A Numerical Study of Lateral Spreading Behind a Caisson Type Quay Wall

A series of centrifuge model tests were conducted at Rensselaer Polytechnic Institute to study the seismic response of a caisson-type waterfront quay wall system, and the liquefaction and deformation characteristics of the saturated cohesionless backfill. Using a nonlinear two-phase (solid-fluid) finite element program, a numerical study of the above centrifuge tests is performed. In this paper, the centrifuge tests and formulation of the employed finite element program are briefly described, and the numerical simulation results are compared to the experimental records. It is shown that the extent of liquefaction, the deformation pattern of the soil-wall system, and the magnitude of lateral spreading obtained from the computational code are similar to actual observations in the centrifuge tests. Computational parametric studies are then conducted by varying soil relative density and soil permeability to investigate the spatial extent of liquefaction in backfill material and its effect on the magnitude of ground lateral spreading. It is concluded that the dynamic properties and permeability of backfill material are among the most influential factors in dictating seismic performance of a quay wall system

DEM Simulation of Liquefaction-Induced Lateral Spreading

This paper reports the results of a model-based simulation of 1g shake table tests of sloping saturated granular deposits subjected to seismic excitations. The simulation technique utilizes a transient fully-coupled continuum fluid discrete particle model of the watersaturated soil. The fluid (water) phase is idealized at a macroscale using an Eulerian averaged form of Navier-Stokes equations. The solid particles are modeled at the microscale as an assemblage of discrete spheres using the discrete element method. The interphase momentum transfer is accounted for using an established relationship. Numerical simulations were conducted to investigate the liquefaction induced lateral spreading of a mild-sloped semi-infinite deposit subjected to a dynamic base excitation. The employed model reproduced a number of response patterns observed in the 1g experiment. In addition, the simulation results captured the initiation of sliding at failure planes, the propagation of liquefaction front and associated large strain localization, and the redistribution of void space during shaking

Anahita: A System for 3D Video Streaming with Depth Customization

Producing high-quality stereoscopic 3D content requires significantly more effort than preparing regular video footage. In order to assure good depth perception and visual comfort, 3D videos need to be carefully adjusted to specific viewing conditions before they are shown to viewers. While most stereoscopic 3D content is designed for viewing in movie theaters, where viewing conditions do not vary significantly, adapting the same content for viewing on home TV-sets, desktop displays, laptops, and mobile devices requires additional adjustments. To address this challenge, we propose a new system for 3D video streaming that provides automatic depth adjustments as one of its key features. Our system takes into account both the content and the display type in order to customize 3D videos and maximize their perceived quality. We propose a novel method for depth adjustment that is well-suited for videos of field sports such as soccer, football, and tennis. Our method is computationally efficient and it does not introduce any visual artifacts. We have implemented our 3D streaming system and conducted two user studies, which show: (i) adapting stereoscopic 3D videos for different displays is beneficial, and (ii) our proposed system can achieve up to 35% improvement in the perceived quality of the stereoscopic 3D content

Complexity analysis for DROPLET: Distributed Operator Placement for IoT Applications Spanning Edge and Cloud Resources

Internet of Things (IoT) applications generate massive amounts of real-time data. Owners of such data strive to make predictions/inference from large streams of complex input such as video feeds, often by deploying applications that involve machine learning and image processing operations. A typical deployment of IoT applications includes edge devices to acquire the input data and provide processing/storage capacity closer to the location where the data is captured. An important challenge for IoT applications is deciding which operations to be executed on an edge device and which operations should be carried out on the cloud, in order to minimize the completion time of all operations. We call this the distributed operator placement problem. In this report, we show that the distributed operator placement problem is NP-complete. We then provide computational complexity analysis of a dynamic programming algorithm called Droplet, which is a heuristic to scalably partition operations in IoT applications across shared edge and cloud resources, while minimizing the completion time of all operations. Our analysis shows that Droplet scales log-linearly in the total number of operations.Ope

BRACELET: Hierarchical Edge-Cloud Microservice Infrastructure for Scientific Instruments’ Lifetime Connectivity

Recent advances in cyber-infrastructure have enabled digital data sharing and ubiquitous network connectivity between scientific instruments and cloud-based storage infrastructure for uploading, storing, curating, and correlating of large amounts of materials and semiconductor fabrication data and metadata. However, there is still a significant number of scientific instruments running on old operating systems that are taken offline and cannot connect to the cloud infrastructure, due to security and performance concerns. In this paper, we propose BRACELET - an edge-cloud infrastructure that augments the existing cloud-based infrastructure with edge devices and helps to tackle the unique performance and security challenges that scientific instruments face when they are connected to the cloud through public network. With BRACELET, we put a networked edge device, called cloudlet, in between the scientific instruments and the cloud as the middle tier of a three-tier hierarchy. The cloudlet will shape and protect the data traffic from scientific instruments to the cloud, and will play a foundational role in keeping the instruments connected throughout its lifetime, and continuously providing the otherwise missing performance and security features for the instrument as its operating system ages.NSF Award Number 1659293NSF Award Number 1443013Ope

A multi-biometric iris recognition system based on a deep learning approach

YesMultimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. In this paper, an efficient and real-time multimodal biometric system is proposed based on building deep learning representations for images of both the right and left irises of a person, and fusing the results obtained using a ranking-level fusion method. The trained deep learning system proposed is called IrisConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without any domain knowledge where the input image represents the localized iris region and then classify it into one of N classes. In this work, a discriminative CNN training scheme based on a combination of back-propagation algorithm and mini-batch AdaGrad optimization method is proposed for weights updating and learning rate adaptation, respectively. In addition, other training strategies (e.g., dropout method, data augmentation) are also proposed in order to evaluate different CNN architectures. The performance of the proposed system is tested on three public datasets collected under different conditions: SDUMLA-HMT, CASIA-Iris- V3 Interval and IITD iris databases. The results obtained from the proposed system outperform other state-of-the-art of approaches (e.g., Wavelet transform, Scattering transform, Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases and a recognition time less than one second per person