In the present scenario, public health is a global challenge. In view of COVID-19 pandemic, interventions of emerging technologies hasbeen highly increased and post-pandemica big technological shift is expected for pr...
In the present scenario, public health is a global challenge. In view of COVID-19 pandemic, interventions of emerging technologies hasbeen highly increased and post-pandemica big technological shift is expected for providing information and communication technology-enabled solutions to healthcare as well a meeting other social challenges. Internet of Things or IoT and Big data are the technologies prominently being used in healthcare applications. In smart city visualization to provide ubiquitous computing environment, urge of smart, small but powerful sensor devices or IoT technology-enabled healthcare solutions deployments done over open networked infrastructure and underlying architecture. Such highly dynamic and heterogeneous environment with rapid digital transformation enforcing trusted security resource-restrictions and performance implication. In this paper, firstly, we explore the existing security, privacy and authentication weakness in reference to IoT or IoMT and big data enabled healthcare applications. Secondly, scaling the low to high security risks done based on the major weaknesses. In this work primarily we focus on most challenging attacks like Denial of Services (DoS), Man in the middle and dynamic intrusions. In winding-up machine learning based intelligent adaptive approach proposed for underlying deficiencies and insufficiencies in IoT enabled Healthcare application security. The key driving forces for the imprecision of trust and security with emerging Big IoT also presented as future scope.
In this experiment, three different cell cultures (A549, WI38VA13 and MCF7) and blank medium (without cells) as a control were used. The electronic nose (E-Nose) was used to sniff the headspace of cultured cells and t...
In this experiment, three different cell cultures (A549, WI38VA13 and MCF7) and blank medium (without cells) as a control were used. The electronic nose (E-Nose) was used to sniff the headspace of cultured cells and the data were recorded. After data pre-processing, two different features were extracted by taking into consideration of both steady state and the transient information. The extracted data are then being processed by multivariate analysis, Linear Discriminant Analysis (LDA) to provide visualization of the clustering vector information in multi-sensor space. The Probabilistic Neural Network (PNN) classifier was used to test the performance of the E-Nose on determining the volatile organic compounds (VOCs) of lung cancer cell line. The LDA data projection was able to differentiate between the lung cancer cell samples and other samples (breast cancer, normal cell and blank medium) effectively. The features extracted from the steady state response reached 100% of classification rate while the transient response with the aid of LDA dimension reduction methods produced 100% classification performance using PNN classifier with a spread value of 0.1. The results also show that E-Nose application is a promising technique to be applied to real patients in further work and the aid of Multivariate Analysis; it is able to be the alternative to the current lung cancer diagnostic methods.
In this paper we present an approach for design of cloud-based systems for storing and processing of sensordata from weather stations. In recent years there are abundant IoT devices that are capable of fairly precise...
In this paper we present an approach for design of cloud-based systems for storing and processing of sensordata from weather stations. In recent years there are abundant IoT devices that are capable of fairly precise data acquisition of local atmospheric parameters and reliable transmission over wireless network. The used dataset is acquired from a modern weather station model from a superior European manufacturer. 17 parameters are used for training neural network based LSTM model. In our research we have also compared several models and approaches for modeling this type of data. We propose a design of container based system for automated processing of the received data, visualization and cloud storage technique. We are comparing two types of open-source container technologies for implementing data processing. We have evaluated the performance and provide the set of criteria for choosing a container platform. Storing the data in the cloud is an important part and we provide our consideration for implementing a particular open source platform that could reduce costs fulfil the requirements for weather data storage. The designed system is expected to be cloud-provider agnostic. The proposed features make the system especially suitable for purposes such as local weather forecast; disaster prediction: hurricane, severe storms, floods, tornadoes, etc.; severe droughts with fire dangers; prediction of landslides. Depending on the availability of additional sensors the future development of the system may be extended with parameters of the soil. Training the model on a freely available data set is in our future plans for system development.
Synthetic Aperture radar (SAR) imageries data have turned out to be one of the essential sources for forest fire mapping, especially in tropical region since the smoke haze obstruct data acquisition by optical sensor....
Synthetic Aperture radar (SAR) imageries data have turned out to be one of the essential sources for forest fire mapping, especially in tropical region since the smoke haze obstruct data acquisition by optical sensor. Despite these limitations, until now, the use of optical sensors still dominates in monitoring forest and land fires in the world. The Sentinel-1 satellites presently offer unreservedly accessible and freely available, world coverage and fast recurrent time (6-12 days), gives Sentinel-1 images the possibility to be broadly utilized for observing the Earth's surface, including forest and land fire phenomenon. However, the use of sentinel-1 data for monitoring and mapping forest and land fires in the tropics of Indonesia, is still limited and has not been widely implemented. This study investigated the use of Sentinel-1, synergy with optical Landsat-8 OLI (Operational Land Imager) data, to identify the burned area, in the tropical region of Indonesia, during 2019 fire season. A pair of Landsat-8 OLI, collected before and after fires, has been used to delineate the boundaries of sample location of burned area. Then, the difference of reflectance and Normalized Burn Ratio were analyzed. A series of Sentinel-1 images, collected before and during/after fires, has been utilized to produce the backscatter values among images. Fire incident causes landcover changes from vegetated land to bareland. This changes can affect the reflectance detected from Landsat-8 OLI. This changes also influence the backscatter recognized from SAR sensor. Then analysis of SAR backscatter on the location of the burned area detected from Landsat-8 was performed. The synergy between SAR polarimetric and optical reflected data, creates a valuable tool for identifying and interpreting burned area following a fire event.
This paper investigates impact degree of blast furnace related elements towards blast furnace gas (BFG) production. BFG is a by-product in the steel industry, which is one of the enterprise's most essential energy...
This paper investigates impact degree of blast furnace related elements towards blast furnace gas (BFG) production. BFG is a by-product in the steel industry, which is one of the enterprise's most essential energy resources. While because multiple factors affect BFG production it has characteristics of large fluctuations. Most works focus on finding a satisfactory method or improving the accuracy of existing methods to predict BFG production. There are no special studies on the factors that affect the production of BFG. Finding the elements that affect BFG production is benefit to production of BFG, which has a significance in economy. We propose a novel framework, combining cross recurrence plot (CRP) and cross recurrence quantification analysis (CRQA). Moreover, it supplies a general method to convert time series of BFG related data into high-dimensional space. This is the first analytical framework that attempts to reveal the inherent dynamic similarities of blast furnace gas-related elements. The experimental results demonstrate that this framework can realize the visualization of the time series. In addition, the results also identify the factor that has the greatest impact on blast furnace gas production by quantitative analysis.
Oil spills have become serious problems for coastal and marine environment. Oil spills incidents often occur in Batam-Bintan and Karawang waters. During Northwest monsoon, oil spills often found littering Batam and Bi...
Oil spills have become serious problems for coastal and marine environment. Oil spills incidents often occur in Batam-Bintan and Karawang waters. During Northwest monsoon, oil spills often found littering Batam and Bintan north coastal area. In Karawang waters there are a lot of oil and gas activities that cause risk of oil spills. Remote sensing technology is a solution to monitor oil spills in the sea. Active remote sensing or radar and passive/optical remote sensing have their own advantages in detecting oil spills. By using multitemporal and multi sensor satellite data, monitoring of oil spill can be done in periodic time in area that are vulnerable to oil spills. This study used SAR and optical data to monitor waters area that are often found oil spills. This study aimed to detect oil spills in Batam-Bintan and Karawang waters using multi sensor and multitemporal satellite data in 2021, and to analyze spatiotemporal distribution of oil spill, and compare oil spill pattern of the two areas. The study used Sentinel-1A, Sentinel-2A and Sentinel-2B data from January to December 2021. Oil spill detection from SAR data used dark spot object detection method. Oil spill detection from Sentinel-2 used RGB composite of SWIR-NIR-Red band. Analysis of oil spill from Sentinel-1 and Sentinel-2 from January to December 2021, in Batam-Bintan waters mostly found from January to April, then decreased, and increase in November, and mostly found in north of Bintan to the north until east of Malay peninsula. In Karawang, oil spills mostly found in March-April, and July-August, but every month oil spills was found in this area. Oil spill in Karawang mostly located from near the coast up to around 20 km to the shore.
'E-SOUNDMAPS' is a distributed microelectronic system for the sound/acoustic monitoring of areas of environmental interest that is based on an appropriately designed wireless acoustic sensor network (WASN). It...
'E-SOUNDMAPS' is a distributed microelectronic system for the sound/acoustic monitoring of areas of environmental interest that is based on an appropriately designed wireless acoustic sensor network (WASN). It involves the automated generation of multi-level sound-maps for environmental assessment of areas of interest. This paper focuses on the method and the software application for the construction of sound-maps, which is developed as part of the integrated 'E-SOUNDMAPS' system. The software application periodically produces geographically-referenced, accurate environmental sound information, based on real- field measurement data, and integrates them in the geographic map of the area of interest in a concise and comprehensive manner. Following the field recording of sound and the hierarchical recognition/classification of sound events and corresponding sources, the obtained sound sources characterization tags feed the specific software application. The output is a multilevel soundmap, constructed on the basis of the data and published electronically on the Web, for human inspection and assessment. All necessary steps for handling, archiving, monitoring, visualization and retrieval of sound data are also presented.
Remote sensing is one of the methods for geothermal exploration. This method can be used to map the geological structures, manifestations, and predict the geothermal potential area. The results from remote sensing wer...
Remote sensing is one of the methods for geothermal exploration. This method can be used to map the geological structures, manifestations, and predict the geothermal potential area. The results from remote sensing were used as guidance for the next step exploration. Analysis of target in remote sensing is an efficient method to delineate geothermal surface manifestation without direct contact to the object. The study took a place in District Merangin, Jambi Province, Indonesia. The area was selected due to existing of Merangin volcanic complex composed by Mounts Sumbing and Hulunilo with surface geothermal manifestations presented by hot springs and hot pools. The location of surface manifestations could be related with local and regional structures of Great Sumatra Fault. The methods used in this study were included identification of volcanic products, lineament extraction, and lineament density quantification. The objective of this study is to delineate the potential zones for sitting the geothermal working site based on Thermal Infrared and Synthetic Aperture radar (SAR) sensors. The lineament-related to geological structures, was aimed for high lineament density, is using ALOS - PALSAR (Advanced Land Observing Satellite - The Phased Array type L-band Synthetic Aperture radar) level 1.1. The Normalized Difference Vegetation Index (NDVI) analysis was used to predict the vegetation condition using Landsat 8 OLI-TIRS (The Operational Land Imager – Thermal Infrared sensor). The brightness temperature was extracted from TIR band to estimate the surface temperature. Geothermal working area identified based on index overlay method from extracted parameter of remote sensing data was located at the western part of study area (Graho Nyabu area). This location was identified because of the existence of high surface temperature about 30°C, high lineament density about 4 - 4.5 km/km2 and low NDVI values less than 0.3.
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