IJSTE – Submit Paper – Call for Paper – Nov 18

IJSTE – International Journal of Science Technology and Engineering
Call for Papers Vol. 5 Issue 5 – Nov 2018

I.C. Value= 62.78 | Impact Factor : 4.753
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Brushless DC Motor Controlled by using Internet of Things | IJSTEV3I9191

Author(s): Dr. R. Nagarajan, Ms. C. Hemalatha, P. Suresh, G. Ganesh Shankar, A. Vijay,
Institute: Gnanamani College of Technology,  Tamil Nadu, India.

Keywords: IoT, IP, BLDC Motor


This paper presents the study and control of the Brushless DC (BLDC) motor by using IoT. The BLDC drive systems are often used in many industrial applications such as robotics, actuation and manipulators. The Internet of Things (IoT) refers to the ever growing network of physical objects that feature an Internet protocol (IP) address for internet connectivity and the communication that occurs between these objects and other internet enabled devices and systems. The BLDC Motor has been widely used in industries because of its properties such as high efficiency, reliability, high weight to torque ratio. By utilizing this IoT control, the rate can be tuned until it gets like the desired output.


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IJSTE (International Journal of Science Technology and Engineering)

ISSN (Online) : 2349-784X

Call for Papers Subject Category : Engineering Science and Technology

Call for Papers Frequency : Monthly, 12 issues per year

IMPACT FACTOR: 4.753 | Index Copernicus Value: 69.90

Submission Last Date: 25-Dec-17
Submit Manuscript Online


Neuromorphic Technology. #IJSTE Journal#


Title: Neuromorphic Technolog:   Computer Chips that Mimic the Human Brain

Even today’s best supercomputers cannot rival the sophistication of the human brain. Computers are linear, moving data back and forth between memory chips and a central processor over a high-speed backbone. The brain, on the other hand, is fully interconnected, with logic and memory intimately cross-linked at billions of times the density and diversity of that found in a modern computer. Neuromorphic chips aim to process information in a fundamentally different way from traditional hardware, mimicking the brain’s architecture to deliver a huge increase in a computer’s thinking and responding power.


Miniaturization has delivered massive increases in conventional computing power over the years, but the bottleneck of shifting data constantly between stored memory and central processors uses large amounts of energy and creates unwanted heat, limiting further improvements. In contrast, neuromorphic chips can be more energy efficient and powerful, combining data-storage and data-processing components into the same interconnected modules. In this sense, the system copies the networked neurons that, in their billions, make up the human brain.

Neuromorphic technology will be the next stage in powerful computing, enabling vastly more rapid processing of data and a better capacity for machine learning. IBM’s million-neuron TrueNorth chip, revealed in prototype in August 2014, has a power efficiency for certain tasks that is hundreds of times superior to a conventional CPU (Central Processing Unit), and more comparable for the first time to the human cortex. With vastly more compute power available for far less energy and volume, neuromorphic chips should allow more intelligent small-scale machines to drive the next stage in miniaturization and artificial intelligence.

Potential applications include: drones better able to process and respond to visual cues, much more powerful and intelligent cameras and smartphones, and data-crunching on a scale that may help unlock the secrets of financial markets or climate forecasting. Computers will be able to anticipate and learn, rather than merely respond in pre-programmed ways.

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