Prof. Milton Halem Profile

Prof. Milton Halem

at Univ of Maryland Baltimore County

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Publications (6)

Proceedings Article | 12 September 2021 Presentation + Paper

Physics-aware deep edge detection network

Kinjal Patel, Jennifer Sleeman, Milton Halem

Proceedings Volume 11859, 1185908 (2021) https://doi.org/10.1117/12.2600327

KEYWORDS: Edge detection, Backscatter, Clouds, Satellites, Satellite imaging, Network architectures, Meteorological satellites, Information theory, Atmospheric sensing

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Proceedings Article | 20 May 2020 Presentation + Paper

A hybrid quantum enabled RBM advantage: convolutional autoencoders for quantum image compression and generative learning

Jennifer Sleeman, John Dorband, Milton Halem

Proceedings Volume 11391, 113910B (2020) https://doi.org/10.1117/12.2558832

KEYWORDS: Binary data, Computer programming, Image compression, Quantum communications, Data modeling, Quantum computing, Image restoration, Machine learning, Annealing, Convolutional neural networks

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Understanding how the D-Wave quantum computer could be used for machine learning problems is of growing interest. Our work explores the feasibility of using the D-Wave as a sampler for a machine learning task. We describe a hybrid method that combines a classical deep neural network autoencoder with a quantum annealing Restricted Boltzmann Machine (RBM) using the D-Wave for image generation. Our method overcomes two key limitations in the 2000-qubit D-Wave processor, namely the limited number of qubits available to accommodate typical problem sizes for fully connected quantum objective functions, and samples that are binary pixel representations. As a consequence of these limitations we are able to show how we achieved nearly a 22-fold compression factor of grayscale 28 x 28 sized images to binary 6 x 6 sized images with a lossy recovery of the original 28 x 28 grayscale images. We further show how generating samples from the D-Wave after training the RBM, resulted in 28 x 28 images that were variations of the original input data distribution, as opposed to recreating the training samples. We evaluated the quality of this method by using a downstream classification method. We formulated a MNIST classification problem using a deep convolutional neural network that used samples from the quantum RBM to train the MNIST classifier and compared the results with a MNIST classifier trained with the original MNIST training data set, as well as a MNIST classifier trained using classical RBM samples. We also explored using a secondary dataset, the MNIST Fashion dataset and demonstrate the first quantum-generated fashion. Our hybrid autoencoder approach indicates advantage for RBM results relative to the use of a current RBM classical computer implementation for image-based machine learning and even more promising results for the next generation D-Wave quantum system. Our method for compression and image mappings is not constrained to RBMs, the autoencoder part of this method could be coupled with other quantum-based algorithms.

Proceedings Article | 18 May 2020 Presentation + Paper

Astor: A compute framework for scalable distributed big data processing

Smriti Prathapan, Navid Golpayegani, Bryan Wyatt, Milton Halem, John Dorband, Jon Trantham, Chris Markey

Proceedings Volume 11395, 113950O (2020) https://doi.org/10.1117/12.2558811

KEYWORDS: Data storage, Advanced distributed simulations, Data processing, Computer programming, Computing systems, Databases, Data centers, Computer architecture, Data communications, On-screen displays

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Proceedings Article | 22 April 2020 Paper

Satellite data fusion of multiple observed XCO2 using compressive sensing

Phuong Nguyen, Samit Shivadekar, Sai Sree Laya Chukkapalli, Milton Halem

Proceedings Volume 11423, 114230Y (2020) https://doi.org/10.1117/12.2558319

KEYWORDS: Satellites, Data fusion, Compressed sensing, Carbon dioxide, Sensors, Image fusion, Spatial resolution, Signal processing, Earth observing sensors, Carbon

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When it entered into the era of big data, Earth observing systems developed into a new stage, namely characterized by low cost, multi-national, multi-sensor and multi-modal with varying spatial and spectral resolutions confronting new challenges and opportunities. Climate data records from multiple data sources are used to infer seasonal and interannual variations which will advance and promote the development of data fusion methods. Compressed sensing is a new framework in which data acquisition and data processing are merged. It provides a new fantastic way to handle multiple observations of the same field view from complementary remote sensing instruments, allowing us to recover information at very low signal-to-noise ratio. We will particularly point out that a Compressive Sensing based framework is flexible enough for combining the two measurement systems by fusing the data from the two satellites, NASA Orbiting Carbon Observatory -2 (OCO-2) and the JAXA Greenhouse gases from Orbiting Satellites (GOSAT) to calculate the interannual Net XCO2 variability over land for three latitudinal regions, Alaska/Canada, United States and the Amazon/Brazil. The OCO-2 design is optimized for sensitivity to XCO2 variations, with an unprecedented combination of spatial resolution (about 3km) with narrow nadir coverage, while GOSAT provides broader spatial coverage (10km) with wider scanning coverage. There are different temporal degradations of both instruments over time because GOSAT was launched in 2009 and OCO-2 was launched in 2014. Both instruments infer CO2 concentration from high-resolution measurements of reflected sunlight and use similar inversion algorithms to retrieve CO2 concentrations. Both are passive satellites providing on-orbit global measurements of the greenhouse gas, XCO2, for the years 2015 -2018. The results of the CS data fusion framework show that the fused data have Root Mean Square Error (RMSE) varying from 1.31 ppm to 4.12 ppm compared with original data, depending on the region of study and gridding resolution. Validation of fused data compared with AmeriFlux station towers observations shows RMSE of 2.68 ppm.

Proceedings Article | 10 March 2020 Paper

Segmentation of stem cell colonies in fluorescence microscopy images with transfer learning

Dorsa Ziaei, David Chapman, Yaacov Yesha, Milton Halem

Proceedings Volume 11313, 113132C (2020) https://doi.org/10.1117/12.2550008

KEYWORDS: Image segmentation, Green fluorescent protein, Stem cells, Microscopy, Data modeling, Phase contrast, Performance modeling, Biomedical optics, Computer programming

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Proceedings Article | 18 May 2013 Paper

A decade of measured greenhouse forcings from AIRS

D. Chapman, P. Nguyen, M. Halem

Proceedings Volume 8743, 874313 (2013) https://doi.org/10.1117/12.2017019

KEYWORDS: Absorption, Infrared radiation, Calibration, MODIS, Climate change, Carbon dioxide, Climatology, Satellites, Temperature metrology, Spectral resolution

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