Методичні рекомендації щодо використання британського досвіду підготовки вчителів-філологів до навчання дорослих у вітчизняній педагогічній практиці

У методичних рекомендаціях охарактеризовано особливості підготовки вчителів-філологів до навчання дорослих у системі неперервної освіти Великої Британії, виокремлено можливості використання британського досвіду у вітчизняній педагогічній теорії та практиці. Методичні рекомендації адресуються науковцям і практикам

Dynamically reconfigurable bio-inspired hardware

During the last several years, reconfigurable computing devices have experienced an impressive development in their resource availability, speed, and configurability. Currently, commercial FPGAs offer the possibility of self-reconfiguring by partially modifying their configuration bitstream, providing high architectural flexibility, while guaranteeing high performance. These configurability features have received special interest from computer architects: one can find several reconfigurable coprocessor architectures for cryptographic algorithms, image processing, automotive applications, and different general purpose functions. On the other hand we have bio-inspired hardware, a large research field taking inspiration from living beings in order to design hardware systems, which includes diverse topics: evolvable hardware, neural hardware, cellular automata, and fuzzy hardware, among others. Living beings are well known for their high adaptability to environmental changes, featuring very flexible adaptations at several levels. Bio-inspired hardware systems require such flexibility to be provided by the hardware platform on which the system is implemented. In general, bio-inspired hardware has been implemented on both custom and commercial hardware platforms. These custom platforms are specifically designed for supporting bio-inspired hardware systems, typically featuring special cellular architectures and enhanced reconfigurability capabilities; an example is their partial and dynamic reconfigurability. These aspects are very well appreciated for providing the performance and the high architectural flexibility required by bio-inspired systems. However, the availability and the very high costs of such custom devices make them only accessible to a very few research groups. Even though some commercial FPGAs provide enhanced reconfigurability features such as partial and dynamic reconfiguration, their utilization is still in its early stages and they are not well supported by FPGA vendors, thus making their use difficult to include in existing bio-inspired systems. In this thesis, I present a set of architectures, techniques, and methodologies for benefiting from the configurability advantages of current commercial FPGAs in the design of bio-inspired hardware systems. Among the presented architectures there are neural networks, spiking neuron models, fuzzy systems, cellular automata and random boolean networks. For these architectures, I propose several adaptation techniques for parametric and topological adaptation, such as hebbian learning, evolutionary and co-evolutionary algorithms, and particle swarm optimization. Finally, as case study I consider the implementation of bio-inspired hardware systems in two platforms: YaMoR (Yet another Modular Robot) and ROPES (Reconfigurable Object for Pervasive Systems); the development of both platforms having been co-supervised in the framework of this thesis

A Social Approach for Target Localization: Simulation and Implementation in the marXbot Robot

Foraging is a common benchmark problem in collective robotics in which a robot (the forager) explores a given environment while collecting items for further deposition at specific locations. A typical real-world application of foraging is garbage collection where robots collect garbage for further disposal in pre-defined locations. This work proposes a method to cooperatively perform the task of finding such locations: instead of using local or global localization strategies relying on pre-installed infrastructure, the proposed approach takes advantage of the knowledge gathered by a population about the localization of the targets. In our approach, robots communicate in an intrinsic way the estimation about how near they are from a target; these estimations are used by neighbour robots for estimating their proximity, and for guiding the navigation of the whole population when looking for these specific areas. We performed several tests in a simulator, and we validated our approach on a population of real robots. For the validation tests we used a mobile robot called marXbot. In both cases (i.e., simulation and implementation on real robots), we found that the proposed approach efficiently guides the robots towards the pre-specified targets while allowing the modulation of their speed

Self-Organizing Machine Architecture

International audienceSOMA is a France-Switzerland collaborative project which aims to develop a computing machine with self-organizing properties inspired by the functioning of the brain. The SOMA project addresses this challenge by lying at the intersection of four main research fields, namely adaptive reconfigurable computing, cellular computing, computational neuroscience, and neuromorphic engineering. In the framework of SOMA, we designed the SCALP platform, a 3D array of FPGAs and processors permitting to prototype and evaluate self-organization mechanisms on physical cellular machines

Neuromorphic hardware as a self-organizing computing system

International audienceThis paper presents the self-organized neuromorphic architecture named SOMA. The objective is to study neural-based self-organization in computing systems and to prove the feasibility of a self-organizing hardware structure. Considering that these properties emerge from large scale and fully connected neural maps, we will focus on the definition of a self-organizing hardware architecture based on digital spiking neurons that offer hardware efficiency. From a biological point of view, this corresponds to a combination of the so-called synaptic and structural plasticities. We intend to define computational models able to simultaneously self-organize at both computation and communication levels, and we want these models to be hardware-compliant, fault tolerant and scalable by means of a neuro-cellular structure

Real-time audio group delay correction with FFT convolution on FPGA

This paper describes the implementation of a digital hardware architecture for correcting the effect produced by group delay distortion on multiway loudspeakers. The correction is performed by a digital filter implemented in the form of an FFT convolution. The application imposes real-time execution on an embedded low-cost platform using a high-resolution audio format (stereo coded on 24 bits @ 96 kHz). The result is a pipelined streaming architecture composed of FFT-complex multiplication-iFFT performed on 32,768 samples using a floating-point representation. The filter computation is performed in 72 ms and the overall maximal audio latency is 169 ms running on a Cyclone V FPGA

UbiManager ::a software tool for managing ubichips

This paper introduces the UbiManager, a tool for managing the ubichip reconfigurable circuit. The ubichip is a custom reconfigurable electronic device for implementing circuits featuring bio-inspired mechanisms like growth, learning, and evolution. The ubichip has been developed in the framework of Perplexus, a European project that aims to develop a scalable hardware platform made of bio-inspired custom reconfigurable devices for simulating large-scale complex systems. In this paper, we present the software tool used for designing, simulating, emulating, debugging, configuring, and monitoring the systems to be implemented in the ubichip. This paper also presents the dissemination plans of the UbiManager, that consist in a web platform allowing researchers to access the hardware platform from any remote base station