Phone Models Research Articles

Study upon the influence of mobile phone protective case on SAR distribution

Abstract The article presents a series of simulations performed with CST Studio software, using the Hugo male model, a member of CST Voxel Family. These simulations aim to study the effect that the conductivity (thermal and electrical) of the phone protective case might have on the SAR distribution. In the first part, details are presented on how to understand and use the SAR parameter, on how CST Studio simulates it, complementary with the method of delivery of the obtained data. All 6 communication ports of the available phone model have been tested, 4 of them intended for 2G, 2G+, 3G, 4G mobile phone technologies, the other 2 being dedicated to the two Wi-Fi standards (2.4 and 5 GHz). The phone has been placed in the left breast pocket (near heart). The SAR values have been compared for three different cases: silicone protective case without conductive foil, with 0.5 mm Aluminum conductive foil, placed between the phone and the case and with foil of the same thickness, but made of Copper. Conclusions have been drawn regarding the SAR reduction percentages as a result of the use of conductive foils. A recommendation regarding an efficient and accessible solution for designing and making phone protective cases has been issued.

I-vector and variability compensation techniques for mobile phone recognition

Mobile phone recognition consists of trying to identify the mobile phone brand or model, which is very important in forensic analysis. In this paper, we exploit the audio recordings to realize this task. Audio data conveys many information, one of them is related to the source of records, i.e., is a specific audio was captured by a given mobile phone. In order to separate between this particular information and the rest of audio content, we introduce the I-vector technique. Additionally, Linear Discriminant Analysis (LDA) and Within-Class Covariance Normalization (WCCN) methods were used in different ways to compensate the speaker and channel variability. We evaluate the performance of our proposed system by correct rate recognition based on Gaussian Probabilistic Linear Discriminant Analysis (GPLDA) and Cosine Similarity Score (CSS) methods. To train the mobile phone models and do tests, two datasets were prepared: the public MOBIPHONE database and a novel local database. Experiments done with different configurations demonstrate the effectiveness of I-vectors models with the combination of LDA – WCCN compensation techniques and CSS as a similarity measurement method. A correct recognition rates of 97.23 %, 98.91 %, and 99.45 % were obtained for MOBIPHONE, the local Same Environment (SE) and Different Environment (DE) databases, respectively.

From investigation of expressiveness and robustness to a comprehensive value-based framework for multiple criteria sorting problems

We adopt an experiment-oriented perspective to investigate two essential characteristics – expressiveness and robustness – of multiple criteria sorting methods. We focus on the approaches from the family of UTADIS, learning the parameters of a value-driven threshold-based model from the Decision Maker’s assignment examples. Even if the considered properties are crucial for the methods’ reliability and usefulness in real-world scenarios, their verification through explicit numerical tests has been so far neglected. On the one hand, expressiveness captures the models’ flexibility to reproduce different preferences, including simple and complex ones, meaningfully and accurately. On the other hand, robustness reflects the ability to deliver valid recommendations and ensure proper conclusiveness given the multiplicity of compatible preference model instances. We consider different variants of UTADIS, from assuming monotonic and preferentially independent criteria to more advanced settings that relax the monotonicity constraints or represent interactions. The experimental results capture the trade-off between the considered quality dimensions, indicating that richer models are characterized by greater expressiveness and lesser robustness. We also formulate a comprehensive framework indicating when some variant should be used, given the nature of supplied preferences or problem characteristics. These findings aid decision analysts in making robust recommendations in different contexts and help refine preference modeling assumptions. The framework’s practical use is illustrated in a case study involving sorting mobile phone models into pre-defined preference-ordered classes.

Determining the relationship between mobile phone network signal strength and radiofrequency electromagnetic field exposure: protocol and pilot study to derive conversion functions

Mobile phones continuously monitor and evaluate indicators of the received signal strengths from surrounding base stations to optimise wireless services. These signal strength indicators (SSIs) offer the potential for assessing radiofrequency electromagnetic field (RF-EMF) exposure on a population scale, as they can be related to exposure from both base stations and handset devices. Within the ETAIN (Exposure To electromAgnetic fields and plaNetary health) project, an open-access RF-EMF exposure app for smartphones, named "5G Scientist Monitor”, has been developed using citizen science. This paper delineates a measurement protocol for deriving formulas to convert the app SSIs into electric field values to estimate RF-EMF exposure. It presents pilot study results from measurements taken at four locations in Lyon, France (FR), and 14 locations in the Netherlands (NL), using three different phone models and the most common network providers in each country. The measurements were conducted while executing different usage scenarios, such as calls or data transmission. The exposimeter ExpoM-RF4 and on-body electric field probes were used to measure exposure from far-field sources and the handset, respectively. Two-minute aggregates were considered the sample unit for analyses (n=891 in NL and n=395 in FR). Regression analyses showed a positive log-linear relationship between Long Term Evolution (LTE) SSIs and far-field RF-EMF exposure when aggregating data by location (coefficients for the normalised RSSI: 0.91 [95% CI: 0.55 - 1.28] in FR, 1.09 [95% CI: 0.96 - 1.22] in NL). Negative log-linear trends were observed for handset-related RF-EMF exposure at the ear (-0.31 [95% CI: -0.46 - -0.16]) and chest (-0.20 [95% CI: -0.37 - -0.03]) during data transmission scenarios. These results demonstrate that the 5G-Scientist-Monitor app can be implemented for smartphone-based RF-EMF estimation. However, uncertainties in individual measurement points highlight the need for further data collection and analysis to improve the accuracy of exposure estimates.

A Deep Learning Framework for Evaluating the Over-the-Air Performance of the Antenna in Mobile Terminals.

This study introduces RTEEMF (Real-Time Evaluation Electromagnetic Field)-PhoneAnts, a novel Deep Learning (DL) framework for the efficient evaluation of mobile phone antenna performance, addressing the time-consuming nature of traditional full-wave numerical simulations. The DL model, built on convolutional neural networks, uses the Near-field Electromagnetic Field (NEMF) distribution of a mobile phone antenna in free space to predict the Effective Isotropic Radiated Power (EIRP), Total Radiated Power (TRP), and Specific Absorption Rate (SAR) across various configurations. By converting antenna features and internal mobile phone components into near-field EMF distributions within a Huygens' box, the model simplifies its input. A dataset of 7000 mobile phone models was used for training and evaluation. The model's accuracy is validated using the Wilcoxon Signed Rank Test (WSR) for SAR and TRP, and the Feature Selection Validation Method (FSV) for EIRP. The proposed model achieves remarkable computational efficiency, approximately 2000-fold faster than full-wave simulations, and demonstrates generalization capabilities for different antenna types, various frequencies, and antenna positions. This makes it a valuable tool for practical research and development (R&D), offering a promising alternative to traditional electromagnetic field simulations. The study is publicly available on GitHub for further development and customization. Engineers can customize the model using their own datasets.

New Digital Divide Shaped by Algorithm? Evidence from Agent-Based Testing on Douyin’s Health-Related Video Recommendation

While recommendation algorithms have significantly empowered human communication process, there is an emerging scholarly and societal concern regarding the potential discrimination inherent in algorithmic decision-making. The present study employs a novel agent-based testing approach to conduct an automated audit of the Douyin algorithm’s recommendations for health-related videos, aiming to investigate its relationship with information inequalities between individuals who are socioeconomically advantaged and disadvantaged. Our findings imply the possible existence of a new digital divide, wherein the algorithm tends to recommend a smaller proportion of authenticated health-related videos on cheap phone models, representing users in low socioeconomic group. Furthermore, this divide was mitigated when both groups demonstrated the same watching strategy for authenticated information. This study implies that recommendation algorithms actively perpetuate and exacerbate discriminatory social structures in reality. We also advocate for increased attention and education among the low socioeconomic group to enhance their health information literacy and understanding of algorithmic mechanisms.

Usability of the BigO system in pediatric obesity treatment: A mixed-methods evaluation of clinical end-users

ObjectiveTo assess technical usability of the BigO app and clinical portal among diverse participants and explore the overall user experiences of both. MethodsMethods included technical usability testing by measuring the relative user efficiency score (RUS) for the app and measuring Relative User Efficiency (RUE) using the ‘think aloud’ method with the clinical portal. Qualitative approaches involved focus groups with adolescent app users and semi-structured one-to-one interviews with clinician participants. Thematic analysis was applied to analyze qualitative data. ParticipantsClinical participants consisted of adolescents seeking treatment for severe obesity and were invited via telephone/face to face to attend technical usability testing and a focus group. Healthcare professionals (HCPs) and researchers using the BigO clinical portal interface were invited to participate in usability testing and semi-structured interviews. ResultsFrom 14 families invited to attend, seven consented to join the study and four adolescents (mean age=13.8 (SD 0.8) years) participated. Additionally, six HCPs and one pediatric obesity researcher took part. RUS for adolescents indicated that the tasks required of them via myBigO app were feasible, and technically efficient. No user-related errors were observed during tasks. Technical barriers reported by adolescents included notifications of battery optimization, misunderstanding image annotation language, and compatibility challenges with certain phone models. RUS for the HCPs and researcher indicated that basic technical skills are a potential barrier for clinical portal use and qualitative findings revealed that clinical users wanted a logging option for monitoring goals and providing feedback on the portal. ConclusionOur study provided valuable formative findings from clinical end-users in Ireland indicating that adolescents being treated for obesity rated myBigO app as usable, acceptable and that it may assist other key stakeholders to understand food marketing and to monitor dietary and physical activity behaviors. Several key suggestions for future iterations of the clinical portal were provided to enhance its value in pediatric obesity treatment.

One Mobile Phone Model of Chinese Medical Qigong Therapies

One Mobile Phone Model of Chinese Medical Qigong Therapies

Smartphone-based colorimetric detection platform using color correction algorithms to reduce external interference

Smartphone-based digital image colorimetry is a powerful, fast, low-cost approach to detecting target analytes. However, lighting conditions and camera parameters easily affect the detection results, significantly curtailing its applicability in multiple scenarios. In this study, an Android-based mobile application (SMP-CC) is developed, which offers a comprehensive package that includes image acquisition, color correction, and colorimetric analysis functions. Using a custom color card, a built-in algorithm in SMP-CC can minimize the color difference between the standard color block image captured by different smartphones under different lighting conditions and the standard value by an LS171 colorimeter less than 4.36. The algorithm significantly eliminates the impacts of external lighting conditions and differences in cell phone models. Furthermore, the feasibility of SMP-CC was verified by successful colorimetric detection of urine pH, glucose, and protein, demonstrating its potential in smartphone-based digital image colorimetry.

Watching the watchers: camera identification and characterization using retro-reflections

A focused imaging system such as a camera will reflect light directly back at a light source in a retro-reflection (RR) or cat-eye reflection. RRs provide a signal that is largely independent of distance providing a way to probe cameras at very long ranges. We find that RRs provide a rich source of information on a target camera that can be used for a variety of remote sensing tasks to characterize a target camera including predictions of rotation and camera focusing depth as well as cell phone model classification. We capture three RR datasets to explore these problems with both large commercial lenses and a variety of cell phones. We then train machine learning models that take as input a RR and predict different parameters of the target camera. Our work has applications as an input device, in privacy protection, identification, and image validation.

Mobile phone price prediction: A comparative study among four models

As science and technology is advancing by leaps and bounds, mobile phones have become part and parcel of people's life. Because the different models of mobile phones which have different structural foundations, the prices of mobile phones are constantly fluctuating. Mobile phone prices forecasts are becoming more precise as artificial intelligence develops. This article compares various machine learning approaches, and the importance of the variables is ranked in order to determine the most accurate way to forecast the prices of mobile phones. The machine learning techniques used are linear regression (LR), random forest regressor (RFR), XGB Regressor and Support Vector Machine regressor (SVM). In order to determine which model predicts the most accurate mobile phone prices, R^2 evaluation is used. The XGB Regressor model had the greatest score (R-squared = 0.95) for prediction of mobile phone prices, compared to the other three models. In a word, with XGB Regressor methodology as a priority for future mobile phone price predicting, which can improve the accuracy of price predicting.

Evaluation of neonatal outcomes according to the specific absorption rate values of phones used during pregnancy.

The aim was to compare neonatal outcomes according to cell phone specific absorption rate (SAR) levels and daily time spent on cell phones by pregnant women. Women who gave birth at Konya City Hospital between September 2020 and February 2021 were included in this retrospective study. Gestational ages, birth weight, birth length, head circumference, sex, 5-minute APGAR scores, neonate postpartum resuscitation requirement, delivery type, the model of phone used by the pregnant women, and the average time spent on the phone during a day were recorded. To determine the relation between the SAR values of the phones used and delivering a small for gestational age (SGA) baby, receiver operating characteristic curve analysis was performed. In total 1495 pregnant women were included. The rate of delivering a SGA fetus was significantly higher in women who used phones with higher SAR values (p=0.001). The cut-off value for the SAR level was 1.23 W/kg with 69.3% sensitivity and 73.0% specificity (area under the curve: 0.685; 95% confidence interval: 0.643-0.726). No correlation was found between time spent on the phone and SGA birth rate. Although both phone SAR values and time spent on the phone were higher in the symmetrical SGA group compared to the asymmetrical SGA group, the difference was not significant (p>0.05). Although the women who had preterm delivery had higher phone SAR values and had spent more time on the phone compared to those who had term deliveries, the difference was again not significant (p>0.05). As the SAR values of cell phones used during pregnancy increased, there was a trend towards delivering a SGA baby.

100-Phones: A Large VI-SLAM Dataset for Augmented Reality Towards Mass Deployment on Mobile Phones.

Visual-inertial SLAM (VI-SLAM) is a key technology for Augmented Reality (AR), which allows the AR device to recover its 6-DoF motion in real-time in order to render the virtual content with the corresponding pose. Nowadays, smartphones are still the mainstream devices for ordinary users to experience AR. However the current VI-SLAM methods, although performing well on high-end phones, still face robustness challenges when deployed on a larger stock of mid- and low-end phones. Existing VI-SLAM datasets use either very ideal sensors or only a limited number of devices for data collection, which cannot reflect the capability gaps that VI-SLAM methods need to solve when deployed on a large variety of phone models. This work proposes 100-Phones. the first VI-SLAM dataset covering a wide range of mainstream phones in the market. The dataset consists of 350 sequences collected by 100 different models of phones. Through analysis and experiments on the collected data, we conclude that the quality of visual-inertial data vary greatly among the mainstream phones, and the current open source VI-SLAM methods still have serious robustness issues when it comes to mass deployment on mobile phones. We release the dataset to facilitate the robustness improvement of VI-SLAM and to promote the mass popularization of AR. Project page: https://github.com/zju3dv/100-Phones.

2022年山西农谷番茄小镇温室串番茄果实数据集

The rapid and high-precision identification of tomato fruit is one of the key technologies to enhancing the picking efficiency and reliable operation of tomato picking robots. Therefore, a substantial volume of tomato image data is essential for in-depth learning and training to build a real-time and accurate identification model of tomato clusters. From July to August 2022, we collected the data of cluster tomato in the glass greenhouse in the Tomato Town of Jinzhong National Agricultural High-tech Industries Demonstration Zone, Getou Village, Fancun Town, Taigu District, Jinzhong City, Shanxi Province. We took pictures of cluster tomato in different angles and directions with different models of mobile phones in different light positionsat different times of the day on sunny days and cloudy days. After sorting and screening, we selected 3,665 images with a size of 5.31 GB. LabelImg tool was used to label the selected images with three types of labels: mature, raw and cover, which are stored as TXT documents supporting yolo format, with a size of 0.8MB. We randomly categorized all the images according to an 8:1:1 ratio for the training set, validation set, and test set, respectively. We further adopted yolo tools to train and test the tomato cluster dataset. All performance indicators of the test results have been improved to varying degrees, thereby ensuring the authenticity and effectiveness of the tomato cluster dataset. This dataset can be effectively used for constructing the convolution neural network model of cluster tomato at various stages of maturity, so as to further accurately realize the research on yield prediction and maturity-based picking decisions of cluster tomato.

모바일 쇼핑 앱의 고객 충성도에 영향을 미치는 요인에 관한 연구

Purpose - This study focused on exploring factors that affect the customer loyalty of mobile shopping apps by targeting Jingdong, one of China’s comprehensive mobile shopping companies
 Design/Methodology/Approach - In this study, a model was established to explore the customer loyalty of mobile shopping apps based on previous studies on factors affecting the customer loyalty of mobile shopping apps. This paper conducted an empirical analysis on factors influencing the customer loyalty of mobile shopping apps using customer satisfaction as a parameter.
 Findings - Factors such as interaction, safety, app design, and the customer satisfaction of mobile shopping apps were found to be important factors affecting customer loyalty. All four factors of interaction, safety, app design, and the customer satisfaction of mobile shopping apps have a significant impact on customer loyalty.
 Research Implications - In this study, several practical implications were raised to increase the customer loyalty of mobile shopping apps based on the derived analysis results. First, different mobile shopping apps should be designed for each cell phone model. Second, customer loyalty should be cultivated sequentially by improving service quality. Third, the service system of online shopping should be completed by strengthening the auxiliary facilities of mobile shopping apps. Fourth, it is necessary to reduce user detection risk by strengthening the interaction of mobile shopping apps.

Application of a Low-Cost mHealth Solution for the Remote Monitoring of Patients With Epilepsy: Algorithm Development and Validation

Background Implementing automated seizure detection in long-term electroencephalography (EEG) analysis enables the remote monitoring of patients with epilepsy, thereby improving their quality of life. Objective The objective of this study was to explore an mHealth (mobile health) solution by investigating the feasibility of smartphones for processing large EEG recordings for the remote monitoring of patients with epilepsy. Methods We developed a mobile app to automatically analyze and classify epileptic seizures using EEG. We used the cross-database model developed in our previous study, incorporating successive decomposition index and matrix determinant as features, adaptive median feature baseline correction for overcoming interdatabase feature variation, and postprocessing-based support vector machine for classification using 5 different EEG databases. The Sezect (Seizure Detect) Android app was built using the Chaquopy software development kit, which uses the Python language in Android Studio. Various durations of EEG signals were tested on different smartphones to check the feasibility of the Sezect app. Results We observed a sensitivity of 93.5%, a specificity of 97.5%, and a false detection rate of 1.5 per hour for EEG recordings using the Sezect app. The various mobile phones did not differ substantially in processing time, which indicates a range of phone models can be used for implementation. The computational time required to process real-time EEG data via smartphones and the classification results suggests that our mHealth app could be a valuable asset for monitoring patients with epilepsy. Conclusions Smartphones have multipurpose use in health care, offering tools that can improve the quality of patients’ lives.

Specific Absorption Rate and Temperature Distributions in the Human Head with Implanted Deep Brain Stimulation Subjected to Mobile Phone Electromagnetic Radiation

Deep Brain Stimulation (DBS), also known as the brain pacemaker, has gradually evolved from a scientific experiment into an effective clinical treatment for movement disorders as a method of improving movement disorders. At present, there are few studies on the effects of 5G mobile phone antenna radiation on the heads of adult patients implanted with DBS. In this study, COMSOL Multiphysics was used to establish a mobile phone model with a 5G/4G patch antenna, a real human head, and the DBS models. Then, we calculated the specific absorption rate (SAR) of various layers of the head tissues with the mobile phone at different distances from the human head, as well as the temperature change rule of the head and the DBS irradiated by the antenna for 30 min. The simulation results showed that when the frequency is 3500 MHz, the electromagnetic radiation of the phone to the patient’s head is generally greater than that of the 2400 MHz. When at 3500 MHz, the distance between the phone and the head is inversely proportional to the SAR value; thus, when the distance between the phone and the head is 1 cm, the maximum SAR value—which is 1.132 W/kg—appeared in the skin layer of the head with implanted DBS. But it is worth noting that the largest temperature rise appeared in the brain layer at 2400 MHz and at a distance of 1 cm, which is 0.2148 °C. Although the SAR values and temperature rise obtained from all simulations are below the limits of 2 W/kg and +1 °C specified by the International Commission on Non-Ionizing Radiation Protection (ICNIRP), we still recommend that patients with implanted DBS maintain a distance when using the phones.

Product Innovation Possibilities by Designing the Smart Phone Exterior Outline Based on 3d Scans of the Hand Imprint

Much is happening in the innovation of smart phones. However, the physical form has still much room for improvement. In this study 3d scans of the hand imprint on a clay model are made and transformed into a smart phone exterior outline. The clay model was made by placing clay around a rectengular plate as clay alone would lead to rounded forms that are too far away from current smart phone models. Eleven participants made an imprint using the phone with one hand (right hand) and using the phone two handed. Of both imprints the average was taken and a 3d print was made. Also, a 3d print was made of a traditional phone of the same size. The comfort of these three 3d printed models was evaluated by 20 participants. The result was that the version based on holding the smart phone in the right hand showed more comfort related to the traditional smart phone of the same size. So, physical form adaptation is certainly an area to be studied in innovating smart phones.

Safety Assessment and Uncertainty Quantification of Electromagnetic Radiation from Mobile Phones to the Human Head

With the rapid development of the mobile communication technology, the design of mobile phones has become more complex, and research on the electromagnetic radiation from mobile phones that reaches the human head has become important. Therefore, first of all, a model of mobile phone daily use was established. Then, based on the established simulation model, the safety of human head exposure to mobile phones was evaluated. The generalized polynomial chaos (gPC) method was used to establish a proxy model of the specific absorption rate (SAR) of the human head at different frequencies to perform a parameter uncertainty quantification (UQ). Finally, the Sobol method was used to quantify the influence of relevant variables on the SAR. The simulation results showed that the gPC method can save time and cost while ensuring accuracy, and the SAR value is greatly influenced by the electromagnetic materials of the mobile phone shell. Combined with the above analysis, this paper can provide reasonable suggestions for the design of mobile phone electromagnetic materials.

Non-Thermal Effect Of The Brand And Generic Mobile Phones Radiofrequency Radiation On The Antioxidant And Histomorphology Of The Epididymis After Prolonged Whole-Body Radiation on Sprague Dawley Rat

Mobile phone usage has increased greatly over time, and there is also the proliferation of models by different manufacturers, some of which are known to produce popular brands while other manufacturers of unknown brands have been identified. So many health conditions have been associated with the radiation emitted from mobile phones and other communication gadgets. Such condition as glioma, schwannoma, and some degenerative tissue conditions has been reported by different researchers both on short and prolonged exposure. The IARC report on radiofrequency radiation as a human “possible carcinogen, group 2B” has also prompted much research on the effect of mobile phone radiofrequency radiation on humans. The mobile phone placement in the pocket, which is close to the male reproductive organ, and the possible radiation emission levels from different models by manufacturers prompted this study. The goal is to investigate the histological alterations, spermatic epididymal contents, and changes in oxidative stress indicators that may occur following prolonged exposure to mobile phone radiofrequency radiation. Forty (40) male Sprague Dawley rats were used for this study. They were grouped into eight groups equally, two (2) control groups and six (6) groups that were exposed to six different mobile phone models. The 8 hours of daily exposure for 6 months was done with phones in active mode. The finding showed a significant reduction of the mature sperms in the lumen of the epididymis. There was focal epithelial hyperplasia in two groups exposed to branded models. No degenerative epithelium was observed in the epididymis. There was also a significant reduction in the motility and sperm count of the exposed groups. The Superoxide dismutase (SOD) was significantly depleted across the exposed groups. There are isolated changes that result from individual phone models, but changes cannot be dichotomized into changes that result from branded or generic phones.