Smartphone Environment Research Articles

Real-Time Moving Object Tracking on Smartphone Using Cradle Head Servo Motor.

The increasing demand for artificially intelligent smartphone cradles has prompted the need for real-time moving object detection. Real-time moving object tracking requires the development of algorithms for instant tracking analysis without delays. In particular, developing a system for smartphones should consider different operating systems and software development environments. Issues in current real-time moving object tracking systems arise when small and large objects coexist, causing the algorithm to prioritize larger objects or struggle with consistent tracking across varying scales. Fast object motion further complicates accurate tracking and leads to potential errors and misidentification. To address these issues, we propose a deep learning-based real-time moving object tracking system which provides an accuracy priority mode and a speed priority mode. The accuracy priority mode achieves a balance between the high accuracy and speed required in the smartphone environment. The speed priority mode optimizes the speed of inference to track fast-moving objects. The accuracy priority mode incorporates CSPNet with ResNet to maintain high accuracy, whereas the speed priority mode simplifies the complexity of the convolutional layer while maintaining accuracy. In our experiments, we evaluated both modes in terms of accuracy and speed.

Development of an integrated solution for patients with neurostimulator for chronic pain in times of COVID-19: A mobile application with a support center

Chronic pain is one of the most prevalent pathologies in the world. Treatment with neurostimulators is carried out in the most extreme cases and requires a large investment of resources. In these times of COVID-19 pandemic, we present a comprehensive solution for monitoring this kind of patient, this solution includes the development of a mobile application and a support center for remote monitoring (SCRM). The project was developed according to the scientific evidence in the following phases: (1) approval in a multidisciplinary clinical committee of implants for chronic pain, (2) setting up a group of experts, (3) protocol adaptation for the follow-up of patients with chronic pain to the Smartphone environment, (4) technology platform adaptation to the clinical protocol (technological environment and workflow between the hospital and the SCRM), and (5) quality evaluation by survey (quantitative and qualitative) of a small series of patients. The application was evaluated by asking for user opinions about design and usefulness with the first implanted patients. Some minor adjustments were made concerning downloadable material and screen color and text. Developing a comprehensive solution should be based on scientific principles and in accordance with established protocols. A support center ensures greater adherence for follow-up and better patient care.

Decision-makers are more impulsive on smartphones than on computers

• Individuals have a higher present bias when they use a smartphone. • Individuals are less willing to invest time to obtain information when using a smartphone. • Decision makers are more impulsive on smartphones than computers. • A novel approach to study costly information search. Abstract 1 1 I acknowledge financial support from The Blavatnik Interdisciplinary Cyber Research Center. I want to thank Ayala Arad, Itai Ater, Shay Danziger , Dotan Persitz, Ariel Rubinstein, and Peter Zubcsek for helpful comments. As the use of smartphones becomes increasingly pervasive, the question arises as to whether the smartphone platform induces different choices than the computer platform. An incentivized-choice lab experiment is carried out to examine the effect of the smartphone environment on intertemporal decision-making and risky choices. In addition, a novel approach is introduced in order to study costly information search. While no significant differences are found between the platforms in the case of risky choices, there was significantly more present bias in the case of the smartphone platform than in the case of the computer platform. Furthermore, smartphone users were significantly less willing to invest time in order to obtain information that may increase their payoff. Overall, the findings suggest that decision makers exhibit more impulsive behavior on the smartphone platform relative to the computer platform.

User Authentication Method via Speaker Recognition and Speech Synthesis Detection

As the Internet has been developed, various online services such as social media services are introduced and widely used by many people. Traditionally, many online services utilize self-certification methods that are made using public certificates or resident registration numbers, but it is found that the existing methods pose the risk of recent personal information leakage accidents. The most popular authentication method to compensate for these problems is biometric authentication technology. The biometric authentication techniques are considered relatively safe from risks like personal information theft, forgery, etc. Among many biometric-based methods, we studied the speaker recognition method, which is considered suitable to be used as a user authentication method of the social media service usually accessed in the smartphone environment. In this paper, we first propose a speaker recognition-based authentication method that identifies and authenticates individual voice patterns, and we also present a synthesis speech detection method that is used to prevent a masquerading attack using synthetic voices.

Development of an integrated solution for patients with neurostimulator for chronic pain in times of COVID-19: A mobile application with a support center

IntroductionChronic pain is one of the most prevalent pathologies in the world. Treatment with neurostimulators is carried out in the most extreme cases and requires a large investment of resources. In these times of the COVID19 pandemic, we present a comprehensive solution for monitoring this kind of patient, this solution includes the development of a mobile application and a support center for remote monitoring (SCRM). Material and methodologyThe project was developed according to the scientific evidence in the following phases: (1) Approval in a multidisciplinary clinical committee of implants for chronic pain, (2) Setting up a group of experts, (3) Protocol adaptation for the follow-up of patients with chronic pain to the Smartphone environment, (4) Technology platform adaptation to the clinical protocol (technological environment and workflow between the hospital and the SCRM), and (5) Quality evaluation by survey (quantitative and qualitative) of a small series of patients. ResultsThe application was evaluated by asking for user opinions about design and usefulness with the first implanted patients. Some minor adjustments were made concerning downloadable material and screen color and text. ConclusionsDeveloping a comprehensive solution should be based on scientific principles and in accordance with established protocols. A support center ensures greater adherence for follow-up and better patient care.

Fast and Accurate Ophthalmic Medication Bottle Identification Using Deep Learning on a Smartphone Device

To assess the accuracy and efficacy of deep learning models, specifically convolutional neural networks (CNNs), to identify glaucoma medication bottles. Algorithm development for predicting ophthalmic medication bottles using a large mobile image-based dataset. A total of 3750 mobile images of 5 ophthalmic medication bottles were included: brimonidine tartrate, dorzolamide-timolol, latanoprost, prednisolone acetate, and moxifloxacin. Seven CNN models were initially pretrained on a large-scale image database and subsequently retrained to classify 5 commonly prescribed topical ophthalmic medications using a training dataset of 2250 mobile-phone captured images. The retrained CNN models' accuracies were compared using k-fold cross-validation (k= 10). The top 2 performing CNN models were then embedded into separate iOS apps and evaluated using 1500 mobile images not included in the training dataset. Prediction accuracy, image processing time. Of the 7 CNN architectures, MobileNet v2 yielded the highest k-fold cross-validation accuracy of 0.974 (95% confidence interval [CI], 0.966-0.980) and the shortest average image processing time at 3.45 (95% CI, 3.13-3.77) sec/image. ResNet V2 had the second highest accuracy of 0.961 (95% CI, 0.952-0.969). When the 2 app-embedded CNNs were compared, in terms of accuracy, MobileNet V2, with an image prediction accuracy of 0.86 (95% CI, 0.84-0.88), was significantly greater than ResNet V2, 0.68 (95% CI, 0.66-0.71) (Table1). Sensitivities and specificities varied between medications (Table1). There was no significant difference inaverage imaging processing time, 0.32 (95% CI, 0.28-0.36) sec/image and 0.31 (95% CI, 0.29-0.33) sec/image for MobileNet V2 and ResNet V2, respectively. Information on beta-testing of the iOS app can be found here: https://lin.hs.uci.edu/research/. We have retrained MobileNet V2 to accurately identify ophthalmic medication bottles and demonstrated that this neural network can operate in a smartphone environment. This work serves as a proof-of-concept for the production of a CNN-based smartphone application to empower patients by decreasing risk for error.

Encryption Image using HiSea Algorithm Based on Android Operating System

The mobile phone environment represents one of the important environments in encryption various multimedia (audio, image, and video) , and this depends on the type of algorithm used in the encryption process, as phones have limited memory and computational resources, Therefore the selection of the algorithm must be compatible with the mobile environment in terms of speed, safety and flexibility in addition to choosing an algorithm that The simplicity and safety of the image encryption process was investigated with lightweight and efficient computing. In this paper, Hybrid Cube Encryption (HiSec) was used. When implementing this algorithm in a smart phone environment, the results showed the ease of encrypting images and retrieving the original image, in addition to that it only requires small computational resources, and the algorithm was very effective in encrypting images on mobile phones. This suggested method has been implemented in the mobile environment with android OS. The proposed method has been programmed in JAVA, and the method has been tried on different types of mobile phones (such as Huawei Nova 2, Huawei Nova 7, HTC, NOT 8, Galaxy S 20, and HONOR).

Lightweight and Secure Face-based Active Authentication for Mobile Users

Active Authentication (AA) systems continuously authenticate users on smartphones. With high quality front-facing cameras available on recent smartphones, face-based active authentication emerges as a good candidate for AA systems. On the other hand, secure authentication of mobile users is a big concern in biometric systems. Mobile match-on-card (MMOC) technique takes advantage of SIM/eSIM card as a secure element (SE) to protect biometric templates and verify users isolated from the smartphone's environment. However, resource limitations of smart cards make MMOC authentication hard to implement. In this paper, we propose two system architectures for MMOC face-based AA systems. In Cloud-assisted MMOC architecture (CA-MMOC), we use cloud resources for model selection and training. Full MMOC architecture (F-MMOC) relies only on SIM/eSIM card's resources for enrollment and verification. A quantization scheme is proposed to make the authentication system implementable on SIM cards, plus a speed-up technique to reduce on-card execution time. Using a public mobile video dataset, we evaluate the proposed system. Our evaluation results show that the proposed MMOC authentication achieves high accuracy in real-time with a small memory footprint on SIM, and is suitable for cross-platform authentication. We also implement the CA-MMOC system on a real smartphone and evaluate the system's performance overhead in terms of power consumption, CPU and memory usage.

Abadan School of Medical Sciences students' viewpoints on information seeking behavior in the mobile smartphone environment

Abadan School of Medical Sciences students' viewpoints on information seeking behavior in the mobile smartphone environment

Implicit authentication method for smartphone users based on rank aggregation and random forest

Currently, the smartphone devices have become an essential part of our daily activities. Smartphone’ users run various essential applications (such as banking and e-health Apps), which contains very confidential information (e.g., credit card number and its PIN). Typically, the smartphone’s user authentication is achieved using mechanisms (password or security pattern) to verify the user identity. Although these mechanisms are cheap, simple, and quick enough for frequent logins, they are vulnerable to attacks such as shoulder surfing or smudge attack. This problem could be addressed by authenticating the users using their behaviour (i.e., touch behaviour) while using their smartphones. Such behaviours include finger’s pressure, size, and pressure time while tapping keys. Selecting features (from these behaviours) could play an important role in the authentication process’s performance. This paper aims to propose an efficient authentication method providing an implicit authentication for smartphone users while not imposing an additional cost of special hardware and addressing the limited smartphone capabilities. We first investigated feature selection techniques from the filter and wrapper approaches and then used the best one to propose our implicit authentication method. The random forest classifier is used to evaluate these techniques. It is also used to achieve the classification task in our authentication method. Using a public dataset, the experimental results showed that the filter-based technique (i.e., rank aggregation) is the best feature selection to build an implicit authentication method for the smartphone environment. It showed accuracy results around 97.80% using only 25 features out of 53 features (i.e., require less mobile resources (memory and processing power) to authenticate users. At the same time, the results showed that our method has less error rate: 2.03 FAR, 0.04 FRR, and 1.04 ERR, comparing to the related work. These promising results would be used to develop a mobile application that allows implicit authentication of legitimate owners while avoiding the traditional authentication problems and using fewer smartphone resources.

The Influence of Smartphone Text Input Method, Posture, and Environment on User Experience

ABSTRACT Text input on smartphones plays an increasingly important role in people’s daily lives. This study aims to explore the impact of posture and environment on the user experience of smartphone input methods. A combination of performance, subjective, and physiological measures is used to assess the user experience of 32 subjects (12 male, 20 female; 18–26 years old) using QWERTY and T9 input methods in walking and sitting postures in indoor and outdoor environments. The results showed that T9 was more effective for indoors and when sitting than QWERTY, while QWERTY was more effective than T9 for outdoors and when walking. In our tests, using T9 indoors while sitting showed a significantly higher performance than QWERTY, while using QWERTY outdoors while walking elicited significantly lower emotional arousal than T9. The findings allow us to propose both the optimal contexts in which to use particular input methods, and to highlight new approaches for improving the user experience of these devices.

"스마트폰 환경에서의 네이버 앱 메인페이지 UX디자인 개선을 위한 사용성(Usability)분석 - 메인화면과 검색기능을 중심으로 -"

"스마트폰 환경에서의 네이버 앱 메인페이지 UX디자인 개선을 위한 사용성(Usability)분석 - 메인화면과 검색기능을 중심으로 -"

Energy Efficient Algorithm for High Speed Packet Data Transfer on Smartphone Environment

Energy efficiency is the main concern of the 21st century. The smartphone could be a trendy battery operated device and also the experience of the smartphone usage is entirely counting on the battery life. The state of the Smartphone can be Screen_OFF and Screen_ON. Majority of the time spent by the user on the phone is in screen off mode however 30% to 40% of the battery power consumption in this state through Always_On Apps. These apps send tiny burst to their server for various functions at a specific interval of time and keep RRC state busy. This paper proposes an energy-efficient algorithm for high-speed packet data transfer on a Smartphone called PSEO (PS Energy Optimization). It schedules data communication throughout the screen off state based on the radio signal quality. PSEO regulate Data_ON and Data_OFF period while not affecting the user convenience based on the independent value of RSRQ. The proposed research saves the energy up to 28.88%, 78.60% and 16.20% on average of total network energy, Screen_Off energy, and overall smartphone energy respectively.

A Frequency-Reconfigurable Antenna With 1-mm Nonground Portion for Metal-Frame and Full-Display Screen Handset Applications Using Mode Control Method

A frequency-reconfigurable antenna featuring four integrated switches for metal-frame and full-display screen handset applications is proposed in this paper. To achieve full-display aesthetics, the nonground portion of the proposed antenna is reduced to only 1 mm, making it impossible to formulate additional parasitic strips to enhance the bandwidth and radiation efficiency of the antenna. Four switches are employed in conjunction with the devised antenna to excite five resonant modes using the mode control method (MCM) to operate from 699 to 960 MHz and from 1710 to 2690 MHz. The proposed antenna is designed by employing several peripheral metallic components, such as steel sheet, USB, speaker box, and full-display screen into consideration, which enables the overall structure to be much closer to a practical smartphone environment. A prototype was fabricated and measured. The experimental results confirm that the proposed antenna features radiation efficiency from 30% to 55% for metal-frame and full-display screen handset applications.

Implementation of Augmented Reality as Information and Promotion Media on Dieng Tourism Area

Information becomes necessary for tourists to get and improve the knowledge of tourism and their experiences about tourist destination. In Dieng tourism area, tourist information and promotional media has not been structured, as well as the need of media. The purpose of this research is produce Augmented Reality application that can visualize information of Dieng tourism, as interactive and interesting information, promotion tourism media. This research produces an android application which is used as information and promotion media of tourism in Dieng. Testing according to ISO 25010 with functional suitability got 100% suitability with Blackbox test, on portability aspect application successfully installed, update and uninstall on various smartphone environment, on performance efficiency aspect show average launch time is 1817 ms and also 0,22% CPU and 177,8 MB Memory usage. Results usability obtained SUS score 79.6 indicates the application get acceptable category to be used as information and tourism promotion media in Dieng.

A Study on the Psychological Analysis System Using Machine Learning

Real-time facial expression recognition and analysis technology is recently drawing attention in areas of computer vision, computer graphics, and HCI. Recognition of user’s emotion on the basis of video and voice is drawing particular interest. The technology may help managers of households or hospitals. In the present study, video and voice were converted into digital data through MATLAB by using PCA(Principal Component Analysis), LDA(Linear Discriminant Analysis), KNN(K Nearest Neighbor) algorithms to analyze emotions through machine learning. The manager of the psychological analysis counseling system may understand a user’s emotion in an smart phone environment. This system of the present study may help the manager to have a smooth conversation or develop a smooth relationship with a user on the basis of the provided psychological analysis results.

Energy Efficient Developments of Smartphone Environment and Cellular Network Opportunities and Challenges

Energy Efficient Developments of Smartphone Environment and Cellular Network Opportunities and Challenges

Mining mobile application usage pattern for demand prediction by considering spatial and temporal relations

Recently, researches on smart phones have received attentions because the wide potential applications. One of interesting and useful topic is mining and predicting the users’ mobile application (App) usage behaviors. With more and more Apps installed in users’ smart phone, the users may spend much time to find the Apps they want to use by swiping the screen. App prediction systems benefit for reducing search time and launching time since the Apps which may be launched can preload in the memory before they are actually used. Although some previous studies had been proposed on the problem of App usage analysis, they recommend Apps for users only based on the frequencies of App usages. We consider that the relationship between App usage demands and users’ recent spatial and temporal behaviors may be strong. In this paper, we propose Spatial and Temporal App Recommender (STAR), a novel framework to predict and recommend the Apps for mobile users under a smart phone environment. The STAR framework consists of four major modules. We first find the meaningful and semantic location movements from the geographic GPS trajectory data by the Spatial Relation Mining Module and generate the suitable temporal segments by the Temporal Relation Mining Module. Then, we design Spatial and Temporal App Usage Pattern Mine (STAUP-Mine) algorithm to efficiently discover mobile users’ Spatial and Temporal App Usage Patterns (STAUPs). Furthermore, an App Usage Demand Prediction Module is presented to predict the following App usage demands according to the discovered STAUPs and spatial/temporal relations. To our knowledge, this is the first study to simultaneously consider the spatial movements, temporal properties and App usage behavior for mining App usage pattern and demand prediction. Through rigorous experimental analysis from two real mobile App datasets, STAR framework delivers an excellent prediction performance.

Implementation of Augmented Reality as Information and Promotion Media on Dieng Tourism Area

Information becomes necessary for tourists to get and improve the knowledge of tourism and their experiences about tourist destination. In Dieng tourism area, tourist information and promotional media has not been structured, as well as the need of media. The purpose of this research is produce Augmented Reality application that can visualize information of Dieng tourism, as interactive and interesting information, promotion tourism media. This research produces an android application which is used as information and promotion media of tourism in Dieng. Testing according to ISO 25010 with functional suitability got 100% suitability with Blackbox test, on portability aspect application successfully installed, update and uninstall on various smartphone environment, on performance efficiency aspect show average launch time is 1817 ms and also 0,22% CPU and 177,8 MB Memory usage. Results usability obtained SUS score 79.6 indicates the application get acceptable category to be used as information and tourism promotion media in Dieng.

Experiences, perceptions and needs on the education of evidence-based medicine among Korean Medicine college students: A nationwide online survey

IntroductionEvidence-based medicine (EBM) has become an important part of medical education. Students’ perception and demand are important in education. The aim of our study was to conduct a nationwide online survey about EBM in traditional Korean Medicine (KM) education. MethodsAn online questionnaire was developed, comprising categories about EBM education in KM. Student’s experience, achievement, satisfaction, understanding and expectation in EBM education were surveyed. Face validity of the developed questionnaire was tested before survey and online links were sent through SurveyMonkey to all KM college students in Korea. ResultsAmong the total 4649 students nationwide, 1292 agreed to participate in the survey (response rate: 27.8%) within 10days. Self-appraisals about the achievement, satisfaction, and understanding of EBM were relatively low. In addition to this, 30% of students answered they did not learn about EBM in regular school curriculum. Expectation of extension of further EBM education curriculum was comparatively high. Most respondents agreed that EBM knowledge would be useful for clinical practice and the undergraduate curriculum. Barriers to enhance EBM education were perceived to be lack of resources and appropriate contents. ConclusionThis first nationwide survey in Korea investigated the experience, perception, and demand for EBM education by KM college students. The importance of EBM education in the KM curriculum needs further improvement and curriculum development based on the students' experiences and perceptions. Additionally, online survey in smartphone environment, which was conducted thorough involvement of student’s representatives seems to be an eligible strategy for a survey of students.