Sara Hashemi

An image contrast enhancement method based on genetic algorithm

Contrast enhancement plays a fundamental role in image/video processing. Histogram Equalization (HE) is one of the most commonly used methods for image contrast enhancement. However, HE and most other contrast enhancement methods may produce un-natural looking images and the images obtained by these methods are not desirable in applications such as consumer electronic products where brightness preservation is necessary to avoid annoying artifacts. To solve such problems, we proposed an efficient contrast enhancement method based on genetic algorithm in this paper. The proposed method uses a simple and novel chromosome representation together with corresponding operators. Experimental results showed that this method makes natural looking images especially when the dynamic range of input image is high. Also, it has been shown by simulation results that the proposed genetic method had better results than related ones in terms of contrast and detail enhancement and the resulted images were suitable for consumer electronic products.

New robust QCA D flip flop and memory structures

Quantum dot Cellular Automata (QCA) is one of the candidate technologies to be replaced with CMOS. Using this technology, extra low power, extremely dense and very high speed structure is achieved. Since flip flops and memory cells are fundamental building blocks of digital circuits, constructing an efficient, dense, and simple QCA memory structure is of great importance. In this paper, using a robust 2:1 multiplexer, efficient level triggered and edge triggered QCA D flip flops and a memory cell with set/reset ability will be introduced. Simulation results demonstrate that the proposed desgins have efficient structures in terms of area, delay and complexity. Also, it is worth mentioning that these designs in contrast to the previous structures do not need any crossover wire. QCA designer, a common QCA layout design and a verification tool is employed to verify and simulate the proposed circuits.

A novel QCA multiplexer design

Quantum-dot Cellular Automata is a novel nanotechnology that promises extra low-power, extremely dense and high speed structure for construction of logical circuits at a nano-scale. Moreover, multiplexer is a useful component for the design of many important circuits. This paper proposes a novel and efficient design of 2:1 multiplexer in the QCA. The proposed multiplexer has been compared to few recent designs in terms of area, speed and complexity. Comparison of results illustrates significant improvements in our design as compared to traditional approaches. Also, simulation proves that the proposed multiplexer design is completely robust and more sustainable to high input frequency, as compared to other designs. Simulations have been carried out using the QCA Designer, a layout and simulation tool for QCA.

An Image Enhancement Method Based on Genetic Algorithm

Contrast enhancement plays a fundamental role in image/video processing. Histogram Equalization (HE) is one of the most commonly used methods for image contrast enhancement. However, HE and most other contrast enhancement methods may produce unnatural looking images. The images obtained by these methods are not suitable to use in applications such as consumer electronic products where brightness preservation is necessary to avoid annoying artifacts. To solve such problems, we proposed a novel and efficient contrast enhancement method based on genetic algorithm in this paper. Simulation results demonstrated that the proposed genetic method was stronger than counterpart methods in terms of contrast and detail enhancement and producing natural looking images. Moreover, experiments showed that resulted images were suitable for consumer electronic products.

A hybrid multiprocessor task scheduling method based on immune genetic algorithm

Multiprocessor task scheduling plays a fundamental role in parallel applications and distributed networks. All of the methods for this kind of scheduling are concerned with achieving optimal running time. In this way parallel execution of tasks on several processors based on precedence graph should be considered. In this study, first a new heuristic method has been introduced which improved the execution time of some precedence graphs. Furthermore, we presented a novel immune genetic approach for multiprocessor task scheduling problem. Finally, combination of the proposed heuristic and the genetic approach makes a new hybrid scheme which is better than other well known and recent methods.

A novel FPGA-programmable switch matrix interconnection element in quantum-dot cellular automata

The Quantum-dot cellular automata (QCA) is a novel nanotechnology, promising extra low-power, extremely dense and very high-speed structure for the construction of logical circuits at a nanoscale. In this paper, initially previous works on QCA-based FPGA’s routing elements are investigated, and then an efficient, symmetric and reliable QCA programmable switch matrix (PSM) interconnection element is introduced. This element has a simple structure and offers a complete routing capability. It is implemented using a bottom-up design approach that starts from a dense and high-speed 2:1 multiplexer and utilise it to build the target PSM interconnection element. In this study, simulations of the proposed circuits are carried out using QCAdesigner, a layout and simulation tool for QCA circuits. The results demonstrate high efficiency of the proposed designs in QCA-based FPGA routing.