Replacing MOSFETs with Single Electron Transistors (SET) to Reduce Power Consumption of an Inverter Circuit

Year: 2015 Volume: 9 Issue: 3 405 - 410 Pages

• Authors:
• Ahmed Shariful Alam
• Abu Hena M. Mustafa Kamal
• M. Abdul Rahman
• M. Nasmus Sakib Khan Shabbir
• Atiqul Islam

Abstract: According to the rules of quantum mechanics there is a non-vanishing probability of for an electron to tunnel through a thin insulating barrier or a thin capacitor which is not possible according to the laws of classical physics. Tunneling of electron through a thin insulating barrier or tunnel junction is a random event and the magnitude of current flowing due to the tunneling of electron is very low. As the current flowing through a Single Electron Transistor (SET) is the result of electron tunneling through tunnel junctions of its source and drain the supply voltage requirement is also very low. As a result, the power consumption across a Single Electron Transistor is ultra-low in comparison to that of a MOSFET. In this paper simulations have been done with PSPICE for an inverter built with both SETs and MOSFETs. 35mV supply voltage was used for a SET built inverter circuit and the supply voltage used for a CMOS inverter was 3.5V.

• Keywords:
• ITRS
• enhancement type MOSFET
• island
• DC analysis
• transient analysis
• power consumption
• background charge co-tunneling.

Automated Textile Defect Recognition System Using Computer Vision and Artificial Neural Networks

Year: 2008 Volume: 2 Issue: 1 73 - 78 Pages

• Authors:
• Atiqul Islam
• Shamim Akhter
• Tumnun E. Mursalin

Abstract: Least Development Countries (LDC) like Bangladesh, whose 25% revenue earning is achieved from Textile export, requires producing less defective textile for minimizing production cost and time. Inspection processes done on these industries are mostly manual and time consuming. To reduce error on identifying fabric defects requires more automotive and accurate inspection process. Considering this lacking, this research implements a Textile Defect Recognizer which uses computer vision methodology with the combination of multi-layer neural networks to identify four classifications of textile defects. The recognizer, suitable for LDC countries, identifies the fabric defects within economical cost and produces less error prone inspection system in real time. In order to generate input set for the neural network, primarily the recognizer captures digital fabric images by image acquisition device and converts the RGB images into binary images by restoration process and local threshold techniques. Later, the output of the processed image, the area of the faulty portion, the number of objects of the image and the sharp factor of the image, are feed backed as an input layer to the neural network which uses back propagation algorithm to compute the weighted factors and generates the desired classifications of defects as an output.

• Keywords:
• Computer vision
• image acquisition device
• machine vision
• multi-layer neural networks.