Bio-Inspired Obstacle Avoidance: from Animals to Intelligent Agents

A considerable amount of research in the field of modern robotics deals with mobile agents and their autonomous operation in unstructured, dynamic, and unpredictable environments. Designing robust controllers that map sensory input to action in order to avoid obstacles remains a challenging task. Several biological concepts are amenable to autonomous navigation and reactive obstacle avoidance. We present an overview of most noteworthy, elaborated, and interesting biologically-inspired approaches for solving the obstacle avoidance problem. We categorize these approaches into three groups: nature inspired optimization, reinforcement learning, and biorobotics. We emphasize the advantages and highlight potential drawbacks of each approach. We also identify the benefits of using biological principles in artificial intelligence in various research areas

Missing signal restoration by means of an entropy criterion

Tese de mestrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Adaptive Boltzmann Medical Dataset Machine Learning

The RBM is a stochastic energy-based model of an unsupervised neural network (RBM). RBM is a key pre-training for Deep Learning. Structure of RBM includes weights and coefficients for neurons. Better network structure allows us to examine data more thoroughly, which is good. We looked at the variance of parameters in learning on demand to fix the problem. To determine why RBM's energy function fluctuates, we'll look at its parameter variance. A neuron generation and annihilation algorithm is smeared with an adaptive RBM learning method to determine the optimal number of hidden neurons for attribute imputation during training. When the energy function isn't converged and parameter variance is high, a hidden neuron is generated. If the neuron doesn't disrupt learning, it'll destroy the hidden neuron. In this study, some yardstick PIMA data sets were tested

Living Innovation Laboratory Model Design and Implementation

Living Innovation Laboratory (LIL) is an open and recyclable way for multidisciplinary researchers to remote control resources and co-develop user centered projects. In the past few years, there were several papers about LIL published and trying to discuss and define the model and architecture of LIL. People all acknowledge about the three characteristics of LIL: user centered, co-creation, and context aware, which make it distinguished from test platform and other innovation approaches. Its existing model consists of five phases: initialization, preparation, formation, development, and evaluation. Goal Net is a goal-oriented methodology to formularize a progress. In this thesis, Goal Net is adopted to subtract a detailed and systemic methodology for LIL. LIL Goal Net Model breaks the five phases of LIL into more detailed steps. Big data, crowd sourcing, crowd funding and crowd testing take place in suitable steps to realize UUI, MCC and PCA throughout the innovation process in LIL 2.0. It would become a guideline for any company or organization to develop a project in the form of an LIL 2.0 project. To prove the feasibility of LIL Goal Net Model, it was applied to two real cases. One project is a Kinect game and the other one is an Internet product. They were both transformed to LIL 2.0 successfully, based on LIL goal net based methodology. The two projects were evaluated by phenomenography, which was a qualitative research method to study human experiences and their relations in hope of finding the better way to improve human experiences. Through phenomenographic study, the positive evaluation results showed that the new generation of LIL had more advantages in terms of effectiveness and efficiency.Comment: This is a book draf

Dynamics meets Morphology: towards Dymorph Computation

In this dissertation, approaches are presented for both technically using and investigating biological principles with oscillators in the context of electrical engineering, in particular neuromorphic engineering. Thereby, dynamics as well as morphology as important neuronal principles were explicitly selected, which shape the information processing in the human brain and distinguish it from other technical systems. The aspects and principles selected here are adaptation during the encoding of stimuli, the comparatively low signal transmission speed, the continuous formation and elimination of connections, and highly complex, partly chaotic, dynamics. The selection of these phenomena and properties has led to the development of a sensory unit that is capable of encoding mechanical stress into a series of voltage pulses by the use of a MOSFET augmented by AlScN. The circuit is based on a leaky integrate and fire neuron model and features an adaptation of the pulse frequency. Furthermore, the slow signal transmission speed of biological systems was the motivation for the investigation of a temporal delay in the feedback of the output pulses of a relaxation oscillator. In this system stable pulse patterns which form due to so-called jittering bifurcations could be observed. In particular, switching between different stable pulse patterns was possible to induce. In the further course of the work, the first steps towards time-varying coupling of dynamic systems are investigated. It was shown that in a system consisting of dimethyl sulfoxid and zinc acetate, oscillators can be used to force the formation of filaments. The resulting filaments then lead to a change in the dynamics of the oscillators. Finally, it is shown that in a system with chaotic dynamics, the extension of it with a memristive device can lead to a transient stabilisation of the dynamics, a behaviour that can be identified as a repeated pass of Hopf bifurcations

Hardrock Seismic Reflection Through Cover: Defining Controls on Mineralization via Reflection Attribute Analysis

This study attempts to modify oil and gas industry seismic processing and interpretation techniques for use in Carlin-type deposit (CTD) gold exploration. Magmatic and deformation overprints on the Nevada carbonate platform-slope setting present challenges in seismic interpretation when compared to conventional seismic data, which is more commonly imaged in petroliferous basins with low levels of deformation. Barrick Gold Corporation provided 2D seismic reflection data for this case study, which assesses the viability of certain seismic practices when applied to hardrock seismic data collected in NE Nevada. Initial seismic interpretations of the pre-stack depth migrated (PSDM) sections located first-order structures and enhanced the geological model. This study uses derivatives of the PSDM, called seismic attributes, in an attempt to improve interpretability. Seismic attributes can reveal structural and stratigraphic features that are not apparent in the conventional PSDM amplitude data. Attribute analysis in this study leverages correlations made from a seismic response database of ~500 petrophysical drill core samples. These petrophysical measurements indicate that the ore zone exhibits a porosity, acoustic impedance, decarbonatization relationship that is distinguishable from unaltered rock. Down-hole geophysical data suggest an even larger contrast between altered and unaltered limestone. Given sufficient data quality, these observations make attribute analysis for detection of CTD alteration viable. An exhaustive calculation of attributes applied to one 2D reflection profile, which transects the Goldrush CTD resource, suggests that energy- and frequency-based attributes best highlight the ore zone, which is expressed as a chaotic zone of reduced amplitude within one 2D profile. RMS amplitude and instantaneous amplitude identify broad zones of low amplitude whereas an average frequency attribute highlights possible high-frequency attenuation effects in the vicinity of the ore-zone. The sweetness and frequency washout attributes combine frequency and amplitude attributes to more effectively highlight the ore zone. However, the erratic response of sweetness and frequency washout suggest that they may be negatively affected by noise. One structural model is also presented, which used the instantaneous phase attribute to better visualize possible thrust faulting