Exploiting peer group concept for adaptive and highly available services

This paper presents a prototype for redundant, highly available and fault tolerant peer to peer framework for data management. Peer to peer computing is gaining importance due to its flexible organization, lack of central authority, distribution of functionality to participating nodes and ability to utilize unused computational resources. Emergence of GRID computing has provided much needed infrastructure and administrative domain for peer to peer computing. The components of this framework exploit peer group concept to scope service and information search, arrange services and information in a coherent manner, provide selective redundancy and ensure availability in face of failure and high load conditions. A prototype system has been implemented using JXTA peer to peer technology and XML is used for service description and interfaces, allowing peers to communicate with services implemented in various platforms including web services and JINI services. It utilizes code mobility to achieve role interchange among services and ensure dynamic group membership. Security is ensured by using Public Key Infrastructure (PKI) to implement group level security policies for membership and service access.Comment: The Paper Consists of 5 pages, 6 figures submitted in Computing in High Energy and Nuclear Physics, 24-28 March 2003 La Jolla California. CHEP0

Localization and segmentation of optic disc in retinal images using circular Hough transform and grow-cut algorithm

Automated retinal image analysis has been emerging as an important diagnostic tool for early detection of eye-related diseases such as glaucoma and diabetic retinopathy. In this paper, we have presented a robust methodology for optic disc detection and boundary segmentation, which can be seen as the preliminary step in the development of a computer-assisted diagnostic system for glaucoma in retinal images. The proposed method is based on morphological operations, the circular Hough transform and the grow-cut algorithm. The morphological operators are used to enhance the optic disc and remove the retinal vasculature and other pathologies. The optic disc center is approximated using the circular Hough transform, and the grow-cut algorithm is employed to precisely segment the optic disc boundary. The method is quantitatively evaluated on five publicly available retinal image databases DRIVE, DIARETDB1, CHASE_DB1, DRIONS-DB, Messidor and one local Shifa Hospital Database. The method achieves an optic disc detection success rate of 100% for these databases with the exception of 99.09% and 99.25% for the DRIONS-DB, Messidor, and ONHSD databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 78.6%, 85.12%, 83.23%, 85.1%, 87.93%, 80.1%, and 86.1%, respectively, for these databases. This unique method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc

The automated detection of proliferative diabetic retinopathy using dual ensemble classification

Objective: Diabetic retinopathy (DR) is a retinal vascular disease that is caused by complications of diabetes. Proliferative diabetic retinopathy (PDR) is the advanced stage of the disease which carries a high risk of severe visual impairment. This stage is characterized by the growth of abnormal new vessels. We aim to develop a method for the automated detection of new vessels from retinal images. Methods: This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel maps which each hold vital information. Local morphology, gradient and intensity features are measured using each binary vessel map to produce two separate 21-D feature vectors. Independent classification is performed for each feature vector using an ensemble system of bagged decision trees. These two independent outcomes are then combined to a produce a final decision. Results: Sensitivity and specificity results using a dataset of 60 images are 1.0000 and 0.9500 on a per image basis. Conclusions: The described automated system is capable of detecting the presence of new vessels

Seeing Tree Structure from Vibration

Humans recognize object structure from both their appearance and motion; often, motion helps to resolve ambiguities in object structure that arise when we observe object appearance only. There are particular scenarios, however, where neither appearance nor spatial-temporal motion signals are informative: occluding twigs may look connected and have almost identical movements, though they belong to different, possibly disconnected branches. We propose to tackle this problem through spectrum analysis of motion signals, because vibrations of disconnected branches, though visually similar, often have distinctive natural frequencies. We propose a novel formulation of tree structure based on a physics-based link model, and validate its effectiveness by theoretical analysis, numerical simulation, and empirical experiments. With this formulation, we use nonparametric Bayesian inference to reconstruct tree structure from both spectral vibration signals and appearance cues. Our model performs well in recognizing hierarchical tree structure from real-world videos of trees and vessels.Comment: ECCV 2018. The first two authors contributed equally to this work. Project page: http://tree.csail.mit.edu

Rocket mode operational analysis of a rocket based combined cycle model engine

LAUREA MAGISTRALEI motori a ciclo combinato basati su endoreattore sono attualmente al centro della ricerca grazie ai vantaggi rispetto ai motori turbogas per velocità supersoniche e ipersoniche. Le turbine a gas che sono state utilizzate nella maggior parte degli aerei commerciali e militari implicano diverse restrizioni strutturali e di progettazione a causa della presenza di parti rotanti nei motori che portano al raggiungimento di un valore massimo del numero di Mach di volo pari a 2. I moderni sistemi di propulsione sono il risultato di una combinazione di endoreattori a propellente solido e ramjets. Questi sistemi sono in grado di modificare la loro modalità operativa da puro endoreattore a ramjet e quindi scramjet, a seconda del numero di Mach, mediante lievi modifiche nella geometria. L’endoreattore funge da acceleratore e il motore ramjet sostiene il volo fino alle velocità più elevate. Questi sistemi sono altamente efficienti in termini di prestazioni propulsive e compattezza, e possiedono un'elevata manovrabilità. Questo studio si concentra sulle condizioni operative del motore RBCC disponibile presso il laboratorio CNR-ICMATE di Milano con l'obiettivo principale di studiare il funzionamento in modalità endoreattore. Diversi test sono stati eseguiti in esclusiva modalità endoreattore al fine di studiare in dettaglio le caratteristiche dell’endoreattore in diverse condizioni operative con e senza accensione della miscela combustibile. Durante le prove di accensione / non accensione sono state mantenute le stesse condizioni operative per comprendere meglio il comportamento dell’endoreattore. Il combustibile e l'ossidante gassosi sono stati alimentati attraverso gli iniettori nella camera di combustione e la misura della pressione e della temperatura è stata condotta durante l'intera durata della prova, che è solo una frazione di secondo a causa del volume di combustibile e ossidante alimentato. Le letture di pressione e temperatura sono state quindi utilizzate per valutare numericamente la spinta, l'efficienza di combustione e l'impulso specifico. I numeri di Mach nelle varie posizioni sono stati valutati usando diverse tecniche di calcolo della media che hanno portato alla valutazione della spinta. Il set di dati di spinta è stato quindi confrontato con quello misurato utilizzando la cella di carico durante le prove al fine di verificare la validità delle misure di pressione e temperatura.Rocket Based combined cycle engines are the prime focus of research these days due to their enhanced advantage over the limitations presented by the gas turbines engines for supersonic and hypersonic speeds. The gas turbines which have been used in most commercial and military airplanes imply several structural and design restrictions because of presence of rotating parts in the engines and hence resulted in achievement of the highest possible speed of Mach 2. Modern propulsion systems are a result of combination of solid propellent rockets and ramjets to create a Ramjet. These systems are capable of modifying their operation mode from pure rocket to ramjet and then scramjet depending on the Mach number by slight modification in the geometry. The rocket combustor in the engine is basically the integral part that serves as a booster and the ramjet engine sustains the operation up to the higher speeds. These systems are highly efficient in terms of propulsive performance, compactness and possess high maneuverability. This study focuses on the operating conditions of laboratory (CNR-IENI Milano) based RBCC Engine in with the main focus on the Rocket mode operation. Several tests were performed in the facility in which the setup was run in the rocket only mode in order to study in detail, the rocket motor characteristics under different operating conditions with and without ignition of the fuel. Same operating conditions were maintained during the ignition/non ignition tests in order to better understand the behavior of the rocket motor. Gaseous fuel and oxidizer were fed through the injectors in to the combustion chamber of the nozzle and pressure and temperature measurement were performed during the whole duration of the test which is a fraction of second because of the volume of fuel and oxidizer fed. The pressure and temperature readings were then utilized to numerically evaluate the thrust, combustion efficiency & specific impulse. Mach numbers at the various locations were evaluated using different averaging techniques which leaded to the evaluation of thrust. The thrust data set was then compared with the one measured using the load cell during the tests in order to cross check the validity of the pressure and temperature measurements

Detection of microaneurysms in retinal images using an ensemble classifier

This paper introduces, and reports on the performance of, a novel combination of algorithms for automated microaneurysm (MA) detection in retinal images. The presence of MAs in retinal images is a pathognomonic sign of Diabetic Retinopathy (DR) which is one of the leading causes of blindness amongst the working age population. An extensive survey of the literature is presented and current techniques in the field are summarised. The proposed technique first detects an initial set of candidates using a Gaussian Matched Filter and then classifies this set to reduce the number of false positives. A Tree Ensemble classifier is used with a set of 70 features (the most commons features in the literature). A new set of 32 MA groundtruth images (with a total of 256 labelled MAs) based on images from the MESSIDOR dataset is introduced as a public dataset for benchmarking MA detection algorithms. We evaluate our algorithm on this dataset as well as another public dataset (DIARETDB1 v2.1) and compare it against the best available alternative. Results show that the proposed classifier is superior in terms of eliminating false positive MA detection from the initial set of candidates. The proposed method achieves an ROC score of 0.415 compared to 0.2636 achieved by the best available technique. Furthermore, results show that the classifier model maintains consistent performance across datasets, illustrating the generalisability of the classifier and that overfitting does not occur

Video content analysis for intelligent forensics

The networks of surveillance cameras installed in public places and private territories continuously record video data with the aim of detecting and preventing unlawful activities. This enhances the importance of video content analysis applications, either for real time (i.e. analytic) or post-event (i.e. forensic) analysis. In this thesis, the primary focus is on four key aspects of video content analysis, namely; 1. Moving object detection and recognition, 2. Correction of colours in the video frames and recognition of colours of moving objects, 3. Make and model recognition of vehicles and identification of their type, 4. Detection and recognition of text information in outdoor scenes. To address the first issue, a framework is presented in the first part of the thesis that efficiently detects and recognizes moving objects in videos. The framework targets the problem of object detection in the presence of complex background. The object detection part of the framework relies on background modelling technique and a novel post processing step where the contours of the foreground regions (i.e. moving object) are refined by the classification of edge segments as belonging either to the background or to the foreground region. Further, a novel feature descriptor is devised for the classification of moving objects into humans, vehicles and background. The proposed feature descriptor captures the texture information present in the silhouette of foreground objects. To address the second issue, a framework for the correction and recognition of true colours of objects in videos is presented with novel noise reduction, colour enhancement and colour recognition stages. The colour recognition stage makes use of temporal information to reliably recognize the true colours of moving objects in multiple frames. The proposed framework is specifically designed to perform robustly on videos that have poor quality because of surrounding illumination, camera sensor imperfection and artefacts due to high compression. In the third part of the thesis, a framework for vehicle make and model recognition and type identification is presented. As a part of this work, a novel feature representation technique for distinctive representation of vehicle images has emerged. The feature representation technique uses dense feature description and mid-level feature encoding scheme to capture the texture in the frontal view of the vehicles. The proposed method is insensitive to minor in-plane rotation and skew within the image. The capability of the proposed framework can be enhanced to any number of vehicle classes without re-training. Another important contribution of this work is the publication of a comprehensive up to date dataset of vehicle images to support future research in this domain. The problem of text detection and recognition in images is addressed in the last part of the thesis. A novel technique is proposed that exploits the colour information in the image for the identification of text regions. Apart from detection, the colour information is also used to segment characters from the words. The recognition of identified characters is performed using shape features and supervised learning. Finally, a lexicon based alignment procedure is adopted to finalize the recognition of strings present in word images. Extensive experiments have been conducted on benchmark datasets to analyse the performance of proposed algorithms. The results show that the proposed moving object detection and recognition technique superseded well-know baseline techniques. The proposed framework for the correction and recognition of object colours in video frames achieved all the aforementioned goals. The performance analysis of the vehicle make and model recognition framework on multiple datasets has shown the strength and reliability of the technique when used within various scenarios. Finally, the experimental results for the text detection and recognition framework on benchmark datasets have revealed the potential of the proposed scheme for accurate detection and recognition of text in the wild

PAR4 (Protease-Activated Receptor 4) Antagonism with BMS-986120 Inhibits Human Ex Vivo Thrombus Formation

Objective-BMS-986120 is a novel first-in-class oral PAR4 (protease-Activated receptor 4) antagonist with potent and selective antiplatelet effects. We sought to determine for the first time, the effect of BMS-986120 on human ex vivo thrombus formation. Approach and Results-Forty healthy volunteers completed a phase 1 parallel-group PROBE trial (Prospective Randomized Open-Label Blinded End Point). Ex vivo platelet activation, platelet aggregation, and thrombus formation were measured at 0, 2, and 24 hours after (1) oral BMS-986120 (60 mg) or (2) oral aspirin (600 mg) followed at 18 hours with oral aspirin (600 mg) and oral clopidogrel (600 mg). BMS-986120 demonstrated highly selective and reversible inhibition of PAR4 agonist peptide (100 μM)-stimulated P-selectin expression, platelet-monocyte aggregates, and platelet aggregation (P<0.001 for all). Compared with pretreatment, total thrombus area (μm2/mm) at high shear was reduced by 29.2% (95% confidence interval, 18.3%-38.7%; P<0.001) at 2 hours and by 21.4% (9.3%-32.0%; P=0.002) at 24 hours. Reductions in thrombus formation were driven by a decrease in platelet-rich thrombus deposition: 34.8% (19.3%-47.3%; P<0.001) at 2 hours and 23.3% (5.1%-38.0%; P=0.016) at 24 hours. In contrast to aspirin alone, or in combination with clopidogrel, BMS-986120 had no effect on thrombus formation at low shear (P=nonsignificant). BMS-986120 administration was not associated with an increase in coagulation times or serious adverse events. Conclusions-BMS-986120 is a highly selective and reversible oral PAR4 antagonist that substantially reduces platelet-rich thrombus formation under conditions of high shear stress. Our results suggest PAR4 antagonism has major potential as a therapeutic antiplatelet strategy. Clinical Trial Registration-URL: http://www.clinicaltrials.gov. Unique identifier: NCT02439190