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

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 “smart tooling” 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

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