scholarly journals Open Technology : The Free EEG 32 project.

2020 ◽  
Author(s):  
Anil Kumar Bheemaiah
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Data Streams ◽  
Spectral Power ◽  
Just In Time ◽  
Success Factor ◽  
Maharishi Mahesh Yogi ◽  
Chaos Analysis ◽  
On Demand ◽  
Open Source Hardware

Open Technology, with just in time manufacturing is illustrated with the Free EEG 32 project, with open source hardware and software fulfilled with Seeed Studios, and Mouser as the vendor. The product illustrates the use of 32 EEG channels for M.L based learning of 32 dimensional data streams for the classification, with deep learning of meditative states, quantified as spectral power ratios, differential spectral power ratios and identification of the various bardos, and biofeedback mechanisms.Inspired by the MUSE headset, soundscape engineering is used as feedback on meditation coherence and bird chirps indicate transcendental success factor thresholds, illustrated with the Transcendental Meditation (™) system of Maharishi Mahesh Yogi. Keywords: ™, EEG based hardware, 32 channels, Free EEG 32, Seeed studio, gerber, just in time, on-demand, drop-shipping, D-Commerce, portals, MUSE headband, FFT, Chaos analysis, deep learning

ON METHODS OF OBJECT DETECTION IN VIDEO STREAMS

2017 ◽  
Vol 2 (1) ◽  
pp. 80-87
Author(s):  
Puyda V. ◽  
◽  
Stoian. A.
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Feature Detection ◽  
Video Stream ◽  
Object Identification ◽  
Vision Systems ◽  
Modern Computer ◽  
Computer Vision Systems ◽  
Open Source Hardware

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed. In this paper, we study three algorithms which can be used to detect objects of different nature and are based on different approaches: detection of color non-uniformities, frame difference and feature detection. As the input data, we use a video stream which is obtained from a video camera or from an mp4 video file. Simulations and testing of the algoritms were done on a universal computer based on an open-source hardware, built on the Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC processor with frequency 1,5GHz. The software was created in Visual Studio 2019 using OpenCV 4 on Windows 10 and on a universal computer operated under Linux (Raspbian Buster OS) for an open-source hardware. In the paper, the methods under consideration are compared. The results of the paper can be used in research and development of modern computer vision systems used for different purposes. Keywords: object detection, feature points, keypoints, ORB detector, computer vision, motion detection, HSV model color

Promoting Hybrid Learning through an Open Source EBook Approach

2010 ◽  
Vol 14 (3) ◽  
Author(s):  
Xin Bai ◽  
Michael B. Smith
Keyword(s):  
Open Source ◽  
Building Blocks ◽  
Educational Technologies ◽  
Just In Time ◽  
Learning Modules ◽  
Learning Content ◽  
Long Run ◽  
E Learning ◽  
Teaching Efficiency ◽  
Content Design

Educational technology is developing rapidly, making education more accessible, affordable, adaptable, and equitable. Students now have the option to choose a campus that can provide excellent blended learning curriculum with minimal geographical restraints. We proactively explore ways to maximize the power of educational technologies to increase enrollment, reduce failure rates, improve teaching efficiency, and cut costs without sacrificing high quality or placing extra burden on faculty. This mission is accomplished through open source learning content design and development. We developed scalable, shareable, and sustainable e-learning modules as book chapters that can be distributed through both computers and mobile devices. The resulting e-learning building blocks can automate the assessment processes, provide just-in-time feedback, and adjust the teaching material dynamically based upon each student’s strengths and weaknesses. Once built, these self-contained learning modules can be easily maintained, shared, and re-purposed, thus cutting costs in the long run. This will encourage faculty from different disciplines to share their best teaching practices online. The end result of the project is a sustainable knowledge base that can grow over time, benefit all the discipline, and promote learning.

MODELS FOR DESCRIBING AND SUPPORTING AUTOMATED SYSTEM SCENARIOS

2020 ◽  
pp. 18-22
Author(s):  
В.П. Хранилов ◽  
П.В. Мисевич ◽  
А.Э. Ермилов
Keyword(s):  
Fuzzy Logic ◽  
Life Cycle ◽  
Automated System ◽  
Just In Time ◽  
Success Factor ◽  
Situational Analysis ◽  
Frame Model ◽  
Information Logistics ◽  
The Right ◽  
The Mathematical Model

В статье представлены модели описания сценариев функционирования автоматизированных систем (АС). Вводится и анализируется категория "жизненный цикл сценариев АС". Наиболее важными этапами жизненного цикла сценария являются следующие: этап формирования событийного набора для формирования сценария, этап выполнения последовательности сценарных событий и этап ситуационного анализа внешней и внутренней среды события. В статье предложена математическая модель функционирования АС, которая используется для поддержки этапа выполнения последовательности сценарных событий и основана на принципе информационной логистики: каждый параметр (набор данных) находится в нужном месте в АС, "точно в срок" и в нужном формате. Для поддержки ситуационного анализа предлагается модифицированная фреймовая модель. Ситуационный анализ используется для разработки алгоритмов событий и определения следующего события в сценарии. Модифицированная фреймовая модель основана на использовании нечетких логических процедур в фреймовой сети. The paper presents models for describing the operating scenarios of automated systems. The authors introduce and analyze the category “the life cycle of automated system scenarios”. The life cycle consists of a sequence of stages. The leading success factor of any scenario is the support of the scenario during all stages of its life cycle. The most important stages of the scenario life cycle are the following: the stage of forming the event set for generating the scenario, the stage of performing the sequence of scenario events and the stage of situational analysis of the external and internal environment of the event. In the article it is proposed to use a theoretical set model in order to select an element from a set of alternatives. The elements are events for designing a scenario. The model uses fuzzy logic and is based on the process of controlling an array of parameters if variants are available. The model is used to support the stage of forming a set of events for generating the scenario. The mathematical model of automated system operation which is used to support the stage of performing the sequence of scenario events is suggested in the article. The model is based on the principle of information logistics: each parameter (a set of data) is in the right place in the automated system, ‘just-in-time’ and in the required format. A modified frame model is proposed to support situational analysis. The situational analysis is used to operate the event algorithms and to determine the next event in the scenario. The modified frame model is based on the use of fuzzy logic procedures in a frame network.

scholarly journals Shock tube data processing tools using open source hardware and software platforms

2020 ◽  
Author(s):  
K. Thirumalesh ◽  
Salgeri Puttaswamy Raju ◽  
Hiriyur Mallaiah Somashekarappa ◽  
Kumaraswamy Swaroop
Keyword(s):  
Data Processing ◽  
Shock Tube ◽  
Open Source ◽  
Software Platforms ◽  
Open Source Hardware

scholarly journals Monitoring System for Tracking a PV Generator in an Experimental Smart Microgrid: An Open-Source Solution

2021 ◽  
Vol 13 (15) ◽  
pp. 8182
Author(s):  
José María Portalo ◽  
Isaías González ◽  
Antonio José Calderón
Keyword(s):  
Open Source ◽  
Monitoring System ◽  
Smart Grids ◽  
Raspberry Pi ◽  
Operator Interface ◽  
Open Source Hardware ◽  
Real Time Information ◽  
And Storage ◽  
Time Information ◽  
Pv Generator

Smart grids and smart microgrids (SMGs) require proper monitoring for their operation. To this end, measuring, data acquisition, and storage, as well as remote online visualization of real-time information, must be performed using suitable equipment. An experimental SMG is being deployed that combines photovoltaics and the energy carrier hydrogen through the interconnection of photovoltaic panels, electrolyser, fuel cell, and load around a voltage bus powered by a lithium battery. This paper presents a monitoring system based on open-source hardware and software for tracking the temperature of the photovoltaic generator in such an SMG. In fact, the increases in temperature in PV modules lead to a decrease in their efficiency, so this parameter needs to be measured in order to monitor and evaluate the operation. Specifically, the developed monitoring system consists of a network of digital temperature sensors connected to an Arduino microcontroller, which feeds the acquired data to a Raspberry Pi microcomputer. The latter is accessed by a cloud-enabled user/operator interface implemented in Grafana. The monitoring system is expounded and experimental results are reported to validate the proposal.

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