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Steven Moran

Researcher at University of California, Los Angeles

Publications -  11
Citations -  91

Steven Moran is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Deep learning & TrueNorth. The author has an hindex of 5, co-authored 9 publications receiving 57 citations.

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Proceedings ArticleDOI

A deep learning approach to spine segmentation using a feed-forward chain of pixel-wise convolutional networks

TL;DR: This work proposes a deep learning approach that uses a series of four pixel-wise segmentation networks to improve vertebrae and disk segmentations results when compared to a state-of-the-art deep-learning segmentation method—the U-net.
Proceedings ArticleDOI

Extremely Flexible (1mm Bending Radius) Biocompatible Heterogeneous Fan-Out Wafer-Level Platform with the Lowest Reported Die-Shift (<6 µm) and Reliable Flexible Cu-Based Interconnects

TL;DR: A flexible fan-out wafer-level packaging (FOWLP) process for heterogeneous integration of high performance dies in a flexible and biocompatible elastomeric package (FlexTrateTM) was used to assemble >600 dies with co-planarity and tilt < 1µm, average die-shift of 3.28 µm with? < 2.23 µm.
Journal ArticleDOI

Quantitative Analysis of Neural Foramina in the Lumbar Spine: An Imaging Informatics and Machine Learning Study.

TL;DR: A linear model encoding variation in lumbar neural foraminal areas in asymptomatic individuals has been established and can be used to make quantitative assessments of neural foramic areas in patients by comparing them to the age-, sex-, and height-adjusted population averages.
Journal ArticleDOI

Total Ionizing Dose Responses of 22-nm FDSOI and 14-nm Bulk FinFET Charge-Trap Transistors

TL;DR: In this paper, total ionizing-dose (TID) effects for 22-nm fully-depleted silicon-on-insulator (FDSOI) and 14-nm bulk FinFET charge-trap memory transistors were investigated.
Journal ArticleDOI

The Impact of Proton-Induced Single Events on Image Classification in a Neuromorphic Computing Architecture

TL;DR: This work explores the effect of proton-induced single-event upsets (SEUs) on a neuromorphic computing architecture engaged in image recognition and finds the overall classification accuracy is unchanged although a high number of hidden, tolerable errors occurred.