scholarly journals Improved RSSI-Based Data Augmentation Technique for Fingerprint Indoor Localisation

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 851 ◽  
Author(s):  
Rashmi Sharan Sinha ◽  
Seung-Hoon Hwang
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Indoor Localization ◽  
Laboratory Simulation ◽  
Test Accuracy ◽  
Learning Approaches ◽  
Positioning Systems ◽  
Positioning Algorithm ◽  
Received Signal Strength Indication ◽  
Indoor Localisation

Recently, deep-learning-based indoor localisation systems have attracted attention owing to their higher performance compared with traditional indoor localization systems. However, to achieve satisfactory performance, the former systems require large amounts of data to train deep learning models. Since obtaining the data is usually a tedious task, this requirement deters the use of deep learning approaches. To address this problem, we propose an improved data augmentation technique based on received signal strength indication (RSSI) values for fingerprint indoor positioning systems. The technique is implemented using available RSSI values at one reference point, and unlike existing techniques, it mimics the constantly varying RSSI signals. With this technique, the proposed method achieves a test accuracy of 95.26% in the laboratory simulation and 94.59% in a real-time environment, and the average location error is as low as 1.45 and 1.60 m, respectively. The method exhibits higher performance compared with an existing augmentation method. In particular, the data augmentation technique can be applied irrespective of the positioning algorithm used.

scholarly journals Data Augmentation Schemes for Deep Learning in an Indoor Positioning Application

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 554 ◽  
Author(s):  
Rashmi Sharan Sinha ◽  
Sang-Moon Lee ◽  
Minjoong Rim ◽  
Seung-Hoon Hwang
Keyword(s):  
Deep Learning ◽  
Indoor Environment ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Indoor Localization ◽  
Reference Points ◽  
Test Accuracy ◽  
Learning Approach ◽  
Location Error ◽  
User Location

In this paper, we propose two data augmentation schemes for deep learning architecture that can be used to directly estimate user location in an indoor environment using mobile phone tracking and electronic fingerprints based on reference points and access points. Using a pretrained model, the deep learning approach can significantly reduce data collection time, while the runtime is also significantly reduced. Numerical results indicate that an augmented training database containing seven days’ worth of measurements is sufficient to generate acceptable performance using a pretrained model. Experimental results find that the proposed augmentation schemes can achieve a test accuracy of 89.73% and an average location error that is as low as 2.54 m. Therefore, the proposed schemes demonstrate the feasibility of data augmentation using a deep neural network (DNN)-based indoor localization system that lowers the complexity required for use on mobile devices.

scholarly journals Deep Learning for Laryngopharyngeal Reflux Diagnosis

2021 ◽  
Vol 11 (11) ◽  
pp. 4753
Author(s):  
Gen Ye ◽  
Chen Du ◽  
Tong Lin ◽  
Yan Yan ◽  
Jack Jiang
Keyword(s):  
Deep Learning ◽  
Speech Processing ◽  
Data Augmentation ◽  
Laryngopharyngeal Reflux ◽  
Ph Monitoring ◽  
Binary Classification ◽  
Classification Problem ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Auc Value

(1) Background: Deep learning has become ubiquitous due to its impressive performance in various domains, such as varied as computer vision, natural language and speech processing, and game-playing. In this work, we investigated the performance of recent deep learning approaches on the laryngopharyngeal reflux (LPR) diagnosis task. (2) Methods: Our dataset is composed of 114 subjects with 37 pH-positive cases and 77 control cases. In contrast to prior work based on either reflux finding score (RFS) or pH monitoring, we directly take laryngoscope images as inputs to neural networks, as laryngoscopy is the most common and simple diagnostic method. The diagnosis task is formulated as a binary classification problem. We first tested a powerful backbone network that incorporates residual modules, attention mechanism and data augmentation. Furthermore, recent methods in transfer learning and few-shot learning were investigated. (3) Results: On our dataset, the performance is the best test classification accuracy is 73.4%, while the best AUC value is 76.2%. (4) Conclusions: This study demonstrates that deep learning techniques can be applied to classify LPR images automatically. Although the number of pH-positive images used for training is limited, deep network can still be capable of learning discriminant features with the advantage of technique.

scholarly journals SDR Based Indoor Beacon Localization Using 3D Probabilistic Multipath Exploitation and Deep Learning

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1323 ◽  
Author(s):  
Donald L. Hall ◽  
Ram M. Narayanan ◽  
David M. Jenkins
Keyword(s):  
Deep Learning ◽  
Software Defined Radio ◽  
High Performance ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Signal Propagation ◽  
Multipath Channel ◽  
K Nearest Neighbor ◽  
Positioning Systems ◽  
Channel Characteristics

Wireless indoor positioning systems (IPS) are ever-growing as traditional global positioning systems (GPS) are ineffective due to non-line-of-sight (NLoS) signal propagation. In this paper, we present a novel approach to learning three-dimensional (3D) multipath channel characteristics in a probabilistic manner for providing high performance indoor localization of wireless beacons. The proposed system employs a single triad dipole vector sensor (TDVS) for polarization diversity, a deep learning model deemed the denoising autoencoder to extract unique fingerprints from 3D multipath channel information, and a probabilistic k-nearest-neighbor (PkNN) to exploit the 3D multipath characteristics. The proposed system is the first to exploit 3D multipath channel characteristics for indoor wireless beacon localization via vector sensing methodologies, a software defined radio (SDR) platform, and multipath channel estimation.

scholarly journals An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Okeke Stephen ◽  
Mangal Sain ◽  
Uchenna Joseph Maduh ◽  
Do-Un Jeong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Data Augmentation ◽  
Classification Performance ◽  
Learning Approaches ◽  
X Ray ◽  
Chest X Ray

This study proposes a convolutional neural network model trained from scratch to classify and detect the presence of pneumonia from a collection of chest X-ray image samples. Unlike other methods that rely solely on transfer learning approaches or traditional handcrafted techniques to achieve a remarkable classification performance, we constructed a convolutional neural network model from scratch to extract features from a given chest X-ray image and classify it to determine if a person is infected with pneumonia. This model could help mitigate the reliability and interpretability challenges often faced when dealing with medical imagery. Unlike other deep learning classification tasks with sufficient image repository, it is difficult to obtain a large amount of pneumonia dataset for this classification task; therefore, we deployed several data augmentation algorithms to improve the validation and classification accuracy of the CNN model and achieved remarkable validation accuracy.

scholarly journals Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

Diagnostics ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 417 ◽  
Author(s):  
Mohammad Farukh Hashmi ◽  
Satyarth Katiyar ◽  
Avinash G Keskar ◽  
Neeraj Dhanraj Bokde ◽  
Zong Woo Geem
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Medical Center ◽  
Training Dataset ◽  
Test Accuracy ◽  
X Rays ◽  
Learning Models ◽  
Novel Approach ◽  
Unseen Data

Pneumonia causes the death of around 700,000 children every year and affects 7% of the global population. Chest X-rays are primarily used for the diagnosis of this disease. However, even for a trained radiologist, it is a challenging task to examine chest X-rays. There is a need to improve the diagnosis accuracy. In this work, an efficient model for the detection of pneumonia trained on digital chest X-ray images is proposed, which could aid the radiologists in their decision making process. A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way. This approach is a supervised learning approach in which the network predicts the result based on the quality of the dataset used. Transfer learning is used to fine-tune the deep learning models to obtain higher training and validation accuracy. Partial data augmentation techniques are employed to increase the training dataset in a balanced way. The proposed weighted classifier is able to outperform all the individual models. Finally, the model is evaluated, not only in terms of test accuracy, but also in the AUC score. The final proposed weighted classifier model is able to achieve a test accuracy of 98.43% and an AUC score of 99.76 on the unseen data from the Guangzhou Women and Children’s Medical Center pneumonia dataset. Hence, the proposed model can be used for a quick diagnosis of pneumonia and can aid the radiologists in the diagnosis process.

scholarly journals Simulation-based Data Augmentation for the Quality Inspection of Structural Adhesive with Deep Learning

2021 ◽  
Author(s):  
Ricardo Peres ◽  
Magno Guedes ◽  
Fábio Miranda ◽  
José Barata
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Data Availability ◽  
Training Data ◽  
Learning Approaches ◽  
Automotive Parts ◽  
Novel Method ◽  
The Cost ◽  
Structural Adhesive ◽  
Data Context

<div>The advent of Industry 4.0 has shown the tremendous transformative potential of combining artificial intelligence, cyber-physical systems and Internet of Things concepts in industrial settings. Despite this, data availability is still a major roadblock for the successful adoption of data-driven solutions, particularly concerning deep learning approaches in manufacturing. Specifically in the quality control domain, annotated defect data can often be costly, time-consuming and inefficient to obtain, potentially compromising the viability of deep learning approaches due to data scarcity. In this context, we propose a novel method for generating annotated synthetic training data for automated quality inspections of structural adhesive applications, validated in an industrial cell for automotive parts. Our approach greatly reduces the cost of training deep learning models for this task, while simultaneously improving their performance in a scarce manufacturing data context with imbalanced training sets by 3.1% ([email protected]). Additional results can be seen at https://git.io/Jtc4b.</div>

scholarly journals A DEEP LEARNING MODEL IMPLEMENTATION BASED ON RSSI FINGERPRINTING FOR LORA-BASED INDOOR LOCALIZATION

2021 ◽  
pp. 40-59
Author(s):  
Irsan Taufik Ali ◽  
Abdul Muis ◽  
Riri Fitri Sari
Keyword(s):  
Deep Learning ◽  
Environmental Changes ◽  
Indoor Localization ◽  
Dynamic Environment ◽  
Reference Database ◽  
Learning Approaches ◽  
Learning Methods ◽  
Success Stories ◽  
Natural Feature ◽  
A New Technique

LoRa technology has received a lot of attention in the last few years. Numerous success stories about using LoRa technology for the Internet of Things in various implementations. Several studies have found that the use of LoRa technology has the opportunity to be implemented in indoor-based applications. LoRa technology is found more stable and is more resilient to environmental changes. Environmental change of the indoor is a major problem to maintain accuracy in position prediction, especially in the use of Received Signal Strength (RSS) fingerprints as a reference database. The variety of approaches to solving accuracy problems continues to improve as the need for indoor localization applications increases. Deep learning approaches as a solution for the use of fingerprints in indoor localization have been carried out in several studies with various novelties offered. Let’s introduce a combination of the use of LoRa technology's excellence with a deep learning method that uses all variations of measurement results of RSS values at each position as a natural feature of the indoor condition as a fingerprint. All of these features are used for training in-deep learning methods. It is DeepFi-LoRaIn which illustrates a new technique for using the fingerprint data of the LoRa device's RSS device on indoor localization using deep learning methods. This method is used to find out how accurate the model produced by the training process is to predict the position in a dynamic environment. The scenario used to evaluate the model is by giving interference to the RSS value received at each anchor node. The model produced through training was found to have good accuracy in predicting the position even in conditions of interference with several anchor nodes. Based on the test results, DeepFi-LoRaIn Technique can be a solution to cope with changing environmental conditions in indoor localization

scholarly journals Synthetic Data Augmentation and Deep Learning for the Fault Diagnosis of Rotating Machines

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2336
Author(s):  
Asif Khan ◽  
Hyunho Hwang ◽  
Heung Soo Kim
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Data Augmentation ◽  
Binary Classification ◽  
Synthetic Data ◽  
Feature Space ◽  
Test Accuracy ◽  
Rotating Machines ◽  
Health States ◽  
Virtual Sensors

As failures in rotating machines can have serious implications, the timely detection and diagnosis of faults in these machines is imperative for their smooth and safe operation. Although deep learning offers the advantage of autonomously learning the fault characteristics from the data, the data scarcity from different health states often limits its applicability to only binary classification (healthy or faulty). This work proposes synthetic data augmentation through virtual sensors for the deep learning-based fault diagnosis of a rotating machine with 42 different classes. The original and augmented data were processed in a transfer learning framework and through a deep learning model from scratch. The two-dimensional visualization of the feature space from the original and augmented data showed that the latter’s data clusters are more distinct than the former’s. The proposed data augmentation showed a 6–15% improvement in training accuracy, a 44–49% improvement in validation accuracy, an 86–98% decline in training loss, and a 91–98% decline in validation loss. The improved generalization through data augmentation was verified by a 39–58% improvement in the test accuracy.

scholarly journals Evaluation of network architecture and data augmentation methods for deep learning in chemogenomics

2019 ◽  
Author(s):  
Benoit Playe ◽  
Véronique Stoven
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Data Augmentation ◽  
Representation Learning ◽  
Learning Approaches ◽  
Neuron Network ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Large Sets ◽  
Proteome Level

AbstractAmong virtual screening methods that have been developed to facilitate the drug discovery process, chemogenomics presents the particularity to tackle the question of predicting ligands for proteins, at at scales both in the protein and chemical spaces. Therefore, in addition to to predict drug candidates for a given therapeutic protein target, like more classical ligand-based or receptor-based methods do, chemogenomics can also predict off-targets at the proteome level, and therefore, identify potential side-effects or drug repositioning opportunities. In this study, we study and compare machine-learning and deep learning approaches for chemogenomics, that are applicable to screen large sets of compounds against large sets of druggable proteins. State-of-the-art drug chemogenomics methods rely on expert-based chemical and protein descriptors or similarity measures. The recent development of deep learning approaches enabled to design algorithms that learn numerical abstract representations of molecular graphs and protein sequences in an end-to-end fashion, i.e., so that the learnt features optimise the objective function of the drug-target interaction prediction task. In this paper, we address drug-target interaction prediction at the druggable proteome-level, with what we define as the chemogenomic neuron network. This network consists of a feed-forward neuron network taking as input the combination of molecular and protein representations learnt by molecular graph and protein sequence encoders. We first propose a standard formulation of this chemogenomic neuron network. Then, we compare the performances of the standard chemogenomic network to reference deep learning or shallow (machine-learning without deep learning) methods. In particular, we show that such a representation learning approach is competitive with state-of-the-art chemogenomics with shallow methods, but not ultimately superior. We evaluate the most promising neuron network architectures and data augmentation techniques, such as multi-view and transfer learning, to improve the prediction performance of the chemogenomic network. Our results shed new insights on the design of chemogenomics approaches based on representation learning algorithms. Most importantly, we conclude from our observations that a promising research direction is to integrate heterogeneous sources of data such as various bioactivity datasets, or independently, multiple molecule and protein attribute views, instead of focusing on sophisticated, yet intuitively relevant, encoder’s neuron network architecture.

scholarly journals An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification

Diagnostics ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1064
Author(s):  
Payam Hosseinzadeh Kasani ◽  
Sang-Won Park ◽  
Jae-Won Jang
Keyword(s):  
Bone Marrow ◽  
Deep Learning ◽  
Blood Cells ◽  
Data Augmentation ◽  
Learning Strategy ◽  
Bone Marrow Failure ◽  
Lymphoblastic Leukemia ◽  
Test Accuracy ◽  
Clinical Factor ◽  
Major Interest

Leukemia is a cancer of blood cells in the bone marrow that affects both children and adolescents. The rapid growth of unusual lymphocyte cells leads to bone marrow failure, which may slow down the production of new blood cells, and hence increases patient morbidity and mortality. Age is a crucial clinical factor in leukemia diagnosis, since if leukemia is diagnosed in the early stages, it is highly curable. Incidence is increasing globally, as around 412,000 people worldwide are likely to be diagnosed with some type of leukemia, of which acute lymphoblastic leukemia accounts for approximately 12% of all leukemia cases worldwide. Thus, the reliable and accurate detection of normal and malignant cells is of major interest. Automatic detection with computer-aided diagnosis (CAD) models can assist medics, and can be beneficial for the early detection of leukemia. In this paper, a single center study, we aimed to build an aggregated deep learning model for Leukemic B-lymphoblast classification. To make a reliable and accurate deep learner, data augmentation techniques were applied to tackle the limited dataset size, and a transfer learning strategy was employed to accelerate the learning process, and further improve the performance of the proposed network. The results show that our proposed approach was able to fuse features extracted from the best deep learning models, and outperformed individual networks with a test accuracy of 96.58% in Leukemic B-lymphoblast diagnosis.

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