Metric learning aims to measure the similarity among samples while using an optimal distance metric for learning tasks. Metric learning methods, which generally use a linear projection, are limited in solving real-world problems demonstrating non-linear characteristics. Kernel approaches are utilized in metric learning to address this problem. In recent years, deep metric learning, which provides a better solution for nonlinear data through activation functions, has attracted researchers’ attention in many different areas. This article aims to reveal the importance of deep metric learning and the problems dealt with in this field in the light of recent studies. As far as the research conducted in this field are concerned, most existing studies that are inspired by Siamese and Triplet networks are commonly used to correlate among samples while using shared weights in deep metric learning. The success of these networks is based on their capacity to understand the similarity relationship among samples. Moreover, sampling strategy, appropriate distance metric, and the structure of the network are the challenging factors for researchers to improve the performance of the network model. This article is considered to be important, as it is the first comprehensive study in which these factors are systematically analyzed and evaluated as a whole and supported by comparing the quantitative results of the methods.

Deep Metric Learning: A Survey

Cardiac arrhythmia is a condition when the heart rate is irregular either the beat is too slow or too fast. It occurs due to improper electrical impulses that coordinates the heart beats. Sudden cardiac death may occurs due to some dangerous arrhythmias conditions. Hence the main objective of the electrocardiogram (ECG) analysis is to detect the life-threatening arrhythmias accurately for appropriate treatment in order to save life. Since the last decades, several methods were reported for automatic ECG beat classifications. In this work, we present a systematic review of the current state-of-the-art methods used to detect cardiac arrhythmia using on ECG signals. It includes the signal decomposition, feature extraction and machine learning approaches used for automatic detection and decision making process. The articles covers the pre-processing, detection of QRS complex, feature extraction and classification of ECG beats. Based on the past studies, it is understood that the automated approach using computer-aided decision making process is highly required for real-time detection of cardiac arrhythmias. The advantages and limitations of different methods are discussed and also the future scopes is highlighted in the process of effective detection of cardiac arrhythmias. This study could be beneficial for researchers to analyze the existing state-of-art techniques used in detection of arrhythmia conditions.

Machine Learning Approach to Detect Cardiac Arrhythmias in ECG Signals: A Survey

Network-based Intrusion Detection Systems (NIDSs) identify malicious activities by analyzing network traffic. NIDSs are trained with the samples of benign and intrusive network traffic. Training samples belong to either majority or minority classes depending upon the number of available instances. Majority classes consist of abundant samples for the normal traffic as well as for recurrent intrusions. Whereas, minority classes include fewer samples for unknown events or infrequent intrusions. NIDSs trained on such imbalanced data tend to give biased predictions against minority attack classes, causing undetected or misclassified intrusions. Past research works handled this class imbalance problem using data-level approaches that either increase minority class samples or decrease majority class samples in the training data set. Although these data-level balancing approaches indirectly improve the performance of NIDSs, they do not address the underlying issue in NIDSs i.e. they are unable to identify attacks having limited training data only. This paper proposes an algorithm-level approach called Improved Siam-IDS (I-SiamIDS), which is a two-layer ensemble for handling class imbalance problem. I-SiamIDS identifies both majority and minority classes at the algorithm-level without using any data-level balancing techniques. The first layer of I-SiamIDS uses an ensemble of binary eXtreme Gradient Boosting (b-XGBoost), Siamese Neural Network (Siamese-NN) and Deep Neural Network (DNN) for hierarchical filtration of input samples to identify attacks. These attacks are then sent to the second layer of I-SiamIDS for classification into different attack classes using multi-class eXtreme Gradient Boosting classifier (m-XGBoost). As compared to its counterparts, I-SiamIDS showed significant improvement in terms of Accuracy, Recall, Precision, F1-score and values of Area Under the Curve (AUC) for both NSL-KDD and CIDDS-001 datasets. To further strengthen the results, computational cost analysis was also performed to study the acceptability of the proposed I-SiamIDS.

https://link.springer.com/content/pdf/10.1007/s10489-020-01886-y.pdf

I-SiamIDS: an improved Siam-IDS for handling class imbalance in network-based intrusion detection systems

Siam-IDS: Handling class imbalance problem in Intrusion Detection Systems using Siamese Neural Network

The displacement responses of a civil engineering structure can provide important information regarding structural behaviors that help in assessing safety and serviceability. A displacement measurement using conventional devices, such as the linear variable differential transformer (LVDT), is challenging owing to issues related to inconvenient sensor installation that often requires additional temporary structures. A promising alternative is offered by computer vision, which typically provides a low-cost and non-contact displacement measurement that converts the movement of an object, mostly an attached marker, in the captured images into structural displacement. However, there is limited research on addressing light-induced measurement error caused by the inevitable sunlight in field-testing conditions. This study presents a computer vision-based displacement measurement approach tailored to a field-testing environment with enhanced robustness to strong sunlight. An image-processing algorithm with an adaptive region-of-interest (ROI) is proposed to reliably determine a marker's location even when the marker is indistinct due to unfavorable light. The performance of the proposed system is experimentally validated in both laboratory-scale and field experiments.

https://www.mdpi.com/1424-8220/17/10/2317/pdf

Computer Vision-Based Structural Displacement Measurement Robust to Light-Induced Image Degradation for In-Service Bridges

Electrocardiogram signal analysis is based on detecting a fiducial point consisting of the onset, offset, and peak of each waveform. The accurate diagnosis of arrhythmias depends on the accuracy of fiducial point detection. Detecting the onset and offset fiducial points is ambiguous because the feature values are similar to those of the surrounding sample. To improve the accuracy of this paper’s fiducial point detection, the signal is represented by a small number of vertices through a curvature-based vertex selection technique using polygonal approximation. The proposed method minimizes the number of candidate samples for fiducial point detection and emphasizes these sample’s feature values to enable reliable detection. It is also sensitive to the morphological changes of various QRS complexes by generating an accumulated signal of the amplitude change rate between vertices as an auxiliary signal. To verify the superiority of the proposed algorithm, error distribution is measured through comparison with the QT-DB annotation provided by Physionet. The mean and standard deviation of the onset and the offset were stable as − 4.02 ± 7.99 ms and − 5.45 ± 8.04 ms, respectively. The results show that proposed method using small number of vertices is acceptable in practical applications. We also confirmed that the proposed method is effective through the clustering of the QRS complex. Experiments on the arrhythmia data of MIT-BIH ADB confirmed reliable fiducial point detection results for various types of QRS complexes.

/pdf/efficient-fiducial-point-detection-of-ecg-qrs-complex-based-5cugg5l98n.pdf

Efficient Fiducial Point Detection of ECG QRS Complex Based on Polygonal Approximation.

Recently, there have been many studies on the automatic extraction of facial information using machine learning. Age estimation from frontal face images is becoming important, with various applications. Our proposed work is based on a binary classifier that only determines whether two input images are clustered in a similar class and trains a convolutional neural network (CNN) model using the deep metric learning method based on the Siamese network. To converge the results of the training Siamese network, two classes, for which age differences are below a certain level of distance, are considered as the same class, so the ratio of positive database images is increased. The deep metric learning method trains the CNN model to measure similarity based only on age data, but we found that the accumulated gender data can also be used to compare ages. Thus, we adopted a multi-task learning approach to consider the gender data for more accurate age estimation. In the experiment, we evaluated our approach using MORPH and MegaAge-Asian datasets, and compared gender classification accuracy only using age data from the training images. In addition, using gender classification, our proposed architecture, which is trained with only age data, performs age comparison using the self-generated gender feature. The accuracy enhancement by multi-task learning, i.e. simultaneously considering age and gender data, is discussed. Our approach results in the best accuracy among the methods based on deep metric learning on MORPH dataset. Additionally, our method has better results than the state of the art in terms of age estimation on MegaAge-Asian and MORPH datasets.

Accurate Age Estimation Using Multi-Task Siamese Network-Based Deep Metric Learning for Frontal Face Images

In this study, we have proposed an algorithm that solves the problems which occur during the recognition of a vehicle license plate through closed-circuit television (CCTV) by using a deep learning model trained with a general database. The deep learning model which is commonly used suffers with a disadvantage of low recognition rate in the tilted and low-resolution images, as it is trained with images acquired from the front of the license plate. Furthermore, the vehicle images acquired by using CCTV have issues such as limitation of resolution and perspective distortion. Such factors make it difficult to apply the commonly used deep learning model. To improve the recognition rate, an algorithm which is a combination of the super-resolution generative adversarial network (SRGAN) model, and the perspective distortion correction algorithm is proposed in this paper. The accuracy of the proposed algorithm was verified with a character recognition algorithm YOLO v2, and the recognition rate of the vehicle license plate image was improved 8.8% from the original images.

Recognition of Vehicle License Plates Based on Image Processing

Arrhythmia is less frequent than a normal heartbeat in an electrocardiogram signal, and the analysis of an electrocardiogram measurement can require more than 24 hours. Therefore, the efficient storage and transmission of electrocardiogram signals have been studied, and their importance has increased recently due to the miniaturization and weight reduction of measurement equipment. The polygonal approximation method based on dynamic programming can effectively achieve signal compression and fiducial point detection by expressing signals with a small number of vertices. However, the execution time and memory area rapidly increase depending on the length of the signal and number of vertices, which are not suitable for lightweight and miniaturized equipment. In this paper, we propose a method that can be applied in embedded environments by optimizing the processing time and memory usage of dynamic programming applied to the polygonal approximation of an ECG signal. The proposed method is divided into three steps to optimize the processing time and memory usage of dynamic programming. The first optimization step is based on the characteristics of electrocardiogram signals in the polygonal approximation. Second, the size of a data bit is used as the threshold for the time difference of each vertex. Finally, a type conversion and memory optimization are applied, which allow real-time processing in embedded environments. After analyzing the performance of the proposed algorithm for a signal length L and number of vertices N, the execution time is reduced from O(L
 2
N) to O(L), and the memory usage is reduced from O(L
 2
N) to O(LN). In addition, the proposed method preserve a performance of fiducial point detection. In a QT-DB experiment provided by Physionet, achieving values of -4.01 ± 7.99 ms and -5.46 ± 8.03 ms.

/pdf/advanced-real-time-dynamic-programming-in-the-polygonal-17ht3munly.pdf

Advanced Real-Time Dynamic Programming in the Polygonal Approximation of ECG Signals for a Lightweight Embedded Device

This paper presents a pan-tilt-zoom (PTZ) camera-based displacement measurement system, specially based on the perspective distortion correction technique for the early detection of building destruction. The proposed PTZ-based vision system rotates the camera to monitor the specific targets from various distances and controls the zoom level of the lens for a constant field of view (FOV). The proposed approach adopts perspective distortion correction to expand the measurable range in monitoring the displacement of the target structure. The implemented system successfully obtains the displacement information in structures, which is not easily accessible on the remote site. We manually measured the displacement acquired from markers which is attached on a sample of structures covering a wide geographic region. Our approach using a PTZ-based camera reduces the perspective distortion, so that the improved system could overcome limitations of previous works related to displacement measurement. Evaluation results show that a PTZ-based displacement sensor system with the proposed distortion correction unit is possibly a cost effective and easy-to-install solution for commercialization.

Yoosoo Jeong

Papers

Efficient Fiducial Point Detection of ECG QRS Complex Based on Polygonal Approximation.

Accurate Age Estimation Using Multi-Task Siamese Network-Based Deep Metric Learning for Frontal Face Images

Recognition of Vehicle License Plates Based on Image Processing

Advanced Real-Time Dynamic Programming in the Polygonal Approximation of ECG Signals for a Lightweight Embedded Device

PTZ Camera-Based Displacement Sensor System with Perspective Distortion Correction Unit for Early Detection of Building Destruction