Towards an Interaction-based Integration of MKM Services into End-User Applications

The Semantic Alliance (SAlly) Framework, first presented at MKM 2012, allows integration of Mathematical Knowledge Management services into typical applications and end-user workflows. From an architecture allowing invasion of spreadsheet programs, it grew into a middle-ware connecting spreadsheet, CAD, text and image processing environments with MKM services. The architecture presented in the original paper proved to be quite resilient as it is still used today with only minor changes. This paper explores extensibility challenges we have encountered in the process of developing new services and maintaining the plugins invading end-user applications. After an analysis of the underlying problems, I present an augmented version of the SAlly architecture that addresses these issues and opens new opportunities for document type agnostic MKM services.Comment: 14 pages, 7 figure

Measuring traffic flow and lane changing from semi-automatic video processing

Comprehensive databases are needed in order to extend our knowledge on the behavior of vehicular traffic. Nevertheless data coming from common traffic detectors is incomplete. Detectors only provide vehicle count, detector occupancy and speed at discrete locations. To enrich these databases additional measurements from other data sources, like video recordings, are used. Extracting data from videos by actually watching the entire length of the recordings and manually counting is extremely time-consuming. The alternative is to set up an automatic video detection system. This is also costly in terms of money and time, and generally does not pay off for sporadic usage on a pilot test. An adaptation of the semi-automatic video processing methodology proposed by Patire (2010) is presented here. It makes possible to count flow and lane changes 90% faster than actually counting them by looking at the video. The method consists in selecting some specific lined pixels in the video, and converting them into a set of space – time images. The manual time is only spent in counting from these images. The method is adaptive, in the sense that the counting is always done at the maximum speed, not constrained by the video playback speed. This allows going faster when there are a few counts and slower when a lot of counts happen. This methodology has been used for measuring off-ramp flows and lane changing at several locations in the B-23 freeway (Soriguera & Sala, 2014). Results show that, as long as the video recordings fulfill some minimum requirements in framing and quality, the method is easy to use, fast and reliable. This method is intended for research purposes, when some hours of video recording have to be analyzed, not for long term use in a Traffic Management Center.Postprint (published version

Moveable worlds/digital scenographies

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ Intellect Ltd 2010.The mixed reality choreographic installation UKIYO explored in this article reflects an interest in scenographic practices that connect physical space to virtual worlds and explore how performers can move between material and immaterial spaces. The spatial design for UKIYO is inspired by Japanese hanamichi and western fashion runways, emphasizing the research production company's commitment to various creative crossovers between movement languages, innovative wearable design for interactive performance, acoustic and electronic sound processing and digital image objects that have a plastic as well as an immaterial/virtual dimension. The work integrates various forms of making art in order to visualize things that are not in themselves visual, or which connect visual and kinaesthetic/tactile/auditory experiences. The ‘Moveable Worlds’ in this essay are also reflections of the narrative spaces, subtexts and auditory relationships in the mutating matrix of an installation-space inviting the audience to move around and follow its sensorial experiences, drawn near to the bodies of the dancers.Brunel University, the British Council, and the Japan Foundation

An Energetic AGN Outburst Powered by a Rapidly Spinning Supermassive Black Hole or an Accreting Ultramassive Black Hole

Powering the 10^62 erg nuclear outburst in the MS0735.6+7421 cluster central galaxy by accretion implies that its supermassive black hole (SMBH) grew by ~6x10^8 solar masses over the past 100 Myr. We place upper limits on the amount of cold gas and star formation near the nucleus of <10^9 solar masses and <2 solar masses per year, respectively. These limits imply that an implausibly large fraction of the preexisting cold gas in the bulge must have been consumed by its SMBH at the rate of ~3-5 solar masses per year while leaving no trace of star formation. Such a high accretion rate would be difficult to maintain by stellar accretion or the Bondi mechanism, unless the black hole mass approaches 10^11 solar masses. Its feeble nuclear luminosities in the UV, I, and X-ray bands compared to its enormous mechanical power are inconsistent with rapid accretion onto a ~5x10^9 solar mass black hole. We suggest instead that the AGN outburst is powered by a rapidly-spinning black hole. A maximally-spinning, 10^9 solar mass black hole contains enough rotational energy, ~10^62 erg, to quench a cooling flow over its lifetime and to contribute significantly to the excess entropy found in the hot atmospheres of groups and clusters. Two modes of AGN feedback may be quenching star formation in elliptical galaxies centered in cooling halos at late times. An accretion mode that operates in gas-rich systems, and a spin mode operating at modest accretion rates. The spin conjecture may be avoided in MS0735 by appealing to Bondi accretion onto a central black hole whose mass greatly exceeds 10^10 solar mass. The host galaxy's unusually large, 3.8 kpc stellar core radius (light deficit) may witness the presence of an ultramassive black hole.Comment: Accepted for publication in ApJ. Modifications: adopted slightly higher black hole mass using Lauer's M_SMBH vs L_bulge relation and adjusted related quantities; considered more seriously the consequences of a ultramassive black hole, motivated by new Kormendy & Bender paper published after our submission; other modifications per referee comments by Ruszkowsk

A Study on Inspection of Defective Tablet Blister Using Image Segmentation Techniques

humansareaffectedfrom many kind ofdiseases. Proper Medicationistheonlywaytoovercome fromsuchdiseases.Somedicinesbecome most important part of human life.Manufacturing of medicines is done in very large scale. Duringmanufacturing, thereare many kind of defects in tablet blister, defects are likebreakage, cracksetcpresentin tablets or capsules.There may be side-effects of these defected tablets or capsules due to variation in dosage when consumed. The manufactured tablets should be properly inspected before reaching to the public, so that they do not cause any side-effects.Manual inspection of such defects in tablet blister may be very challenging task.Image segmentation is an important technique for automation of visual inspection. Hence, it is important to propose some approaches to detect these defects in tablet blister. In literaturesurvey many researchers have proposed multiple procedures for identifying defects in tablet blister. In this research work we review allthe methods used to identify defects in tablets blister

Fully automated segmentation and tracking of the intima media thickness in ultrasound video sequences of the common carotid artery

Abstract—The robust identification and measurement of the intima media thickness (IMT) has a high clinical relevance because it represents one of the most precise predictors used in the assessment of potential future cardiovascular events. To facilitate the analysis of arterial wall thickening in serial clinical investigations, in this paper we have developed a novel fully automatic algorithm for the segmentation, measurement, and tracking of the intima media complex (IMC) in B-mode ultrasound video sequences. The proposed algorithm entails a two-stage image analysis process that initially addresses the segmentation of the IMC in the first frame of the ultrasound video sequence using a model-based approach; in the second step, a novel customized tracking procedure is applied to robustly detect the IMC in the subsequent frames. For the video tracking procedure, we introduce a spatially coherent algorithm called adaptive normalized correlation that prevents the tracking process from converging to wrong arterial interfaces. This represents the main contribution of this paper and was developed to deal with inconsistencies in the appearance of the IMC over the cardiac cycle. The quantitative evaluation has been carried out on 40 ultrasound video sequences of the common carotid artery (CCA) by comparing the results returned by the developed algorithm with respect to ground truth data that has been manually annotated by clinical experts. The measured IMTmean ± standard deviation recorded by the proposed algorithm is 0.60 mm ± 0.10, with a mean coefficient of variation (CV) of 2.05%, whereas the corresponding result obtained for the manually annotated ground truth data is 0.60 mm ± 0.11 with a mean CV equal to 5.60%. The numerical results reported in this paper indicate that the proposed algorithm is able to correctly segment and track the IMC in ultrasound CCA video sequences, and we were encouraged by the stability of our technique when applied to data captured under different imaging conditions. Future clinical studies will focus on the evaluation of patients that are affected by advanced cardiovascular conditions such as focal thickening and arterial plaques

A High-Performance System Architecture for Medical Imaging

Medical imaging is classified into different modalities such as ultrasound, X-ray, computed tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI), single-photon emission tomography (SPECT), nuclear medicine (NM), mammography, and fluoroscopy. Medical imaging includes various imaging diagnostic and treatment techniques and methods to model the human body, and therefore, performs an essential role to improve the health care of the community. Medical imaging, scans (such as X-Ray, CT, etc.) are essential in a variety of medical health-care environments. With the enhanced health-care management and increase in availability of medical imaging equipment, the number of global imaging-based systems is growing. Effective, safe, and high-quality imaging is essential for the medical decision-making. In this chapter, we proposed a medical imaging-based high-performance hardware architecture and software programming toolkit called high-performance medical imaging system (HPMIS). The HPMIS can perform medical image registration, storage, and processing in hardware with the support of C/C++ function calls. The system is easy to program and gives high performance to different medical imaging applications

New sensors benchmark report on Kompsat-3

The following document has been drawn up as a follow up to the Quality Control Record L [i] on the commissioning phase of the Kompsat-3 imagery, planned benchmarking tests as well as the methodology used in the tests. Benchmarking is necessary to be performed in order to estimate the usability of the imagery collected by particular sensor in The Common Agricultural Policy (CAP) image acquisition Campaign. The main requirement that should be fulfilled concerns the planimetric accuracy of the orthoimagery which should not exceed particular thresholds given in VHR Specifications [iii]. The methodologies used in the benchmarking tests were performed based on Guidelines for Best Practice and Quality Checking of Ortho Imagery [ii]. However, in addition the tests were performed according to alternative methodology, described in [i], which differs from the standard one, the GCPs selection/measurement phase i.e. image to image correlation techniques are used.JRC.H.6-Digital Earth and Reference Dat

Segmentation of heart chambers in 2-D heart ultrasounds with deep learning

Echocardiography is a non-invasive image diagnosis technique where ultrasound waves are used to obtain an image or sequence of the structure and function of the heart. The segmentation of the heart chambers on ultrasound images is a task usually performed by experienced cardiologists, in which they delineate and extract the shape of both atriums and ventricles to obtain important indexes of a patient’s heart condition. However, this task is usually hard to perform accurately due to the poor image quality caused by the equipment and techniques used and due to the variability across different patients and pathologies. Therefore, medical image processing is needed in this particular case to avoid inaccuracy and obtain proper results. Over the last decade, several studies have proved that deep learning techniques are a possible solution to this problem, obtaining good results in automatic segmentation. The major problem with deep learning techniques in medical image processing is the lack of available data to train and test these architectures. In this work we have trained, validated, and tested a convolutional neural network based on the architecture of U-Net for 2D echocardiogram chamber segmentation. The data used for the training of the convolutional neural network was the B-Mode 4-chamber apical view Echogan dataset with data augmentation techniques applied. The novelty of this work is the hyperparameter and architecture optimizations to reduce the computation time while obtaining significant training and testing accuraciesObjectius de Desenvolupament Sostenible::3 - Salut i Benesta