Global and robust formation-shape stabilization of relative sensing networks

This paper proposes a simple, distributed algorithm that achieves global stabilization of formations for relative sensing networks in arbitrary dimensions with fixed topology. Assuming the network runs an initialization procedure to equally orient all agent reference frames, convergence to the desired formation shape is guaranteed even in partially asynchronous settings. We characterize the algorithm robustness against several sources of errors: link failures, measurement errors, and frame initialization errors. The technical approach combines algebraic graph theory, multidimensional scaling, and distributed linear iterations.     

An 802.11e HCCA scheduler with an end-to-end quality aware territory method

In this paper we present a solution for the IEEE 802.11e HCCA (Hybrid coordination function Controlled Channel Access) mechanism which aims both at supporting strict real-time traffic requirements and, simultaneously, at handling TCP applications efficiently. Our proposal combines a packet scheduler and a dynamic resource allocation algorithm. The scheduling discipline is based on the Monolithic Shaper-Scheduler, which is a modification of a General Processor Sharing (GPS) related scheduler. It supports minimum-bandwidth and delay guarantees and, at the same time, it achieves the optimal latency of all the GPS-related schedulers. In addition, our innovative resource allocation procedure, called the territory method, aims at prioritizing real time services and at improving the performance of TCP applications. For this purpose, it splits the wireless channel capacity (in terms of transmission opportunities) into different territories for the different types of traffic, taking into account the end-to-end network dynamics. In order to give support to the desired applications, we consider the following traffic classes: conversational, streaming, interactive and best-effort. The so called territories shrink or expand depending on the current quality experienced by the corresponding traffic class. We evaluated the performance of our solution through extensive simulations in a heterogeneous wired-cum-wireless scenario under different traffic conditions. Additionally, we compare our proposal to other HCCA scheduling algorithms, the HCCA reference scheduler and Fair Hybrid Coordination Function (FHCF). The results show that the combination of the MSS and the territory method obtains higher system capacity for VoIP traffic (up to 32 users) in the simulated scenario, compared to FHCF and the HCCA reference scheduler (13 users). In addition, the MSS with the territory method also improves the throughput of TCP sources (one FTP application achieves between 6.1 Mbps without VoIP traffic and 2.1 Mbps with 20 VoIP users) compared to the reference scheduler (at most 388 kbps) and FHCF (with a maximum FTP throughput of 4.8 Mbps).     

Multivariable control strategy based on bifurcation analysis of an industrial gas-phase polymerization reactor

In an industrial gas-phase polyethylene reactor, the safe operating range of temperature is rather narrow. Even within this temperature range, temperature excursions must be avoided because they can result in low catalyst productivity and significant changes in product properties. If the manipulated variable for temperature control saturates (i.e., the cooling water valve position is completely open), then the reactor operates without a feedback temperature controller, leading to oscillatory behavior and limit cycles. In this work, it has been demonstrated that the saturation in the manipulated variable and the complex non-linear dynamic behavior are removed when auxiliary manipulated variables, obtained by bifurcation analysis, are used in a multivariable control strategy for the reactor temperature control. Two control structures are proposed and compared considering their impact in the reactor production and polymer melt index. In the first control structure, the designed PID controller for the reactor temperature is considered and a switching strategy with a PI controller for the auxiliary manipulated variables is included. In the second control structure, the designed PID controller for the reactor temperature is also used, however, a MPC controller for the auxiliary manipulated variables is considered. The results suggest that the use of gain-scheduling strategy in the PID temperature controller with a MPC controller for the auxiliary manipulated variables avoids the saturation of the manipulated variable and, hence, the undesired non-linear dynamic behavior, reducing the production loss and improving the product quality.     

Using new scheduling heuristics based on resource consumption information for increasing throughput on rule‐based spam filtering systems

The large increase of spam deliveries since the first half of 2013 entailed hard to solve troubles in spam filters. In order to adequately fight spam, the throughput of spam filtering platforms should be necessarily increased. In this context, and taking into consideration the widespread utilization of rule‐based filtering frameworks in the spam filtering domain, this work proposes three novel scheduling strategies for optimizing the time needed to classify new incoming e‐mails through an intelligent management of computational resources depending on the Central Processing Unit (CPU) usage and Input/Output (I/O) delays. In order to demonstrate the suitability of our approaches, we include in our experiments a comparative study in contrast to other successful heuristics previously published in the scientific literature. Results achieved demonstrated that one of our alternative heuristics allows time savings of up to 10% in message filtering, while keeping the same classification accuracy. Copyright © 2015 John Wiley & Sons, Ltd.     

Relational Learning with GPUs: Accelerating Rule Coverage

Relational learning algorithms mine complex databases for interesting patterns. Usually, the search space of patterns grows very quickly with the increase in data size, making it impractical to solve important problems. In this work we present the design of a relational learning system, that takes advantage of graphics processing units (GPUs) to perform the most time consuming function of the learner, rule coverage. To evaluate performance, we use four applications: a widely used relational learning benchmark for predicting carcinogenesis in rodents, an application in chemo-informatics, an application in opinion mining, and an application in mining health record data. We compare results using a single and multiple CPUs in a multicore host and using the GPU version. Results show that the GPU version of the learner is up to eight times faster than the best CPU version.     

Self-adaptation and secure information flow in multiparty communications

We present a comprehensive model of structured communications in which self-adaptation and security concerns are jointly addressed. More specifically, we propose a model of multiparty, self-adaptive communications with access control and secure information flow guarantees. In our model, multiparty protocols (choreographies) are described as global types; security violations occur when process implementations of protocol participants attempt to read or write messages of inappropriate security levels within directed exchanges. Such violations trigger adaptation mechanisms that prevent the violations to occur and/or to propagate their effect in the choreography. Our model is equipped with local and global adaptation mechanisms for reacting to security violations of different gravity; type soundness results ensure that the overall multiparty protocol is still correctly executed while the system adapts itself to preserve the participants’ security.     

A PC-based architecture for parameter analysis of vector-controlled induction motor drive

In this article a methodology for constructing a user interface for system control and data acquisition of a drive which is suitable for three-phase induction motors (3?IM) is presented. The entire hardware implementation is shown, including power and digital stages. Communication between the computer and the controller is engaged in order to enhance an analysis of power quality, adjust the controller parameters for tuning the flux and speed loops, and performance of the embedded algorithm. USB and Ethernet protocols have been put into operation in the user front-end because a high speed sample frequency is required in order to guarantee the real-time operation of the whole system. A software interface is developed using LabVIEW environment enabling features not only as filtering signals of phase voltage and current but also power spectrum measurements. Vector-controlled drive is programmed on a digital signal processor (DSP) in order to ensure efficient use of energy in the power stage and proper tracking of the reference at low and high speeds.     

Non-photorealistic, depth-based image editing

Recent works in image editing are opening up new possibilities to manipulate and enhance input images. Within this context, we leverage well-known characteristics of human perception along with a simple depth approximation algorithm to generate non-photorealistic renditions that would be difficult to achieve with existing methods. Once a perceptually plausible depth map is obtained from the input image, we show how simple algorithms yield powerful new depictions of such an image. Additionally, we show how artistic manipulation of depth maps can be used to create novel non-photorealistic versions, for which we provide the user with an intuitive interface. Our real-time implementation on graphics hardware allows the user to efficiently explore artistic possibilities for each image. We show results produced with six different styles proving the versatility of our approach, and validate our assumptions and simplifications by means of a user study.