Pub Date : 2023-06-01DOI: 10.1109/IS3C57901.2023.00091
Jian-Jiun Ding, T. Tseng
Context modeling plays a critical role in the adaptive arithmetic coding process. It classifies the causal part into several classes according to the features extracted from the causal neighboring pixels. However, when the feature value is around the border of the ranges of two adjacent contexts, its corresponding probability model cannot be estimated accurately. In this paper, we propose an advanced way for context assignment. We make the contexts overlapped in both the training phase and the coding phase. With the proposed method, more than one context wm be assigned for each input data. Then, the probability model generated by weighted combination is applied to encode the input data. Then, the frequency table corresponds to the context whose range overlaps with the input data value wm be adjusted. Experimental results on lossless image coding show that, with the proposed algorithm, a high coding efficiency can be achieved.
{"title":"Overlapped Context Modeling Using Feature Mapping Functions in the Adaptive Arithmetic Coding Process for Lossless Encoding","authors":"Jian-Jiun Ding, T. Tseng","doi":"10.1109/IS3C57901.2023.00091","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00091","url":null,"abstract":"Context modeling plays a critical role in the adaptive arithmetic coding process. It classifies the causal part into several classes according to the features extracted from the causal neighboring pixels. However, when the feature value is around the border of the ranges of two adjacent contexts, its corresponding probability model cannot be estimated accurately. In this paper, we propose an advanced way for context assignment. We make the contexts overlapped in both the training phase and the coding phase. With the proposed method, more than one context wm be assigned for each input data. Then, the probability model generated by weighted combination is applied to encode the input data. Then, the frequency table corresponds to the context whose range overlaps with the input data value wm be adjusted. Experimental results on lossless image coding show that, with the proposed algorithm, a high coding efficiency can be achieved.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130839875","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 : 2023-06-01DOI: 10.1109/IS3C57901.2023.00079
Yu-Kuo Chang, Kao-chin Lee, Chen-Kang Huang
18650 Li-Ion batteries are used in a lot of applications. In this study, 18650 batteries were explored to have a method to estimate its SOH quickly. Batteries were discharged with a high current for a short period of time. According to the voltage histories for the period, the internal resistance and SOH could be derived. With the current and rated capacity, two parameters could be found. The relationship between SOH and SOC could be found. In short, the proposed method was able to estimate the SOH and SOC with data from high current discharging for a short period time.
{"title":"Quick SOH and SOC estimation for commercial 18650 Li-Ion Batteries","authors":"Yu-Kuo Chang, Kao-chin Lee, Chen-Kang Huang","doi":"10.1109/IS3C57901.2023.00079","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00079","url":null,"abstract":"18650 Li-Ion batteries are used in a lot of applications. In this study, 18650 batteries were explored to have a method to estimate its SOH quickly. Batteries were discharged with a high current for a short period of time. According to the voltage histories for the period, the internal resistance and SOH could be derived. With the current and rated capacity, two parameters could be found. The relationship between SOH and SOC could be found. In short, the proposed method was able to estimate the SOH and SOC with data from high current discharging for a short period time.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130873901","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 : 2023-06-01DOI: 10.1109/IS3C57901.2023.00092
Chuan-Kang Liu, Chi-Hui Chiang
Federated learning has been regarded as emerging machine learning framework due to its privacy protection. In the IoT trend, federated learning enables edge clients to predict or classify local detected data with a global model that is computed by a FL server through the aggregation of all local models trained by a base FL algorithm. However, meanwhile, its distributed nature also brings several security challenges. Poisoning attacks are the main security risks that can easily and efficiently affect the accuracy of the global learning model. Previous work proposed a voting strategy which can predict the label of the input robustly no matter the attacks the malicious users use. However, its accuracy also easily falls down as the number of malicious user increases while the number of groups is fixed. This paper proposes a new attack defense algorithm against poisoning attacks in federated learning. This paper uses ID-distribution features to group all clients, including normal and malicious ones. The main idea of this proposed scheme is to put those potential malicious clients in specified groups. Hence, the resulting vote output can accurately classify the dataset inputs, regardless of the number of the groups the learning framework has. Our analytical results also show that our scheme exactly perform better compared to original voting scheme.
{"title":"A Collaboration Federated Learning Framework with a Grouping Scheme against Poisoning Attacks","authors":"Chuan-Kang Liu, Chi-Hui Chiang","doi":"10.1109/IS3C57901.2023.00092","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00092","url":null,"abstract":"Federated learning has been regarded as emerging machine learning framework due to its privacy protection. In the IoT trend, federated learning enables edge clients to predict or classify local detected data with a global model that is computed by a FL server through the aggregation of all local models trained by a base FL algorithm. However, meanwhile, its distributed nature also brings several security challenges. Poisoning attacks are the main security risks that can easily and efficiently affect the accuracy of the global learning model. Previous work proposed a voting strategy which can predict the label of the input robustly no matter the attacks the malicious users use. However, its accuracy also easily falls down as the number of malicious user increases while the number of groups is fixed. This paper proposes a new attack defense algorithm against poisoning attacks in federated learning. This paper uses ID-distribution features to group all clients, including normal and malicious ones. The main idea of this proposed scheme is to put those potential malicious clients in specified groups. Hence, the resulting vote output can accurately classify the dataset inputs, regardless of the number of the groups the learning framework has. Our analytical results also show that our scheme exactly perform better compared to original voting scheme.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131835428","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 : 2023-06-01DOI: 10.1109/IS3C57901.2023.00009
Hong-Yu Chuang, Ruey-Maw Chen
Malicious attack detection becomes a critical issue in Industrial IoT(IIoT) environments. Meanwhile, the IoT market is constantly growing, and new IoT devices are connected to the Internet day by day, causing a rapid increase in network traffic. To enable IDS to detect malicious attacks in high-load network environments, a lightweight IDS is required. Therefore, Machine Learning (ML) based intrusion detection systems (IDS) with fewer features to meet the lightweight IDS are applied to the TON_IoT dataset. A Pearson correlation coefficient (PCC) is applied to calculate correlations among features, followed by Jamovi analysis software’s frequency table to analyze the core features of the TON_IoT dataset. Finally, the original 45 features are reduced to 10 core features for IDS to detect malicious activity. To verify the performance of malicious attack activities with the reduced 10 core features, four evaluation criteria are used: accuracy, precision, recall, and F1 score. Two ML techniques, KNN and RF, are applied for testing. According to experimental results, both ML techniques can detect multiple types of attacks with an accuracy of over 99%, indicating that using the proposed 10 core features for attack detection can still yield high accuracy.
{"title":"Detection of Attacks on Industrial Internet of Things Using Fewer Features","authors":"Hong-Yu Chuang, Ruey-Maw Chen","doi":"10.1109/IS3C57901.2023.00009","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00009","url":null,"abstract":"Malicious attack detection becomes a critical issue in Industrial IoT(IIoT) environments. Meanwhile, the IoT market is constantly growing, and new IoT devices are connected to the Internet day by day, causing a rapid increase in network traffic. To enable IDS to detect malicious attacks in high-load network environments, a lightweight IDS is required. Therefore, Machine Learning (ML) based intrusion detection systems (IDS) with fewer features to meet the lightweight IDS are applied to the TON_IoT dataset. A Pearson correlation coefficient (PCC) is applied to calculate correlations among features, followed by Jamovi analysis software’s frequency table to analyze the core features of the TON_IoT dataset. Finally, the original 45 features are reduced to 10 core features for IDS to detect malicious activity. To verify the performance of malicious attack activities with the reduced 10 core features, four evaluation criteria are used: accuracy, precision, recall, and F1 score. Two ML techniques, KNN and RF, are applied for testing. According to experimental results, both ML techniques can detect multiple types of attacks with an accuracy of over 99%, indicating that using the proposed 10 core features for attack detection can still yield high accuracy.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131567727","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}
With the progress of the laser application, the laser manufacturing technology is acceptable for the production line in many industries. The trend of laser applications is one laser source serves one laser manufacturing machine. In this research, we design a system which divide the laser beam into 2 beams. However, this system can enhance the efficiency for not only mass production but also small amount of variety. Moreover, the output laser beam can be individually controlled.
{"title":"A simple laser beam divider for mass-production/small-amount-of-variety applications","authors":"Ying-Chang Li, Chu-En Lin, Meng-Hua Yen, Faizal Aprillian, Chia-Yu Hsieh","doi":"10.1109/IS3C57901.2023.00071","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00071","url":null,"abstract":"With the progress of the laser application, the laser manufacturing technology is acceptable for the production line in many industries. The trend of laser applications is one laser source serves one laser manufacturing machine. In this research, we design a system which divide the laser beam into 2 beams. However, this system can enhance the efficiency for not only mass production but also small amount of variety. Moreover, the output laser beam can be individually controlled.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132972251","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}
Due to the high cost of manually labeling data and sometimes requiring domain expertise, semi-supervised methods have received a lot of attention. Self-training is a very effective semi-supervised method that greatly improves the problem of insufficient labeled data in classification tasks. In this paper, we propose a semi-supervised classification algorithm based on self-training and label propagation. Specifically, our self-training architecture uses two soft pseudo-labels obtained by the fine-tuned model and label propagation as input to obtain the output of the pseudo-label prediction model, and then selects the high-confidence output of the pseudo-label prediction model as the pseudo-label data. Additionally, we use ImageNet pre-train models for fine-tuning, which greatly reduces learning time and improves accuracy. Experiments show that our method can achieve effective accuracy improvement on a large amount of unlabeled data.
{"title":"Self-training and Label Propagation for Semi-supervised Classification","authors":"Yu-An Wang, Che-Jui Yeh, Kai-Wen Chen, Chen-Kuo Chiang","doi":"10.1109/IS3C57901.2023.00101","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00101","url":null,"abstract":"Due to the high cost of manually labeling data and sometimes requiring domain expertise, semi-supervised methods have received a lot of attention. Self-training is a very effective semi-supervised method that greatly improves the problem of insufficient labeled data in classification tasks. In this paper, we propose a semi-supervised classification algorithm based on self-training and label propagation. Specifically, our self-training architecture uses two soft pseudo-labels obtained by the fine-tuned model and label propagation as input to obtain the output of the pseudo-label prediction model, and then selects the high-confidence output of the pseudo-label prediction model as the pseudo-label data. Additionally, we use ImageNet pre-train models for fine-tuning, which greatly reduces learning time and improves accuracy. Experiments show that our method can achieve effective accuracy improvement on a large amount of unlabeled data.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133476360","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 : 2023-06-01DOI: 10.1109/IS3C57901.2023.00015
Chien-Feng Chiu, Yu-Hao Lee, An-Bang Liu, Hsin-Ru Liu, Wei-Min Liu
Zebrafish is one of the most widely used model organisms for behavior research in biomedical and pharmaceutical field. Many zebrafish studies used drugs to test its responses, then tracked its movement and analyzed the locomotor features. Such tracking analysis is a challenging task due to the complex body deformation, occasional occlusions, and its “burst” movements. In this study an object detection model YOLOv7 and a multi-object tracking method StrongSORT were integrated to develop an automated zebrafish tracking system and generate relevant locomotor features. Several analyses can be performed through the system. First, we proposed to use approximate entropy to quantify a series of locomotor feature change to evaluate the regularity and unpredictability of movement. Second, through the tracking function we can establish the locomotor trajectory data and collect the time series of several locomotor features including distance, velocity, and different types of body angles when a zebrafish moving in a camera-monitored tank. These analyses help us further understand the impact of drugs through zebrafish’s movement change. The experimental results showed the capabilities of the proposed system and demonstrated that the extracted motion features can be used to distinguish healthy versus diseased groups of zebrafish. The proposed system provides a useful and friendly tool for zebrafish research.
{"title":"Tracking and Analyzing Locomotor Changes in Zebrafish","authors":"Chien-Feng Chiu, Yu-Hao Lee, An-Bang Liu, Hsin-Ru Liu, Wei-Min Liu","doi":"10.1109/IS3C57901.2023.00015","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00015","url":null,"abstract":"Zebrafish is one of the most widely used model organisms for behavior research in biomedical and pharmaceutical field. Many zebrafish studies used drugs to test its responses, then tracked its movement and analyzed the locomotor features. Such tracking analysis is a challenging task due to the complex body deformation, occasional occlusions, and its “burst” movements. In this study an object detection model YOLOv7 and a multi-object tracking method StrongSORT were integrated to develop an automated zebrafish tracking system and generate relevant locomotor features. Several analyses can be performed through the system. First, we proposed to use approximate entropy to quantify a series of locomotor feature change to evaluate the regularity and unpredictability of movement. Second, through the tracking function we can establish the locomotor trajectory data and collect the time series of several locomotor features including distance, velocity, and different types of body angles when a zebrafish moving in a camera-monitored tank. These analyses help us further understand the impact of drugs through zebrafish’s movement change. The experimental results showed the capabilities of the proposed system and demonstrated that the extracted motion features can be used to distinguish healthy versus diseased groups of zebrafish. The proposed system provides a useful and friendly tool for zebrafish research.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130514228","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 : 2023-06-01DOI: 10.1109/IS3C57901.2023.00068
Chu-En Lin, Bo Yu, H. You, Yi-Ching Cheng, Jung-Chih Lin, C. Wu
A nonvolatile oxide-based resistive memory device by using chemical displacing technique (CDT) copper as the metal electrode was demonstrated in this paper. The advantages of CDT Cu include low-cost, high selectivity, and low-temperature process. The fabricated CDT Cu film performed a rough surface, which was beneficial to the filament pathway formation of the electrochemical metallization (ECM) type ReRAM device. We compared the roughness of CDT Cu films in different chemical displacing time, and demonstrated the impact of the CDT Cu electrode to the electrical properties of the resistive memory devices. The obtained results show that the device with short CDT time, which have rough surface, exhibits low operation electric field and good reliability. This is because low voltage is needed and thus effect of Joule heating can be effectively diminished during operation.
{"title":"Effects of Chemical Displacing Time for the Characteristics of the Nonvolatile Oxide-based Resistive Memory Devices","authors":"Chu-En Lin, Bo Yu, H. You, Yi-Ching Cheng, Jung-Chih Lin, C. Wu","doi":"10.1109/IS3C57901.2023.00068","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00068","url":null,"abstract":"A nonvolatile oxide-based resistive memory device by using chemical displacing technique (CDT) copper as the metal electrode was demonstrated in this paper. The advantages of CDT Cu include low-cost, high selectivity, and low-temperature process. The fabricated CDT Cu film performed a rough surface, which was beneficial to the filament pathway formation of the electrochemical metallization (ECM) type ReRAM device. We compared the roughness of CDT Cu films in different chemical displacing time, and demonstrated the impact of the CDT Cu electrode to the electrical properties of the resistive memory devices. The obtained results show that the device with short CDT time, which have rough surface, exhibits low operation electric field and good reliability. This is because low voltage is needed and thus effect of Joule heating can be effectively diminished during operation.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"491 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123195713","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 : 2023-06-01DOI: 10.1109/IS3C57901.2023.00013
Dai-Wei Huang, Ji-Lan Liu, Ching-Te Kuo
This paper presents a self-powered flexible pressure sensor called the TVAP sensor for throat vibration and wrist arterial pulse monitoring. The sensor is fabricated using PVDF-TrFE nanofibers, which are more suitable for wearable devices than conventional piezoelectric ceramics due to their low cost, high flexibility, and biocompatibility. The TVAP sensor is able to convert mechanical energy into electricity and vice versa and has a sensitivity of 102 mV/N for sensing force. Experimental results demonstrate the TVAP sensor’s ability to detect pressure changes and promising potential for detecting early onset of cardiovascular disease and assessing personal health status.
{"title":"Wearable PVDF-TrFE-based Pressure Sensors for Throat Vibrations and Arterial Pulses Monitoring","authors":"Dai-Wei Huang, Ji-Lan Liu, Ching-Te Kuo","doi":"10.1109/IS3C57901.2023.00013","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00013","url":null,"abstract":"This paper presents a self-powered flexible pressure sensor called the TVAP sensor for throat vibration and wrist arterial pulse monitoring. The sensor is fabricated using PVDF-TrFE nanofibers, which are more suitable for wearable devices than conventional piezoelectric ceramics due to their low cost, high flexibility, and biocompatibility. The TVAP sensor is able to convert mechanical energy into electricity and vice versa and has a sensitivity of 102 mV/N for sensing force. Experimental results demonstrate the TVAP sensor’s ability to detect pressure changes and promising potential for detecting early onset of cardiovascular disease and assessing personal health status.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114710908","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 : 2023-06-01DOI: 10.1109/IS3C57901.2023.00086
C. Tseng, Su-Ling Lee
Polynomial graph filter (PGF) is an important tool for processing the irregular data captured from various complex networks, so the design of PGF is studied in this paper. First, Legendre polynomials are briefly reviewed and the basics of graph signal processing (GSP) are described. Second, the PGF design using Legendre polynomials is presented. The closed-form solution of filter coefficients is derived for lowpass, bandpass and highpass filters. Third, an efficient implementation structure of PGF based on recurrence relation of Legendre polynomials is investigated. Finally, the signal denoising application of sensor network data is demonstrated to show that the PGF method has better performance than the conventional smoothness-based method in term of the improvement of signal to noise ratio.
{"title":"Polynomial Graph Filter Design Using Legendre Polynomials","authors":"C. Tseng, Su-Ling Lee","doi":"10.1109/IS3C57901.2023.00086","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00086","url":null,"abstract":"Polynomial graph filter (PGF) is an important tool for processing the irregular data captured from various complex networks, so the design of PGF is studied in this paper. First, Legendre polynomials are briefly reviewed and the basics of graph signal processing (GSP) are described. Second, the PGF design using Legendre polynomials is presented. The closed-form solution of filter coefficients is derived for lowpass, bandpass and highpass filters. Third, an efficient implementation structure of PGF based on recurrence relation of Legendre polynomials is investigated. Finally, the signal denoising application of sensor network data is demonstrated to show that the PGF method has better performance than the conventional smoothness-based method in term of the improvement of signal to noise ratio.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130572549","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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