Rear Camera Research Articles

Correlation of Trichoscopic Findings with Severity in Androgenetic Alopecia

Abstract Background: Androgenetic Alopecia (AGA) refers to the appearance of common non-scarring progressive loss of terminal hair. Trichoscopy shows a magnified view of the hair shafts and hair follicle openings and helps us to diagnose, prognosticate and determine the disease severity. Aims: The aim of the study was to identify the trichoscopic findings in patients with androgenetic alopecia and associate these findings with disease severity. Methods: This was a cross-sectional analytical study of both male and female patients with AGA. The Hamilton Norwood staging was used in males and Ludwig’s staging was used in females. The NC 2 Heine Dermoscope was used to assess the scalp and a 64 MP rear camera was used for the photographic assessment. A Chi-square test was performed to find out the association between the stage of alopecia and trichoscopic findings. Results: A total of 63 patients were assessed in this study. Trichoscopic examination showed Hair Shaft Heterogeneity (HSTH) in all patients of AGA. Scalp honeycomb pigmentation and brown peripilar signs were seen in more than half of the patients. The white peripilar sign was associated with advanced stages in both male and female patients, (P = 0.015) and (P = 0.038) respectively. Focal atrichia was also associated with advanced stages in both male and female patients, (P = 0.001) and (P = 0.05) respectively. Conclusion: HSTH was the characteristic trichoscopic feature of AGA. White peripilar sign and focal atrichia correlated with the severity of the disease. Trichoscopy-based assessment can be used to individualise the treatment options and counsel the patients regarding prognosis.

Development of intelligent traction walking plate for tension overhead construction

AbstractIn the process of tension wire construction of transmission lines, the key state quantities such as tension, inclination and spatial position of the traction walking plate directly affect the construction safety. At present, the construction personnel use visual observation to obtain the state quantity of traction walking plate in the process of tension wire construction. This monitoring method is not only inefficient, and there are personal subjective factors of judgment errors. For this reason, this paper develops a kind of intelligent traction walking plate for tension wire construction, based on the principle of resistance strain type and gyroscope to obtain the tension and inclination of the traction walking plate, respectively, using two front cameras and two rear cameras to obtain the spatial position of the traction walking plate, and finally completing the real‐time transmission of the monitoring data through the Lora technology. The developed intelligent traction walking plate was applied in Shandong Heze Yongfeng 220 kV transmission line project on 11 October 2020, which improved the construction efficiency by 15.71% while ensuring the construction safety.

Accreditation of the handheld 3-dimensional scanner and conventional photo images for area measurement.

Accurate assessment of wound areas is crucial in making therapeutic decisions, as the prognosis and changes in the size of the wound over time play a significant role. An ideal assessment method should possess qualities such as speed, affordability, accuracy, user-friendliness for both patients and healthcare professionals, and suitability for daily clinical practice. This study aims to introduce a handheld 3-dimensional (3D) scanner and evaluate its accuracy in measuring wound areas. Engineers from the Industrial Technology Research Institute in Taiwan developed a handheld 3D scanner with the intention of extending its application to the medical field. A project was conducted to validate the accuracy of this 3D scanner. We utilized a smartphone (Asus ZenFone 2 with a 13-million-pixel rear camera), a digital single-lens reflex digital camera (Nikon, D5000, Tokyo, Japan), and the 3D scanner to repeatedly measure square papers of known size that were affixed to the curved surface of life-size facial mask or medical teaching breast models. The "Image J" software was employed for 2-dimensional image measurements, while the "3D Edit" software was used to assess the "area of interest" on 3D objects. By using square papers with predetermined dimensions, the measurement-associated error rate (ER) could be calculated for each image. Three repeated measurements were performed using the "Image J" software for each square paper. The ERs of the 3D scan images were all below 3%, with an average ER of 1.64% in this study. The close-up mode of the smartphone exhibited the highest ER. It was observed that as the area increased, the ER also increased in the digital single-lens reflex camera group. The extension distortion effect caused by the wide-angle lens on the smartphone may increase the ER. However, the definition of a healthy skin edge may vary, and different algorithms for calculating the measurement area are employed in various 3D measurement software. Therefore, further validation of their accuracy for medical purposes is necessary. Effective communication with software engineers and discussions on meeting clinical requirements are crucial steps in enhancing the functionality of the 3D scanner.

Smartphone‐based colorimetric determination of some physicochemical properties of polyaniline on flexible cellulose substrate

AbstractPolymer‐based materials are of high potential for application as active materials in contemporary electronic gadgets. The functionality of such materials is highly dependent on their oxidation state and requires fine control during their synthesis. Owing to that, we have designed a cost‐effective, simple, portable, system for calibration and the detection of the optical and electrical properties of polyaniline loaded on a paper substrate using a smartphone‐based colorimetric sensor. The vapor phase polymerization technique was adopted for preparing polyaniline. Colorimetric measurements of polyaniline‐loaded papers were achieved by estimating the hue, saturation and value (HSV) color parameter values using an open‐source smartphone application called “Color Grab (Color Detection)” during synthesis at different time intervals. The proposed sensor relies on the principle of Beer–Lambert law. The rear camera of the smartphone acts as a sensor and captures the light signal from the samples and provides information to the colorimetric app. The colorimetric results showed a promising correlation between the color parameter values with their electrical properties. The optical absorbance results of all polyaniline‐loaded papers are also in accordance with the H parameter values. The proposed sensor can be used as a portable smartphone‐based point‐of‐care testing kit to bring in‐situ control during synthesis.Highlights Conducting polymer on flexible cellulose substrate. Smartphone‐based visual colorimetric sensor. Biodegradable and environment‐friendly electronic material. Variation of electrical properties in terms of hue values. Cheap, portable sensor for point‐of‐care testing.

Mobile Phone Price Range Prediction Based on Machine Learning Algorithms

Mobile phones with different price points on the market have different parameter configurations, which provide a lot of information related to the price. Mobile phone prices are also the focus of attention of the mobile phone market, manufacturers and consumers. This study used machine learning technology to investigate the issues in mobile phone sales, mainly the prediction of mobile phone price range and the feature contribution of the price range. The data resource includes the release price of mobile phones in the European market from 2018 to 2021. The data augmentation technologies are also applied in this study. Among the three established machine learning models, the accuracy rate of the training set of the Catboost model exceeds 80%, which is the model with the highest accuracy rate. The accuracy rate of Bagging Classifier is slightly lower than that of CatBoost, but the prediction result is the most stable. Such a result conforms to the characteristics of the algorithm itself. The conclusion of the analysis of feature contribution of numerical data to accuracy rate is that rear camera pixels, number of cameras and screen size have negligible impact on model prediction accuracy, which means feature contribution is minimal. The research results are reasonable and can provide references for merchants to set prices and for users to purchase mobile phones.

Energy-based Out-of-distribution Detection Network and Diffusion Model-based Dataset for Soiling Detection in Rear Camera Scenes of Autonomous Vehicles

Energy-based Out-of-distribution Detection Network and Diffusion Model-based Dataset for Soiling Detection in Rear Camera Scenes of Autonomous Vehicles

Penerapan Metode ARAS dalam Rekomendasi Handphone Terbaik untuk Youtuber Pemula dengan Pembobotan ROC

Mobile phones have evolved into versatile devices that can be used for a variety of purposes, including sending and receiving multimedia messages, surfing the internet, taking photos and videos, playing music, playing games, and running various applications. Mobile is not only a communication tool, but also a multimedia and productivity tool that is very important in everyday life, like a novice YouTuber who can use a cellphone to create his YouTube content. Beginner YouTubers refer to individuals who are just starting their journey in creating and uploading videos on YouTube. When creating video content, a novice YouTuber can use a cellphone, but when choosing a cellphone for creating content, a YouTuber must really choose. Because there are many choices of cellphones available now, it makes a novice YouTuber confused in determining the best cellphone to use. In choosing a cellphone for a novice YouTuber, there are several things to consider, namely Front Camera Resolution, Rear Camera Resolution, RAM, Internal Memory, Battery Capacity and Price. Then we need this information system which is also called a Decision Support System (DSS). SPK is a system that can help companies or organizations to make decisions on a problem they face. In this study, we applied the Additive Ratio Assessment (ARAS) method and ROC weighting, which is a method for making decisions and scoring criteria for obtaining the best cellphone recommendations for novice YouTubers, namely Infinix Zero 20 with a score of 0.92711 as an alternative that gets the highest score.

Your smartphone could act as a pulse-oximeter and as a single-lead ECG

In the post-covid19 era, every new wave of the pandemic causes an increased concern/interest among the masses to learn more about their state of well-being. Therefore, it is the need of the hour to come up with ubiquitous, low-cost, non-invasive tools for rapid and continuous monitoring of body vitals that reflect the status of one’s overall health. In this backdrop, this work proposes a deep learning approach to turn a smartphone—the popular hand-held personal gadget—into a diagnostic tool to measure/monitor the three most important body vitals, i.e., pulse rate (PR), blood oxygen saturation level (aka SpO2), and respiratory rate (RR). Furthermore, we propose another method that could extract a single-lead electrocardiograph (ECG) of the subject. The proposed methods include the following core steps: subject records a small video of his/her fingertip by placing his/her finger on the rear camera of the smartphone, and the recorded video is pre-processed to extract the filtered and/or detrended video-photoplethysmography (vPPG) signal, which is then fed to custom-built convolutional neural networks (CNN), which eventually spit-out the vitals (PR, SpO2, and RR) as well as a single-lead ECG of the subject. To be precise, the contribution of this paper is twofold: (1) estimation of the three body vitals (PR, SpO2, RR) from the vPPG data using custom-built CNNs, vision transformer, and most importantly by CLIP model (a popular image-caption-generator model); (2) a novel discrete cosine transform+feedforward neural network-based method that translates the recorded video-PPG signal to a single-lead ECG signal. The significance of this work is twofold: (i) it allows rapid self-testing of body vitals (e.g., self-monitoring for covid19 symptoms), (ii) it enables rapid self-acquisition of a single-lead ECG, and thus allows early detection of atrial fibrillation (abormal heart beat or arrhythmia), which in turn could enable early intervention in response to a range of cardiovascular diseases, and could help save many precious lives. Our work could help reduce the burden on healthcare facilities and could lead to reduction in health insurance costs.

A smartphone-integrated imaging device for measuring nitrate and phosphate in soil and water samples

This research presents a smartphone-integrated imaging device to detect nitrate (NO3–) and phosphate (PO43-) in soil and water samples for agricultural input management and environmental pollution monitoring. This low-cost and field-deployable device uses a smartphone with a digital camera to take a color photograph of the extracted solution under test. The sensing principle is based on the standard colorimetric phenoldisulfonic acid and ascorbic acid methods for NO3– and PO43- in soil and water samples, respectively. A 3D printed device that houses the sensor's optical source and other optical components is coupled to the phone's built-in rear camera and turned into a compact handheld system. An android application, 'SMART NP,' is also developed to quantify the concentrations of NO3– and PO43- in soil and water samples using the Value (V) component of the Hue Saturation and Value (HSV) color space model. The results obtained with the proposed device were comparable to laboratory-grade spectrophotometer data. The limit of detection of the device for NO3– in soil, NO3– in water, PO43- in soil, and PO43- in water were calculated as 0.1 mg L-1, 0.07 mg L-1, 0.001 mg L-1, and 0.02 mg L-1, respectively, with good accuracy (≤1% bias) and precision (∼2% residual standard deviation). The device exhibited a sensitivity of 0.17 mg L-1, 0.12 mg L-1, 0.026 mg L-1, and 0.49 mg L-1 for NO3– in soil, NO3– in water, PO43- in soil, and PO43- in water, respectively. Overall, this sensing device has the potential to assist farmers or scientists in easily measuring NO3– and PO43- concentrations in soil and water media, without the need for laboratory equipment.

"Click" Reaction-Mediated Silk Fibroin-Functionalized Thiol-Branched Graphene Oxide Quantum Dots for Smart Sensing of Tetracycline.

The abuse of tetracycline (TC) antibiotics causes the accumulation of their residue in the environment, which has an irreversible impact on food safety and human health. In light of this, it is vital to offer a portable, quick, efficient, and selective sensing platform to detect TC instantly. Herein, we have successfully developed a sensor using silk fibroin-decorated thiol-branched graphene oxide quantum dots through a well-known thiol-ene click reaction. It is applied to ratiometric fluorescence sensing of TC in real samples in the linear range of 0-90 nM, with the detection limit of 49.69, 47.76, 55.25, 47.90, and 45.78 nM for deionized water, chicken sample, fish sample, human blood serum, and honey sample, respectively. With the gradual addition of TC to the liquid media, the sensor develops a synergetic luminous effect in which the fluorescence intensity of the nanoprobe steadily declines at 413 nm, while the intensity of a newly emerging peak increases at 528 nm, maintaining a ratio that is dependent on the analyte concentration. The increase of luminescence properties in the liquid media is clearly visible by naked eyes in the presence of 365 nm UV light. The result helps us in building a filter paper strip-based portable smart sensor using an electric circuit comprising a 365 nm LED (light-emitting diode) powered by a mobile phone battery which is attached just below the rear camera of a smartphone. The camera of the smartphone captures the color changes that occur throughout the sensing process and translates into readable RGB data. The dependency of color intensity with respect to the concentration of TC was evaluated by deducing a calibration curve from where the limit of detection was calculated and found to be 0.125 μM. These kinds of gadgets are important for the possible real-time, on-the-spot, quick detection of analytes in situations where high-end approaches are not easily accessible.

Aspect-based Sentiment Analysis on Car Reviews Using SpaCy Dependency Parsing and VADER

All businesses, including car manufacturers, need to understand what aspects of their products are perceived as positive and negative based on user reviews so that they can make improvements for the negative aspects and maintain the already positive aspects of their products. One of the available tools for this task is Sentiment Analysis. The traditional document-level and sentence-level sentiment analysis will only classify each document / sentence into a class. This approach is incapable of finding the more fine-grained sentiment for a specific aspect of interest, for example, comfort, price, engine, paint, etc. Therefore, in this case, Aspect-based Sentiment Analysis is used. A total of 22.702 rows of car review data are scraped from the Edmunds website (www.edmunds.com) for a specific car manufacturer. Dependency Parsing and noun phrase extraction were carried out using the SpaCy module in Python, and VADER sentiment analysis was used to determine the polarity of the sentiment for each noun phrase. Results showed that the vast majority of the sentiments are on the positive aspects: comfortable to drive, good fuel economy / mileage, reliability, spaciousness, value for money, helpful rear camera, quiet ride, good acceleration, well-designed, good sound system, and solid build. The results for the negative aspects have some similar aspects with those in the positive class but has a very low frequency. This finding means that the vast majority of the users are satisfied with multiple aspects of the produced cars. The limitation of this research and future research direction are discussed.

Channel Intensity and Edge-Based Estimation of Heart Rate via Smartphone Recordings

Smartphones, today, come equipped with a wide variety of sensors and high-speed processors that can capture, process, store, and communicate different types of data. Coupled with their ubiquity in recent years, these devices show potential as practical and portable healthcare monitors that are both cost-effective and accessible. To this end, this study focuses on examining the feasibility of smartphones in estimating the heart rate (HR), using video recordings of the users’ fingerprints. The proposed methodology involves two-stage processing that combines channel-intensity-based approaches (Channel-Intensity mode/Counter method) and a novel technique that relies on the spatial and temporal position of the recorded fingerprint edges (Edge-Detection mode). The dataset used here included 32 fingerprint video recordings taken from 6 subjects, using the rear camera of 2 smartphone models. Each video clip was first validated to determine whether it was suitable for Channel-Intensity mode or Edge-Detection mode, followed by further processing and heart rate estimation in the selected mode. The relative accuracy for recordings via the Edge-Detection mode was 93.04%, with a standard error of estimates (SEE) of 6.55 and Pearson’s correlation r > 0.91, while the Channel-Intensity mode showed a relative accuracy of 92.75%, with an SEE of 5.95 and a Pearson’s correlation r > 0.95. Further statistical analysis was also carried out using Pearson’s correlation test and the Bland–Altman method to verify the statistical significance of the results. The results thus show that the proposed methodology, through smartphones, is a potential alternative to existing technologies for monitoring a person’s heart rate.

Socialistic 3D tracking of humans from a mobile robot for a ‘human following robot’ behaviour

AbstractRobotic guides take visitors on a tour of a facility. Such robots must always know the position of the visitor for decision-making. Current tracking algorithms largely assume that the person will be nearly always visible. In the robotic guide application, a person’s visibility could be often lost for prolonged periods, especially when the robot is circumventing a corner or making a sharp turn. In such cases, a person cannot quickly come behind the limited field of view rear camera. We propose a new algorithm that can track people for prolonged times under such conditions. The algorithm is benefitted from an application-level heuristic that the person will be nearly always following the robot, which can be used to guess the motion. The proposed work uses a Particle Filter with a ‘follow-the-robot’ motion model for tracking. The tracking is performed in 3D using a monocular camera. Unlike approaches in the literature, the proposed work observes from a moving base that is especially challenging since a rotation of the robot can cause a large sudden change in the position of the human in the image plane that the approaches in the literature would filter out. Tracking in 3D can resolve such errors. The proposed approach is tested for three different indoor scenarios. The results showcase that the approach is significantly better than the baselines including tracking in the image and projecting in 3D, tracking using a randomized (non-social) motion model, tracking using a Kalman Filter and using LSTM for trajectory prediction.

Rendering automatic bokeh recommendation engine for photos using deep learning algorithm

Abstract Automatic bokeh is one of the smartphone’s essential photography effects. This effect enhances the quality of the image where the subject background gets out of focus by providing a soft (i.e., diverse) background. Most smartphones have a single rear camera that is lacking to provide which effects need to be applied to which kind of images. To do the same, smartphones depend on different software to generate the bokeh effect on images. Blur, Color-point, Zoom, Spin, Big Bokeh, Color Picker, Low-key, High-Key, and Silhouette are the popular bokeh effects. With this wide range of bokeh types available, it is difficult for the user to choose a suitable effect for their images. Deep Learning (DL) models (i.e., MobileNetV2, InceptionV3, and VGG16) are used in this work to recommend high-quality bokeh effects for images. Four thousand five hundred images are collected from online resources such as Google images, Unsplash, and Kaggle to examine the model performance. 85% accuracy has been achieved for recommending different bokeh effects using the proposed model MobileNetV2, which exceeds many of the existing models.

HandyGaze: A Gaze Tracking Technique for Room-Scale Environments using a Single Smartphone

We propose HandyGaze, a 6-DoF gaze tracking technique for room-scale environments that can be carried out by simply holding a smartphone naturally without installing any sensors or markers in the environment. Our technique simultaneously employs the smartphone’s front and rear cameras: The front camera estimates the user’s gaze vector relative to the smartphone, while the rear camera (and depth sensor, if available) performs self-localization by reconstructing a pre-obtained 3D map of the environment. To achieve this, we implemented a prototype that works on iOS smartphones by running an ARKit-based algorithm for estimating the user’s 6-DoF head orientation. We additionally implemented a novel calibration method that offsets the user-specific deviation between the head and gaze orientations. We then conducted a user study (N=10) that measured our technique’s positional accuracy to the gaze target under four conditions, based on combinations of use with and without a depth sensor and calibration. The results show that our calibration method was able to reduce the mean absolute error of the gaze point by 27%, with an error of 0.53 m when using the depth sensor. We also report the target size required to avoid erroneous inputs. Finally, we suggest possible applications such as a gaze-based guidance application for museums.

Teknologi Augmented Reality untuk instalasi kelistrikan gedung

The electrical installation stage in the process of building construction have risks to work safety and the operation of the results if the work is not following the General Electrical Installation Requirements (PUIL). This study aims to apply the development of Augmented Reality (AR) technology to provide convenience in the process of working on the electrical installation. Development of AR-based applications by utilizing android-based mobile phone devices which are now commonly owned by the public. The research was conducted by analyzing the electrical installation system in accordance with PUIL standards and its application in an easy-to-understand format, then processing electrical component data as input for 3D objects. Furthermore, the technical data of the building is used to produce AR applications that can be applied to the location of electrical installation work. The application that was built was successfully implemented on an Android-based cellular phone device. The rear camera is used to detect markers as triggers for the emergence of 3D objects in the form of electrical networks and electrical installation components. There are also info features and 3D object animations, as well as video calls as an alternative option for assistance if there are problems during electrical installation work.

Sistem Edukasi Pengenalan Rumus Matematika Menggunakan Aughmented Reality Berbasis Android

The introduction of spatial media, especially in mathematics, is still often applied to learning module media such as books or whiteboard media as happened. In this case it is less effective because students are less able to absorb what is conveyed by the teacher because the media is still in the form of 2D images. This study aims to build learning augmented reality with markerless method based on android. With the application of augmented reality objects in the recognition of spatial shapes can be used as 3D images. The spatial applications that will be made are cubes, blocks, triangular prisms, triangular pyramids, rectangular pyramids, cones, spheres and regular hexagon prisms. The final result obtained is an educational application for the introduction of mathematical formulas, especially in building spaces using Android-based augmented reality, and in the form of a marker book. It is possible that this technology can be used as a tool for more innovative methods of introducing mathematical geometric formulas. By shining on the rear camera on the marker book, users can immediately see in 3D on the smartphone screen the shape of the shapes and formulas that exist in real time. This will be an interesting lesson, especially for fifth graders at SDIT At Taubah Batam.

Lighting measurement with a 360° panoramic camera: Part 2 – Applications

This study developed a 360° field of view (FOV) lighting measurement method with the aid of the Ricoh Theta Z1 panoramic camera. As versatile lighting information from all viewing directions within 360° FOV can be retrieved from a single 360° high dynamic range image, this new method improves the lighting measurement efficiency. Potential unique applications are reported in the present paper as Part 2 of this study. Corresponding lighting information covers a 360° luminance map, a 360° coefficient of variation map of luminance (CVL), a 360° luminance contrast ( C1) map or ratio ( Cr1) map between any interested target located at the viewing direction and its near background, a 360° luminance contrast ( C2) map or ratio ( Cr2) map between the same target and the ambient environment and a 360° illuminance map and its single coefficient of variation of illuminance (CVE) value of all illuminances on the illuminance map. All lighting information is measured at the same point with 360° viewing directions to analyse the uniformity and continuity of the lighting environment. An angular distance weighted illuminance calibration is used to correct possible mismatched white balance of the front and rear camera lenses in a scenario with multiple light sources of dramatically different Correlated Colour Temperature (CCTs). All customized camera-control and data treatment programs, including MATLAB code and Python programs, are uploaded to the project GitHub site for online sharing.

Compact Smartphone-Based Laser Speckle Contrast Imaging Endoscope Device for Point-of-Care Blood Flow Monitoring.

Laser speckle contrast imaging (LSCI) is a powerful visualization tool for quantifying blood flow in tissues, providing simplicity of configuration, ease of use, and intuitive results. With recent advancements, smartphone and camera technologies are suitable for the development of smartphone-based LSCI applications for point-of-care (POC) diagnosis. A smartphone-based portable LSCI endoscope system was validated for POC diagnosis of vascular disorders. The endoscope consisted of compact LED and laser illumination, imaging optics, and a flexible fiberscope assembled in a 3D-printed hand-held cartridge for access to body cavities and organs. A smartphone’s rear camera was mounted thereto, enabling endoscopy, LSCI image acquisition, and processing. Blood flow imaging was calibrated in a perfused tissue phantom consisting of a microparticle solution pumped at known rates through tissue-mimicking gel and validated in a live rat model of BBN-induced bladder cancer. Raw LSCI images successfully visualized phantom flow: speckle flow index showed linearity with the pump flow rate. In the rat model, healthy and cancerous bladders were distinguishable in structure and vasculature. The smartphone-based low-cost portable mobile endoscope for monitoring blood flow and perfusion shows promise for preclinical applications and may be suitable for primary diagnosis at home or as a cost-effective POC testing assay.

A smartphone-based photometric and fluorescence sensing for accurate estimation of zinc ion in water

This paper demonstrates the development of a smartphone-based analytical system to determine zinc ion (Zn2+) concentration in water. The proposed system has been designed for simultaneous sensing in photometric and fluorescence mode on a single platform. Two inbuilt sensors of the phone, namely, the ambient light sensor (ALS) and the rear camera, have been utilized to develop the proposed sensing system. For photometric-based sensing, the ALS receives the transmitted modulated signal from the sample, while the rear camera records the fluorescence signal emitted from the sample at right angle to the direction of the excitation signal. The holder for the proposed sensing system has been fabricated in a 3D printer. A custom-designed android application has been developed to convert the proposed platform into a standalone tool. Using the designed sensor, Zn2+ ion concentration of field-collected water samples has been estimated successfully. Responses of the designed platform have been compared with the standard laboratory counterparts. The limit of detection and the percentage recovery of the proposed sensor are found to be 0.1 ppm and 103.10% respectively.