Pub Date : 2021-11-01DOI: 10.1109/CONF-SPML54095.2021.00013
Ruo-Nan Du
Gbps business will become an important part of the future mobile communications systems. It has been shown that non-orthogonal multiple access (NOMA) based on power multiplexing could potentially offer a robust performance in the spectrum utilization efficiency. However, when the terminal performs demodulation, the difference in user power superposition and the non-uniformity of user distribution may lead to some severe problems such as intensive excessive power or insufficient signal-to-noise ratio (SNR) under different scenarios, the performance of the communication system is reduced. Therefore, in this paper, a two-stage adaptive weight-adjusting interference cancellation (AWIC) demodulation technology based on symbol level based interference cancellation (SLIC) and code word level interference cancellation (CWIC) has been developed and presented. Moreover, we analyzed the downlink transmission performance of NOMA, innovated the multi-stage adaptive weight-adjusting serial interference cancellation (SIC) demodulation technology, and adjusted the depth of the demodulation algorithm according to the posterior decoding performance feedback. It improves NOMA demodulation performance under a low SNR environment and reduced the complexity under a high SNR environment. According to the computer simulations, under the average bit error rate (BER) of $3times 10^{-2}$, the improved NOMA interference cancellation approach proposed in this paper has a 5.09 dB performance improvement compared to SLIC and 9.8 dB compared to CWIC.
{"title":"A Two-stage Adaptive Weight-adjusting Interference Cancellation Demodulation Technology Based on SLIC and CWIC for NOMA","authors":"Ruo-Nan Du","doi":"10.1109/CONF-SPML54095.2021.00013","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00013","url":null,"abstract":"Gbps business will become an important part of the future mobile communications systems. It has been shown that non-orthogonal multiple access (NOMA) based on power multiplexing could potentially offer a robust performance in the spectrum utilization efficiency. However, when the terminal performs demodulation, the difference in user power superposition and the non-uniformity of user distribution may lead to some severe problems such as intensive excessive power or insufficient signal-to-noise ratio (SNR) under different scenarios, the performance of the communication system is reduced. Therefore, in this paper, a two-stage adaptive weight-adjusting interference cancellation (AWIC) demodulation technology based on symbol level based interference cancellation (SLIC) and code word level interference cancellation (CWIC) has been developed and presented. Moreover, we analyzed the downlink transmission performance of NOMA, innovated the multi-stage adaptive weight-adjusting serial interference cancellation (SIC) demodulation technology, and adjusted the depth of the demodulation algorithm according to the posterior decoding performance feedback. It improves NOMA demodulation performance under a low SNR environment and reduced the complexity under a high SNR environment. According to the computer simulations, under the average bit error rate (BER) of $3times 10^{-2}$, the improved NOMA interference cancellation approach proposed in this paper has a 5.09 dB performance improvement compared to SLIC and 9.8 dB compared to CWIC.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114411658","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 : 2021-11-01DOI: 10.1109/CONF-SPML54095.2021.00045
Guoyuan Li
Wireless communication has developed rapidly in recent years. The fifth-generation(5G) era has arrived in an all-round way, and 5G technology has achieved a wide range of applications and brought great convenience to our life. With the all-round development of 5G technology, we begin to look forward to the future sixth-generation(6G) technology. In many conferences and reports, we have put the 6G technology research on the agenda. As an effective way to control the wireless environment channel manually, Intelligent Reflecting Surface(IRS)1 has become one of the 6G developable technologies and is getting more and more attention. IRS assisted wireless channel and the change of channel model are important research contents in the future wireless communication. Channel model is divided into large-scale model and small-scale model, among which the research of large-scale model is the most important one in the future technical research. The model of IRS Assisted Wireless Channel is still relatively basic in the current research, so this paper aims to further explore and research. In this paper, based on the model of path loss in free space, we analyze the path model of IRS assisted wireless communication transmission. We divide the path loss model into two scenarios: near field and far field. At the end of the article, we use matlab to simulate the theoretical model numerically, and plot the change of path loss results with different distances. We find that the simulation results are basically consistent with the theoretical model.
{"title":"Transmission Channel modeling and analysis of Intelligent Reflecting Surface","authors":"Guoyuan Li","doi":"10.1109/CONF-SPML54095.2021.00045","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00045","url":null,"abstract":"Wireless communication has developed rapidly in recent years. The fifth-generation(5G) era has arrived in an all-round way, and 5G technology has achieved a wide range of applications and brought great convenience to our life. With the all-round development of 5G technology, we begin to look forward to the future sixth-generation(6G) technology. In many conferences and reports, we have put the 6G technology research on the agenda. As an effective way to control the wireless environment channel manually, Intelligent Reflecting Surface(IRS)1 has become one of the 6G developable technologies and is getting more and more attention. IRS assisted wireless channel and the change of channel model are important research contents in the future wireless communication. Channel model is divided into large-scale model and small-scale model, among which the research of large-scale model is the most important one in the future technical research. The model of IRS Assisted Wireless Channel is still relatively basic in the current research, so this paper aims to further explore and research. In this paper, based on the model of path loss in free space, we analyze the path model of IRS assisted wireless communication transmission. We divide the path loss model into two scenarios: near field and far field. At the end of the article, we use matlab to simulate the theoretical model numerically, and plot the change of path loss results with different distances. We find that the simulation results are basically consistent with the theoretical model.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130196003","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 : 2021-11-01DOI: 10.1109/CONF-SPML54095.2021.00042
Yinyihong Liu
Being one of the largest online accommodation booking platforms, Airbnb has many hosts who are seeking for more proper prices to increase their booking rate. To develop a good pricing prediction model, this paper has employed machine learning models including KNN, MLR, LASSO regression, Ridge regression, Random Forest, Gradient Boosting and XGBoost etc. While past studies on Airbnb pricing have applied quantitative pricing, some face the problems that the models are not robust enough and some face the problem of not training the model plentily. To fill this gap, we give careful consideration in exploratory data analysis to make the dataset more reasonable, apply many robust models ranging from regularized regression to ensemble models and use cross validation and random search to tune each parameter in each model. In this way, we not only select XGBoost as the best model for price prediction with R2 score 0.6321, but also uncover the features which have statistical significance with the target price.
{"title":"Airbnb Pricing Based on Statistical Machine Learning Models","authors":"Yinyihong Liu","doi":"10.1109/CONF-SPML54095.2021.00042","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00042","url":null,"abstract":"Being one of the largest online accommodation booking platforms, Airbnb has many hosts who are seeking for more proper prices to increase their booking rate. To develop a good pricing prediction model, this paper has employed machine learning models including KNN, MLR, LASSO regression, Ridge regression, Random Forest, Gradient Boosting and XGBoost etc. While past studies on Airbnb pricing have applied quantitative pricing, some face the problems that the models are not robust enough and some face the problem of not training the model plentily. To fill this gap, we give careful consideration in exploratory data analysis to make the dataset more reasonable, apply many robust models ranging from regularized regression to ensemble models and use cross validation and random search to tune each parameter in each model. In this way, we not only select XGBoost as the best model for price prediction with R2 score 0.6321, but also uncover the features which have statistical significance with the target price.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125235590","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 : 2021-11-01DOI: 10.1109/CONF-SPML54095.2021.00017
Chao Wu
Generative Adversarial Networks (GAN) contributed many significant works in computer vision tasks in different research areas. But, to author’s knowledge, there is no research discussion about GAN’s performance in a bi-directional task. In this paper, we utilize Pix2pix network as a GAN example to test its performance in a bi-directional task, which is to transfer daylight image to night image and transfer night image back to daylight image. The experimental results review both success cases and fail cases to get several interesting observations regarding the influence of human’s perception in evaluation.
{"title":"Performance Review of Generative Adversarial Network for a Bi-directional Task","authors":"Chao Wu","doi":"10.1109/CONF-SPML54095.2021.00017","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00017","url":null,"abstract":"Generative Adversarial Networks (GAN) contributed many significant works in computer vision tasks in different research areas. But, to author’s knowledge, there is no research discussion about GAN’s performance in a bi-directional task. In this paper, we utilize Pix2pix network as a GAN example to test its performance in a bi-directional task, which is to transfer daylight image to night image and transfer night image back to daylight image. The experimental results review both success cases and fail cases to get several interesting observations regarding the influence of human’s perception in evaluation.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116449250","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 : 2021-11-01DOI: 10.1109/CONF-SPML54095.2021.00033
Qingyuan Jiang
Different population among the states shows a heterogeneous housing price trend during the past years. Any possible abnormal migration will cause price change. Thus, the migration could be tackled by comparing the current price trend with the data in past 10 years. COVID-19 is a strong effect which could cause migration. In order to observe the possible migration under this situation, wo high-population states were chosen as examples – California and New York, to compare with two low-population states – Nevada and Ohio. Three machine learning techniques have been used (Random Forest, XGboost, and Ridge and Lasso regression) to forecast housing price in U.S.: the difference between the real price and forecast price trend will show the amount of real estate transactions affect by the pandemic. The observed data was compared with the predicted results after COVID-19. The final result didn’t show a strong evidence that would verify a possible migration, but the answer will be clearer with further studies.
在过去的几年里，不同的人口在各州之间表现出不同的房价趋势。任何可能的异常迁移都会引起价格变动。因此，可以通过比较当前的价格趋势与过去10年的数据来解决人口迁移问题。COVID-19是一种可能导致移民的强烈影响。为了观察在这种情况下可能发生的移民，我们选择了两个人口高的州——加利福尼亚和纽约作为例子，与两个人口低的州——内华达州和俄亥俄州进行比较。利用随机森林(Random Forest)、XGboost、Ridge and Lasso回归等3种机器学习技术预测了美国的房价，通过实际价格和预测价格趋势之间的差异，可以显示受疫情影响的房地产交易量。将观察数据与COVID-19后的预测结果进行比较。最终的结果并没有显示出强有力的证据来证实可能的迁移，但随着进一步的研究，答案将更加清晰。
{"title":"Estimate the Housing Price Under the Impact Of COVID-19 and Possible Migration Due to the Demand for Density","authors":"Qingyuan Jiang","doi":"10.1109/CONF-SPML54095.2021.00033","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00033","url":null,"abstract":"Different population among the states shows a heterogeneous housing price trend during the past years. Any possible abnormal migration will cause price change. Thus, the migration could be tackled by comparing the current price trend with the data in past 10 years. COVID-19 is a strong effect which could cause migration. In order to observe the possible migration under this situation, wo high-population states were chosen as examples – California and New York, to compare with two low-population states – Nevada and Ohio. Three machine learning techniques have been used (Random Forest, XGboost, and Ridge and Lasso regression) to forecast housing price in U.S.: the difference between the real price and forecast price trend will show the amount of real estate transactions affect by the pandemic. The observed data was compared with the predicted results after COVID-19. The final result didn’t show a strong evidence that would verify a possible migration, but the answer will be clearer with further studies.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116807041","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 : 2021-11-01DOI: 10.1109/CONF-SPML54095.2021.00025
Jailin Chen
Stabilization is one of the most significant step of automatic control system design. In this paper, we propose a stabilization method for linear time-invariant SISO systems from the view of signal processing. In this method, the complex plane is regarded as a frequency plane, and the control law to improve the stability of the system is designed with the idea of filtering, which has the advantages of intuitive design and easy adjustment. The feasibility of this method is proved by simulation, and the relationship between the design of control law and the dynamic characteristics of the system is analyzed.
{"title":"Stabilization with the Idea of Notch Filter in Automatic Control System","authors":"Jailin Chen","doi":"10.1109/CONF-SPML54095.2021.00025","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00025","url":null,"abstract":"Stabilization is one of the most significant step of automatic control system design. In this paper, we propose a stabilization method for linear time-invariant SISO systems from the view of signal processing. In this method, the complex plane is regarded as a frequency plane, and the control law to improve the stability of the system is designed with the idea of filtering, which has the advantages of intuitive design and easy adjustment. The feasibility of this method is proved by simulation, and the relationship between the design of control law and the dynamic characteristics of the system is analyzed.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114951585","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 : 2021-11-01DOI: 10.1109/CONF-SPML54095.2021.00046
Zhi Yi, Yuyang Wang
For the treatment of Interstitial Lung Disease, it is crucial to have an early diagnosis. However, doctors still have a lot of controversy in the diagnosis of lung nodules even with today’s highly developed medical imaging technology. In this article, we summarized the five major challenges we face in medical image recognition and systematically listed the applications from traditional image recognition technology to deep learning in lung CT image recognition. Compared to the traditional convolutional neural network built and trained from scratch, it is beneficial to apply transfer learning to the recognition of lung nodules. Transfer learning focus on transferring knowledge from previous well-trained task to target learning task. Transferring means pretrained networks utilize fine-tuning to reduce iteration times of weight so that it can cope with the problem of lack of high quality images. Various experiments demonstrate that transfer learning performances better than traditional convolutional neural network under complicated circumstances of image recognition such as medical images. In this article, transfer learning is classified into 3 types: inductive transfer learning, transductive transfer learning and unsupervised transfer learning. The main difference between them is label quantity of target training set. Inductive transfer learning highly depends on feature engineering. Compared to it, training sets of two remaining has few labels. However, transductive transfer learning and unsupervised transfer learning are unstable while facing sophisticated cases.
{"title":"Transfer Learning on Interstitial Lung Disease Classification","authors":"Zhi Yi, Yuyang Wang","doi":"10.1109/CONF-SPML54095.2021.00046","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00046","url":null,"abstract":"For the treatment of Interstitial Lung Disease, it is crucial to have an early diagnosis. However, doctors still have a lot of controversy in the diagnosis of lung nodules even with today’s highly developed medical imaging technology. In this article, we summarized the five major challenges we face in medical image recognition and systematically listed the applications from traditional image recognition technology to deep learning in lung CT image recognition. Compared to the traditional convolutional neural network built and trained from scratch, it is beneficial to apply transfer learning to the recognition of lung nodules. Transfer learning focus on transferring knowledge from previous well-trained task to target learning task. Transferring means pretrained networks utilize fine-tuning to reduce iteration times of weight so that it can cope with the problem of lack of high quality images. Various experiments demonstrate that transfer learning performances better than traditional convolutional neural network under complicated circumstances of image recognition such as medical images. In this article, transfer learning is classified into 3 types: inductive transfer learning, transductive transfer learning and unsupervised transfer learning. The main difference between them is label quantity of target training set. Inductive transfer learning highly depends on feature engineering. Compared to it, training sets of two remaining has few labels. However, transductive transfer learning and unsupervised transfer learning are unstable while facing sophisticated cases.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130160801","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 : 2021-11-01DOI: 10.1109/CONF-SPML54095.2021.00040
Leyao Huang
LiDAR-based Simultaneous Localization and Mapping (LiDAR-SLAM) uses the LiDAR sensor to localize itself by observing environmental features and incrementally build the map of the surrounding environment. In this way, the purpose of simultaneous localization and mapping in the unknown environment can be achieved. Localization and mapping with high robustness, high accuracy, and high practicability is a complex and hot issue in recent years. This paper will briefly introduce the information background, classification and development history of LiDAR-SLAM. We will also summarize the common frameworks of LiDAR-SLAM and the function of core modules in the existing LiDAR-SLAM. Additionally, the state-of-the-art multi-sensor fusion-based LiDAR-SLAM techniques are investigated, and the future development trend of LiDAR-SLAM is discussed.
{"title":"Review on LiDAR-based SLAM Techniques","authors":"Leyao Huang","doi":"10.1109/CONF-SPML54095.2021.00040","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00040","url":null,"abstract":"LiDAR-based Simultaneous Localization and Mapping (LiDAR-SLAM) uses the LiDAR sensor to localize itself by observing environmental features and incrementally build the map of the surrounding environment. In this way, the purpose of simultaneous localization and mapping in the unknown environment can be achieved. Localization and mapping with high robustness, high accuracy, and high practicability is a complex and hot issue in recent years. This paper will briefly introduce the information background, classification and development history of LiDAR-SLAM. We will also summarize the common frameworks of LiDAR-SLAM and the function of core modules in the existing LiDAR-SLAM. Additionally, the state-of-the-art multi-sensor fusion-based LiDAR-SLAM techniques are investigated, and the future development trend of LiDAR-SLAM is discussed.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117070132","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 : 2021-11-01DOI: 10.1109/CONF-SPML54095.2021.00018
Ping Xia
Parkinson’s disease (PD) is a neurodegenerative disease. The subject of PD patients are between 60 and 90 years old, and human patients suffer from PD before the age of 50 are particularly small. Deep brain stimulation (DBS) is one of the most efficient method to treat PD. It is found that there are some problems in the current clinical use of DBS (which is always called conventional deep brain stimulation (cDBS), such as the adjustment of parameters requires doctors to adjust parameters by experience. In the improvement process of DBS, adaptive deep brain stimulation (aDBS) has been widely studied because of its ability to better adjust the parameters. The cDBS is an open-loop system, and aDBS is a closed-loop system which the parameters can be better matched. Artificial intelligence deep brain stimulation (AIDBS) is not only an intelligent device, which can adjust the parameters quickly and accurately, furthermore it can be used to find the biomarkers of PD in future studies.
帕金森病(PD)是一种神经退行性疾病。PD患者的研究对象多在60 ~ 90岁之间，50岁以前患PD的人类患者尤其少。深部脑刺激(DBS)是治疗帕金森病最有效的方法之一。发现目前DBS(常被称为常规深部脑刺激)在临床应用中存在一些问题，如参数的调整需要医生凭经验调整参数。在DBS的改进过程中，适应性深部脑刺激(adaptive deep brain stimulation, aDBS)因其具有较好的参数调节能力而得到了广泛的研究。cDBS为开环系统，aDBS为闭环系统，参数匹配性较好。人工智能脑深部刺激(AIDBS)不仅是一种可以快速准确调节参数的智能设备，而且在未来的研究中可以用于寻找PD的生物标志物。
{"title":"Advances in the Deep Brain Stimulation for Parkinson’s Disease","authors":"Ping Xia","doi":"10.1109/CONF-SPML54095.2021.00018","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00018","url":null,"abstract":"Parkinson’s disease (PD) is a neurodegenerative disease. The subject of PD patients are between 60 and 90 years old, and human patients suffer from PD before the age of 50 are particularly small. Deep brain stimulation (DBS) is one of the most efficient method to treat PD. It is found that there are some problems in the current clinical use of DBS (which is always called conventional deep brain stimulation (cDBS), such as the adjustment of parameters requires doctors to adjust parameters by experience. In the improvement process of DBS, adaptive deep brain stimulation (aDBS) has been widely studied because of its ability to better adjust the parameters. The cDBS is an open-loop system, and aDBS is a closed-loop system which the parameters can be better matched. Artificial intelligence deep brain stimulation (AIDBS) is not only an intelligent device, which can adjust the parameters quickly and accurately, furthermore it can be used to find the biomarkers of PD in future studies.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123512152","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 : 2021-11-01DOI: 10.1109/CONF-SPML54095.2021.00037
Yiran Hu
This paper studies the battery monitoring technology based on the Internet of Things, which is applied to monitor the operation and performance of the battery in the smart grid. Through the research on the development background and research status of the battery monitoring industry, based on the structure of the Internet of Things and battery monitoring, the construction method of the battery monitoring system composed of data acquisition system, communication system and monitoring platform is proposed.
{"title":"Research on Battery Monitoring Technology Based on Internet of Things","authors":"Yiran Hu","doi":"10.1109/CONF-SPML54095.2021.00037","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00037","url":null,"abstract":"This paper studies the battery monitoring technology based on the Internet of Things, which is applied to monitor the operation and performance of the battery in the smart grid. Through the research on the development background and research status of the battery monitoring industry, based on the structure of the Internet of Things and battery monitoring, the construction method of the battery monitoring system composed of data acquisition system, communication system and monitoring platform is proposed.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"17 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116854894","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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