Information cut for clustering using a gradient descent approach

We introduce a new graph cut for clustering which we call the Information Cut. It is derived using Parzen windowing to estimate an information theoretic distance measure between probability density functions. We propose to optimize the Information Cut using a gradient descent-based approach. Our algorithm has several advantages compared to many other graph-based methods in terms of determining an appropriate affinity measure, computational complexity, memory requirements and coping with different data scales. We show that our method may produce clustering and image segmentation results comparable or better than the state-of-the art graph-based methods.     

Simultaneous Spark Plasma Synthesis and Densification of TiC–TiB2 Composites

The simultaneous synthesis and densification of dense TiC–TiB₂ composite has been investigated starting from Ti, B₄C, and C as reactants, and using the spark plasma sintering (SPS) technique. The optimal conditions for complete conversion of the reactants to the composite were determined for different applied DC current levels. A kinetic investigation performed allows us to conclude that solid-state diffusion is the mechanism governing the synthesis process. It is seen that TiC is the first phase formed, while TiB₂ formation occurs afterward. Two intermediate boride phases, i.e. TiB and Ti₃B₄, are also formed but, as the SPS holding time was augmented, they were gradually and completely converted to TiB₂. Moreover, it is found that in order to reach relatively high dense products, an electric current needs to be applied for time intervals longer than those required for obtaining complete conversion. A pure dense product (relative density ∼98%) was obtained when an electric pulsed current of 1100 A and a mechanical pressure of 20 MPa were applied for about 4 min.     

Transporting Deformations of Face Emotions in the Shape Spaces: A Comparison of Different Approaches

Studying the changes of shape is a common concern in many scientific fields. We address here two problems: (1) quantifying the deformation between two given shapes and (2) transporting this deformation to morph a third shape. These operations can be done with or without point correspondence, depending on the availability of a surface matching algorithm, and on the type of mathematical procedure adopted. In computer vision, the re-targeting of emotions mapped on faces is a common application. We contrast here four different methods used for transporting the deformation toward a target once it was estimated upon the matching of two shapes. These methods come from very different fields such as computational anatomy, computer vision and biology. We used the large diffeomorphic deformation metric mapping and thin plate spline, in order to estimate deformations in a deformational trajectory of a human face experiencing different emotions. Then we use naive transport (NT), linear shift (LS), direct transport (DT) and fanning scheme (FS) to transport the estimated deformations toward four alien faces constituted by 240 homologous points and identifying a triangulation structure of 416 triangles. We used both local and global criteria for evaluating the performance of the 4 methods, e.g., the maintenance of the original deformation. We found DT, LS and FS very effective in recovering the original deformation while NT fails under several aspects in transporting the shape change. As the best method may differ depending on the application, we recommend carefully testing different methods in order to choose the best one for any specific application.

     

Algorithms for processing the group K nearest-neighbor query on distributed frameworks

Distributed and Parallel Databases - Given two datasets of points (called Query and Training), the Group (K) Nearest-Neighbor (GKNN) query retrieves (K) points of the Training with the smallest sum...     

Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

Neural Computing and Applications - In this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble...     

Analysis of information exchange activities to actualize and validate situation awareness during shift changeovers in nuclear power plants

Shift work situations occur in almost all safety‐critical organizations, and the investigations of some catastrophes like Chernobyl, Exxon Valdez, and the Gol/Legacy mid‐air collision indicated that shift work information exchange played an important role during the evolution of the situation before the accidents. Inadequate communications during shift changeovers challenged operators' work in the moments that preceded these accidents, because they got inadequate information about the current situation. Our research focuses on the information exchange activities (verbal, written, and nonverbal) of nuclear power plant control operators during shift changeovers. Our aim is to investigate how verbal exchanges and other representations enable operator crews to share information regarding the events that occurred in the previous shift to achieve adequate situation awareness. Our findings indicated the importance and richness of the information exchange during the shift changeover process to update and validate individual and collective situation awareness, showing that information adequately shared enables the ad hoc configurations of regulation loops and a safer use of simplified strategies that can be understood and be validated by other operators, reducing the occurrence of cognitive overloads and contributing to the construction of a common cognitive ground that enhances system resilience. © 2010 Wiley Periodicals, Inc.     

A fuzzy queue-aware routing approach for wireless mesh networks

Recent advances in Wireless Mesh Networks (WMNs) have overcome the drawbacks of traditional wired and ad-hoc networks and now they are seen as a means of allowing last mile communications with quality level assurance in Future Multimedia Systems. However, new routing schemes are needed to provide end-to-end Quality of Service (QoS) and Quality of Experience (QoE) support for delay/loss/jitter-sensitive multimedia applications. The well-known OLSR (Optimized Link State Routing) protocol with ETX (Expected Transmission Count) metric brings many benefits to the path selection process, but has a drawback with regard to queue availability management, which reduces the system performance. This problem is caused when OLSR-EXT control messages are exchanged and the queues of mesh routers along the end-to-end communication path are overloaded. As a result, multimedia-related packets will suffer from loss/delay/jitter and the overall system performance will decrease. This paper proposes the Optimized Link State Routing-Fuzzy ETX Queue (OLSR-FEQ) protocol to overcome the limitations of OLSR-ETX regarding queue availability, QoS and QoE assurance. OLSR-FEQ optimizes network and user-based parameters by coordinating queue availability, QoS and fuzzy issues in the routing decision process as a way of allocating the best paths for multimedia applications. Performance evaluations were carried out with the Network Simulator (NS-2.34) to show the benefits of the proposed solution when compared with existing routing schemes, namely OLSR-ETX, OLSR-FLC, OLSR-MD and HWMP (IEEE 802.11s standard), regarding QoS (unsuccessful packet delivery and throughput) and QoE (PSNR, SSIM, VQM and MOS) parameters.     

Automotive observers based on multibody models and the extended Kalman filter

This work is part of a project aimed to develop automotive real-time observers based on detailed nonlinear multibody models and the extended Kalman filter (EKF). In previous works, a four-bar mechanism was studied to get insight into the problem. Regarding the formulation of the equations of motion, it was concluded that the state-space reduction method known as matrix-R is the most suitable one for this application. Regarding the sensors, it was shown that better stability, accuracy and efficiency are obtained as the sensored magnitude is a lower derivative and when it is a generalized coordinate of the problem. In the present work, the automotive problem has been addressed, through the selection of a Volkswagen Passat as a case-study. A model of the car containing fifteen degrees of freedom has been developed. The observer algorithm that combines the equations of motion and the integrator has been reformulated so that duplication of the problem size is avoided, in order to improve efficiency. A maneuver of acceleration from rest and double lane change has been defined, and tests have been run for the “prototype,” the “model” and the “observer,” all the three computational, with the model having 100 kg more than the prototype. Results have shown that good convergence is obtained for position level sensors, but the computational cost is high, still far from real-time performance.     

PhotoGeo: a photo digital library with spatial-temporal support and self-annotation

The recent popularity of digital cameras has posed a new problem: how to efficiently store and retrieve the very large number of digital photos captured and chaotically stored in multiple locations without any annotation. This paper proposes an infrastructure, called PhotoGeo, which aims at helping users with the people photo annotation, event photo annotation, storage and retrieval of personal digital photos. To achieve the desired objective, PhotoGeo uses new algorithms that make it possible to annotate photos with the key metadata to facilitate their retrieval, such as: the people who were shown in the photo (who); where it was captured (where); the date and time of capture (when); and the event that was captured. The paper concludes with a detailed evaluation of these algorithms.     

An infrastructure for smart hospitals

In healthcare facilities most of the daily activities require strict coordination between clinicians, who often operate under heavy workloads and minimal workforce conditions in environments filled with increasingly complex technology. Ubiquitous Computing applications constitute a suitable solution for both reducing medical costs and improving patient safety by better supporting clinical processes. In this study we introduce an intelligent infrastructure for smart hospitals which implements basic services to optimize medical staff/patient interactions and grants ubiquitous and transparent access to clinical data stored in standard clinical databases. This infrastructure relies on the integration of Radio Frequency IDentification (RFID) and photosensor technologies to identify, locate and track clinicians and patients equipped with mobile devices and wearable RFID tags.