Mobile Devices Research Articles

A Brief Review of Lightweighting Methods for Vision Transformers (ViT)

The Vision Transformer (ViT) has emerged as a powerful model in recent years, surpassing traditional Convolutional Neural Networks (CNNs) in various benchmarks. However, its large model architecture and high parameter count present significant challenges for mobile deployment. Consequently, the exceptional performance of ViT in computer vision tasks is often overshadowed by its difficulties in being deployed on mobile devices due to its large parameter size and high computational demands. This paper provides a comprehensive review of the literature on lightweight ViT models, focusing on model optimization strategies such as post-training modifications—quantization, pruning, and knowledge distillation—as well as architectural changes including hybrid CNN-Transformer, MLP-based, and sparse models. These strategies are aimed at improving efficiency for mobile platforms. The review aims to clarify current techniques for mobile ViT, guide future research, stimulate innovation, and contribute to the development of efficient ViT models for mobile environments.

Development of the Japanese version of the Mobile Device Proficiency Questionnaire: A cross-sectional validation study.

In response to the growing popularity of mobile devices among older adults in Japan, this study aimed to establish a reliable and valid measure of mobile device proficiency by developing a Japanese version of the Mobile Device Proficiency Questionnaire (MDPQ-J) for the Japanese population. To evaluate the reliability and validity of the MDPQ-J, we administered the questionnaire to 100 young or middle-aged participants (37.78 ± 13.90 years, 57.00% women), and 62 older participants (75.90 ± 6.01 years; 59.68% women). The MDPQ-J showed a high degree of internal consistency (Cronbach's α = 0.98). Moreover, MDPQ-J scores in older participants tended to be lower than those in young or middle-aged participants. In terms of validity, the MDPQ-J score of older participants was significantly associated with age (ρ = -0.31), the amount of time using a mobile device per day (ρ = 0.64), and the system usability scale (ρ = 0.39). Additionally, the MDPQ-J scores were significantly positively associated with the importance (ρ = 0.37), performance (ρ = 0.57), and satisfaction (ρ = 0.29) associated with daily mobile device use in older participants. The high reliability and validity of the MDPQ-J in the Japanese population highlight its utility in adapting mobile devices for older adults, which is crucial in an increasingly digital society. Further research should explore the mediating role of mobile device proficiency in various health-related outcomes. Geriatr Gerontol Int 2024; ••: ••-••.

Spatial Data Infrastructure and Mobile Big Data for Urban Planning Based on the Example of Mikolajki Town in Poland

Spatial Data Infrastructure (SDI) is a decision-making tool that is often used in the area of urban planning. At the same time, many other data sources with great utility potential, such as Big Data, can be identified. The aim of the paper is to present the possibility of using mobile Big Data collections with data from Polish SDI, for the purposes of local spatial planning on the example of the tourist town, Mikolajki in Poland. The publication also focuses on assessing the quality of data, as well as the decision-making process supported by these sources. The draft of the local spatial development plan was verified based on integrated data sources. The results showed that the visualization of Big Data as a heat map may be used in urban tasks and as the thematic layer integrated with vector and raster data sets from the SDI in the geographic information system software. The contribution is the practical example how information about users of mobile devices and some information from behavioral profiles may be analyzed for the purposes of verifying planned land use.

Lithosphere infiltrometer: A low cost, automated infiltrometer (permeameter) for measurement of soil health, infiltration, and hydraulic conductivity

Accurate measurement of infiltration rate and hydraulic conductivity is important for assessing soil physical health, irrigation management and catchment modelling. Commercially available infiltrometers are often expensive, fragile, and require specialist knowledge to operate and perform complex post-measurement calculations. We developed the Lithosphere Infiltrometer (permeameter) to be usable for both experienced soil researchers, and users without prior technical or scientific expertise in soil science. The Lithosphere Infiltrometer is able measure both surface and subsoil infiltration rate and hydraulic conductivity. It is manufactured using 3D-printed parts and off-the-shelf electronic components. It has a robust, quad-leg design that allows for rapid leveling of the device and accurate setting of water depth via a novel 3D-printed bell chamber. The water level is measured using a U85 JRT laser with ±1 mm accuracy connected to an Adafruit Huzzah 32 microcontroller. Infiltration rate and hydraulic conductivity are calculated at 1 to 10 min time steps, and data is presented in real-time on an OLED display. Data is stored on the device and available for download to a mobile device by Wi-Fi.

Charge Transport in Interband Cascade Lasers: An Ab‐Initio Self‐Consistent Model

AbstractInterband cascade lasers (ICLs) stand out due to their low threshold current and minimal power consumption, rendering them viable sources for compact and mobile devices in the mid‐infrared. Since their first demonstration, they experienced major performance improvements. Mostly they originate from either improved material quality or the outcomes of numerical analysis of secluded parts. Encouraged by the impact of secluded models, an ICL‐specific simulation tool can lead to performance breakthroughs and a better comprehension of governing mechanisms. Drawing from an evaluation of existing tools designed for quantum cascade structures, a self‐consistent density matrix rate equation model is implemented to simulate the transport in both conduction and valence band heterostructures. Albeit the extensive inclusion of the quantum effects, special care was taken to maintain a high numerical efficiency. The charge transport model additionally considers optical field calculations, allowing for predictive calculations of light–current–voltage curves. The model is benchmarked against well‐established ICL designs and demonstrate reliable performance predictability. Additionally, detailed insights into device characteristics extracted from the model are provided. This ultimately allows to deepen the understanding of ICL and not only refine existing ones but also generate novel optimized designs.

Ecological functional near-infrared spectroscopy in mobile children: using short separation channels to correct for systemic contamination during naturalistic neuroimaging.

The advances and miniaturization in functional near-infrared spectroscopy (fNIRS) instrumentation offer the potential to move the classical laboratory-based cognitive neuroscience investigations into more naturalistic settings. Wearable and mobile fNIRS devices also provide a novel child-friendly means to image functional brain activity in freely moving toddlers and preschoolers. Measuring brain activity in more ecologically valid settings with fNIRS presents additional challenges, such as the increased impact of physiological interferences. One of the most popular methods for minimizing such interferences is to regress out short separation channels from the long separation channels [i.e., superficial signal regression (SSR)]. Although this has been extensively investigated in adults, little is known about the impact of systemic changes on the fNIRS signals recorded in children in either classical or novel naturalistic experiments. We aim to investigate if extracerebral physiological changes occur in toddlers and preschoolers and whether SSR can help minimize these interferences. We collected fNIRS data from 3- to 7-year-olds during a conventional computerized static task and in a dynamic naturalistic task in an immersive virtual reality (VR) cave automatic virtual environment. Our results show that superficial signal contamination data are present in young children as in adults. Importantly, we find that SSR helps in improving the localization of functional brain activity, both in the computerized task and, to a larger extent, in the dynamic VR task. Following these results, we formulate suggestions to advance the field of developmental neuroimaging with fNIRS, particularly in ecological settings.

Bulk acoustic wave—Solidly mounted resonator with a-SiOCN:H as low-Z material

Bulk acoustic wave (BAW) filters have been proven to be of high demand in today's low power RF front-ends for mobile communication devices. Within the launch of 5G applications worldwide, especially in the region of new radio (nr-1) up to 6 GHz, defined frequency bands require filters of wide bandwidths, while simultaneously featuring steep edges and high out-of-band rejection. Due to the coexistence with 4G LTE (long term evolution) wireless standards as well as advanced data transfer concepts such as carrier aggregation, the increasing complexity of antenna systems forces the implementation of highly selective acoustic filters. In contrast to the widely used surface acoustic wave (SAW) technology, BAW filters appear with superior performance for frequencies above 1 GHz. This work describes the fabrication of a BAW-solidly mounted resonator (BAW-SMR) with a tailored material system of a-SiOCN:H as a low impedance (low-Z) material integrated within its acoustic Bragg mirror. A direct comparison to the widely used low-Z material of SiO2 with an acoustic impedance of around 13 MRayl is demonstrated by two equal resonator stacks by replacing only the uppermost low-Z thin film of the acoustic reflector. A single-mask design was chosen with platinum as a bottom electrode to ensure the most equal growth conditions for the piezoelectric aluminum nitride (AlN) layers on both reflectors’ surfaces featuring either the tailored a-SiOCN:H or standard SiO2. The a-SiOCN:H thin films were deposited by plasma-enhanced chemical vapor deposition and the according acoustic impedance was priorly depicted to 7.1 MRayl, which could be exploited to achieve an increase in the effective coupling coefficient beyond 7% as well as a resonator bandwidth of more than 60 MHz.

CO-TSM: A Flexible Model for Secure Embedded Device Ownership and Management

The Consumer-Oriented Trusted Service Manager (CO-TSM) model has been recognised as a significant advancement in managing applications on Near Field Communication (NFC)-enabled mobile devices and multi-application smart cards. Traditional Trusted Service Manager (TSM) models, while useful, often result in market fragmentation and limit widespread adoption due to their centralised control mechanisms. The CO-TSM model addresses these issues by decentralising management and offering greater flexibility and scalability, making it more adaptable to the evolving needs of embedded systems, particularly in the context of the Internet of Things (IoT) and Radio Frequency Identification (RFID) technologies. This paper provides a comprehensive analysis of the CO-TSM model, highlighting its application in various technological domains such as smart cards, HCE-based NFC mobile phones, TEE-enabled smart home IoT devices, and RFID-based smart supply chains. By evaluating the CO-TSM model’s architecture, implementation challenges, and practical deployment scenarios, this paper demonstrates how CO-TSM can overcome the limitations of traditional TSM approaches. The case studies presented offer practical insights into the model’s adaptability and effectiveness in real-world scenarios. Through this examination, the paper aims to underscore the CO-TSM model’s role in enhancing scalability, flexibility, and user autonomy in secure embedded device management, while also identifying areas for future research and development.

Optimizing Touchless Fingerprint Identification: A Machine Learning Approach to Modelling and Performance Evaluation

This paper explored the modelling and performance analysis of a smartphone-based fingerprint identification system using Convolutional Neural Networks (CNN). The research developed a theoretical framework to validate picture-based fingerprint identification as a feasible alternative to traditional touch-based methods. A modified Automated Fingerprint Identification System (AFIS) model served as the study's foundation. To enhance the model's capabilities, data from two databases, IIT India and SOCOFing, were utilized. The evaluation of the CNN architecture focused on mobile device fingerprint recognition. It emphasized key processes such as data pre-processing, model training, and the optimization of the CNN through a Siamese-CNN approach to boost accuracy and efficiency. Python scripts developed for this purpose were converted to Android code using TensorFlow for deployment on Android devices. Performance metrics, including identification accuracy, processing speed, and resource utilization, were analysed to determine the system's feasibility. The results demonstrated that CNN-based fingerprint identification systems hold significant promise for delivering robust and reliable biometric authentication on smartphones, highlighting both their practical applications and limitations. decrease medical as well as financial burden, hence improving the management of cirrhotic patients. These predictors, however, need further work to validate reliability.

Mobile Assisted Language Learning: Ecuadorian Undergraduate Polytechnic Students' Perceptions

Despite the increased use of mobile devices for language learning, little research has examined Ecuadorian university students' perceptions of Mobile-Assisted Language Learning (MALL). This study aims to explore undergraduate polytechnic students' perceptions of using MALL for English skills development. Also, it looks at the different factors associated with the acceptance of technology and identifies those that are more common. This quantitative research design uses a survey adapted from the Technology Adoption Model (TAM). It counts with the participation of 126 Ecuadorian undergraduate polytechnic students taking English as a foreign language classes. Participants were very positive about using MALL applications to learn English during their off-classroom time. The average total mean score was 4.373. It was also determined, through multiple regression analysis, that Perceived Usefulness (t = 6.025, p < 0.000), Social Influence (t = 2.876, p < 0.000), and Perceived Outcomes (t = 3.903, p <0.000) were the highest predictors of MALL acceptance. The two main contributions of this study to the literature are that students feel a connection between their language learning aims and attaining them by using MALL. Perceived Outcome is a high predictor of MALL usage. The findings suggest that Ecuadorian educational practices should increasingly integrate Mobile-Assisted Language Learning (MALL) technologies, as students perceive them as highly useful, especially in improving language skills, fostering social collaboration, and offering personalized, flexible learning experiences that align with their technological readiness and cultural context.

Real-time Deep Learning Based Mobile Application for Detecting Edible Fungi: Mushapp

Mushroom consumption and wild mushroom gathering are increasing in our country and in the world. Mushroom poisoning has an important place in food poisoning cases. Mushroom poisoning accounts for approximately 7% of poisoning cases in adults. Mushroom collection and consumption is common in many regions of our country. In this study, a deep learning based mobile application was developed to reduce the incidence of mushroom poisoning by taking a photo of a mushroom and determining the type and toxicity of the mushroom from the photo. This mobile application is called MushAPP. In the first phase of the study, 5150 mushroom images of 20 mushroom species were used to create the dataset. The dataset was then pre-processed and converted into a format that can be used by the deep learning algorithm. The mobile application side of the project was developed in Android Studio IDE environment. An artificial intelligence model was integrated into the designed mobile application. In the application, the type and toxicity status of the mushroom viewed from the mobile device camera are determined and presented to the user. The research findings were analyzed and it was determined that the accuracy rate of the application in detecting the mushroom species was 99.8%.

A Survey on Mobile Digital Forensic: Taxonomy, Tools, and Challenges

ABSTRACTModern time largely rely on mobile gadgets as a means of communication, and entertainment. These gadgets now plays a vital role in forensic analysis and criminal investigations as it store a plethora of private and sensitive data. Investigating feasible digital incidents and preserving mobile device security became crucial as the proliferation of mobile devices continues to influence modern society. In this survey; mobile threats, investigative process models for mobile forensics, evidence sources, forensics tools, and field problems are all covered in‐depth. Further, a comprehensive analysis examining the capabilities, features, and efficiency of available distinct mobile forensic tools namely Belkasoft, MOBILedit, and Magnet AXIOM for retrieving, analyzing, and presenting digital evidence is being presented. The objective of the study is to assist forensic investigators in choosing the best tool for specific investigations. The findings of our study reveals the complexity of mobile operating systems, investigative process supported with sources of evidence, and forensic tools which makes it clear that the area of mobile forensics is dynamic and require modern skill adaptation along and innovative development in future. Finally, the study presents current challenges encountered during mobile forensic investigations as researchers can enhance their procedures and tactics for mobile device examination and evidence extraction by fostering a greater knowledge of the difficulties involved in mobile forensics.

A Developer-Oriented Framework for Assessing Power Consumption in Mobile Applications: Android Energy Smells Case Study

Currently, people spend a lot of time using their mobile devices. With such ubiquity of mobile devices in our daily life, battery capacity and quality are of utmost importance. Running software applications (called apps) are one of the major factors influencing the power consumption in mobile devices. In order to meet user needs, mobile apps are becoming inherently complex and resource greedy. Therefore, fulfilling these requirements at the pace imposed by the market may degrade software construction quality and insert so-called energy code smells: bad patterns in the source code of an app that indicate a deeper problem and adversely affect power consumption. This work proposes a developer-oriented framework for identifying and fixing patterns via analyzing different application code flavors in a user-driven test scenario. A special app was designed in order to validate the Android implementation of the proposed methodology. The study results have shown significant improvement regarding energy efficiency after correcting one or more energy code smells, with a 4 to 30 percent decrease in battery drain. Additionally, the power consumption signature term is defined in the context of mobile applications. This paper presents a developer-oriented framework for assessing power consumption in mobile applications. Our key contributions include identifying significant energy code smells, demonstrating their impact on power consumption, and providing a toolset for developers to improve energy efficiency.

Utility of ultra-portable ultrasound devices for the risk-stratification of thyroid dysfunctions in an emergency setting.

We previously showed that the rapid TSH (rTSH) screening is able to detect a high prevalence of thyroid diseases in patients presenting to the Emergency department (ED), with a 7% prevalence of undiagnosed thyroid dysfunctions. The purpose of the present study is to implement our diagnostic flow-chart for thyroid dysfunctions in the ED with a rapid point-of-care thyroid ultrasound (rPOCUS). rPOCUS was performed by an app-based mobile ultrasound device (Lumify® by Philips Healthcare) in patients with suppressed rTSH undergoing urgent procedures requiring iodinate contrast media. Our results suggest that rPOCUS is cost- and time-effective for the management of patients with a newly diagnosed hyperthyroidism requiring urgent iodinated contrast media or amiodarone administration. Moreover, this handled US scanner avoided rTSH measurement in patients found to have a normal thyroid gland, and detected some incidental findings (nodules, heterogeneous echotexture), which would lead to further diagnostic investigations. We demonstrate, for the first time, that rPOCUS greatly improves the management of patients attending the ED, including the prompt characterization and correct treatment of hyperthyroidism, and the prevention of iodine-induced thyrotoxicosis.

Exploring the Affordances of Vernacular Digital Games in Developing 21st-Century Skills

The past decade has witnessed an unprecedented surge in technological advancements and an exponential growth in mobile device ownership world-wide. Such transformations have intensified access to digital content, opening up new avenues for non-formal learning. This was particularly true during subsequent crises such as the COVID-19 Pandemic and natural and man-made disasters which have disrupted formal learning and left many learners immobilised. This paper contends that capitalising on the intersection of formal and non-formal learning experiences is integral to innovative planning and value-added education. To dismiss engagement in non-formal leaning transactions is to exclude a significant part of learners’ cognitive processes, preferences and experiences. In particular, this paper focuses on vernacular digital games as an apt example of pervasive technology and a non-formal learning arena. Described as a popular culture text, vernacular games are originally intended for entertainment, as opposed to pedagogical, purposes. However, embedded in their designs are social and developmental affordances that render them potent tools for serendipitous learning and dynamic spaces for developing competencies. In the light of this, this paper proposes a move from gamification, i.e. adding a gaming layer to non-gaming contexts, to adding an educational layer to non-formal digital gaming contexts. To consolidate this proposal, it investigates the 21st-century competencies reportedly acquired and developed through digital gameplay. It also delves into possible design-related paradoxes. More precisely, the paper reflects on how key game design features such as multimodality, adaptivity, cultural narratives and affinity spaces can be harnessed to better cultivate much in-demand 21st-century skills. Although learning skills (in their metacognitive form of higher-order thinking) are part and parcel of 21st-century skills, literacy skills and life skills are equally important. The further longer-term aim is to map development opportunities and optimise learning in ways that are in harmony with today’s changing scenarios, demands and objectives.

Frequent Use of Mobile Devices and its Impact on Early Childhood

The study consists of a systematic review, where the objective was set: to analyze the impact of the use of mobile devices in early childhood, answering the following questions: What is the frequency of the use of mobile devices in early childhood? What is the impact of frequent use of mobile devices in early childhood? Scientific productions from the SCOPUS, Web of Science, Gale OneFile Psychology and ProQuest databases were analyzed, publications made from 2020 to 2023. The study is based on the PRISMA methodology, which consists of a flow diagram that allows selecting the studies for their construction ( Higgins and Green, 2011), which have allowed the methods and techniques of the studies to be systematized in an organized way. The reviewed productions provide empirical and theoretical data in the field of psychology and social sciences. The results reveal that 22.7% of 18-month-old children and 94.6% of 5-year-old children use the mobile device 60 minutes a day. High levels of mobile device use at the age of 5 years are associated with hyperactivity, difficulties with attention, concentration and behavioral problems. Frequent mobile device use at the age of 18 months can cause many attention difficulties because they are susceptible to external experiences and excessive media use that can hinder development in attention networks.

The practice and effect of multi-disciplinary cooperative mobile devices in improving the satisfaction of inpatients on drug education

Objective: To explore the effect of multi-disciplinary cooperative mobile device-assisted drug education on the satisfaction of inpatients drug education. Methods: Mobile nursing vehicle, IPAD, and mobile phone were used to scan QR code to play drug propaganda videos and distribute drug propaganda materials. Doctors, nurses and pharmacists cooperated to complete drug education for inpatients. The satisfaction on drug education of inpatients was collected through a third-party satisfaction survey. Results: Before implementation, the four satisfactions of communicating about the drug, understanding the purpose of the drug, informing the role of the drug and informing the side effect of the drug were 83.9%, 80.4%, 83.8% and 83.9%, respectively, and after the implementation increased to 88.2%, 86.4%, 89.6% and 87.3% (P<0.05). The effective rate of drug education among inpatients increased from 95% before the implementation to 99.4%, and the medication compliance of inpatients increased from 93.1% before the implementation to 98.75%. Conclusion: Multi-subject cooperative mobile device-assisted drug education can improve the satisfaction of inpatients drug education, the efficiency of drug education and drug compliance. Keywords: Multidisciplinary collaboration; mobile devices; drug education; patient satisfaction.

Development of an Automatic Rock Mass Classification System Using Digital Tunnel Face Mapping

To mitigate unforeseen incidents, such as key block failure or tunnel collapse during excavation, an appropriate support pattern that correlates with the geological conditions of the rock mass at the tunnel face should be designed. Rock mass evaluations should be conducted through geological face mapping during the construction phase, alongside predictions based on field investigations during the design phase. When marked discrepancies are identified, it is customary to convene an on-site evaluation involving a committee of experts. This study develops a digital tunnel face mapping system that utilises mobile devices to facilitate online evaluations during the construction phase. This system effectively replaces the traditional on-site field evaluation method. Tunnel face mapping can be promptly accomplished using images captured at the excavation face, enabling rapid analysis. In conjunction with the mapping capabilities, the developed system was designed to digitally store geological information, which includes parameters such as rock strength distribution, the spacing and length of discontinuities observed during the mapping process, as well as data pertaining to weathering and the groundwater conditions of those discontinuities. This information was then correlated with the rock mass rating sheet to automate the determination of ratings for each parameter, ultimately leading to a conclusive classification of the rock mass quality. By employing this system for tunnel face mapping and rock quality evaluation, we significantly reduced the discrepancies in the evaluation results that often arise due to the subjective judgement of geologists, as well as human errors that can occur throughout the rating process.

Smartphone Addiction among Adolescents in Southern Italy and Correlation with Other Risky Behaviors.

Addiction to smartphone is defined as the disability to cease using the device despite the physical, psychological, or social consequences. The diagnosis of this disorder is increasing exponentially among teenagers, even more so after Corona Virus Disease 2019 (COVID-19) pandemic. This study aims to analyze the risk of mobile phone addiction among teenagers in Italy and its relationship with other risk behaviors, such as drug consumption and emotional well-being, following the COVID-19 pandemic. A cross-sectional observational study was conducted in Catania, Sicily, during 2022. The study sample comprised 342 students from a secondary education center that answered to a validated Italian version of the Smartphone Addiction Scale Short Version (SAS-SV) accessing through computers and mobile devices. Subjective Wellbeing was measured with the Personal Wellbeing Index-School Children (PWI-SC). Also, they completed the NIAAA Brief Alcohol Use Screener. Our results showed that 30.1% of students who participated in the survey were classified with a smartphone addiction. Besides, most of the participants used the Smartphone for social networks (86.0%). Regarding the other aspects measured, the mean self-perceived well-being score measured with the PWI-SC was 55.5, and 36.2% of participants were categorized as "high risk" for depression. Finally, four out of ten participants were at "moderate or high risk" of an alcohol use disorder. Our results suggest that the use of mobile phones is increasing in adolescents, an age group also exposed to social and health problems.

Fluorescence Polarization Assay for Infection Diagnostics: A Review

Rapid and specific diagnosis is necessary for both the treatment and prevention of infectious diseases. Bacteria and viruses that enter the bloodstream can trigger a strong immune response in infected animals and humans. The fluorescence polarization assay (FPA) is a rapid and accurate method for detecting specific antibodies in the blood that are produced in response to infection. One of the first examples of FPA is the non-competitive test for detecting brucellosis in animals, which was followed by the development of other protocols for detecting various infections. Fluorescently labeled polysaccharides (in the case of brucellosis and salmonellosis) or specific peptides (in the case of tuberculosis and salmonellosis, etc.) can be used as biorecognition elements for detecting infections. The availability of new laboratory equipment and mobile devices for fluorescence polarization measurements outside the laboratory has stimulated the development of new fluorescence polarization assays (FPAs) and the emergence of commercial kits on the market for the detection of brucellosis, tuberculosis, and equine infectious anemia viruses. It has been shown that, in addition to antibodies, the FPA method can detect both viruses and nucleic acids. The development of more specific and sensitive biomarkers is essential for the diagnosis of infections and therapy monitoring. This review summarizes studies published between 2003 and 2023 that focus on the detection of infections using FPA. Furthermore, it demonstrates the potential for using new biorecognition elements (e.g., aptamers, proteins, peptides) and the combined use of FPA with new technologies, such as PCR and CRISPR/Cas12a systems, for detecting various infectious agents.