scholarly journals Superimposed polygonal approximation analysis comparing 2D photography and 3D scanned images of bite marks on human skin

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ain Ashraf Rizwal ◽  
Nursyereen Azahar ◽  
Nor Hidayah Reduwan ◽  
Mohd Yusmiaidil Putera Mohd Yusof
Keyword(s):  
Standard Operating Procedure ◽  
Absolute Error ◽  
Polygonal Approximation ◽  
Bite Marks ◽  
Surface Areas ◽  
Bite Mark ◽  
Scanned Image ◽  
Approximation Analysis ◽  
The Mean ◽  
Scanned Images

Abstract Background Preservation of bite marks evidence has always been a major problem in forensic odontology due to progressive loss of details as time passes. The use of 2D photographs has been widely used to document forensic evidence and preserving bite marks; however, there are limitations to this method. This study aims to measure the accuracy of the 3D scanned image in comparison to 2D photograph registration of experimental bite marks. Thirty volunteers performed self-exertions of a bite mark on the respective forearm of subjects. A 2D photograph and 3D scanned image was immediately registered following bite mark exercise using a conventional camera and Afinia EinScan-Pro 2X PLUS Handheld 3D Scanner, respectively. The outlines of the bite mark were transformed into a polygonal shape. Next, the polygonal approximation analysis was performed by an arbitrary superimposition method. The difference between surface areas of both images was calculated (2D photographs ̶ 3D scanned images). Results A paired t test was used to measure significance with α = 0.05. The mean surface area of 2D photographs and 3D scanned images is 31.535 cm2 and 31.822 cm2, respectively. No statistical difference was found between both mean surface areas (p > 0.05). The mean error (ME) is 0.287 ± 3.424 cm2 and the mean absolute error (MAE) is 1.733 ± 1.149 cm2. Conclusion Bite marks registered with the 3D scanned image are comparable to the standard 2D photograph for bite mark evaluations. The use of a 3D scan may be adopted as a standard operating procedure in the forensic application, especially for evidence preservation.

scholarly journals A Novel Computer-Assisted Method of Bite Mark Analysis for Gender Determination

2018 ◽  
Vol 2018 ◽  
pp. 1-4 ◽  
Author(s):  
Anirban Maji ◽  
Tanya Khaitan ◽  
Rupam Sinha ◽  
Soumyabrata Sarkar ◽  
Pratik Verma ◽  
...  
Keyword(s):  
Forensic Odontology ◽  
Criminal Court ◽  
Computer Assisted ◽  
Bite Marks ◽  
Normal Occlusion ◽  
Bite Mark ◽  
Males And Females ◽  
The Mean ◽  
Dental Evidence ◽  
Intercanine Distance

Background. Bite mark analysis is an imperative area of forensic odontology and considered the commonest form of dental evidence presented in the criminal court. The process of comparing bite marks with a suspect’s dentition includes analysis and measurement of shape, size, and position of an individual’s teeth. The present study was aimed to evaluate the bite marks of males and females using a novel indirect computer-assisted method and explicate its application in forensic odontology. Materials and methods. 60 subjects (30 males and 30 females) with normal occlusion were included in the present study. Bite registrations were obtained with help of modelling waxes, and positive replicas were prepared with dental stone and barium powder. Intraoral periapical radiographs were taken for the same. The radiographs obtained were scanned and analyzed by measuring tools using Sidexis Next Generation software. Intercanine distance (ICD), line AB, angle ABX, and angle ABY were measured. The Kruskal–Wallis test was performed to compare the bite marks of males and females. Results. The mean ICD of males and females was found to be 32.95 mm and 29.84 mm, respectively, and was statistically highly significant with a p value <0.001. The mean ICD, line AB, and angle ABX were found to be higher in males when compared to females. Conclusion. Analysis of bite marks using this novel computer-assisted method is a simple, reliable, easily reproducible, and economical technique with confidentiality of the identity of the participants involved.

scholarly journals Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3771
Author(s):  
Alexey Kashevnik ◽  
Walaa Othman ◽  
Igor Ryabchikov ◽  
Nikolay Shilov
Keyword(s):  
Respiratory Rate ◽  
Evaluation System ◽  
Body Movement ◽  
Absolute Error ◽  
The Body ◽  
Meditation Practice ◽  
Body Parts ◽  
Mental Health Training ◽  
Negative Thoughts ◽  
The Mean

Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displacement between frames, filtering and de-noising the chest movement signal, and calculating the number of real peaks in this signal. We also present an approach to detecting the movement of different body parts (head, thorax, shoulders, elbows, wrists, stomach and knees). We have collected a non-annotated dataset for meditation practice videos consists of ninety videos and the annotated dataset consists of eight videos. The non-annotated dataset was categorized into beginner and professional meditators and was used for the development of the algorithm and for tuning the parameters. The annotated dataset was used for evaluation and showed that human activity during meditation practice could be correctly estimated by the presented approach and that the mean absolute error for the respiratory rate is around 1.75 BPM, which can be considered tolerable for the meditation application.

scholarly journals Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 68
Author(s):  
Jiwei Fan ◽  
Xiaogang Yang ◽  
Ruitao Lu ◽  
Xueli Xie ◽  
Weipeng Li
Keyword(s):  
Deep Learning ◽  
Autonomous Navigation ◽  
Detection Method ◽  
Active Role ◽  
Absolute Error ◽  
Face Mask ◽  
Learning Technology ◽  
Flight System ◽  
Crowd Density ◽  
The Mean

Unmanned aerial vehicles (UAV) and related technologies have played an active role in the prevention and control of novel coronaviruses at home and abroad, especially in epidemic prevention, surveillance, and elimination. However, the existing UAVs have a single function, limited processing capacity, and poor interaction. To overcome these shortcomings, we designed an intelligent anti-epidemic patrol detection and warning flight system, which integrates UAV autonomous navigation, deep learning, intelligent voice, and other technologies. Based on the convolution neural network and deep learning technology, the system possesses a crowd density detection method and a face mask detection method, which can detect the position of dense crowds. Intelligent voice alarm technology was used to achieve an intelligent alarm system for abnormal situations, such as crowd-gathering areas and people without masks, and to carry out intelligent dissemination of epidemic prevention policies, which provides a powerful technical means for epidemic prevention and delaying their spread. To verify the superiority and feasibility of the system, high-precision online analysis was carried out for the crowd in the inspection area, and pedestrians’ faces were detected on the ground to identify whether they were wearing a mask. The experimental results show that the mean absolute error (MAE) of the crowd density detection was less than 8.4, and the mean average precision (mAP) of face mask detection was 61.42%. The system can provide convenient and accurate evaluation information for decision-makers and meets the requirements of real-time and accurate detection.

Explanation Plus Prediction—The Logical Focus of Project Management Research

2021 ◽  
pp. 875697282199994
Author(s):  
Joseph F. Hair ◽  
Marko Sarstedt
Keyword(s):  
Project Management ◽  
Statistical Models ◽  
Predictive Power ◽  
Mean Absolute Error ◽  
Explanatory Power ◽  
Absolute Error ◽  
Model Parameters ◽  
Mean Square ◽  
Management Research ◽  
The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

60 Contact Hand Burns in Children: Still a Major Prevention Need

2020 ◽  
Vol 41 (Supplement_1) ◽  
pp. S39-S40
Author(s):  
Jaclyn M McBride ◽  
Kathleen S Romanowski ◽  
Soman Sen ◽  
Tina L Palmieri ◽  
David G Greenhalgh
Keyword(s):  
Graft Loss ◽  
Dorsal Surface ◽  
Skin Grafting ◽  
Secondary Surgery ◽  
Surface Areas ◽  
Palmar Surface ◽  
The Mean ◽  
Total Body ◽  
Hand Burns ◽  
Palmar Aspect

Abstract Introduction Since toddlers explore with their hands, contact burns continue to be a major pediatric problem. The purpose of this report is to review a pediatric burn unit’s 8-year experience with contact burns of the hand. Methods After IRB approval, a review of pediatric contact hand burns that occurred between 2006–2014 was performed. We examined the causes and outcomes in pediatric contact hand burns in a single pediatric burn program. Results In the 8-year span, 535 children suffered contact burns to the hand (67 per year). The majority suffered hands burns from an oven or stove (120). The other etiologies included burns from a fireplace (76), clothing iron (65), curling or straightening iron (50), and firepit or campfire (46). The mean age at time of injury was 2.62 years old, with a range of 2 months old to18 years old. Male children (339) typically burned their hands more than females (197). Locations of injury included the palmar surface, dorsal surface, fingers tips/thumb, wrist or a combination of several different areas. Most children burned the palmar aspect of their hand (384) compared to the dorsal aspect (61). These burns typically cover small total body surface areas (mean 1.08% TBSA), with only 2% of burns comprising &gt;5% TBSA. Approximately, 84% of these patients did not need surgery, but 86 (16%) had skin grafting (usually full-thickness) and 26% needed a secondary surgery. Of those that needed more than two, the average number of procedures was 3.6. Approximately 4.1% of patients needed a tertiary surgery. Causes for tertiary surgeries included contractures and graft loss. Out of twenty-two patients that needed a third surgery, 59% were due to graft loss and 41% were due to contractures. Conclusions Contact burns to the hand continue to be a major problem for toddlers. Children are most likely to burn themselves on an oven or stove, fireplace, clothing iron or curling/straightening iron. The palmar surface of the hand is the most likely site. While most children do not require surgery, approximately 16% require grafting. A significant number of those patients need reconstructive surgery. Clearly, current prevention efforts have failed to reduce these injuries. Applicability of Research to Practice Palm burns are common in young children. Efforts should focus on preventing these injuries.

scholarly journals An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1867
Author(s):  
Tasbiraha Athaya ◽  
Sunwoong Choi
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Health ◽  
Estimation Method ◽  
Absolute Error ◽  
British Hypertension Society ◽  
Non Invasive ◽  
Arterial Blood ◽  
Blood Pressure Waveform ◽  
The Mean ◽  
Mimic Iii

Blood pressure (BP) monitoring has significant importance in the treatment of hypertension and different cardiovascular health diseases. As photoplethysmogram (PPG) signals can be recorded non-invasively, research has been highly conducted to measure BP using PPG recently. In this paper, we propose a U-net deep learning architecture that uses fingertip PPG signal as input to estimate arterial BP (ABP) waveform non-invasively. From this waveform, we have also measured systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP). The proposed method was evaluated on a subset of 100 subjects from two publicly available databases: MIMIC and MIMIC-III. The predicted ABP waveforms correlated highly with the reference waveforms and we have obtained an average Pearson’s correlation coefficient of 0.993. The mean absolute error is 3.68 ± 4.42 mmHg for SBP, 1.97 ± 2.92 mmHg for DBP, and 2.17 ± 3.06 mmHg for MAP which satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed method is an efficient process to estimate ABP waveform directly using fingertip PPG.

scholarly journals A Multi-Channel Uncertainty-Aware Multi-Resolution Network for MR to CT Synthesis

2021 ◽  
Vol 11 (4) ◽  
pp. 1667
Author(s):  
Kerstin Klaser ◽  
Pedro Borges ◽  
Richard Shaw ◽  
Marta Ranzini ◽  
Marc Modat ◽  
...  
Keyword(s):  
Medical Image Analysis ◽  
Absolute Error ◽  
Magnetic Resonance Images ◽  
The Body ◽  
Whole Body ◽  
Image Size ◽  
Body Images ◽  
Image Translation ◽  
The Mean ◽  
Synthetic Ct

Synthesising computed tomography (CT) images from magnetic resonance images (MRI) plays an important role in the field of medical image analysis, both for quantification and diagnostic purposes. Convolutional neural networks (CNNs) have achieved state-of-the-art results in image-to-image translation for brain applications. However, synthesising whole-body images remains largely uncharted territory, involving many challenges, including large image size and limited field of view, complex spatial context, and anatomical differences between images acquired at different times. We propose the use of an uncertainty-aware multi-channel multi-resolution 3D cascade network specifically aiming for whole-body MR to CT synthesis. The Mean Absolute Error on the synthetic CT generated with the MultiResunc network (73.90 HU) is compared to multiple baseline CNNs like 3D U-Net (92.89 HU), HighRes3DNet (89.05 HU) and deep boosted regression (77.58 HU) and shows superior synthesis performance. We ultimately exploit the extrapolation properties of the MultiRes networks on sub-regions of the body.

Visualizing the Variance of a Random Variable

2011 ◽  
Vol 18 (01) ◽  
pp. 71-85
Author(s):  
Fabrizio Cacciafesta
Keyword(s):  
Stochastic Dominance ◽  
Mean Absolute Error ◽  
Random Variable ◽  
Absolute Error ◽  
Second Order ◽  
Taylor Formula ◽  
Order Stochastic Dominance ◽  
The Mean ◽  
Second Order Stochastic Dominance ◽  
Options Theory

We provide a simple way to visualize the variance and the mean absolute error of a random variable with finite mean. Some application to options theory and to second order stochastic dominance is given: we show, among other, that the "call-put parity" may be seen as a Taylor formula.

scholarly journals Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception

2018 ◽  
Vol 10 (12) ◽  
pp. 4863 ◽  
Author(s):  
Chao Huang ◽  
Longpeng Cao ◽  
Nanxin Peng ◽  
Sijia Li ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Power Plants ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Multilayer Perception ◽  
Squared Error ◽  
The Mean ◽  
Effectiveness And Efficiency ◽  
Mlp Network ◽  
Grid Operation ◽  
Better Than

Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

scholarly journals Direct Calculation of Thermodynamic Wet-Bulb Temperature as a Function of Pressure and Elevation

2013 ◽  
Vol 30 (8) ◽  
pp. 1757-1765 ◽  
Author(s):  
Sayed-Hossein Sadeghi ◽  
Troy R. Peters ◽  
Douglas R. Cobos ◽  
Henry W. Loescher ◽  
Colin S. Campbell
Keyword(s):  
Air Temperature ◽  
Mean Absolute Error ◽  
Direct Calculation ◽  
Ambient Air ◽  
Absolute Error ◽  
Mean Square ◽  
Air Temperatures ◽  
Order Polynomial ◽  
The Mean ◽  
Very High

Abstract A simple analytical method was developed for directly calculating the thermodynamic wet-bulb temperature from air temperature and the vapor pressure (or relative humidity) at elevations up to 4500 m above MSL was developed. This methodology was based on the fact that the wet-bulb temperature can be closely approximated by a second-order polynomial in both the positive and negative ranges in ambient air temperature. The method in this study builds upon this understanding and provides results for the negative range of air temperatures (−17° to 0°C), so that the maximum observed error in this area is equal to or smaller than −0.17°C. For temperatures ≥0°C, wet-bulb temperature accuracy was ±0.65°C, and larger errors corresponded to very high temperatures (Ta ≥ 39°C) and/or very high or low relative humidities (5% &lt; RH &lt; 10% or RH &gt; 98%). The mean absolute error and the root-mean-square error were 0.15° and 0.2°C, respectively.

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