Bio-inspired Dynamic Control Systems with Time Delays

The world around us exhibits a rich and ever changing environment of startling, bewildering and fascinating complexity. Almost everything is never as simple as it seems, but through the chaos we may catch fleeting glimpses of the mechanisms within. Throughout the history of human endeavour we have mimicked nature to harness it for our own ends. Our attempts to develop truly autonomous and intelligent machines have however struggled with the limitations of our human ability. This has encouraged some to shirk this responsibility and instead model biological processes and systems to do it for us. This Thesis explores the introduction of continuous time delays into biologically inspired dynamic control systems. We seek to exploit rich temporal dynamics found in physical and biological systems for modelling complex or adaptive behaviour through the artificial evolution of networks to control robots. Throughout, arguments have been presented for the modelling of delays not only to better represent key facets of physical and biological systems, but to increase the computational potential of such systems for the synthesis of control. The thorough investigation of the dynamics of small delayed networks with a wide range of time delays has been undertaken, with a detailed mathematical description of the fixed points of the system and possible oscillatory modes developed to fully describe the behaviour of a single node. Exploration of the behaviour for even small delayed networks illustrates the range of complex behaviour possible and guides the development of interesting solutions. To further exploit the potential of the rich dynamics in such systems, a novel approach to the 3D simulation of locomotory robots has been developed focussing on minimising the computational cost. To verify this simulation tool a simple quadruped robot was developed and the motion of the robot when undergoing a manually designed gait evaluated. The results displayed a high degree of agreement between the simulation and laser tracker data, verifying the accuracy of the model developed. A new model of a dynamic system which includes continuous time delays has been introduced, and its utility demonstrated in the evolution of networks for the solution of simple learning behaviours. A range of methods has been developed for determining the time delays, including the novel concept of representing the time delays as related to the distance between nodes in a spatial representation of the network. The application of these tools to a range of examples has been explored, from Gene Regulatory Networks (GRNs) to robot control and neural networks. The performance of these systems has been compared and contrasted with the efficacy of evolutionary runs for the same task over the whole range of network and delay types. It has been shown that delayed dynamic neural systems are at least as capable as traditional Continuous Time Recurrent Neural Networks (CTRNNs) and show significant performance improvements in the control of robot gaits. Experiments in adaptive behaviour, where there is not such a direct link between the enhanced system dynamics and performance, showed no such discernible improvement. Whilst we hypothesise that the ability of such delayed networks to generate switched pattern generating nodes may be useful in Evolutionary Robotics (ER) this was not borne out here. The spatial representation of delays was shown to be more efficient for larger networks, however these techniques restricted the search to lower complexity solutions or led to a significant falloff as the network structure becomes more complex. This would suggest that for anything other than a simple genotype, the direct method for encoding delays is likely most appropriate. With proven benefits for robot locomotion and the open potential for adaptive behaviour delayed dynamic systems for evolved control remain an interesting and promising field in complex systems research

Low Power Circuits for Smart Flexible ECG Sensors

Cardiovascular diseases (CVDs) are the world leading cause of death. In-home heart condition monitoring effectively reduced the CVD patient hospitalization rate. Flexible electrocardiogram (ECG) sensor provides an affordable, convenient and comfortable in-home monitoring solution. The three critical building blocks of the ECG sensor i.e., analog frontend (AFE), QRS detector, and cardiac arrhythmia classifier (CAC), are studied in this research. A fully differential difference amplifier (FDDA) based AFE that employs DC-coupled input stage increases the input impedance and improves CMRR. A parasitic capacitor reuse technique is proposed to improve the noise/area efficiency and CMRR. An on-body DC bias scheme is introduced to deal with the input DC offset. Implemented in 0.35m CMOS process with an area of 0.405mm2, the proposed AFE consumes 0.9W at 1.8V and shows excellent noise effective factor of 2.55, and CMRR of 76dB. Experiment shows the proposed AFE not only picks up clean ECG signal with electrodes placed as close as 2cm under both resting and walking conditions, but also obtains the distinct -wave after eye blink from EEG recording. A personalized QRS detection algorithm is proposed to achieve an average positive prediction rate of 99.39% and sensitivity rate of 99.21%. The user-specific template avoids the complicate models and parameters used in existing algorithms while covers most situations for practical applications. The detection is based on the comparison of the correlation coefficient of the user-specific template with the ECG segment under detection. The proposed one-target clustering reduced the required loops. A continuous-in-time discrete-in-amplitude (CTDA) artificial neural network (ANN) based CAC is proposed for the smart ECG sensor. The proposed CAC achieves over 98% classification accuracy for 4 types of beats defined by AAMI (Association for the Advancement of Medical Instrumentation). The CTDA scheme significantly reduces the input sample numbers and simplifies the sample representation to one bit. Thus, the number of arithmetic operations and the ANN structure are greatly simplified. The proposed CAC is verified by FPGA and implemented in 0.18m CMOS process. Simulation results show it can operate at clock frequencies from 10KHz to 50MHz. Average power for the patient with 75bpm heart rate is 13.34W

Diel Rhythmicity Found in Behavior but Not Biogenic Amine Levels in the Funnel-Web Spider Agelenopsis pennsylvanica (Araneae, Agelenidae)

Quantifying individual differences in behavior and the extent that behavior is influenced by circadian control is of paramount importance in behavioral ecology. In addition, the proximate mechanisms underlying behavior are also critical in order to obtain a more complete picture of how behavior evolves. Biogenic amines (BAs) are simple nitrogenous compounds derived from amino acids and have been consistently and extensively linked to behavior. For this study, we analyzed temporal patterns of BAs in relation to the antipredator (boldness) and aggressive behavior in female Agelenopsis pennsylvanica, a funnel-web spider. Using HPLC-ED, we compared behavioral responses to temporal patterns of octopamine and serotonin, two BAs known to influence behavior in invertebrates. Our results suggest that, while there was a clear diel cycling pattern of both aggression and boldness, BAs do not follow this same pattern, suggesting that oscillations in absolute levels of BAs are not the underpinnings of behavioral oscillations

Drivers of introgression and fitness in the Saltmarsh-Nelson\u27s Sparrow hybrid zone

Hybrid zones can provide an understanding of the genetic basis of biodiversity maintenance and as well as insight into how interacting species respond to climate change, and how climate change may alter patterns of introgression. This body of research focuses on dynamics of hybridization between the Saltmarsh (Ammospiza caudacutus) and Nelson’s Sparrow (A. nelsoni) across two populations in the center of the hybrid zone to gain a window into both the evolutionary processes underlying the relationship between these species and the role of climate change and adaptive introgression on the future persistence of the two sparrows. In Chapter 1, I determined patterns of introgression between Saltmarsh and Nelson’s Sparrows on a fine-scale across a habitat gradient and on a broad-scale through comparison with known patterns in the southern range of the zone. I explored the fitness consequences of hybridization to female Saltmarsh, Nelson’s and hybrid sparrows in relation to environmental conditions and tidal marsh nesting adaptations in Chapter 2. Finally, in Chapter 3, I evaluated the relative fitness of male Saltmarsh, Nelson’s and hybrid individuals in relation to competitive ability and male condition. I intensively sampled sparrow adults (n = 218) and chicks (n = 326) and determined the success of 201 nests over two years at two marshes in the center of the hybrid zone located at Popham Beach State Park and Wharton Point on Maquoit Bay on the northeastern coast of the United States, between Brunswick, Maine and Phippsburg, Maine. I used a ddRAD sequencing approach to identify a panel of135 fixed SNPs, which I used to calculate a hybrid index and determine the genotypic composition of individuals and the level of admixture of the populations. In addition, a separate panel of 589 SNPs was used to assign paternity to offspring and reconstruct mating pairs. I compared genotypic composition and patterns of introgression across two sites in the center of the hybrid zone with previous work done in the southern portion of the hybrid zone. I tested for reduced survival of hybrid females in support of Haldane’s Rule and also for assortative mating between the species. I modeled daily nest survival and fledging success between Saltmarsh, Nelson’s and hybrid females in relation to tidal cycles and known tidal marsh nesting adaptations. Lastly, I compared the number of offspring sired by Saltmarsh, Nelson’s and hybrid males in relation to male condition, as measured by three secondary and one primary male sexual traits. I found that population density differences across the hybrid zone influenced patterns of introgression, such that in the center of the zone there is relatively equal backcrossing in both the Saltmarsh and Nelson’s Sparrow direction compared to asymmetric backcrossing toward the Saltmarsh Sparrow in the southern hybrid zone (Walsh et al., 2015a). Local site-specific characteristics of the two study populations influenced the distribution of genotypes and patterns of introgression across a tidal marsh habitat gradient, such that there were a higher number of hybrids and more backcrossing towards Nelson’s Sparrow at the inland than coastal site. I also observed twice as many recent-generation hybrid female nestlings than adults in the population, supporting Haldane’s Rule, and a significant correlation between mother and father hybrid index (r = 0.73, P \u3c0.0001), indicative of assortative mating. I found differential fitness among Saltmarsh, Nelson’s and hybrid females. Birds with predominantly Saltmarsh Sparrow alleles had higher reproductive success than birds with predominantly Nelson’s Sparrows alleles, with hybrids being intermediate between the two. Fledging success models suggested that the number of offspring fledged also increased with two known tidal marsh nesting adaptations: nest height and nesting synchrony with tidal cycles. I found a positive relationship between hybrid index and fitness in daily nest survival in 2016, but not across both breeding seasons (2016 & 2017) combined, likely due to differing levels of nest flooding. The strongest and most consistent predictors of daily nest survival were nesting synchrony with lunar tidal flooding cycles (female behavioral adaptation) and daily maximum tide height. I also found differential male fitness, with Saltmarsh Sparrows siring more offspring than Nelson’s Sparrows (ANOVA; F = 3.81, P =0.04) and hybrids intermediate in fitness, although more similar to Nelson’s Sparrows. Cloacal Protuberance (CP) volume and body mass were significant predictors of interspecific fitness, providing evidence that pre and post copulatory sexual selection may be acting on body size and CP volume (as a proxy for sperm competition) to drive mating patterns within and between the Saltmarsh and Nelson’s Sparrows. Saltmarsh-Nelson’s Sparrow hybrid zone structure and maintenance appear to be driven by endogenous and exogenous factors at multiple spatial scales. Fitness differences among parental species and hybrids, relative population densities and species distributions, differential adaptation to local environments, and pre-zygotic and post-zygotic reproductive isolating mechanisms all play a role in the dynamics of this hybrid zone

Aerospace Medicine and Biology. A continuing bibliography with indexes, supplement 151

This bibliography lists 195 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1976

Biologically inspired learning system

Learning Systems used on robots require either a-priori knowledge in the form of models, rules of thumb or databases or require that robot to physically execute multitudes of trial solutions. The first requirement limits the robot’s ability to operate in unstructured changing environments, and the second limits the robot’s service life and resources. In this research a generalized approach to learning was developed through a series of algorithms that can be used for construction of behaviors that are able to cope with unstructured environments through adaptation of both internal parameters and system structure as a result of a goal based supervisory mechanism. Four main learning algorithms have been developed, along with a goal directed random exploration routine. These algorithms all use the concept of learning from a recent memory in order to save the robot/agent from having to exhaustively execute all trial solutions. The first algorithm is a reactive online learning algorithm that uses a supervised learning to find the sensor/action combinations that promote realization of a preprogrammed goal. It produces a feed forward neural network controller that is used to control the robot. The second algorithm is similar to first in that it uses a supervised learning strategy, but it produces a neural network that considers past values, thus providing a non-reactive solution. The third algorithm is a departure from the first two in that uses a non-supervised learning technique to learn the best actions for each situation the robot encounters. The last algorithm builds a graph of the situations encountered by agent/robot in order to learn to associate the best actions with sensor inputs. It uses an unsupervised learning approach based on shortest paths to a goal situation in the graph in order to generate a non-reactive feed forward neural network. Test results were good, the first and third algorithms were tested in a formation maneuvering task in both simulation and onboard mobile robots, while the second and fourth were tested simulation

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Lessons Learned in Engineering

This Contractor Report (CR) is a compilation of Lessons Learned in approximately 55 years of engineering experience by each James C. Blair, Robert S. Ryan, and Luke A. Schutzenhofer. The lessons are the basis of a course on Lessons Learned that has been taught at Marshall Space Flight Center. The lessons are drawn from NASA space projects and are characterized in terms of generic lessons learned from the project experience, which are further distilled into overarching principles that can be applied to future projects. Included are discussions of the overarching principles followed by a listing of the lessons associated with that principle. The lesson with sub-lessons are stated along with a listing of the project problems the lesson is drawn from, then each problem is illustrated and discussed, with conclusions drawn in terms of Lessons Learned. The purpose of this CR is to provide principles learned from past aerospace experience to help achieve greater success in future programs, and identify application of these principles to space systems design. The problems experienced provide insight into the engineering process and are examples of the subtleties one experiences performing engineering design, manufacturing, and operations