SOME ADVANCES IN ENVIRONMENTAL ANALYTICS AND MONITORING

2007 ◽  
pp. 161-174
Author(s):  
Agata Kot-Wasik ◽  
Jacek Namieśnik
Keyword(s):  
Environmental Analytics

Applications in: Environmental Analytics (fine particles)

2019 ◽  
Vol 4 (6) ◽  
Author(s):  
Markus Lankers
Keyword(s):  
Raman Spectroscopy ◽  
Detailed Analysis ◽  
Fine Particles ◽  
Raman Microscopy ◽  
New Developments ◽  
Micro Raman Spectroscopy ◽  
Environmental Analytics

Abstract Micro Raman spectroscopy has been applied very early in environmental analytics. However, until now the field of application is quite limited. The main reasons for the low acceptance are high cost of the method and the low throughput. New developments in technology lead to cheaper instrumentation. Automation of Raman microscopy of particles might be a solution for a higher throughput and a broader application in environmental analytics. A more detailed analysis of aerosols and microplastic is good examples that could benefit from this development.

scholarly journals Aptamers for pharmaceuticals and their application in environmental analytics

2011 ◽  
Vol 4 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Beate Strehlitz ◽  
Christine Reinemann ◽  
Soeren Linkorn ◽  
Regina Stoltenburg
Keyword(s):  
Environmental Analytics

Trends in Environmental Analytics and Monitoring

2000 ◽  
Vol 30 (2-3) ◽  
pp. 221-269 ◽  
Author(s):  
Jacek Namieśnik
Keyword(s):  
Environmental Analytics

Dynamic Fog Computing

2019 ◽  
pp. 24-47
Author(s):  
SANDER SOO ◽  
Chii Chang ◽  
Seng W. Loke ◽  
Satish Narayana Srirama
Keyword(s):  
Fog Computing ◽  
Mobility Support ◽  
Task Distribution ◽  
Distribution Process ◽  
Promising Solution ◽  
The Common ◽  
Computational Resources ◽  
Edge Networks ◽  
Environmental Analytics ◽  
Computing Approach

The emerging Internet of Things (IoT) systems enhance various mobile ubiquitous applications such as augmented reality, environmental analytics, etc. However, the common cloud-centric IoT systems face limitations on the agility needed for real-time applications. This motivates the Fog computing architecture, where IoT systems distribute their processes to the computational resources at the edge networks near data sources and end-users. Although fog computing is a promising solution, it also raises a challenge in mobility support for mobile ubiquitous applications. Lack of proper mobility support will increase the latency due to various factors such as package drop, re-assigning tasks to fog servers, etc. To address the challenge, this chapter proposes a dynamic and proactive fog computing approach, which improves the task distribution process in fog-assisted mobile ubiquitous applications and optimizes the task allocation based on runtime context information. The authors have implemented and validated a proof-of-concept prototype and the chapter discusses the findings.

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