Sajjad Waheed

A new approach of presenting reversible logic gate in nanoscale

Conventional lithography-based VLSI design technology deployed to optimize low-powered-computing and higher scale integration of semiconductor components. However, this downscaling trend confronts serious challenges of tunneling and leakage current increment to the Complementary Metal–Oxide–Semiconductor (CMOS) technology on nanoscale regimes. To resolve the physical restriction of the CMOS, Quantum-dot Cellular Automata (QCA) technology dedicates for the nanoscale technology that embrace a new information transformation technique. However, QCA is limited to the design of the sequential and combinational circuits only. This paper presents some highly scalable features reversible logic gate for the QCA technology. In addition, proposed layout compared with CMOS technology, offer a better reduction in size up to 233 times.

A novel presentation of reversible logic gate in Quantum-dot Cellular Automata (QCA)

In last few decades, low power processing and small scaling have been successfully achieved by conventional lithography-based VLSI technology. However, this trend confronts serious challenges due to fundamental physical limits of Complementary Metal-Oxide-Semiconductor (CMOS) technology such as ultra-thin gate oxides, short channel effects, doping fluctuations at nano-scale regimes. Quantum-dot Cellular Automata (QCA) technology has been extensively conceivable as one of the alternative, that gives not only the solution of CMOS technology at nano-scale, but also it offers a new method of computation and information transformation. This paper presents a novel representation of reversible gate in QCA. Those proposed circuit has a promising future in constructing of nano-scale low power consumption information processing system and can be stimulated higher digital applications in QCA.

Design and implementation of an efficient single layer five input majority voter gate in quantum-dot cellular automata

The fundamental logical element of a quantum-dot cellular automata (QCA) circuit is majority voter gate (MV). The efficiency of a QCA circuit is depends on the efficiency of the MV. This paper presents an efficient single layer five-input majority voter gate (MV5). The structure of proposed MV5 is very simple and easy to implement in any logical circuit. This proposed MV5 reduce number of cells and use conventional QCA cells. However, using MV5 a multilayer 1-bit full-adder (FA) is designed. The functional accuracy of the proposed MV5 and FA are confirmed by QCADesigner a well-known QCA layout design and verification tools. Furthermore, the power dissipation of proposed circuits are estimated, which shows that those circuits dissipate extremely small amount of energy and suitable for reversible computing. The simulation outcomes demonstrate the superiority of the proposed circuit.

Implementation of Reversible Logic Gate in Quantum Dot Cellular Automata

Quantum Dot Cellular Automata (QCA) is a nanotechnology with many attractive features such as higher speed, smaller size, higher switching frequency, higher scale integration and low power consumption. There are many researches have been reported on the design of reversible logic gates compared to the reversible TR. This paper proposes a modified design of the reversible Feynman gate and also propose reversible TR gate, then design 1-bit comparator using reversible TR gates and Feynman Gate. The result shows an efficient technique to design Feynman gate and one bit comparator. The proposed gates can be easily used to design complex circuits which are used in the Central Processing Unit (CPU) and microcontrollers. General Terms Reversible logic gates, Quantum Dot Cellular Automata.

Optimized design of full-subtractor using new SRG reversible logic gates and VHDL simulation

Reversible logic has comprehensive applications in communications, quantum computing, low power VLSI design, computer graphics, cryptography, nanotechnology and optical computing. It has received significant attention in low power dissipating circuit design in the past few years. While several researchers have inspected the design of reversible logic units, there is not much reported works on reversible subtractors. In this paper we proposed the quantum equivalent circuit for SRG (SRG refers to Samiur Rahman Gate) gate and we have computed the quantum cost of SRG gate. We also showed that how SRG gate can work singly as a Full-subtractor circuit. It is being tried to design the circuit optimal in terms of number of reversible gates, number of garbage outputs, number of constant inputs and quantum cost with compared to the existing circuits. All the designs have nanometric scales.

Gastrointestinal polyp detection in endoscopic images using an improved feature extraction method

Gastrointestinal polyps are treated as the precursors of cancer development. So, possibility of cancers can be reduced at a great extent by early detection and removal of polyps. The most used diagnostic modality for gastrointestinal polyps is video endoscopy. But, as an operator dependant procedure, several human factors can lead to miss detection of polyps. In this peper, an improved computer aided polyp detection method has been proposed. Proposed improved method can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention. Color wavelet features and convolutional neural network features are extracted from endoscopic images, which are used for training a support vector machine. Then a target endoscopic image will be given to the classifier as input in order to find whether it contains any polyp or not. If polyp is found, it will be marked automatically. Experiment shows that, color wavelet features and convolutional neural network features together construct a highly representative of endoscopic polyp images. Evaluations on standard public databases show that, proposed system outperforms state-of-the-art methods, gaining accuracy of 98.34%, sensitivity of 98.67% and specificity of 98.23%. In this paper, the strength of color wavelet features and power of convolutional neural network features are combined. Fusion of these two methodology and use of support vector machine results in an improved method for gastrointestinal polyp detection. An analysis of ROC reveals that, proposed method can be used for polyp detection purposes with greater accuracy than state-of-the-art methods.

Stock market prediction using an improved training algorithm of neural network

Predicting closing stock price accurately is an challenging task. Computer aided systems have been proved to be helpful tool for stock prediction such as Artificial Neural Net-work(ANN), Adaptive Neuro Fuzzy Inference System (ANFIS) etc. Latest research works prove that Adaptive Neuro Fuzzy Inference System shows better results than Neural Network for stock prediction. In this paper, an improved Levenberg Marquardt(LM) training algorithm of artificial neural network has been proposed. Improved Levenberg Marquardt algorithm of neural network can predict the possible day-end closing stock price with less memory and time needed, provided previous historical stock market data of Dhaka Stock Exchange such as opening price, highest price, lowest price, total share traded. Morever, improved LM algorithm can predict day-end stock price with 53% less error than ANFIS and traditional LM algorithm. It also requires 30% less time, 54% less memory than traditional LM and 47% less time, 59% less memory than ANFIS.

Randomly encrypted key generation algorithm against side channel attack in cloud computing

Cloud computing offers both services that provide resources over the Internet and economic benefits for using these resources. Economic benefit plays a vital role both for users and providers of cloud. The users pay as they use on the other hand resources of providers never remain idle. The cloud users hive away their valuable data in the cloud and perform their activity with the data. The security of these valuable data should be ensured. The side channel attack is one of the common attacks in cloud computing. Attackers use a malicious virtual machine to retrieve data from the cloud. In this paper, implements a randomly encrypted key generation algorithm against side channel attack in cloud computing. As anyone can use the cloud, it makes easy for the attackers to attack the desired users data. Also, a process has been developed that generates random keys to encrypt data of the cloud users at the time of storage and transmission. The attackers may find the encrypted data when they grab the users data that may no further value for them.

Application of neural networks in talent management

Study of talent management is getting more attentions in the recent years. It was found that there are no easy classification methods for verifying talents. This paper discusses the application of neural networks for a talent matrix based talent classification process. The proposed method is easy to implement, and free from biasing and nepotism.

An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features

Gastrointestinal polyps are considered to be the precursors of cancer development in most of the cases. Therefore, early detection and removal of polyps can reduce the possibility of cancer. Video endoscopy is the most used diagnostic modality for gastrointestinal polyps. But, because it is an operator dependent procedure, several human factors can lead to misdetection of polyps. Computer aided polyp detection can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention to. In this paper, an automatic system has been proposed as a support to gastrointestinal polyp detection. This system captures the video streams from endoscopic video and, in the output, it shows the identified polyps. Color wavelet (CW) features and convolutional neural network (CNN) features of video frames are extracted and combined together which are used to train a linear support vector machine (SVM). Evaluations on standard public databases show that the proposed system outperforms the state-of-the-art methods, gaining accuracy of 98.65%, sensitivity of 98.79%, and specificity of 98.52%.