Technology, Nature, Software and Networks: Materializing the Post-Romantic Landscape

Leonardo ◽  
2011 ◽  
Vol 44 (2) ◽  
pp. 101-106 ◽  
Author(s):  
Chris Welsby
Keyword(s):  
New Media ◽  
Real Time ◽  
Large Scale ◽  
Natural World ◽  
Time Data ◽  
Real Time Data ◽  
Weather Stations ◽  
Selection Of ◽  
Made In ◽  
Media Installation

The author began making films and installations in the early 1970s. Although he has worked across a range of media, he has always concentrated on one particular theme that he conceptualizes as a two-sided question: How do we see ourselves in relation to the natural world, and how should we position our selves and our technologies within it? This essay traces some of the threads of these seminal ideas through a selection of works made in the years between 1974 and 2009. The author concludes with a detailed description of Tree Studies, a large-scale new media installation powered by real-time data from weather stations around the planet.

scholarly journals Aesthetic Archives

2016 ◽  
Vol 41 (1) ◽  
pp. 11-23
Author(s):  
Michael Takeo Magruder ◽  
Jeremy Pilcher
Keyword(s):  
New Media ◽  
Real Time ◽  
Emerging Technologies ◽  
Distributed Data ◽  
Time Data ◽  
The Real ◽  
New Media Art ◽  
Data Flows ◽  
Real Time Data ◽  
Visual Artist

Michael Takeo Magruder, visual artist and researcher, discusses his digital and new media art and practice with Jeremy Pilcher, lawyer and academic, whose research engages with the intersection of art and law. Takeo's work asks viewers to question their relationship both to and within the real-time data flows generated by emerging technologies and the implications these have for archives. His art concerns the way institutions use such systems to create narratives that structure societies. This conversation discusses how Takeo's practice invites us, as individuals, to critically reflect on the implications of the stories that are both told to and about us by using gathered and distributed data.

A distributed real-time data prediction framework for large-scale time-series data using stream processing

2017 ◽  
Vol 10 (2) ◽  
pp. 145-165 ◽  
Author(s):  
Kehe Wu ◽  
Yayun Zhu ◽  
Quan Li ◽  
Ziwei Wu
Keyword(s):  
Time Series ◽  
Real Time ◽  
Large Scale ◽  
Time Series Data ◽  
Data Sources ◽  
Series Data ◽  
Time Data ◽  
Content Type ◽  
Data Prediction ◽  
Real Time Data

Purpose The purpose of this paper is to propose a data prediction framework for scenarios which require forecasting demand for large-scale data sources, e.g., sensor networks, securities exchange, electric power secondary system, etc. Concretely, the proposed framework should handle several difficult requirements including the management of gigantic data sources, the need for a fast self-adaptive algorithm, the relatively accurate prediction of multiple time series, and the real-time demand. Design/methodology/approach First, the autoregressive integrated moving average-based prediction algorithm is introduced. Second, the processing framework is designed, which includes a time-series data storage model based on the HBase, and a real-time distributed prediction platform based on Storm. Then, the work principle of this platform is described. Finally, a proof-of-concept testbed is illustrated to verify the proposed framework. Findings Several tests based on Power Grid monitoring data are provided for the proposed framework. The experimental results indicate that prediction data are basically consistent with actual data, processing efficiency is relatively high, and resources consumption is reasonable. Originality/value This paper provides a distributed real-time data prediction framework for large-scale time-series data, which can exactly achieve the requirement of the effective management, prediction efficiency, accuracy, and high concurrency for massive data sources.

scholarly journals Wind Power Integration: An Experimental Investigation for Powering Local Communities

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 621 ◽  
Author(s):  
Mazhar Hussain Baloch ◽  
Dahaman Ishak ◽  
Sohaib Tahir Chaudary ◽  
Baqir Ali ◽  
Ali Asghar Memon ◽  
...  
Keyword(s):  
Real Time ◽  
Local Communities ◽  
Time Data ◽  
Wind Rose ◽  
The Real ◽  
Wind Power Integration ◽  
Course Of Action ◽  
Wind Potential ◽  
Real Time Data ◽  
Selection Of

The incorporation of wind energy as a non-conventional energy source has received a lot of attention. The selection of wind turbine (WT) prototypes and their installation based on assessment and analysis is considered as a major problem. This paper focuses on addressing the aforementioned issues through a Weibull distribution technique based on five different methods. The accurate results are obtained by considering the real-time data of a particular site located in the coastal zone of Pakistan. Based on the computations, it is observed that the proposed site has most suitable wind characteristics, low turbulence intensity, wind shear exponent located in a safe region, adequate generation with the most adequate capacity factor and wind potential. The wind potential of the proposed site is explicitly evaluated with the support of wind rose diagrams at different heights. The energy generated by ten different prototypes will suggest the most optimum and implausible WT models. Correspondingly, the most capricious as well as optimal methods are also classified among the five Weibull parameters. Moreover, this study provides a meaningful course of action for the selection of a suitable site, WT prototype and parameters evaluation based on the real-time data for powering local communities.

INS real-time data processing composite system using large scale general purpose computers

1983 ◽  
Vol 213 (2-3) ◽  
pp. 317-327 ◽  
Author(s):  
Jun Kokame ◽  
Motonobu Takano ◽  
Tomoko Oshikubo ◽  
Kurazo Chiba ◽  
Kumataro Ukai ◽  
...  
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Large Scale ◽  
Composite System ◽  
General Purpose ◽  
Time Data ◽  
Real Time Data ◽  
Real Time Data Processing

scholarly journals MIRRA: A Modular and Cost-Effective Microclimate Monitoring System for Real-Time Remote Applications

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4615
Author(s):  
Olivier Pieters ◽  
Emiel Deprost ◽  
Jonas Van Der Donckt ◽  
Lore Brosens ◽  
Pieter Sanczuk ◽  
...  
Keyword(s):  
Climate Change ◽  
Real Time ◽  
Monitoring System ◽  
Low Cost ◽  
Cost Effective ◽  
Time Data ◽  
Real Time Data ◽  
Microclimate Monitoring ◽  
Weather Stations

Monitoring climate change, and its impacts on ecological, agricultural, and other societal systems, is often based on temperature data derived from official weather stations. Yet, these data do not capture most microclimates, influenced by soil, vegetation and topography, operating at spatial scales relevant to the majority of organisms on Earth. Detecting and attributing climate change impacts with confidence and certainty will only be possible by a better quantification of temperature changes in forests, croplands, mountains, shrublands, and other remote habitats. There is an urgent need for a novel, miniature and simple device filling the gap between low-cost devices with manual data download (no instantaneous data) and high-end, expensive weather stations with real-time data access. Here, we develop an integrative real-time monitoring system for microclimate measurements: MIRRA (Microclimate Instrument for Real-time Remote Applications) to tackle this problem. The goal of this platform is the design of a miniature and simple instrument for near instantaneous, long-term and remote measurements of microclimates. To that end, we optimised power consumption and transfer data using a cellular uplink. MIRRA is modular, enabling the use of different sensors (e.g., air and soil temperature, soil moisture and radiation) depending upon the application, and uses an innovative node system highly suitable for remote locations. Data from separate sensor modules are wirelessly sent to a gateway, thus avoiding the drawbacks of cables. With this sensor technology for the long-term, low-cost, real-time and remote sensing of microclimates, we lay the foundation and open a wide range of possibilities to map microclimates in different ecosystems, feeding a next generation of models. MIRRA is, however, not limited to microclimate monitoring thanks to its modular and wireless design. Within limits, it is suitable or any application requiring real-time data logging of power-efficient sensors over long periods of time. We compare the performance of this system to a reference system in real-world conditions in the field, indicating excellent correlation with data collected by established data loggers. This proof-of-concept forms an important foundation to creating the next version of MIRRA, fit for large scale deployment and possible commercialisation. In conclusion, we developed a novel wireless cost-effective sensor system for microclimates.

scholarly journals Impact of Fog and Cloud Computing Applications to Reserve a Seat in Railways

2020 ◽  
Vol 13 (1) ◽  
pp. 28
Author(s):  
Gajender Kumar Kumar
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Real Data ◽  
Third Parties ◽  
College Level ◽  
Time Data ◽  
Data Pipeline ◽  
Real Time Data ◽  
Multiple Devices ◽  
Sensor Cloud

As India's largest employer has gone digital, this is the beginning of mass acquisition in a new dimension. Indian Railways serves on a peripheral scale in the size of each aircraft. It carries 23,000 passengers and over 3 million tonnes daily, with more than 12,000 passengers traveling at 115,000 km and 7000 stations. But as the nation's largest employer has begun to digitalize its services operations, it is beginning to gain mass in an emerging dimension. The network collects around 100 datasets per user (one truffle is enough to store 500–1000 movies). This passenger booking plate has 25 million users, leading to approximately 800,000 daily transactions. Such is the creation of a digital scale railway ecosystem, a fertile ground for entrepreneurs, employers and large scale technology for testing and testing. And while Railways has made a good start by integrating third parties with third parties, the company can create a seamless experience in booking, payment and other services at the top of the railway data pipeline. Here we have several ways to verify a passenger or passenger ticket, which will be discussed one by one on Fig.1. We understand here that the N computer system (server) is available in the availability center and the availability train provided by the NECC (North Eastern Carrying Corporation Ltd). Each seat or chair acts as a spring (Smart) chair, which means that we are connected to a ticket scanner or chair that can scan tickets and send the actual data to the field node (fog node). Later, the latest analysis will result in the proposed data and the final data that will be sent to the cloud. We are connected to multiple devices to share real-time data and updated real-time data on the cloud. We have created a fog environment and a cloud environment. We have implemented this with the help of clipping and sending data by sending real data separately. We have created a smart gateway between military (Servers) and customer (sensor), cloud and customer (sensor). In this paper, there is the solution that can enhanced the chances of getting confirm ticket, based on individual server load in coaches of the train, according to the increase in the number of sensors request, in the context of processing and considering three cases , it is an answer by analysing cloud-fog scenario. Up to this implementation the simple round robin algorithm is also used solution for load balancing purpose. The method is implemented by using a class student attendance system in a college level.

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