An information architecture for a performance management framework by collaborating SMEs

Recently, much attention has been given to performance management within collaborative environments resulting in a wide variety of performance measurement/management frameworks. However, practically all of these frameworks present a common gap regarding the information treatment needs that support these frameworks. This issue is even more important in the case of Small and Medium-sized Enterprises (SMEs) due to their special characteristics. For that purpose, this paper presents an approach called Global Performance Management (GPM) Framework composed of a methodology, an information architecture and a technological solution, focusing mainly on the information architecture. The methodology guides SMEs when defining, developing, deploying and operating the GPM framework. The information architecture consists of a set of elements that support the tasks of collection, treatment and analysis of the used information, which complement the methodology to build a real model for the GPM framework. Finally, the technological solution bridges the engineering and conceptual environment, with the real-world operational and executable environment, following the path defined by the methodology. The information architecture was applied to two European pilots, achieving satisfactory results, which are highlighted in the paper as well as the main barriers encountered and solutions provided.     

Learning a generic 3D face model from 2D image databases using incremental Structure-from-Motion

Over the last decade 3D face models have been extensively used in many applications such as face recognition, facial animation and facial expression analysis. 3D Morphable Models (MMs) have become a popular tool to build and fit 3D face models to images. Critical to the success of MMs is the ability to build a generic 3D face model. Major limitations in the MMs building process are: (1) collecting 3D data usually involves the use of expensive laser scans and complex capture setups, (2) the number of available 3D databases is limited, and typically there is a lack of expression variability and (3) finding correspondences and registering the 3D model is a labor intensive and error prone process.     

New techniques for simulating high performance MPI applications on large storage networks

In this work, we propose new techniques to analyze the behavior, the performance, and specially the scalability of High Performance Computing (in short, HPC) applications on different computing architectures. Our final objective is to test applications using a wide range of architectures (real or merely designed) and scaling it to any number of nodes or components. This paper presents a new simulation framework, called SIMCAN, for HPC architectures. The main characteristic of the proposed simulation framework is the ability to be configured for simulating a wide range of possible architectures that involve any number of components. SIMCAN is developed to simulate complete HPC architectures, but putting special emphasis on the storage and network subsystems. The SIMCAN framework can handle complete components (nodes, racks, switches, routers, etc.), but also key elements of the storage and network subsystems (disks, caches, sockets, file systems, schedulers, etc.). We also propose several methods to implement the behavior of HPC applications. Each method has its own advantages and drawbacks. In order to evaluate the possibilities and the accuracy of the SIMCAN framework, we have tested it by executing a HPC application called BIPS3D on a hardware-based computing cluster and on a modeled environment that represent the real cluster. We also checked the scalability of the application using this kind of architecture by simulating the same application with an increased number of computing nodes.     

Learning Photometric Invariance for Object Detection

Color is a powerful visual cue in many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions that negatively affect the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, this approach may be too restricted to model real-world scenes in which different reflectance mechanisms can hold simultaneously.     

Detecting skin in face recognition systems: A colour spaces study

Skin colour detection is a technique very used in most of face detectors to find faces in images or videos. However, there is not a common opinion about which colour space is the best choice to do this task. Therefore, the motivation for our study is to discover which colour model is the best option to build an efficient face detector which can be embedded in a functional face recognition system. We have studied 10 of the most common and used colour spaces doing different comparisons among them, in order to know which one is the best option for human skin colour detection. In concrete, we have studied the models: RGB, CMY, YUV, YIQ, YPbPr, YCbCr, YCgCr, YDbDr, HSV—or HSI—and CIE-XYZ. To make the comparison among them, we have used 15 truth images where the skin colour of a face is clearly separated from the rest of the image (background, eyes, lips, hair, etc.). Thus we can compare at level pixel each colour model, doing a detailed study of each format. We present the final conclusions comparing different results, such as: right detections, false positives and false negatives for each colour space. According to the obtained results, the most appropriate colour spaces for skin colour detection are HSV model (the winner in our study), and the models YCgCr and YDbDr.     

Simultaneous algorithm for trajectory planning

This paper addresses the solution of smooth trajectory planning for industrial robots in environments with obstacles using a direct method, creating the trajectory gradually as the robot moves. The presented method deals with the uncertainties associated with the lack of knowledge of kinematic properties of intermediate via‐points since they are generated as the algorithm evolves looking for the solution. Several cost functions are also proposed, which use the time that has been calculated to guide the robot motion. The method has been applied successfully to a PUMA 560 robot and four operational parameters (execution time, computational time, distance travelled and number of configurations) have been computed to study the properties and influence of each cost function on the trajectory obtained. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Optimization–Simulation Model for Planning Supply Transport to Large Infrastructure Public Works Located in Congested Urban Areas

This article proposes an optimization–simulation model for planning the transport of supplies to large public infrastructure works located in congested urban areas. The purpose is to minimize their impact on the environment and on private transportation users on the local road network. To achieve this goal, the authors propose and solve an optimization problem for minimizing the total system cost made up of operating costs for various alternatives for taking supplies to the worksite and the costs supported by private vehicle users as a result of increased congestion due to the movement of heavy goods vehicles transporting material to the worksite. The proposed optimization problem is a bi-level Math Program model. The upper level defines the total cost of the system, which is minimized taking into account environmental constraints on atmospheric and noise pollution. The lower level defines the optimization problem representing the private transportation user behavior, assuming they choose the route that minimizes their total individual journey costs. Given the special characteristics of the problem, a heuristic algorithm is proposed for finding optimum solutions. Both the model developed and the specific solution algorithm are applied to the real case of building a new port at Laredo (Northern Spain). A series of interesting conclusions are obtained from the corresponding sensitivity analysis.     

Liquid recirculation in microfluidic channels by the interplay of capillary and centrifugal forces

We demonstrate a technique to recirculate liquids in a microfluidic channel by alternating predominance of centrifugal and capillary forces to rapidly bring the entire volume of a liquid sample to within one diffusion length, δ, of the surface, even for sample volumes hundreds of times the product of δ and the geometric device area. This is accomplished by repetitive, random sampling of an on-disc sample reservoir to form a thin fluid layer of thickness δ in a microchannel, maintaining contact for the diffusion time, then rapidly exchanging the fluid layer for a fresh aliquot by disc rotation and stoppage. With this technique, liquid volumes of microlitres to millilitres can be handled in many sizes of microfluidic channels, provided the channel wall with greatest surface area is hydrophilic. We present a theoretical model describing the balance of centrifugal and capillary forces in the device and validate the model experimentally.     

Real-time massive convolution for audio applications on GPU

Massive convolution is the basic operation in multichannel acoustic signal processing. This field has experienced a major development in recent years. One reason for this has been the increase in the number of sound sources used in playback applications available to users. Another reason is the growing need to incorporate new effects and to improve the hearing experience. Massive convolution requires high computing capacity. GPUs offer the possibility of parallelizing these operations. This allows us to obtain the processing result in much shorter time and to free up CPU resources. One important aspect lies in the possibility of overlapping the transfer of data from CPU to GPU and vice versa with the computation, in order to carry out real-time applications. Thus, a synthesis of 3D sound scenes could be achieved with only a peer-to-peer music streaming environment using a simple GPU in your computer, while the CPU in the computer is being used for other tasks. Nowadays, these effects are obtained in theaters or funfairs at a very high cost, requiring a large quantity of resources. Thus, our work focuses on two mains points: to describe an efficient massive convolution implementation and to incorporate this task to real-time multichannel-sound applications.     

A parameterized shared-memory scheme for parameterized metaheuristics

This paper presents a parameterized shared-memory scheme for parameterized metaheuristics. The use of a parameterized metaheuristic facilitates experimentation with different metaheuristics and hybridation/combinations to adapt them to the particular problem we are working with. Due to the large number of experiments necessary for the metaheuristic selection and tuning, parallelism should be used to reduce the execution time. To obtain parallel versions of the metaheuristics and to adapt them to the characteristics of the parallel system, a unified parameterized shared-memory scheme is developed. Given a particular computational system and fixed parameters for the sequential metaheuristic, the appropriate selection of parameters in the unified parallel scheme eases the development of parallel efficient metaheuristics.