15 research outputs found

    Liquid Metal-Based Multifunctional Micropipette for 4D Single Cell Manipulation.

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    A novel manufacturing approach to fabricate liquid metal-based, multifunctional microcapillary pipettes able to provide electrodes with high electrical conductivity for high-frequency electrical stimulation and measurement is proposed. 4D single cell manipulation is realized by applying multifrequency, multiamplitude, and multiphase electrical signals to the microelectrodes near the pipette tip to create 3D dielectrophoretic trap and 1D electrorotation, simultaneously. Functions such as single cell trapping, patterning, transfer, and rotation are accomplished. Cell viability and multiday proliferation characterization has confirmed the biocompatibility of this approach. This is a simple, low-cost, and fast fabrication process that requires no cleanroom and photolithography step to manufacture 3D microelectrodes and microchannels for easy access to a wide user base for broad applications

    A Knowledge-based Recommendation System for Time Series Classification

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    Time series data sets reflect the state and extent of things as they change over time. Information extraction based on such data plays an important role in many fields. The time series classification is a typical supervised learning problem, which is applied in speech recognition, image processing and so on. However, because the attributes of time series data don't make sense and the feature dimensions are particularly large, people can't treat them as general machine learning classification problems. Currently, many different time series classification problems have been proposed. But how to choose and use these methods is still a huge problem for non-computer professional researchers. This article uses the ontology technology to build a recommendation system that contains the details and features of such algorithms. When the users input the characteristics of the data and the task requirements, they can get reasonable suggestions and a description of the workflow of the algorithm. Such a system saves the user a lot of analysis and comparison time. It also makes such problems easier to understand

    An Ontology of Machine Learning Algorithms for Human Activity Data Processing

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    Machine learning algorithms are the main tools in the field of data analysis. However, extracting knowledge from data sets originating in real life requires complex data processing. Obtaining the available tidy data sets and selecting the appropriate analysis algorithm are important issues for data analysts. Because of the complexity of the dataset and the diversity of the algorithms the researchers take too much time in selecting and comparing these algorithms. Human Activity Recognition is a typical example in Internet of Things. Its principle is to identify human behavior by analyzing the coordinate data from the sensors on the human body so that we can achieve remote monitoring. A precise Human Activity Recognition application can serve as a real-time monitoring of the elderly or vulnerable behavior. However, due to the unpredictability of human behavior, these sensor data require relatively complex processing. Therefore, we propose an ontology-based algorithm recommendation system. It consists of several parts: algorithm pool, data features, model features, and mathematical theory. The framework provides data researchers with reasonable solutions based on the characteristics of the data set and the task requirements. Especially for the Internet of Things data such as Human Activity Recognition data set, its recommendations can save users much time for analysis and comparison

    Urban intelligent assistant on the example of the escalator passenger safety management at the subway stations

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    Abstract Intelligent assistants often struggle with the complexity of spatiotemporal models used for understanding objects and environments. The construction and usage of such models demand significant computational resources. This article introduces a novel multilevel spatiotemporal model and a computationally efficient construction method. To facilitate model construction on different levels, we employ a meta-mining technique. Furthermore, the proposed model is specifically designed to excel in foggy environments. As a practical application, we develop an intelligent assistant focused on enhancing subway passenger safety. We present case examples involving jammed objects, such as shoes, in escalator combs. Our results demonstrate the effectiveness of the proposed model and method. Specifically, the accuracy of breakdown detection has improved by 10% compared to existing information systems used in subways. Moreover, the time required to build a spatiotemporal model is reduced by 2.3 times, further highlighting the efficiency of our approach. Our research offers a promising solution for intelligent assistants dealing with complex spatiotemporal modeling, with practical applications in ensuring subway passenger safety

    Graphene woven fabric as high-resolution sensing element of contact-lens tonometer

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    In our work, the graphene woven fabrics (GWFs) are investigated as the sensing element of the contact-lens tonometer, which enables precisely monitoring IOP all the daytime. The current-voltage relationship of the device was tested under voltage sweep and the relationships between resistance change and deformation were calculated. Eight devices with GWF in different sizes and CVD conditions were fabricated and the relationship between the current changes of each device and effective IOP increasing, when keeping the voltage constant, was obtained. Combining the highly strain sensing sensitivity and transparency, the contact-lens tonometers with GWF as high-resolution sensing element have a promising prospective. ? 2014 IEEE.EICPCI-S(ISTP)

    Narrower Nanoribbon Biosensors Fabricated by Chemical Lift-off Lithography Show Higher Sensitivity

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    Wafer-scale nanoribbon field-effect transistor (FET) biosensors fabricated by straightforward top-down processes are demonstrated as sensing platforms with high sensitivity to a broad range of biological targets. Nanoribbons with 350 nm widths (700 nm pitch) were patterned by chemical lift-off lithography using high-throughput, low-cost commercial digital versatile disks (DVDs) as masters. Lift-off lithography was also used to pattern ribbons with 2 μm or 20 μm widths (4 or 40 μm pitches, respectively) using masters fabricated by photolithography. For all widths, highly aligned, quasi-one-dimensional (1D) ribbon arrays were produced over centimeter length scales by sputtering to deposit 20 nm thin-film In2O3 as the semiconductor. Compared to 20 μm wide microribbons, FET sensors with 350 nm wide nanoribbons showed higher sensitivity to pH over a broad range (pH 5 to 10). Nanoribbon FETs functionalized with a serotonin-specific aptamer demonstrated larger responses to equimolar serotonin in high ionic strength buffer than those of microribbon FETs. Field-effect transistors with 350 nm wide nanoribbons functionalized with single-stranded DNA showed greater sensitivity to detecting complementary DNA hybridization vs 20 μm microribbon FETs. In all, we illustrate facile fabrication and use of large-area, uniform In2O3 nanoribbon FETs for ion, small-molecule, and oligonucleotide detection where higher surface-to-volume ratios translate to better detection sensitivities
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