In this paper, we propose a method to analyse the coherence of existing curricula at higher education institution. We focus our attention to engineering programmes at universities but the proposed method is by no means restricted to those cases. In contrast to other known methods, our approach is quantitative, decentralised, and asynchronous and allows to analyse entire programmes (in contrast to single courses) and does not depend on using specific teaching methods or tools. We propose to perform this quantitative assessment in two steps: first, representing the university programme as an opportune graph with courses and concepts as nodes and connections between courses and concepts as edges; second, analysing the structure of the programme using methods from graph theory. We thus perform two investigations, both leveraging a practical case – data collected from three engineering programmes at two Swedish universities: (a) how to represent university programmes in terms of graphs (here called concepts-courses graph (CCG)) and (b) how to reinterpret the most classical graph-theoretical node centrality indexes and connectivity and network flow results in order to analyse the programme structure, including to discover flows and mismatches.

This paper studies how to describe, using a piecewise linear dynamical model, the short-term effects of fatigue and recovery on the strength of pelvic floor muscles. Specifically, we first adapt a known model that describes short-term fatigue in skeletal muscles to the specific problem of describing fatigue in pelvic floor muscles when performing Kegel exercises, and then propose a strategy to learn the modelřs parameters from field data. In details, we estimate the model parameters using a least squares approach starting from measurement data that has been obtained from three healthy women using a dedicated vaginal pressure sensor array and a connected mobile app which gamifies the Kegel exercising experience. We show that describing the pelvic floor muscles behaviour in terms of short-term fatigue and recovery factors plus learning the associated parameters from data from healthy women leads to the possibility of precisely forecasting how much pressure the players will exert while playing the game. By cross-learning and cross-testing individual models from the three volunteers we also discover that the models need to be individualized: indeed, the numerical results indicate that, generically, using data from one player to model another leads to potentially drastically lower forecasting capabilities.

We consider the problem of distributedly estimating Gaussian processes in multi-agent frameworks. Each agent collects few measurements and aims to collaboratively reconstruct a common estimate based on all data. Agents are assumed with limited computational and communication capabilities and to gather M noisy measurements in total on input locations independently drawn from a known common probability density. The optimal solution would require agents to exchange all the M input locations and measurements and then invert an M×M matrix, a non-scalable task. Differently, we propose two suboptimal approaches using the first E orthonormal eigenfunctions obtained from the Karhunen-Loève (KL) expansion of the chosen kernel, where typically E≪M. The benefits are that the computation and communication complexities scale with E and not with M, and computing the required statistics can be performed via standard average consensus algorithms. We obtain probabilistic non-asymptotic bounds that determine a priori the desired level of estimation accuracy, and new distributed strategies relying on Stein's unbiased risk estimate (SURE) paradigms for tuning the regularization parameters and applicable to generic basis functions (thus not necessarily kernel eigenfunctions) and that can again be implemented via average consensus. The proposed estimators and bounds are finally tested on both synthetic and real field data.

In this work, we study the problem of unconstrained convex optimization in a fully distributed multiagent setting, which includes asynchronous computation and lossy communication. In particular, we extend a recently proposed algorithm named Newton-Raphson consensus by integrating it with a broadcast-based average consensus algorithm, which is robust to packet losses. We show via the separation of time-scale principle that under mild conditions (i.e., persistency of the agents activation and bounded consecutive communication failures), the proposed algorithm is provably locally exponentially stable with respect to the optimal global solution. Finally, we complement the theoretical analysis with numerical simulations and comparisons based on real datasets.

We consider the problem of occupancy estimation in buildings using available environmental information. In particular, we study the problem of how to collect data that is informative enough for occupancy estimation purposes. We thus propose an application-oriented input design approach for designing the ventilation signal to be used while collecting the system identification datasets. The main goal of the method is to guarantee a certain accuracy in the estimated occupancy levels while minimizing the experimental time and effort. To take into account potential limitations on the actuation signals we moreover formulate the problem as a recursive nonlinear and nonconvex optimization problem, solved then using exhaustive search methods. We finally corroborate the theoretical findings with some numerical examples, which results show that computing ventilation signals using experiment design concepts leads to occupancy estimator performing 4 times better in terms of Mean Square Error (MSE).

This paper investigates the problem of calibrating sensors affected by (i) heteroskedastic measurement noise and (ii) a polynomial bias, describing a systematic distortion of the measured quantity. First, a set of increasingly complex statistical models for the measurement process was proposed. Then, for each model the authors design a Bayesian parameters estimation method handling heteroskedasticity and capable to exploit prior information about the model parameters. The Bayesian problem is solved using MCMC methods and reconstructing the unknown parameters posterior in sampled form. The authors then test the proposed techniques on a practically relevant case study, the calibration of Light Detection and Ranging (Lidar) sensor, and evaluate the different proposed procedures using both artificial and field data.

This paper proposes individualized, dynamical and data-driven models that describe pelvic floor muscle responses in women that use vaginal dilation. Specifically, the models describe how the volume of an inflatable balloon inserted at the vaginal introitus dynamically affects the aggregated pressure exerted by the pelvic floor muscles of the person. The paper inspects the approximation capabilities of different model structures, such as Hammerstein-Wiener and NARX, for this specific application, and finds the specific model structures and orders that best describe the recorded measurement data. Hence, although the current dataset is drawn from a sample of healthy volunteers, this paper is an initial step towards better understanding women’s responses to vaginal dilation and facilitating individualised medical vaginal dilation treatment.

Women affected by pain during penetrative sexual intercourse are often treated using fixed-size vaginal dilators that are regularly perceived as uncomfortable and leading to premature treatment drop-outs. These dilators could be improved by making them adaptive, i.e., able to exert dynamically different pressures on the vaginal duct to simultaneously guarantee comfort levels and achieve the medical dilation objectives. Implementing feedback control would then benefit from models that connect the patients’ comfort levels with their experienced physiological stimuli. Here we address the problem of data-driven quantitative modelling of pain/pleasure self-assessments obtained through medical trials. More precisely, we consider time-series records of Pelvic Floor Muscles (PFM) pressure, vaginal dilation, and pain/pleasure evaluations, and model the relations among these quantities using statistical analysis tools. Besides this, we also address the important issue of the individualization of these models: different persons may respond differently, but these variations may sometimes be so small that it may be beneficial to learn from several individuals simultaneously. We here numerically validate the previous claim by verifying that clustering patients in groups may lead, from a data-driven point of view, to models with a significantly improved statistical performance.

When cooling and exhaust air flows in air-cooled datacenters mix, the energetic efficiency of the cooling operations drops. One way to prevent this mixing of happening is by augmenting the air tightness of the hot and cold aisles; this, however, requires installing opportune hardware that may be expensive and require time consuming installations. Alternatively, one may try to minimize cooling and exhaust air flows mixing by opportunely controlling the speeds of the fans of the Computer Room Air Handling (CRAH) units so that the distribution of the air pressure field within the computer room is favorable. Implementing this type of flow control requires both detecting when there actually is some type of flow mixing somewhere, plus understanding how to operate the cooling infrastructure so that these mixings do not happen. To this aim, there is the need for models that can both help deciding whether these mixing events occur, plus designing automatic control strategies for reducing the risks that they will happen. In this manuscript, we propose an ad-hoc methodology for the data-driven derivation of control-oriented models that serve the purposes above. The methodology is built on classical Prediction Error Method (PEM) approaches to the system identification problem, and on laddering on the peculiarities of the physics of the phenomena under consideration. Moreover, we test and assess the methodology on a industrial-scale air-cooled datacenter with an installed capacity of 240 kW, and verify that the obtained models are able to capture the dynamics of the system in all its potential regimes.

This paper suggests a method for developing, implementing and assessing a concept inventory test for electrical engineering students (CITE). The aim of this test is to help students better understand and learn core concepts, plus increase their awareness about links between the different courses and other themes of the program. Our and other experiences show that students often struggle to understand and use fundamental concepts, and how these relate to the various courses. This issue is probably due to the fact that traditional exams mainly focus on assessing procedural tasks (e.g., directly solving specific problems following step-by-step approaches). The investigated programs at Uppsala University (UU) and Luleå Uni-versity of Technology (LTU), nonetheless, have no tool for collecting quantitative data on how students develop conceptual knowledge throughout the programs, and thus no means to obtain an holistic view about their learning process. The here proposed methodology thus describes how to develop tests that would not only provide students with valuable feedback on their progression, but also equip teachers and program boards with high-end data for pedagogical and course development purposes. Besides illustrating the developmental methodology, the paper includes reactions and remarks from students on what the tests would provide and what would motivate them to take it.