scholarly journals Design and Validation of a Camera-Based Safety System for Fenceless Robotic Work Cells

2021 ◽  
Vol 11 (24) ◽  
pp. 11679
Author(s):  
Merdan Ozkahraman ◽  
Cuneyt Yilmaz ◽  
Haydar Livatyali
Keyword(s):  
High Speed ◽  
Learning Algorithm ◽  
Cost Effective ◽  
Safety System ◽  
International Standards ◽  
Optimized Design ◽  
Price Ratio ◽  
Camera System ◽  
Deep Learning Algorithm ◽  
Camera Systems

A two-dimensional (2-D) camera system with a real-time image processing-based safety technology is a cost-effective alternative that needs optimization of the cell layout, the number of cameras, and the camera’s locations and orientations. A design optimization study was performed using the multi-criteria linear fractional programming method and considering the number of cameras, the resolution, as well as camera positions and orientations. A table-top experimental setup was designed and built to test the effectiveness of the optimized design using two cameras. The designs at optimal and nonoptimal parameters were compared using a deep learning algorithm, ResNet-152. To eliminate blind spots, a simple but novel 2-D image merging technique was proposed as an alternative to commonly employed stereo imaging methods. Verification experiments were conducted by using two camera resolutions with two graphic processors under varying illuminance. It was validated that high-speed entrances to the safety system were detected reliably and with a 0.1 s response time. Moreover, the system was proven to work effectively at a minimum illuminance of 120 lux, while commercial systems cannot be operated under 400 lux. After determining the most appropriate 2-D camera type, positions, and angles within the international standards, the most cost-effective solution set with a performance-to-price ratio up to 15 times higher than high-cost 3-D camera systems was proposed and validated.

scholarly journals Performance of a deep learning CNN model for the automated detection of 13 common conditions on Chest X-rays

2021 ◽  
Author(s):  
Tirupathi Karthik ◽  
Vijayalakshmi Kasiraman ◽  
Bhavani Paski ◽  
Kashyap Gurram ◽  
Amit Talwar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Delayed Diagnosis ◽  
Cost Effective ◽  
Radiology Workflow ◽  
X Rays ◽  
Term Care ◽  
X Ray ◽  
Intelligence System ◽  
Deep Learning Algorithm

Background and aims: Chest X-rays are widely used, non-invasive, cost effective imaging tests. However, the complexity of interpretation and global shortage of radiologists have led to reporting backlogs, delayed diagnosis and a compromised quality of care. A fully automated, reliable artificial intelligence system that can quickly triage abnormal images for urgent radiologist review would be invaluable in the clinical setting. The aim was to develop and validate a deep learning Convoluted Neural Network algorithm to automate the detection of 13 common abnormalities found on Chest X-rays. Method: In this retrospective study, a VGG 16 deep learning model was trained on images from the Chest-ray 14, a large publicly available Chest X-ray dataset, containing over 112,120 images with annotations. Images were split into training, validation and testing sets and trained to identify 13 specific abnormalities. The primary performance measures were accuracy and precision. Results: The model demonstrated an overall accuracy of 88% in the identification of abnormal X-rays and 87% in the detection of 13 common chest conditions with no model bias. Conclusion: This study demonstrates that a well-trained deep learning algorithm can accurately identify multiple abnormalities on X-ray images. As such models get further refined, they can be used to ease radiology workflow bottlenecks and improve reporting efficiency. Napier Healthcare’s team that developed this model consists of medical IT professionals who specialize in AI and its practical application in acute & long-term care settings. This is currently being piloted in a few hospitals and diagnostic labs on a commercial basis.

scholarly journals An Automated Heart Rate-based Algorithm for Sleep Stage Classification: Validation Using Conventional PSG and Innovative Wearable ECG Device

2021 ◽  
Author(s):  
Nicolò Pini ◽  
Ju Lynn Ong ◽  
Gizem Yilmaz ◽  
Nicholas I. Y. N. Chee ◽  
Zhao Siting ◽  
...  
Keyword(s):  
New York ◽  
Learning Algorithm ◽  
Sleep Stage ◽  
Cost Effective ◽  
Deep Sleep ◽  
Sleep Staging ◽  
Deep Learning Algorithm ◽  
Best Value ◽  
Sleep Stage Classification

Study Objectives: Validate a HR-based deep-learning algorithm for sleep staging named Neurobit-HRV (Neurobit Inc., New York, USA). Methods: The algorithm can perform classification at 2-levels (Wake; Sleep), 3-levels (Wake; NREM; REM) or 4- levels (Wake; Light; Deep; REM) in 30-second epochs. The algorithm was validated using an open-source dataset of PSG recordings (Physionet CinC dataset, n=994 participants) and a proprietary dataset (Z3Pulse, n=52 participants), composed of HR recordings collected with a chest-worn, wireless sensor. A simultaneous PSG was collected using SOMNOtouch. We evaluated the performance of the models in both datasets using Accuracy (A), Cohen's kappa (K), Sensitivity (SE), Specificity (SP). Results: CinC - The highest value of accuracy was achieved by the 2-levels model (0.8797), while the 3-levels model obtained the best value of K (0.6025). The 4-levels model obtained the lowest SE (0.3812) and the highest SP (0.9744) for the classification of Deep sleep segments. AHI and biological sex did not affect sleep scoring, while a significant decrease of performance by age was reported across the models. Z3Pulse - The highest value of accuracy was achieved by the 2-levels model (0.8812), whereas the 3-levels model obtained the best value of K (0.611). For classification of the sleep states, the lowest SE (0.6163) and the highest SP (0.9606) were obtained for the classification of Deep sleep segment. Conclusions: Results demonstrate the feasibility of accurate HR-based sleep staging. The combination of the illustrated sleep staging algorithm with an inexpensive HR device, provides a cost-effective and non-invasive solution easily deployable in the home.

scholarly journals Transfer Learning Strategies for Deep Learning-based PHM Algorithms

2020 ◽  
Vol 10 (7) ◽  
pp. 2361
Author(s):  
Fan Yang ◽  
Wenjin Zhang ◽  
Laifa Tao ◽  
Jian Ma
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Learning Strategies ◽  
Transfer Learning ◽  
High Speed ◽  
Health Management ◽  
Learning Algorithm ◽  
Learning Technology ◽  
Deep Learning Algorithm ◽  
Transfer Strategies

As we enter the era of big data, we have to face big data generated by industrial systems that are massive, diverse, high-speed, and variability. In order to effectively deal with big data possessing these characteristics, deep learning technology has been widely used. However, the existing methods require great human involvement that is heavily depend on domain expertise and may thus be non-representative and biased from task to similar task, so for a wide variety of prognostic and health management (PHM) tasks, how to apply the developed deep learning algorithms to similar tasks to reduce the amount of development and data collection costs has become an urgent problem. Based on the idea of transfer learning and the structures of deep learning PHM algorithms, this paper proposes two transfer strategies via transferring different elements of deep learning PHM algorithms, analyzes the possible transfer scenarios in practical application, and proposes transfer strategies applicable in each scenario. At the end of this paper, the deep learning algorithm of bearing fault diagnosis based on convolutional neural networks (CNN) is transferred based on the proposed method, which was carried out under different working conditions and for different objects, respectively. The experiments verify the value and effectiveness of the proposed method and give the best choice of transfer strategy.

scholarly journals Automated Arrhythmia Detection Based on RR Intervals

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1446
Author(s):  
Oliver Faust ◽  
Murtadha Kareem ◽  
Ali Ali ◽  
Edward J. Ciaccio ◽  
U. Rajendra Acharya
Keyword(s):  
Learning Algorithm ◽  
Cost Effective ◽  
Arrhythmia Detection ◽  
Rr Interval ◽  
Deep Learning Algorithm ◽  
Number Of Patients ◽  
Life Threatening ◽  
Term Monitoring ◽  
Fold Cross Validation

Abnormal heart rhythms, also known as arrhythmias, can be life-threatening. AFIB and AFL are examples of arrhythmia that affect a growing number of patients. This paper describes a method that can support clinicians during arrhythmia diagnosis. We propose a deep learning algorithm to discriminate AFIB, AFL, and NSR RR interval signals. The algorithm was designed with data from 4051 subjects. With 10-fold cross-validation, the algorithm achieved the following results: ACC = 99.98%, SEN = 100.00%, and SPE = 99.94%. These results are significant because they show that it is possible to automate arrhythmia detection in RR interval signals. Such a detection method makes economic sense because RR interval signals are cost-effective to measure, communicate, and process. Having such a cost-effective solution might lead to widespread long-term monitoring, which can help detecting arrhythmia earlier. Detection can lead to treatment, which improves outcomes for patients.

scholarly journals An AI-assisted Online Tool for Cognitive Impairment Detection Using Images from the Clock Drawing Test

2021 ◽  
Author(s):  
Samad Amini ◽  
Lifu Zhang ◽  
Boran Hao ◽  
Aman Gupta ◽  
Mengting Song ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cognitive Impairment ◽  
Screening Tool ◽  
Learning Algorithm ◽  
Cost Effective ◽  
Clock Drawing Test ◽  
Clock Drawing ◽  
Deep Learning Algorithm ◽  
Drawing Test ◽  
Deployable Mechanism

AbstractBackgroundWidespread early dementia detection could drastically increase clinical trial candidates and enable early interventions. Since the Clock Drawing Test (CDT) can be potentially used for diagnosing dementia related diseases, it can be leveraged to devise a computer-aided screening tool.ObjectiveThis work aims to develop an online screening tool by leveraging Artificial Intelligence and the CDT.MethodsImages of an analog clock drawn by 3, 263 cognitively intact and 160 impaired subjects were used. First, we processed the images from the CDT by a deep learning algorithm to obtain dementia scores. Then, individuals were classified as belonging to either category by combining CDT image scores with the participant’s age.ResultsWe have evaluated the performance of the developed models by applying 5-fold cross validation on 20% of the dataset. The deep learning model generates dementia scores for the CDT images with an Area Under the ROC Curve (AUC) of 81.3% ± 4.3%. A composite logistic regression model using age and the generated dementia scores, yielded an average AUC and average weighted F1 score of 92% ± 0.8% and 94.4% ± 0.7%, respectively.DiscussionCDT images were subjected to distortion consistent with an image drawn on paper and photographed by a cell phone. The model offers a cost-effective and easily deployable mechanism for detecting cognitive impairment online, without the need to visit a clinic.

scholarly journals Challenges in building image-based diagnostic support deep-learning algorithm for acute burns

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
C Boissin ◽  
J Fransén ◽  
F Huss ◽  
L Wallis ◽  
N Allorto ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Cost Effective ◽  
Clinical Image ◽  
Body Parts ◽  
Mortality And Morbidity ◽  
Deep Learning Algorithm ◽  
Burn Depth

Abstract Background Acute burns are complex to diagnose and erroneous assessments impact on the victim’s mortality and morbidity. Specialists operate from a small number of burns centres and the capacity to provide timely assistance to front line clinicians is therefore limited. Diagnostic assistance through artificial intelligence could be an option to provide a timely and equitable diagnosis. This project sheds light on the feasibility of the development of an artificial intelligence algorithm for assisted diagnosis of acute burns and clarifies challenges faced along the way. Methods A bank of images has been built from a number of burn centres in South Africa and is continuously being updated (currently about 1200 images). Attempts have been made to train deep learning algorithms to diagnose the burn depth, an element that is challenging both at bedside and using image-based teleconsultation. We came across methodological challenges that need further consideration. Results Some challenges are clinical, i.e. inherent to the complexity of burn diagnosis, and imply the need of having an accurate diagnosis for the burn images on which the algorithm is trained. Other challenges pertain to the actual development of an algorithm. Prior to identifying burn depth a complex task relates to the feasibility of finding the burn itself in images of varying body parts and backgrounds. Further, training an algorithm for diagnosing burn depth also requires, large numbers of varying cases, the accurate labelling of the wound area for training, and decisions to be made as regards the best outcome to train upon. Current preliminary results indicate satisfactory identification of the burn area and promising results with regards burn depth diagnosis. Conclusions Development of artificial intelligence algorithms require strong collaborations and discussions between technical and clinical experts but are showing promising results. Key messages Development of clinical image-based automated diagnosis involve a number of critical challenges on both the technical and clinical sides that need to be addressed prior to optimization. Once optimally developed, deep learning algorithms are a potential solution to assist with the reduction of the burden of burns on the health services by providing timely, and cost-effective advice.

scholarly journals Performance of a deep learning CNN model for the automated detection of 13 common conditions on Chest X-rays

2021 ◽  
Author(s):  
Tirupathi Karthik ◽  
Vijayalakshmi Kasiraman ◽  
Bhavani Paski ◽  
Kashyap Gurram ◽  
Amit Talwar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Delayed Diagnosis ◽  
Cost Effective ◽  
Radiology Workflow ◽  
X Rays ◽  
Term Care ◽  
X Ray ◽  
Intelligence System ◽  
Deep Learning Algorithm

Background and aims: Chest X-rays are widely used, non-invasive, cost effective imaging tests. However, the complexity of interpretation and global shortage of radiologists have led to reporting backlogs, delayed diagnosis and a compromised quality of care. A fully automated, reliable artificial intelligence system that can quickly triage abnormal images for urgent radiologist review would be invaluable in the clinical setting. The aim was to develop and validate a deep learning Convoluted Neural Network algorithm to automate the detection of 13 common abnormalities found on Chest X-rays. Method: In this retrospective study, a VGG 16 deep learning model was trained on images from the Chest-ray 14, a large publicly available Chest X-ray dataset, containing over 112,120 images with annotations. Images were split into training, validation and testing sets and trained to identify 13 specific abnormalities. The primary performance measures were accuracy and precision. Results: The model demonstrated an overall accuracy of 88% in the identification of abnormal X-rays and 87% in the detection of 13 common chest conditions with no model bias. Conclusion: This study demonstrates that a well-trained deep learning algorithm can accurately identify multiple abnormalities on X-ray images. As such models get further refined, they can be used to ease radiology workflow bottlenecks and improve reporting efficiency. Napier Healthcare’s team that developed this model consists of medical IT professionals who specialize in AI and its practical application in acute & long-term care settings. This is currently being piloted in a few hospitals and diagnostic labs on a commercial basis.

The Radio Probe™: A New Measurement Tool for High Speed Integrated Circuits

2005 ◽  
Author(s):  
Mark Kimball
Keyword(s):  
Integrated Circuits ◽  
Integrated Circuit ◽  
High Speed ◽  
Attenuation Factor ◽  
Cost Effective ◽  
Frequency Measurement ◽  
Single Frequency ◽  
Measurement Tool ◽  
Probe Design ◽  
Differential Measurement

Abstract This article presents a novel tool designed to allow circuit node measurements in a radio frequency (RF) integrated circuit. The discussion covers RF circuit problems; provides details on the Radio Probe design, which achieves an input impedance of 50Kohms and an overall attenuation factor of 0 dB; and describes signal to noise issues in the output signal, along with their improvement techniques. This cost-effective solution incorporates features that make it well suited to the task of differential measurement of circuit nodes within an RF IC. The Radio Probe concept offers a number of advantages compared to active probes. It is a single frequency measurement tool, so it complements, rather than replaces, active probes.

Sign in / Sign up

Export Citation Format

Share Document