Smart parking in IoT-enabled cities: A survey

Fetal electrocardiography (fECG) is a promising alternative to cardiotocography continuous fetal monitoring. Robust extraction of the fetal signal from the abdominal mixture of maternal and fetal electrocardiograms presents the greatest challenge to effective fECG monitoring. This is mainly due to the low amplitude of the fetal versus maternal electrocardiogram and to the non-stationarity of the recorded signals. In this review, we highlight key developments in advanced signal processing algorithms for non-invasive fECG extraction and the available open access resources (databases and source code). In particular, we highlight the advantages and limitations of these algorithms as well as key parameters that must be set to ensure their optimal performance. Improving or combining the current or developing new advanced signal processing methods may enable morphological analysis of the fetal electrocardiogram, which today is only possible using the invasive scalp electrocardiography method.

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A Review of Signal Processing Techniques for Non-Invasive Fetal Electrocardiography

Issues and research on foetal electrocardiogram signal elicitation

Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL algorithms may vary depending upon the data sets being used, as well as on the suitability of algorithms to the data and the application domain under consideration. Hence, determining which ML/DL algorithm is most suitable for a specific application domain and its related data sets would be a key advantage. To respond to this need, a comparative analysis of well-known ML/DL techniques, including Multilayer Perceptron, K-Nearest Neighbors, Decision Tree, Random Forest, and Voting Classifier (or the Ensemble Learning Approach) for the prediction of parking space availability has been conducted. This comparison utilized Santander's parking data set, initiated while working on the H2020 WISE-IoT project. The data set was used in order to evaluate the considered algorithms and to determine the one offering the best prediction. The results of this analysis show that, regardless of the data set size, the less complex algorithms like Decision Tree, Random Forest, and KNN outperform complex algorithms such as Multilayer Perceptron, in terms of higher prediction accuracy, while providing comparable information for the prediction of parking space availability. In addition, in this paper, we are providing Top-K parking space recommendations on the basis of distance between current position of vehicles and free parking spots.

https://hal.archives-ouvertes.fr/hal-02429337/document

A comparative analysis of machine/deep learning models for parking space availability prediction

Fetal electrocardiography is among the most promising methods of modern electronic fetal monitoring. However, before they can be fully deployed in the clinical practice as a gold standard, the challenges associated with the signal quality must be solved. During the last two decades, a great amount of articles dealing with improving the quality of the fetal electrocardiogram signal acquired from the abdominal recordings have been introduced. This article aims to present an extensive literature survey of different non-adaptive signal processing methods applied for fetal electrocardiogram extraction and enhancement. It is limiting that a different non-adaptive method works well for each type of signal, but independent component analysis, principal component analysis and wavelet transforms are the most commonly published methods of signal processing and have good accuracy and speed of algorithms.

Non-Adaptive Methods for Fetal ECG Signal Processing: A Review and Appraisal.

In many applications, it is often required to separate mixtures of sub-Gaussian, skewed, near Gaussian, and super-Gaussian source signals so as to obtain desired source signals. In this paper, we propose a fast and adaptive ICA algorithm based on fully-multiplicative orthogonal-group (FastAdaptiveOgICA), which not only can instantaneously separate mixtures of sub-Gaussian and super-Gaussian source signals, but also can separate skewed and/or near Gaussian signals, which are common in some key application areas, such as biomedical signal processing and telecommunications. Separation performance highly depends on nonlinear functions, so a self-adaptive nonlinear function, which adjusts its parameters to achieve better performance according to the estimation of moments of source signals, is presented in our algorithm. We successfully applied the algorithm to obtain fetal electrocardiogram (FECG) signals with better separation performance and faster convergence speed, compared with several famous ICA algorithms.

A fast and adaptive ICA algorithm with its application to fetal electrocardiogram extraction

In this paper, the drawbacks of the conventional f-t frame based maximum torque per ampere (MTPA) control schemes are analyzed and mathematically proved. In order to inherit the merits of both the direct flux vector control (DFVC) in field weakening region and field orientated control (FOC) in constant torque region while avoiding their disadvantages, an integrated control scheme is proposed. The proposed control scheme integrates the FOC into f-t reference frame at low speeds to achieve a relatively accurate and robust MTPA control, while at high speeds, the DFVC is adopted to utilize the advantages of f-t frame based control scheme in field weakening region. A shape function is utilized by the proposed control scheme to achieve a smooth transition between the two control schemes. The proposed control scheme is verified by experiments under various operation conditions on a prototype IPMSM drive. The simulation and experimental results illustrate that the proposed control scheme could achieve a better MTPA control accuracy in constant torque region and a better field weakening performance in the constant power region. Meanwhile the complex look-up tables for FOC in field weakening region and the difficulties in observing flux vector at low speed are avoided.

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Integration of FOC With DFVC for Interior Permanent Magnet Synchronous Machine Drives

It's getting more difficult to find available parking spaces with the rapid growth of the vehicles, especially in big cities. The parking guidance system (PGS) which powered by real time data or historical data could reduce the time spent on looking for parking spaces and alleviate the heavy traffic around the parking lots. However, the PGS is completely ineffective when the parking lots missing data or even have no parking data. This paper makes an exploration on data repairing by digging geospatial data and historical data. First, this paper proposes a method to verify the possibility of parking data similarity when parking lots have spatial similarity. Then take the known data of parking lots as samples and generate reparative data through the Recurrent GANs. The experiment shows, when parking lots have similar spatial features, the parking data, in a high probability, have some similarity with each other, too. And the data generated by Recurrent GANs have the same distribution with real data. In fact, this paper provides a new idea to solve the problems of timeseries data repairing, to a certain extent, this method can help to save the cost of equipment and time.

Exploration on Spatiotemporal Data Repairing of Parking Lots Based on Recurrent GANs

Nowadays, it's difficult to find available parking spaces in big cities within China due to the rapid growth of the vehicles. Parking guidance system(PGS) would play an important role to reduce the time spent on looking for parking spaces. An intuitional idea is to collect the real-time occupancy information of city-wide parking lots and guide the vehicles to the proper one on demands. But the idea is hardly implemented practically because of the huge financial and time costs on linking all city-wide parking lots in early deployment stage. Unlike the expensive real-time data, the public information of parking lots is easily obtained with little costs. What if we dig these free public data and use them in parking recommendation? Inspired by the classic Page rank algorithm used in web searching, this paper propose a novel method, named Parking rank, to sort the parking lots based on the public information totally, such as price, location, total spaces, etc. And a driving-cost sensitive recommendation method is presented in this paper. The simulation shows, when working with the Parking rank algorithm, the recommendation algorithm can help vehicles find the proper parking lots efficiently and reasonably, even in the urban district lack of any real-time information. In fact, Parking rank algorithm can be used as pre-processing of static ranking of city-wide parking lots, and can work with other recommendation algorithm related to real-time occupancy information, or work alone if there is only the public information about parking lots.

Lei Peng

Papers

A fast and adaptive ICA algorithm with its application to fetal electrocardiogram extraction

A fast and adaptive ICA algorithm with its application to fetal electrocardiogram extraction

Integration of FOC With DFVC for Interior Permanent Magnet Synchronous Machine Drives

Exploration on Spatiotemporal Data Repairing of Parking Lots Based on Recurrent GANs

Parking rank: A novel method of parking lots sorting and recommendation based on public information