Vibration energy harvesting is receiving a considerable amount of interest as a means for powering wireless sensor nodes. This paper presents a small (component volume 0.1 cm3, practical volume 0.15 cm3) electromagnetic generator utilizing discrete components and optimized for a low ambient vibration level based upon real application data. The generator uses four magnets arranged on an etched cantilever with a wound coil located within the moving magnetic field. Magnet size and coil properties were optimized, with the final device producing 46 µW in a resistive load of 4 k! from just 0.59 m s! 2 acceleration levels at its resonant frequency of 52 Hz. A voltage of 428 mVrms was obtained from the generator with a 2300 turn coil which has proved sufficient for subsequent rectification and voltage step-up circuitry. The generator delivers 30% of the power supplied from the environment to useful electrical power in the load. This generator compares very favourably with other demonstrated examples in the literature, both in terms of normalized power density and efficiency. (Some figures in this article are in colour only in the electronic version)

This paper presents the modeling and optimization of an electromagnetic-based generator for generating power from ambient vibrations. Basic equations describing such generators are presented and the conditions for maximum power generation are described. Two-centimeter scale prototype generators, which consist of magnets suspended on a beam vibrating relative to a coil, have been built and tested. The measured power and modeled results are compared. It is shown that the experimental results confirm the optimization theory

As autonomous vehicles become more common on the roads, their advancement draws on safety concerns for vulnerable road users, such as pedestrians and cyclists. This paper presents a review of recent developments in pedestrian and cyclist detection and intent estimation to increase the safety of autonomous vehicles, for both the driver and other road users. Understanding the intentions of the pedestrian/cyclist enables the self-driving vehicle to take actions to avoid incidents. To make this possible, development of methods/techniques, such as deep learning (DL), for the autonomous vehicle will be explored. For example, the development of pedestrian detection has been significantly advanced using DL approaches, such as; Fast Region-Convolutional Neural Network (R-CNN) , Faster R-CNN and Single Shot Detector (SSD). Although DL has been around for several decades, the hardware to realise the techniques have only recently become viable. Using these DL methods for pedestrian and cyclist detection and applying it for the tracking, motion modelling and pose estimation can allow for a successful and accurate method of intent estimation for the vulnerable road users. Although there has been a growth in research surrounding the study of pedestrian detection using vision-based approaches, further attention should include focus on cyclist detection. To further improve safety for these vulnerable road users (VRUs), approaches such as sensor fusion and intent estimation should be investigated.