Stevens Institute of Technology

About: Stevens Institute of Technology is a education organization based out in Hoboken, New Jersey, United States. It is known for research contribution in the topics: Computer science & Cognitive radio. The organization has 5440 authors who have published 12684 publications receiving 296875 citations. The organization is also known as: Stevens & Stevens Tech.

Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset

Abstract: Lung segmentation constitutes a critical procedure for any clinical-decision supporting system aimed to improve the early diagnosis and treatment of lung diseases. Abnormal lungs mainly include lung parenchyma with commonalities on CT images across subjects, diseases and CT scanners, and lung lesions presenting various appearances. Segmentation of lung parenchyma can help locate and analyze the neighboring lesions, but is not well studied in the framework of machine learning. We proposed to segment lung parenchyma using a convolutional neural network (CNN) model. To reduce the workload of manually preparing the dataset for training the CNN, one clustering algorithm based method is proposed firstly. Specifically, after splitting CT slices into image patches, the k-means clustering algorithm with two categories is performed twice using the mean and minimum intensity of image patch, respectively. A cross-shaped verification, a volume intersection, a connected component analysis and a patch expansion are followed to generate final dataset. Secondly, we design a CNN architecture consisting of only one convolutional layer with six kernels, followed by one maximum pooling layer and two fully connected layers. Using the generated dataset, a variety of CNN models are trained and optimized, and their performances are evaluated by eightfold cross-validation. A separate validation experiment is further conducted using a dataset of 201 subjects (4.62 billion patches) with lung cancer or chronic obstructive pulmonary disease, scanned by CT or PET/CT. The segmentation results by our method are compared with those yielded by manual segmentation and some available methods. A total of 121,728 patches are generated to train and validate the CNN models. After the parameter optimization, our CNN model achieves an average F-score of 0.9917 and an area of curve up to 0.9991 for classification of lung parenchyma and non-lung-parenchyma. The obtain model can segment the lung parenchyma accurately for 201 subjects with heterogeneous lung diseases and CT scanners. The overlap ratio between the manual segmentation and the one by our method reaches 0.96. The results demonstrated that the proposed clustering algorithm based method can generate the training dataset for CNN models. The obtained CNN model can segment lung parenchyma with very satisfactory performance and have the potential to locate and analyze lung lesions.

Ultrabright Quantum Photon Sources on Chip.

Abstract: Quantum photon sources of high rate, brightness, and purity are increasingly desirable as quantum information systems are quickly scaled up and applied to many fields. Using a periodically poled lithium niobate microresonator on chip, we demonstrate photon-pair generation at high rates of 8.5 and 36.3 MHz using only 3.4 and 13.4 μW pump power, respectively, marking orders of magnitude improvement over the state of the art, across all material platforms. These results constitute the first direct measurement of the device's giant single photon nonlinearity. The measured coincidence to accidental ratio is well above 100 at those high rates and reaches 14682±4427 at a lower pump power. The same chip enables heralded single-photon generation at tens of megahertz rates, each with low autocorrelation g_{H}^{(2)}(0)=0.008 and 0.097 for the microwatt pumps, which marks a new milestone. Such distinct performance, facilitated by the chip device's noiseless and giant optical nonlinearity, will contribute to the forthcoming pervasive adoption of quantum optical information technologies.

Length based attack and braid groups: cryptanalysis of Anshel-Anshel-Goldfeld key exchange protocol

Abstract: The length based attack on Anshel-Anshel-Goldfeld commutator key-exchange protocol [1] was initially proposed by Hughes and Tannenbaum in [9]. Several attempts have been made to implement the attack [6], but none of them had produced results convincing enough to believe that attack works. In this paper we show that accurately designed length based attack can successfully break a random instance of the simultaneous conjugacy search problem for certain parameter values and argue that the public/private information chosen uniformly random leads to weak keys.

Improved photoresponse with enhanced photoelectric contribution in fully suspended graphene photodetectors

Abstract: Graphene's unique optoelectronic properties are promising to realize photodetectors with ultrafast photoresponse over a wide spectral range from far-infrared to ultraviolet radiation. The underlying mechanism of the photoresponse has been a particular focus of recent work and was found to be either photoelectric or photo-thermoelectric in nature and enhanced by hot carrier effects. Graphene supported by a substrate was found to be dominated by the photo-thermoelectric effect, which is known to be an order of magnitude slower than the photoelectric effect. Here we demonstrate fully-suspended chemical vapor deposition grown graphene microribbon arrays that are dominated by the faster photoelectric effect. Substrate removal was found to enhance the photoresponse by four-fold compared to substrate-supported microribbons. Furthermore, we show that the light-current input/output curves give valuable information about the underlying photophysical process responsible for the generated photocurrent. These findings are promising towards wafer-scale fabrication of graphene photodetectors approaching THz cut-off frequencies.