Initiating android phone technology using QR codes to make innovative functional clothes

Nofal, Reem M.

doi:10.1108/ijcst-12-2018-0153

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Wearable device technology includes five items: (1) smartphones, such as Google Android and Apple iOS operating system (OS); (2) wireless communication including Bluetooth, Wi-Fi, near-field communication (NFC) and radio frequency identification (RFID); (3) microcontrollers; (4) various sensor types, such as environmental sensors, biological sensors and position tracking sensors; (5) wearable devices, such as smart clothing, smart eyewear and smart watches. Wearable device technology should be further used in the internet of things (IOT) applications (An et al , 2017; Mardonova and Choi, 2018; Nofal, 2020).…”

Section: Introductionmentioning

confidence: 99%

Quadri-directional optical bending sensor using side-emitting POF integrated with wearable device technology in smart glove

Lin

2022

IJCST

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PurposeIt is important to monitor wrist four direction movements (flexion, extension, adduction and abduction) for hand healthcare, wrist rehabilitation and upper limb exercise, and so on. The purpose of this study is to develop a quadri-directional optical bending sensor that integrated wearable device technology in a smart glove to detect wrist four direction movements.Design/methodology/approachThe quadri-directional optical bending sensor was designed with a microcontroller board, a Bluetooth wireless module, a side-emitting polymeric optical fibre (POF), an infrared light emitting diode and four phototransistors. A linear equation was deduced to calculate bending angle from detecting sensor value of Arduino microcontroller. The bending angle values could be seen by the smartphone screen, so the system has a good human–machine interface function.FindingsThe light emission by macro-bending of the side-emitting POFs that the transmittance of the outer side is greater than the inner. The bending POFs lateral emission phenomenon integrated with phototransistors on the edge is suitable for the development of bending sensors.Originality/valueThis study is to develop a novel quadri-directional optical bending sensor to replace two bi-direction sensors or four uni-direction sensors for wrist four direction movements monitoring.

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Section: Introductionmentioning

confidence: 99%

Quadri-directional optical bending sensor using side-emitting POF integrated with wearable device technology in smart glove

Lin

2022

IJCST

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“…Wearable devices include five technologies; (1) Google Android and Apple iOS operating system (OS) smart phones; (2) wireless communication including Bluetooth, Wi-Fi, near field communication (NFC) and radio frequency identification (RFID); (3) microcontrollers; (4) various sensor types, such as environmental sensors, biological sensors and position tracking sensors; (5) wearable devices, such as smart clothing, smart eyewear and smart watches (An et al, 2017;Mardonova and Choi, 2018;Nofal, 2020).…”

Section: Introductionmentioning

confidence: 99%

Sensor and photovoltaic dual functions of a-Si:H solar cell in smart clothing and eyewear

Lin

2021

IJCST

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PurposeFlexible hydrogenated amorphous silicon (a-Si:H) solar cells have many advantages, including lower weight, good flexibility and light sensitivity. Moreover, a-Si:H solar cells can be used as sensors, as indoor light sources and can also generate electricity. These solar cells are suitable for the design of portable systems and curved materials. The purpose of this study was to integrate flexible a-Si:H solar cells and wearable technology and to apply the dual functions of photovoltaics and photo sensors to smart clothing and eyewear.Design/methodology/approachThe integration of flexible a-Si:H solar cells and tri-colour light-emitting diodes (LEDs) was used to develop smart auto-flashing clothing. In addition, we combined flexible a-Si:H solar cells and twisted nematic (TN) liquid crystal (LC) cells to design smart self-activation eyewear.FindingsThe maximum power resistance value of flexible a-Si:H solar cells was used to deduce the equation of solar cell voltage value generated by different percentages of SUN (100% SUN means 100 mW/cm2). A solar cell was used as a photo sensor that connects a resistor in a series to the Arduino to detect the voltage value, and then different percentages of SUN are calculated from the equation. Applying the deduced equation to the smart phone APP and Arduino code, we developed a human–machine interface (HMI) to facilitate user operation.Originality/valueIn this study, the flexible a-Si:H solar cell performs the function of not only photovoltaic power generation but also that of a photo sensor. The smart auto-flashing clothing is suitable for traffic guides, joggers and people engaging in other night activities. This smart self-activating eyewear can adjust to light and protect the eyes.

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“…With the popularity of smart phones and 4G networks, QR codes are widely used in logistics and transportation, financial institutions, advertising and marketing, security, and anti-counterfeiting [1][2][3][4][5]. It records data symbol information according to certain rules and distributes black and white images on a plane, and it has the advantages of low printing cost, easy production, and durability.…”

Section: Introductionmentioning

confidence: 99%

Digital Forensics of Scanned QR Code Images for Printer Source Identification Using Bottleneck Residual Block

Guo

Zheng

You

et al. 2020

Sensors

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With the rapid development of information technology and the widespread use of the Internet, QR codes are widely used in all walks of life and have a profound impact on people’s work and life. However, the QR code itself is likely to be printed and forged, which will cause serious economic losses and criminal offenses. Therefore, it is of great significance to identify the printer source of QR code. A method of printer source identification for scanned QR Code image blocks based on convolutional neural network (PSINet) is proposed, which innovatively introduces a bottleneck residual block (BRB). We give a detailed theoretical discussion and experimental analysis of PSINet in terms of network input, the first convolution layer design based on residual structure, and the overall architecture of the proposed convolution neural network (CNN). Experimental results show that the proposed PSINet in this paper can obtain extremely excellent printer source identification performance, the accuracy of printer source identification of QR code on eight printers can reach 99.82%, which is not only better than LeNet and AlexNet widely used in the field of digital image forensics, but also exceeds state-of-the-art deep learning methods in the field of printer source identification.

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Initiating android phone technology using QR codes to make innovative functional clothes

Cited by 6 publications

References 10 publications

Quadri-directional optical bending sensor using side-emitting POF integrated with wearable device technology in smart glove

Quadri-directional optical bending sensor using side-emitting POF integrated with wearable device technology in smart glove

Sensor and photovoltaic dual functions of a-Si:H solar cell in smart clothing and eyewear

Digital Forensics of Scanned QR Code Images for Printer Source Identification Using Bottleneck Residual Block

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