A hierarchical approach to multi-project planning under uncertainty

We survey several viewpoints on the management of the planning complexity of multi-project organisations under uncertainty. A positioning framework is proposed to distinguish between different types of project-driven organisations, which is meant to aid project management in the choice between the various existing planning approaches. We discuss the current state of the art of hierarchical planning approaches both for traditional manufacturing and for project environments. We introduce a generic hierarchical project planning and control framework that serves to position planning methods for multi-project planning under uncertainty. We discuss multiple techniques for dealing with the uncertainty inherent to the different hierarchical stages in a multi-project organisation. In the last part of this paper we discuss two cases from practice and we relate these practical cases to the positioning framework that is put forward in the paper

Convolutional Neural Networks Via Node-Varying Graph Filters

Convolutional neural networks (CNNs) are being applied to an increasing number of problems and fields due to their superior performance in classification and regression tasks. Since two of the key operations that CNNs implement are convolution and pooling, this type of networks is implicitly designed to act on data described by regular structures such as images. Motivated by the recent interest in processing signals defined in irregular domains, we advocate a CNN architecture that operates on signals supported on graphs. The proposed design replaces the classical convolution not with a node-invariant graph filter (GF), which is the natural generalization of convolution to graph domains, but with a node-varying GF. This filter extracts different local features without increasing the output dimension of each layer and, as a result, bypasses the need for a pooling stage while involving only local operations. A second contribution is to replace the node-varying GF with a hybrid node-varying GF, which is a new type of GF introduced in this paper. While the alternative architecture can still be run locally without requiring a pooling stage, the number of trainable parameters is smaller and can be rendered independent of the data dimension. Tests are run on a synthetic source localization problem and on the 20NEWS dataset.Comment: Submitted to DSW 2018 (IEEE Data Science Workshop

A hierarchical approach to multi-project planning under uncertainty.

We survey several viewpoints on the management of the planning complexity of multi-project organisations under uncertainty. A positioning framework is proposed to distinguish between different types of project-driven organisations, which is meant to aid project management in the choice between the various existing planning approaches. We discuss the current state of the art of hierarchical planning approaches both for traditional manufacturing and for project environments. We introduce a generic hierarchical project planning and control framework that serves to position planning methods for multi-project planning under uncertainty. We discuss multiple techniques for dealing with the uncertainty inherent to the different hierarchical stages in a multi-project organisation. In the last part of this paper we discuss two cases from practice and we relate these practical cases to the positioning framework that is put forward in the paper.Choice; Complexity; Framework; Hierarchical models; Management; Manufacturing; Methods; Multi-project organisations; Planning; Project management; Project planning; Uncertainty;

Compressive Sampling based Multiple Symbol Differential Detection for UWB Communications

Compressive sampling (CS) based multiple sym- bol differential detectors are proposed for impulse-radio ultra- wideband signaling, using the principles of generalized likelihood ratio tests. The CS based detectors correspond to two communica- tion scenarios. One, where the signaling is fully synchronized at the receiver and the other, where there exists a symbol level synchro- nization only. With the help of CS, the sampling rates are reduced much below the Nyquist rate to save on the high power consumed by the analog-to-digital converters. In stark contrast to the usual compressive sampling practices, the proposed detectors work on the compressed samples directly, thereby avoiding a complicated reconstruction step and resulting in a reduction of the implemen- tation complexity. To resolve the detection of multiple symbols, compressed sphere decoders are proposed as well, for both com- munication scenarios, which can further help to reduce the sys- tem complexity. Differential detection directly on the compressed symbols is generally marred by the requirement of an identical measurement process for every received symbol. Our proposed detectors are valid for scenarios where the measurement process is the same as well as where it is different for each received symbol

Study into the potential of UWB applications in the process industry

The advances in mobile technology, such as ultra wideband (UWB), enable the use of advanced applications in the process industry. The main challenge in the first phase of designing UWB networks and applications is getting to know what the process industry, i.e. the potential customer, wants. In this paper we present how we applied a user requirements elicitation process to get information about the expectation from the process industry regarding wireless networks and more specifically ultra wideband. We describe the design process of the balanced decisions that have to be made regarding these elements, i.e. applications as demanded by the process industry, the technology as offered by the ICT industry and the value network that has to guarantee benefits for all involved partners. The outcome of this feasibility study leads to the decision for going on with the next step, i.e. the design and building of an UWB testbed