2024 IEEE International Conference on Consumer Electronics (ICCE)最新文献_第3页

The LanternPredator: A Machine-learning-based Robot for Controlling the Spread of Invasive Species 灯笼捕食者基于机器学习的控制入侵物种扩散的机器人

2024 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2024-01-06 DOI: 10.1109/ICCE59016.2024.10444478

Wei Gou, David Banyamin, Mark Rezk, Wictor Fedorowait, Daniel Burbano, Sasan Haghani

引用次数: 0

A Fast Adaptive Motion Vector Resolution Algorithm for AVS3 用于 AVS3 的快速自适应运动矢量解析算法

2024 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2024-01-06 DOI: 10.1109/ICCE59016.2024.10444166

Yukun Zhang, Guoqing Xiang, Chen Li, Peng Zhang, Hao Lin, Wei Yan

引用次数: 0

Traditional Transformation Theory Guided Model for Learned Image Compression 传统变换理论指导下的学习型图像压缩模型

2024 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2024-01-06 DOI: 10.1109/ICCE59016.2024.10444483

Zhiyuan Li, Chenyang Ge, Shun Li

引用次数: 0

A Distributed Anonymous Reputation System for V2X Communication 用于 V2X 通信的分布式匿名信誉系统

2024 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2024-01-06 DOI: 10.1109/ICCE59016.2024.10444501

Shahidatul Sadiah, Toru Nakanishi

引用次数: 0

A Comprehensive Study of Scene Text Recognition in Scene Text Image Super-Resolution with Parametric Frameworks 利用参数框架对场景文本图像超分辨率中的场景文本识别进行综合研究

2024 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2024-01-06 DOI: 10.1109/ICCE59016.2024.10444229

Supatta Viriyavisuthisakul, P. Sanguansat, Toshihiko Yamasaki

{"title":"A Comprehensive Study of Scene Text Recognition in Scene Text Image Super-Resolution with Parametric Frameworks","authors":"Supatta Viriyavisuthisakul, P. Sanguansat, Toshihiko Yamasaki","doi":"10.1109/ICCE59016.2024.10444229","DOIUrl":"https://doi.org/10.1109/ICCE59016.2024.10444229","url":null,"abstract":"Scene Text Recognition (STR) is a technique to detect and recognize text in images. Predicting text in real-world scene images is challenging due to various uncontrollable environmental factors. State-of-the-art text detection and recognition models leverage deep learning and Transformer architectures, consequently achieving impressive accuracy on benchmark datasets. However, challenges persist in accurately processing text within real-world images, often due to unseen data or limited datasets. Both the limitations of STR and the quality of scene text images are crucial factors. Recently, a parametric weight and multiple parametric regularizations were proposed to improve the quality of real-world scene text images. Different from previous surveys in this area, this study has three main objectives. Firstly, to confirm the performance of parametric methods, the text recognition accuracy between with and without methods is compared by using different STR methods. Second, to make the comprehensive experiments, the outcomes of each STR method are compared to show their prediction performances. Third, several existing challenges and research directions are discussed.","PeriodicalId":518694,"journal":{"name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","volume":"95 6","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531651","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}

引用次数: 0

Forecasting Indoor Air Quality Using Machine Learning Models 利用机器学习模型预测室内空气质量

2024 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2024-01-06 DOI: 10.1109/ICCE59016.2024.10444387

Ashay Singh, Mohaiminul Islam, Nga Dinh

{"title":"Forecasting Indoor Air Quality Using Machine Learning Models","authors":"Ashay Singh, Mohaiminul Islam, Nga Dinh","doi":"10.1109/ICCE59016.2024.10444387","DOIUrl":"https://doi.org/10.1109/ICCE59016.2024.10444387","url":null,"abstract":"As people typically spend a significant portion of their time indoors, indoor air pollution is the primary cause of nausea, dizziness, headaches and other health issues. Therefore, indoor air quality (IAQ) monitoring and prediction is important to protect people from indoor air pollution. The indoor pollutant prediction can be efficiently tackled by using machine learning (ML) models. This paper focuses on predicting IAQ based on several important pollutants including CO2, humidity, PM10, PM2.5, temperature, and volatile organic compounds (VOC). In particular, we evaluate and compare eight ML models namely Light Gradient Boosting Machines (LightGBM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Decision Tree (DT), Linear Regression (LR), and Long Short-Term Memory (LSTM). These ML models are trained and then predict pollutants on GAMS dataset which is not well-investigated in literature. The evaluation of the models employs standard measures such as mean square error (MSE) and mean absolute percentage error (MAPE). Our results highlight LightGBM, SVR, and XGBoost as optimal models for IAQ prediction. Specifically, LightGBM achieves an impressive CO2 prediction MAPE score of 0.0960%. SVR demonstrates strong MAPE scores for humidity (0.0185%), temperature (0.0264%), and VOC (3.1953%) predictions. XGBoost attains notable MAPE scores of 0.0414% and 0.0399% for PM10 and PM2.5 models respectively. Thus, advocating for their application across diverse settings—residences, offices, educational institutions, and healthcare facilities—to forecast and monitor IAQ, the study contributes to mitigating health risks tied to indoor air pollution.","PeriodicalId":518694,"journal":{"name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","volume":"92 9","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531657","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}

引用次数: 0

Enhancing Viewer Experience: Combining TV and Projector Technologies for a New Era of Entertainment 增强观众体验：结合电视和投影机技术，开创娱乐新时代

2024 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2024-01-06 DOI: 10.1109/ICCE59016.2024.10444273

Junhee Woo, Daeki Kim, Jisu Lee, Sungchang Jang

引用次数: 0

A Practical Framework for Designing and Deploying Tiny Deep Neural Networks on Microcontrollers 在微控制器上设计和部署微型深度神经网络的实用框架

2024 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2024-01-06 DOI: 10.1109/ICCE59016.2024.10444435

Brenda Zhuang, Danilo Pau

{"title":"A Practical Framework for Designing and Deploying Tiny Deep Neural Networks on Microcontrollers","authors":"Brenda Zhuang, Danilo Pau","doi":"10.1109/ICCE59016.2024.10444435","DOIUrl":"https://doi.org/10.1109/ICCE59016.2024.10444435","url":null,"abstract":"For many applications, Deep Neural Networks (DNNs) trained on powerful CPUs and GPUs are expected to efficiently perform inference on tiny devices. However, deploying productively un-constrained complex models to microcontrollers (MCUs) remains a time-consuming task. In this paper, a comprehensive methodology is presented that combines advanced optimization techniques in hyperparameter search, model compression, and deployability evaluation using benchmark data.MCUs typically have low-power processors, limited embedded RAM memory and FLASH storage, providing orders of magnitude fewer computational resources than what cloud assets offer. Designing DNNs for such platforms requires effective strategies to balance high accuracy performance with low memory usage and inference latency. To address this challenge, Bayesian optimization has been applied, a powerful complexity-bounded technique, to hyperparameter tuning to select tiny model architecture candidates. Several pruning and quantization methods have been developed to compress all the models and evaluated the numerical performance after compression. Additionally, cloud-based deployment tools have been utilized to iteratively validate the on-device memory and latency performance on off-the-shelf MCUs. Through evaluating the benchmarks against the stringent requirements of tiny devices at the edge, practical insights have been gained into these models.Multiple image classification applications have been applied on a variety of STM32 MCUs. The practical framework can: a) maintain top-1 classification accuracy within tolerance from the floating-point network after compression; b) reduce memory footprint by at least 4 times; c) reduce inference runtime significantly by avoiding external RAM usage; d) adaptable to many different applications.","PeriodicalId":518694,"journal":{"name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","volume":"88 12","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531661","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}

引用次数: 0

Face Image Restoration Method Using Semantic and Transformer Splitting Networks 使用语义和变换器分割网络的人脸图像修复方法

2024 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2024-01-06 DOI: 10.1109/ICCE59016.2024.10444243

Hyoungki Choi, Jinsol Choi, Heunseung Lim, Joonki Paik

{"title":"Face Image Restoration Method Using Semantic and Transformer Splitting Networks","authors":"Hyoungki Choi, Jinsol Choi, Heunseung Lim, Joonki Paik","doi":"10.1109/ICCE59016.2024.10444243","DOIUrl":"https://doi.org/10.1109/ICCE59016.2024.10444243","url":null,"abstract":"This paper delves into the hardware constraints of consumer-grade surveillance camera systems, proposing a unique network architecture that splits into four distinct branches tailored for mainstream consumer electronics. While there have been significant advancements in consumer camera technology, the financial barriers related to surveillance applications in consumer markets remain notably high. Responding to this, our research presents a state-of-the-art method, optimized for everyday consumer devices, to enhance facial regions in videos by utilizing our specialized splitting network design. This model, ideal for consumer technology applications, demonstrates the capacity to precisely reconstruct damaged facial features at a pixel-level, all the while preserving the true aesthetics and authenticity of human faces. Recognizing the critical role of facial regions for personal safety in consumer settings, our solution presents a compelling answer to current challenges. This research accentuates the profound potential of advanced deep learning techniques to fortify personal safety in the modern consumer electronics landscape.","PeriodicalId":518694,"journal":{"name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","volume":"24 5","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531781","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}

引用次数: 0

A Strategy to Maximize the Utilization of AI Neural Processors on an Automotive Computing Platform 在汽车计算平台上最大限度利用人工智能神经处理器的策略

2024 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2024-01-06 DOI: 10.1109/ICCE59016.2024.10444298

Kiwon Sohn, Insup Choi, Seongwan Kim, Jaeho Lee, Jungyong Lee, Joonghang Kim

{"title":"A Strategy to Maximize the Utilization of AI Neural Processors on an Automotive Computing Platform","authors":"Kiwon Sohn, Insup Choi, Seongwan Kim, Jaeho Lee, Jungyong Lee, Joonghang Kim","doi":"10.1109/ICCE59016.2024.10444298","DOIUrl":"https://doi.org/10.1109/ICCE59016.2024.10444298","url":null,"abstract":"Advancements in AI are transforming the automotive industry, creating opportunities for AI-powered software and hardware. AI-driven features in automobiles are increasingly embraced due to their potential to significantly improve the driving experience. High-performance computing, particularly with NPUs, becomes crucial for executing the AI features. To maximize the efficiency and utilization of NPUs, DAIMO-NPU optimizes the inference sequence of the DNN models that form the backbones of the AI features. Not only does it organize and schedule the model inference tasks but also supports the tasks to be executed on heterogeneous NPU settings. Three main components are involved in the implementation of DAIMO-NPU. The schedule-table generator is responsible for creating a detailed plan for the model inference tasks, which is to be updated whenever an AI feature is added, removed, or upgraded. The onboard operator reads the schedule table and carries out the tasks accordingly. And, by dividing models into smaller segments, while not mandatory, the schedule table can be further optimized. In the subsequent developments, the integration of additional NPU hardware properties into DAIMO-NPU will be pursued.","PeriodicalId":518694,"journal":{"name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","volume":"22 6","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531782","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}

引用次数: 0