Smart Mobile Devices Research Articles

Non‐Interleaved Bilayer Complex‐Amplitude Janus Metasurface Enabling Energy‐Tailorable Bidirectional Wave Modulation

AbstractJanus metasurfaces have attracted significant attention due to their key feature of asymmetric transmission in numerous practical applications, such as asymmetric data inscription in communications and dual side displays in smart mobile devices. More compact and integrated spatial wave modulation components call for implementations of non‐interleaved bilayer complex‐amplitude (CA) Janus metasurfaces. Here, by introducing composite geometry phase into propagation phase, direction‐dependent decoupling relation between amplitude and phase engineering is derived, and the mechanism of energy‐tailorable bidirectional wave modulation is elucidated via extremely simple single‐pixeled Janus elements. A non‐interleaved bilayer CA Janus metasurface is thus proposed. Bidirectional amplitude‐phase profiles with both‐transmission (T–T) or transmission–reflection‐integration (T–R) operational modes can readily be achieved. Numerical analysis and experimental verification of direction‐dependent energy allocation and reciprocal asymmetric holograms are performed to demonstrate the excellent manipulation accuracy and decorrelation under bidirectional illuminations. On this basis, a series of coplanar image syntheses, including frequency–polarization multitasking and T–R dual‐channel multiplexing, based on the proposed bidirectional CA modulation mechanism are experimentally demonstrated in the microwave region. This asymmetric transmission mechanism will promote developments of Janus metasurfaces for multichannel processing and information multiplexing applications in miniaturized and highly integrated systems.

Optimal multi-user offloading with resources allocation in mobile edge cloud computing

Mobile cloud computing (MCC), as a prospective computing paradigm, can significantly enhance computation capability and save energy of smart mobile device (SMD) by offloading computation-intensive tasks from resource-constrained SMDs onto the resource-rich central cloud. Compared to central cloud, edge cloud can provide services to nearby SMDs with lower latency. However, the edge cloud may be mobile and its resources are limited to multiple nearby users. Therefore, how to obtain an optimal offloading policy under the constraints of mobility and limited resource remains a challenging issue. In this paper, we aim to minimize the total execution cost of multiple devices by offloading the computations from SMDs onto edge clouds in edge cloud computing (ECC) system. By considering the mobility of SMDs and edge clouds, we first formulate the total cost minimization problem under the constraints of application completion deadline and connection time between SMDs and edge clouds as well as the limited computation resource of both edge clouds and SMDs. Then, by solving the minimization problem, we propose an optimal offloading selection strategy based on game model, and an edge cloud payoff competition algorithm to optimally allocate edge cloud resource to SMDs to achieve the minimum total execution cost. Experimental results show that our offloading strategy can effectively reduce energy consumption and application completion time compared with the state-of-the-art methods.

Research on Output Power of Series–Series Resonance Wireless Power Transmission System

The popularization of smart mobile devices has led to the research on wireless power transfer (WPT) technology. Previous research shows that increasing the excitation coil or the energy transfer coil can increase the energy transmission distance of the WPT system. But the mutual inductance calculation of coils is not accurate enough when the distance is close. This paper presents an improved formula to calculate the mutual inductance between two round inductance coils. Contrasts with the previous calculation methods, computer simulation and experimental results are done to validate the accuracy of the formula, especially when the two inductance coils are close together. On the basis of this formula, the maximum output power (MOP) and the maximum output power distance (MOPD) of the two-, three- and four-coil series–series resonance WPT systems based on circuit models are studied and compared. The simulation and experimental results show a high consistency with the theoretical calculation results. We can find that the farther the MOPD of the system is, the smaller the MOP is. Moreover, when the operating frequency increases, the MOPD of the two-coil conformation increases, while the four-coil conformation is just opposite.

Design and Usability Testing of an Augmented Reality (AR) Environment in Pharmacy Education—Presenting a Pilot Study on Comparison between AR Smart Glasses and a Mobile Device in a Laboratory Course

An essential feature of pharmacy education is the teaching of theoretical knowledge with the support of practical work in the laboratory. When properly utilized, laboratory activities have the potential to enhance students’ achievement, conceptual understanding, and positive attitudes towards learning. In this pilot study, an augmented reality (AR) environment was designed and introduced for teaching laboratory skills in pharmacy education at the university level. The AR environment was used by pharmacy students (n = 36), featuring gate questions, information screens, Quick Response codes, think-aloud questions, and instant feedback. The environment was utilized with smart glasses and mobile devices with the aim of comparing the support to students’ performance. User experience was evaluated through self-efficacy beliefs and anxiety towards the technology. As a result, students found the environment a useful supplement to traditional laboratory teaching. Smart glasses and mobile devices were both accepted with great positivity but neither being clearly preferred over the other. Smart glasses were noted to provide sufficient feedback in the right stages of work. In contrast, mobile devices promoted the learning process more than the smart glasses. The self-efficacy results for mobile device use were higher, especially related to device handling and operating the AR environment. The pilot study gives educators valuable insights on the usability of AR technology in guiding laboratory tasks, although future work should involve larger and more diverse samples, as well as different learning tasks.

Impact of built environment on residential online car-hailing trips: Based on MGWR model.

With the development of smart mobile devices and global positioning technology, people's daily travel has become increasingly dependent on online car-hailing. Meanwhile, it has also become possible to use multi-source data to explore the factors influencing urban residents' car-hailing trips. Using online data on car-hailing trajectories, points of interest (POIs) data and other auxiliary data, the paper explores how the built environment impacts online car-hailing passengers. Within a 200 x 200m research grid, the unique spatiotemporal patterns of weekday car-hailing trips during a one-week period are analyzed, using statistics on pick-ups and drop-offs at different time of the day. By combining these data with built environment variables and various economic and traffic indicators, a multi-scale geographically weighted regression (MGWR) model is developed for different time scales. The MGWR model outperforms the classical geographically weighted regression (GWR) model and the ordinary least squares (OLS) regression model in terms of goodness of fit and all other aspects. More importantly, this study finds a high degree of temporal and spatial heterogeneity in the impact of built environment factors on local car-hailing trips across different regions, and the paper analyzes the business residence coefficient in detail. The study provides valuable insights to help improve the level of urban transportation services, as well as urban transportation planning and construction.

Dealing With Inaccurate Sensor Data in the Context of Mobile Crowdsensing and mHealth.

The technological capabilities and ubiquity of smart mobile devices favor the combined utilization of Ecological Momentary Assessments (EMA) and Mobile Crowdsensing (MCS). In the healthcare domain, this combination particularly enables the collection of ecologically valid and longitudinal data. Furthermore, the context in which these data are collected can be captured through the use of smartphone sensors as well as externally connected sensors. The TrackYourTinnitus (TYT) mobile platform uses these concepts to collect the user's individual subjective perception of tinnitus as well as an objective environmental sound level. However, the sound level data in the TYT database are subject to several possible sensor errors and therefore do not allow a meaningful interpretation in terms of correlation with tinnitus symptoms. To this end, a data-centric approach based on Principal Component Analysis (PCA) is proposed in this paper to cleanse MCS mHealth data sets from erroneous sensor data. To further improve the approach, additional information (i.e., responses to the EMA questionnaire) is considered in the PCA and a prior check for constant values is performed. To demonstrate the practical feasibility of the approach, in addition to TYT data, where it is generally unknown which sensor measurements are actually erroneous, a simulation with generated data was designed and performed to evaluate the performance of the approach with different parameters based on different quality metrics. The results obtained show that the approach is able to detect an average of 29.02% of the errors, with an average false-positive rate of 14.11%, yielding an overall error reduction of 22.74%.

The Effects of Text Type and Text Length on the Experience of Mandarin Voice Perception on Smart Mobile Devices

Voice interaction is a user-friendly interaction method for Mandarin Chinese. Voice perception has been widely implemented on smart devices for Mandarin voice interaction. However, previous studies gave little attention to how language features, such as the text type and text length affect the voice perception experience. Moreover, how the device type influences Mandarin voice perception is unclear, as the device type has been shown to have a significant effect on other interaction methods, such as interactions with keyboard entry and screen displays. Thus, the aim of this study was to investigate the effects of the text type, text length, and device type on the voice perception experience. This study invited seventy-two participants (36 females and 36 males) for an empirical experiment. Three text types, i.e., classical Chinese text, professional text, and daily text, three text lengths, namely, short (i.e., 10 characters), medium (i.e., 20 characters), and long (i.e., 30 characters), and two device types, i.e., mobile phones and smartwatches, were considered. Four measurements were used to evaluate the voice perception experience, namely, performance (i.e., self-reported performance and correct ratio), the satisfaction rating, mental workload, and skin conductance. The results indicate that the text type, text length, and device type had significant effects on the four measurements. Specifically, the voice perception experience was best for daily text and worst for classical Chinese text, shorter text led to a better voice perception experience, and users of smartwatches had a better voice perception experience than mobile phones. Furthermore, the design implications for improving the voice perception experience that consider the text type, text length, and device type were discussed.

Deep Mobile Linguistic Therapy for Patients with ASD.

Autistic spectrum disorder (ASD) is one of the most complex groups of neurobehavioral and developmental conditions. The reason is the presence of three different impaired domains, such as social interaction, communication, and restricted repetitive behaviors. Some children with ASD may not be able to communicate using language or speech. Many experts propose that continued therapy in the form of software training in this area might help to bring improvement. In this work, we propose a design of software speech therapy system for ASD. We combined different devices, technologies, and features with techniques of home rehabilitation. We used TensorFlow for Image Classification, ArKit for Text-to-Speech, Cloud Database, Binary Search, Natural Language Processing, Dataset of Sentences, and Dataset of Images with two different Operating Systems designed for Smart Mobile devices in daily life. This software is a combination of different Deep Learning Technologies and makes Human–Computer Interaction Therapy very easy to conduct. In addition, we explain the way these were connected and put to work together. Additionally, we explain in detail the architecture of software and how each component works together as an integrated Therapy System. Finally, it allows the patient with ASD to perform the therapy anytime and everywhere, as well as transmitting information to a medical specialist.

Enhancing mHealth data collection applications with sensing capabilities.

Smart mobile devices such as smartphones or tablets have become an important factor for collecting data in complex health scenarios (e.g., psychological studies, medical trials), and are more and more replacing traditional pen-and-paper instruments. However, simply digitizing such instruments does not yet realize the full potential of mobile devices: most modern smartphones have a variety of different sensor technologies (e.g., microphone, GPS data, camera, ...) that can also provide valuable data and potentially valuable insights for the medical purpose or the researcher. In this context, a significant development effort is required to integrate sensing capabilities into (existing) data collection applications. Developers may have to deal with platform-specific peculiarities (e.g., Android vs. iOS) or proprietary sensor data formats, resulting in unnecessary development effort to support researchers with such digital solutions. Therefore, a cross-platform mobile data collection framework has been developed to extend existing data collection applications with sensor capabilities and address the aforementioned challenges in the process. This framework will enable researchers to collect additional information from participants and environment, increasing the amount of data collected and drawing new insights from existing data.

Examining the influence of the performance of mobile devices on teaching quality based on the technological acceptance mode

PurposeThis study aims to examine the effects of iPad distribution on all teachers in a university and its application in teaching and student learning at home via wireless network during the coronavirus (COVID-19) pandemic. The attitude towards the use of iPads, behavioural intentions and the impact on the quality of teaching were evaluated.Design/methodology/approachThis study used the technology acceptance model to explore the use of iPad smart mobile devices in multimedia teaching applications by university teachers. Furthermore, it used the structural equation modelling (SEM) for data analysis to explore the causal relationship between model variables, and it aimed to examine the causal relationship between variables to verify the theory. The SEM analysis included the following two stages: measurement model analysis and structural model analysis.FindingsThe “Internet information environment” had a significant positive impact on “perceived usefulness” and “perceived ease of use”. Amongst them, perceived usefulness had a significant positive effect on the use attitude, and use attitude had a significant positive effect on behaviour intention.Originality/valueThe findings confirmed that a good information network environment will directly and positively affect the perceived usefulness and the ease of use of iPad smart devices, of which the perceived usefulness will further positively affect teachers' perception of iPad smart devices. The attitude and behaviour of using such devices will in turn positively affect the quality of teaching. The results of the quality performance evaluation can be referenced further by manufacturers and scholars regarding the use of iPad smart devices for work at home during the COVID-19 pandemic.

Multiobjective Edge Server Placement in Mobile-Edge Computing Using a Combination of Multiagent Deep Q-Network and Coral Reefs Optimization

The growth of telecommunication technologies, especially 5G, the growing popularity of smart mobile devices, the emergence of smart cities and Internet of Things (IoT), and the easy use of these equipments have led the cloud users to utilize their various services. Real-time applications and the use of big data have caused cloud service providers (CSPs) to move their servers to the edge of the network and in the vicinity of users to maintain the quality of their services. For this purpose, the concept of mobile-edge computing (MEC) was formed. Applications often have heavy computing complexity on mobile devices or require a lot of data to process. Moreover, in order to save energy consumption of the batteries of this equipment, offloading them on the network resources can transfer the computational complexity from the users’ equipment to the network resources. The resource placement (RP) is one of the major challenges in this area. Improper resource topology upsets their load balancing and increases access latency. In the proposed method of this article, the cellular mobile network is divided into smaller areas and using the coral reefs optimization (CRO) algorithm, the optimal placement of resources in each of these areas will be locally performed. The deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network (DQN) and Markov game (MG) are used to optimize global RP to reduce global latency and to improve resource load balancing as its two objectives. The results of the experiments show that the proposed method has significantly improved its objectives and server’s energy efficiency, compared to some similar works in this area.

Using Social Media Data to Explore Urban Land Value and Sentiment Inequality: A Case Study of Xiamen, China

Differences in urban land values affect residents’ living experiences and may contribute to sentiment inequality. Due to the popularity of smart mobile devices and social media platforms, online tweets with location information can be used as objective information to reflect sentiment differences of urban residents in different locations, overcoming the limitations of previous studies with small sample sizes or a lack of spatial information. Sentiment quantification based on deep learning enables the identification of spatial patterns of urban residents’ sentiments. It also provides a new approach for analyzing data from big data platforms using an intelligent computing platform. This paper quantitatively analyzes the sentiment contained in social media tweets using a deep learning sentiment analysis algorithm to reveal inequalities between urban residents’ sentiments and land values. The Baidu Intelligent Cloud sentiment analysis platform is used to identify 460,000 Weibo tweets in Xiamen, China, in 2020. We quantitatively analyze the positive and negative sentiments of residents and create a spatial distribution map. The concentration curve indicates sentiment inequality and the impact of high land values on residents’ sentiments. The positive sentiment concentration index (CI) and correlation analysis show that the CI value is 0.07, and significant sentiment inequality exists due to the high land value. The use of social media tweet data to analyze sentiment inequality provides a reference for future interdisciplinary research in psychology, urban planning, geography, and sociology. The proposed approach of analyzing social media data using an intelligent computing platform provides new insights into multiplatform data interaction in the context of the Internet of Everything.

Online computation offloading with double reinforcement learning algorithm in mobile edge computing

Smart mobile devices have recently emerged as a promising computing platform for computation tasks. However, the task performance is restricted by the computing power and battery capacity of mobile devices. Mobile edge computing, an extension of cloud computing, solves this problem well by providing computational support to mobile devices. In this paper, we discuss a mobile edge computing system with a server and multiple mobile devices that need to perform computation tasks with priorities. The limited resources of the mobile edge computing server and mobile device make it challenging to develop an offloading strategy to minimize both delay and energy consumption in the long term. To this end, an online algorithm is proposed, namely, the double reinforcement learning computation offloading (DRLCO) algorithm, which jointly decides the offloading decision, the CPU frequency, and transmit power for computation offloading. Concretely, we first formulate the power scheduling problem for mobile users to minimize energy consumption. Inspired by reinforcement learning, we solve the problem by presenting a power scheduling algorithm based on the deep deterministic policy gradient (DDPG). Then, we model the task offloading problem to minimize the delay of tasks and propose a double Deep Q-networks (DQN) based algorithm. In the decision-making process, we fully consider the influence of task queue information, channel state information, and task information. Moreover, we propose an adaptive prioritized experience replay algorithm to improve the model training efficiency. We conduct extensive simulations to verify the effectiveness of the scheme, and the simulation results show that compared with the conventional schemes, our method reduces the delay by 48% and the energy consumption by 53%.

An intelligent hybrid method: Multi-objective optimization for MEC-enabled devices of IoE

Emerging Internet-of-Everything (IoE) services require connected devices to respond instantly and operate for long durations. As smart mobile devices (SMDs) are often powered by batteries of limited capacity, offloading some computational tasks to nearby edge servers is a promising solution to reduce the latency and energy consumption of SMDs in operation. However, a challenge of computation offloading in the IoE system is the large number of SMDs that need to be handled. To address this problem, in this paper, we propose an intelligent two-stage computation offloading scheme with multiple optimization objectives. In the first stage, we categorize the computation tasks into classes (e.g., computation-intensive, data-intensive) and make early offloading decisions on some classes of tasks with offloading preferences. In the second stage, we make offloading decisions on the remaining tasks by solving a multi-objective optimization problem using the powerful Non-dominated Sorting Genetic Algorithm (NSGA-II). This two-stage design can help reduce the size of task instances in the second optimization stage, thus accelerating the convergence of the NSGA-II algorithm. The extensive simulation results show that, compared to the existing NSGA-II algorithm-based optimization methods, the proposed offloading scheme improves the performance by 10% in terms of latency and energy consumption.

Concept for Use of Consumer Mobile Devices and Smart Applications for Preservation of Cultural Heritage

One of the challenges to the digitalisation and preservation of the cultural heritage is the need for expert human labour and specialized hardware/software. With the advent of sophisticated personal smart mobile devices and applications, we expect that soon every citizen will be able to participate and contribute to this process.

Joint Offloading and Resource Allocation Using Deep Reinforcement Learning in Mobile Edge Computing

Mobile edge computation offloading (MECO) has recently emerged as a promising method to support computation-intensive and latency-sensitive applications, significantly saving the battery energy of smart mobile devices (SMDs). However, on the one hand, the energy consumption depends on both the SMD and the MEC server, which makes it necessary to consider these two entities to achieve energy sustainability jointly. On the other hand, for a real-time mobile edge computing (MEC) system, efficient optimization algorithms based on binary offloading have received significant attention, while efficient algorithms for partial offloading under time-varying channels are seldom investigated. In this paper, we propose an energy-efficient algorithm based on deep reinforcement learning to optimize the overall energy cost in a real-time multi-user MEC system. We decompose the energy minimization problem into two sub-problems, where a deep neural network learns the optimal mapping between wireless channels and offloading ratios, and a closed-form solution for the optimal local frequency and a convex optimization algorithm are used to solve the resource allocation sub-problem. Finally, the extensive experiments demonstrate the effectiveness of our proposed algorithm in reducing the total energy consumption of the MECO system against several offloading schemes and achieving low processing latency fit to the time-varying wireless channels.

Cost-Efficient Resources Scheduling for Mobile Edge Computing in Ultra-Dense Networks

With the development of 5G communication technologies and smart mobile devices, various computation-intensive and delay-sensitive tasks continue to increase. The combination of Mobile Edge Computing (MEC) and Ultra-Dense Networks (UDN) increases the network capacity and improves the computing capability of mobile devices, which effectively meets the transmission and computing demands of tasks. However, the ultra-dense deployment of network infrastructures causes energy shortage and channel interference, making it challenging to reduce the system cost. In this paper, we investigate the task offloading and resources scheduling problem in UDN with MEC. In order to minimize the total system cost including delay and energy consumption in the intensive deployment environment of edge servers and base stations (BSs) simultaneously, we design the strategy of task offloading, BS selection and resources scheduling of mobile devices. Because of the complex coupling of decision variables, the original problem is decomposed into two sub-problems. We propose Newton-IPM based Computing Resource Allocation (NICRA) algorithm and Genetic Algorithm based BS Selection and Resources Scheduling (GABSRS) algorithm to solve these two sub-problems, respectively. Then, we prove the number of iterations can be reduced effectively by the GABSRS algorithm while reaching the optimal solution through mathematical analysis. Through experiments analysis, the effectiveness of the GABSRS algorithm is validated.

Study on user stickiness of Ximalaya APP Based on basic analysis of SPSS

With the development of network technology, the traditional radio station began to have a new development opportunities, mobile Internet radio began to come out. The audience can listen to the radio in different use scenarios such as traditional listening devices, smart mobile devices, vehicle devices and so on. In August 2012, Himalaya FM began to build an online audio sharing platform. Today, Ximalaya FM is firmly ranked as the largest number of users and the largest platform in China. As of 2019, Ximalaya apps have more than 600 million active users and have more than 80 million monthly active users, but how to enhance user engagement, improve user activity and usage duration is still a very important issue in the Ximalaya FM operation process. This paper mainly explores the factors influencing the stickiness of the Ximalaya FM users, and uses the literature research method and the questionnaire survey method to analyze the questionnaire sample through the SPSS data analysis. In-depth analysis of the Ximalaya FM users, including their use of behavior characteristics, attitude influence, and the use of quantitative research methods to analyze the factors affecting user stickiness.

Adaptiveness of RGB-image derived algorithms in the measurement of fractional vegetation coverage

BackgroundFractional vegetation coverage (FVC) is a crucial parameter in determining vegetation structure. Automatic measurement of FVC using digital images captured by mobile smart devices is a potential direction for future research on field survey methods in plant ecology, and this algorithm is crucial for accurate FVC measurement. However, there is a lack of insight into the influence of illumination on the accuracy of FVC measurements. Therefore, the main objective of this research is to assess the adaptiveness and performance of different algorithms under varying light conditions for FVC measurements and to deepen our understanding of the influence of illumination on FVC measurement.Methods and resultsBased on a literature survey, we selected four algorithms that have been reported to have high accuracy in automatic FVC measurements. The first algorithm (Fun01) identifies green plants based on the combination of R/G, B/G, and ExG (R, G, and B are the actual pixel digital numbers from the images based on each RGB channel, ExG is the abbreviation of the Excess Green index), the second algorithm (Fun02) is a decision tree that uses color properties to discriminate plants from the background, the third algorithm (Fun03) uses ExG-ExR (ExR is the abbreviation of the Excess Red index) to recognize plants in the image, and the fourth algorithm (Fun04) uses ExG and O{text{tsu}} to separate the plants from the background. Otsu is an algorithm used to determine a threshold to transform the image into a binary image for the vegetation and background. We measured the FVC of several surveyed quadrats using these four algorithms under three scenarios, namely overcast sky, solar forenoon, and solar noon. FVC values obtained using the Photoshop-assisted manual identification method were used as a reference to assess the accuracy of the four algorithms selected. Results indicate that under the overcast sky scenario, Fun01 was more accurate than the other algorithms and the MAPE (mean absolute percentage error), BIAS, relBIAS (relative BIAS), RMSE (root mean square error), and relRMSE (relative RMSE) are 8.68%, 1.3, 3.97, 3.13, and 12.33%, respectively. Under the scenario of the solar forenoon, Fun02 (decision tree) was more accurate than other algorithms, and the MAPE, BIAS, relBIAS, RMSE, and relRMSE are 22.70%, − 2.86, − 7.70, 5.00, and 41.23%. Under the solar noon scenario, Fun02 was also more accurate than the other algorithms, and the MAPE, BIAS, relBIAS, RMSE, and relRMSE are 20.60%, − 6.39, − 20.67, 7.30, and 24.49%, respectively.ConclusionsGiven that each algorithm has its own optimal application scenario, among the four algorithms selected, Fun01 (the combination of R/G, B/G, and ExG) can be recommended for measuring FVC on cloudy days. Fun02 (decision tree) is more suitable for measuring the FVC on sunny days. However, it considerably underestimates the FVC in most cases. We expect the findings of this study to serve as a useful reference for automatic vegetation cover measurements.

How Do Digital Market Platform Hosts Exercise Control Over Sellers?

High speed connectivity, smart mobile devices, social media, and rapidly-proliferating end-user applications have given rise to digital markets. This paper investigates how digital market hosts exercise control over sellers. The authors propose that findings from prior empirical studies informed by control theory in other contexts might not fully apply to digital markets, since these platforms are loosely connected to participants. The three-case study revealed that, similar to controllers in other contexts, digital market hosts do utilize a mix of formal and informal control mechanisms, yet their seller control portfolios also differ importantly from control portfolios in other contexts. This paper presents the study findings, considers questions arising from the findings, and provides a useful foundation for further research that can consider why digital markets entail challenges that give rise to novel control portfolios.