Mobile cloud computing for ECG telemonitoring and real-time coronary heart disease risk detection

Venkatesan, C.; Karthigaikumar, P.; Satheeskumaran, S.

doi:10.1016/j.bspc.2018.04.013

Cited by 64 publications

(35 citation statements)

References 24 publications

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“…Although the main focus is to assess the toxicity in water, it is possible to include other environment sensors such as temperature, CO 2 , and humidity using cloud‐based environment assessment. Mobile cloud approach is required in many IoT applications to store the information and to process them . Figure depicts the cloud platform for receiving information from various sensors and environment assessment.…”

Section: Methodsmentioning

confidence: 99%

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Microbial fuel cell–based self‐powered biosensor for environment monitoring in IoT cloud framework

Saravanakumar

Rajeswari²

2019

Concurrency and Computation

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Summary Renewable energy sources are useful for sustainable monitoring, but still very limited today due to various implementation constraints. Microbial fuel cells (MFCs) are considered a promising renewable power source for remote monitoring applications. They are used as wireless temperature sensors and biosensors due to their ability in powering environmental sensors. MFCs can provide ultralow and dynamic power, and hence, energy improvement is crucial for self‐powered biosensors. Cloud computing–based IoT framework is proposed for environment monitoring using MFC‐based biosensors. This paper presents the electric energy harvesting from Oryza Sativa plants with bacteria as the catalyst. It adopts the technology of MFC in the plants to extract the maximum energy. An effective power management with IoT cloud framework is presented in this work to independently operate multiple MFCs to generate maximum power. Independently operated MFCs with electrically isolated electrodes have been utilized in the design of a suitable power management system. Cloud computing is utilized in this work to process the data generated in continuous monitoring of environment. Experimental results show that the proposed framework can achieve sustainable power for sensor nodes and achieves maximum performance in environment monitoring using cloud‐based IoT platform.

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Section: Methodsmentioning

confidence: 99%

“…Mobile cloud approach is required in many IoT applications to store the information and to process them. 25 Figure 5 depicts the cloud platform for receiving information from various sensors and environment assessment. The data collected from various sensors are stored in the cloud server.…”

Section: Iv) Cloud-based Environment Assessmentmentioning

confidence: 99%

Microbial fuel cell–based self‐powered biosensor for environment monitoring in IoT cloud framework

Saravanakumar

Rajeswari²

2019

Concurrency and Computation

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“…The preprocessing stage enhances the input image so that the speed-limit sign can be extracted easily. MSER detection [7] is used in detection stage to find Region of Interest (ROI). In first method, the recognition stage extracts HOG features from the ROI, which is the input to trained SVM for sign classification.…”

Section: System Overviewmentioning

confidence: 99%

“…In this stage, MSERs [7] are detected to find probable speed limit sign region. The advantage of MSER detection is that it is robust in various environmental conditions.…”

Section: Roi Detection Using Msermentioning

confidence: 99%

Automatic Speed-Limit Sign Detection and Recognition for Advanced Driver Assistance Systems

2019

IJITEE

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 Abstract -In recent years, traffic accidents have become the major cause to injuries, deaths and property damages. One of the main reasons to such accidents is due to high speed of vehicles. In order to maintain proper speed limit and thus provide significant contribution to improve safety, we propose Speed Limit sign detection and recognition method which is one of the features of Advanced Driver Assistance System (ADAS). In this paper we propose two approaches, i.e., histogram oriented gradient feature with SVM classifier namely HOG-SVM and CNN based approach. In these approaches we first pre-process the image using red color enhancement method and then we detect the Region of Interest using Maximally Stable Extremal Regions (MSER). Later, we classify the image by using different classifiers. In the HOG-SVM method, we are using HOG for feature extraction and Support Vector Machine (SVM) classifier for classification. In the CNN approach, we are using Convolutional Neural Networks (CNN) both for feature extraction and classification. Performance analysis of SVM classifier and CNN classifier are first evaluated on simple German Traffic Sign Recognition Benchmark (GTSRB) dataset using 5 fold classification, we got accuracy 100% for SVM classifier and 98.5% for CNN classifier. Also Further evaluated on German Traffic Sign Detection and Recognition Benchmark datasets and the experimental results show detection accuracy upto 93.6% for SVM classifier and 85.8% for CNN classifier.

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“…Six types of supervised learning algorithms are used, each with ten different training algorithms for the neural networks. [23][24][25] The selection of the supervised learning algorithms is based on an extensive literature survey done for completion of this work. The most suitable algorithms for gas detection are identified and used in the developed system.…”

Section: Data Processing and Classification Using Neural Networkmentioning

confidence: 99%

Qualitative Detection of Nitro-Aromatic Explosives using Supervised Learning Access

2019

IJITEE

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Mobile cloud computing for ECG telemonitoring and real-time coronary heart disease risk detection

Cited by 64 publications

References 24 publications

Microbial fuel cell–based self‐powered biosensor for environment monitoring in IoT cloud framework

Microbial fuel cell–based self‐powered biosensor for environment monitoring in IoT cloud framework

Automatic Speed-Limit Sign Detection and Recognition for Advanced Driver Assistance Systems

Qualitative Detection of Nitro-Aromatic Explosives using Supervised Learning Access

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