Home Devices Research Articles

Why do people use mobile food ordering apps?: Extended UTAUT2

With the advancement of technology, many habits of people have changed. One of these is the habit of ordering food. While people used to place food orders through traditional means such as phone or email, nowadays, they can do so more quickly and easily through mobile food ordering applications (MFOAs). The increasing use of MFOAs has necessitated the exploration of factors influencing individuals’ intention to reuse these applications. The aim of this study is to investigate the factors influencing individuals’ intention to reuse MFOAs. The study is based on marketing theory and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. In addition to the seven variables proposed by UTAUT2, variables related to the characteristic features of MFOAs, such as online reviews, online ratings, and online order tracking, have been added to the same model. The research data were obtained from surveys conducted with 441 MFOA users between December 2021 and March 2022. The collected data were analyzed using structural equation modeling in the R Software. As a result, it was observed that online ratings, effort expectation, online order tracking, price value, habit, and hedonic motivation variables have a significant and positive impact on the intention to reuse MFOAs. Social influence, facilitating conditions, performance expectation, and online reviews were found to have no effect on customers’ reuse intentions toward MFOAs. The findings of the study provide insights into understanding consumer preferences and purchase intentions, offering a prediction for MFOA service providers competing for a larger market share.

Rotor speed signature analysis‐based inter‐turn short circuit fault detection for permanent magnet synchronous machines

AbstractA rotor speed signature analysis‐ (RSSA‐)based inter‐turn short circuit (ITSC) fault diagnostic method is proposed, which is robust with regard to speed‐ and current‐loop bandwidths of permanent magnet synchronous machine (PMSM) drive systems. Conventional ITSC fault detection solutions that rely on current signals or extra devices. Thus, an initial step of investigation on the validity of RSSA is taken for residual insulation capacity monitoring of the ITSC fault at the incipient stage. The Vold–Kalman filtering order tracking method is employed for the real‐time extraction of fault features. Besides, the impact of speed‐ and current‐loop controller bandwidths on ITSC fault diagnosis is also analysed and an Adaline estimator is designed to decouple their impacts. Finally, the effectiveness of the proposed method is verified on a faulty PMSM, which exhibits satisfying results in tracking the degradation of residual insulation, even if the phase currents are significantly distorted due to low switching frequency of the inverter.

Faculty of Management, Sremski Karlovci, “Union-Nikola Tesla“ University

Re-defining important changes in the music and film restoration industry, exploring the possibility to: 1. Extract individually chosen sounds from the final movie track in order to re-balance (re-mix) its audio content. 2. Extract chosen sounds from the original audio recording and re-mix, with separated audio stems. The Results section starts by describing the process of creating a completely new mixdown made from AI-generated audio stems (previously extracted from the original mono recordings) joining it with restored original 16 mm video footage, resulting in a full eight-hour movie, separated into three episodes, each with a duration between two and three hours, covering about one week each, of the 21 days of studio time. The material was recorded during the filming of The Beatles’ “Get Back” sessions at Twickenham Studios, London, in January 1969, and chronicles the making of “Let It Be” and the band’s famed final “Rooftop Concert.” The second episode describes the audio/video production of the last-ever song by The Beatles (“Now and Then”) literally made from scratch, as the core of the project was only a poor-quality analog recording made by John Lennon, singing and playing piano to an old, mono, compact cassette recorder.

Everyday artificial intelligence unveiled: Societal awareness of technological transformation

Research background: As Artificial Intelligence (AI) weaves into the fabric of daily life, its societal and economic implications underscore the urgency of embracing an environment conducive to its informed adoption. This requires a sophisticated understanding of the societal perception and adaptability to AI, emphasizing the importance of developing comprehensive AI literacy. Purpose of the article: This study inquiries into the sociodemographic underpinnings of AI literacy, aiming to demystify how knowledge about AI's capabilities in everyday tasks varies across individual population segments. It allows us to define the basic determinants that influence the differences in the individual population structures. It also reveals the potential risks associated with the use of AI. Methods: This study investigates the awareness of Artificial Intelligence (AI) in daily lives of the Czech population, focusing on the influence of socio-demographic factors. Utilizing computer-assisted web interviewing, we surveyed 1,041 respondents in April 2023, ensuring representativeness by applying quotas for age, gender, education, region, and residential area size. Our investigation spanned AI applications in sectors like customer service, music playlist recommendation, email sorting, healthcare, online shopping, and home devices. Findings & value added: Findings taken from descriptive statistics reveal variable AI awareness levels across different domains, with younger demographics exhibiting notably lower awareness in several areas. Regression analysis highlighted that awareness is significantly associated with gender, age, and education level. Regression analysis showed that males, younger age groups and those with higher levels of education were more likely to correctly answer majority of questions about the role of AI in everyday life. These insights are crucial for stakeholders aiming to enhance AI literacy, tailor communication strategies, and develop digital platforms, offering guidance for policymakers and market analysts in optimizing AI-related initiatives.

FREIGHT MANAGEMENT SYSTEM: RIGHTWAYS AIR LOGISTICS SOLUTION

Abstract In modern logistics and supply chain management, the freight management system (FMS) is a key tool for optimizing the transportation and storage of goods. This is because the global trade has become more extensive and the supply chain solution was required to grow and incorporate FMS solutions, in order to enhance speed, productivity, and business outcome. This is because the abstract of FMS detailed highlights FMS elements and its functions, how it improves the efficiency of the operation, reduces cost and its connection with the customers. Core Modules of a Freight Management System: order management, transportation management, warehouse management, tracking and visibility, and compliance and risk management. Technologies from the Internet of Things (IoT), artificial intelligence (AI) and blockchain to autonomous vehicles are integrated into each of the modules to improve performance and reliability. To keep tabs on shipments and ensure they are delivered in time, you need visibility. Index Terms – Web-Based Application, Pickup Creation, Consignment tracking, and Reports creation.

Matter and ECHONET Lite: Similarities, differences, and a bridge solution for interoperability

With the increasing number of connected devices in smart homes, ensuring compatibility and interoperability is becoming increasingly more important. Matter and ECHONET Lite have been introduced to tackle this issue. While Matter is gaining significant attention globally as the future protocol for smart homes, ECHONET Lite has a long history of development and adoption in the smart home in Japan. Therefore, it is essential that both Matter and ECHONET Lite devices are interoperable to provide maximum benefits to users.In this paper, we provide an in-depth analysis that compares the architecture and features of Matter and ECHONET Lite, highlighting both their commonalities and differences. On top of that, we introduce a solution to bridge ECHONET Lite devices into the Matter ecosystem, enabling them to collaborate seamlessly with other Matter devices. This bridge solution is developed, implemented, and tested within a real smart home testbed installed with commercial and experimental ECHONET Lite devices. Our results show that the proposed solution effectively enables the smooth integration of ECHONET Lite devices into the Matter ecosystem, making it suitable for everyday use in smart home environments.

Children with medical complexity receiving home healthcare devices in Japan: a retrospective cohort study

BackgroundLimited evidence exists regarding children receiving home healthcare devices (HHDs). This study aimed to describe the range and type of HHD use by children with chronic medical conditions in Japan...

Budget Gadget a Price Comparison Website

Abstract—In today’s dynamic technological landscape, the quest for budget-friendly yet highquality electronic gadgets remains a paramount concern for consumers worldwide. “Budget Gadget” emerges as a pioneering solution to cater to this burgeoning demand, offering an intuitive platform designed to simplify the process of discovering affordable gadgets across diverse categories. Leveraging state-of-the-art technologies such as React.js, MongoDB, and Node.js, Budget Gadget sets out to revolutionize the way users explore and acquire electronic devices. The essence of Budget Gadget lies in its user-centric approach, prioritizing accessibility, affordability, and functionality. Through a seamless integration of intuitive user interfaces and robust backend infrastructure, Budget Gadget aims to empower users with the ability to make informed purchasing decisions tailored to their specific budgetary constraints and preferences. By curating a diverse selection of gadgets spanning smartphones, laptops, smart home devices, wearables, and more, Budget Gadget endeavours to serve as a comprehensive hub for tech enthusiasts and budget-conscious consumers alike..As a testament to its commitment to excellence, Budget Gadget undergoes continuous refinement and enhancement, driven by user feedback, market trends, and technological advancements. With a relentless focus on delivering unparalleled value and utility, Budget Gadget stands poised to redefine the landscape of budget-friendly gadget shopping, offering a transformative experience that empowers consumers to navigate the digital marketplace with confidence and ease. Keywords – Budget Gadget Platform, Affordable Tech, Price Comparison, Informed Decisions, User Empowerment, Digital Marketplace, Gadget Shopping, Tech Enthusiasts, Innovation, Consumer Electronics.

SMART CONNECT: SIMPLIFYING BLUETOOTH HOME AUTOMATION CONTROL

The emergence of smart home technologies has fundamentally transformed the way individuals interact with their living spaces, promising unprecedented levels of convenience, comfort, and energy efficiency. In response to this paradigm shift, the SmartConnect project introduces a pioneering solution that harnesses the power of Bluetooth connectivity to establish a centralized home automation control center. This innovative system aims to streamline the management of diverse smart home devices, encompassing lights, thermostats, and sensors, by providing users with a seamless interface accessible via smartphones or tablets. With a primary objective of empowering users to remotely monitor and control their home environment, SmartConnect facilitates personalized automation routines and responsive adjustments to changing environmental conditions. Leveraging Arduino microcontroller technology and Bluetooth communication protocols, SmartConnect offers a robust and scalable platform for home automation, designed to accommodate future expansions and updates while ensuring compatibility and functionality over the long term. In essence, SmartConnect represents a significant stride towards realizing the smart home vision, embodying a fusion of innovation, efficiency, and user-centric design. Keywords- HC-05 Bluetooth Module, Arduino UNO Micro-controller.

Social impact of decarbonization objectives through smart homes: Survey and analysis

The technological development and economic growth of renewable energy are the key solution for almost full decarbonization of the energy systems. The sustainability and energy efficiency, artificial intelligence and connectivity will be the determining elements towards reaching this ambitious goal. Household appliances and tools for controlling energy consumption, temperature, lighting, etc. In the smart home will play a key role to increase awareness of families' energy consumption. IoT (Internet of Things) devices for the smart home allow us to monitor consumption and pay attention to efficiently energy use. However, savings are not the only important element of the smart home. Together with the digital control of the home these are elements that include a fundamental dimension: decarbonization, which is the cornerstone of the energy transition, which must involve an epochal change of mentality to our society where digitalization and people's well-being meet. The paper deals with social analysis of large cities population perception on integrating renewable energy sources and necessary devices to transform existing houses in smart homes towards economic wellbeing and decarbonization of the energy sector. Different classes of respondents were interviewed – owned and rented fome, rural and urban inhabitants, different segments of age, various number of inhabitants in urban areas and the capital, women and men, high-income and low-income, different levels of education – to support or disregard smart home technological development and reveal the different perception towards further usage of smart devices for daily benefits.

TFASOT for bearing damage feature extraction and enhancement under complex working conditions

Abstract In some complex working conditions, such as variable speeds and variable loadings, events like bearing skidding and/or contact angle changing may happen during the operations. In such cases, order tracking based on the shaft speed, i.e. the first order tracking (FOT), can no longer completely eliminate the smearing effect on spectrum, and thus affects the bearing damage detection precision and accuracy. The existing second order tracking is unable to resolve these issues either in some cases. To resolve these problems, a new approach called time frequency analysis based second order tracking is developed in this paper. Based on the FOT, short-time Fourier transform and synchro-reassigning transform are used to obtain the actual fault characteristic order (FCO) information. Then the FCO is extracted by ridge searching approach to set new synchronous resampling clock. Finally, synchronous resampling and order tracking are combined to correct and enhance the bearing damage feature, resulting in more accurate damage feature extraction. The effectiveness of proposed method is verified by numerical simulations and practical examples as well. The results show that the proposed method can effectively correct the dispersion and deviation of the FCO induced by speed variations as well as operation condition variations, and thus significantly improve the damage feature extraction.

An IoT-assisted Intelligent Monitoring System

Objective: The usage of power and automated control systems can save energy, but creating an intelligent control system via the Internet of Things (IoT) is an advanced technique because it provides remote access and control through the Internet, especially for those who are facing issues with monitoring their home’s smart gears remotely. In this sense, NodeMcu is an internet-enabled device that operates under different conditions to monitor smart home gear securely. Methods: Therefore, this research has been conducted to provide a solid platform for remote home control and monitoring, together with a complete solution. In this research, we have built a small model by integrating hardware components and connecting online to Google Firebase, allowing the end-user to monitor and control their online home environment efficiently. Results: The online interface helps end-users care for their home equipment, lock doors, and install all intelligent gear inside smart homes. This interface ensures efficient and seamless interaction with all integrated smart home devices, enhancing the user experience and providing a higher level of convenience and security. Through this platform, end-users can perform a wide range of tasks such as adjusting lighting, regulating temperature, monitoring security cameras, and managing energy consumption. The system’s user-friendly design and reliable connectivity ensure that users can maintain control over their home environment from any location with internet access. Conclusion: The proposed model is Cost effective, making it accessible to a boarder demographic. This advancement in home automation enhances security and improves the overall management of smart home system.

Voice Over for Home Security System

The Smart Home Voice Control Hub is an advanced, integrated system design to seamlessly manage and operate a variety of smart home devices using voice commands. Recent years have witnessed significant advancements in home automation driven by wireless technologies. Among these, the IEEE 802.15.4 standard, RF, stands out for its low data rate, extended battery lif e, and secure networking capabilities. This article details a method for testing voice control implementation in smart homes, emphasizing compatibility with existing infrastructure. Utilizing low-power RF wireless modules and a microcontroller, the system caters to the needs of elderly and disabled individuals living independently. By focusing on voice recognition, it off ers enhanced security and control over household lighting and appliances. Experimental validation demonstrates the system's effectiveness in practical applications.

Security Analysis of Low-Budget IoT Smart Home Appliances Embedded Software and Connectivity

This paper investigates the challenge of finding and analyzing security vulnerabilities among widely available low-budget Internet of Things smart home appliances. It considers the identification of security vulnerabilities within the appliances’ embedded software and connectivity functions over wired and wireless channels in local networks and external communications with manufacturers’ cloud services. To analyze the security of these appliances, a universal laboratory test bench is proposed and a set of methodologies for testing the security of smart home devices is described. The proposed testing platform offers a practical solution for security analysis of Internet of Things smart home devices and it can serve as a reference approach for future research. The results from the research indicated varying levels of susceptibility across different types of devices. A list of recommendations for manufacturers and others to improve the security level of these appliances is provided. The findings emphasize the need for regular security assessments of smart home devices, to maintain the protection of personal and sensitive information.

Smart Devices Require Skilled Users: Home Automation Performance Tests among the Dutch Population

The benefits obtained from home automation are promising and will become more pronounced as smart home technologies continue to develop. To achieve benefits, users require operational, data, and strategic skills to control and automate smart devices, retrieve and understand collected data, and make informed decisions. These skills were tested by providing assignments in a virtual test environment to 100 Dutch adult participants. The assignments were designed to measure different facets of all skills by using the functions, data, and automations of smart home devices. The results suggest that the Dutch adult population is not sufficiently skilled in using the smart home to its full potential; several skills related problems occurred in the tests. Furthermore, in terms of gender, age, and education, home automation further reinforces existing social-digital inequalities. Thus, earlier digital inequalities will remain present for some time, despite increasing device autonomy.

Quantitative Detection of Vertical Track Irregularities under Non-Stationary Conditions with Variable Vehicle Speed.

Track irregularities directly affect the quality and safety of railway vehicle operations. Quantitative detection and real-time monitoring of track irregularities are of great importance. However, due to the frequent variable vehicle speed, vehicle operation is a typical non-stationary process. The traditional signal analysis methods are unsuitable for non-stationary processes, making the quantitative detection of the wavelength and amplitude of track irregularities difficult. To solve the above problems, this paper proposes a quantitative detection method of track irregularities under non-stationary conditions with variable vehicle speed by order tracking analysis for the first time. Firstly, a simplified wheel-rail dynamic model is established to derive the quantitative relationship between the axle-box vertical vibration and the track vertical irregularities. Secondly, the Simpson double integration method is proposed to calculate the axle-box vertical displacement based on the axle-box vertical acceleration, and the process error is optimized. Thirdly, based on the order tracking analysis theory, the angular domain resampling is performed on the axle-box vertical displacement time-domain signal in combination with the wheel rotation speed signals, and the quantitative detection of the track irregularities is achieved. Finally, the proposed method is validated based on simulation and field test analysis cases. We provide theoretical support and method reference for the quantitative detection method of track irregularities.

Implementation of Lightweight Machine Learning-Based Intrusion Detection System on IoT Devices of Smart Homes

Smart home devices, also known as IoT devices, provide significant convenience; however, they also present opportunities for attackers to jeopardize homeowners’ security and privacy. Securing these IoT devices is a formidable challenge because of their limited computational resources. Machine learning-based intrusion detection systems (IDSs) have been implemented on the edge and the cloud; however, IDSs have not been embedded in IoT devices. To address this, we propose a novel machine learning-based two-layered IDS for smart home IoT devices, enhancing accuracy and computational efficiency. The first layer of the proposed IDS is deployed on a microcontroller-based smart thermostat, which uploads the data to a website hosted on a cloud server. The second layer of the IDS is deployed on the cloud side for classification of attacks. The proposed IDS can detect the threats with an accuracy of 99.50% at cloud level (multiclassification). For real-time testing, we implemented the Raspberry Pi 4-based adversary to generate a dataset for man-in-the-middle (MITM) and denial of service (DoS) attacks on smart thermostats. The results show that the XGBoost-based IDS detects MITM and DoS attacks in 3.51 ms on a smart thermostat with an accuracy of 97.59%.

Cloud computing and rural globalization: Evidence for the U.S. nonfarm economy

We use confidential firm-level data to examine the association between export intensity and subscription to cloud computer services – a technology that often requires very high-speed broadband. Our focus on rural nonfarm exports is motivated by the increasing concentration of manufacturing—the dominant export sector—in nonmetropolitan counties and the large public investment in extending high-speed broadband to underserved parts of the U.S. We find that cloud computing is associated with higher export intensity that is consistent with exporting activity placing new demands on IT-enabled functions such as order fulfillment and tracking, marketing, or document control.

Network intrusion detection system by applying ensemble model for smart home

The exponential advancements in recent technologies for surveillance become an important part of life. Though the internet of things (IoT) has gained more attention to develop smart infrastructure, it also provides a large attack surface for intruders. Therefore, it requires identifying the attacks as soon as possible to provide a secure environment. In this work, the network intrusion detection system, by applying the ensemble model (NIDSE) for Smart Homes is designed to identify the attacks in the smart home devices. The problem of classifying attacks is considered a classification predictive modeling using eXtreme gradient boosting (XGBoosting). It is an ensemble approach where the models are added sequentially to correct the errors until no further improvements or high performance can be made. The performance of the NIDSE is tested on the IoT network intrusion (IoT-NI) dataset. It has various types of network attacks, including host discovery, synchronized sequence number (SYN), acknowledgment (ACK), and hypertext transfer protocol (HTTP) flooding. Results from the cross-validation approach show that the XGBoosting classifier classifies the nine attacks with micro average precision of 94% and macro average precision of 85%.

User authentication using gait and enhanced attribute-based encryption: a case of smart home

With the increasing popularity of the internet of things (IoT) application such as smart home, more data is being collected, and subsequently, concerns about preserving the privacy and confidentiality of these data are growing. When intruders attack and get control of smart home devices, privacy is compromised. Attribute-based encryption (ABE) is a new technique proposed to solve the data privacy issue in smart homes. However, ABE involves high computational cost, and the length of its ciphertext/private key increases linearly with the number of attributes, thus limiting the usage of ABE. This study proposes an enhanced ABE that utilises gait profile. By combining lesser number of attributes and generating a profiling attribute that utilises gait, the proposed technique solves two issues: computational cost and one-to-one encryption. Based on experiment conducted, computational time has been reduced by 55.27% with nine static attributes and one profile attribute. Thus, enhanced ABE is better in terms of computational time.