Vehicles Recognition Using Fuzzy Descriptors of Image Segments

In this paper a vision-based vehicles recognition method is presented. Proposed method uses fuzzy description of image segments for automatic recognition of vehicles recorded in image data. The description takes into account selected geometrical properties and shape coefficients determined for segments of reference image (vehicle model). The proposed method was implemented using reasoning system with fuzzy rules. A vehicles recognition algorithm was developed based on the fuzzy rules describing shape and arrangement of the image segments that correspond to visible parts of a vehicle. An extension of the algorithm with set of fuzzy rules defined for different reference images (and various vehicle shapes) enables vehicles classification in traffic scenes. The devised method is suitable for application in video sensors for road traffic control and surveillance systems.Comment: The final publication is available at http://www.springerlink.co

F-transforms for the definition of contextual fuzzy partitions

Fuzzy partitions can be defined in many different ways, but usually, it is done taking into account the whole universe. In this paper, we present a method to define fuzzy partitions according to those elements in the universe holding certain fuzzy attribute. Specifically, we show how to define those fuzzy partitions by means of F-transforms.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech This work has been partially supported by the Spanish Ministry of Science by the projects TIN15-70266-C2-P-1 and TIN2016-76653-

Illuminating spindle convex bodies and minimizing the volume of spherical sets of constant width

A subset of the d-dimensional Euclidean space having nonempty interior is called a spindle convex body if it is the intersection of (finitely or infinitely many) congruent d-dimensional closed balls. The spindle convex body is called a "fat" one, if it contains the centers of its generating balls. The core part of this paper is an extension of Schramm's theorem and its proof on illuminating convex bodies of constant width to the family of "fat" spindle convex bodies.Comment: 17 page

Belief Hierarchical Clustering

In the data mining field many clustering methods have been proposed, yet standard versions do not take into account uncertain databases. This paper deals with a new approach to cluster uncertain data by using a hierarchical clustering defined within the belief function framework. The main objective of the belief hierarchical clustering is to allow an object to belong to one or several clusters. To each belonging, a degree of belief is associated, and clusters are combined based on the pignistic properties. Experiments with real uncertain data show that our proposed method can be considered as a propitious tool

Opaque Service Virtualisation: A Practical Tool for Emulating Endpoint Systems

Large enterprise software systems make many complex interactions with other services in their environment. Developing and testing for production-like conditions is therefore a very challenging task. Current approaches include emulation of dependent services using either explicit modelling or record-and-replay approaches. Models require deep knowledge of the target services while record-and-replay is limited in accuracy. Both face developmental and scaling issues. We present a new technique that improves the accuracy of record-and-replay approaches, without requiring prior knowledge of the service protocols. The approach uses Multiple Sequence Alignment to derive message prototypes from recorded system interactions and a scheme to match incoming request messages against prototypes to generate response messages. We use a modified Needleman-Wunsch algorithm for distance calculation during message matching. Our approach has shown greater than 99% accuracy for four evaluated enterprise system messaging protocols. The approach has been successfully integrated into the CA Service Virtualization commercial product to complement its existing techniques.Comment: In Proceedings of the 38th International Conference on Software Engineering Companion (pp. 202-211). arXiv admin note: text overlap with arXiv:1510.0142

Jagged1 intracellular domain-mediated inhibition of Notch1 signalling regulates cardiac homeostasis in the postnatal heart.

AIMS: Notch1 signalling in the heart is mainly activated via expression of Jagged1 on the surface of cardiomyocytes. Notch controls cardiomyocyte proliferation and differentiation in the developing heart and regulates cardiac remodelling in the stressed adult heart. Besides canonical Notch receptor activation in signal-receiving cells, Notch ligands can also activate Notch receptor-independent responses in signal-sending cells via release of their intracellular domain. We evaluated therefore the importance of Jagged1 (J1) intracellular domain (ICD)-mediated pathways in the postnatal heart. METHODS AND RESULTS: In cardiomyocytes, Jagged1 releases J1ICD, which then translocates into the nucleus and down-regulates Notch transcriptional activity. To study the importance of J1ICD in cardiac homeostasis, we generated transgenic mice expressing a tamoxifen-inducible form of J1ICD, specifically in cardiomyocytes. Using this model, we demonstrate that J1ICD-mediated Notch inhibition diminishes proliferation in the neonatal cardiomyocyte population and promotes maturation. In the neonatal heart, a response via Wnt and Akt pathway activation is elicited as an attempt to compensate for the deficit in cardiomyocyte number resulting from J1ICD activation. In the stressed adult heart, J1ICD activation results in a dramatic reduction of the number of Notch signalling cardiomyocytes, blunts the hypertrophic response, and reduces the number of apoptotic cardiomyocytes. Consistently, this occurs concomitantly with a significant down-regulation of the phosphorylation of the Akt effectors ribosomal S6 protein (S6) and eukaryotic initiation factor 4E binding protein1 (4EBP1) controlling protein synthesis. CONCLUSIONS: Altogether, these data demonstrate the importance of J1ICD in the modulation of physiological and pathological hypertrophy, and reveal the existence of a novel pathway regulating cardiac homeostasis

Tuning the Level of Concurrency in Software Transactional Memory: An Overview of Recent Analytical, Machine Learning and Mixed Approaches

Synchronization transparency offered by Software Transactional Memory (STM) must not come at the expense of run-time efficiency, thus demanding from the STM-designer the inclusion of mechanisms properly oriented to performance and other quality indexes. Particularly, one core issue to cope with in STM is related to exploiting parallelism while also avoiding thrashing phenomena due to excessive transaction rollbacks, caused by excessively high levels of contention on logical resources, namely concurrently accessed data portions. A means to address run-time efficiency consists in dynamically determining the best-suited level of concurrency (number of threads) to be employed for running the application (or specific application phases) on top of the STM layer. For too low levels of concurrency, parallelism can be hampered. Conversely, over-dimensioning the concurrency level may give rise to the aforementioned thrashing phenomena caused by excessive data contention—an aspect which has reflections also on the side of reduced energy-efficiency. In this chapter we overview a set of recent techniques aimed at building “application-specific” performance models that can be exploited to dynamically tune the level of concurrency to the best-suited value. Although they share some base concepts while modeling the system performance vs the degree of concurrency, these techniques rely on disparate methods, such as machine learning or analytic methods (or combinations of the two), and achieve different tradeoffs in terms of the relation between the precision of the performance model and the latency for model instantiation. Implications of the different tradeoffs in real-life scenarios are also discussed

Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera

Thermal errors are often quoted as being the largest contributor to CNC machine tool errors, but they can be effectively reduced using error compensation. The performance of a thermal error compensation system depends on the accuracy and robustness of the thermal error model and the quality of the inputs to the model. The location of temperature measurement must provide a representative measurement of the change in temperature that will affect the machine structure. The number of sensors and their locations are not always intuitive and the time required to identify the optimal locations is often prohibitive, resulting in compromise and poor results. In this paper, a new intelligent compensation system for reducing thermal errors of machine tools using data obtained from a thermal imaging camera is introduced. Different groups of key temperature points were identified from thermal images using a novel schema based on a Grey model GM (0, N) and Fuzzy c-means (FCM) clustering method. An Adaptive Neuro-Fuzzy Inference System with Fuzzy c-means clustering (FCM-ANFIS) was employed to design the thermal prediction model. In order to optimise the approach, a parametric study was carried out by changing the number of inputs and number of membership functions to the FCM-ANFIS model, and comparing the relative robustness of the designs. According to the results, the FCM-ANFIS model with four inputs and six membership functions achieves the best performance in terms of the accuracy of its predictive ability. The residual value of the model is smaller than ± 2 μm, which represents a 95% reduction in the thermally-induced error on the machine. Finally, the proposed method is shown to compare favourably against an Artificial Neural Network (ANN) model