RW
R. Wijlens
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
4 records found
1
Engineering Signal Analysis
From Fourier to filtering: Exercises
This book, Exercises, contains hundreds of exercises, including answers and worked examples, for studying and practicing the theory. Python scripts illustrate how to perform spectral signal analysis on a computer.
The companion book, Theory, is an introductory textbook on the analysis of signals in time and frequency. It takes an engineer’s perspective and discusses how to characterize, analyze and operate on signals. The basic theoretical concepts, Fourier series and transform, are explained in continuous time. It then introduces discrete-time signals, addressing how sampling and finite signal duration affect spectral analysis. It discusses the discrete Fourier transform and its use in spectral estimation. The Theory book concludes with an introduction to linear systems and signal filtering.
The companion book, Engineering Signal Analysis: From Fourier to filtering: Theory ...
The companion book, Theory, is an introductory textbook on the analysis of signals in time and frequency. It takes an engineer’s perspective and discusses how to characterize, analyze and operate on signals. The basic theoretical concepts, Fourier series and transform, are explained in continuous time. It then introduces discrete-time signals, addressing how sampling and finite signal duration affect spectral analysis. It discusses the discrete Fourier transform and its use in spectral estimation. The Theory book concludes with an introduction to linear systems and signal filtering.
The companion book, Engineering Signal Analysis: From Fourier to filtering: Theory ...
This book, Exercises, contains hundreds of exercises, including answers and worked examples, for studying and practicing the theory. Python scripts illustrate how to perform spectral signal analysis on a computer.
The companion book, Theory, is an introductory textbook on the analysis of signals in time and frequency. It takes an engineer’s perspective and discusses how to characterize, analyze and operate on signals. The basic theoretical concepts, Fourier series and transform, are explained in continuous time. It then introduces discrete-time signals, addressing how sampling and finite signal duration affect spectral analysis. It discusses the discrete Fourier transform and its use in spectral estimation. The Theory book concludes with an introduction to linear systems and signal filtering.
The companion book, Engineering Signal Analysis: From Fourier to filtering: Theory
The companion book, Theory, is an introductory textbook on the analysis of signals in time and frequency. It takes an engineer’s perspective and discusses how to characterize, analyze and operate on signals. The basic theoretical concepts, Fourier series and transform, are explained in continuous time. It then introduces discrete-time signals, addressing how sampling and finite signal duration affect spectral analysis. It discusses the discrete Fourier transform and its use in spectral estimation. The Theory book concludes with an introduction to linear systems and signal filtering.
The companion book, Engineering Signal Analysis: From Fourier to filtering: Theory
On the road to comfort
Evaluating the influence of motion predictability on motion sickness in automated vehicles
Journal article
(2025)
-
Rowenna Wijlens, Boris J.V. Englebert, Atsushi Takamatsu, Mitsuhiro Makita, Hikaru Sato, Takahiro Wada, Joost C.F. de Winter, Marinus M. van Paassen, Max Mulder
Automated vehicles could increase the risk of motion sickness because occupants are not involved in driving and do not watch the road. This paper aimed to investigate the influence of motion predictability on motion sickness in automated vehicles, as better motion anticipation is believed to mitigate motion sickness. In a simulator-based study, twenty participants experienced two driving conditions differing only in turn directions. The repetitive condition featured a repeating turn direction pattern. The non-repetitive condition contained pseudo-randomly ordered turn directions. To mimic an ‘eyes-off-the-road’ setting and prevent visual motion anticipation, road visuals were omitted. No significant differences in sickness or head motion, a metric for motion anticipation, were found between the conditions. No participant recognised the repeating turn pattern. This suggests no increased motion anticipation in the repetitive condition, possibly due to a reduced ability to recognise a repeating motion pattern in one degree of freedom within more complex motion.
...
Automated vehicles could increase the risk of motion sickness because occupants are not involved in driving and do not watch the road. This paper aimed to investigate the influence of motion predictability on motion sickness in automated vehicles, as better motion anticipation is believed to mitigate motion sickness. In a simulator-based study, twenty participants experienced two driving conditions differing only in turn directions. The repetitive condition featured a repeating turn direction pattern. The non-repetitive condition contained pseudo-randomly ordered turn directions. To mimic an ‘eyes-off-the-road’ setting and prevent visual motion anticipation, road visuals were omitted. No significant differences in sickness or head motion, a metric for motion anticipation, were found between the conditions. No participant recognised the repeating turn pattern. This suggests no increased motion anticipation in the repetitive condition, possibly due to a reduced ability to recognise a repeating motion pattern in one degree of freedom within more complex motion.
Journal article
(2022)
-
Rowenna Wijlens, Marinus M. van Paassen, Max Mulder, Atsushi Takamatsu, Mitsuhiro Makita, Takahiro Wada
Without intervention the widespread adoption of autonomous vehicles could be compromised by an increased incidence of motion sickness compared to conventional cars. To investigate whether passengers' motion sickness can be reduced by manipulating an autonomous vehicle's accelerations on a fixed route without altering the travel time, a human-out-of-the-loop experiment was performed in the SIMONA Research Simulator at Delft University of Technology. The experiment consisted of two different driving conditions, in which an identical 22-km road including 52 curves was travelled in 30 minutes. Condition 1 comprised larger longitudinal, but smaller lateral, acceleration values compared to Condition 2. Experimental results suggested that Condition 1 resulted in more severe motion sickness than Condition 2, with fitted learning curves providing final MIsery SCale scores of 1.19 vs. 0.80. A similar relative difference between the two conditions had been predicted by the 6-DOF Subjective Vertical Conflict model. Hence, this model has the potential to, once further developed, support the design of autonomous vehicles by reducing the need to perform costly, time-consuming experiments.
...
Without intervention the widespread adoption of autonomous vehicles could be compromised by an increased incidence of motion sickness compared to conventional cars. To investigate whether passengers' motion sickness can be reduced by manipulating an autonomous vehicle's accelerations on a fixed route without altering the travel time, a human-out-of-the-loop experiment was performed in the SIMONA Research Simulator at Delft University of Technology. The experiment consisted of two different driving conditions, in which an identical 22-km road including 52 curves was travelled in 30 minutes. Condition 1 comprised larger longitudinal, but smaller lateral, acceleration values compared to Condition 2. Experimental results suggested that Condition 1 resulted in more severe motion sickness than Condition 2, with fitted learning curves providing final MIsery SCale scores of 1.19 vs. 0.80. A similar relative difference between the two conditions had been predicted by the 6-DOF Subjective Vertical Conflict model. Hence, this model has the potential to, once further developed, support the design of autonomous vehicles by reducing the need to perform costly, time-consuming experiments.
This paper presents the results of a training and retention experiment conducted to objectively and quantitatively evaluate the acquisition, decay, and retention of skill-based manual control behavior in a compensatory dual-axis roll and pitch attitude tracking task. In this study, thirty-eight fully task-naive participants were trained in a fixed-base setting in the Human-Machine Interaction Laboratory at Delft University of Technology and subsequently divided into three matched groups based on their training performance and control behavior. Performance of the first group was re-evaluated after a period of non-practice of six months, whereas the second group was retested at both three and six months after training, and skill retention of the third group was measured after two, four, and six months. The goal of the experiment was to model the decay curve of skill-based manual control behavior and to determine the re-acquisition rate of lost skills compared to their initial acquisition rate. To explicitly quantify changes in manual control skills, learning curve models were fitted to metrics of task performance and control activity. The results suggest that control skills decay following a negatively accelerating decay curve and that lost skills are re-acquired at a higher rate than their initial development rate.
...
This paper presents the results of a training and retention experiment conducted to objectively and quantitatively evaluate the acquisition, decay, and retention of skill-based manual control behavior in a compensatory dual-axis roll and pitch attitude tracking task. In this study, thirty-eight fully task-naive participants were trained in a fixed-base setting in the Human-Machine Interaction Laboratory at Delft University of Technology and subsequently divided into three matched groups based on their training performance and control behavior. Performance of the first group was re-evaluated after a period of non-practice of six months, whereas the second group was retested at both three and six months after training, and skill retention of the third group was measured after two, four, and six months. The goal of the experiment was to model the decay curve of skill-based manual control behavior and to determine the re-acquisition rate of lost skills compared to their initial acquisition rate. To explicitly quantify changes in manual control skills, learning curve models were fitted to metrics of task performance and control activity. The results suggest that control skills decay following a negatively accelerating decay curve and that lost skills are re-acquired at a higher rate than their initial development rate.