A Multi-Purpose CO Poisoning Detection System using Mobile Phones

Nowadays, Technology is advancing day by day. Most of the thing are done today with the help of technology. Technology makes the things automate and reduce human efforts. Technology plays a vital role in everyone’s life either in the form of comfort or security. To provide a input in enhancing the security, we are proposing a Human detection system. System is based on today’s most popular and growing IOT technology and PIR Sensor. System is operated through wireless network suing mobile phones. PIR sensor is used here to detect presence of human in nearby areas. System generates the alert if presence of human is detected. System ensures the security in restricted areas.

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Design and Implementation of Fall Detection System Based on ZigBee

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.397-400.1446 ◽

2013 ◽

Vol 397-400 ◽

pp. 1446-1450

Author(s):

Yi Zhang ◽

Gang Tan ◽

Yuan Luo ◽

Yang Li

Keyword(s):

Mobile Phones ◽

Detection System ◽

Fall Detection ◽

The Elderly ◽

Sensor Data ◽

Fusion Algorithm ◽

Design And Implementation ◽

Weighted Fusion ◽

Detection Technology ◽

Alert Message

Elderly people are unable to be rescued promptly when they fall down from the chair or bed, and may be injured seriously. A fall detection system for chair based on ZigBee was investigated in this paper for this problem. Gotten the fallen information by multi-sensor data acquisition and detection technology, uploaded to PC or Android terminals through ZigBee-WiFi gateway, and sent the information to mobile phone in SMS through GSM module. The adaptive weighted fusion algorithm was used to improve the accuracy of monitoring-data. The results show that one who take the system can get the alert message instantly by PC, Android terminals and mobile phones, so it has the potential to satisfy the application of the elderly peoples care problem.

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Guide for Modelling a Network Flow-Based Detection System for Malware Categorization

Advances in Digital Crime, Forensics, and Cyber Terrorism - Applying Methods of Scientific Inquiry Into Intelligence, Security, and Counterterrorism ◽

10.4018/978-1-5225-8976-1.ch007 ◽

2019 ◽

pp. 150-178

Author(s):

Joshua Chibuike Sopuru ◽

Murat Akkaya

Keyword(s):

Operating System ◽

Mobile Phones ◽

Operating Systems ◽

Network Flow ◽

Detection System ◽

Malicious Software ◽

Android Os ◽

Definition Of ◽

Improved Technology ◽

Over Time

Improved technology has led to significant changes in society over time. This has been accompanied by significant changes in the economy. The improvement in technology has also been accompanied by significant changes in the modeling of network-based systems. This is comprised of significant updates of computer and mobile operating systems. The development of mobile phones and operating systems have endangered essential individual and corporate data over time by making it vulnerable and prone to viruses, worms, and malware. This chapter focuses on reviewing literature that serves as guides for modeling a network flow-based detection system for malware categorization. The Author begins with an in-depth definition of mobile devices and how they have eased the spread of malicious software. Identifying Android OS as the most used operating system, Android OS operating system layer was explained, and the reason for user preferability unveiled. The chapter continued with a review of known malware and their behaviors as has been observed over time.

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Design of Identity Recognition and Liveness Detection System for Mobile Phones

2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN) ◽

10.1109/indo-taiwanican48429.2020.9181332 ◽

2020 ◽

Author(s):

Ding-Jia Lee ◽

Tse-Yu Pan ◽

Min-Chun Hu

Keyword(s):

Mobile Phones ◽

Detection System ◽

Identity Recognition ◽

Liveness Detection

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Design of a Radio Frequency Detection System for Mobile Phones

2018 IEEE 38th Central America and Panama Convention (CONCAPAN XXXVIII) ◽

10.1109/concapan.2018.8596394 ◽

2018 ◽

Author(s):

Brian Meneses-Claudio ◽

Witman Alvarado-Diaz ◽

Avid Roman-Gonzalez

Keyword(s):

Radio Frequency ◽

Mobile Phones ◽

Detection System ◽

Frequency Detection

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PerFallD: A pervasive fall detection system using mobile phones

2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops) ◽

10.1109/percomw.2010.5470652 ◽

2010 ◽

Cited By ~ 16

Author(s):

Jiangpeng Dai ◽

Xiaole Bai ◽

Zhimin Yang ◽

Zhaohui Shen ◽

Dong Xuan

Keyword(s):

Mobile Phones ◽

Detection System ◽

Fall Detection

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AI-Based Vehicular Network toward 6G and IoT: Deep Learning Approaches

ACM Transactions on Management Information Systems ◽

10.1145/3466691 ◽

2022 ◽

Vol 13 (1) ◽

pp. 1-12

Author(s):

Mu-Yen Chen ◽

Min-Hsuan Fan ◽

Li-Xiang Huang

Keyword(s):

Deep Learning ◽

Mobile Phones ◽

Vehicular Networks ◽

Detection System ◽

High Reliability ◽

Vehicular Network ◽

Driving Behavior ◽

Learning Approaches ◽

Automobile Accidents ◽

Dangerous Driving

In recent years, vehicular networks have become increasingly large, heterogeneous, and dynamic, making it difficult to meet strict requirements of ultralow latency, high reliability, high security, and massive connections for next generation (6G) networks. Recently, deep learning (DL ) has emerged as a powerful artificial intelligence (AI ) technique to optimize the efficiency and adaptability of vehicle and wireless communication. However, rapidly increasing absolute numbers of vehicles on the roads are leading to increased automobile accidents, many of which are attributable to drivers interacting with their mobile phones. To address potentially dangerous driver behavior, this study applies deep learning approaches to image recognition to develop an AI-based detection system that can detect potentially dangerous driving behavior. Multiple convolutional neural network (CNN )-based techniques including VGG16, VGG19, Densenet, and Openpose were compared in terms of their ability to detect and identify problematic driving.

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Analysis of electron-diffraction intensity profiles using a photodiode-array detection system

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100075403 ◽

1983 ◽

Vol 41 ◽

pp. 322-323

Author(s):

J. B. Warren

Keyword(s):

Electron Diffraction ◽

Diffraction Pattern ◽

Detection System ◽

High Energy ◽

Photographic Emulsion ◽

Diffraction Intensity ◽

Silicon Photodiode ◽

Photodiode Array Detection ◽

Analog To Digital ◽

Photodiode Array

Electron diffraction intensity profiles have been used extensively in studies of polycrystalline and amorphous thin films. In previous work, diffraction intensity profiles were quantitized either by mechanically scanning the photographic emulsion with a densitometer or by using deflection coils to scan the diffraction pattern over a stationary detector. Such methods tend to be slow, and the intensities must still be converted from analog to digital form for quantitative analysis. The Instrumentation Division at Brookhaven has designed and constructed a electron diffractometer, based on a silicon photodiode array, that overcomes these disadvantages. The instrument is compact (Fig. 1), can be used with any unmodified electron microscope, and acquires the data in a form immediately accessible by microcomputer.Major components include a RETICON 1024 element photodiode array for the de tector, an Analog Devices MAS-1202 analog digital converter and a Digital Equipment LSI 11/2 microcomputer. The photodiode array cannot detect high energy electrons without damage so an f/1.4 lens is used to focus the phosphor screen image of the diffraction pattern on to the photodiode array.

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