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Simulation learning

Marianne Hollensteiner, David Fürst, Peter Augat, Falk Schrödl, Benjamin Esterer, Stefan Gabauer, Stefan Hunger, Michael Malek, Daniel Stephan, Andreas Schrempf
Cranial grafts are favored to reconstruct skeletal defects because of their reduced resorption and their histocompatibility. Training possibilities for novice surgeons include the "learning by doing" on the patient, specimens or simulators. Although the acceptance of simulators is growing, the major drawback is the lack of validated bone models. The aim of this study was to create and validate a realistic skull cap model and to show superiority compared to a commercially available skull model. Characteristic forces during machinery procedures were recorded and thickness parameters from the bony layers were obtained...
August 17, 2018: Journal of Materials Science. Materials in Medicine
Julie Chang, Vincent Sitzmann, Xiong Dun, Wolfgang Heidrich, Gordon Wetzstein
Convolutional neural networks (CNNs) excel in a wide variety of computer vision applications, but their high performance also comes at a high computational cost. Despite efforts to increase efficiency both algorithmically and with specialized hardware, it remains difficult to deploy CNNs in embedded systems due to tight power budgets. Here we explore a complementary strategy that incorporates a layer of optical computing prior to electronic computing, improving performance on image classification tasks while adding minimal electronic computational cost or processing time...
August 17, 2018: Scientific Reports
Loren C Hoffmann, S James Zara, Evan D DeLord, Michael D Mauk
Transforming a brief sensory event into a persistent neural response represents a mechanism for linking temporally-disparate stimuli together to support learning. The cerebellum requires this type of persistent input during trace conditioning to engage associative plasticity and acquire adaptively-timed conditioned responses (CRs). An initial step toward identifying the sites and mechanisms generating and transmitting persistent signals to the cerebellum is to identify the input pathway. The medial auditory thalamic nuclei (MATN) are the necessary and sufficient source of auditory input to the cerebellum for delay conditioning in rodents and a possible input to forebrain sites generating persistent signals...
August 17, 2018: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
Ziyi Wang, Aiying Yang, Peng Guo, Pinjing He
The optical signal-to-noise ratio (OSNR) and fiber nonlinearity are critical factors in evaluating the performance of high-speed optical fiber communication systems. Recently, several deep learning based methods have been put forward to monitor OSNR of a fiber communication system. In this work, we propose a long short-term memory (LSTM) network based method to simultaneously estimate OSNR and nonlinear noise power caused by fiber nonlinearity. In the training step, LSTM network extracts the essential features in frequency domain of the input signal...
August 6, 2018: Optics Express
Zewei He, Yanpeng Cao, Yafei Dong, Jiangxin Yang, Yanlong Cao, Christel-Löic Tisse
Fixed-pattern noise (FPN), which is caused by the nonuniform opto-electronic responses of microbolometer focal-plane-array (FPA) optoelectronics, imposes a challenging problem in infrared imaging systems. In this paper, we successfully demonstrate that a better single-image-based non-uniformity correction (NUC) operator can be directly learned from a large number of simulated training images instead of being handcrafted as before. Our proposed training scheme, which is based on convolutional neural networks (CNNs) and a column FPN simulation module, gives rise to a powerful technique to reconstruct the noise-free infrared image from its corresponding noisy observation...
June 20, 2018: Applied Optics
Yunpeng Liu, Yuan Tian, Xiaozhou Fan, Yanan Bu, Bowen Wang
At present, transformer winding strain monitoring is divided mainly into off-line detection and on-line detection. Due to the interference of the complex electromagnetic environment, on-line detection has not been widely used. Although off-line detection is more mature, it can not accurately judge the winding strain form. Based on the above problems, this research investigated a strain gauge strain detection method based on distributed fiber optic sensing, and proposes a winding strain identification method based on the S-transform and an extreme learning machine (ELM)...
August 1, 2018: Applied Optics
Rishad Khan, Joanne Plahouras, Bradley C Johnston, Michael A Scaffidi, Samir C Grover, Catharine M Walsh
BACKGROUND: Endoscopy has traditionally been taught with novices practicing on real patients under the supervision of experienced endoscopists. Recently, the growing awareness of the need for patient safety has brought simulation training to the forefront. Simulation training can provide trainees with the chance to practice their skills in a learner-centred, risk-free environment. It is important to ensure that skills gained through simulation positively transfer to the clinical environment...
August 17, 2018: Cochrane Database of Systematic Reviews
Pier Luigi Ingrassia, Jeffrey Michael Franc, Luca Carenzo
Objective: Medical simulation competitions are a growing reality. This study aims at exploring if a novel format of simulation competition (SIMCUP) can be an effective educational format in post-graduate education. Design: We designed a 2-day event that included scientific educational lectures, an orientation to the competition, familiarization with the simulation lab, and competition time. Day 1 was devoted to preliminary rounds and was structured using an Objective Structured Clinical Examination (OSCE)-like system...
2018: Advances in Simulation
Pilar García Díaz, Juan Antonio Martínez Rojas, Manuel Utrilla Manso, Leticia Monasterio Expósito
A new haptic sensor that is based on vibration produced by mechanical excitation from a clock coupled to a resonant cavity is presented. This sensor is intended to determine the chemical composition of liquid mixtures in a completely non-destructive method. In this case, a set of 23 samples of water, ethanol, and fructose mixtures has been used to simulate different kinds of alcoholic beverage. The spectral information from the vibrational absorption bands of liquid samples is analyzed by a Grouping Genetic Algorithm...
August 16, 2018: Sensors
Tonghe Wang, Nivedh Manohar, Yang Lei, Anees Dhabaan, Hui-Kuo Shu, Tian Liu, Walter J Curran, Xiaofeng Yang
Magnetic resonance imaging (MRI)-only radiotherapy treatment planning is attractive since MRI provides superior soft tissue contrast without ionizing radiation compared with computed tomography (CT). However, it requires the generation of pseudo CT from MRI images for patient setup and dose calculation. Our machine-learning-based method to generate pseudo CT images has been shown to provide pseudo CT images with excellent image quality, while its dose calculation accuracy remains an open question. In this study, we aim to investigate the accuracy of dose calculation in brain frameless stereotactic radiosurgery (SRS) using pseudo CT images which are generated from MRI images using the machine learning-based method developed by our group...
August 14, 2018: Medical Dosimetry: Official Journal of the American Association of Medical Dosimetrists
Shay Elmalem, Raja Giryes, Emanuel Marom
Modern consumer electronics market dictates the need for small-scale and high-performance cameras. Such designs involve trade-offs between various system parameters. In such trade-offs, Depth Of Field (DOF) is a significant issue very often. We propose a computational imaging-based technique to overcome DOF limitations. Our approach is based on the synergy between a simple phase aperture coding element and a convolutional neural network (CNN). The phase element, designed for DOF extension using color diversity in the imaging system response, causes chromatic variations by creating a different defocus blur for each color channel of the image...
June 11, 2018: Optics Express
Laurens R Krol, Juliane Pawlitzki, Fabien Lotte, Klaus Gramann, Thorsten O Zander
BACKGROUND: Electroencephalography (EEG) is a popular method to monitor brain activity, but it is difficult to evaluate EEG-based analysis methods because no ground-truth brain activity is available for comparison. Therefore, in order to test and evaluate such methods, researchers often use simulated EEG data instead of actual EEG recordings. Simulated data can be used, among other things, to assess or compare signal processing and machine learning algorithms, to model EEG variabilities, and to design source reconstruction methods...
August 13, 2018: Journal of Neuroscience Methods
Gong Zhang, Tian Guan, Zhiyuan Shen, Xiangnan Wang, Tao Hu, Delai Wang, Yonghong He, Ni Xie
Traditional digital holographic imaging algorithms need multiple iterations to obtain focused reconstructed image, which is time-consuming. In terms of phase retrieval, there is also the problem of phase compensation in addition to focusing task. Here, a new method is proposed for fast digital focus, where we use U-type convolutional neural network (U-net) to recover the original phase of microscopic samples. Generated data sets are used to simulate different degrees of defocused image, and verify that the U-net can restore the original phase to a great extent and realize phase compensation at the same time...
July 23, 2018: Optics Express
Zixia Zhou, Yuanyuan Wang, Jinhua Yu, Yi Guo, Wei Guoand, Yanxing Qi
In recent years, plane-wave imaging (PWI) has attracted considerable attention because of its high temporal resolution. However, the low spatial resolution of PWI limits its clinical applications, which has inspired various studies on the high spatial resolution reconstruction of plane-wave (PW) ultrasound images. Although compounding methods and traditional high spatial resolution reconstruction approaches can improve the image quality, these techniques decrease the temporal resolution. Since learning methods can fully reserve the high temporal resolution of PW ultrasounds, a novel convolutional neural network (CNN) model for the high spatial-temporal resolution reconstruction of PW ultrasound images is proposed in this paper...
August 14, 2018: IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Faisal Mahmood, Richard Chen, Sandra Sudarsky, Daphne Yu, Nicholas J Durr
Deep learning has emerged as a powerful artificial intelligence tool to interpret medical images for a growing variety of applications. However, the paucity of medical imaging data with high-quality annotations that is necessary for training such methods ultimately limits their performance. Medical data is challenging to acquire due to privacy issues, shortage of experts available for annotation, limited representation of rare conditions and cost. This problem has previously been addressed by using synthetically generated data...
August 16, 2018: Physics in Medicine and Biology
J Gunasagaran, R J Rasid, S Mappiare, C Devarajooh, T S Ahmad
Introduction: Microsurgery is a subspecialised field which requires high technical skill. Laboratory training offers good opportunity for novice surgeons to learn and repetitively practise their skills prior to hands-on clinical practice. Commonly, the training programme consists of models in a stepwise increase in fidelity: from latex sheet to anaesthetised rat. We introduce microgrids model as a daily warm up procedure in a 5-day basic microsurgery course. The purpose of this study is to evaluate the correlation between microgrids colouring under magnification with microsuturing proficiency among novice surgeons...
July 2018: Malaysian Orthopaedic Journal
Michele S Barnhill, Mark Real, James H Lewis
Drug-induced liver injury (DILI) remains an increasingly recognized cause of hepatotoxicity and liver failure worldwide. In 2017, we continued to learn about predicting, diagnosing, and prognosticating drug hepatotoxicity. Areas covered: In this review, we selected from over 1200 articles from 2017 to synopsize updates in DILI. There were new HLA haplotypes associated with medications including HLA-C0401 and HLA-B*14. There has been continued work with quantitative systems pharmacology, particularly with the DILIsym® initiative, which employs mathematical representations of DILI mechanisms to predict hepatotoxicity in simulated populations...
August 15, 2018: Expert Review of Gastroenterology & Hepatology
Nicholas Wagner, Danilo Puggioni, James M Rondinelli
Statistical analysis of local atomic distortions in crystalline materials is a powerful tool for understanding coupled electronic and structural phase transitions in transition metal compounds. The analyses of such complex materials, however, often require significant domain knowledge to recognize limitations in the available data, whether it be experimentally reported crystal structures, property measurements, or computed quantities, and to understand when additional experiments or simulations may be necessary...
August 16, 2018: Journal of Chemical Information and Modeling
Saleem Aslam, Ju Wook Jang, Kyung-Geun Lee
Cognitive capabilities are indispensable for the Internet of Things (IoT) not only to equip them with learning, thinking, and decision-making capabilities but also to cater to their unprecedented huge spectrum requirements due to their gigantic numbers and heterogeneity. Therefore, in this paper, a novel unified channel management framework (CMF) is introduced for cognitive radio sensor networks (CRSNs), which comprises an (1) opportunity detector (ODR), (2) opportunity scheduler (OSR), and (3) opportunity ranker (ORR) to specifically address the immense and diverse spectrum requirements of CRSN-aided IoT...
August 14, 2018: Sensors
Anna Sapienza, Alain Barrat, Ciro Cattuto, Laetitia Gauvin
Recent advances in data collection have facilitated the access to time-resolved human proximity data that can conveniently be represented as temporal networks of contacts between individuals. While the structural and dynamical information revealed by this type of data is fundamental to investigate how information or diseases propagate in a population, data often suffer from incompleteness, which possibly leads to biased estimations in data-driven models. A major challenge is thus to estimate the outcome of spreading processes occurring on temporal networks built from partial information...
July 2018: Physical Review. E
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