Farzam Tajdari

Bio: Farzam Tajdari is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Control theory & Computer science. The author has an hindex of 8, co-authored 22 publications receiving 132 citations. Previous affiliations of Farzam Tajdari include Amirkabir University of Technology & Aalto University.

Feedback-Based Ramp Metering and Lane-Changing Control With Connected and Automated Vehicles

Abstract: Aiming at operating effectively future traffic systems, we propose here a novel methodology for integrated lane-changing and ramp metering control that exploits the presence of connected vehicles. In particular, we assume that a percentage of vehicles can receive and implement specific control tasks (e.g., lane-changing commands), while ramp metering is available via an infrastructure-based system or enabled by connected vehicles. The proposed approach is designed to robustly maximise the throughput at motorway bottlenecks employing a feedback controller, formulated as a Linear Quadratic Integral regulator, which is based on a simplified linear time invariant traffic flow model. We also present an extremum seeking algorithm to compute the optimal set-points used in the feedback controller, employing only the measurement of a cost that is representative of the achieved traffic conditions. The method is evaluated via simulation experiments, performed on a first-order, multi-lane, macroscopic traffic flow model, also featuring the capacity drop phenomenon, which allows to demonstrate the effectiveness of the developed methodology and to highlight the improvement in terms of the generated congestion.

A novel online method for identifying motion artifact and photoplethysmography signal reconstruction using artificial neural networks and adaptive neuro-fuzzy inference system

Abstract: Photoplethysmography (PPG) is a noninvasive technique to measure blood volume changes in blood vessels. Despite the wide usage of PPG signal in medical and non-medical applications, this signal can be affected by the motion artifacts leading to data loss. In this paper, we proposed an algorithm to detect motion artifacts and reconstruct the corrupted parts of the signal using real-time modeling based on multilayer perceptron (MLP), radial basis function (RBF) artificial neural networks (ANNs) and adaptive-neuro fuzzy inference system (ANFIS). The developed algorithm was applied to reconstruct the corrupted parts of PPG signals of 23 healthy 25- to 28-year-old volunteers. In the experimental phase, the left- and right-hand PPG signals of the volunteers were simultaneously obtained. While the left hand of the subjects were fixed, they were asked to shake their hands without any predetermined pattern, to simulate the real-life motion accelerations. To statistically and physiologically evaluate the performance of the proposed models, Pearson correlation coefficient (PCC), intraclass correlation coefficient (ICC), Bland–Altman plot (with the 95% limits of agreement), and time-domain feature analysis tests were adopted. The results indicated that the ANFIS with subtractive clustering algorithm shows the best performance in modeling the lost parts of the right-hand signals with an average PCC and ICC of 0.80 and 0.77, respectively, with the reference signal (left-hand signals) over all the tests. Also, the proposed ANFIS-based algorithm had an ability to retrieve the important time-domain PPG signal features, namely mean of inter-beat intervals (NN), standard deviations of NN (SDNN), root mean square of standard deviations (RMSSD) and standard deviation of standard deviations (SDSD) without any significant difference at p < 0.05 level to those of the reference signal (left-hand signal).

Design of a new, light and portable mechanism for knee CPM machine with a user-friendly interface

Abstract: After a knee joint surgery, due to severe pain and immobility of the patient, the tissue around the knee become harder and knee stiffness will occur, which may causes many problems such as scar tissue swelling, bleeding, and fibrosis. A CPM (Continuous Passive Motion) machine is an apparatus that is being used to patient recovery, retrieving moving abilities of the knee, and reducing tissue swelling, after the knee joint surgery. This device prevents frozen joint syndrome (adhesive capsulitis), joint stiffness, and articular cartilage destruction by stimulating joint tissues, and flowing synovial fluid and blood around the knee joint. In this study, a new, light, and portable CPM machine with an appropriate interface, is designed and manufactured. The knee joint can be rotated from the range of −15° to 120° with a pace of 0.1 degree/sec to 1 degree/sec by this machine. One of the most important advantages of this new machine is its own user-friendly interface. This apparatus is controlled via an Android-based application; therefore, the users can use this machine easily via their own smartphones without the necessity to an extra controlling device. Besides, because of its apt size, this machine is a portable device. Smooth movement without any vibration and adjusting capability for different anatomies are other merits of this new CPM machine.

New fuzzy solution for determining anticipation and evaluation behavior during car-following maneuvers:

Abstract: Nowadays, vehicles are the most important means of transportation in our daily lifes. During the last few decades, many studies have been carried out in the field of intelligent vehicles and signif...

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Current progress of photoplethysmography and SPO2 for health monitoring.

Abstract: A photoplethysmograph (PPG) is a simple medical device for monitoring blood flow and transportation of substances in the blood. It consists of a light source and a photodetector for measuring transmitted and reflected light signals. Clinically, PPGs are used to monitor the pulse rate, oxygen saturation, blood pressure, and blood vessel stiffness. Wearable unobtrusive PPG monitors are commercially available. Here, we review the principle issues and clinical applications of PPG for monitoring oxygen saturation.

Behavior measurement, analysis, and regime classification in car following

Abstract: This paper first reports a data acquisition method that the authors used in a project on modeling driver behavior for microscopic traffic simulations. An advanced instrumented vehicle was employed to collect driver-behavior data, mainly car-following and lane-changing patterns, on Swedish roads. To eliminate the measurement noise in acquired car-following patterns, the Kalman smoothing algorithm was applied to the state-space model of the physical states (acceleration, speed, and position) of both instrumented and tracked vehicles. The denoised driving patterns were used in the analysis of driver properties in the car-following stage. For further modeling of car-following behavior, we developed and implemented a consolidated fuzzy clustering algorithm to classify different car-following regimes from the preprocessed data. The algorithm considers time continuity of collected driver-behavior patterns and can be more reliably applied in the classification of continuous car-following regimes when the classical fuzzy C-means algorithm gives unclear results.

Photoplethysmogram Analysis and Applications: An Integrative Review

Abstract: Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.