114 research outputs found
A process for scheduling urban interchange reconstruction
The researchers from Iowa State University worked with the Iowa DOT, and developed computer-based schedules for the corridor I235 using Microsoft Project 2000, to predict project completion, expose and adjust conflicts between trades or subcontractors, evaluate the effect of changes on project completion and cost, track projects\u27 progress, and so on. This thesis will describe a method to create schedules for urban freeway interchange reconstruction projects and procedures and tables that assist with planning and accelerating. The use of the above is demonstrated in a case study: Martin Luther King Jr. (MLK) and Cottage Grove Avenue projects in the corridor I23
Comparative Life Cycle Assessment of Single-Serve Coffee Packaging in Ontario
Single-serve coffee pods are occupying a growing share in the coffee market. In Ontario, with 14 million people, it is estimated that 2 billion single-serve coffee pods are consumed annually, the consumption of which generates 30,000 tons of landfill waste in Ontario, equivalent to 0.3% of total landfill waste generated in the province in 2014.
Different formats of coffee pods have been introduced, and each addresses the waste problem differently. Two examples are recyclable coffee pods made of aluminum and compostable coffee pods made from biodegradable polymers. In this research, these two coffee pod formats are investigated together with a typical petroleum-based plastic coffee pod, which represents the baseline landfilling scenario. A cradle-to-grave life cycle assessment (LCA) is conducted to quantify and compare the environmental effects of these systems, with a special focus on packaging materials and end-of-life management.
The results show that among the three investigated coffee pods, the recyclable aluminum format has the highest potential environmental effects across nine impact categories. Whereas, the Biodegradable Pod, which is assumed to be composted in 40% of uses, has reduced greenhouse gas emissions and landfill waste generation potential when compared with the petroleum-based plastic coffee pod. After applying a standard LCA weighting, results indicate that human toxicity is the most important life cycle impact assessment indicator result associated with all three of coffee pod formats.
This research is important from both a biodegradable material and a circular economy perspective. From a biodegradable material perspective, this study is the first to compare polylactic acid, a bio-based biodegradable polymer, with polystyrene, a petroleum-based non-degradable plastic. Biodegradable materials enable consumers easily to compost the coffee waste together with the coffee pod, but at the same time, it requires an extra plastic packaging warp for each coffee pod. From a circular economy perspective, the study is important because the results indicate the strength of using compostable biological nutrients over recyclable technical nutrients in the context of small single-use food products. Like all LCA studies, the results are dependent on specific assumptions and scenarios analyzed
Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue
Histological staining is a vital step used to diagnose various diseases and
has been used for more than a century to provide contrast to tissue sections,
rendering the tissue constituents visible for microscopic analysis by medical
experts. However, this process is time-consuming, labor-intensive, expensive
and destructive to the specimen. Recently, the ability to virtually-stain
unlabeled tissue sections, entirely avoiding the histochemical staining step,
has been demonstrated using tissue-stain specific deep neural networks. Here,
we present a new deep learning-based framework which generates
virtually-stained images using label-free tissue, where different stains are
merged following a micro-structure map defined by the user. This approach uses
a single deep neural network that receives two different sources of information
at its input: (1) autofluorescence images of the label-free tissue sample, and
(2) a digital staining matrix which represents the desired microscopic map of
different stains to be virtually generated at the same tissue section. This
digital staining matrix is also used to virtually blend existing stains,
digitally synthesizing new histological stains. We trained and blindly tested
this virtual-staining network using unlabeled kidney tissue sections to
generate micro-structured combinations of Hematoxylin and Eosin (H&E), Jones
silver stain, and Masson's Trichrome stain. Using a single network, this
approach multiplexes virtual staining of label-free tissue with multiple types
of stains and paves the way for synthesizing new digital histological stains
that can be created on the same tissue cross-section, which is currently not
feasible with standard histochemical staining methods.Comment: 19 pages, 5 figures, 2 table
Ensemble learning of diffractive optical networks
A plethora of research advances have emerged in the fields of optics and
photonics that benefit from harnessing the power of machine learning.
Specifically, there has been a revival of interest in optical computing
hardware, due to its potential advantages for machine learning tasks in terms
of parallelization, power efficiency and computation speed. Diffractive Deep
Neural Networks (D2NNs) form such an optical computing framework, which
benefits from deep learning-based design of successive diffractive layers to
all-optically process information as the input light diffracts through these
passive layers. D2NNs have demonstrated success in various tasks, including
e.g., object classification, spectral-encoding of information, optical pulse
shaping and imaging, among others. Here, we significantly improve the inference
performance of diffractive optical networks using feature engineering and
ensemble learning. After independently training a total of 1252 D2NNs that were
diversely engineered with a variety of passive input filters, we applied a
pruning algorithm to select an optimized ensemble of D2NNs that collectively
improve their image classification accuracy. Through this pruning, we
numerically demonstrated that ensembles of N=14 and N=30 D2NNs achieve blind
testing accuracies of 61.14% and 62.13%, respectively, on the classification of
CIFAR-10 test images, providing an inference improvement of >16% compared to
the average performance of the individual D2NNs within each ensemble. These
results constitute the highest inference accuracies achieved to date by any
diffractive optical neural network design on the same dataset and might provide
a significant leapfrog to extend the application space of diffractive optical
image classification and machine vision systems.Comment: 22 Pages, 4 Figures, 1 Tabl
Universal Linear Intensity Transformations Using Spatially-Incoherent Diffractive Processors
Under spatially-coherent light, a diffractive optical network composed of
structured surfaces can be designed to perform any arbitrary complex-valued
linear transformation between its input and output fields-of-view (FOVs) if the
total number (N) of optimizable phase-only diffractive features is greater than
or equal to ~2 Ni x No, where Ni and No refer to the number of useful pixels at
the input and the output FOVs, respectively. Here we report the design of a
spatially-incoherent diffractive optical processor that can approximate any
arbitrary linear transformation in time-averaged intensity between its input
and output FOVs. Under spatially-incoherent monochromatic light, the
spatially-varying intensity point spread functon(H) of a diffractive network,
corresponding to a given, arbitrarily-selected linear intensity transformation,
can be written as H(m,n;m',n')=|h(m,n;m',n')|^2, where h is the
spatially-coherent point-spread function of the same diffractive network, and
(m,n) and (m',n') define the coordinates of the output and input FOVs,
respectively. Using deep learning, supervised through examples of input-output
profiles, we numerically demonstrate that a spatially-incoherent diffractive
network can be trained to all-optically perform any arbitrary linear intensity
transformation between its input and output if N is greater than or equal to ~2
Ni x No. These results constitute the first demonstration of universal linear
intensity transformations performed on an input FOV under spatially-incoherent
illumination and will be useful for designing all-optical visual processors
that can work with incoherent, natural light.Comment: 29 Pages, 10 Figure
Pyramid diffractive optical networks for unidirectional magnification and demagnification
Diffractive deep neural networks (D2NNs) are composed of successive
transmissive layers optimized using supervised deep learning to all-optically
implement various computational tasks between an input and output field-of-view
(FOV). Here, we present a pyramid-structured diffractive optical network design
(which we term P-D2NN), optimized specifically for unidirectional image
magnification and demagnification. In this P-D2NN design, the diffractive
layers are pyramidally scaled in alignment with the direction of the image
magnification or demagnification. Our analyses revealed the efficacy of this
P-D2NN design in unidirectional image magnification and demagnification tasks,
producing high-fidelity magnified or demagnified images in only one direction,
while inhibiting the image formation in the opposite direction - confirming the
desired unidirectional imaging operation. Compared to the conventional D2NN
designs with uniform-sized successive diffractive layers, P-D2NN design
achieves similar performance in unidirectional magnification tasks using only
half of the diffractive degrees of freedom within the optical processor volume.
Furthermore, it maintains its unidirectional image
magnification/demagnification functionality across a large band of illumination
wavelengths despite being trained with a single illumination wavelength. With
this pyramidal architecture, we also designed a wavelength-multiplexed
diffractive network, where a unidirectional magnifier and a unidirectional
demagnifier operate simultaneously in opposite directions, at two distinct
illumination wavelengths. The efficacy of the P-D2NN architecture was also
validated experimentally using monochromatic terahertz illumination,
successfully matching our numerical simulations. P-D2NN offers a
physics-inspired strategy for designing task-specific visual processors.Comment: 26 Pages, 7 Figure
Learning Inter- and Intra-frame Representations for Non-Lambertian Photometric Stereo
In this paper, we build a two-stage Convolutional Neural Network (CNN)
architecture to construct inter- and intra-frame representations based on an
arbitrary number of images captured under different light directions,
performing accurate normal estimation of non-Lambertian objects. We
experimentally investigate numerous network design alternatives for identifying
the optimal scheme to deploy inter-frame and intra-frame feature extraction
modules for the photometric stereo problem. Moreover, we propose to utilize the
easily obtained object mask for eliminating adverse interference from invalid
background regions in intra-frame spatial convolutions, thus effectively
improve the accuracy of normal estimation for surfaces made of dark materials
or with cast shadows. Experimental results demonstrate that proposed masked
two-stage photometric stereo CNN model (MT-PS-CNN) performs favorably against
state-of-the-art photometric stereo techniques in terms of both accuracy and
efficiency. In addition, the proposed method is capable of predicting accurate
and rich surface normal details for non-Lambertian objects of complex geometry
and performs stably given inputs captured in both sparse and dense lighting
distributions.Comment: 9 pages,8 figure
Rapid Sensing of Hidden Objects and Defects using a Single-Pixel Diffractive Terahertz Processor
Terahertz waves offer numerous advantages for the nondestructive detection of
hidden objects/defects in materials, as they can penetrate through most
optically-opaque materials. However, existing terahertz inspection systems are
restricted in their throughput and accuracy (especially for detecting small
features) due to their limited speed and resolution. Furthermore, machine
vision-based continuous sensing systems that use large-pixel-count imaging are
generally bottlenecked due to their digital storage, data transmission and
image processing requirements. Here, we report a diffractive processor that
rapidly detects hidden defects/objects within a target sample using a
single-pixel spectroscopic terahertz detector, without scanning the sample or
forming/processing its image. This terahertz processor consists of passive
diffractive layers that are optimized using deep learning to modify the
spectrum of the terahertz radiation according to the absence/presence of hidden
structures or defects. After its fabrication, the resulting diffractive
processor all-optically probes the structural information of the sample volume
and outputs a spectrum that directly indicates the presence or absence of
hidden structures, not visible from outside. As a proof-of-concept, we trained
a diffractive terahertz processor to sense hidden defects (including
subwavelength features) inside test samples, and evaluated its performance by
analyzing the detection sensitivity as a function of the size and position of
the unknown defects. We validated its feasibility using a single-pixel
terahertz time-domain spectroscopy setup and 3D-printed diffractive layers,
successfully detecting hidden defects using pulsed terahertz illumination. This
technique will be valuable for various applications, e.g., security screening,
biomedical sensing, quality control, anti-counterfeiting measures and cultural
heritage protection.Comment: 23 Pages, 5 Figure
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