Navid Mohajer

Bio: Navid Mohajer is an academic researcher from Deakin University. The author has contributed to research in topics: Computer science & Model predictive control. The author has an hindex of 5, co-authored 21 publications receiving 135 citations. Previous affiliations of Navid Mohajer include University of Tehran.

Vehicle motion simulators, a key step towards road vehicle dynamics improvement

Abstract: Real road vehicle tests are time consuming, laborious, and costly, and involve several safety concerns Road vehicle motion simulators (RVMS) could assist with vehicle testing, and eliminate or reduce the difficulties traditionally associated with conducting vehicle tests However, such simulators must exhibit a high level of fidelity and accuracy in order to provide realistic and reliable outcomes In this paper, we review existing RVMS and discuss each of the major RVMS subsystems related to the research and development of vehicle dynamics The possibility of utilising motion simulators to conduct ride and handling test scenarios is also investigated

Human passive motions and a user-friendly energy harvesting system

Abstract: Energy harvesting for wireless sensors and consumer electronic devices can significantly improve reliability and environmental sustainability of the devices. This is achieved by eliminating the dependency of these devices on rechargeable batteries, using clean and/or renewable energy sources. Energy harvesting from various energy sources is widely discussed among researchers and entrepreneurs, including harvesting energy from microscale phenomena. This topic is receiving increasing attention due to the rising numbers of low-power consumer electronic devices and wireless sensors, but also the increasing demand for more convenient and available devices. This article presents a feasibility study for an energy harvesting system based on a human’s breathing motion. The system is based on a modified pants belt that is integrated with an array of piezoelectric films and a harvesting circuit. The proposed energy harvester generates electricity from reciprocal abdominal motions of the human subject. In comparison ...

Enhancing Passenger Comfort in Autonomous Vehicles Through Vehicle Handling Analysis and Optimization

Abstract: Autonomous Vehicles (AVs) offer significant advantages in terms of traffic and fuel efficiency accident prevention, and reduced travel time. ‘Sense-plan-act’™ cycle of the AV has been designed and implemented to achieve a high level of active safety that considerably reduces the risk to passengers and pedestrians. However, within existing path planning and tracking algorithms, the subject of passenger comfort has received less attention compared to other AV topics. This paper is aimed at characterizing vehicle handling criteria and path profiles configuration in order to identify self-driving requirements for passenger comfort. The emphasis is given to the efficacy and usability of the classical handling analysis methods from a self-driving perspective. To these aims, we initially establish a framework which is defined by comfortable path profiles and a versatile handling model of an AV. Then, we introduce a directional path tracking unit which optimally implements AV’s trajectories using a controller and a speed regulator. The path tracking unit includes a multi-objective optimizer enabling improvement the handling behavior of the AV. The integration of the proposed methodology into the state-of-the-art AVs’ control system can lead to an enhanced comfort level.

A review of energy harvesting using piezoelectric materials: state-of-the-art a decade later (2008–2018)

Abstract: Energy harvesting technologies have been explored by researchers for more than two decades as an alternative to conventional power sources (e.g. batteries) for small-sized and low-power electronic devices. The limited life-time and necessity for periodic recharging or replacement of batteries has been a consistent issue in portable, remote, and implantable devices. Ambient energy can usually be found in the form of solar energy, thermal energy, and vibration energy. Amongst these energy sources, vibration energy presents a persistent presence in nature and manmade structures. Various materials and transduction mechanisms have the ability to convert vibratory energy to useful electrical energy, such as piezoelectric, electromagnetic, and electrostatic generators. Piezoelectric transducers, with their inherent electromechanical coupling and high power density compared to electromagnetic and electrostatic transducers, have been widely explored to generate power from vibration energy sources. A topical review of piezoelectric energy harvesting methods was carried out and published in this journal by the authors in 2007. Since 2007, countless researchers have introduced novel materials, transduction mechanisms, electrical circuits, and analytical models to improve various aspects of piezoelectric energy harvesting devices. Additionally, many researchers have also reported novel applications of piezoelectric energy harvesting technology in the past decade. While the body of literature in the field of piezoelectric energy harvesting has grown significantly since 2007, this paper presents an update to the authors' previous review paper by summarizing the notable developments in the field of piezoelectric energy harvesting through the past decade.

Characterization of Human Body-Based Thermal and Vibration Energy Harvesting for Wearable Devices

Abstract: Energy harvesting is an important enabling technology necessary to unleash the next shift in mm-scale and μW power computing devices, especially for wireless sensor nodes. Energy harvesting could play an important role in biomedical devices where it extends the lifetime of the system. Furthermore, it eliminates the need for periodic maintenance such as exchanging or recharging the battery. This paper presents experimental results of thermal and vibration energy harvested from human body using the thermoelectric generator and the piezo electric harvester, respectively. Contemporary research revealed that most of the published data, including harvesters datasheets, are adjusted for industrial or laboratory-setting environment. This paper focuses on obtaining experimental data from the human body using off-the-shelf harvesters, and discrete electrical components. Our experimental results showed that for 9 cm 2 area of thermoelectric generator, up to 20 μW of power can be generated at 22 ° C room temperature. In addition, 0.5 cm 3 piezo electric harvester can generate up to 3.7 μW when running at 7 mi/h. These data correspond to a power density of 2.2 μW/cm 2 and 7.4 μW/cm 3 for thermoelectric generator and piezo electric harvester, respectively. As such, the harvested energy from thermal and vibration of human body could potentially power autonomous wearable and implantable devices.

Ride comfort of a higher speed rail vehicle using a magnetorheological suspension system

Abstract: In a railway vehicle, vibrations are generated due to the interaction between wheel and track. To evaluate the effect of vibrations on the ride quality and comfort of a passenger vehicle, the Sperl...

Electroencephalogram Emotion Recognition Based on Empirical Mode Decomposition and Optimal Feature Selection

Abstract: Electroencephalogram (EEG) emotion recognition based on a hybrid feature extraction method in empirical mode decomposition domain combining with optimal feature selection based on sequence backward selection is proposed, which can reflect subtle information of multiscale components of unstable and nonlinear EEG signals and remove the reductant features to improve the performance of emotion recognition. The proposal is tested on DEAP dataset, in which the emotional states in the Valance dimension and Arousal dimension are classified by both ${K}$ -nearest neighbor and support vector machine, respectively. In the experiments, temporal windows of different length and three kinds of rhythms of EEG signal are taken into account for comparison, from which the results show that EEG signal with 1s temporal window achieves highest recognition accuracy of 86.46% in Valence dimension and 84.90% in Arousal dimension, respectively, which is superior to some state-of-the-art works. The proposed method would be applied to real-time emotion recognition in multimodal emotional communication-based humans–robots interaction system.