Pub Date : 2023-07-02DOI: 10.1109/SSP53291.2023.10207956
Jiachao Liang, Yongwei Huang
Consider a joint optimal transmit waveform and receive beamforming design problem for a dual-functional radar and communication (DFRC) system. The DFRC base station sends signals to communicate with the downlink users while detecting a multiple-input multiple-output radar target. The system performance is evaluated by an affine combination between the communication multi-user interference energy and the reciprocal of the radar output signal-to-interference-plus-noise ratio. Then a joint minimization problem of the affine function is formulated, subject to constant modulus constraints. This is a typical nonconvex optimization problem. In the paper, we propose a new online learning network (OLN) scheme to solve it, by setting proper trainable network parameters, formulating a loss function, and selecting a suitable learning rate for the OLN. Simulation results are presented to demonstrate the higher performance for the DFRC system by the proposed OLN method than that by a traditional optimization method.
{"title":"Online Learning Network Methods for a Joint Transmit Waveform and Receive Beamforming Design for a DFRC System","authors":"Jiachao Liang, Yongwei Huang","doi":"10.1109/SSP53291.2023.10207956","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207956","url":null,"abstract":"Consider a joint optimal transmit waveform and receive beamforming design problem for a dual-functional radar and communication (DFRC) system. The DFRC base station sends signals to communicate with the downlink users while detecting a multiple-input multiple-output radar target. The system performance is evaluated by an affine combination between the communication multi-user interference energy and the reciprocal of the radar output signal-to-interference-plus-noise ratio. Then a joint minimization problem of the affine function is formulated, subject to constant modulus constraints. This is a typical nonconvex optimization problem. In the paper, we propose a new online learning network (OLN) scheme to solve it, by setting proper trainable network parameters, formulating a loss function, and selecting a suitable learning rate for the OLN. Simulation results are presented to demonstrate the higher performance for the DFRC system by the proposed OLN method than that by a traditional optimization method.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115481965","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10208079
Furkan Batuhan Okumus, E. Panayirci, M. Khalighi
In this paper, we propose a new and low-complexity channel estimation algorithm for the generalized LED index modulation (GLIM), recently proposed for visible-light communication systems based on multi-input multi-output (MIMO) and orthogonal frequency-division multiplexing (OFDM). For this scheme, denoted by GLIM-OFDM, we investigate the bit-error rate (BER), the mean-square error (MSE) of channel estimation, as well as the Cramer-Rao bound on the latter. Furthermore, we present a novel physical layer security (PLS) technique for the GLIM-OFDM scheme using precoding at the transmitter assuming it has the channel state information (CSI) between the LEDs and a legitimate user, but no knowledge of the CSI corresponding to eavesdroppers. The efficiency of the proposed PLS technique is demonstrated through numerical results.
{"title":"Channel Estimation and Physical Layer Security in Optical MIMO-OFDM based LED Index Modulation","authors":"Furkan Batuhan Okumus, E. Panayirci, M. Khalighi","doi":"10.1109/SSP53291.2023.10208079","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208079","url":null,"abstract":"In this paper, we propose a new and low-complexity channel estimation algorithm for the generalized LED index modulation (GLIM), recently proposed for visible-light communication systems based on multi-input multi-output (MIMO) and orthogonal frequency-division multiplexing (OFDM). For this scheme, denoted by GLIM-OFDM, we investigate the bit-error rate (BER), the mean-square error (MSE) of channel estimation, as well as the Cramer-Rao bound on the latter. Furthermore, we present a novel physical layer security (PLS) technique for the GLIM-OFDM scheme using precoding at the transmitter assuming it has the channel state information (CSI) between the LEDs and a legitimate user, but no knowledge of the CSI corresponding to eavesdroppers. The efficiency of the proposed PLS technique is demonstrated through numerical results.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122805819","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10208051
T. Chu, H. Zepernick, T. Duong
Fifth-generation (5G) and future sixth-generation (6G) mobile networks aim at offering ultra-reliable, low-latency, and massive machine-type communications. In this context, this paper studies full-duplex (FD) cooperative non-orthogonal multiple access (C-NOMA) short-packet communications (SPC) with K-means clustering of user equipment regarding block error rate (BLER) and sum rate. Analytical expressions are derived for the BLER and sum rate allowing to assess the performance of the considered system. The numerical results reveal the benefits of the FD C-NOMA SPC system, illustrate trade-offs between BLER and sum rate, and show the impact of the transmit signal-to-noise ratio and number of channel uses on the performance.
{"title":"Full-Duplex Cooperative NOMA Short-Packet Communications with K-Means Clustering","authors":"T. Chu, H. Zepernick, T. Duong","doi":"10.1109/SSP53291.2023.10208051","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208051","url":null,"abstract":"Fifth-generation (5G) and future sixth-generation (6G) mobile networks aim at offering ultra-reliable, low-latency, and massive machine-type communications. In this context, this paper studies full-duplex (FD) cooperative non-orthogonal multiple access (C-NOMA) short-packet communications (SPC) with K-means clustering of user equipment regarding block error rate (BLER) and sum rate. Analytical expressions are derived for the BLER and sum rate allowing to assess the performance of the considered system. The numerical results reveal the benefits of the FD C-NOMA SPC system, illustrate trade-offs between BLER and sum rate, and show the impact of the transmit signal-to-noise ratio and number of channel uses on the performance.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123370936","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10207985
Ketan Atul Bapat, M. Chakraborty
In this paper, we present Simultaneous Lorentzian Iterative Hard Thresholding (SLIHT) algorithm for recovering complex valued, jointly sparse signals corrupted by heavy tailed noise in the multiple measurement vector model in compressed sensing. The proposed algorithm uses Lorentzian norm as the underlying cost function which provides robustness against heavy tailed noise, e.g., impulsive noise. Analysis is carried out for the proposed algorithm using Majorization-Minimization framework and we show that under proper selection of parameters, the proposed SLIHT algorithm produces a sequence of row sparse estimates for which the Lorentzian norm of the residual is non-increasing. Extensive simulation studies are carried out against state of the art methods and it is observed that performance of the proposed algorithm is better or at least at par with the current methods.
{"title":"Hard Thresholding based Robust Algorithm for Multiple Measurement Vectors","authors":"Ketan Atul Bapat, M. Chakraborty","doi":"10.1109/SSP53291.2023.10207985","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207985","url":null,"abstract":"In this paper, we present Simultaneous Lorentzian Iterative Hard Thresholding (SLIHT) algorithm for recovering complex valued, jointly sparse signals corrupted by heavy tailed noise in the multiple measurement vector model in compressed sensing. The proposed algorithm uses Lorentzian norm as the underlying cost function which provides robustness against heavy tailed noise, e.g., impulsive noise. Analysis is carried out for the proposed algorithm using Majorization-Minimization framework and we show that under proper selection of parameters, the proposed SLIHT algorithm produces a sequence of row sparse estimates for which the Lorentzian norm of the residual is non-increasing. Extensive simulation studies are carried out against state of the art methods and it is observed that performance of the proposed algorithm is better or at least at par with the current methods.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128566390","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10208009
Minhhuy Le, V. Luong, K. Nguyen, Tien Dat Le, Dang-Khanh Le
Remote sensing of vital signals, including respiration and heartbeat, is an important application used in smart homes, smart hospitals, or car driver assistant systems. Ultra-wideband impulse (UWB) radar recently became popular because of its ability to sense tiny motions from breathing and cardiac activities. The heartbeat signal is in order of magnitudes smaller than the respiration signal and is usually buried in a noisy signal. In this research, we propose a multivariate signal decomposition for efficiently extracting the heartbeat signal. The results show that the proposed method significantly improves the accuracy of the signal-to-noise ratio of the heartbeat signal compared to the recent advanced methods such as wavelet transform, singular spectral analysis, and multivariate singular spectral analysis. The proposed method also improves the stability of heartbeat monitoring in real-time applications.
{"title":"Multivariate Signal Decomposition for Vital Signal Extraction using UWB Impulse Radar","authors":"Minhhuy Le, V. Luong, K. Nguyen, Tien Dat Le, Dang-Khanh Le","doi":"10.1109/SSP53291.2023.10208009","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208009","url":null,"abstract":"Remote sensing of vital signals, including respiration and heartbeat, is an important application used in smart homes, smart hospitals, or car driver assistant systems. Ultra-wideband impulse (UWB) radar recently became popular because of its ability to sense tiny motions from breathing and cardiac activities. The heartbeat signal is in order of magnitudes smaller than the respiration signal and is usually buried in a noisy signal. In this research, we propose a multivariate signal decomposition for efficiently extracting the heartbeat signal. The results show that the proposed method significantly improves the accuracy of the signal-to-noise ratio of the heartbeat signal compared to the recent advanced methods such as wavelet transform, singular spectral analysis, and multivariate singular spectral analysis. The proposed method also improves the stability of heartbeat monitoring in real-time applications.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"19 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121276875","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10207948
A. Back, Janet Wiles
Modeling unknown natural sequences is a challenging area. Here we consider an information theoretic approach for analyzing probabilistic natural sequences in the context of synthetic languages, which are characterized by having no available language models. Based on the notion of efficient short-term entropy estimators, we examine the concept of extending information geometry to information topology as a method of characterizing natural sequences. A normalized relative difference entropy method is described, which is required to apply the technique to sub-word models derived from natural sequences. Visualization of information topological spaces is considered, and some aspects are considered for future work. The approach is shown to provide potential as a new method for modeling the probabilistic structure of synthetic language sequences.
{"title":"Estimation of Statistical Manifold Properties of Natural Sequences using Information Topology","authors":"A. Back, Janet Wiles","doi":"10.1109/SSP53291.2023.10207948","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207948","url":null,"abstract":"Modeling unknown natural sequences is a challenging area. Here we consider an information theoretic approach for analyzing probabilistic natural sequences in the context of synthetic languages, which are characterized by having no available language models. Based on the notion of efficient short-term entropy estimators, we examine the concept of extending information geometry to information topology as a method of characterizing natural sequences. A normalized relative difference entropy method is described, which is required to apply the technique to sub-word models derived from natural sequences. Visualization of information topological spaces is considered, and some aspects are considered for future work. The approach is shown to provide potential as a new method for modeling the probabilistic structure of synthetic language sequences.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125808199","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10208085
J. Delmas, H. Abeida
The statistical performance of subspace-based algorithms depends on the deterministic and stochastic statistical model of the noisy linear mixture of the data, the estimate of the projector, and the algorithm that estimates the parameters from the projector. This paper presents different circular and non-circular complex elliptically symmetric (CES) models of the data and different associated non-robust and robust covariance estimators whose asymptotic distributions are derived. This allows us to unify and complement the asymptotic distribution of subspace projectors adapted to these models and to prove several invariance properties that have impacts on the parameters to be estimated in CES data models.
{"title":"Unified asymptotic distribution of subspace projectors in complex elliptically symmetric models","authors":"J. Delmas, H. Abeida","doi":"10.1109/SSP53291.2023.10208085","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208085","url":null,"abstract":"The statistical performance of subspace-based algorithms depends on the deterministic and stochastic statistical model of the noisy linear mixture of the data, the estimate of the projector, and the algorithm that estimates the parameters from the projector. This paper presents different circular and non-circular complex elliptically symmetric (CES) models of the data and different associated non-robust and robust covariance estimators whose asymptotic distributions are derived. This allows us to unify and complement the asymptotic distribution of subspace projectors adapted to these models and to prove several invariance properties that have impacts on the parameters to be estimated in CES data models.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"515 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131864519","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10207960
Swati Rai, Jignesh S. Bhatt, S. K. Patra
Medical image translation is an ill-posed problem. Unlike existing networks, we consider unpaired medical images as input, and provide a strictly bound generative network that yields a stable cyclic (bidirectional) translation. It consists of two cyclically connected conditional GANs where both generators (32 layers each) are conditioned with concatenation of alternate unpaired patches from input and target images of the same organ. The key idea is to exploit cross-neighborhood contextual feature information to bound translation space and boost generalization. Further, the generators are equipped with adaptive dictionaries which are learned from the cross-contextual patches to reduce possible degradation. Discriminators are 15-layer deep networks which employ minimax function to validate the translated imagery. A combined loss function is formulated with adversarial, non-adversarial, forward-backward cyclic, and identity losses that further minimize variance of the proposed learning machine. Qualitative, quantitative, and ablation analysis show superior results on real CT and MRI datasets.
{"title":"A Strictly Bounded Deep Network for Unpaired Cyclic Translation of Medical Images","authors":"Swati Rai, Jignesh S. Bhatt, S. K. Patra","doi":"10.1109/SSP53291.2023.10207960","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207960","url":null,"abstract":"Medical image translation is an ill-posed problem. Unlike existing networks, we consider unpaired medical images as input, and provide a strictly bound generative network that yields a stable cyclic (bidirectional) translation. It consists of two cyclically connected conditional GANs where both generators (32 layers each) are conditioned with concatenation of alternate unpaired patches from input and target images of the same organ. The key idea is to exploit cross-neighborhood contextual feature information to bound translation space and boost generalization. Further, the generators are equipped with adaptive dictionaries which are learned from the cross-contextual patches to reduce possible degradation. Discriminators are 15-layer deep networks which employ minimax function to validate the translated imagery. A combined loss function is formulated with adversarial, non-adversarial, forward-backward cyclic, and identity losses that further minimize variance of the proposed learning machine. Qualitative, quantitative, and ablation analysis show superior results on real CT and MRI datasets.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131092237","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10208077
Xiaoyu Sui, Zhenlong Xiao, S. Tomasin
Distributed learning has attracted considerable interests in literatures because the collaborations of multiple agents would help to solve complicated engineering problems. Robustness issue plays an important role in distributed learning since attacks on agents would strongly affect the convergence performance and even lead the collaboration to an incorrect global solution. In this paper, we consider the attacks as disturbance and propose a joint time-graph filtering to defend against the attacks in distributed learning. The coefficients of joint filtering can be determined based on the coefficients of time-domain and graph-domain filters that are designed separately. If there is no attack in distributed learning, the joint time-graph filtering can also contribute to the convergence performance acceleration. Numerical studies demonstrate that the joint filtering in both time domain and graph domain can defend against attacks with noise and outperforms several existing algorithms.
{"title":"Disturbance Rejection for Robust Distributed Learning via Time-Vertex Filtering","authors":"Xiaoyu Sui, Zhenlong Xiao, S. Tomasin","doi":"10.1109/SSP53291.2023.10208077","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208077","url":null,"abstract":"Distributed learning has attracted considerable interests in literatures because the collaborations of multiple agents would help to solve complicated engineering problems. Robustness issue plays an important role in distributed learning since attacks on agents would strongly affect the convergence performance and even lead the collaboration to an incorrect global solution. In this paper, we consider the attacks as disturbance and propose a joint time-graph filtering to defend against the attacks in distributed learning. The coefficients of joint filtering can be determined based on the coefficients of time-domain and graph-domain filters that are designed separately. If there is no attack in distributed learning, the joint time-graph filtering can also contribute to the convergence performance acceleration. Numerical studies demonstrate that the joint filtering in both time domain and graph domain can defend against attacks with noise and outperforms several existing algorithms.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115936396","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10207986
Bouthaina Slika, F. Dornaika, K. Hammoudi, Vinh Truong Hoang
A large number of well-maintained datasets are needed for the diagnosis and assessment of the severity of the new disease (COVID-19) using chest radiographs (CXR). To achieve the best results, current methods for quantifying severity require complex methods and large datasets for training. Medical professionals must have access to systems that can quickly and automatically identify COVID-19 patients and predict severity. In this work, we measure the severity of COVID-19 using an efficient neural network consisting of a CNN backbone and a regression head to automatically predict lung infection scores. In addition, we investigate the efficiency of some augmentation methods to increase the potential of the deep model. A comparative study was conducted using several state-of-the-art deep learning methods on the public RALO dataset. The experimental results show that our model has the potential to perform best on severity quantification tasks and demonstrate the impact of lung segmentation on performance.
{"title":"Automatic Quantification of Lung Infection Severity in Chest X-ray Images","authors":"Bouthaina Slika, F. Dornaika, K. Hammoudi, Vinh Truong Hoang","doi":"10.1109/SSP53291.2023.10207986","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207986","url":null,"abstract":"A large number of well-maintained datasets are needed for the diagnosis and assessment of the severity of the new disease (COVID-19) using chest radiographs (CXR). To achieve the best results, current methods for quantifying severity require complex methods and large datasets for training. Medical professionals must have access to systems that can quickly and automatically identify COVID-19 patients and predict severity. In this work, we measure the severity of COVID-19 using an efficient neural network consisting of a CNN backbone and a regression head to automatically predict lung infection scores. In addition, we investigate the efficiency of some augmentation methods to increase the potential of the deep model. A comparative study was conducted using several state-of-the-art deep learning methods on the public RALO dataset. The experimental results show that our model has the potential to perform best on severity quantification tasks and demonstrate the impact of lung segmentation on performance.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123800769","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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