Robustness of machine learning pedestrian signal detection applied to pedestrian guidance device for persons with visual impairment

2018 ◽  
Author(s):  
Shinnosuke Suda ◽  
Kengo Ohnishi ◽  
Yuki Iwazaki ◽  
Takuya Asami
Keyword(s):  
Machine Learning ◽  
Signal Detection ◽  
Visual Impairment ◽  
Guidance Device

scholarly journals Traffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning

2019 ◽  
Vol 10 (1) ◽  
pp. 101-119 ◽  
Author(s):  
Y. Méneroux ◽  
A. Le Guilcher ◽  
G. Saint Pierre ◽  
M. Ghasemi Hamed ◽  
S. Mustière ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Signal Detection ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Traffic Signal

scholarly journals Prediction of Function in ABCA4-Related Retinopathy Using Ensemble Machine Learning

2020 ◽  
Vol 9 (8) ◽  
pp. 2428 ◽  
Author(s):  
Philipp L. Müller ◽  
Tim Treis ◽  
Alexandru Odainic ◽  
Maximilian Pfau ◽  
Philipp Herrmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
Visual Impairment ◽  
Age Of Onset ◽  
Outer Nuclear Layer ◽  
Stargardt Disease ◽  
Full Field ◽  
Corrected Visual Acuity ◽  
Ensemble Machine Learning ◽  
Prediction Of Function ◽  
Patient Burden

Full-field electroretinogram (ERG) and best corrected visual acuity (BCVA) measures have been shown to have prognostic value for recessive Stargardt disease (also called “ABCA4-related retinopathy”). These functional tests may serve as a performance-outcome-measure (PerfO) in emerging interventional clinical trials, but utility is limited by variability and patient burden. To address these limitations, an ensemble machine-learning-based approach was evaluated to differentiate patients from controls, and predict disease categories depending on ERG (‘inferred ERG’) and visual impairment (‘inferred visual impairment’) as well as BCVA values (‘inferred BCVA’) based on microstructural imaging (utilizing spectral-domain optical coherence tomography) and patient data. The accuracy for ‘inferred ERG’ and ‘inferred visual impairment’ was up to 99.53 ± 1.02%. Prediction of BCVA values (‘inferred BCVA’) achieved a precision of ±0.3LogMAR in up to 85.31% of eyes. Analysis of the permutation importance revealed that foveal status was the most important feature for BCVA prediction, while the thickness of outer nuclear layer and photoreceptor inner and outer segments as well as age of onset highly ranked for all predictions. ‘Inferred ERG’, ‘inferred visual impairment’, and ‘inferred BCVA’, herein, represent accurate estimates of differential functional effects of retinal microstructure, and offer quasi-functional parameters with the potential for a refined patient assessment, and investigation of potential future treatment effects or disease progression.

scholarly journals Postsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Fatin Nabihah Jais ◽  
Mohd Zulfaezal Che Azemin ◽  
Mohd Radzi Hilmi ◽  
Mohd Izzuddin Mohd Tamrin ◽  
Khairidzan Mohd Kamal
Keyword(s):  
Machine Learning ◽  
Visual Acuity ◽  
Visual Impairment ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Machine Learning Technique ◽  
Corrected Visual Acuity ◽  
Learning Technique ◽  
Best Corrected Visual Acuity

Introduction. Early detection of visual symptoms in pterygium patients is crucial as the progression of the disease can cause visual disruption and contribute to visual impairment. Best-corrected visual acuity (BCVA) and corneal astigmatism influence the degree of visual impairment due to direct invasion of fibrovascular tissue into the cornea. However, there were different characteristics of pterygium used to evaluate the severity of visual impairment, including fleshiness, size, length, and redness. The innovation of machine learning technology in visual science may contribute to developing a highly accurate predictive analytics model of BCVA outcomes in postsurgery pterygium patients. Aim. To produce an accurate model of BCVA changes of postpterygium surgery according to its morphological characteristics by using the machine learning technique. Methodology. A retrospective of the secondary dataset of 93 samples of pterygium patients with different pterygium attributes was used and imported into four different machine learning algorithms in RapidMiner software to predict the improvement of BCVA after pterygium surgery. Results. The performance of four machine learning techniques were evaluated, and it showed the support vector machine (SVM) model had the highest average accuracy (94.44% ± 5.86%), specificity (100%), and sensitivity (92.14% ± 8.33%). Conclusion. Machine learning algorithms can produce a highly accurate postsurgery classification model of BCVA changes using pterygium characteristics.

Machine Learning Assisted Signal Detection in Ambient Backscatter Communication Networks

2021 ◽  
Author(s):  
Usman Saleh Toro ◽  
Basem M. Elhalawany ◽  
Aslan B Wong ◽  
Lu Wang ◽  
Kaishun Wu
Keyword(s):  
Machine Learning ◽  
Signal Detection ◽  
Communication Networks ◽  
Backscatter Communication ◽  
Ambient Backscatter

The paper describes ML techniques that can be leveraged to assist signal detection in ambient backscatter communication networks.

Machine Learning-based Signal Detection for PMH Signals in Load-modulated MIMO System

2021 ◽  
pp. 1-1
Author(s):  
Jinle Zhu ◽  
Qiang Li ◽  
Li Hu ◽  
Hongyang Chen ◽  
Nirwan Ansari
Keyword(s):  
Machine Learning ◽  
Signal Detection ◽  
Mimo System

scholarly journals Improving signal detection accuracy at FC of a CRN using machine learning and fuzzy rules

2021 ◽  
Vol 21 (2) ◽  
pp. 1140
Author(s):  
Md Abul Kalam Azad ◽  
Anup Majumder ◽  
Jugal Krishna Das ◽  
Md Imdadul Islam
Keyword(s):  
Machine Learning ◽  
Signal Detection ◽  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Energy Detection ◽  
Decision Time ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Detection Accuracy ◽  
Inference System

<span>The performance of a cognitive radio network (CRN) mainly depends on the faithful signal detection at fusion center (FC). In this paper, the concept of weighted Fuzzy rule in Iris data classification, as well as, four machine learning techniques named fuzzy inference system (FIS), fuzzy <em>c</em>-means clustering (FCMC), support vector machine (SVM) and convolutional neural network (CNN) are applied in signal detection at FC taking signal-to-interference plus noise ratio of secondary users as parameter. The weighted Fuzzy rule gave the detection accuracy of 86.6%, which resembles the energy detection model of majority rule of FC; however, CNN gave an accuracy of 91.3% at the expense of more decision time. The FIS, FCMC and SVM gave some intermediate results; however, the combined method gave the best result compared to that of any individual technique.</span>

scholarly journals Machine Learning Assisted Signal Detection in Ambient Backscatter Communication Networks

2021 ◽  
Author(s):  
Usman Saleh Toro ◽  
Basem M. Elhalawany ◽  
Aslan B Wong ◽  
Lu Wang ◽  
Kaishun Wu
Keyword(s):  
Machine Learning ◽  
Signal Detection ◽  
Communication Networks ◽  
Backscatter Communication ◽  
Ambient Backscatter

The paper describes ML techniques that can be leveraged to assist signal detection in ambient backscatter communication networks.

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