scholarly journals Hands on Wheel Classification Based on Depth Images and Neural Networks

2020 ◽  
Vol 308 ◽  
pp. 06003
Author(s):  
Jan-Christoph Schmitz ◽  
Stephan Tilgner ◽  
Kathrin Kalischewski ◽  
Daniel Wagner ◽  
Anton Kummert
Keyword(s):  
Embedded System ◽  
Class Imbalance ◽  
Computational Effort ◽  
Training Data ◽  
Steering Wheel ◽  
Depth Information ◽  
Further Training ◽  
Depth Images ◽  
Hands On ◽  
Roof Module

This paper describes a system to automatically observe if the driver has his hands on the wheel, which is important to know that he can intervene if necessary. To accomplish this an artificial neural network is used, which utilizes depth information captured by a camera in the roof module of the car. This means that the driver and the steering wheel are viewed from above. The created classification system is described. It is designed to require as little computational effort as possible, since the target application is on an embedded system in the car. A dataset is presented and the effect of a class imbalance that is incorporated in it is studied. Furthermore, it is examined which part, i.e. the depth or the intensity image, of the available data is important to achieve the best possible performance. Finally, by examining a learning curve, an experiment is made to find out whether the recording of further training data would be reasonable.

scholarly journals Coronary Centerline Extraction from CCTA Using 3D-UNet

2021 ◽  
Vol 13 (4) ◽  
pp. 101
Author(s):  
Alexandru Dorobanțiu ◽  
Valentin Ogrean ◽  
Remus Brad
Keyword(s):  
Computed Tomography ◽  
Cardiac Computed Tomography ◽  
Three Dimensional ◽  
Class Imbalance ◽  
Training Data ◽  
Cardiac Computed Tomography Angiography ◽  
Centerline Extraction ◽  
Network Training ◽  
Dimensional Reconstruction ◽  
Coronary Centerline

The mesh-type coronary model, obtained from three-dimensional reconstruction using the sequence of images produced by computed tomography (CT), can be used to obtain useful diagnostic information, such as extracting the projection of the lumen (planar development along an artery). In this paper, we have focused on automated coronary centerline extraction from cardiac computed tomography angiography (CCTA) proposing a 3D version of U-Net architecture, trained with a novel loss function and with augmented patches. We have obtained promising results for accuracy (between 90–95%) and overlap (between 90–94%) with various network training configurations on the data from the Rotterdam Coronary Artery Centerline Extraction benchmark. We have also demonstrated the ability of the proposed network to learn despite the huge class imbalance and sparse annotation present in the training data.

scholarly journals Recognition of Symbolic Gestures Using Depth Information

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Hasan Mahmud ◽  
Md. Kamrul Hasan ◽  
Abdullah-Al-Tariq ◽  
Md. Hasanul Kabir ◽  
M. A. Mottalib
Keyword(s):  
Contextual Information ◽  
Depth Map ◽  
Recognition System ◽  
Support Vector ◽  
Svm Classifier ◽  
Depth Information ◽  
Scale Invariant ◽  
Binary Images ◽  
Depth Images ◽  
Symbolic Gestures

Symbolic gestures are the hand postures with some conventionalized meanings. They are static gestures that one can perform in a very complex environment containing variations in rotation and scale without using voice. The gestures may be produced in different illumination conditions or occluding background scenarios. Any hand gesture recognition system should find enough discriminative features, such as hand-finger contextual information. However, in existing approaches, depth information of hand fingers that represents finger shapes is utilized in limited capacity to extract discriminative features of fingers. Nevertheless, if we consider finger bending information (i.e., a finger that overlaps palm), extracted from depth map, and use them as local features, static gestures varying ever so slightly can become distinguishable. Our work here corroborated this idea and we have generated depth silhouettes with variation in contrast to achieve more discriminative keypoints. This approach, in turn, improved the recognition accuracy up to 96.84%. We have applied Scale-Invariant Feature Transform (SIFT) algorithm which takes the generated depth silhouettes as input and produces robust feature descriptors as output. These features (after converting into unified dimensional feature vectors) are fed into a multiclass Support Vector Machine (SVM) classifier to measure the accuracy. We have tested our results with a standard dataset containing 10 symbolic gesture representing 10 numeric symbols (0-9). After that we have verified and compared our results among depth images, binary images, and images consisting of the hand-finger edge information generated from the same dataset. Our results show higher accuracy while applying SIFT features on depth images. Recognizing numeric symbols accurately performed through hand gestures has a huge impact on different Human-Computer Interaction (HCI) applications including augmented reality, virtual reality, and other fields.

scholarly journals An Artificial Intelligence–Assisted Design Method for Topology Optimization without Pre-Optimized Training Data

2021 ◽  
Vol 11 (19) ◽  
pp. 9041
Author(s):  
Alex Halle ◽  
Lucio Flavio Campanile ◽  
Alexander Hasse
Keyword(s):  
Topology Optimization ◽  
Input Data ◽  
Design Method ◽  
Design Procedure ◽  
Computational Effort ◽  
Training Data ◽  
Geometry Design ◽  
Random Input Data ◽  
Degree Of Filling ◽  
Initial Process

Engineers widely use topology optimization during the initial process of product development to obtain a first possible geometry design. The state-of-the-art method is iterative calculation, which requires both time and computational power. This paper proposes an AI-assisted design method for topology optimization, which does not require any optimized data. An artificial neural network—the predictor—provides the designs on the basis of boundary conditions and degree of filling as input data. In the training phase, the so-called evaluators evaluate the generated geometries on the basis of random input data with respect to given criteria. The results of those evaluations flow into an objective function, which is minimized by adapting the predictor’s parameters. After training, the presented AI-assisted design procedure generates geometries that are similar to those of conventional topology optimizers, but require only a fraction of the computational effort. We believe that our work could be a clue for AI-based methods that require data that are difficult to compute or unavailable.

scholarly journals Automatic Landmark Detection of Human Back Surface from Depth Images via Deep Learning

2021 ◽  
Author(s):  
Masumeh Delgarmi ◽  
Hamed Heravi ◽  
Ali Rahimpour Jounghani ◽  
Abdullah Shahrezie ◽  
Afshin Ebrahimi ◽  
...  
Keyword(s):  
Network Architecture ◽  
The Body ◽  
Microsoft Kinect ◽  
Training Data ◽  
Back Surface ◽  
Anatomical Landmarks ◽  
X Rays ◽  
Depth Images ◽  
Physical Dysfunction ◽  
The Status

AbstractStudying human postural structure is one of the challenging issues among scholars and physicians. The spine is known as the central axis of the body, and due to various genetic and environmental reasons, it could suffer from deformities that cause physical dysfunction and correspondingly reduce people’s quality of life. Radiography is the most common method for detecting these deformities and requires monitoring and follow-up until full treatment; however, it frequently exposes the patient to X-rays and ionization and as a result, cancer risk is increased in the patient and could be highly dangerous for children or pregnant women. To prevent this, several solutions have been proposed using topographic data analysis of the human back surface. The purpose of this research is to provide an entirely safe and non-invasive method to examine the spiral structure and its deformities. Hence, it is attempted to find the exact location of anatomical landmarks on the human back surface, which provides useful and practical information about the status of the human postural structure to the physician.In this study, using Microsoft Kinect sensor, the depth images from the human back surface of 105 people were recorded and, our proposed approach - Deep convolution neural network-was used as a model to estimate the location of anatomical landmarks. In network architecture, two learning processes, including landmark position and affinity between the two associated landmarks, are successively performed in two separate branches. This is a bottom-up approach; thus, the runtime complexity is considerably reduced, and then the resulting anatomical points are evaluated concerning manual landmarks marked by the operator as the benchmark. Our results showed that 86.9% of PDJ and 80% of PCK. According to the results, this study was more effective than other methods with more than thousands of training data.

scholarly journals Multi-label pathway prediction based on active dataset subsampling

2020 ◽  
Author(s):  
Abdur Rahman M. A. Basher ◽  
Steven J. Hallam
Keyword(s):  
Machine Learning ◽  
Microbial Communities ◽  
Performance Metrics ◽  
Class Imbalance ◽  
Training Data ◽  
Great Promise ◽  
Biological Organization ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Pathway Prediction

AbstractMachine learning methods show great promise in predicting metabolic pathways at different levels of biological organization. However, several complications remain that can degrade prediction performance including inadequately labeled training data, missing feature information, and inherent imbalances in the distribution of enzymes and pathways within a dataset. This class imbalance problem is commonly encountered by the machine learning community when the proportion of instances over class labels within a dataset are uneven, resulting in poor predictive performance for underrepresented classes. Here, we present leADS, multi-label learning based on active dataset subsampling, that leverages the idea of subsampling points from a pool of data to reduce the negative impact of training loss due to class imbalance. Specifically, leADS performs an iterative process to: (i)-construct an acquisition model in an ensemble framework; (ii) select informative points using an appropriate acquisition function; and (iii) train on selected samples. Multiple base learners are implemented in parallel where each is assigned a portion of labeled training data to learn pathways. We benchmark leADS using a corpora of 10 experimental datasets manifesting diverse multi-label properties used in previous pathway prediction studies, including manually curated organismal genomes, synthetic microbial communities and low complexity microbial communities. Resulting performance metrics equaled or exceeded previously reported machine learning methods for both organismal and multi-organismal genomes while establishing an extensible framework for navigating class imbalances across diverse real world datasets.Availability and implementationThe software package, and installation instructions are published on github.com/[email protected]

Computer Intelligence in Healthcare

2013 ◽  
pp. 716-715 ◽  
Author(s):  
Pramit Ghosh ◽  
Debotosh Bhattacharjee ◽  
Mita Nasipuri ◽  
Dipak Kumar Basu
Keyword(s):  
Embedded System ◽  
Room Temperature ◽  
Cooling System ◽  
Low Cost ◽  
Environmental Parameters ◽  
Training Data ◽  
Data Sets ◽  
Air Conditioner ◽  
Time Data ◽  
Full Speed

Low cost solutions for the development of intelligent bio-medical devices that not only assist people to live in a better way but also assist physicians for better diagnosis are presented in this chapter. Two such devices are discussed here, which are helpful for prevention and diagnosis of diseases. Statistical analysis reveals that cold and fever are the main culprits for the loss of man-hours throughout the world, and early pathological investigation can reduce the vulnerability of disease and the sick period. To reduce this cold and fever problem a household cooling system controller, which is adaptive and intelligent in nature, is designed. It is able to control the speed of a household cooling fan or an air conditioner based on the real time data, namely room temperature, humidity, and time for which system is active, which are collected from environment. To control the speed in an adaptive and intelligent manner, an associative memory neural network (Kramer) has been used. This embedded system is able to learn from training set; i.e., the user can teach the system about his/her feelings through training data sets. When the system starts up, it allows the fan to run freely at full speed, and after certain interval, it takes the environmental parameters like room temperature, humidity, and time as inputs. After that, the system takes the decision and controls the speed of the fan.

A Framework for Homogeneous Cross-Project Defect Prediction

2021 ◽  
Vol 9 (1) ◽  
pp. 52-68
Author(s):  
Lipika Goel ◽  
Mayank Sharma ◽  
Sunil Kumar Khatri ◽  
D. Damodaran
Keyword(s):  
Class Imbalance ◽  
The Other ◽  
Training Data ◽  
Defect Prediction ◽  
Rank Test ◽  
Ensemble Model ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Proposed Model ◽  
Cross Project

Often, the prior defect data of the same project is unavailable; researchers thought whether the defect data of the other projects can be used for prediction. This made cross project defect prediction an open research issue. In this approach, the training data often suffers from class imbalance problem. Here, the work is directed on homogeneous cross-project defect prediction. A novel ensemble model that will perform in dual fold is proposed. Firstly, it will handle the class imbalance problem of the dataset. Secondly, it will perform the prediction of the target class. For handling the imbalance problem, the training dataset is divided into data frames. Each data frame will be balanced. An ensemble model using the maximum voting of all random forest classifiers is implemented. The proposed model shows better performance in comparison to the other baseline models. Wilcoxon signed rank test is performed for validation of the proposed model.

scholarly journals Remote Laboratory Portal for Robotic and Embedded System Experiments

2013 ◽  
Vol 9 (S8) ◽  
pp. 23 ◽  
Author(s):  
Raivo Sell
Keyword(s):  
Embedded System ◽  
Engineering Education ◽  
The Internet ◽  
Remote Laboratory ◽  
Experiment System ◽  
Practical Experiment ◽  
Hands On ◽  
E Learning ◽  
Education Process

Engineering education process is heavily relying on the practical hands-on experimentation. However, todayâ??s education is involving more and more e-learning aspects and learners expect to get most of the content and activity available over the Internet. Practical experiments is not trivial to carry out over the Internet, but using novel ICT technologies and integrated solution, it is possible to offer real experimentation over the Internet. This paper describes and presents the remote practical experiment system in robotic and embedded system domain.

An Investigation of Imbalanced Ensemble Learning Methods for Cross-Project Defect Prediction

2019 ◽  
Vol 33 (12) ◽  
pp. 1959037 ◽  
Author(s):  
Shaojian Qiu ◽  
Lu Lu ◽  
Siyu Jiang ◽  
Yang Guo
Keyword(s):  
Ensemble Learning ◽  
Class Imbalance ◽  
Training Data ◽  
Defect Prediction ◽  
Class Imbalance Problem ◽  
Learning Methods ◽  
Imbalance Problem ◽  
Intelligent Software ◽  
Under Sampling ◽  
Cross Project

Machine-learning-based software defect prediction (SDP) methods are receiving great attention from the researchers of intelligent software engineering. Most existing SDP methods are performed under a within-project setting. However, there usually is little to no within-project training data to learn an available supervised prediction model for a new SDP task. Therefore, cross-project defect prediction (CPDP), which uses labeled data of source projects to learn a defect predictor for a target project, was proposed as a practical SDP solution. In real CPDP tasks, the class imbalance problem is ubiquitous and has a great impact on performance of the CPDP models. Unlike previous studies that focus on subsampling and individual methods, this study investigated 15 imbalanced learning methods for CPDP tasks, especially for assessing the effectiveness of imbalanced ensemble learning (IEL) methods. We evaluated the 15 methods by extensive experiments on 31 open-source projects derived from five datasets. Through analyzing a total of 37504 results, we found that in most cases, the IEL method that combined under-sampling and bagging approaches will be more effective than the other investigated methods.

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