Distributed, layered and reliable computing nets to represent neuronal receptive fields

Abstract. Receptive fields of retinal and other sensory neurons show a large variety of spatiotemporal linear and non linear types of responses to local stimuli. In visual neurons, these responses present either asymmetric sensitive zones or center-surround organization. In most cases, the nature of the responses suggests the existence of a kind of distributed computation prior to the integration by the final cell which is evidently supported by the anatomy. We describe a new kind of discrete and continuous filters to model the kind of computations taking place in the receptive fields of retinal cells. To show their performance in the analysis of diferent non-trivial neuron-like structures, we use a computer tool specifically programmed by the authors to that efect. This tool is also extended to study the efect of lesions on the whole performance of our model nets

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

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area

Disturbance rejection for U.A.S. aircraft using bio-inspired strain sensing

A bio inspired gust rejection mechanism based on structural inputs is proposed. Insect wings possess a wealth of sensor systems which typically consist of fast reflexive neuronal paths. Stretch and strain sensors on insect wings are used for flight control and can be found across many species. These are used for monitoring of bending and torsion during flight. The fast reflexive and proprioceptive mechanisms based on strain sensing found in nature are the inspiration for this work. A strain feedback controller allows for anticipation of the onset of rigid body dynamics due to gust perturbations. This anticipation stems from sensing of higher order states and the possibility of reacting before lower order states are reached. High bandwidth inner loop compensation is therefore enabled. Forces and moments are proportional to wing strain patterns and can be used in fast reaction inner loops. Strain sensors are used for providing an indirect estimation of the differential forces applied to the aircraft wing and therefore to the aircraft rigid body. These sensors can be distributed over the surface of the aircraft wing to encode multiple degree of freedom disturbances. Sensor locations for disturbance rejection are determined based on metrics associated to the observability Grammian. The locations are preselected based on modal energy analyses and are chosen according to wide field integration patterns. A model for wide field integrated strain based on mass participation factors is proposed as well as one which is based on the physics of the forces and moments acting on the wing producing strain patterns which can be used for disturbance rejection. Models of the differential forces via strains on the wings are proposed. Strain feedback was implemented in four platforms under different types of disturbances. The platforms consisted of a glider, a quadrotor, a wing section for wind tunnel testing and an RC airplane with a full span wing. The disturbances included discrete gusts as well as turbulence. The results of using strain feedback showed not only to be faster than IMU estimations but also to be better when compared to a classical attitude controller implementation

Material-based design computation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Architecture, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 306-328).The institutionalized separation between form, structure and material, deeply embedded in modernist design theory, paralleled by a methodological partitioning between modeling, analysis and fabrication, resulted in geometric-driven form generation. Such prioritization of form over material was carried into the development and design logic of CAD. Today, under the imperatives and growing recognition of the failures and environmental liabilities of this approach, modern design culture is experiencing a shift to material aware design. Inspired by Nature's strategies where form generation is driven by maximal performance with minimal resources through local material property variation, the research reviews, proposes and develops models and processes for a material-based approach in computationally enabled form-generation. Material-based Design Computation is developed and proposed as a set of computational strategies supporting the integration of form, material and structure by incorporating physical form-finding strategies with digital analysis and fabrication. In this approach, material precedes shape, and it is the structuring of material properties as a function of structural and environmental performance that generates design form. The thesis proposes a unique approach to computationally-enabled form-finding procedures, and experimentally investigates how such processes contribute to novel ways of creating, distributing and depositing material forms. Variable Property Design is investigated as a theoretical and technical framework by which to model, analyze and fabricate objects with graduated properties designed to correspond to multiple and continuously varied functional constraints. The following methods were developed as the enabling mechanisms of Material Computation: Tiling Behavior & Digital Anisotropy, Finite Element Synthesis, and Material Pixels. In order to implement this approach as a fabrication process, a novel fabrication technology, termed Variable Property Rapid Prototyping has been developed, designed and patented. Among the potential contributions is the achievement of a high degree of customization through material heterogeneity as compared to conventional design of components and assemblies. Experimental designs employing suggested theoretical and technical frameworks, methods and techniques are presented, discussed and demonstrated. They support product customization, rapid augmentation and variable property fabrication. Developed as approximations of natural formation processes, these design experiments demonstrate the contribution and the potential future of a new design and research field.by Neri Oxman.Ph.D

A review of UAV autonomous navigation in GPS-denied environments

Unmanned aerial vehicles (UAVs) have drawn increased research interest in recent years, leading to a vast number of applications, such as, terrain exploration, disaster assistance and industrial inspection. Unlike UAV navigation in outdoor environments that rely on GPS (Global Positioning System) for localization, indoor navigation cannot rely on GPS due to the poor quality or lack of signal. Although some reviewing papers particularly summarized indoor navigation strategies (e.g., Visual-based Navigation) or their specific sub-components (e.g., localization and path planning) in detail, there still lacks a comprehensive survey for the complete navigation strategies that cover different technologies. This paper proposes a taxonomy which firstly classifies the navigation strategies into Mapless and Map-based ones based on map usage and then, respectively categorizes the Mapless navigation into Integrated, Direct and Indirect approaches via common characteristics. The Map-based navigation is then split into Known Map/Spaces and Map-building via prior knowledge. In order to analyze these navigation strategies, this paper uses three evaluation metrics (Path Length, Deviation Rate and Exploration Efficiency) according to the common purposes of navigation to show how well they can perform. Furthermore, three representative strategies were selected and 120 flying experiments conducted in two reality-like simulated indoor environments to show their performances against the evaluation metrics proposed in this paper, i.e., the ratio of Successful Flight, the Mean time of Successful Flight, the Mean Length of Successful Flight, the Mean time of Flight, and the Mean Length of Flight. In comparison to the CNN-based Supervised Learning (directly maps visual observations to UAV controls) and the Frontier-based navigation (necessitates continuous global map generation), the experiments show that the CNN-based Distance Estimation for navigation trades off the ratio of Successful Flight and the required time and path length. Moreover, this paper identifies the current challenges and opportunities which will drive UAV navigation research in GPS-denied environments

Advances towards behaviour-based indoor robotic exploration

215 p.The main contributions of this research work remain in object recognition by computer vision, by one side, and in robot localisation and mapping by the other. The first contribution area of the research address object recognition in mobile robots. In this area, door handle recognition is of great importance, as it help the robot to identify doors in places where the camera is not able to view the whole door. In this research, a new two step algorithm is presented based on feature extraction that aimed at improving the extracted features to reduce the superfluous keypoints to be compared at the same time that it increased its efficiency by improving accuracy and reducing the computational time. Opposite to segmentation based paradigms, the feature extraction based two-step method can easily be generalized to other types of handles or even more, to other type of objects such as road signals. Experiments have shown very good accuracy when tested in real environments with different kind of door handles. With respect to the second contribution, a new technique to construct a topological map during the exploration phase a robot would perform on an unseen office-like environment is presented. Firstly a preliminary approach proposed to merge the Markovian localisation in a distributed system, which requires low storage and computational resources and is adequate to be applied in dynamic environments. In the same area, a second contribution to terrain inspection level behaviour based navigation concerned to the development of an automatic mapping method for acquiring the procedural topological map. The new approach is based on a typicality test called INCA to perform the so called loop-closing action. The method was integrated in a behaviour-based control architecture and tested in both, simulated and real robot/environment system. The developed system proved to be useful also for localisation purpose

Biochemical sensing based on metal-organic architectures

This research work is about the use of metal–organic frameworks (MOFs) as a platform for biochemical sensing purposes. Different metal–organic architectures were used and individual approaches were pursued, such as the synthesis of electrically conductive hybrid MOF structures as chemiresistive sensing material and the integration of MOF particles into a polymer membrane to explore their potential for sweat biomarker detection using Raman spectroscopy. The focus in each project was on the application of our MOF as sensor material and the evaluation of the signal response upon exposure to relevant analytes. The achievements presented in this work emphasize the great potential that metal–organic architectures have as active material for the sensing of biochemical analytes

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201