Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review

Human gesture detection, obstacle detection, collision avoidance, parking aids, automotive driving, medical, meteorological, industrial, agriculture, defense, space, and other relevant fields have all benefited from recent advancements in mmWave radar sensor technology. A mmWave radar has several advantages that set it apart from other types of sensors. A mmWave radar can operate in bright, dazzling, or no-light conditions. A mmWave radar has better antenna miniaturization than other traditional radars, and it has better range resolution. However, as more data sets have been made available, there has been a significant increase in the potential for incorporating radar data into different machine learning methods for various applications. This review focuses on key performance metrics in mmWave-radar-based sensing, detailed applications, and machine learning techniques used with mmWave radar for a variety of tasks. This article starts out with a discussion of the various working bands of mmWave radars, then moves on to various types of mmWave radars and their key specifications, mmWave radar data interpretation, vast applications in various domains, and, in the end, a discussion of machine learning algorithms applied with radar data for various applications. Our review serves as a practical reference for beginners developing mmWave-radar-based applications by utilizing machine learning techniques.

Application of MEMS Accelerometers in Dynamic Vibration Monitoring of a Vehicle

In this paper, the viability of MEMS accelerometers is investigated to measure vibration parameters related to different locations of a vehicle with respect to the automotive dynamic functions. The data is collected to compare the accelerometer performances in different locations on the vehicle, including on the hood above the engine, on the hood above the radiator fan, over the exhaust pipe, and on the dashboard. The power spectral density (PSD), together with the time and frequency domain results, confirm the strength and frequencies of the sources of vehicle dynamics. The frequencies obtained from the vibrations of the hood above the engine and radiator fan are approximately 44.18 Hz and 38 Hz, respectively. In terms of the vibration amplitude, the measured amplitudes are between 0.5 g and 2.5 g in both cases. Furthermore, the time domain data collected on the dashboard during driving mode reflects the road condition. Overall, the knowledge obtained from the various tests conducted in this paper can be advantageous for further control and development of vehicle diagnostics, safety, and comfort.

Energy Management and Control in Multiple Storage Energy Units (Battery-Supercapacitor) of Fuel Cell Electric Vehicles

This paper presents a new approach of energy management for a fuel cell electric vehicle traction system. This system includes a supercapacitor, a traction battery of valve-regulated sealed lead-acid type, a high-performance permanent magnet traction system, and a power electronics converter. Special attention was placed on the coordination for managing the flow of energy from several sources to treat the concerns of prolonged electric vehicle mileage and battery lifetime for drivetrains of electric vehicles. Connection to a supercapacitor in parallel with the electric vehicle's battery affects electric vehicle battery lifetime and its range. The paper used a study case of an all-electric train, but the used methods can be applied on hybrid or electric train cases. Fuzzy logic control and proportional integral control methods were used to control the electric vehicle system. The results of these two control methods were examined and compared. The simulation results were compared between the proposed electric vehicle system and the traditional system to show the effectiveness of the proposed method. Comparison of waveforms was made with and without the supercapacitor. The proposed optimized energy management strategy could improve the overall performance of the hybrid system and reduce the power consumption.

Single-Channel FMCW-Radar-Based Multi-Passenger Occupancy Detection Inside Vehicle

In this paper, we provide the results of multi-passenger occupancy detection inside a vehicle obtained using a single-channel frequency-modulated continuous-wave radar. The physiological characteristics of the radar signal are analyzed in a time-frequency spectrum, and features are proposed based on these characteristics for multi-passenger occupancy detection. After clutter removal is applied, the spectral power and Wiener entropy are proposed as features to quantify physiological movements arising from breathing and heartbeat. Using the average means of both the power and Wiener entropy at seats 1 and 2, the feature distributions are expressed, and classification is performed. The multi-passenger occupancy detection performance is evaluated using linear discriminant analysis and maximum likelihood estimation. The results indicate that the proposed power and Wiener entropy are effective features for multi-passenger occupancy detection.

Influence of Ionomer Content in the Catalytic Layer of MEAs Based on Aquivion® Ionomer

Perfluorinated sulfonic acid (PFSA) polymers such as Nafion^® are widely used for both electrolyte membranes and ionomers in the catalytic layer of membrane-electrode assemblies (MEAs) because of their high protonic conductivity, σ_H, as well as chemical and thermal stability. The use of PFSA polymers with shorter side chains and lower equivalent weight (EW) than Nafion^®, such as Aquivion^® PFSA ionomers, is a valid approach to improve fuel cell performance and stability under drastic operative conditions such as those related to automotive applications. In this context, it is necessary to optimize the composition of the catalytic ink, according to the different ionomer characteristics. In this work, the influence of the ionomer amount in the catalytic layer was studied, considering the dispersing agent used to prepare the electrode (water or ethanol). Electrochemical studies were carried out in a single cell in the presence of H₂-air, at intermediate temperatures (80-95 °C), low pressure, and reduced humidity ((50% RH). %). The best fuel cell performance was found for 26 wt.% Aquivion^® at the electrodes using ethanol for the ink preparation, associated to a maximum catalyst utilization.

Design and Intensive Experimental Evaluation of an Enhanced Visible Light Communication System for Automotive Applications

As the interest toward communication-based vehicle safety applications is increasing, the development of secure wireless communication techniques has become an important research area. In this context, the article addresses issues that are related to the use of the visible light communication (VLC) technology in vehicular applications. Thus, it provides an extensive presentation concerning the main challenges and issues that are associated to vehicular VLC applications and of some of the existing VLC solutions. Moreover, the article presents the aspects related to the design and intensive experimental evaluation of a new automotive VLC system. The experimental evaluation performed in indoor and outdoor conditions shows that the proposed system can achieve communication distances up to 50 m and bit error ratio (BER) lower than 10-6, while being exposed to optical and weather perturbations. This article provides important evidence concerning the snowfall effect on middle to long range outdoor VLC, as the proposed VLC system was also evaluated in snowfall conditions. Accordingly, the experimental evaluation showed that snowfall and heavy gust could increase bit error rate by up to 10,000 times. Even so, this article provides encouraging evidence that VLC systems will soon be able to reliably support V2X communications.

Nonlinear Constrained Moving Horizon Estimation Applied to Vehicle Position Estimation

The design of high–performance state estimators for future autonomous vehicles constitutes a challenging task, because of the rising complexity and demand for operational safety. In this application, a vehicle state observer with a focus on the estimation of the quantities position, yaw angle, velocity, and yaw rate, which are necessary for a path following control for an autonomous vehicle, is discussed. The synthesis of the vehicle’s observer model is a trade-off between modelling complexity and performance. To cope with the vehicle still stand situations, the framework provides an automatic event handling functionality. Moreover, by means of an efficient root search algorithm, map-based information on the current road boundaries can be determined. An extended moving horizon state estimation algorithm enables the incorporation of delayed low bandwidth Global Navigation Satellite System (GNSS) measurements—including out of sequence measurements—as well as the possibility to limit the vehicle position change through the knowledge of the road boundaries. Finally, different moving horizon observer configurations are assessed in a comprehensive case study, which are compared to a conventional extended Kalman filter. These rely on real-world experiment data from vehicle testdrive experiments, which show very promising results for the proposed approach.

A Deep Neural Network Based Model for a Kind of Magnetorheological Dampers

In this paper, a deep neural network based model for a set of small-scale magnetorheological dampers (MRD) is developed where relevant parameters that have a physical meaning are inputs to the model. An experimental platform and a 3D-printing rapid prototyping facility provided a set of different conditions including MRD filled with two different MR fluids, which were used to train a Deep Neural Network (DNN), which is the core of the proposed model. Testing results indicate the model could forecast the hysteretic response of magnetorheological dampers for different load conditions and various physical configurations.

Giant Magnetoresistance: Basic Concepts, Microstructure, Magnetic Interactions and Applications

The giant magnetoresistance (GMR) effect is a very basic phenomenon that occurs in magnetic materials ranging from nanoparticles over multilayered thin films to permanent magnets. In this contribution, we first focus on the links between effect characteristic and underlying microstructure. Thereafter, we discuss design criteria for GMR-sensor applications covering automotive, biosensors as well as nanoparticular sensors.