Addressing the facilities layout design problem through constraint logic programming

One of the most difficult problems that face researchers experimenting with complex systems in real world applications is the Facility Layout Design Problem. It relies with the design and location of production lines, machinery and equipment, inventory storage and shipping facilities. In this work it is intended to address this problem through the use of Constraint Logic Programming (CLP) technology. The use of Genetic Algorithms (GA) as optimisation technique in CLP environment is also an issue addressed. The approach aims the implementation of genetic algorithm operators following the CLP paradigm.

Programming atomic multicasts in CAN

In Distributed Computer-Controlled Systems (DCCS), a special emphasis must be given to the communication infrastructure, which must provide timely and reliable communication services. CAN networks are usually suitable to support small-scale DCCS. However, they are known to present some reliability problems, which can lead to an unreliable behaviour of the supported applications. In this paper, an atomic multicast protocol for CAN networks is proposed. This protocol explores the CAN synchronous properties, providing a timely and reliable service to the supported applications. The implementation of such protocol in Ada, on top of the Ada version of Real-Time Linux is presented, which is used to demonstrate the advantages and disadvantages of the platform to support reliable communications in DCCS.

Nano-CF: a Coordination Framework for macro-programming in wireless sensor networks

Wireless Sensor Networks (WSN) are being used for a number of applications involving infrastructure monitoring, building energy monitoring and industrial sensing. The difficulty of programming individual sensor nodes and the associated overhead have encouraged researchers to design macro-programming systems which can help program the network as a whole or as a combination of subnets. Most of the current macro-programming schemes do not support multiple users seamlessly deploying diverse applications on the same shared sensor network. As WSNs are becoming more common, it is important to provide such support, since it enables higher-level optimizations such as code reuse, energy savings, and traffic reduction. In this paper, we propose a macro-programming framework called Nano-CF, which, in addition to supporting in-network programming, allows multiple applications written by different programmers to be executed simultaneously on a sensor networking infrastructure. This framework enables the use of a common sensing infrastructure for a number of applications without the users having to worrying about the applications already deployed on the network. The framework also supports timing constraints and resource reservations using the Nano-RK operating system. Nano- CF is efficient at improving WSN performance by (a) combining multiple user programs, (b) aggregating packets for data delivery, and (c) satisfying timing and energy specifications using Rate- Harmonized Scheduling. Using representative applications, we demonstrate that Nano-CF achieves 90% reduction in Source Lines-of-Code (SLoC) and 50% energy savings from aggregated data delivery.

A parallel programming model for ada

Over the last three decades, computer architects have been able to achieve an increase in performance for single processors by, e.g., increasing clock speed, introducing cache memories and using instruction level parallelism. However, because of power consumption and heat dissipation constraints, this trend is going to cease. In recent times, hardware engineers have instead moved to new chip architectures with multiple processor cores on a single chip. With multi-core processors, applications can complete more total work than with one core alone. To take advantage of multi-core processors, parallel programming models are proposed as promising solutions for more effectively using multi-core processors. This paper discusses some of the existent models and frameworks for parallel programming, leading to outline a draft parallel programming model for Ada.

Inter-application redundancy elimination in sensor networks with compiler-assisted scheduling

Most current-generation Wireless Sensor Network (WSN) nodes are equipped with multiple sensors of various types, and therefore support for multi-tasking and multiple concurrent applications is becoming increasingly common. This trend has been fostering the design of WSNs allowing several concurrent users to deploy applications with dissimilar requirements. In this paper, we extend the advantages of a holistic programming scheme by designing a novel compiler-assisted scheduling approach (called REIS) able to identify and eliminate redundancies across applications. To achieve this useful high-level optimization, we model each user application as a linear sequence of executable instructions. We show how well-known string-matching algorithms such as the Longest Common Subsequence (LCS) and the Shortest Common Super-sequence (SCS) can be used to produce an optimal merged monolithic sequence of the deployed applications that takes into account embedded scheduling information. We show that our approach can help in achieving about 60% average energy savings in processor usage compared to the normal execution of concurrent applications.

A framework for programming sensor networks with scheduling and resource-sharing optimizations

Several projects in the recent past have aimed at promoting Wireless Sensor Networks as an infrastructure technology, where several independent users can submit applications that execute concurrently across the network. Concurrent multiple applications cause significant energy-usage overhead on sensor nodes, that cannot be eliminated by traditional schemes optimized for single-application scenarios. In this paper, we outline two main optimization techniques for reducing power consumption across applications. First, we describe a compiler based approach that identifies redundant sensing requests across applications and eliminates those. Second, we cluster the radio transmissions together by concatenating packets from independent applications based on Rate-Harmonized Scheduling.

sMapReduce: a programming pattern for wireless sensor networks

Wireless Sensor Networks (WSNs) are increasingly used in various application domains like home-automation, agriculture, industries and infrastructure monitoring. As applications tend to leverage larger geographical deployments of sensor networks, the availability of an intuitive and user friendly programming abstraction becomes a crucial factor in enabling faster and more efficient development, and reprogramming of applications. We propose a programming pattern named sMapReduce, inspired by the Google MapReduce framework, for mapping application behaviors on to a sensor network and enabling complex data aggregation. The proposed pattern requires a user to create a network-level application in two functions: sMap and Reduce, in order to abstract away from the low-level details without sacrificing the control to develop complex logic. Such a two-fold division of programming logic is a natural-fit to typical sensor networking operation which makes sensing and topological modalities accessible to the user.

Direct search optimization application programming interface with remote access

Search Optimization methods are needed to solve optimization problems where the objective function and/or constraints functions might be non differentiable, non convex or might not be possible to determine its analytical expressions either due to its complexity or its cost (monetary, computational, time,...). Many optimization problems in engineering and other fields have these characteristics, because functions values can result from experimental or simulation processes, can be modelled by functions with complex expressions or by noise functions and it is impossible or very difficult to calculate their derivatives. Direct Search Optimization methods only use function values and do not need any derivatives or approximations of them. In this work we present a Java API that including several methods and algorithms, that do not use derivatives, to solve constrained and unconstrained optimization problems. Traditional API access, by installing it on the developer and/or user computer, and remote API access to it, using Web Services, are also presented. Remote access to the API has the advantage of always allow the access to the latest version of the API. For users that simply want to have a tool to solve Nonlinear Optimization Problems and do not want to integrate these methods in applications, also two applications were developed. One is a standalone Java application and the other a Web-based application, both using the developed API.

Web-based application programming interface to solve nonlinear optimization problems

Nonlinear Optimization Problems are usual in many engineering fields. Due to its characteristics the objective function of some problems might not be differentiable or its derivatives have complex expressions. There are even cases where an analytical expression of the objective function might not be possible to determine either due to its complexity or its cost (monetary, computational, time, ...). In these cases Nonlinear Optimization methods must be used. An API, including several methods and algorithms to solve constrained and unconstrained optimization problems was implemented. This API can be accessed not only as traditionally, by installing it on the developer and/or user computer, but it can also be accessed remotely using Web Services. As long as there is a network connection to the server where the API is installed, applications always access to the latest API version. Also an Web-based application, using the proposed API, was developed. This application is to be used by users that do not want to integrate methods in applications, and simply want to have a tool to solve Nonlinear Optimization Problems.

An integer programming framework for sequencing cutting patterns based on interval graph completion

We derived a framework in integer programming, based on the properties of a linear ordering of the vertices in interval graphs, that acts as an edge completion model for obtaining interval graphs. This model can be applied to problems of sequencing cutting patterns, namely the minimization of open stacks problem (MOSP). By making small modifications in the objective function and using only some of the inequalities, the MOSP model is applied to another pattern sequencing problem that aims to minimize, not only the number of stacks, but also the order spread (the minimization of the stack occupation problem), and the model is tested.

An integer programming model for the minimum interval graph completion problem

The minimum interval graph completion problem consists of, given a graph G = ( V, E ), finding a supergraph H = ( V, E ∪ F ) that is an interval graph, while adding the least number of edges |F| . We present an integer programming formulation for solving the minimum interval graph completion problem recurring to a characteri- zation of interval graphs that produces a linear ordering of the maximal cliques of the solution graph.

Novel biosensing device for point-of-care applications with plastic antibodies grown on Au-Screen Printed Electrodes

A gold screen printed electrode (Au-SPE) was modified by merging Molecular Imprinting and Self-Assembly Monolayer techniques for fast screening cardiac biomarkers in point-of-care (POC). For this purpose, Myoglobin (Myo) was selected as target analyte and its plastic antibody imprinted over a glutaraldehyde (Glu)/cysteamine (Cys) layer on the gold-surface. The imprinting effect was produced by growing a reticulated polymer of acrylamide (AAM) and N,N′-methylenebisacrylamide (NNMBA) around the Myo template, covalently attached to the biosensing surface. Electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) studies were carried out in all chemical modification steps to confirm the surface changes in the Au-SPE. The analytical features of the resulting biosensor were studied by different electrochemical techniques, including EIS, square wave voltammetry (SWV) and potentiometry. The limits of detection ranged from 0.13 to 8 μg/mL. Only potentiometry assays showed limits of detection including the cut-off Myo levels. Quantitative information was also produced for Myo concentrations ≥0.2 μg/mL. The linear response of the biosensing device showed an anionic slope of ~70 mV per decade molar concentration up to 0.3 μg/mL. The interference of coexisting species was tested and good selectivity was observed. The biosensor was successfully applied to biological fluids.

A system model and stack for the parallelization of time-critical applications on many-core architectures

3rd Workshop on High-performance and Real-time Embedded Systems (HIRES 2015). 21, Jan, 2015. Amsterdam, Netherlands.

A Modular Programming Approach for IoTBased Wireless Sensor Networks

Poster presented in 12th European Conference on Wireless Sensor Network (EWSN 2015). 9 to 11, Feb, 2015. Porto, Portugal.

SHõWA: A Self-healing Framework for Web-based Applications

The complexity of systems is considered an obstacle to the progress of the IT industry. Autonomic computing is presented as the alternative to cope with the growing complexity. It is a holistic approach, in which the systems are able to configure, heal, optimize, and protect by themselves. Web-based applications are an example of systems where the complexity is high. The number of components, their interoperability, and workload variations are factors that may lead to performance failures or unavailability scenarios. The occurrence of these scenarios affects the revenue and reputation of businesses that rely on these types of applications. In this article, we present a self-healing framework for Web-based applications (SHõWA). SHõWA is composed by several modules, which monitor the application, analyze the data to detect and pinpoint anomalies, and execute recovery actions autonomously. The monitoring is done by a small aspect-oriented programming agent. This agent does not require changes to the application source code and includes adaptive and selective algorithms to regulate the level of monitoring. The anomalies are detected and pinpointed by means of statistical correlation. The data analysis detects changes in the server response time and analyzes if those changes are correlated with the workload or are due to a performance anomaly. In the presence of per- formance anomalies, the data analysis pinpoints the anomaly. Upon the pinpointing of anomalies, SHõWA executes a recovery procedure. We also present a study about the detection and localization of anomalies, the accuracy of the data analysis, and the performance impact induced by SHõWA. Two benchmarking applications, exercised through dynamic workloads, and different types of anomaly were considered in the study. The results reveal that (1) the capacity of SHõWA to detect and pinpoint anomalies while the number of end users affected is low; (2) SHõWA was able to detect anomalies without raising any false alarm; and (3) SHõWA does not induce a significant performance overhead (throughput was affected in less than 1%, and the response time delay was no more than 2 milliseconds).