This paper presents a preliminary version of an Active Learning (AL) scheme for the sample
selection aimed at the development of a surrogate model for the uncertainty quantification based on the Gaussian Process regression. The proposed AL strategy iteratively searches for new candidate points to be included within the training set by trying to minimize the relative posterior standard deviation provided by the Gaussian Process regression surrogate. The above scheme has been applied for the construction of a surrogate model for the statistical analysis of the efficiency of a switching buck converter as a function of 7 uncertain parameters. The performance of the surrogate model constructed via the proposed active learning method are compared with the ones provided by an equivalent model built via a latin hypercube sampling. The results of a Monte Carlo simulation with the computational model are used as reference.

The step-size parameter and the equalizer's tap length are the system parameters in the blind adaptive equalization design. Choosing a large step-size parameter causes the equalizer to converge faster compared with applying a smaller value for the step size parameter. But, a higher step-size parameter leaves the system with a higher residual inter-symbol-interference (ISI) than does a lower step-size parameter. The equalizer's tap length should be set large enough to compensate for the channel distortions. But, since the channel parameters are unknown, the required equalizer's tap length is also unknown. The system parameters are usually designed via simulation trials, in such a way that the equalizer's performance from the residual ISI point of view reaches a system desired residual ISI level. Recently, a closed-form approximated expression was derived for the residual ISI as a function of the system parameters, input sequence statistics and channel power. This expression was obtained under the assumption having a value for the equalizer's tap length that is sufficient to compensate for the channel distortions. Based on this approximated expression, the outcome from the step-size parameter multiplied by the equalizer's tap length can be derived when the residual ISI is given. But, by choosing a step-size parameter, we automatically have also the value for the equalizer's tap length which might now not be large enough to compensate for the channel distortions and thus leaving the system with a higher residual ISI than the required one. In this work, we derive an expression that sets a condition on the equalizer's tap length based on the input sequence statistics, on the chosen equalizer's characteristics and required residual ISI. In addition, highlights are supplied on how to set the equalizer's tap length for different channel cases based on this new derived expression. The findings are accompanied by simulation results.

The COVID-19 pandemic has caused havoc in many economic areas such as those related to tourism. This creates the need for alternative activities in this sector, especially given that it is not clear when will end the present emergency and there could new situations of this kind. We consider here two main possibilities (virtual models and remote observations) for tourism related to geological objects (including those used by humans) and processes. These approaches could help to promote remote-operated tourism in other celestial bodies, helping to promote this kind of enterprise. These activities could be prepared with variable connection to education (for publics with diverse age ranges), prompting their use at any time of the year (hence minimizing the issue of seasonality). Our discussion suggests that remote observations will be the most interesting option since they could potentially give the users an unlimited diversity of experiences, it might give higher return to local communities (but also higher loads on local environments) and they could find additional value in other geological applications. While our analysis is certainly very speculative at present, it can be submitted to falsification by the financial results.

During microwave design, it is of practical interest to obtain insight in the statistical variability of a device’s frequency response with respect to several sources of variation. Unfortunately, the frequency response acquisition can be particularly time-consuming or expensive. This makes uncertainty quantification unfeasible when dealing with complex networks. Generative modeling techniques that are based on machine learning can reduce the computation load by learning the underlying stochastic process from few instances of the device response and generating new ones by executing an inexpensive sampling strategy. This way, an arbitrary number of frequency responses can be obtained that are drawn from a probability distribution that resembles the original one. The use of Gaussian Process Latent Variable Models (GP-LVM) and Variational Autoencoders (VAE) as modeling algorithms will be evaluated in a generative framework. The framework includes a Vector Fitting (VF) pre-processing step which guarantees stability and reciprocity of S-matrices by converting them into a suitable rational model. Both GP-LVM and VAE are tested on the S-parameter responses of two linear multi-port network examples.

This paper presents realistic system-level modeling of effective noise sources in a coupled resonating mode-localized MEMS sensors. A governing set of differential equations are used to build a numerical model of a mechanical noise source in a coupled-resonator sensor and an effective thermo-mechanical noise is quantified through the simulation performed via SIMULINK. On a similar note, an effective noise that stems from the electronic readout used for the coupled resonating MEMS sensors is also quantified. Various noise sources in electronic readout are identified and the contribution of each is quantified. A comparison between an effective mechanical and electronic noise in a sensor system aids in identifying the dominant noise source in a sensor system. A method to optimize the system noise floor for an amplitude-based readout is presented. The proposed models present a variety of operating conditions, such as finite quality factor, varying coupled electrostatic spring strength, and operation with in-phase and out-of-phase mode. The proposed models aim to study the impact of fundamental noise processes that govern the ultimate resolution into a coupled resonating system used for various sensing applications.

The need for accurate physical measurements is omnipresent in both scientific and engineering applications. Such measurements can be used to explore and characterize the behavior of a system over the parameters of interest. These procedures are often very costly and time-consuming, requiring many measurements or samples. Therefore, a suitable data collection strategy can be used to reduce the cost of acquiring the required samples. One important consideration which often surfaces in physical experiments, like near-field measurements for electromagnetic compliance testing, is the total path length between consecutively visited samples by the measurement probe, as the time needed to travel along this path is often a limiting factor. A line-based sampling strategy optimizes the sample locations in order to reduce the overall path length while achieving the intended goal. Previous research on line-based sampling techniques solely focused on exploring the measurement space. None of these techniques considered the actual measurements themselves despite these values hold the potential to identify interesting regions in the parameter space, such as an optimum, quickly during the sampling process. In this paper, we extend Bayesian optimization, a point-based optimization technique into a line-based setting. The proposed algorithm is assessed using an artificial example and an electromagnetic compatibility use-case. The results show that our line-based technique is able to find the optimum using a significantly shorter total path length compared to the point-based approach.

The continuous miniaturization of sensors, as well as the progression in wearable electronics, embedded software, digital signal processing and biomedical technologies, have led to new user-centric networks, where devices can be carried in the user’s pockets, attached to the user’s body. Ultra wide band (UWB) technology is an innovative wireless technology which can transmit digital data over a wide frequency spectrum with very low power and a very high data rate. Actually, wireless body sensor networks comprise with sensor nodes that is very adjacent to the body on or in daily wear. It is mainly advantageous applications to the biomedical, such as human body probing, health monitoring, real-time diagnosis etc. Ultra Wideband (UWB) has enormous prospective for applications in the wireless body area networks (WBANs). Designing an efficient antenna with UWB technology is a great initiative as an application of WBAN. To design an efficient antenna with band-notch technology is a good key. This paper offers the antenna design & performance study of Ultra Wideband band-notch antenna for the wireless body sensor networks. Both the free space & the on-body behaviour of the antenna with FR4 substrate is investigated and analysed. After the antenna modelling in free space, it is modelled on human test phantom to study the on-body performance. The free space and on-body performance of this antenna were compared and it shows very good performance. For the better comparisons, important antenna factors such as gain, return loss response, efficiency & radiation pattern are closely analysed. The antenna shows good on-body behaviour & proper band-notch characteristic and as a result it will be appropriate candidate for wireless body sensor networks for medical applications. Moreover body-centric applications of the antenna such as GPS, healthcare, military applications and fire fighter personal communications are at peak demand as well.

Development and investigation of a miniaturized ultra-wideband band notch antenna is demonstrated in this paper. The antenna was modeled and simulated using Computer Simulation Technology (CST)TM Microwave Studio software. The simulated results of this antenna are presented and analyzed. The performance parameters such as return loss, gain, radiation efficiency, radiation patterns are simulation based results provided here. The main objective of this paper was to obtain band notch characteristics at the Wireless Local Area Network (5.15-5.8 GHz) and WiMax (5.25-5.85 GHz) in the UWB frequency ranges of 3.1 to 10.6 GHz in order to avoid interference. Results and analysis show that the antenna meets the objective and shows very good results. It has very compact size as well which is attractive feature of this antenna that will make it suitable for ultra-wideband wireless communication systems.