1,027 research outputs found
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Micro Thin Film Sensor Embedding in Metal Structures for Rapid Production of Miniature Smart Metal Tooling
In-situ monitoring and control of temperature and strain is important to improve
product quality for numerous mesoscale manufacturing processes. However, it is difficult for
conventional sensors to provide measurements with a high spatial and temporal resolution at
critical locations. This paper studies the fabrication and calibration of micro thin film sensors
embedded in metal structures for miniature tooling applications. Micro thin film sensors have
been successfully fabricated on various metal substrates and advanced embedding techniques
have been developed to ensure sensor function inside metal structures. Specifically, multilayer
dielectric/metal thin film micro sensors were embedded into layered metal structures by
ultrasonic welding (USW). These embedded sensors provided superior spatial and temporal
resolutions. Smart tooling technique will improve safety and reliability significantly for
manufacturing processes.Mechanical Engineerin
Biological Lasers for Biomedical Applications
A biolaser utilizes biological materials as part of its gain medium and/or part of its cavity. It can also be a micro- or nanosized laser embedded/integrated within biological materials. The biolaser employs lasing emission rather than regular fluorescence as the sensing signal and therefore has a number of unique advantages that can be explored for broad applications in biosensing, labeling, tracking, contrast agent development, and bioimaging. This article reports on the progress in biolasers with focus on the work done in the past five years. In the end, the possible future directions of the biolaser are discussed.Biolasers and their applications in biology and biomedicine are reviewed in this progress report. The biolaser employs lasing emission rather than regular fluorescence as the sensing signal and therefore has a number of unique advantages that can be explored for broad applications in biosensing, labeling, tracking, contrast agent development, and bioimaging.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151258/1/adom201900377.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151258/2/adom201900377_am.pd
Selective Refinement Network for High Performance Face Detection
High performance face detection remains a very challenging problem,
especially when there exists many tiny faces. This paper presents a novel
single-shot face detector, named Selective Refinement Network (SRN), which
introduces novel two-step classification and regression operations selectively
into an anchor-based face detector to reduce false positives and improve
location accuracy simultaneously. In particular, the SRN consists of two
modules: the Selective Two-step Classification (STC) module and the Selective
Two-step Regression (STR) module. The STC aims to filter out most simple
negative anchors from low level detection layers to reduce the search space for
the subsequent classifier, while the STR is designed to coarsely adjust the
locations and sizes of anchors from high level detection layers to provide
better initialization for the subsequent regressor. Moreover, we design a
Receptive Field Enhancement (RFE) block to provide more diverse receptive
field, which helps to better capture faces in some extreme poses. As a
consequence, the proposed SRN detector achieves state-of-the-art performance on
all the widely used face detection benchmarks, including AFW, PASCAL face,
FDDB, and WIDER FACE datasets. Codes will be released to facilitate further
studies on the face detection problem.Comment: The first two authors have equal contributions. Corresponding author:
Shifeng Zhang ([email protected]
Enhancement of Multimodal Traffic Safety in High-Quality Transit Areas
Numerous extant studies are dedicated to enhancing the safety of active transportation modes, but very few studies are devoted to safety analysis surrounding transit stations, which serve as an important modal interface for pedestrians and bicyclists. This study bridges the gap by developing joint models based on the multivariate conditionally autoregressive (MCAR) priors with a distance-oriented neighboring weight matrix. For this purpose, transit-station-centered data in Los Angeles County were used for model development. Feature selection relying on both random forest and correlation analyses was employed, which leads to different covariate inputs to each of the two jointed models, resulting in increased model flexibility. Utilizing an Integrated Nested Laplace Approximation (INLA) algorithm and various evaluation criteria, the results demonstrate that models with a correlation effect between pedestrians and bicyclists perform much better than the models without such an effect. The joint models also aid in identifying significant covariates contributing to the safety of each of the two active transportation modes. The research results can furnish transportation professionals with additional insights to create safer access to transit and thus promote active transportation
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Rapid Fabrication of Smart Tooling with Embedded Sensors by Casting in Molds Made by Three Dimensional Printing
This paper is to investigate the feasibility of constructing âsmart toolingâ by embedding thin film
sensors, specifically, thin film thermocouples (TFTC) in castings made by molds formed by 3
Dimensional Printing (3DP). This study investigates whether thin film sensors can effectively be
cast into larger metal structures and if the sensors survive the casting process. The investigation
includes making 3DP molds to produce cast lap joint test bars of aluminum A356 and
electroplated nickel to characterize by mechanical testing to find the best process conditions to
maximize bond strength between the embedded thin film sensors and the cast material. Lastly
molds were made and embedded sensors were placed inside the mold for casting. Some of the
embedded sensors survived the casting process. In-situ monitoring of casting process with the
embedded sensors was accomplished.Mechanical Engineerin
Chromatin Laser Imaging Reveals Abnormal Nuclear Changes for Early Cancer Detection
We developed and applied rapid scanning laser-emission microscopy to detect
abnormal changes in cell nuclei for early diagnosis of cancer and cancer
precursors. Regulation of chromatins is essential for genetic development and
normal cell functions, while abnormal nuclear changes may lead to many
diseases, in particular, cancer. The capability to detect abnormal changes in
apparently normal tissues at a stage earlier than tumor development is critical
for cancer prevention. Here we report using LEM to analyze colonic tissues from
mice at-risk for colon cancer by detecting prepolyp nuclear abnormality. By
imaging the lasing emissions from chromatins, we discovered that, despite the
absence of observable lesions, polyps, or tumors under stereoscope, high-fat
mice exhibited significantly lower lasing thresholds than low-fat mice. The low
lasing threshold is, in fact, very similar to that of adenomas and is caused by
abnormal cell proliferation and chromatin deregulation that can potentially
lead to cancer. Our findings suggest that conventional methods, such as
colonoscopy, may be insufficient to reveal hidden or early tumors under
development. We envision that this work will provide new insights into LEM for
early tumor detection in clinical diagnosis and fundamental biological and
biomedical research of chromatin changes at the biomolecular level of cancer
development
Primary oral and maxillofacial liposarcoma: a clinicopathological and immunohistochemical study of eleven cases
Relational Learning for Joint Head and Human Detection
Head and human detection have been rapidly improved with the development of
deep convolutional neural networks. However, these two tasks are often studied
separately without considering their inherent correlation, leading to that 1)
head detection is often trapped in more false positives, and 2) the performance
of human detector frequently drops dramatically in crowd scenes. To handle
these two issues, we present a novel joint head and human detection network,
namely JointDet, which effectively detects head and human body simultaneously.
Moreover, we design a head-body relationship discriminating module to perform
relational learning between heads and human bodies, and leverage this learned
relationship to regain the suppressed human detections and reduce head false
positives. To verify the effectiveness of the proposed method, we annotate head
bounding boxes of the CityPersons and Caltech-USA datasets, and conduct
extensive experiments on the CrowdHuman, CityPersons and Caltech-USA datasets.
As a consequence, the proposed JointDet detector achieves state-of-the-art
performance on these three benchmarks. To facilitate further studies on the
head and human detection problem, all new annotations, source codes and trained
models will be public
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