Pub Date : 2020-12-09DOI: 10.1109/ISSPIT51521.2020.9408820
Bilal Hassan, S. Qin, Ramsha Ahmed
Optical coherence tomography (OCT) is a label-free, non-invasive imaging technique that is widely used in the diagnosis of various ophthalmic diseases. The diagnostic information related to these diseases is embodied in the texture and geometric features of the OCT scans, which are used by the retinal experts for interpretation and classification. However, due to the large number of OCT scans obtained every day, doctors and hospital staff are unable to meaningfully examine the potential retinal pathological conditions (RPCs), resulting in unexpected delays in the diagnosis and treatment of RPCs. In this paper, we propose an automated deep recurrent residual inception network, RRI-Net, for the classification of retinal OCT scans into diagnostically relevant classes, including healthy, age-related macular degeneration (AMD), diabetic macular edema (DME) and choroidal neovascularization (CNV). The proposed RRI-Net employs residual connections with cascaded multi-kernel convolutions to provide optimal training and classification results. In addition, we conducted extensive training of RRI-Net using 108,312 OCT scans, and tested the performance of the proposed framework over 1,000 OCT scans. The results show that RRI-Net achieves 98.8% accuracy in multi-class classification problem between healthy, AMD, DME and CNV, with 97.6% true positive rate and 99.2% true negative rate, outperforming other state-of-the-art methods.
{"title":"RRI-Net: Classification of Multi-class Retinal Diseases with Deep Recurrent Residual Inception Network using OCT Scans","authors":"Bilal Hassan, S. Qin, Ramsha Ahmed","doi":"10.1109/ISSPIT51521.2020.9408820","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408820","url":null,"abstract":"Optical coherence tomography (OCT) is a label-free, non-invasive imaging technique that is widely used in the diagnosis of various ophthalmic diseases. The diagnostic information related to these diseases is embodied in the texture and geometric features of the OCT scans, which are used by the retinal experts for interpretation and classification. However, due to the large number of OCT scans obtained every day, doctors and hospital staff are unable to meaningfully examine the potential retinal pathological conditions (RPCs), resulting in unexpected delays in the diagnosis and treatment of RPCs. In this paper, we propose an automated deep recurrent residual inception network, RRI-Net, for the classification of retinal OCT scans into diagnostically relevant classes, including healthy, age-related macular degeneration (AMD), diabetic macular edema (DME) and choroidal neovascularization (CNV). The proposed RRI-Net employs residual connections with cascaded multi-kernel convolutions to provide optimal training and classification results. In addition, we conducted extensive training of RRI-Net using 108,312 OCT scans, and tested the performance of the proposed framework over 1,000 OCT scans. The results show that RRI-Net achieves 98.8% accuracy in multi-class classification problem between healthy, AMD, DME and CNV, with 97.6% true positive rate and 99.2% true negative rate, outperforming other state-of-the-art methods.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134330293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-09DOI: 10.1109/ISSPIT51521.2020.9408852
Alejandro Giraldo-Quintero, Daniel Sierra-Sosa, Juan Guillermo Lalinde Pulido
Quantum Computing fast development is leading to the emergence of a wide variety of software development frameworks. In general, in these frameworks users can implement quantum algorithms and circuits, evaluating their behavior through simulations, and in some cases, executing them on Noisy Intermediate-Scale Quantum (NISQ) devices. IBM has been a pioneer in this field, providing public access to their devices through the IBM Q Experience Platform, using Python’s open-source framework Qiskit. In this paper, we present the development of a n-bitstring half-adder and half-subtractor algorithm in Qiskit, analyzing the behavior on the IBM Q Experience simulator and real quantum processors.
{"title":"Qiskit n-Bitstring Quantum Half-adder and Half-substractor","authors":"Alejandro Giraldo-Quintero, Daniel Sierra-Sosa, Juan Guillermo Lalinde Pulido","doi":"10.1109/ISSPIT51521.2020.9408852","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408852","url":null,"abstract":"Quantum Computing fast development is leading to the emergence of a wide variety of software development frameworks. In general, in these frameworks users can implement quantum algorithms and circuits, evaluating their behavior through simulations, and in some cases, executing them on Noisy Intermediate-Scale Quantum (NISQ) devices. IBM has been a pioneer in this field, providing public access to their devices through the IBM Q Experience Platform, using Python’s open-source framework Qiskit. In this paper, we present the development of a n-bitstring half-adder and half-subtractor algorithm in Qiskit, analyzing the behavior on the IBM Q Experience simulator and real quantum processors.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127682332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-09DOI: 10.1109/ISSPIT51521.2020.9408754
Zhaohui Wang
Wavelet-feature Markov clustering algorithm for the remotely sensed data is based on an accurate description of abrupt spectral features and an optimized Markov clustering in the wavelet feather space. The peak points can be captured and identified by applying wavelet transform on the expanded multispectral data. The correlation ratio between the two samples is a statistical calculation of the matched peak point positions on the wavelet-feature within an adjustable spectrum domain or a range of wavelet scales. The evenly sampled data can be used to create class centers, depending on the correlation ratio threshold at each Markov step, accelerating the clustering speed by avoiding computation of Euclidean distance for traditional clustering algorithms, such as K-means and ISODATA. By applying a simulated annealing method and gradually shrunk clustering size, Markov clustering leads to the best class centers quickly at each clustering temperature. The experimental results about TM data have verified its acceptable clustering accuracy and high convergence velocity.
{"title":"Unsupervised Wavelet-Feature Markov Clustering Algorithm for Remotely Sensed Images","authors":"Zhaohui Wang","doi":"10.1109/ISSPIT51521.2020.9408754","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408754","url":null,"abstract":"Wavelet-feature Markov clustering algorithm for the remotely sensed data is based on an accurate description of abrupt spectral features and an optimized Markov clustering in the wavelet feather space. The peak points can be captured and identified by applying wavelet transform on the expanded multispectral data. The correlation ratio between the two samples is a statistical calculation of the matched peak point positions on the wavelet-feature within an adjustable spectrum domain or a range of wavelet scales. The evenly sampled data can be used to create class centers, depending on the correlation ratio threshold at each Markov step, accelerating the clustering speed by avoiding computation of Euclidean distance for traditional clustering algorithms, such as K-means and ISODATA. By applying a simulated annealing method and gradually shrunk clustering size, Markov clustering leads to the best class centers quickly at each clustering temperature. The experimental results about TM data have verified its acceptable clustering accuracy and high convergence velocity.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131210525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-09DOI: 10.1109/ISSPIT51521.2020.9409010
S. Madasu
Sensor issues arise quite often in many fields such as data showing anomalous behavior or data being corrupt. This involves finding either faulty, noisy and malfunctioning sensors or anomalous behavior of the physical system deviating from the normal behavior indicating either new physics or the assumptions of the current model are being violated. This necessitates integration of domain-specific reduced form physics-based engineering models with data-driven modeling techniques to model effectively by covering wider data space. There could be sensors on the order of thousand but not every sensor is relevant and useful to the system modeling. Real-time drilling modeling is used as a prototype for demonstrating the new algorithm to deal with modeling efficiently virtual sensors. This paper provides a new real-time model with deep neural network (DNN) using a new hybrid physics/data driven algorithm that can intelligently pick the models to retrain and predict accurately for virtual sensing. This approach offers an improved and efficient methodology to arrive at the decision of whether the sensors are malfunctioning, or the physics models needs to be updated to model the new behavior. This method was applied to predict rate of penetration (ROP) with automatic sensor value predictions of hookload (HL), rotations per minute (RPM), pressure (P) and How rate (Q) for drilling. The physics model is obtained from the engineering models produced from domain insight. Thus, the modeling integrates reduced form physics-based engineering models into DNN framework. The generated data from the engineering model are needed to fill the void space in the surface not covered by the real-time measured data. The hybrid physics/data driven algorithm is fast, as the training is performed whenever the deviation occurs either between the model predictions and sensor values or ROP predictions deviate or both occur. The hybrid model uses the DNN framework to speed up the predictions and improve the accuracy of the ROP. The new hybrid modeling approach developed in this paper for virtual sensors can be applied to any real-time modeling system.
{"title":"A Hybrid Physics/Data Driven Modeling Approach for Virtual Sensors","authors":"S. Madasu","doi":"10.1109/ISSPIT51521.2020.9409010","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9409010","url":null,"abstract":"Sensor issues arise quite often in many fields such as data showing anomalous behavior or data being corrupt. This involves finding either faulty, noisy and malfunctioning sensors or anomalous behavior of the physical system deviating from the normal behavior indicating either new physics or the assumptions of the current model are being violated. This necessitates integration of domain-specific reduced form physics-based engineering models with data-driven modeling techniques to model effectively by covering wider data space. There could be sensors on the order of thousand but not every sensor is relevant and useful to the system modeling. Real-time drilling modeling is used as a prototype for demonstrating the new algorithm to deal with modeling efficiently virtual sensors. This paper provides a new real-time model with deep neural network (DNN) using a new hybrid physics/data driven algorithm that can intelligently pick the models to retrain and predict accurately for virtual sensing. This approach offers an improved and efficient methodology to arrive at the decision of whether the sensors are malfunctioning, or the physics models needs to be updated to model the new behavior. This method was applied to predict rate of penetration (ROP) with automatic sensor value predictions of hookload (HL), rotations per minute (RPM), pressure (P) and How rate (Q) for drilling. The physics model is obtained from the engineering models produced from domain insight. Thus, the modeling integrates reduced form physics-based engineering models into DNN framework. The generated data from the engineering model are needed to fill the void space in the surface not covered by the real-time measured data. The hybrid physics/data driven algorithm is fast, as the training is performed whenever the deviation occurs either between the model predictions and sensor values or ROP predictions deviate or both occur. The hybrid model uses the DNN framework to speed up the predictions and improve the accuracy of the ROP. The new hybrid modeling approach developed in this paper for virtual sensors can be applied to any real-time modeling system.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131365217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-09DOI: 10.1109/ISSPIT51521.2020.9408988
Bilal Hassan, S. Qin, Ramsha Ahmed
In ophthalmology, symptomatic exudate-associated derangement (SEAD) lesions play an important role in the timely intervention and treatment of maculopathy. Optical coherence tomography (OCT) imaging, due to its ability to visualize early symptoms linked with chronic retinal conditions, is mainly used for screening maculopathy and related SEAD lesions. However, in OCT scans, the inter- and intra-observer variability of manual estimation of SEAD lesions is high, which may lead to serious inconsistencies in the treatment of macular diseases. In this context, an automated SEAD segmentation algorithm can be regarded as a feasible approach. This paper proposes a novel deep encoder-decoder architecture called SEADNet, that performs the joint segmentation and extraction of three SEAD lesions including intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED). SEADNet comprises of three main modules, namely feature encoder, feature decoder and a newly introduced extractor module that further extracts the multi-scale enriched features of candidate SEAD lesions. The proposed framework is trained using 7064 OCT scans and tested over 4270 OCT scans acquired from three different OCT imaging devices. The simulation results show that the segmentation performance of SEADNet is better than the existing algorithms, with mean dice scores of 0.909, 0.913 and 0.918 for IRF, SRF and PED, respectively.
{"title":"SEADNet: Deep learning driven segmentation and extraction of macular fluids in 3D retinal OCT scans","authors":"Bilal Hassan, S. Qin, Ramsha Ahmed","doi":"10.1109/ISSPIT51521.2020.9408988","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408988","url":null,"abstract":"In ophthalmology, symptomatic exudate-associated derangement (SEAD) lesions play an important role in the timely intervention and treatment of maculopathy. Optical coherence tomography (OCT) imaging, due to its ability to visualize early symptoms linked with chronic retinal conditions, is mainly used for screening maculopathy and related SEAD lesions. However, in OCT scans, the inter- and intra-observer variability of manual estimation of SEAD lesions is high, which may lead to serious inconsistencies in the treatment of macular diseases. In this context, an automated SEAD segmentation algorithm can be regarded as a feasible approach. This paper proposes a novel deep encoder-decoder architecture called SEADNet, that performs the joint segmentation and extraction of three SEAD lesions including intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED). SEADNet comprises of three main modules, namely feature encoder, feature decoder and a newly introduced extractor module that further extracts the multi-scale enriched features of candidate SEAD lesions. The proposed framework is trained using 7064 OCT scans and tested over 4270 OCT scans acquired from three different OCT imaging devices. The simulation results show that the segmentation performance of SEADNet is better than the existing algorithms, with mean dice scores of 0.909, 0.913 and 0.918 for IRF, SRF and PED, respectively.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117349141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-09DOI: 10.1109/ISSPIT51521.2020.9408808
Mingwei Wang, Fangshun Li, Decui Liang
Consensus boost and opinion guidance are two important problems during the opinion management process. Considering that opinion interaction with opinion dynamics, this paper formalizes the two problems as markov decision process. To solve the two problems with minimum cost, we proposes consensus boost algorithm and opinion guidance algorithm based on reinforcement learning. Meantime, we construct opinion management framework by combining consensus boost algorithm and opinion guidance algorithm which is beneficial to the opinion management of managers. Finally, through experimental analysis, we verify the effectiveness and properties of the proposed framework.
{"title":"Opinion dynamics and consensus achievement strategy based on reinforcement learning","authors":"Mingwei Wang, Fangshun Li, Decui Liang","doi":"10.1109/ISSPIT51521.2020.9408808","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408808","url":null,"abstract":"Consensus boost and opinion guidance are two important problems during the opinion management process. Considering that opinion interaction with opinion dynamics, this paper formalizes the two problems as markov decision process. To solve the two problems with minimum cost, we proposes consensus boost algorithm and opinion guidance algorithm based on reinforcement learning. Meantime, we construct opinion management framework by combining consensus boost algorithm and opinion guidance algorithm which is beneficial to the opinion management of managers. Finally, through experimental analysis, we verify the effectiveness and properties of the proposed framework.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122203046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, a derivative-based MUSIC (DB-MUSIC) algorithm for two-dimensional (2-D) direction-of-arrival (DOA) estimation is proposed using an L-shaped uniform array. It transforms the traditional 2-D search problem into a one-dimensional (1-D) one using a derivative based optimization method, taking into consideration the structure of the steering vector and the associated cost function. As a result, the proposed algorithm has a significantly low computational complexity with the additional benefit of no need for 2-D angle pairing. Simulation results show that the proposed algorithm has better estimation accuracy than some existing representative 2-D DOA estimation algorithms falling into the same category, i.e., low complexity through 1-D search with no need for pairing.
{"title":"A Derivative-Based MUSIC Algorithm for Two-Dimensional Angle Estimation Employing an L-Shaped Array","authors":"Jingjing Cai, Huanyin Zhang, Wei Liu, Fuwei Tan, Yang-yang Dong","doi":"10.1109/ISSPIT51521.2020.9408790","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408790","url":null,"abstract":"In this paper, a derivative-based MUSIC (DB-MUSIC) algorithm for two-dimensional (2-D) direction-of-arrival (DOA) estimation is proposed using an L-shaped uniform array. It transforms the traditional 2-D search problem into a one-dimensional (1-D) one using a derivative based optimization method, taking into consideration the structure of the steering vector and the associated cost function. As a result, the proposed algorithm has a significantly low computational complexity with the additional benefit of no need for 2-D angle pairing. Simulation results show that the proposed algorithm has better estimation accuracy than some existing representative 2-D DOA estimation algorithms falling into the same category, i.e., low complexity through 1-D search with no need for pairing.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122240043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-09DOI: 10.1109/ISSPIT51521.2020.9408879
A. Syed, Anup Kumar, Daniel Sierra-Sosa, Adel Said Elmaghraby
Fall detection has been an important consideration in the field of human activity recognition and has garnered significant interest from researchers. A typical aim within fall detection systems is the determination of whether a fall has occurred or not. However, less attention has been provided to the problem of fall direction detection and severity. In this paper, we experiment with the detection of direction and severity in falls using the SisFall dataset. We perform this by using a combination of time and frequency domain features on inertial measurement sensor values along with a Support Vector Machine classifier. We are able to achieve promising results for the considered task.
{"title":"Determining Fall direction and severity using SVMs","authors":"A. Syed, Anup Kumar, Daniel Sierra-Sosa, Adel Said Elmaghraby","doi":"10.1109/ISSPIT51521.2020.9408879","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408879","url":null,"abstract":"Fall detection has been an important consideration in the field of human activity recognition and has garnered significant interest from researchers. A typical aim within fall detection systems is the determination of whether a fall has occurred or not. However, less attention has been provided to the problem of fall direction detection and severity. In this paper, we experiment with the detection of direction and severity in falls using the SisFall dataset. We perform this by using a combination of time and frequency domain features on inertial measurement sensor values along with a Support Vector Machine classifier. We are able to achieve promising results for the considered task.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122698674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-09DOI: 10.1109/ISSPIT51521.2020.9408845
Maria K. Krizea, J. Gialelis, Anastasios Kladas, G. Theodorou, Grigoris Protopsaltis, S. Koubias
In recent years, the demand for wrist wearable devices to monitor continuously critical physiological parameters in real time that are limited by designated hospital monitoring equipment is steadily increasing. In the medical field, one of the main issues that wearable devices could sufficiently address is the pervasive monitoring of vital signs and the corresponding health status assessment of the rapidly growing elderly population in real time. Main advantages in the adoption of wearable devices for the real time monitoring are the significant decrease of the cost both for the health system and subsequently the patient as well as the dramatic decrease of the waiting time in the hospital emergency rooms.Reflectance pulse oximetry being the right mode to be used at the wrist for measurements such as Heart Rate (HR), Peripheral Capillary Oxygen Saturation (SpO2) and Respiratory Rate (RR) imposes many technical challenges with its excessive sensitivity to all types of entailed artifacts due to arm/hand/body motions to be amongst the major ones.This work introduces a low-power wrist wearable device comprising a Photoplethysmography (PPG) array sensor special extraction algorithms to estimate HR and SpO2 parameters and a Multiple Linear Regression model, which after training performs considerable reduction of the imposed Motion Artifacts (Mas) thus enabling more accurate reading outputs.
{"title":"Accurate Detection of Heart Rate and Blood Oxygen Saturation in Reflective Photoplethysmography","authors":"Maria K. Krizea, J. Gialelis, Anastasios Kladas, G. Theodorou, Grigoris Protopsaltis, S. Koubias","doi":"10.1109/ISSPIT51521.2020.9408845","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408845","url":null,"abstract":"In recent years, the demand for wrist wearable devices to monitor continuously critical physiological parameters in real time that are limited by designated hospital monitoring equipment is steadily increasing. In the medical field, one of the main issues that wearable devices could sufficiently address is the pervasive monitoring of vital signs and the corresponding health status assessment of the rapidly growing elderly population in real time. Main advantages in the adoption of wearable devices for the real time monitoring are the significant decrease of the cost both for the health system and subsequently the patient as well as the dramatic decrease of the waiting time in the hospital emergency rooms.Reflectance pulse oximetry being the right mode to be used at the wrist for measurements such as Heart Rate (HR), Peripheral Capillary Oxygen Saturation (SpO2) and Respiratory Rate (RR) imposes many technical challenges with its excessive sensitivity to all types of entailed artifacts due to arm/hand/body motions to be amongst the major ones.This work introduces a low-power wrist wearable device comprising a Photoplethysmography (PPG) array sensor special extraction algorithms to estimate HR and SpO2 parameters and a Multiple Linear Regression model, which after training performs considerable reduction of the imposed Motion Artifacts (Mas) thus enabling more accurate reading outputs.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128650348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we study the robustness of least squares (LS) estimation for the modeling of nonlinear systems, and propose an estimation method with enhanced robustness. We first show some motivations for improving the robustness when estimating coefficients of a nonlinear model. In particular, without a robust estimation, two recent linearization techniques would fail to linearize a practical nonlinear system. Then, we analyze the commonly-used LS estimation in the application of the nonlinear system modeling, and show its poor robustness is originated from the correlation effects. As a result, the estimated coefficients will deviate unpredictably from the true coefficients. Based on the above analysis, we present a ridge regression method to remove the correlation effects, and hence improve the robustness of the coefficients estimation. Some data is captured from a practical 1-watt power amplifier (PA) to estimate the coefficients of the PA model, and the superiority of our estimation method over the conventional LS-based method is demonstrated.
{"title":"A Robust Estimation Method for Nonlinear Model Coefficients Using Ridge Regression","authors":"Qiang Xu, Wei Zhang, Guizhen Wang, Xiangjie Xia, Ying Liu, Youxi Tang","doi":"10.1109/ISSPIT51521.2020.9408986","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408986","url":null,"abstract":"In this paper, we study the robustness of least squares (LS) estimation for the modeling of nonlinear systems, and propose an estimation method with enhanced robustness. We first show some motivations for improving the robustness when estimating coefficients of a nonlinear model. In particular, without a robust estimation, two recent linearization techniques would fail to linearize a practical nonlinear system. Then, we analyze the commonly-used LS estimation in the application of the nonlinear system modeling, and show its poor robustness is originated from the correlation effects. As a result, the estimated coefficients will deviate unpredictably from the true coefficients. Based on the above analysis, we present a ridge regression method to remove the correlation effects, and hence improve the robustness of the coefficients estimation. Some data is captured from a practical 1-watt power amplifier (PA) to estimate the coefficients of the PA model, and the superiority of our estimation method over the conventional LS-based method is demonstrated.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130643243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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