Seal and Sea lion Whiskers Detect Slips of Vortices Similar as Rats Sense Textures

Pinnipeds like seals and sea lions use their whiskers in hunting their prey in dark and turbid conditions. There is no theoretical model or a hypothesis to explain the interaction of whiskers with hydrodynamic fish trails. The present work provides insight into the mechanism behind the Strouhal frequency identification from a Von-Karman vortex street behind bluff bodies, similar to the inverted hydrodynamic fish trail. Flow over 3D printed sea lion head with integrated whiskers of similar geometrical and material properties was investigated when being exposed to vortex streets behind cylindrical bluff bodies. It is found that the whiskers respond to the vortices by a jerky motion similar to the stick-slip response of rat whiskers on different surface textures. The Strouhal frequency of the upstream wake is clearly decoded with the time-derivative of the whisker response rather than the displacement response, which increases the sensing efficiency in noisy environments. It is hypothesized from the work that the time derivative of the bending moment of the whiskers is the best physical variable, which can be used as the input to the neural system of the pinnipeds

The design and intelligent control of an autonomous mobile robot

This thesis presents an investigation into the problems of exploration, map building and collision free navigation for intelligent autonomous mobile robots. The project began with an extensive review of currently available literature in the field of mobile robot research, which included intelligent control techniques and their application. It became clear that there was scope for further development with regard to map building and exploration in new and unstructured environments. Animals have an innate propensity to exhibit such abilities, and so the analogous use of artificial neural networks instead of actual neural systems was examined for use as a method of robot mapping. A simulated behaviour based mobile robot was used in conjunction with a growing cell structure neural network to map out new environments. When using the direct application of this algorithm, topological irregularities were observed to be the direct result of correlations within the input data stream. A modification to this basic system was shown to correct the problem, but further developments would be required to produce a generic solution. The mapping algorithms gained through this approach, although more similar to biological systems, are computationally inefficient in comparison to the methods which were subsequently developed. A novel mapping method was proposed based on the robot creating new location vectors, or nodes, when it exceeded a distance threshold from its mapped area. Network parameters were developed to monitor the state of growth of the network and aid the robot search process. In simulation, the combination of the novel mapping and search process were shown to be able to construct maps which could be subsequently used for collision free navigation. To develop greater insights into the control problem and to validate the simulation work the control structures were ported to a prototype mobile robot. The mobile robot was of circular construction, with a synchro-drive wheel configuration, and was equipped with eight ultrasonic distance sensors and an odometric positioning system. It was self-sufficient, incorporating all its power and computational resources. The experiments observed the effects of odometric drift and demonstrated methods of re-correction which were shown to be effective. Both the novel mapping method, and a new algorithm based on an exhaustive mesh search, were shown to be able to explore different environments and subsequently achieve collision free navigation. This was shown in all cases by monitoring the estimates in the positional error which remained within fixed bounds

A role for sensory areas in coordinating active sensing motions

Active sensing, which incorporates closed-loop behavioral selection of information during sensory acquisition, is an important feature of many sensory modalities. We used the rodent whisker tactile system as a platform for studying the role cortical sensory areas play in coordinating active sensing motions. We examined head and whisker motions of freely moving mice performing a tactile search for a randomly located reward, and found that mice select from a diverse range of available active sensing strategies. In particular, mice selectively employed a strategy we term contact maintenance, where whisking is modulated to counteract head motion and sustain repeated contacts, but only when doing so is likely to be useful for obtaining reward. The context dependent selection of sensing strategies, along with the observation of whisker repositioning prior to head motion, suggests the possibility of higher level control, beyond simple reflexive mechanisms. In order to further investigate a possible role for primary somatosensory cortex (SI) in coordinating whisk-by-whisk motion, we delivered closed-loop optogenetic feedback to SI, time locked to whisker motions estimated through facial electromyography. We found that stimulation regularized whisking (increasing overall periodicity), and shifted whisking frequency, changes that emulate behaviors of rodents actively contacting objects. Importantly, we observed changes to whisk timing only for stimulation locked to whisker protractions, possibly encoding that natural contacts are more likely during forward motion of the whiskers. Simultaneous neural recordings from SI show cyclic changes in excitability, specifically that responses to excitatory stimulation locked to whisker retractions appeared suppressed in contrast to stimulation during protractions that resulted in changes to whisk timing. Both effects are evident within single whisks. These findings support a role for sensory cortex in guiding whisk-by-whisk motor outputs, but suggest a coupling that depends on behavioral context, occurring on multiple timescales. Elucidating a role for sensory cortex in motor outputs is important to understanding active sensing, and may further provide novel insights to guide the design of sensory neuroprostheses that exploit active sensing context

Crystallographic influences on the nanomanipulation of gold nanoclusters on molybdenum disulfide

This work investigates the manipulation behavior of thermally deposited gold nanoclusters with tens of nanometers in size on monocrystalline Molybdenum Disulfide (MoS2) surfaces. Using scan raster patterns in the order of several m, dozens of Au islands can be displaced with a single scan, revealing a directional locking effect caused by the epitaxial nature of the nanoparticle growth on the MoS2 surface. Statistical analysis of tapping mode manipulation scans using pyramidal and conical AFM tips along with MD simulations lead to the conclusion that frictional anitrosopy governs the direction of displacement, with the preference to move along the zigzag- or armchair direction of the hexagonally structured surface. It further investigates the manipulation behavior on CVD grown mono- and bilayer MoS2 with the goal of formation of gold nanowires. For this several nanomanipulation and nanoscratching techniques are deployed to exploit the unique movement behavior of gold islands on a crystalline surface

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School of Energy and Chemical Engineering (Chemical Engineering)Nowadays, there has been demand for advanced composite materials due to their outstanding characteristics in wide ranges of academic and industrial fields. Composite materials usually possess improved properties not being observed in pure material itself. Particularly, polymer composite materials composed of constituents based on polymer matrix have been widely researched due to their highly enhanced capabilities (e.g., elasticity, flexibility, conductivity, hardness, stretchable, scalable, and so on) in various fields. Polymers, which are composed of structural repeat units with covalent bonds, have been gradually becoming essential and indispensable materials in the recent world owing to their high flexibility, elasticity, ease of processing, low cost, light weight, and other unique properties. Therefore, to utilize polymers more effectively for advanced composite materials, many fundamental studies have been researched to discover fundamental reasons (i.e., molecular origins) for their intrinsic characteristics corresponding to the polymer physics and rheology. Recent experimental techniques offer some microscopic information. Nevertheless, it is still challenging issue to conduct a full atomic level analysis through only experimental approach. As such, depending on the rapid increase in computing power, multi-scale computer simulation methods have been developed to reveal the fundamental origin for some unique phenomena observed at the macroscopic level. Therefore, we conducted a detailed numerical analysis on rheological and mechanical properties of polymeric materials via mainly nonequilibrium molecular dynamics (NEMD) simulations and finite-element-method (FEM) simulations (Abaqus CAE and COMSOL Multiphysics). In this dissertation, we first present comprehensive analysis on the shear rheology of polymers for various molecular architectures (linear, ring, and short-chain branched) in the bulk and confined systems using atomistic NEMD simulations. In comparison to bulk polymeric system, the interfacial chain dynamics near the boundary solid walls in the confined system are interesting. Detailed molecular-level analysis of the individual chain motions for various molecular architectures are carried out to characterize the intrinsic molecular mechanisms for interfacial chains in three characteristic flow regimes (weak, intermediate, and strong) regarding to the interfacial slip behavior (i.e., degree of slip). Based on fundamental studies for polymers, we additionally modeled and analyzed polymer nanocomposites to fabricate versatile sensor devices using FEM simulations collaborated with experimental approach. Through a precise modeling in consideration to (particularly) mechanical properties, we found the most optimized construction of the nanostructured polymeric materials with highly improved sensing performances (ultrahigh sensitivity, linear sensing capability, and broad sensing range). Finally, we demonstrated highly sensitive triboelectric, ferroelectric, mechanochromic, and piezoresistive sensors with a proper physical (fundamental) mechanism to improve sensing ability.ope

Advanced Knowledge Application in Practice

The integration and interdependency of the world economy leads towards the creation of a global market that offers more opportunities, but is also more complex and competitive than ever before. Therefore widespread research activity is necessary if one is to remain successful on the market. This book is the result of research and development activities from a number of researchers worldwide, covering concrete fields of research

Multiphysics Modeling And Simulation Process To Develop Thin Piezoelectric Film Sensors To Measure The Vibration Of Structures With Complex Shapes And Boundary Conditions.

Piezoelectricity was discovered in 1880 by Jacques and Pierre Curie. Its application has since been extended to actuators and sensors, widely used in industry, automotive, and aerospace applications. The last two decades have seen intensive research in piezoelectric theory in an effort to effectively capture and control the distinctive coupling of electricity and elasticity. However, due to the complexity of the theory involved, finite element and numerical methods are often used in the process. Limited analytical exact solutions are also found in literature. The objective of this work is to devise a multiphysics modeling and simulation process to develop thin piezoelectric film sensors to measure the vibration of structures with complex shapes and boundary conditions. First, the output charge of generic piezoelectric films, respectively attached to a beam and a plate, is modeled using ANSYS and experimentally verified. Second, the modeling method is extended to a cylindrical shell followed by experimental verifications. Appropriate material properties obtained from past researches were incorporated as required. Finally, shaped sensors for the measurement of specific dynamic characteristics of a beam, plate and cylindrical shell respectively, are developed and experimentally validated. The results show that Multiphysics modeling can be an efficient design tool and be effectively used to simulate complex systems. This tool can be also used to detect or simulate design flaws and errors

Human Health Engineering Volume II

In this Special Issue on “Human Health Engineering Volume II”, we invited submissions exploring recent contributions to the field of human health engineering, i.e., technology for monitoring the physical or mental health status of individuals in a variety of applications. Contributions could focus on sensors, wearable hardware, algorithms, or integrated monitoring systems. We organized the different papers according to their contributions to the main parts of the monitoring and control engineering scheme applied to human health applications, namely papers focusing on measuring/sensing physiological variables, papers highlighting health-monitoring applications, and examples of control and process management applications for human health. In comparison to biomedical engineering, we envision that the field of human health engineering will also cover applications for healthy humans (e.g., sports, sleep, and stress), and thus not only contribute to the development of technology for curing patients or supporting chronically ill people, but also to more general disease prevention and optimization of human well-being

The temporal pattern of impulses in primary afferents analogously encodes touch and hearing information

An open question in neuroscience is the contribution of temporal relations between individual impulses in primary afferents in conveying sensory information. We investigated this question in touch and hearing, while looking for any shared coding scheme. In both systems, we artificially induced temporally diverse afferent impulse trains and probed the evoked perceptions in human subjects using psychophysical techniques. First, we investigated whether the temporal structure of a fixed number of impulses conveys information about the magnitude of tactile intensity. We found that clustering the impulses into periodic bursts elicited graded increases of intensity as a function of burst impulse count, even though fewer afferents were recruited throughout the longer bursts. The interval between successive bursts of peripheral neural activity (the burst-gap) has been demonstrated in our lab to be the most prominent temporal feature for coding skin vibration frequency, as opposed to either spike rate or periodicity. Given the similarities between tactile and auditory systems, second, we explored the auditory system for an equivalent neural coding strategy. By using brief acoustic pulses, we showed that the burst-gap is a shared temporal code for pitch perception between the modalities. Following this evidence of parallels in temporal frequency processing, we next assessed the perceptual frequency equivalence between the two modalities using auditory and tactile pulse stimuli of simple and complex temporal features in cross-sensory frequency discrimination experiments. Identical temporal stimulation patterns in tactile and auditory afferents produced equivalent perceived frequencies, suggesting an analogous temporal frequency computation mechanism. The new insights into encoding tactile intensity through clustering of fixed charge electric pulses into bursts suggest a novel approach to convey varying contact forces to neural interface users, requiring no modulation of either stimulation current or base pulse frequency. Increasing control of the temporal patterning of pulses in cochlear implant users might improve pitch perception and speech comprehension. The perceptual correspondence between touch and hearing not only suggests the possibility of establishing cross-modal comparison standards for robust psychophysical investigations, but also supports the plausibility of cross-sensory substitution devices

Instrumentation, Data, And Algorithms For Visually Understanding Haptic Surface Properties

Autonomous robots need to efficiently walk over varied surfaces and grasp diverse objects. We hypothesize that the association between how such surfaces look and how they physically feel during contact can be learned from a database of matched haptic and visual data recorded from various end-effectors\u27 interactions with hundreds of real-world surfaces. Testing this hypothesis required the creation of a new multimodal sensing apparatus, the collection of a large multimodal dataset, and development of a machine-learning pipeline. This thesis begins by describing the design and construction of the Portable Robotic Optical/Tactile ObservatioN PACKage (PROTONPACK, or Proton for short), an untethered handheld sensing device that emulates the capabilities of the human senses of vision and touch. Its sensory modalities include RGBD vision, egomotion, contact force, and contact vibration. Three interchangeable end-effectors (a steel tooling ball, an OptoForce three-axis force sensor, and a SynTouch BioTac artificial fingertip) allow for different material properties at the contact point and provide additional tactile data. We then detail the calibration process for the motion and force sensing systems, as well as several proof-of-concept surface discrimination experiments that demonstrate the reliability of the device and the utility of the data it collects. This thesis then presents a large-scale dataset of multimodal surface interaction recordings, including 357 unique surfaces such as furniture, fabrics, outdoor fixtures, and items from several private and public material sample collections. Each surface was touched with one, two, or three end-effectors, comprising approximately one minute per end-effector of tapping and dragging at various forces and speeds. We hope that the larger community of robotics researchers will find broad applications for the published dataset. Lastly, we demonstrate an algorithm that learns to estimate haptic surface properties given visual input. Surfaces were rated on hardness, roughness, stickiness, and temperature by the human experimenter and by a pool of purely visual observers. Then we trained an algorithm to perform the same task as well as infer quantitative properties calculated from the haptic data. Overall, the task of predicting haptic properties from vision alone proved difficult for both humans and computers, but a hybrid algorithm using a deep neural network and a support vector machine achieved a correlation between expected and actual regression output between approximately ρ = 0.3 and ρ = 0.5 on previously unseen surfaces