caxenie Goto Github PK
Name: Cristian Axenie
Type: User
Company: TH Nürnberg
Bio: Full-time Researcher, Part-time Hacker, Any-time Innovator.
Twitter: cristian_axenie
Location: Munich & Nürnberg, Germany
Name: Cristian Axenie
Type: User
Company: TH Nürnberg
Bio: Full-time Researcher, Part-time Hacker, Any-time Innovator.
Twitter: cristian_axenie
Location: Munich & Nürnberg, Germany
ARTEMIC = Advanced Real-time Embedded Mobile robot Intelligent ControllerReal-time Linux fault tolerant control application for mobile robots. Multi-level control application: Fuzzy Sliding-Mode kinematic controller, EKF fault tolerance module and ultrasound based SLAM module.
Lectures and labs from BaseCamp DATA SCIENCE TRAINING PROGRAM.
End-to-End example with Big Data tools. Data from a Sumo simulation are sent to Kafka, processed by Flink, and real-time plotted by matplotlib.
CHIMERA: Combining Mechanistic Models and Machine Learning for Personalized Chemotherapy and Surgery Sequencing in Breast Cancer
Various code snippets
Simple implementation of the distributed cognitive maps for environment interpretation.
Simple implementation of the distributed cognitive maps for environment interpretation. Use artificial data selected / given by user. The input activity mildly influences the belief of the cortically inspired relaxation network. The network dynamics ensures convergence to a stable state in which all quantities are in agreement given the mathematical constraints. The constraints are learned through biologically plausible mechanisms from data.
Simple implementation of the distributed cognitive maps for environment interpretation. Use artificial data selected / given by user. The input activity mildly influences the belief of the cortically inspired relaxation network. The network dynamics ensures convergence to a stable state in which all quantities are in agreement given the mathematical constraints. The constraints are learned through biologically plausible mechanisms from data. Sensory correlation learning neural network. Unsupervised learning of functional relationships between 2 input sensory streams. The network uses SOMs to encode the input data into a population code and Hebbian learning to extract the co-activation patterns encoding the function. Python implementation.
About Unsupervised learning of relations following the network combining WTA, HL and HAR over the projections sharpening architecture. Python jupyter demo.
Quadrotor implementation of the distributed cognitive maps for environment interpretation. Use artificial data selected / given by user. The input activity mildly influences the belief of the cortically inspired relaxation network. The network dynamics ensures convergence to a stable state in which all quantities are in agreement given the mathematical constraints. The constraints are learned through biologically plausible mechanisms from data. Sensory correlation learning neural network. Unsupervised learning of functional relationships between 2 input sensory streams. The network uses SOMs to encode the input data into a population code and Hebbian learning to extract the co-activation patterns encoding the function. Datasets from quadrotor system.
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Customer churn prediction using Fuzzy Inference Systems.
Unsupervised sensory correlation learning using Self-Organizing-Maps for sensor fusion. Each sensory variable projects onto a SOM network which depending on the global network connectivity (1, 2, ... , N vars) connects to other SOM associated with other sensory variables. Extension to extract also temporal correlations using a recursive architecture.
Simple implementation of the distributed cognitive maps for environment interpretation. The input activity mildly influences the belief of the cortically inspired relaxation network. The network dynamics ensures convergence to a stable state in which all quantities are in agreement given the mathematical constraints. The constraints are learned through biologically plausible mechanisms from data. Sensory correlation learning neural network. Unsupervised learning of functional relationships between 2 input sensory streams. The network uses SOMs to encode the input data into a population code and Hebbian learning to extract the co-activation patterns encoding the function. Modelling human circuitry development for multisensory integration. C code implementation.
Dynamic association model to learn correlations between paired modalities samples acquired from different sensors current scenario is using only 2 input modalities.
Test eimp throughput for NST WiFi modules interfacing.
Simple embededed serial (USART) to Ethernet (TCP/IP) converter using AVR32 and ATMega16 platforms. Development scheme for industrial automation and supervisory control and distributed data acquisition systems.
Fork of emokit with addon for the Neural Engineering class @ TUM
Hack Cambridge 2017 - Real-time Event-based Monitoring System for Seniors and Elderly using Neural Networks
Feature-Extraction - Theoretische Grundlagen, Methodik und Umsetzung in der Medizin (Brustkrebs-Datenanalyse)
Einführung in die Feature-Auswahl für die Data Science Pipeline
Simple implementation of the distributed cognitive maps for environment interpretation. Use artificial data selected / given by user. The input activity mildly influences the belief of the cortically inspired relaxation network. The network dynamics ensures convergence to a stable state in which all quantities are in agreement given the mathematical constraints.
Simple implementation of the distributed cognitive maps for environment interpretation. Use artificial data selected / given by user. The input activity mildly influences the belief of the cortically inspired relaxation network. The network dynamics ensures convergence to a stable state in which all quantities are in agreement given the mathematical constraints. Matlab version for easy prototyping and parametrisation.
Demo software usign population code for estimating arbitrary functions. The setup contains 2 input populations each coding some scalar (unimodal) variable which are projected onto a 2D network of units with neurons exhibiting short range excitation and long range inhibition dynamics. The ouput from the intermediate layer is projected to an output population the network has no explicit input and output as each of the populations may be considered inputs / outputs and the processing happens in the intermediate layer.
Trajectory tracking control for wheeled mobile robots in a robot soccer field using Fuzzy Logic.
Garmin Activity Tracker Datenanalyse
Sample demo software for Genetic Algorithms for RF antenna design. The task is to find an optimal wire antenna design (shape) knowing the number of points, the frequency and the desired gain.
Simple Genetic Algorithm for function optimization demo.
Sample demo software for Genetic Algorithms for solving Traveling Salesman Problem.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
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TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
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We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.