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Background: To increase the consultation rate of potential major depressive disorder (MDD) patients, we developed a contact-type fingertip photoplethysmography-based MDD screening system. With the outbreak of SARS-CoV-2, we developed an alternative to contact-type fingertip photoplethysmography: a novel web camera-based contact-free MDD screening system (WCF-MSS) for non-contact measurement of autonomic transient responses induced by a mental task. Methods: The WCF-MSS measures time-series interbeat intervals (IBI) by monitoring color tone changes in the facial region of interest induced by arterial pulsation using a web camera (1920 × 1080 pixels, 30 frames/s). Artifacts caused by body movements and head shakes are reduced. The WCF-MSS evaluates autonomic nervous activation from time-series IBI by calculating LF (0.04–0.15 Hz) components of heart rate variability (HRV) corresponding to sympathetic and parasympathetic nervous activity and HF (0.15–0.4 Hz) components equivalent to parasympathetic activities. The clinical test procedure comprises a pre-rest period (Pre-R; 140 s), mental task period (MT; 100 s), and post-rest period (Post-R; 120 s). The WCF-MSS uses logistic regression analysis to discriminate MDD patients from healthy volunteers via an optimal combination of four explanatory variables determined by a minimum redundancy maximum relevance algorithm: HF during MT (HF MT ), the percentage change of LF from pre-rest to MT (%ΔLF (Pre–R⇒ MT) ), the percentage change of HF from pre-rest to MT (%ΔHF (Pre–R⇒ MT) ), and the percentage change of HF from MT to post-rest (%ΔHF (MT⇒ Post–R) ). To clinically test the WCF-MSS, 26 MDD patients (16 males and 10 females, 20–58 years) were recruited from BESLI Clinic in Tokyo, and 27 healthy volunteers (15 males and 12 females, 18–60 years) were recruited from Tokyo Metropolitan University and RICOH Company, Ltd. Electrocardiography was used to calculate HRV variables as references. Result The WCF-MSS achieved 73% sensitivity and 85% specificity on 5-fold cross-validation. IBI correlated significantly with IBI from reference electrocardiography ( r = 0.97, p < 0.0001). Logit scores and subjective self-rating depression scale scores correlated significantly ( r = 0.43, p < 0.05). Conclusion The WCF-MSS seems a promising contact-free MDD screening apparatus. This method enables web camera built-in smartphones to be used as MDD screening systems.
In recent years, many researchers have shown increasing interest in music information retrieval (MIR) applications, with automatic chord recognition being one of the popular tasks. Many studies have achieved/demonstrated considerable improvement using deep learning based models in automatic chord recognition problems. However, most of the existing models have focused on simple chord recognition, which classifies the root note with the major, minor, and seventh chords. Furthermore, in learning-based recognition, it is critical to collect high-quality and large amounts of training data to achieve the desired performance. In this paper, we present a multi-task learning (MTL) model for a guitar chord recognition task, where the model is trained using a relatively large-vocabulary guitar chord dataset. To solve data scarcity issues, a physical data augmentation method that directly records the chord dataset from a robotic performer is employed. Deep learning based MTL is proposed to improve the performance of automatic chord recognition with the proposed physical data augmentation dataset. The proposed MTL model is compared with four baseline models and its corresponding single-task learning model using two types of datasets, including a human dataset and a human combined with the augmented dataset. The proposed methods outperform the baseline models, and the results show that most scores of the proposed multi-task learning model are better than those of the corresponding single-task learning model. The experimental results demonstrate that physical data augmentation is an effective method for increasing the dataset size for guitar chord recognition tasks.
Traditional engineering courses solely focused on certain engineering theoretical knowledge and skills while in current society it is urgently needed graduates who have interdisciplinary knowledge and skills. Engineering curriculums- electronics and environmental engineering programs at the National University of Mongolia and electrical and construction engineering programs at the Institute of Engineering and technology has adopted Conceive, Design, Implement and Operate (CDIO) framework which supported the revision of learning outcomes and teaching-learning approaches, through the introduction of multidisciplinary projects. Introduction of Multidisciplinary project on Smart Building with collaboration between students from four programs to design a smart energy-efficient building was successfully implemented. The ideas of tasks were to design a building with the following requirements: energy efficient with low heat loss, selection of suitable low cost, eco-material, and 80 % of energy production should be from solar panels. Also, students should choose an appropriate technology for the treatment of greywater and sludge from toilets. During the implementation of the project, students were able to expand their problem reasoning and solving, teamwork and communication skills.
Mouse tracking serves as an alternative to eye tracking in measuring the learning process in education because of its affordability. Moreover, mouse tracking does not require extra hardware, as in the case of eye tracking, because it is a feature in personal computers by default. Therefore, it is possible to implement mouse tracking in a massive open scale. However, mouse tracking has only been implemented in a laboratory setting to date, ostensibly because of the associated extremely high running costs. Nonetheless, there is no available data to support the claim of high resource costs, which has resulted in much speculation among implementers. In general, the implementation of mouse tracking in a non-laboratory environment is still rare. Therefore, the authors developed an application to investigate real-time mouse tracking online. It was implemented on the Moodle learning management system and tested on an online quiz session accessed abroad. Additionally, the application can handle tracking on mobile devices. In this work, the main resources that include CPU, network, RAM, and storage costs were measured when mouse tracking was used. These results can serve as a reference for network and server administrators during future implementation of this technique. It was determined that the characteristics of mouse activities were dynamic in that occasional surges and lulls were observed. Additionally, this article also discussed the advantage of real-time and online implementation to regular online implementation and showed that there is a possibility of implementing mouse tracking on a large scale if mouse tracking data are not aggregated and transmitted as a single data package.
In this paper, we propose a new framework for generating big sized dataset using synthetic data generation by robotics. In learning-based recognition, for example, using convolutional neural networks (CNNs), it is critical for the performance, to collect high-quality and large amounts of training data. Previously, to increase the training data set, a data augmentation technique based on digital signal processing were applied to the original sound data. However, the data augmentation based on digital signal processing data is a limited method, because it depends on some previous knowledge of the data and cannot perform for all domains. On the other hand, we propose a new dataset collection technique using a robot that automatically plays instruments, by which it becomes possible to add high-quality data to training samples. Experimental results for guitar chord recognition show that the proposed method using CNNs and a guitar robot can outperform the CNN systems with the traditional data augmentation.
Due to accelerated urbanization since the 1950s, most of the Mongolian population, or about 68%, live in urban areas. The systematic understanding of urban expansion is a crucial clue for urban planning and sustainable land development. Therefore, in this paper, we used the Markov chain model and an artificial neural network to simulate and predict current and future urban areas expansion in Erdenet city (third largest urban area of Mongolia and fourth copper mining industry in the world) vicinity. Clark Lab’s (Clark University) IDRISI software’s Land change modeler module had been used for the urban expansion prediction. The Land change modeler module was developed by the Multilayer perceptron neural network with the Markov chain model. Multilayer perceptron neural network allows the integration of factors to interpret land change, while the Markov chain model with cellular automata restructuring of them spatially. This model has been used in our country for the first time in the case of Erdenet city. The main aims of the study are (i) land use and land cover change detection; (ii) simulation of urban expansion in different scenarios; (iii) provision of reference information for urban planning and land development. Urban expansion predicted 2046’s trend based on a historical expansion of Erdenet city between 1996, 2006 and 2016, which were prepared according to input model requirements. Low and medium resolution satellite images are allowed cities and regions to regularly monitor their spatial and temporal dimensions and land-use changes. Landsat imageries (Landsat TM 5, Landsat ETM 7 and Landsat OLI 8) of 1996, 2006 and 2016 were used to derive land use maps. In the urbanization process, many socioeconomic and physical factors and their current situation have a significant impact. Digital elevation model, slope, distance to road, population growth rate, distance to economic centers, suitable lands, road network, possible area for urban transition were used as an explanatory factor map of urban land-use change. The model proposes three spatial alternatives for the future expansion of Erdenet city. These include spontaneous scenario, environment-protecting scenario, and resources-saving scenario. The impacts of variations are different, but for the entire period, urban expansion rates are likely to increase. The land-use transition into urban areas has similar changes in environmental protection and resources-saving scenarios, while spontaneous expansion is significantly different from others. By spontaneous scenarios of 2046, open space and the unused areas will be gradually reduced by built-up areas. The land-use change in the north-east and northwest area is estimated to have the largest increase in comparison with other locations. The spatial pattern of the modeled scenarios will provide the stakeholders and planners with information on where and how will go urban expansion process by 2046 and will give important information for future development projects. Simulation performance of the Markov chain model with an Artificial neural network model can be used to improve the understanding of the urban expansion processes while also allowing for better planning of Erdenet city, as well as for other rapidly developing regions of Mongolia.
The primary cause of death among children under age 5 years is acute respiratory infection, such as pneumonia. Detection of infection at the earliest point of contagion is necessary, to reduce morbidity and prevent infectious disease epidemics; therefore, identifying abnormal vital signs is essential. For early detection of pediatric infections, we developed a low-cost, portable, rapid screening system of pediatric infection. The system simultaneously measures three vital signs: heart rate (HR), respiration rate (RR), and body temperature (Temp) within 10 seconds using a pulse sensor, Doppler radar, and an infrared thermopile. Vital sign signal processing and computation are conducted using an Arduino Nano microprocessor, enabling the small, lightweight, and portable design of this system. Moreover, the cost-effectiveness of the system facilitates system applications in developing countries, which have the highest levels of pediatric mortality. We conducted trial measurement in Bayangol Health Center, Ulaanbaatar, Mongolia in 2019. A total of 50 children (age 1-14 years, 26 boys/24 girls) were enrolled in this study. Bland-Altman plot and Pearson correlation methods were used to evaluate the accuracy of the proposed system. The correlation coefficients were calculated as HR: r=0.92, RR: r=0.8, and Temp: r=0.82, with p<; 0.01. The system appears promising for rapid and convenient detection of infection in children.
Үр дүнд суурилсан, оюутан төвтэй сургалтад шилжих шаардлагатай тулгараад буй өнөө үед оюутныг ойлгож, нөхцөл байдлыг нь мэдрэх, бодитоор үнэлэх асуудал сурах явцыг сайжруулах, шалгалтын арга хэлбэрийг илүү боловсронгуй болгох ач холбогдолтой юм. Оюутан өөрийн мэдлэг, шалгалтын бэлтгэл болон зан араншингаасаа хамааран аман шалгалтын явцад сандрах, баярлах, түгших хэлбэрээр сэтгэл нь хөдөлж улмаар энэ нь бодит үнэлгээнд нөлөөлөх талтай. Энэхүү ажлаар оюутны сэтгэл хөдлөл болон үнэлгээний хамаарлыг нөхцөл байдал, асуултын төрлөөс хамааруулан судаллаа. Сэтгэл хөдлөлийг тухайн оюутны арьсны цахилгаан дамжуулал, хөмсөг болон хацрын булчингийн цахилгаан идэвхжилийн утгаар тодорхойлсон. Туршилтыг хоёр үе шаттай явууллаа. 28 оюутныг мэдрүүлтэй болон ердийн гэсэн хоёр бүлэгт хуваан ижил агуулгаар аман шалгалт авсан эхний туршилтаар мэдрүүл зүүх нь оюутны сэтгэл хөдлөл, хариулсан үр дүнд мэдэгдэхүйц нөлөө үзүүлэхгүй гэж дүгнэж болохоор байна. Хоёр дахь туршилтад мэргэжлийн суурь болон мэргэшүүлэх хичээл сонгон тус бүр 10 оюутнаас энгийн тав, шалгалтын таван асуулт асууж сэтгэл хөдлөлийн өөрчлөлтийг судлав. Туршилтаар оюутан энгийн асуултад хариулахдаа сэтгэл хөдлөлийн төлөв нь бага өөрчлөгдөж байсан бол шалгалтын асуултад бүх оюутан илүү төвлөрснөөс гадна ахлах ангийн оюутнууд жигд бага зэрэг сөрөг сэтгэл хөдлөл үзүүлсэн бол бага ангийн оюутнууд эерэг сэтгэл хөдлөл илүүтэй харуулсан. Энэ судалгааг цаашид өргөжүүлэн төрөл бүрийн мэргэжил, нөхцөл байдлаар өргөтгөснөөр шалгалтын арга хэлбэрийг шинэчлэх, оюутны сэтгэл хөдлөл өөрийгөө илэрхийлэхэд нөлөөлдөг эсэхийг тодорхойлох, улмаар онлайн сургалтын шалгалтыг илүү баталгаатай болгох суурь нь болно.
Үр дүнд суурилсан, оюутан төвтэй сургалтад шилжих шаардлагатай тулгараад буй өнөө үед оюутныг ойлгож, нөхцөл байдлыг нь мэдрэх, бодитоор үнэлэх асуудал сурах явцыг сайжруулах, шалгалтын арга хэлбэрийг илүү боловсронгуй болгох ач холбогдолтой юм. Оюутан өөрийн мэдлэг, шалгалтын бэлтгэл болон зан араншингаасаа хамааран аман шалгалтын явцад сандрах, баярлах, түгших хэлбэрээр сэтгэл нь хөдөлж улмаар энэ нь бодит үнэлгээнд нөлөөлөх талтай. Энэхүү ажлаар оюутны сэтгэл хөдлөл болон үнэлгээний хамаарлыг нөхцөл байдал, асуултын төрлөөс хамааруулан судаллаа. Сэтгэл хөдлөлийг тухайн оюутны арьсны цахилгаан дамжуулал, хөмсөг болон хацрын булчингийн цахилгаан идэвхжилийн утгаар тодорхойлсон. Туршилтыг хоёр үе шаттай явууллаа. 28 оюутныг мэдрүүлтэй болон ердийн гэсэн хоёр бүлэгт хуваан ижил агуулгаар аман шалгалт авсан эхний туршилтаар мэдрүүл зүүх нь оюутны сэтгэл хөдлөл, хариулсан үр дүнд мэдэгдэхүйц нөлөө үзүүлэхгүй гэж дүгнэж болохоор байна. Хоёр дахь туршилтад мэргэжлийн суурь болон мэргэшүүлэх хичээл сонгон тус бүр 10 оюутнаас энгийн тав, шалгалтын таван асуулт асууж сэтгэл хөдлөлийн өөрчлөлтийг судлав. Туршилтаар оюутан энгийн асуултад хариулахдаа сэтгэл хөдлөлийн төлөв нь бага өөрчлөгдөж байсан бол шалгалтын асуултад бүх оюутан илүү төвлөрснөөс гадна ахлах ангийн оюутнууд жигд бага зэрэг сөрөг сэтгэл хөдлөл үзүүлсэн бол бага ангийн оюутнууд эерэг сэтгэл хөдлөл илүүтэй харуулсан. Энэ судалгааг цаашид өргөжүүлэн төрөл бүрийн мэргэжил, нөхцөл байдлаар өргөтгөснөөр шалгалтын арга хэлбэрийг шинэчлэх, оюутны сэтгэл хөдлөл өөрийгөө илэрхийлэхэд нөлөөлдөг эсэхийг тодорхойлох, улмаар онлайн сургалтын шалгалтыг илүү баталгаатай болгох суурь нь болно.
One of the objectives of the performance measurement of gradebased higher education is to reduce the failure rate of students. To identify and reduce the number of failing students, the learning activities and behaviors of students in the classroom must be continuously monitored; however, monitoring a large number of students is an extremely difficult task. A penetration of webbased learning systems in academic institutions revealed the possibility of evaluating student activities via these systems. In this paper, we propose an early prediction scheme to identify students at risk of failing in a blended learning course. We employ a neural network on the set of prediction variables extracted from the online learning activities of students in a learning management system. The experiments were based on data from 1110 student who attended a compulsory, sophomore-level course. The results indicate that a neural-network-based approach can achieve early identification of students that are likely to fail; 25% of the failing students were correctly identified after the first quiz submission. After the mid-term examination, 65% of the failing students were correctly predicted.
Орчин үед машин сургалтын арга судалгаа шинжилгээнд өргөнөөр ашиглагдаж хэрэглээний түвшинд нэвтэрсээр байна. Ялангуяа дүрс боловсруулалтын чиглэлд ихээр ашиглагдаж байгаа билээ. Хүний нүүрний төрхөөр хүйсийг тодорхойлох нь царай таних системийн нэг функц болон ашиглагддаг. Энэхүү ажилд Монгол хүний зургаас хүйсийг тодорхойлохыг зорьсон. Үүнд царайг илрүүлэх Landmark 68 цэгт арга[1], онцлогуудыг ялган авах VGG16[2] болон Facenet[3] алгоритмуудыг ашиглаж улмаар машин сургалтын шатанд AdaBoost[4] болон SVM[5] аргуудаар ангилан сургаж хүйсээр нь ялгасан. Эндээс үзэхэд AdaBoost сургалтын үр дүн SVM аргаас сайн үр дүн үзүүлсэн.
Орчин үед машин сургалтын арга судалгаа шинжилгээнд өргөнөөр ашиглагдаж хэрэглээний түвшинд нэвтэрсээр байна. Ялангуяа дүрс боловсруулалтын чиглэлд ихээр ашиглагдаж байгаа билээ. Хүний нүүрний төрхөөр хүйсийг тодорхойлох нь царай таних системийн нэг функц болон ашиглагддаг. Энэхүү ажилд Монгол хүний зургаас хүйсийг тодорхойлохыг зорьсон. Үүнд царайг илрүүлэх Landmark 68 цэгт арга[1], онцлогуудыг ялган авах VGG16[2] болон Facenet[3] алгоритмуудыг ашиглаж улмаар машин сургалтын шатанд AdaBoost[4] болон SVM[5] аргуудаар ангилан сургаж хүйсээр нь ялгасан. Эндээс үзэхэд AdaBoost сургалтын үр дүн SVM аргаас сайн үр дүн үзүүлсэн.
Despite a reduction in pneumonia-related mortality, pneumonia remains a leading cause of death among children aged 0–5 years. Most of these deaths occur in developing countries. However, more than half of pneumonia-related deaths are preventable with improved facilities and health strategies. Early rapid diagnosis is important to decrease pneumonia mortality. We developed a portable, cost-effective, rapid pneumonia screening system using a random tree algorithm to support early detection of pneumonia in children. We enrolled 105 participants: 57 patients aged 1–13 years (33 boys, 24 girls) diagnosed with pneumonia by chest radiograph and 48 normal volunteers aged 2–14 years (25 boys, 22 girls). We conducted a clinical trial in the Bayangol District Geriatric and Pediatric Hospital, Ulaanbaatar, Mongolia from January 12–19, 2019. Our screening system measured heart rate, respiration rate and skin temperature within 10 seconds and used a random tree algorithm to distinguish patients with pneumonia and normal volunteers. The system uses a photosensor, Doppler radar, and infrared thermophile to determine vital signs and an Arduino Nano microprocessor to perform computations. Paired t-tests were used to compare vital signs between patients with pneumonia and normal volunteers. The random tree algorithm achieved sensitivity of 96.5%, specificity of 81.3%, positive predictive value of 85.9%, and negative predictive value of 95.1%. The paired t-tests showed strong statistically significant differences in all three vital signs between patients with pneumonia and normal volunteers. Our random tree algorithm-based screening system offers an effective, rapid, and convenient tool for early detection of pneumonia in children. Its cost-effectiveness enables application in low-income countries. The system measures multiple vital signs simultaneously within 10 seconds, which may be useful for initial physical examinations in pediatric hospitals.
We previously reported in Journal of Infection an infection screening method using a combination of neural network and k-means clustering algorithm.1 After a pandemic of a novel influenza A/H1N1pdm09 in 2009, we started to develop vital sign-based infection screening systems and conducted several preliminary infection screenings in Japan, Mongolia and Vietnam.2–4 Although the previous method achieved up to 98% sensitivity and 96% negative predictive value (NPV), this study was limited to patients with comparatively uniform backgrounds, i.e., hospitalized influenza patients who were soldiers of the Japan Self-Defense Forces.
E-learning can be a potential solution to educational inequality problem in developing countries, like Mongolia, with vast land and sparse population. With the introduction of Massive Open Online Courses (MOOCs), some impressive cases bring the attention of the public towards it. But there is no specific evidence that shows Mongolian students’ experiences related to these online courses yet. Purpose of this study was to examine Mongolian students’ MOOC perception and experience, especially influence of access, skills, and preferences in their practice. We used a 15-item questionnaire and results were based on 6846 students’ responses. The study population consists of undergraduate students of the National University of Mongolia and high school students from 8 schools in Ulaanbaatar city. There was a significant difference between university and high school students in the awareness and enrollment rates. 47% of the students have heard of MOOCs and 2518 respondents (37%) had an experience of taking MOOCs. Our results show that students have a doubt about MOOC’s academical quality and consider it as an additional source of learning materials. The results of this study can be used to compare students’ perception from other developing countries.
Fever is one significant sign of infection. Hence, infrared thermography systems are important for detecting infected suspects in public places. Reliable temperature measurements by thermography are influenced by several factors, including environmental conditions. This paper proposes a linear regression analysis-based facial temperature optimization method to improve the accuracy of multiple vital signs-based infection screening at various ambient temperatures. To obtain the relationship between ambient temperature and thermography measurements, 20 instances of axillary temperature, thermography measurements of facial temperature, and five different ambient temperature values at the time of measurement were used as a training set for a linear regression model. Temperatures from a total of 30 subjects were recalculated by the model. The screening system was evaluated using the temperature both before and after optimization to demonstrate the accuracy of the optimization method. A k-nearest neighbor algorithm was used to classify potentially infected patients from healthy subjects. Although the system has already been evaluated in restricted environmental conditions, this is the first time it was tested in Ulaanbaatar, Mongolia. The results show that the Pearson's correlation coefficient between optimum and axillary temperatures increased to r = 0.82. Paired t-tests revealed that the optimized temperature became statistically highly significant (p<;0.001) for differentiating potentially infected patients from healthy subjects. Finally, the system achieved a sensitivity score of 91% and a negative predictive value of 92%. These values are higher than those obtained without temperature optimization. The proposed optimization method is feasible and can notably improve screening performance.
Over 350 million people across the world suffer from major depressive disorder (MDD). More than 10% of MDD patients have suicide intent, while it has been reported that more than 40% patients did not consult their doctors for MDD. In order to increase consultation rate of potential MDD patients, we developed a novel MDD screening system which can be used at home without help of health-care professionals. Using a fingertip photoplethysmograph (PPG) sensor as a substitute of electrocardiograph (ECG), the system discriminates MDD patients from healthy subjects using autonomic nerve transient responses induced by a mental task (random number generation) via logistic regression analysis. The nine logistic regression variables are averages of heart rate (HR), high frequency (HF) component of heart rate variability (HRV), and the low frequency (LF)/HF ratio of HRV before, during, and after the mental task. We conducted a clinical test of the proposed system. Participants were 6 MDD patients (4 females and 2 males, aged 23–60 years) from Shizuoka Saiseikai General Hospital psychiatry outpatient unit and 14 healthy volunteers from University of Electro-Communications (6 females and 8 males, aged 21–63 years). The average PPG- and ECG (as a reference)-derived HR, HF and LF/HF were significantly correlated with each other (HR; r = 1.00, p < 0.0001, HF; r = 0.98, p < 0.0001, LF/HF; r = 0.98, p < 0.0001). Leave-one-out cross validation (LOOCV) revealed 83% sensitivity and 93% specificity. The proposed system appears promising for future MDD self-screening at home and are expected to encourage psychiatric visits for potential MDD patients.
Pandemics of Severe Acute Respiratory Syndrome (SARS) in 2002 and H1N1 Flu (Swine Flu) in 2009 indicated repeated epidemics in various regions around the world. In order to prevent such spread of diseases, most effective approach is to detect infected individuals in their early stages. We have developed a novel infection screening system which detects the possibility of infection from measured vital signs, such as, heart rate, respiratory rate, and facial temperature within ten seconds. This study aims to evaluate the infection screening system for its accuracy when used in Mongolia, especially for tuberculosis patients and feverous ambulant patients. At present, we modified an infection screening system (KAZEKAMO) in National University of Mongolia for the clinical study at the National Centre. Prior to the clinical study in Mongolia, we conducted a clinical trial in Japan. Within ten seconds, the infection screening system with classification algorithm using a neural network discriminates potentially infected individuals from normal subjects using heart rates, respiratory rates, and facial temperatures measured by a pulse sensor, a compact radar, and thermography, respectively. The system was tested on 57 seasonal influenza patients after antiviral agent uptake (35.70C ≤ body temperature0C ≤ 38.30C ≤, 19-40 years) and 35 normal control subjects (35.50C ≤ body temperature0C ≤ 36.90C, 21-35 years) at the Japan Self-defense Forces Central Hospital. At the time of measurement, approximately half of the influenza patients have already became afebrile because of antiviral agents. The system achieved sensitivity of 98%, NPV of 82%, respectively. Our system appears promising for first order screening application of tuberculosis patients who sometimes develop only low-grade fever, because this system showed its efficacy for unfebrile patients.
Integral imaging (InIm) is an interesting research area in the three-dimensional (3-D) display technology. While it is simple in structure, it shows full color and full parallax 3-D images without the necessity of special glasses. InIm display usually uses the simplest lens array, and hence displayed 3-D image suffers from distortions. A dominating distortion is a Petzval curvature. To the authors' best knowledge, we have firstly analyzed an effect of the Petzval curvature in InIm display. The immediate consequence of Petzval curvature is that the depth plane of InIm display becomes a curved plane array. Using simulation, the effect of Petzval curvature is found to reduce the depth range, change the viewing direction, and increase the black stripe. The result indicates that the lens array in the InIm display should be customized to reduce these undesirable effects.
Integral imaging (InIm) is an interesting research area in the three-dimensional (3-D) display technology. While it is simple in structure, it shows full color and full parallax 3-D images without the necessity of special glasses. InIm display usually uses the simplest lens array, and hence displayed 3-D image suffers from distortions. A dominating distortion is a Petzval curvature. To the authors' best knowledge, we have firstly analyzed an effect of the Petzval curvature in InIm display. The immediate consequence of Petzval curvature is that the depth plane of InIm display becomes a curved plane array. Using simulation, the effect of Petzval curvature is found to reduce the depth range, change the viewing direction, and increase the black stripe. The result indicates that the lens array in the InIm display should be customized to reduce these undesirable effects.
