Arithmetic Logic Unit(ALU) as one of the main parts of any computing hardware plays an important role in digital computers. In quantum computers which can be realized by reversible logics and circuits, reversible ALUs...Arithmetic Logic Unit(ALU) as one of the main parts of any computing hardware plays an important role in digital computers. In quantum computers which can be realized by reversible logics and circuits, reversible ALUs should be designed. In this paper, we proposed three different designs for reversible 1-bit ALUs using our proposed 3×3 and 4×4 reversible gates called MEB3 and MEB4(Moallem Ehsanpour Bolhasani) gates, respectively. The first proposed reversible ALU consists of six logical operations. The second proposed ALU consists of eight operations, two arithmetic, and six logical operations. And finally, the third proposed ALU consists of sixteen operations, four arithmetic operations, and twelve logical operations. Our proposed ALUs can be used to construct efficient quantum computers in nanotechnology, because the proposed designs are better than the existing designs in terms of quantum cost, constant input, reversible gates used, hardware complexity, and functions generated.展开更多
In this paper,a computer vision-based algorithm for golden delicious apple grading is proposed which works in six steps.Non-apple pixels as background are firstly removed from input images.Then,stem end is detected by...In this paper,a computer vision-based algorithm for golden delicious apple grading is proposed which works in six steps.Non-apple pixels as background are firstly removed from input images.Then,stem end is detected by combination of morphological methods and Mahalanobis distant classifier.Calyx region is also detected by applying K-means clustering on the Cb component in YCbCr color space.After that,defects segmentation is achieved using Multi-Layer Perceptron(MLP)neural network.In the next step,stem end and calyx regions are removed from defected regions to refine and improve apple grading process.Then,statistical,textural and geometric features from refined defected regions are extracted.Finally,for apple grading,a comparison between performance of Support Vector Machine(SVM),MLP and K-Nearest Neighbor(KNN)classifiers is done.Classification is done in two manners which in the first one,an input apple is classified into two categories of healthy and defected.In the second manner,the input apple is classified into three categories of first rank,second rank and rejected ones.In both grading steps,SVM classifier works as the best one with recognition rate of 92.5%and 89.2%for two categories(healthy and defected)and three quality categories(first rank,second rank and rejected ones),among 120 different golden delicious apple images,respectively,considering K-folding with K=5.Moreover,the accuracy of the proposed segmentation algorithms including stem end detection and calyx detection are evaluated for two different apple image databases.展开更多
文摘Arithmetic Logic Unit(ALU) as one of the main parts of any computing hardware plays an important role in digital computers. In quantum computers which can be realized by reversible logics and circuits, reversible ALUs should be designed. In this paper, we proposed three different designs for reversible 1-bit ALUs using our proposed 3×3 and 4×4 reversible gates called MEB3 and MEB4(Moallem Ehsanpour Bolhasani) gates, respectively. The first proposed reversible ALU consists of six logical operations. The second proposed ALU consists of eight operations, two arithmetic, and six logical operations. And finally, the third proposed ALU consists of sixteen operations, four arithmetic operations, and twelve logical operations. Our proposed ALUs can be used to construct efficient quantum computers in nanotechnology, because the proposed designs are better than the existing designs in terms of quantum cost, constant input, reversible gates used, hardware complexity, and functions generated.
基金The authors would like to thank Prof.Jose Blasco et al.[11]for making valuable golden delicious apple images database and sharing with us this database and corresponding manual classification which was done by a human expert.
文摘In this paper,a computer vision-based algorithm for golden delicious apple grading is proposed which works in six steps.Non-apple pixels as background are firstly removed from input images.Then,stem end is detected by combination of morphological methods and Mahalanobis distant classifier.Calyx region is also detected by applying K-means clustering on the Cb component in YCbCr color space.After that,defects segmentation is achieved using Multi-Layer Perceptron(MLP)neural network.In the next step,stem end and calyx regions are removed from defected regions to refine and improve apple grading process.Then,statistical,textural and geometric features from refined defected regions are extracted.Finally,for apple grading,a comparison between performance of Support Vector Machine(SVM),MLP and K-Nearest Neighbor(KNN)classifiers is done.Classification is done in two manners which in the first one,an input apple is classified into two categories of healthy and defected.In the second manner,the input apple is classified into three categories of first rank,second rank and rejected ones.In both grading steps,SVM classifier works as the best one with recognition rate of 92.5%and 89.2%for two categories(healthy and defected)and three quality categories(first rank,second rank and rejected ones),among 120 different golden delicious apple images,respectively,considering K-folding with K=5.Moreover,the accuracy of the proposed segmentation algorithms including stem end detection and calyx detection are evaluated for two different apple image databases.
