In this paper, we propose a novel Prior-Guided Parallel Residual Bi-Fusion Feature Pyramid Network (PPRB-FPN) for accurate obstacle detection in unmanned surface vehicle (USV) sailing. Our method tackles the challenge of detecting small objects, which are prone to information vanishing. To the end, we leverage the PRB-FPN for small object detection and YOLOv7 as a single-stage object detector to effectively identify obstacles. Our experimental results on the Obstacle Detection Challenge dataset at the 1st Workshop on Maritime Computer Vision (MaCVi) demonstrate that our method outperforms both Mask R-CNN (mrcnn) and YOLOv7, achieving an F_avg score of 0.514.
{"title":"Prior-Guided Parallel Residual Bi-Fusion Network in USV Obstacle Detection","authors":"Chih-Chung Hsu, Sophia Yang, Xiu-Yu Hou, Yu-An Jhang","doi":"10.1109/ICCE-Taiwan58799.2023.10226878","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226878","url":null,"abstract":"In this paper, we propose a novel Prior-Guided Parallel Residual Bi-Fusion Feature Pyramid Network (PPRB-FPN) for accurate obstacle detection in unmanned surface vehicle (USV) sailing. Our method tackles the challenge of detecting small objects, which are prone to information vanishing. To the end, we leverage the PRB-FPN for small object detection and YOLOv7 as a single-stage object detector to effectively identify obstacles. Our experimental results on the Obstacle Detection Challenge dataset at the 1st Workshop on Maritime Computer Vision (MaCVi) demonstrate that our method outperforms both Mask R-CNN (mrcnn) and YOLOv7, achieving an F_avg score of 0.514.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121740726","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226887
Po-Ying Huang, Po-Yung Chou, Chu-Hsing Lin
Basketball analysis systems are essential tools in modern basketball, where identifying the ball handler is one of the most critical tasks. The reason for this challenge comes from the overlapping of players in basketball, which makes it easy for the analysis system to misjudge the ball handler. We found that it is easy to misjudge ball handler using traditional algorithms, such as calculating the degree of intersection over the union or calculating the coordinate distance between the player and the ball. In this paper, we propose a transformer-based object relationship finder to classify the relationship between players and the ball, which uses features of different objects, such as the use of coordinate information and skeleton information as inputs, to learn the relationship between players and the ball through self-attention. Experimental results show that our method achieves an accuracy of ball handler up to 91.2% based on a smaller dataset, surpassing the 83.9% accuracy of traditional algorithms and the 77.8% accuracy of Resnet-based convolutional neural networks.
{"title":"A Transformer-based Object Relationship Finder for Object Status Analysis","authors":"Po-Ying Huang, Po-Yung Chou, Chu-Hsing Lin","doi":"10.1109/ICCE-Taiwan58799.2023.10226887","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226887","url":null,"abstract":"Basketball analysis systems are essential tools in modern basketball, where identifying the ball handler is one of the most critical tasks. The reason for this challenge comes from the overlapping of players in basketball, which makes it easy for the analysis system to misjudge the ball handler. We found that it is easy to misjudge ball handler using traditional algorithms, such as calculating the degree of intersection over the union or calculating the coordinate distance between the player and the ball. In this paper, we propose a transformer-based object relationship finder to classify the relationship between players and the ball, which uses features of different objects, such as the use of coordinate information and skeleton information as inputs, to learn the relationship between players and the ball through self-attention. Experimental results show that our method achieves an accuracy of ball handler up to 91.2% based on a smaller dataset, surpassing the 83.9% accuracy of traditional algorithms and the 77.8% accuracy of Resnet-based convolutional neural networks.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134109136","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}
Traffic flow is one of the most important information for traffic management. Traditionally, the data were obtained only from intersection monitors, which lack a macroscopic view of the entire roadway. Recently, the applications of unmanned aerial vehicles (UAV) have been widely applied to many fields and have become popular. Due to the programmable path planning and 3D movement characteristics of UAVs, we integrate edge computing for image recognition processing with UAVs to perform traffic flow analysis. This study successfully developed a prototype system to analyze the road segment video that is recorded from UAV-mounted cameras. A deep learning technique will be used to perform vehicle identification and tracking tasks. The average vehicle speed and vehicle flow can then be determined. In addition, violation event detection (including speeding, illegal parking, etc.) can also be reported. The system will automatically produce the diagnosis report. It can greatly reduce the burden of traditional manual image viewing, and the analyzed results can be used for traffic management units to improve traffic strategies.
{"title":"UAV-Assisted Intelligent Traffic Diagnosis System Design","authors":"Yu-Ying Wang, Chen-Wei Huang, Yi-Hua Huang, Chu-Fu Wang, Yu-Huan Hung","doi":"10.1109/ICCE-Taiwan58799.2023.10226961","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226961","url":null,"abstract":"Traffic flow is one of the most important information for traffic management. Traditionally, the data were obtained only from intersection monitors, which lack a macroscopic view of the entire roadway. Recently, the applications of unmanned aerial vehicles (UAV) have been widely applied to many fields and have become popular. Due to the programmable path planning and 3D movement characteristics of UAVs, we integrate edge computing for image recognition processing with UAVs to perform traffic flow analysis. This study successfully developed a prototype system to analyze the road segment video that is recorded from UAV-mounted cameras. A deep learning technique will be used to perform vehicle identification and tracking tasks. The average vehicle speed and vehicle flow can then be determined. In addition, violation event detection (including speeding, illegal parking, etc.) can also be reported. The system will automatically produce the diagnosis report. It can greatly reduce the burden of traditional manual image viewing, and the analyzed results can be used for traffic management units to improve traffic strategies.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134406874","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10227053
Po-Yu Kuo, Ming-Hsiu Chan, Yi-Zhan Zhuang
With the rapid change in technology, portable electronic products are designed to extend battery life through low power consumption and small size. The low-dropout regulator (LDO) is a crucial power management block and it is widely used in electronic products. Although the conventional LDO has the ability of voltage regulation, it cannot achieve low power consumption and fast transient response at the same time. In this paper, an output capacitor-less LDO with a fast transient response was proposed based on the extract power supply ripple technique. By adding a proposed voltage buffer and low-pass filter, was further applied to improve the transient response. Moreover, a cross-coupled technique was enhanced the power supply rejection ratio (PSRR) compared with the conventional LDO. The proposed regulator was fabricated using TSMC 0.18 μm CMOS process technology and subjected to simulations using a 1.8V power supply. From the results, the proposed LDO achieved a fast transient response of 0.27 μs and low PSRR of .-76 dB at 1 kHz .
{"title":"An Output Capacitorless Low-Dropout Regulator Design Based on Cross-Coupled Technique with Extract Power Supply Ripple Technique","authors":"Po-Yu Kuo, Ming-Hsiu Chan, Yi-Zhan Zhuang","doi":"10.1109/ICCE-Taiwan58799.2023.10227053","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10227053","url":null,"abstract":"With the rapid change in technology, portable electronic products are designed to extend battery life through low power consumption and small size. The low-dropout regulator (LDO) is a crucial power management block and it is widely used in electronic products. Although the conventional LDO has the ability of voltage regulation, it cannot achieve low power consumption and fast transient response at the same time. In this paper, an output capacitor-less LDO with a fast transient response was proposed based on the extract power supply ripple technique. By adding a proposed voltage buffer and low-pass filter, was further applied to improve the transient response. Moreover, a cross-coupled technique was enhanced the power supply rejection ratio (PSRR) compared with the conventional LDO. The proposed regulator was fabricated using TSMC 0.18 μm CMOS process technology and subjected to simulations using a 1.8V power supply. From the results, the proposed LDO achieved a fast transient response of 0.27 μs and low PSRR of .-76 dB at 1 kHz .","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134333144","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226879
Han-Chieh Chia, Ke-Sih Yang, Chen-Chiung Hsieh
Due to the complex track environment, the use of only one stage for track component identification is prone to inaccurate component positioning, resulting in misjudgment of the target coordinate system and other problems. This paper proposes a two-stage recognition method based on YOLOX-nano and ResNet34, hoping to solve the problem of inaccurate component positioning in the existing classification system and also improve the recognition accuracy. In the first stage, the entire image is preliminarily screened through YOLOX-nano, so that the system can understand the image structure, obtain the possible range of components, and then obtain the leftmost and rightmost positions of the track through Hough Transform. Next, calculate the intersection with the sleeper range obtained in the first stage, and calculate the possible relative position of the component base on the intersection, thereby locking the range where the component is located, and handing this range to ResNet34 in the second stage for component defect detection.
{"title":"A Real-time System of Two-stage Track Component Classification based on YOLOX-nano and ResNet34","authors":"Han-Chieh Chia, Ke-Sih Yang, Chen-Chiung Hsieh","doi":"10.1109/ICCE-Taiwan58799.2023.10226879","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226879","url":null,"abstract":"Due to the complex track environment, the use of only one stage for track component identification is prone to inaccurate component positioning, resulting in misjudgment of the target coordinate system and other problems. This paper proposes a two-stage recognition method based on YOLOX-nano and ResNet34, hoping to solve the problem of inaccurate component positioning in the existing classification system and also improve the recognition accuracy. In the first stage, the entire image is preliminarily screened through YOLOX-nano, so that the system can understand the image structure, obtain the possible range of components, and then obtain the leftmost and rightmost positions of the track through Hough Transform. Next, calculate the intersection with the sleeper range obtained in the first stage, and calculate the possible relative position of the component base on the intersection, thereby locking the range where the component is located, and handing this range to ResNet34 in the second stage for component defect detection.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133595016","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226802
Po-Yu Kuo, Yi-Zhen Chen, Li-An Tsai
This paper presents a 12-bit 45.5Ms/s SAR ADC in TSMC 0.18μm CMOS process. With the proposed architecture, a multi-segment split capacitor DAC was used to reduce the total capacitance and the possibility of capacitor switching. To reduce the swing range of the common voltage, hybrid capacitive switching was applied to the MSB and monotonic capacitive switching was applied to the LSB. In the CMOS process, split capacitors cannot achieve accurate fractional values. Therefore, non-fractional values are also used to solve the problem of capacitor mismatch. The final measurement data shows INL ranges around -0.71 to 0.64 and DNL ranges around -1.0 to 1.032. Total capacitance reduced by 75% relative to CDAC.
提出了一种采用台积电0.18μm CMOS工艺的12位45.5Ms/s SAR ADC。在该架构下，采用了多段分裂电容DAC来减少总电容和电容切换的可能性。为了减小共电压的摆幅范围，采用混合容性开关控制主侧电压，单调容性开关控制主侧电压。在CMOS工艺中，分体电容不能实现精确的分数值。因此，非分数值也被用来解决电容失配的问题。最终测量数据显示，INL范围约为-0.71至0.64,DNL范围约为-1.0至1.032。总电容相对于CDAC降低75%。
{"title":"10-bit 45.5Ms/s SAR ADC based on Multi-Segmentation Split-Capacitive DAC","authors":"Po-Yu Kuo, Yi-Zhen Chen, Li-An Tsai","doi":"10.1109/ICCE-Taiwan58799.2023.10226802","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226802","url":null,"abstract":"This paper presents a 12-bit 45.5Ms/s SAR ADC in TSMC 0.18μm CMOS process. With the proposed architecture, a multi-segment split capacitor DAC was used to reduce the total capacitance and the possibility of capacitor switching. To reduce the swing range of the common voltage, hybrid capacitive switching was applied to the MSB and monotonic capacitive switching was applied to the LSB. In the CMOS process, split capacitors cannot achieve accurate fractional values. Therefore, non-fractional values are also used to solve the problem of capacitor mismatch. The final measurement data shows INL ranges around -0.71 to 0.64 and DNL ranges around -1.0 to 1.032. Total capacitance reduced by 75% relative to CDAC.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131756073","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226944
Bo-Wei Chen, Tsung-Jung Liu, Kuan-Hsien Liu
Image inpainting has been researched for many years. From traditional methods to current CNN models, they all pursue two targets (structural stability and texture consistency). In this paper, we propose the multi-shift CSWin Transformer (MSCSWin Transformer) and the HSV loss to focus on colors to inpaint images for these two targets. At last, we compare our model with state-of-the-art methods on the Places2 dataset to confirm our proposed module is indeed working.
{"title":"Image Inpainting Using MSCSWin Transformer and Color Correction","authors":"Bo-Wei Chen, Tsung-Jung Liu, Kuan-Hsien Liu","doi":"10.1109/ICCE-Taiwan58799.2023.10226944","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226944","url":null,"abstract":"Image inpainting has been researched for many years. From traditional methods to current CNN models, they all pursue two targets (structural stability and texture consistency). In this paper, we propose the multi-shift CSWin Transformer (MSCSWin Transformer) and the HSV loss to focus on colors to inpaint images for these two targets. At last, we compare our model with state-of-the-art methods on the Places2 dataset to confirm our proposed module is indeed working.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124251046","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226905
Jehn-Ruey Jiang, Chien-Kai Liao
In this paper, we propose a credit card fraud detection method that leverages DeepInsight and deep learning. The proposed method employs the DeepInsight mechanism to convert non-image credit card transaction data into structured images. These images are then processed by a parallel convolutional neural network (CNN) deep learning model to extract crucial hidden features for credit card fraud detection. To evaluate the performance of our method, we utilize European credit card transaction data. The evaluation results are compared with those of related methods, demonstrating the superiority of our proposed method in terms of the accuracy, true positive rate, true negative rate, and Matthews correlation coefficient.
{"title":"Credit Card Fraud Detection Based on DeepInsight and Deep Learning","authors":"Jehn-Ruey Jiang, Chien-Kai Liao","doi":"10.1109/ICCE-Taiwan58799.2023.10226905","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226905","url":null,"abstract":"In this paper, we propose a credit card fraud detection method that leverages DeepInsight and deep learning. The proposed method employs the DeepInsight mechanism to convert non-image credit card transaction data into structured images. These images are then processed by a parallel convolutional neural network (CNN) deep learning model to extract crucial hidden features for credit card fraud detection. To evaluate the performance of our method, we utilize European credit card transaction data. The evaluation results are compared with those of related methods, demonstrating the superiority of our proposed method in terms of the accuracy, true positive rate, true negative rate, and Matthews correlation coefficient.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114353626","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10227037
Wen-Ho Juang, Meng-Chang Wu, Y. Sheu, J. Shieh, Tung-Hsien Hsieh
This work presents a cost-efficient hardware architecture design of 2-D sliding discrete Fourier transform (SDFT). The proposed design requires the lowest eight real adders and six real multipliers in hardware resource, compared with Park’s method reduced by 11.1% and 25%, respectively. In the FPGA implementation, the proposed hardware accelerator is operated at 47.47 MHz, and then it is very suitable for time-frequency analysis in real time.
{"title":"A Cost-efficient Hardware Accelerator Design for 2D Sliding Discrete Fourier Transform","authors":"Wen-Ho Juang, Meng-Chang Wu, Y. Sheu, J. Shieh, Tung-Hsien Hsieh","doi":"10.1109/ICCE-Taiwan58799.2023.10227037","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10227037","url":null,"abstract":"This work presents a cost-efficient hardware architecture design of 2-D sliding discrete Fourier transform (SDFT). The proposed design requires the lowest eight real adders and six real multipliers in hardware resource, compared with Park’s method reduced by 11.1% and 25%, respectively. In the FPGA implementation, the proposed hardware accelerator is operated at 47.47 MHz, and then it is very suitable for time-frequency analysis in real time.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114438254","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226713
Hung-Chung Li, Chun-Hsun Huang, P. Sun
The study conducts a psychophysical experiment to explore the subjective emotional perception of different colored light sources in a beauty aromatherapy condition. The results show that the illuminance level, chroma, and hue of different colored light sources obviously influence subjective emotional perception. In addition, the study also established a perceptual evaluation model based on the experimental data that can be used to design more suitable lighting conditions for beauty aromatherapy settings.
{"title":"Establishing Suitable Lighting Conditions and Perception Models for Beauty Aromatherapy using Colored Light Sources","authors":"Hung-Chung Li, Chun-Hsun Huang, P. Sun","doi":"10.1109/ICCE-Taiwan58799.2023.10226713","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226713","url":null,"abstract":"The study conducts a psychophysical experiment to explore the subjective emotional perception of different colored light sources in a beauty aromatherapy condition. The results show that the illuminance level, chroma, and hue of different colored light sources obviously influence subjective emotional perception. In addition, the study also established a perceptual evaluation model based on the experimental data that can be used to design more suitable lighting conditions for beauty aromatherapy settings.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117296107","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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