Pedro Rocha

IoT interoperability for actuating applications through standardised M2M communications

Smartphones, with vast connectivity and sensing capabilities, are the natural choice to serve as gateways for physiologic sensors and body area networks. Machine-to-Machine (M2M) middleware standards are driving the emergence of Internet of Things (IoT) applications by providing autonomic interoperability. However, standards remain opaque and difficult to interpret, throttling implementation. In this work, we report the design and development of an ETSI M2M Gateway (GW) on a mobile device, instantiated in a smartphone. We describe our reasoning in interpreting the standard for the varied implementation challenges faced. Further, we develop libraries to ease the deployment of IoT applications using the ETSI M2M ecosystem with reduced development costs. Finally, we validated the implementation with a mobile e-health pilot with 10 participants during 3 weeks.

Circle Covering Representation for Nesting problems with continuous rotations

Abstract This paper analyses distinct methods to represent a polygon through circle covering, which satisfy specific requirements, that impact primarily the feasibility and the quality of the layout of final solution. The trade-off between the quality of the polygonal representation and its derived number of circles is also discussed, showing the impact on the resolution of the problem, in terms of computational efficiency. The approach used to tackle the Nesting problem in strip packing uses a Non-Linear Programming model. Addressing these problems allows to tackle real world problems with continuous rotations.

Circle Covering Using Medial Axis

A good representation of a simple polygon, with a desired degree of approximation and complexity, is critical in many applications. This paper presents a method to achieve a complete Circle Covering Representation of a simple polygon, through a topological skeleton, the Medial Axis. The aim is to produce an efficient circle representation of irregular pieces, while considering the approximation error and the resulting complexity, i.e. the number of circles. This will help to address limitations of current approaches to some problems, such as Irregular Placement problems, which will, in turn, provide a positive economic and environmental impact where similar problems arise.

Constraint Aggregation in Non-linear Programming Models for Nesting Problems

The Nesting problem is a complex problem that arises in industries where sets of pieces or space must be efficiently placed or allocated in order to minimize wasted space or wasted raw materials, without overlaps between pieces and fully contained inside a container. This paper analyses the impact that aggregating constraints can achieve in the reduction of computational cost of a Non-Linear Programming model for Nesting problems with continuous rotations. This is achieved by aggregating non-overlapping constraints and using spatial partition and hierarchical overlap detection methods. When aggregating constraints there is also an effect of reducing the sensitivity of the solver, which may reduce the quality of the final layout. Analyzing the trade-off between constraints aggregation and the impact on the quality of the final solution is an important issue to handle nesting instances with a large number of pieces. Computational experiments show that aggregating non-overlapping constraints allows the Non-Linear Programming model for Nesting problems to scale well to tackle large size real world problems with continuous rotations.

GPU-Based Computing for Nesting Problems: The Importance of Sequences in Static Selection Approaches

In this paper, we address the irregular strip packing problem (or nesting problem) where irregular shapes have to be placed on strips representing a piece of material whose width is constant and length is virtually unlimited. We explore a constructive heuristic that relies on the use of graphical processing units to accelerate the computation of different geometrical operations. The heuristic relies on static selection processes, which assume that a sequence of pieces to be placed is defined a priori. Here, the emphasis is put on the analysis of the impact of these sequences on the global performance of the solution algorithm. Computational results on benchmark datasets are provided to support this analysis, and guide the selection of the most promising methods to generate these sequences.

Sensor placement for real-time dynamic state estimation in power systems: Structural systems approach

This paper studies the problem of sensor placement design for efficient dynamic real-time state estimation in electric power networks. Given a (linearized) dynamic physical model of the power system, efficient sensor placement strategies are proposed that minimize the observability index of the system. The observability index plays a key role in determining the minimum window length of filters that guarantee stable estimation error and minimizing this index allows the design of memory and computationally efficient filtering schemes with performance guarantees. Specifically, given the system dynamics, the paper addresses the following two sensor placement design problems: (1) determining the minimal number and placement of sensors that achieves a certain desired system observability index, and (2) given the number of sensors to be deployed, obtaining the placement achieving minimal system observability index. These problems are addressed in a structural systems framework, i.e., the placement strategies are obtained on the basis of the sparsity pattern (location of zeroes/non-zeroes) of the system coupling matrix, and the design guarantees hold for almost all numerical parametric realizations of the system. Finally, an example is provided which illustrates the analytical findings.