Optical coherence tomography (OCT) is a non-invasive imaging modality which is widely used in clinical ophthalmology. OCT images are capable of visualizing deep retinal layers which is crucial for early diagnosis of retinal diseases. In this paper, we describe a comprehensive open-access database containing more than 500 highresolution images categorized into different pathological conditions. The image classes include Normal (NO), Macular Hole (MH), Age-related Macular Degeneration (AMD), Central Serous Retinopathy (CSR), and Diabetic Retinopathy (DR). The images were obtained from a raster scan protocol with a 2mm scan length and 512x1024 pixel resolution. We have also included 25 normal OCT images with their corresponding ground truth delineations which can be used for an accurate evaluation of OCT image segmentation. In addition, we have provided a user friendly GUI which can be used by clinicians for manual (and semi-automated) segmentation.

this study is the first to perform wound bioprinting based on image segmentation. It also compares several segmentation methods used for this purpose to determine the best.

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We propose a non-deterministic CNOT gate based on a quantum cloner, a quantum switch based on all optical routing of single photon by single photon, a quantum-dot spin in a double-sided optical microcavity with two photonic qubits, delay lines and other linear optical photonic devices. Our CNOT provides a fidelity of 78% with directly useful outputs for a quantum computing circuit and requires no ancillary qubits or electron spin measurements.

Data mining techniques, extracting patterns from large databases have become widespread in all life’s aspect. One of the most important data mining tasks is classification. Classification is an important and widely studied topic in many disciplines, including statistics, artificial intelligent, operations research, computer science and data mining and knowledge discovery. One of the important things that should be done before using classification algorithms is preprocessing operations which cause to improve the accuracy of classification algorithms. Preprocessing operations include various methods that one of them is normalization. In this paper, we selected five applicable normalization methods and then we normalized selected data sets afterward we calculated the accuracy of classification algorithm before and after normalization. In this study the SVM algorithm was used in classification because this algorithm works based on n-dimension space and if the data sets become normalized the improvement of results will be expected. Eventually Data Envelopment Analysis (DEA) is used for ranking normalization methods. We have used four data sets in order to rank the normalization methods due to increase the accuracy then using DEA and AP-model outrank these methods.