Implementation of Mobile Point-of-sale Cashier Management System

Tsai, Hsiu-Fen; Chang, Bao Rong; Huang, Hsia-Chung; Huang, Chien‐Feng

doi:10.18494/sam.2019.2427

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Moreover, the large camera sensor built in the UAV can take live top-view images of a grass lawn. Both types of information are saved in an in-cloud database (24) and concurrently displayed on the app to inform the user, as shown in Figs. 14(a) and 14(b).…”

Section: Gas Detection and Top View Of Groundmentioning

confidence: 99%

IoT-connected Group Deployment of Unmanned Vehicles with Sensing Units: iUAGV System

Chang¹,

Tsai²,

Lyu³

et al. 2021

Sensors and Materials

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In recent years, the integration of air vehicles and ground vehicles has been a technological development with huge potential. However, making both types of vehicle work together concurrently is a major challenge. Therefore, we have developed a novel cooperative communication protocol to efficiently implement the collaborative operation between an unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV). Through position sensing and time sensing, the UAV and UGV can achieve autonomous control by sending commands to each other. For example, the UGV can instantly pass a sensing-related task such as gas detection or obtaining a view of the ground from the air to the UAV, and the user can track the trajectory of the UAV flight through position sensing and time sensing to determine whether the UAV has done a task. Similarly, the UAV can send commands to the UGV to carry out sensing-related tasks such as face recognition and infrared thermal imaging of people on the ground. The UAV and UGV are not only equipped with a gas sensor and camera sensor, but they also have the built-in functions of position sensing and time sensing. According to experiments on the collaborative operation between UAVs and UGVs, the proposed approach enables the group deployment of unmanned vehicles to execute successfully three-dimensional collaborative operations that can be operated via an app without a ground control station. As a result, the proposed cooperative communication protocol through position sensing and time sensing efficiently realizes the IoT-connected group deployment of unmanned vehicles with sensing units.

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Section: Gas Detection and Top View Of Groundmentioning

confidence: 99%

IoT-connected Group Deployment of Unmanned Vehicles with Sensing Units: iUAGV System

Chang¹,

Tsai²,

Lyu³

et al. 2021

Sensors and Materials

Self Cite

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“…As shown in Fig. 17, the technological background (21,22) for the iTrashCan encompasses the following four areas: the IoT, mobile application and database system, virtualization management, and deep learning.…”

Section: Technical Requirements Of Systemmentioning

confidence: 99%

Smart Trash Can Robot System with Integration of Internet of Things and Mobile Applications

Chang¹,

Tsai²,

Lyu³

et al. 2019

Sensors and Materials

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positioning, smart trash can robot, intelligent path planning People have become busier in the modern world with both work and housework. In addition to the different types of robotic cleaner, it would be quite helpful to have a smart robotic trash collection and dumping system that provides on-call services so that a user does not need to physically get up and put trash into a trash can. As the bin reaches its maximum capacity, it also dumps the trash automatically without the user's instructions. Available smart trash cans are usually focused on determining if the trash is recyclable. Most trash cans also do not have the capability to move autonomously. In this research, we utilize fingerprint mapping for wireless indoor positioning towards the implementation of an autonomous vehicle with a mounted trash can. The user may make an indoor call to the trash-can-mounted vehicle via a mobile application under an IoT and cloud computing environment. The vehicle can position itself in front of the user for trash collection through automatic obstacle avoidance navigation with smart path planning from deep learning. The system can also monitor the amount of accumulated trash and dump trash at a fixed location before returning to the start point. This research on a smart trash-collecting robot can provide significant assistance to people who are busy, those with impaired or limited movements, and the elderly.

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“…This innovation made instant payment options much more effortless . In 2010 and 2015, mobile point-of-sale devices and EMV (Europay, Mastercard, and Visa) chip cards emerged, which brought customers the convenience of card payments in any location (Tsai et al, 2019). EMV card chips overlaid the security of card payments since the information was encrypted and tokenised before being transmitted (Watt, 2017).…”

Section: Introductionmentioning

confidence: 99%

Behavioural Biases Influencing Technology Adoption by South African Bank Customers

2023

HKJSS

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Artificial intelligence technology has the potential to accelerate the financial industry's transformation by offering tailor-made products and services and improving customer experience. This transformation could give banks in South Africa a competitive advantage in the market and globally. This paper aimed to identify the behavioral biases that influence customers' readiness and adoption of innovative technology within their bank. Under a positivist paradigm, nonprobability convenience and snowball sampling were used to collect the data using an online questionnaire. The sample size consisted of 346 banking customers in South Africa. Using factor analysis (EFA), the study found that customers exhibit three dimensions: optimism, innovativeness, and insecurity, which describe technology readiness as a psychological state in which individuals are ready to accept new technology. Additionally, using descriptive and correlation analysis, the findings indicated that customers exhibit overconfidence, anchoring, and loss aversion as the main behavioral biases influencing new technology adoption within banks. The empirical findings of this study are essential as they provide South African banks and risk managers with improved insights and comprehension regarding the profile of customers who may be ready to adopt artificially intelligent banking products. Furthermore, it is recommended that future research follow a mixedmethods approach by also incorporating qualitative interviews to examine the rationales as to why certain behavioral finance biases influence the adoption of AI and others not.

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Implementation of Mobile Point-of-sale Cashier Management System

Cited by 5 publications

References 9 publications

IoT-connected Group Deployment of Unmanned Vehicles with Sensing Units: iUAGV System

IoT-connected Group Deployment of Unmanned Vehicles with Sensing Units: iUAGV System

Smart Trash Can Robot System with Integration of Internet of Things and Mobile Applications

Behavioural Biases Influencing Technology Adoption by South African Bank Customers

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