN). Despite their high classification accuracy, CNNs require significant computational resources and power. This makes the mapping of CNNs on resource- And power-constrained wearable devices challenging. In this paper, we propose heartbeat classification using spiking neural networks (SNN), an alternative approach based on a biologically inspired, event-driven neural networks. SNNs compute and transfer information using discrete spikes that require fewer operations and less complex hardware resources, making them energy-efficient compared to CNNs. However, due to complex error-backpropagation involving spikes, supervised learning of deep SNNs remains challenging. We propose an alternative approach to SNN-based heartbeat classification. We start with an optimized CNN implementation of the heartbeat classification task and then convert the CNN operations, such as multiply-accumulate, pooling and softmax, into spiking equivalent with a minimal loss of accuracy. We evaluate the SNN-based heartbeat classification using publicly available ECG database of the Massachusetts Institute of Technology and Beth Israel Hospital (MIT/BIH), and demonstrate a minimal loss in accuracy when compared to 85.92% accuracy of a CNN-based hearbeat classification. We demonstrate that, for every operation, the activation of SNN neurons in each layer is sparse when compared to CNN neurons, in the same layer. We also show that this sparsity increases with an increase in the number of layers of the neural network. In addition, we detail the power-accuracy trade-off of the SNN and show a 87.76% and 96.82% reduction in SNN neuron and synapse activity, respectively, for accuracy loss ranging between 0.6% and 1.00%, when compared to a CNN-only implementation. ispartof: JOURNAL OF LOW POWER ELECTRONICS vol:14 issue:4 pages:508-519 status: published

The finger knuckle print (FKP) of a particular person is found to be unique and can serve as a biometric feature has been revealed recently by the researchers. In this research Finger Knuckle Print has been used as a biometric feature. Hybrid Wavelet Type I and Hybrid Wavelet Type II were used for feature extraction from the images in order to process it further. The important role of hybrid wavelet transform is to combine the key features of two different orthogonal transforms so that the strengths of both the transform wavelets are used. In this research the different transforms like (Discrete Cosine Transform) DCT, Haar. Hartley, Walsh and Kekre are used in combination for generation of 20 different hybrid wavelets. These hybrid wavelets are applied on the database images to generate feature vector coefficients and they are then subjected to Intra Class testing And Inter Class Testing and their performance is evaluated and compared. Proposed system has given up to 80% of EER for TAR-TRR (PI) for hybrid wavelet formed using (Discrete Cosine Transform) DCT and Kekre transform for the multimodal multi-instance implementation.

The finger knuckle print (FKP) of a particular person is found to be unique and can serve as a biometric feature has been revealed recently by the researchers. In this research Finger Knuckle Print has been used as a biometric feature. Hybrid Wavelet Type I and Hybrid Wavelet Type II were used for feature extraction from the images in order to process it further. The important role of hybrid wavelet transform is to combine the key features of two different orthogonal transforms so that the strengths of both the transform wavelets are used. In this research the different transforms like (Discrete Cosine Transform) DCT, Haar. Hartley, Walsh and Kekre are used in combination for generation of 20 different hybrid wavelets. These hybrid wavelets are applied on the database images to generate feature vector coefficients and they are then subjected to Intra Class testing And Inter Class Testing and their performance is evaluated and compared. Proposed system has given up to 77% of EER for TAR-TRR (PI) for multi-algorithmic implementation of HWI+HWII.

Key Clinical Message We report an extremely rare case of acute myelomonocytic leukemia (M4) with an MLL-ELL fusion gene lacking the PML-RARα rearrangement that transformed from hypergranular acute promyelocytic leukemia (APL) without showing any karyotypic evolution. The treatment was effective with chemotherapy for M4 and idarubicin plus a cytarabine-based chemotherapy protocol without ATRA.

Signals captured in rotating machines to obtain the status of their components can be considered as a source of massive information. In current methods based on artificial intelligence to fault severity assessment, features are first generated by advanced signal processing techniques. Then feature selection takes place, often requiring human expertise. This approach, besides time-consuming, is highly dependent on the machinery configuration as in general the results obtained for a mechanical system cannot be reused by other systems. Moreover, the information about time events is often lost along the process, preventing the discovery of faulty state patterns in machines operating under time-varying conditions. In this paper a novel method for automatic feature extraction and estimation of fault severity is proposed to overcome the drawbacks of classical techniques. The proposed method employs a Deep Convolutional Neural Network pre-trained by a Stacked Convolutional Autoencoder. The robustness and accuracy of this new method are validated using a dataset with different severity conditions on failure mode in a helical gearbox, working in both constant and variable speed of operation. The results show that the proposed unsupervised feature extraction method is effective for the estimation of fault severity in helical gearbox, and it has a consistently better performance in comparison with other reported feature extraction methods. (C) 2017 Elsevier B.V. All rights reserved. R&D projects Ministeriode Economia yCompet-itividad of Cobierno de Espana [TH12012-37434, T1N2013-41086-P] European FEDER funds GIDTEC [002-002-2016-03-03] Universidad Politecnica Salesians sede Cuenca Secretariat for Higher Education, Science.Technology and Innovation (SENESCVT) of the Republic of Ecuador info:eu-repo/semantics/publishedVersion

The Differential Transform method is an effective method used in recent years in the solving of differential equations. This study benefits from the use of the Differential Transform method in the preliminary free vibration analysis of tall buildings composed from tubular structures. In this study, in accordance with the literature, the tube-in-tube tall building was modeled as an orthotropic beam. The solution was completed in the governing equation written according to this model by applying the Differential Transform method to convert the differential equation into an algebraic equation. In the study, the Mathematic program was used in the execution of the Differential Transform Method. At the end of the study, two examples gathered from the literature were solved and assessed in order to investigate the validity of the suggested method.

A contemporary Australian health system must be characterised by partnerships with consumers. Partnering with consumers is about healthcare organisations, healthcare providers and policy makers actively working with consumers to ensure that health policy, information, systems and services meet their needs. Recognition of the need for a more active role for consumers in healthcare is gaining momentum. Currently in Australia, emphasis is being given to the role consumers and communities can play in the governance of PHNs and local hospital networks, with some accompanying investment in recent years to provide governance training. However, there is a considerable way to go in harnessing the role consumers can play as agents of change in the health system. According to Alison Trimble, 2018 (King’s Fund, UK )“Consumers are renewable energy and the secret ingredient to transformational change within the health and care system”. Collaborative practice underpins co-design and is essential to implementing current health reforms in Australia. The King’s Fund,(an independent charity in the UK) have developed a program Collaborative Pairs which is designed to support the development of the mindset and practices that underpin the culture of shared leadership and partnership and, specifically, joint clinician-consumer approaches to program and service development. The program consists of five one day sessions undertaken over a five month period to enable pairs to work on a shared work challenge in between sessions. The Consumers Health Forum of Australia in collaboration with the Western Health Alliance (operating the Western New South Wales Primary Health Network (WNSW PHN)) and three other PHNS and the Australian Commission on Safety and Quality in Health Care (ACSQHC)are implementing Collaborative Pairs as part of a National Demonstration Trial in Australia. As one of the largest rural and remote PHNs in Australia, Western NSW PHN is well placed to test the program in a rural and remote context. This contribution will provide an overview of the Collaborative Pairs Program, the model being implemented in the National Demonstration Trial with a specific focus on Western NSW. The challenges and opportunities of delivering a world class innovative program in a remote location will be discussed as well as the need to build capacity of both consumer and clinical leaders to work together in rural and remote Australia. The importance of collaborative practice and the ways that it is being practiced, supported and integrated in Western NSW as compared to other more urban sites will also be discussed.

Leadership is one of the important topics in today's economic science. The topicality of the research is based on economic development requirements. Leadership influences important processes and development in the company. The paper contains a theoretical analysis of the term ,,transformational leadership” aspects presented in scientific literature and a determination of transformational leadership structure and research results. In the paper, the author researches four dimensions of transformational leadership. The research is based on an analysis of the specific literature and scientific publications, research papers, studies, survey results and other sources of information. Research methods: the monographic or descriptive method for research of the transformational leadership concept, the interview method. The aim of the paper is to research the conception and theoretical aspects of transformational leadership in contemporary economic science in order to develop suggestions for a company’s leaders. Tasks of the paper: to find out the main aspects of the term ,,transformational leadership”; to make interviews with a company’s leaders in order to clarify transformational leadership influence on interactions between the leaders and their followers; to develop conclusions and suggestions. The author concludes that main aspect of transformational leadership is symbiosis between the leader and the followers - transformational leaders transform the personal values of their followers to support the vision and goals of the organization.

Background After traumatic brain injury (TBI), the ability of cerebral vessels to appropriately react to changes in arterial blood pressure (pressure reactivity) is impaired, leaving patients vulnerable to cerebral hypo- or hyperperfusion. Although, the traditional pressure reactivity index (PRx) has demonstrated that impaired pressure reactivity is associated with poor patient outcome, PRx is sometimes erratic and may not be reliable in various clinical circumstances. Here, we introduce a more robust transform-based wavelet pressure reactivity index (wPRx) and compare its performance with the widely used traditional PRx across 3 areas: its stability and reliability in time, its ability to give an optimal cerebral perfusion pressure (CPPopt) recommendation, and its relationship with patient outcome. Methods and findings Five hundred and fifteen patients with TBI admitted in Addenbrooke’s Hospital, United Kingdom (March 23rd, 2003 through December 9th, 2014), with continuous monitoring of arterial blood pressure (ABP) and intracranial pressure (ICP), were retrospectively analyzed to calculate the traditional PRx and a novel wavelet transform-based wPRx. wPRx was calculated by taking the cosine of the wavelet transform phase-shift between ABP and ICP. A time trend of CPPopt was calculated using an automated curve-fitting method that determined the cerebral perfusion pressure (CPP) at which the pressure reactivity (PRx or wPRx) was most efficient (CPPopt_PRx and CPPopt_wPRx, respectively). There was a significantly positive relationship between PRx and wPRx (r = 0.73), and wavelet wPRx was more reliable in time (ratio of between-hour variance to total variance, wPRx 0.957 ± 0.0032 versus PRx and 0.949 ± 0.047 for PRx, p = 0.002). The 2-hour interval standard deviation of wPRx (0.19 ± 0.07) was smaller than that of PRx (0.30 ± 0.13, p < 0.001). wPRx performed better in distinguishing between mortality and survival (the area under the receiver operating characteristic [ROC] curve [AUROC] for wPRx was 0.73 versus 0.66 for PRx, p = 0.003). The mean difference between the patients’ CPP and their CPPopt was related to outcome for both calculation methods. There was a good relationship between the 2 CPPopts (r = 0.814, p < 0.001). CPPopt_wPRx was more stable than CPPopt_PRx (within patient standard deviation 7.05 ± 3.78 versus 8.45 ± 2.90; p < 0.001). Key limitations include that this study is a retrospective analysis and only compared wPRx with PRx in the cohort of patients with TBI. Prior prospective validation is required to better assess clinical utility of this approach. Conclusions wPRx offers several advantages to the traditional PRx: it is more stable in time, it yields a more consistent CPPopt recommendation, and, importantly, it has a stronger relationship with patient outcome. The clinical utility of wPRx should be explored in prospective studies of critically injured neurological patients., Using continuous monitoring data in traumatic brain inury patients, Xiuyun Liu and colleagues compare the performance of cerebrovascular pressure reactivity monitoring using wavelet analysis to the pressure reactivity index., Author summary Why was this study done? The brain is vulnerable to damage from too little (ischemia) or too much (hyperemia) blood flow following traumatic brain injury (TBI). A physiological mechanism called cerebral autoregulation (CA) exists to maintain stable blood flow even if cerebral perfusion pressure (CPP) is changing, and an assessment of CA as part of bedside neuro-monitoring of patients with TBI could facilitate individualized treatment. A robust method for assessing CA in TBI is not yet available. The traditional measure used, the pressure reactivity index (PRx), provides inherently noisy estimates and may not be reliable in various clinical circumstances. What did the researchers do and find? We studied a new method to assess cerebral CA using continuous, real-time assessment of CA from available neuro-monitoring data called transform-based wavelet pressure reactivity index (wPRx). Using data collected from the continuous monitoring of arterial blood pressure (ABP) and intracranial pressure (ICP) from 515 patients with TBI, we compared the performance of wPRx with PRx across 3 areas: its stability/reliability in time, its ability to give an optimal cerebral perfusion pressure (CPPopt) recommendation, and its relationship with patient outcome. We found a significant positive relationship between PRx and wPRx, with wPRx being more stable in time. wPRx performed better in distinguishing different patient outcome groups, while CPPopt calculated using wPRx had lower variance and higher overall yield (percent of time that the value could be calculated). What do these findings mean? In this study, we found wPRx to be more stable in time and yield a more consistent CPPopt recommendation compared to PRx. Importantly, wPRx had a stronger relationship with patient outcome compared to PRx in our study. The clinical utility of wPRx should be explored further in prospective studies of critically injured neurological patients, particularly in circumstances in which reliability of PRx has been questioned, e.g., inpatient after decompressive craniectomy.

A depth image provides partial geometric information of a 3D scene, namely the shapes of physical objects as observed from a particular viewpoint. This information is important when synthesizing images of different virtual camera viewpoints via depth-image-based rendering (DIBR). It has been shown that depth images can be efficiently coded using contour-adaptive codecs that preserve edge sharpness, resulting in visually pleasing DIBR-synthesized images. However, contours are typically losslessly coded as side information (SI), which is expensive if the object shapes are complex. In this paper, we pursue a new paradigm in depth image coding for color-plus-depth representation of a 3D scene: we pro-actively simplify object shapes in a depth and color image pair to reduce depth coding cost, at a penalty of a slight increase in synthesized view distortion. Specifically, we first mathematically derive a distortion upper-bound proxy for 3DSwIM---a quality metric tailored for DIBR-synthesized images. This proxy reduces interdependency among pixel rows in a block to ease optimization. We then approximate object contours via a dynamic programming (DP) algorithm to optimally trade off coding cost of contours using arithmetic edge coding (AEC) with our proposed view synthesis distortion proxy. We modify the depth and color images according to the approximated object contours in an inter-view consistent manner. These are then coded respectively using a contour-adaptive image codec based on graph Fourier transform (GFT) for edge preservation and HEVC intra. Experimental results show that by maintaining sharp but simplified object contours during contour-adaptive coding, for the same visual quality of DIBR-synthesized virtual views, our proposal can reduce depth image coding rate by up to 22% compared to alternative coding strategies such as HEVC intra., 13 pages, submitted to IEEE Transactions on Circuits and Systems for Video Technology

Utilization of wind energy as an alternative energy source has been growing in Turkey due to limited fuels and environmental pollution. An integrated site investigation is crucial as the wind turbines are subjected to strong static and dynamic loads. This study presents a geological, geotechnical, and geophysical investigation of the Hasanbeyli wind turbine area in Turkey. Results of the laboratory tests and field exploration (borehole, pressuremeter, seismic refraction, point load, unconfined compression) were analyzed to develop geotechnical engineering recommendations and a generalized framework for foundation design and construction of wind turbines.

Transformation is often loosely defined. We argue that the reason for this is its inherent complexity. Paradoxically, its lack of definition is an asset, which provides an opportunity to rethink and research transformation in higher education, rather than an urgent problem to be solved. In this article, the possibilities for researching and rethinking transformation are based on an exploration of transformation in higher education and the policy directives that influenced its development in South Africa. A meta-study was used to determine the national publishing trends in the South African Journal of Higher Education and the way transformation in higher education is situated in some of the articles in this journal. The population of the study comprised 1050 articles published between 2005 and 2015 in the South African Journal of Higher Education. These were analysed to determine the broad national publishing trends. Thereafter, an in-depth analysis was conducted on 30 of these articles to determine the approach to transformation in higher education and the context in which it was discussed. Based on the findings of the analysis, we recommend future directions for rethinking and researching transformation in higher education.

In vertical seismic profiling (VSP) data processing, the first step might be to separate the down-going wavefield from the up-going wavefield. When using a masking filter for VSP wave separation, there are difficulties associated with two termination ends of the up-going waves. A critical challenge is how the masking filter can restore the energy tails, the edge effect associated with these terminations uniquely exist in VSP data. An effective strategy is to implement masking filters in both τ-p and f-k domain sequentially. Meanwhile it uses a median filter, producing a clean but smooth version of the down-going wavefield, used as a reference data set for designing the masking filter. The masking filter is implemented adaptively and iteratively, gradually restoring the energy tails cut-out by any surgical mute. While the τ-p and the f-k domain masking filters target different depth ranges of VSP, this combination strategy can accurately perform in wave separation from field VSP data.

The issue of how to efficiently represent the data collected by a network of microphones recording spatio-temporal acoustic wave fields is addressed. Each sensor node in the network samples the sound field, quantizes the samples and transmits the encoded samples to some central unit, which computes an estimate of the original sound field based on the information received from all the microphones. Our analysis is based on the spectral properties of the sound field, which are induced by the physics of wave propagation and have a significant impact on the efficiency of the chosen sampling lattice and coding scheme. As field acquisition by a sensor network typically implies spatio-temporal sampling of the field, a multidimensional sampling theorem for homogeneous random fields with compactly supported spectral measures is proved. To assess the loss of information implied by source coding, rate distortion functions for various coding schemes and sampling lattices are determined. In particular, centralized coding, independent coding and some multiterminal schemes are compared. Under the assumption of spectral whiteness of the sound field, it is shown that sampling with a quincunx lattice followed by independent coding is optimal as it achieves the lower bound given by centralized coding.

A software application for automatic descriptive feature extraction from image-objects, FETEX 2.0, is presented and described in this paper. The input data include a multispectral high resolution digital image and a vector file in shapefile format containing the polygons or objects, usually extracted from a geospatial database. The design of the available descriptive features or attributes has been mainly focused on the description of agricultural parcels, providing a variety of information: spectral information from the different image bands; textural descriptors of the distribution of the intensity values based on the grey level co-occurrence matrix, the wavelet transform and a factor of edgeness; structural features describing the spatial arrangement of the elements inside the objects, based on the semivariogram curve and the Hough transform; and several descriptors of the object shape. The output file is a table that can be produced in four alternative formats, containing a vector of features for every object processed. This table of numeric values describing the objects from different points of view can be externally used as input data for any classification software. Additionally, several types of graphs and images describing the feature extraction procedure are produced, useful for interpretation and understanding the process. A test of the processing times is included, as well as an application of the program in a real parcel-based classification problem, providing some results and analyzing the applicability, the future improvement of the methodologies, and the use of additional types of data sets. This software is intended to be a dynamic tool, integrating further data and feature extraction algorithms for the progressive improvement of land use/land cover database classification and agricultural database updating processes. © 2011 Elsevier B.V., The authors appreciate the financial support provided by the Spanish Ministerio de Ciencia e Innovacion and the FEDER in the framework of the Project CGL2009-14220 and CGL2010-19591/BTE, the Spanish Institut Geografico Nacional (IGN), Institut Cartografico Valenciano (ICV), Institut Murciano de Investigacion y Desarrollo Agrario y Alimentario (IMIDA) and Banco de Terras de Galicia (Bantegal).

We propose an efficient orthonormal wavelet-domain video denoising algorithm based on an appropriate integration of motion compensation into an adapted version of our recently devised Stein's unbiased risk estimator-linear expansion of thresholds (SURE-LET) approach. To take full advantage of the strong spatio-temporal correlations of neighboring frames, a global motion compensation followed by a selective block-matching is first applied to adjacent frames, which increases their temporal correlations without distorting the interframe noise statistics. Then, a multiframe interscale wavelet thresholding is performed to denoise the current central frame. The simulations we made on standard grayscale video sequences for various noise levels demonstrate the efficiency of the proposed solution in reducing additive white Gaussian noise. Obtained at a lighter computational load, our results are even competitive with most state-of-the-art redundant wavelet-based techniques. By using a cycle-spinning strategy, our algorithm is in fact able to outperform these methods.

A distinction is usually made between wavelet bases and wavelet frames. The former are associated with a one-to-one representation of signals, which is somewhat constrained but most efficient computationally. The latter are over-complete, but they offer advantages in terms of flexibility (shape of the basis functions) and shift-invariance. In this paper, we propose a framework for improved wavelet analysis based on an appropriate pairing of a wavelet basis with a mildly redundant version of itself (frame). The processing is accomplished in four steps: 1) redundant wavelet analysis, 2) wavelet-domain processing, 3) projection of the results onto the wavelet basis, and 4) reconstruction of the signal from its nonredundant wavelet expansion. The wavelet analysis is pyramid-like and is obtained by simple modification of Mallat's filterbank algorithm (e.g., suppression of the down-sampling in the wavelet channels only). The key component of the method is the subband regression filter (Step 3) which computes a wavelet expansion that is maximally consistent in the least squares sense with the redundant wavelet analysis. We demonstrate that this approach significantly improves the performance of soft-threshold wavelet denoising with a moderate increase in computational cost. We also show that the analysis filters in the proposed framework can be adjusted for improved feature detection; in particular, a new quincunx Mexican-hat-like wavelet transform that is fully reversible and essentially behaves the (gamma/2)th Laplacian of a Gaussian.

This manuscript presents the details about the novel controller using wavelet transform and fuzzy logic tuning for speed control of an induction motor drive. The conventional proportional-integral (PI) speed controller in an indirect vector control of induction motor drive has been replaced by the proposed controller for an improved transient and steady state performances. The discrete wavelet transform has been used to decompose the error speed into different frequency components and the fuzzy logic is used to generate the scaling gains of the wavelet controller. The complete model of the proposed controller along with an induction motor drive incorporating the proposed controller has been developed in numerical simulation software using Matlab/Simulink environment and a set of results are provided within this paper in comparison to the conventional controller scheme.

Background Analysis of encounter data relevant to the diagnostic process sourced from routine electronic medical record (EMR) databases represents a classic example of the concept of a learning healthcare system (LHS). By collecting International Classification of Primary Care (ICPC) coded EMR data as part of the Transition Project from Dutch and Maltese databases (using the EMR TransHIS), data mining algorithms can empirically quantify the relationships of all presenting reasons for encounter (RfEs) and recorded diagnostic outcomes. We have specifically looked at new episodes of care (EoC) for two urinary system infections: simple urinary tract infection (UTI, ICPC code: U71) and pyelonephritis (ICPC code: U70). Methods Participating family doctors (FDs) recorded details of all their patient contacts in an EoC structure using the ICPC, including RfEs presented by the patient, and the FDs’ diagnostic labels. The relationships between RfEs and episode titles were studied using probabilistic and data mining methods as part of the TRANSFoRm project. Results The Dutch data indicated that the presence of RfE’s “Cystitis/Urinary Tract Infection”, “Dysuria”, “Fear of UTI”, “Urinary frequency/urgency”, “Haematuria”, “Urine symptom/complaint, other” are all strong, reliable, predictors for the diagnosis “Cystitis/Urinary Tract Infection” . The Maltese data indicated that the presence of RfE’s “Dysuria”, “Urinary frequency/urgency”, “Haematuria” are all strong, reliable, predictors for the diagnosis “Cystitis/Urinary Tract Infection”. The Dutch data indicated that the presence of RfE’s “Flank/axilla symptom/complaint”, “Dysuria”, “Fever”, “Cystitis/Urinary Tract Infection”, “Abdominal pain/cramps general” are all strong, reliable, predictors for the diagnosis “Pyelonephritis” . The Maltese data set did not present any clinically and statistically significant predictors for pyelonephritis. Conclusions We describe clinically and statistically significant diagnostic associations observed between UTIs and pyelonephritis presenting as a new problem in family practice, and all associated RfEs, and demonstrate that the significant diagnostic cues obtained are consistent with the literature. We conclude that it is possible to generate clinically meaningful diagnostic evidence from electronic sources of patient data. Electronic supplementary material The online version of this article (doi:10.1186/s12875-015-0271-4) contains supplementary material, which is available to authorized users.

International audience; The production of multimedia documents of a specified genre from indexed multimedia sources is an important research area. The area adds value to digital library resources and enables the delivery of specific multimedia documents. In this article we explain how the principles of structural computing may be applied to the production of series of documents complying with some specific genre. The research is inspired by Markov transforms as used in natural language processing. We introduce TTL (Tree Transformation Language) a declarative XML-based formalism for the specification of structural transformation rules. We show, with the aid of a number of examples how this formalism may be used to specify the narration, rhetoric, and argument structures which respect generic style constraints, and we show how documents are generated. The interpretation of TTL is itself specified as a set of transformation rules, which, together with the SYGMART engine, constitute the SYG-XML environment for producing target documents.

Makeover television is generally interested in transforming individuals. Often these programmes alter participants on an individual basis, ignore structural problems, and enforce normative ideas of the good citizen. An important role is preserved for celebrities, considered able to help these individuals by performing the role of expert. This article aims to demonstrate how celebrities are legitimised with the power to transform young individuals in a television programme. Specifically, the article inquires how celebrities are represented as key actors in the process of transforming young individuals within a Dutch context in which ordinariness is promoted. To this end, a textual analysis is conducted of the Dutch programme It Gets Better. This article reveals how the programme constructs celebrities as the only way to solve the participants’ problems. On the one hand, they are able to connect with audiences and participants because their ordinariness eases identification and access. The programme particularly emphasises the ordinariness of Dutch celebrities while connecting it to their personal experiences with the issue at stake in the past. On the other hand, their extraordinary status in society gives them authority. The articulation of the celebrity status to personal experiences is pivotal for the transformation to take place. The critical moment of change is when celebrities deliver their messages to the individuals personally and act as a friend; consequently, individuals feel honoured to interact with them and take celebrities’ messages seriously.

With the advent of modern computing and imaging technologies, digital holography is becoming widespread in various scientific disciplines such as microscopy, interferometry, surface shape measurements, vibration analysis, data encoding, and certification. Therefore, designing an efficient data representation technology is of particular importance. Off-axis holograms have very different signal properties with respect to regular imagery, because they represent a recorded interference pattern with its energy biased toward the high-frequency bands. This causes traditional images' coders, which assume an underlying 1/f(2) power spectral density distribution, to perform suboptimally for this type of imagery. We propose a JPEG 2000-based codec framework that provides a generic architecture suitable for the compression of many types of off-axis holograms. This framework has a JPEG 2000 codec at its core, extended with (1) fully arbitrary wavelet decomposition styles and (2) directional wavelet transforms. Using this codec, we report significant improvements in coding performance for off-axis holography relative to the conventional JPEG 2000 standard, with Bjontegaard delta-peak signal-to-noise ratio improvements ranging from 1.3 to 11.6 dB for lossy compression in the 0.125 to 2.00 bpp range and bit-rate reductions of up to 1.6 bpp for lossless compression. c The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

The aim of the present research was to reduce accidents by assisting the driver in various aspects of driving such as lane detection, pedestrian and car detection, driver drowsiness detection and rear view parking assistance. The methodology combines the computer vision techniques with pattern recognition, feature extraction, machine learning, object recognition, human computer interaction and parallel processing in a nutshell. The proposed system provides robust extraction of lane markings in various types and alerts the driver attempting to drift from the lane. It also detects the pedestrians and cars which are at a vulnerable distance to be hit by the vehicle and alarms the driver well ahead of time. The system uses eye closure based decision algorithm to detect driver drowsiness in all conditions and also warns by interactive voice early enough to avoid the accidents. It also assists the driver while reversing the vehicle, by providing a clear view of his blind spot areas. Computer vision algorithms like Hough’s Transform, Canny Edge detection and HAAR classifiers were applied to meet the objectives. The integrated module was analyzed and tested in different terrains and various lighting condition to produce an accurate and robust real-time assistance system (Sivaraman et al., 2014). iCar is an innovative prototype in the Information Technology with minimum hardware like low cost webcams. It emerged as an Interactive Technology with an interactive audio, visual, touch and touch-less interfaces. These can assist to avoid accidents in the world by intelligently ignoring certain hardware sensors like IR, UV, Acoustic, Proximity and mechanical devices like costlier LIDAR (Light Detection and Ranging) fitted in Google Car. Present research findings outperform the state of the art research like CalTech (Aly et al., 1997). Attempts of depth sensing even using Microsoft Kinect could be ignored by the present technology, the iCar. Keywords : iCar; Canny Edge detection; HAAR Classifier; Probabilistic Hough’s; Transform

LQR control of wind induced motion of a benchmark building is considered. The building is fitted with a semiactive variable stiffness tuned mass damper adapted from the literature. The nominal stiffness of the device corresponds to the fundamental frequency of the building and is included in the system matrix. This results in a linear time-invariant system, for which the desired control force is computed using LQR control. The control force thus computed is then realized by varying the device stiffness around its nominal value by using a simple control law. A nonlinear static analysis is performed in order to establish the range of linearity, in terms of the device (configuration) angle, for which the control law is valid. Results are obtained for the cases of zero and nonzero structural stiffness variation. The performance criteria evaluated show that the present method provides displacement control that is comparable with that of two existing controllers. The acceleration control, while not as good as that obtained with the existing active controller, is comparable or better than that obtained with the existing semiactive controller. By using substantially less power as well as control force, the present control yields comparable displacement control and reasonable acceleration control.

The Hatay Triple Junction (HTJ) is a tectonically complex area located at the intersection between the left-lateral East Anatolian fault (EAF), the Cyprus subduction arc and the left-lateral Dead Sea fault (DSF) which is a transform boundary between the Arabian and Sinai plates as they converge toward Eurasia. Previous GPS studies indicate a left-lateral strike-slip rate across the DSF varying from 5 mm/yr (along the southern part) to 2 mm/yr (along the northern part) ( Alchalbi et al., 2010 , Gomez et al., 2007 , Le Beon et al., 2008 , Mahmoud et al., 2005 , Al-Tarazi et al., 2011 ). In contrast, the EAF has a roughly constant velocity along strike estimated at 9.7 + 0.9 mm/yr ( Reilinger et al., 2006 ). The HTJ contains several well-identified active fault segments (DSF, EAF, Osmaniye fault, Karasu fault, Latakia fault, Jisr-al-shuggur fault, Idleb fault and Afrin fault) ( Meghraoui et al., 2011 ), the fault-slip rates for which are poorly constrained. In order to constrain better the slip rate on faults, we established a network of 57 GPS sites in NW Syria and in SE Turkey. The first campaign was carried out in September 2009; a second took place in September and November 2010 and a third (only in Turkey) in September 2011. Although the velocity field vectors computed from the 2009, 2010 and 2011 measurements appear consistent with other local studies, the results are hampered by large uncertainties due to the short observation period. However, preliminary interpretations are consistent with decreasing velocity along the DSF from south to north reported previously.

International audience; Ground penetrating radar (GPR) and electric resistivity tomography (ERT) are well assessed and accurate geophysical methods for the investigation of subsurface geological sections. In this paper, we present the joint exploitation of these methods at the Montagnole (French Alps) experimental site with the final aim to study and monitor effects of possible catastrophic rockslides in transport infrastructures. The overall goal of the joint GPR–ERT deployment considered here is the careful monitoring of the subsurface structure before and after a series of high energetic mechanical impacts at ground level. It is known that factors such as the ambiguity of geophysical field examination, the complexity of geological scenarios and the low signal-to-noise ratio affect the possibility of building reliable physical–geological models of subsurface structure. Here, we applied to the GPR and ERT methods at the Montagnole site, recent advances in wavelet theory and data mining. The wavelet approach was specifically used to obtain enhanced images (e.g. coherence portraits) resulting from the integration of the different geophysical fields. This methodology, based on the matching pursuit combined with wavelet packet dictionaries, permitted us to extract desired signals under different physical–geological conditions, even in the presence of strongly noised data. Tools such as complex wavelets employed for the coherence portraits, and combined GPR–ERT coherency orientation angle, to name a few, enable non-conventional operations of integration and correlation in subsurface geophysics to be performed. The estimation of the above-mentioned parameters proved useful not only for location of buried inhomogeneities but also for a rough estimation of their electromagnetic and related properties. Therefore, the combination of the above approaches has allowed us to set up a novel methodology, which may enhance the reliability and confidence of each separate geophysical method and their integration.

Cancer cell lines are widely used for research purposes in laboratories all over the world. Computer-assisted classification of cancer cells can alleviate the burden of manual labeling and help cancer research. In this paper, we present a novel computerized method for cancer cell line image classification. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by subwindows which correspond to foreground pixels. For each subwindow, a covariance descriptor utilizing the dual-tree complex wavelet transform (DT-[Formula: see text]WT) coefficients and several morphological attributes are computed. Directionally selective DT-[Formula: see text]WT feature parameters are preferred primarily because of their ability to characterize edges at multiple orientations which is the characteristic feature of carcinoma cell line images. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel is employed for final classification. Over a dataset of 840 images, we achieve an accuracy above 98%, which outperforms the classical covariance-based methods. The proposed system can be used as a reliable decision maker for laboratory studies. Our tool provides an automated, time- and cost-efficient analysis of cancer cell morphology to classify different cancer cell lines using image-processing techniques, which can be used as an alternative to the costly short tandem repeat (STR) analysis. The data set used in this manuscript is available as supplementary material through http://signal.ee.bilkent.edu.tr/cancerCellLineClassificationSampleImages.html.

We present a GPU-based framework for extracting surface and curve skeletons of 3D shapes represented as large polygonal meshes. We use an efficient parallel search strategy to compute point-cloud skeletons and their distance and feature transforms with user-defined precision. We regularize skeletons by a new GPU-based geodesic tracing technique which is orders of magnitude faster and more accurate than comparable techniques. We reconstruct the input surface from skeleton clouds using a fast and accurate image-based method. We also show how to reconstruct the skeletal manifold structure as a polygon mesh and the curve skeleton as a polyline. Compared to recent skeletonization methods, our approach offers two orders of magnitude speed-up, high precision, and low memory footprints. We demonstrate our framework on several complex 3D models. Keywords: Medial axes, geodesics, skeleton regularization

Camouflage is an attempt to conceal a target by making it look similar to the background to make the detection and recognition of the target difficult for the observer (a man or a machine or both). Detecting a camouflaged target in a video, captured in visible band is a big challenge. One can use the infrared (IR) video where the visibility of the same target is much better, but the problem with IR band is that most of the contrast, color, and edge information about the background are lost and thus localizing the target becomes difficult. The objective of this work is to fuse registered videos, captured in visible and IR band such that the target is no longer camouflaged and hence clearly visible to the human monitor. More importantly, this should be done without losing the background details. We have proposed four different video fusion methods. All the proposed methods are intensity invariant so that camouflaged targets are detected independent of the illumination conditions. The performance of these methods has been compared using Wang–Bovik and Petrovic–Xydeas fusion metric along with other information theoretic indices. Experimental video results show that the proposed methods improve the perception and thus facilitates detection and localization of a camouflaged target. Moreover, the fused video has minimum artefacts as indicated by the highest peak signal-to-noise ratio, which is one of the most desired quality of a good fusion method.

Int J Cardiol

Spontaneous fixations onto shapes are driven by a structural analysis. But is such analysis also carried out during free viewing of real-world scenes? Here, we analyze how fixation locations in such scenes are related to their region using the region's symmetric axes as a reference. Each fixation location is compared with respect to its nearest symmetric-axis segment by a latitude and a longitude measure. Analyzing the distributions for the two measures we find that there exist fixation biases for L features and parallel contours, suggesting that structural analysis may play a role in saccadic target selection during free viewing.

ACL-11-01; International audience; Quantifying and monitoring the structure and degradation of tropical forests over regional to global scales is gaining increasing scientific and societal importance. Reliable automated methods are only beginning to appear; for instance, through the recent development of textural approaches applied to high resolution optical imagery. In particular, the Fourier Transform Textural Ordination (FOTO) method shows some potential to provide non-saturating estimates of tropical forest structure, including for large scale applications. However, we need to understand more precisely how canopy structure interacts with physical signals (light) to produce a given texture, notably to assess the method's sensitivity to varying sun-view acquisition conditions. In this study, we take advantage of the detailed description of canopy topography provided by airborne small footprint LiDAR data acquired over the Paracou forest experimental station in French Guiana. Using hillshade models and a range of sun-view angles identical to the actual parameter distributions found for Quickbird™ images over the Amazon, we study noise and bias in texture estimation induced by the changing configurations. We introduce the bidirectional texture function, which summarizes these effects, and in particular the existence of a textural ‘hot spot', similar to a well-known feature of bidirectional reflectance studies. For texture, this effect implies that coarseness decreases in configurations for which shadows are concealed to the observer. We also propose a method, termed partitioned standardization, that allows mitigating acquisition effects and discuss the potential for an operational use of VHR optical imagery and the FOTO method in the current context of international decisions to reduce CO2 emissions due to deforestation and forest degradation

cited By (since 1996)4; International audience; This paper is a contribution to the emergy evaluation of systems involving recycling or reuse of waste. If waste exergy (its residual usefulness) is not negligible, wastes could serve as input to another process or be recycled. In cases of continuous waste recycle or reuse, what then is the role of emergy? Emergy is carried by matter and its value is shown to be the product of specific energy with mass flow rate and its transformity. This transformity (τ) given as the ratio of the total emergy input and the useful available energy in the product (exergy) is commonly calculated over a specific period of time (usually yearly) which makes transformity a time dependent factor. Assuming a process in which a part of the non-renewable input is an output (waste) from a previous system, for the waste to be reused, an emergy investment is needed. The transformity of the reused or recycled material should be calculated based on the pathway of the reused material at a certain time (T) which results in a specific transformity value (τ). In case of a second recycle of the same material that had undergone the previous recycle, the material pathway has a new time (T+T 1) which results in a transformity value (τ 1). Recycling flows as in the case of feedback is a dynamic process and as such the process introduces its own time period depending on its pathway which has to be considered in emergy evaluations. Through the inspiration of previous emergy studies, authors have tried to develop formulae which could be used in such cases of continuous recycling of material in this paper. The developed approach is then applied to a case study to give the reader a better understanding of the concept. As a result, a 'factor' is introduced which could be included on emergy evaluation tables to account for subsequent transformity changes in multiple recycling. This factor can be used to solve the difficulties in evaluating aggregated systems, serve as a correction factor to up-level such models keeping the correct evaluation and also solve problems of memory loss in emergy evaluation. The discussion deals with the questions; is it a pure mathematical paradox in the rules of emergy? Is it consistent with previous work? What were the previous solutions to avoid the cumulative problem in a reuse? What are the consequences?. © 2011 Elsevier B.V.

We present a phase-shifting holography method in k space that allows us to investigate the phase and amplitude of optical Bloch waves. The method is based on a Fourier space imaging technique combined with a Mach-Zehnder interferometer. Using a W3 photonic crystal waveguide as a model system, we determine, with high accuracy, both the intensity and the phase of the envelope function of the Bloch modes propagating in such waveguides. We show that local variation of the dielectric map as low as 20 nm can be clearly observed from the part of the angular spectrum of field located inside the light cone.