Optimal sensing for fish school identification

Fish schooling implies an awareness of the swimmers for their companions. In flow mediated environments, in addition to visual cues, pressure and shear sensors on the fish body are critical for providing quantitative information that assists the quantification of proximity to other swimmers. Here we examine the distribution of sensors on the surface of an artificial swimmer so that it can optimally identify a leading group of swimmers. We employ Bayesian experimental design coupled with two-dimensional Navier Stokes equations for multiple self-propelled swimmers. The follower tracks the school using information from its own surface pressure and shear stress. We demonstrate that the optimal sensor distribution of the follower is qualitatively similar to the distribution of neuromasts on fish. Our results show that it is possible to identify accurately the center of mass and even the number of the leading swimmers using surface only information

From Biological Cilia to Artificial Flow Sensors: Biomimetic Soft Polymer Nanosensors with High Sensing Performance

We report the development of a new class of miniature all-polymer flow sensors that closely mimic the intricate morphology of the mechanosensory ciliary bundles in biological hair cells. An artificial ciliary bundle is achieved by fabricating bundled polydimethylsiloxane (PDMS) micro-pillars with graded heights and electrospinning polyvinylidenefluoride (PVDF) piezoelectric nanofiber tip links. The piezoelectric nature of a single nanofiber tip link is confirmed by X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). Rheology and nanoindentation experiments are used to ensure that the viscous properties of the hyaluronic acid (HA)-based hydrogel are close to the biological cupula. A dome-shaped HA hydrogel cupula that encapsulates the artificial hair cell bundle is formed through precision drop-casting and swelling processes. Fluid drag force actuates the hydrogel cupula and deflects the micro-pillar bundle, stretching the nanofibers and generating electric charges. Functioning with principles analogous to the hair bundles, the sensors achieve a sensitivity and threshold detection limit of 300 mV/(m/s) and 8 μm/s, respectively. These self-powered, sensitive, flexible, biocompatibale and miniaturized sensors can find extensive applications in navigation and maneuvering of underwater robots, artificial hearing systems, biomedical and microfluidic devices.Singapore. National Research Foundation (Singapore-MIT Alliance for Research and Technology)Singapore-MIT Alliance for Research and Technology (SMART) (Innovation Grants ING148079- ENG

From Biological Cilia to Artificial Flow Sensors: Biomimetic Soft Polymer Nanosensors with High Sensing Performance.

We report the development of a new class of miniature all-polymer flow sensors that closely mimic the intricate morphology of the mechanosensory ciliary bundles in biological hair cells. An artificial ciliary bundle is achieved by fabricating bundled polydimethylsiloxane (PDMS) micro-pillars with graded heights and electrospinning polyvinylidenefluoride (PVDF) piezoelectric nanofiber tip links. The piezoelectric nature of a single nanofiber tip link is confirmed by X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). Rheology and nanoindentation experiments are used to ensure that the viscous properties of the hyaluronic acid (HA)-based hydrogel are close to the biological cupula. A dome-shaped HA hydrogel cupula that encapsulates the artificial hair cell bundle is formed through precision drop-casting and swelling processes. Fluid drag force actuates the hydrogel cupula and deflects the micro-pillar bundle, stretching the nanofibers and generating electric charges. Functioning with principles analogous to the hair bundles, the sensors achieve a sensitivity and threshold detection limit of 300 mV/(m/s) and 8 μm/s, respectively. These self-powered, sensitive, flexible, biocompatibale and miniaturized sensors can find extensive applications in navigation and maneuvering of underwater robots, artificial hearing systems, biomedical and microfluidic devices

Artificial lateral-line system for imaging dipole sources using Beamforming techniques

AbstractIn nature, fish have the ability to localize prey, school, navigate, etc. using the lateral-line organ [1]. Here we present the use of biomimetic artificial hair-based flow-sensors arranged as lateral-line system in combination with beamforming techniques for dipole source localization in air. Modelling and measurement results show the artificial lateral-line ability to image the position of dipole sources accurately. Such systems open possibilities for flow-based near-field environment mapping which can be beneficial e.g. to robot guidance applications

Nature-Inspired Self-Powered Sensors and Energy Harvesters

Chapter 3 presents a comprehensive review of the various biomimetic self-powered and low-powered MEMS pressure and flow sensors that take inspiration from the biological flow sensors found in the marine world. The sensing performance of the biological flow sensors in marine animals has inspired engineers and scientists to develop efficient state-of-the-art sensors for a variety of real-life applications. In an attempt to achieve high-performance artificial flow sensors, researchers have mimicked the morphology, sensing principle, materials, and functionality of the biological sensors. Inspiration was derived from the survival hydrodynamics featured by various marine animals to develop sensors for sensing tasks in underwater vehicles. The mechanoreceptors of crocodiles have inspired the development of slowly and rapidly adapting MEMS sensory domes for passive underwater sensing. Likewise, the lateral line sensing system in fishes which is capable of generating a three-dimensional map of the surroundings was mimicked to achieve artificial hydrodynamic vision on underwater vehicles. Harbor seals are known to achieve high sensitivity in sensing flows within the wake street of a swimming fish due to the undulatory geometry of the whiskers. Whisker inspired structures were embedded into MEMS sensing membranes to understand their vortex shedding behavior. At the outset, this work comprehensively reviews the sensing mechanisms observed in fishes, crocodiles, and harbor seals. In addition, this chapter presents an in-depth commentary on the recent developments in this area where different researchers have taken inspiration from these aforementioned underwater creatures and developed some of the most efficient artificial sensing systems

Recurrent neural networks for hydrodynamic imaging using a 2D-sensitive artificial lateral line

The lateral line is a mechanosensory organ found in fish and amphibians that allows them to sense and act on their near-field hydrodynamic environment. We present a 2D-sensitive Artificial lateral line (ALL) comprising eight all-optical flow sensors, which we use to measure hydrodynamic velocity profiles along the sensor array in response to a moving object in its vicinity. We then use the measured velocity profiles to reconstruct the objects location, via two types of neural networks: feed-forward and recurrent. Several implementations of feed-forward neural networks for ALL source localisation exist, while recurrent neural networks may be more appropriate for this task. The performance of a recurrent neural network (the Long Short-Term Memory, LSTM) is compared to that of a feed-forward neural network (the Online-Sequential Extreme Learning Machine, OS-ELM) via localizing a 6 cm sphere moving at 13 cm/s. Results show that, in a 62 cm × 9.5 cm area of interest, the LSTM outperforms the OS-ELM with an average localisation error of 0.72 cm compared to 4.27 cm respectively. Furthermore, the recurrent network is relatively less affected by noise, indicating that recurrent connections can be beneficial for hydrodynamic object localisation

Multisensor Processing Algorithms for Underwater Dipole Localization and Tracking Using MEMS Artificial Lateral-Line Sensors

An engineered artificial lateral-line system has been recently developed, consisting of a 16-element array of finely spaced MEMS hot-wire flow sensors. This represents a new class of underwater flow sensing instruments and necessitates the development of rapid, efficient, and robust signal processing algorithms. In this paper, we report on the development and implementation of a set of algorithms that assist in the localization and tracking of vibrational dipole sources underwater. Using these algorithms, accurate tracking of the trajectory of a moving dipole source has been demonstrated successfully

Cupula-Inspired Hyaluronic Acid-Based Hydrogel Encapsulation to Form Biomimetic MEMS Flow Sensors

Blind cavefishes are known to detect objects through hydrodynamic vision enabled by arrays of biological flow sensors called neuromasts. This work demonstrates the development of a MEMS artificial neuromast sensor that features a 3D polymer hair cell that extends into the ambient flow. The hair cell is monolithically fabricated at the center of a 2 µm thick silicon membrane that is photo-patterned with a full-bridge bias circuit. Ambient flow variations exert a drag force on the hair cell, which causes a displacement of the sensing membrane. This in turn leads to the resistance imbalance in the bridge circuit generating a voltage output. Inspired by the biological neuromast, a biomimetic synthetic hydrogel cupula is incorporated on the hair cell. The morphology, swelling behavior, porosity and mechanical properties of the hyaluronic acid hydrogel are characterized through rheology and nanoindentation techniques. The sensitivity enhancement in the sensor output due to the material and mechanical contributions of the micro-porous hydrogel cupula is investigated through experiments.Singapore. National Research Foundation (Campus for Research Excellence and Technological Enterprise programme