Nanowire-Based Sensor Electronics for Chemical and Biological Applications

The Analyst ◽  
2021 ◽  
Author(s):  
Guozhu Zhang ◽  
HAO ZENG ◽  
Jiangyang Liu ◽  
Kazuki Nagashima ◽  
Tsunaki Takahashi ◽  
...  
Keyword(s):  
Big Data ◽  
Biological Species ◽  
Space Time ◽  
Biological Applications ◽  
Sensing Applications ◽  
Fundamental Science ◽  
Detection And Recognition

Detection and recognition of chemical and biological species via sensor electronics is important not only for various sensing applications but alos fundamental science utilizing collected big data in space-time. In...

Space-Time Analytics for Spatial Dynamics

Data Mining ◽  
2013 ◽  
pp. 2117-2131
Author(s):  
May Yuan ◽  
James Bothwell
Keyword(s):  
Big Data ◽  
Temperature Change ◽  
Spatial Dynamics ◽  
Space Time ◽  
Data Sets ◽  
Spatial Processes ◽  
Multiple Sources ◽  
Time Concepts ◽  
New Thinking ◽  
New Space

The so-called Big Data Challenge poses not only issues with massive volumes of data, but issues with the continuing data streams from multiple sources that monitor environmental processes or record social activities. Many statistics tools and data mining methods have been developed to reveal embedded patterns in large data sets. While patterns are critical to data analysis, deep insights will remain buried unless we develop means to associate spatiotemporal patterns to the dynamics of spatial processes that essentially drive the formation of patterns in the data. This chapter reviews the literature with the conceptual foundation for space-time analytics dealing with spatial processes, discusses the types of dynamics that have and have not been addressed in the literature, and identifies needs for new thinking that can systematically advance space-time analytics to reveal dynamics of spatial processes. The discussion is facilitated by an example to highlight potential means of space-time analytics in response to the Big Data Challenge. The example shows the development of new space-time concepts and tools to analyze data from two common General Circulation Models for climate change predictions. Common approaches compare temperature changes at locations from the NCAR CCSM3 and from the CNRM CM3 or animate time series of temperature layers to visualize the climate prediction. Instead, new space-time analytics methods are shown here the ability to decipher the differences in spatial dynamics of the predicted temperature change in the model outputs and apply the concepts of change and movement to reveal warming, cooling, convergence, and divergence in temperature change across the globe.

scholarly journals Suspended-Core Microstructured Polymer Optical Fibers and Potential Applications in Sensing

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3449 ◽  
Author(s):  
Wanvisa Talataisong ◽  
Rand Ismaeel ◽  
Martynas Beresna ◽  
Gilberto Brambilla
Keyword(s):  
Optical Fibers ◽  
Material Selection ◽  
Biological Species ◽  
Single Step ◽  
3D Printer ◽  
Sensing Applications ◽  
Wide Range ◽  
Potential Applications ◽  
Pressure Chemical ◽  
Polymer Optical Fibers

The study of the fabrication, material selection, and properties of microstructured polymer optical fibers (MPOFs) has long attracted great interest. This ever-increasing interest is due to their wide range of applications, mainly in sensing, including temperature, pressure, chemical, and biological species. This manuscript reviews the manufacturing of MPOFs, including the most recent single-step process involving extrusion from a modified 3D printer. MPOFs sensing applications are then discussed, with a stress on the benefit of using polymers.

scholarly journals Emergent Technologies in Big Data Sensing: A Survey

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Ting Zhu ◽  
Sheng Xiao ◽  
Qingquan Zhang ◽  
Yu Gu ◽  
Ping Yi ◽  
...  
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Science ◽  
Mobile Sensing ◽  
Emergent Technologies ◽  
Sensing Applications ◽  
Crowd Sensing ◽  
Multiple Data ◽  
Challenges And Opportunities ◽  
Research Architecture

When the number of data generating sensors increases and the amount of sensing data grows to a scale that traditional methods cannot handle, big data methods are needed for sensing applications. However, big data is a fuzzy data science concept and there is no existing research architecture for it nor a generic application structure in the field of sensing. In this survey, we explore many scattered results that have been achieved by combining big data techniques with sensing and present our vision of big data in sensing. Firstly, we outline the application categories to generally summarize existing research achievements. Then we discuss the techniques proposed in these studies to demonstrate challenges and opportunities in this field. Finally, we present research trends and list some directions of big data in future sensing. Overall, mobile sensing and its related studies are hot topics, but other large-scale sensing researches are flourishing too. Although there are no “big data” techniques acting as research platforms or infrastructures to support various applications, multiple data science technologies, such as data mining, crowd sensing, and cloud computing, serve as foundations and bases of big data in the world of sensing.

Efficient dimension reduction of hyperspectral images for big data remote sensing applications

2020 ◽  
Vol 14 (03) ◽  
pp. 1
Author(s):  
Beatriz P. Garcia-Salgado ◽  
Volodymyr I. Ponomaryov ◽  
Sergiy Sadovnychiy ◽  
Rogelio Reyes-Reyes
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Dimension Reduction ◽  
Hyperspectral Images ◽  
Sensing Applications ◽  
Remote Sensing Applications

NANOSCALE FIELD IONIZATION SENSORS: A REVIEW

2005 ◽  
Vol 04 (05n06) ◽  
pp. 945-949
Author(s):  
NIKHIL KORATKAR
Keyword(s):  
Gas Sensing ◽  
Rensselaer Polytechnic Institute ◽  
Biological Species ◽  
Field Ionization ◽  
Ultra Low Power ◽  
Field Amplification ◽  
Β Phase ◽  
Sensing Applications ◽  
Promising Area ◽  
Nanoscale Science

So far, one of the most promising applications of nanoscale science and technology has been in the area of field emission. The electric field amplification effects associated with sharp nanostructure tips can be used to significantly reduce the emission voltages. Another equally promising area that also takes advantage of the field amplification effects is the area of field ionization. The extremely high electrical fields generated near the vicinity of sharp nanostructure tips can be used to ionize chemical or biological species at a fraction of the voltage of a traditional ionizer. In this article we review two of the very first reported papers related to nanoscale field ionization published by our group at the Rensselaer Polytechnic Institute. The first paper describes a carbon nanotube gas ionizer, which shows potential for gas sensing applications. The second paper describes an ultra low-power gas ionizer featuring β-phase Tungsten nanorod electrodes. We end with a review of the major challenges that must be overcome to develop nanoscale ionization sensors.

An Extensive Review of Nanotubes Based Mass-Sensors

2021 ◽  
Author(s):  
Dinesh Deshwal ◽  
Anil Kumar Narwal
Keyword(s):  
Boron Nitride Nanotubes ◽  
Biological Applications ◽  
Mass Sensor ◽  
Mass Sensing ◽  
Electromechanical Systems ◽  
Nanomechanical Resonators ◽  
Mass Sensors ◽  
Sensing Applications ◽  
Chemical Inertness ◽  
Non Linear

Abstract Sensors have tremendous demand in Industry because of their properties like sensitiveness, responsiveness, stability, selectiveness, and cost-effectiveness. Therefore, it is a dire need to develop advanced sensing materials and technologies. With the rapid advancement in micro and nanotechnologies in Micro-electromechanical Systems/ Nano-electromechanical Systems (MEMS/NEMS), more emphasis has to develop micro and nanomechanical resonators, having great interest for engineering fields. When MEMS/NEMS resonators are used for advancement in sensors, then they could perform both detection and sensing. Both BNNT and CNT are the strongest lightweight nanomaterials used for mass sensing applications. BNNT contradict to CNT have nontoxic property towards health and environment because of its structural stability and chemical inertness, which makes it more suitable for biological applications. From various studies, the conclusion comes out that the non-linear dynamic behavior of Boron Nitride Nanotubes-based mass sensors has not yet been explored. It is required strongly to study the non-linear conduct of BNNT for designing a better performing mass sensor.

scholarly journals Evaluation of Biomedical Imaging in Deep Neural Networks

2021 ◽  
pp. 26-33
Author(s):  
Karthikeyan K
Keyword(s):  
Image Processing ◽  
Big Data ◽  
Deep Neural Networks ◽  
Historical Background ◽  
X Rays ◽  
Biological Applications ◽  
Resonance Imaging ◽  
Magnet Resonance Imaging ◽  
Medical Field ◽  
Magnet Resonance

Whereas the historical background of medical field started in 1895 with Roentgen Wilhelm participating in the first x-rays photograph and proceeded through 1913 with the discovery of mammogram and 1927 with first cerebellar echocardiogram, advanced medicine tomography came into focus in the 1950s with the discovery of PET and ultrasonic image processing. The first computed tomography (CT) scanners was created by Hounsfield Godfrey and CoreMark Allanin 1972, while the first commercialized Magnet Resonance Imaging (MRI) scanners were produced by Raymond Dalmatian in 1977. The creation of general methods and terminology of digitized signal and image processing occurred in tandem with the growth of medical imaging technology in the 1970s and beyond, as well as the advent of digital processors. In an examination of biological applications and analysis in the era of big data and deep learning, this article analyzes background and phraseology: Pattern Classification, Artificial Intelligence, Machine Learning, Big Data.

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