Poster Abstract: Automated Detection of the Onset of Ventricular Depolarization in Challenging Clinical ECG Data

2019 ◽  
Author(s):  
Christopher Baek ◽  
Kanav Saraf ◽  
Michael Wasko ◽  
Xu Zhang ◽  
Yi Zheng ◽  
...  
Keyword(s):  
Automated Detection ◽  
Poster Abstract ◽  
Ventricular Depolarization ◽  
Ecg Data

Automated detection of epithelial changes in colorectal carcinoma

2012 ◽  
Vol 50 (05) ◽  
Author(s):  
G Valcz ◽  
I Bándi ◽  
B Wichmann ◽  
A Patai ◽  
D Szabó ◽  
...  
Keyword(s):  
Colorectal Carcinoma ◽  
Automated Detection

Design of Portable Electrocardiograph Monitoring System

2020 ◽  
Author(s):  
Jia Hua-Ping ◽  
Zhao Jun-Long ◽  
Liu Jun
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Data Storage ◽  
P Wave ◽  
Ecg Monitoring ◽  
The Public ◽  
Ecg Signals ◽  
Early Warning Mechanism ◽  
Timely Treatment ◽  
Ecg Data

Cardiovascular disease is one of the major diseases that threaten the human health. But the existing electrocardiograph (ECG) monitoring system has many limitations in practical application. In order to monitor ECG in real time, a portable ECG monitoring system based on the Android platform is developed to meet the needs of the public. The system uses BMD101 ECG chip to collect and process ECG signals in the Android system, where data storage and waveform display of ECG data can be realized. The Bluetooth HC-07 module is used for ECG data transmission. The abnormal ECG can be judged by P wave, QRS bandwidth, and RR interval. If abnormal ECG is found, an early warning mechanism will be activated to locate the user’s location in real time and send preset short messages, so that the user can get timely treatment, avoiding dangerous occurrence. The monitoring system is convenient and portable, which brings great convenie to the life of ordinary cardiovascular users.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

ECG Data Compression Using Modified Cortes

2015 ◽  
Vol 3 (2) ◽  
pp. 13-18
Author(s):  
Himani Tiwari ◽  
◽  
V. K. Giri ◽  
Keyword(s):  
Data Compression ◽  
Ecg Data Compression ◽  
Ecg Data

Automated Detection of Pin Defects on Counterfeit Microelectronics

2018 ◽  
Author(s):  
Pallabi Ghosh ◽  
Domenic Forte ◽  
Damon L. Woodard ◽  
Rajat Subhra Chakraborty
Keyword(s):  
Integrated Circuits ◽  
Depth Map ◽  
Optical Microscope ◽  
Automated Detection ◽  
Detection Methods ◽  
Global Supply Chains ◽  
Subject Matter Experts ◽  
Security Breaches ◽  
The Past ◽  
Skilled Subject

Abstract Counterfeit electronics constitute a fast-growing threat to global supply chains as well as national security. With rapid globalization, the supply chain is growing more and more complex with components coming from a diverse set of suppliers. Counterfeiters are taking advantage of this complexity and replacing original parts with fake ones. Moreover, counterfeit integrated circuits (ICs) may contain circuit modifications that cause security breaches. Out of all types of counterfeit ICs, recycled and remarked ICs are the most common. Over the past few years, a plethora of counterfeit IC detection methods have been created; however, most of these methods are manual and require highly-skilled subject matter experts (SME). In this paper, an automated bent and corroded pin detection methodology using image processing is proposed to identify recycled ICs. Here, depth map of images acquired using an optical microscope are used to detect bent pins, and segmented side view pin images are used to detect corroded pins.

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