2023 IEEE Region 10 Symposium (TENSYMP)最新文献_第3页

Optimizing the Efficiency of Winner-Takes-All Neuromorphic Circuit Optimization Using Self-Adaptive Multi-Population Quadratic Approximation Guided Jaya Algorithm 基于自适应多种群二次逼近的Jaya算法优化赢者通吃的神经形态电路优化效率

2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223677

R. Das, K. Das

{"title":"Optimizing the Efficiency of Winner-Takes-All Neuromorphic Circuit Optimization Using Self-Adaptive Multi-Population Quadratic Approximation Guided Jaya Algorithm","authors":"R. Das, K. Das","doi":"10.1109/TENSYMP55890.2023.10223677","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223677","url":null,"abstract":"Metaheuristics are robust and sophisticated approaches to solving Electronic Design Optimization problems. However, due to the non-linearity of these optimization problems, the complexity increases and many of these algorithms do not deliver the global optimum. Additional difficulties include diverse constraints, inherent errors, conflicting objectives, and multiple local optima. Consequently, significant variations in the final results of these problems could be observed across multiple iterations, even after using traditional meta-heuristics. Therefore, proper tuning of the control parameters of these algorithms is very important, since it is proportional to their numerical cost and accuracy. The primary objective of this investigation is to enhance both the stability and quality of outcomes while optimizing the Winner-Takes-All neuromorphic circuit using a recently proposed parameter-free approach called Self-adaptive multi-population Quadratic Approximation guided Jaya algorithm. Extensive experimentations with promising outcomes confirm its efficiency compared to other state-of-the-art counterparts. Finally, validation is performed using the circuit design tool Cadence Virtuoso, further illustrating a close agreement with the algorithmic results.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121590302","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

Meat Freshness Classifier with Machine and AI 基于机器和人工智能的肉类新鲜度分类器

2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223681

Zarif Wasif Bhuiyan, Syed Ali Redwanul Haider, Adiba Haque, Mahady Hasan, Mohammad Rejwan Uddin

{"title":"Meat Freshness Classifier with Machine and AI","authors":"Zarif Wasif Bhuiyan, Syed Ali Redwanul Haider, Adiba Haque, Mahady Hasan, Mohammad Rejwan Uddin","doi":"10.1109/TENSYMP55890.2023.10223681","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223681","url":null,"abstract":"Using machine learning and artificial intelligence techniques, this thesis presents a novel approach to detecting meat freshness. The proposed system consists of two gas sensors MQ135 and MQ4 to capture the odors emitted by the meat samples, an ESP32-CAM, and an Arduino UNO microcontroller to process the sensor data and extract relevant features. A machine learning model is trained using a dataset of labeled meat samples with known freshness levels. The proposed technique accurately categorizes the freshness of meat samples with a classification accuracy of over 90%, showing the potential of machine learning and artificial intelligence in improving the precision and effectiveness of this procedure. The technology is transportable and compatible with current meat processing equipment. This gives the food business a dependable, automated method to raise the security and caliber of meat goods. Overall, the study's findings show that the suggested system is a reliable way to classify the freshness of meat. This project proposes a novel approach to detect meat freshness using two gas sensors along with a camera that employs image processing AI techniques to overcome challenges posed by added color in meat. Although there were some limitations regarding Data Availability, Subjectivity of freshness Determination and many other real-time assessments. Despite the limitations the ML and AI can help to mitigate some of the limitations and improve overall performance.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125100571","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 Real-Time Chinese Food Auto Billing System Based on Instance Segmentation 基于实例分割的中餐实时自动计费系统

2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223619

Qiushi Guo, Yifan Chen, Yihang Yao, Tengteng Zhang, Jin Ma

{"title":"A Real-Time Chinese Food Auto Billing System Based on Instance Segmentation","authors":"Qiushi Guo, Yifan Chen, Yihang Yao, Tengteng Zhang, Jin Ma","doi":"10.1109/TENSYMP55890.2023.10223619","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223619","url":null,"abstract":"Recently, food segmentation has been a hot topic in both academia and industry. Solutions for western food segmentation have been proposed and the performance is promising which meets the requirements in scenarios like diet managements and calorie estimation. Inspired by these achievements, we decide to design a Chinese food Price automatic billing system based on instance segmentation methods. However, how to segment Chinese food remains a challenge due to the wide variety of ingredients and cook styles. It's impossible to collect sufficient images to train a segmentation model to detect all kinds of potential Chinese food. To overcome these issues, rather than detecting each singular dish, we reformulate the task by segmenting a set of selected plates with Chinese food, we propose a FoodSyn module, which synthesis images by cropping food part in UECFoodPIX and pasting them on plates images. The generated images are then fed into encoder-decoder network to segment instances for training. Extensive experiments show that our proposed approach performs well in practical scenarios with mIoU over 950/0. The fps is over 20 when deployed on OnePlus 9 Pro. Codes will be released once the paper is accepted.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128001878","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

Accurate Prediction of Pulmonary Fibrosis Progression Using EfficientNet and Quantile Regression: A High Performing Approach 使用高效网和分位数回归准确预测肺纤维化进展:一种高效的方法

2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223673

Rofiqul Alam Shehab, Kaysarul Anas Apurba, Md. Ahsanuzzaman, Tanzilur Rahman

{"title":"Accurate Prediction of Pulmonary Fibrosis Progression Using EfficientNet and Quantile Regression: A High Performing Approach","authors":"Rofiqul Alam Shehab, Kaysarul Anas Apurba, Md. Ahsanuzzaman, Tanzilur Rahman","doi":"10.1109/TENSYMP55890.2023.10223673","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223673","url":null,"abstract":"Pulmonary fibrosis (PF) is a chronic lung disease characterized by the formation of scar tissue in the lungs, leading to difficulty breathing and a reduced ability to oxygenate the blood. The progression of PF is difficult to predict, and current methods of diagnosis and treatment are often ineffective. In this study, we propose to use EfficientNet, utilizing a cutting-edge convolutional neural network (CNN) architecture and quantile regression (QR) to predict the progression of PF in patients. Our approach includes analyzing data from the OSIC dataset, the biggest publicly accessible dataset containing medical imaging, patient demographics, and lab results. The analyzed data was trained on an EfficientNet model and QR to predict the progression of the disease, as well as estimate the uncertainty of the predictions. The performance of the model was evaluated using Laplace-Log-Likelihood. The results demonstrate that the proposed approach outperforms existing literature in predicting pulmonary fibrosis progression, with the highest score (-6.64). This approach has the potential to aid in the development of new treatments for this disease.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133765915","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

An IoT-Based Bed Fall Prediction System Using Force Sensitive Resistor 基于物联网的力敏电阻床坠预测系统

2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223481

Md Fahad Wafiq, Mohsina Taz, Fariha Nowrin, Abrar Mahmud Chowdhury, Amin Rahim, Md. Mehedi Hasan Shawon, Md Rakibul Hasan, Tasfin Mahmud

{"title":"An IoT-Based Bed Fall Prediction System Using Force Sensitive Resistor","authors":"Md Fahad Wafiq, Mohsina Taz, Fariha Nowrin, Abrar Mahmud Chowdhury, Amin Rahim, Md. Mehedi Hasan Shawon, Md Rakibul Hasan, Tasfin Mahmud","doi":"10.1109/TENSYMP55890.2023.10223481","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223481","url":null,"abstract":"Patients with impaired mobility and neurological disorders such as Alzheimer's disease, Parkinson's disease, dementia etc. are vulnerable to bed falls, which can be damaging to their physical and psychological well-being. Existing systems are mostly fall detection based on wearable devices, which can be uncomfortable to wear or ambient devices such as cameras that invade privacy. A bed falls prediction system using force sensitive resistors (FSR) has been proposed in this paper. It is designed to eliminate privacy intrusion and discomfort issues. The system can identify the patient's different on-bed positions and determine the possibility of bed falls. In case of any risky position, the caretaker will be alerted to mobile applications via the Internet of Things (IoT), making patient monitoring more accessible and manageable. This integrated system yields an average of 92% accuracy for 5 different on-bed positions. The bed fall prediction system will facilitate caretakers/nurses to take care conveniently at homes, hospitals and assisted care facilities to ensure patients' health and safety.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124041230","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

CycleMVAE: Benchmarking End-to-End Cycle-Consistent Multi-Task Variational Autoencoder for EEG-Based Emotion Recognition 基于eeg的情绪识别的基准端到端周期一致多任务变分自编码器

2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223616

Kranti S. Kamble, J. Sengupta

{"title":"CycleMVAE: Benchmarking End-to-End Cycle-Consistent Multi-Task Variational Autoencoder for EEG-Based Emotion Recognition","authors":"Kranti S. Kamble, J. Sengupta","doi":"10.1109/TENSYMP55890.2023.10223616","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223616","url":null,"abstract":"Affective computing, particularly the identification of emotions from multichannel electroencephalography (EEG) signals, has gained importance. In this study, we propose a novel deep neural network model called Cycle Consistent Multi-Task Variational Autoencoder (CycleMVAE) to simultaneously investigate pairwise translation of emotional features, signal reconstruction, and emotion classification across two different EEG recording samples. CycleMVAE comprises two Variational Autoencoders (VAEs) and a supervised classifier. Each VAE consists of an encoder and a decoder. The encoder of the first VAE transfers emotional properties from EEG sample X to a compact latent space Z, while the decoder retrieves these features from Z to transfer them to EEG sample Y. Similarly, the second VAE uses the compact latent space Z’ to transfer emotion features from EEG sample Y to EEG sample X. This forms a cyclic translation of feature among EEG sample recordings. The model is trained using reconstructed loss, cycle consistency loss, and latent vector regularization loss, with a supervised classifier used to categorize emotions into arousal, valence, and dominance categories. The proposed approach improves EEG classification performance while reducing pre-processing complexity. The effectiveness of the proposed approach has been validated by experimental findings on the multimodal DREAMER emotional database.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"735 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128920800","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

Zero-Shot Learning For Text Classification: Extending Classifiability Beyond Conventional Techniques 文本分类的零射击学习:超越传统技术的可分类性扩展

2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223610

Muthu Palaniappan M, Adithya Vedhamani, Sundharakumar K B

{"title":"Zero-Shot Learning For Text Classification: Extending Classifiability Beyond Conventional Techniques","authors":"Muthu Palaniappan M, Adithya Vedhamani, Sundharakumar K B","doi":"10.1109/TENSYMP55890.2023.10223610","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223610","url":null,"abstract":"Text classification plays a crucial role in organizing and understanding huge amounts of text data. However, traditional text classification methods often face challenges when dealing with unseen or novel classes. Zero-shot learning (ZSL) offers a promising solution to this problem by enabling the classification of text instances into classes that have not been encountered during training. There is a plethora of potential benefits of ZSL in several applications, emphasizing its ability to handle new classes and adapt to evolving domains. In this paper, we have used the AG news dataset which is a commonly used benchmark dataset for text classification tasks. It consists of news articles from the AG's corpus, collected from four different categories: World, Sports, Business, and Science/Technology. Each article is assigned a label corresponding to one of these categories. We applied state-of-the-art deep learning algorithms such as Convolutional Neural Networks and Recurrent Neural Networks to compare the performance with Zero Shot Learning (ZSL). ZSL proved to be robust and performed better compared to the other algorithms in terms of accuracy and F1 Score.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129182034","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

Impact of AC vs DC Distribution on System Efficiency in a Nanogrid Office 交流与直流分布对纳米电网办公室系统效率的影响

2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223664

Olivia Graillet, F. Alicalapa, D. Genon-Catalot, P. lucas de Peslouan, Laurent Lemaitre, J. Chabriat

{"title":"Impact of AC vs DC Distribution on System Efficiency in a Nanogrid Office","authors":"Olivia Graillet, F. Alicalapa, D. Genon-Catalot, P. lucas de Peslouan, Laurent Lemaitre, J. Chabriat","doi":"10.1109/TENSYMP55890.2023.10223664","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223664","url":null,"abstract":"Reunion Island is a French overseas department located in the southwestern part of the Indian Ocean. Due to its geographical location, it needs to develop local sources of energy production, such as solar energy, to achieve energy autonomy. By reducing energy conversion steps, LVDC (Low-Voltage Direct Current) nanogrids can contribute to this objective by reducing the energy consumption of buildings and optimizing electrical installations. This paper proposes a hybrid nanogrid architecture currently deployed in an office building in Réunion island. The hybrid nanogrid includes a PV power plant, a LiFePO4 battery, a LVDC distribution (48VDC loads such as lighting and fans) and an Alternating Current (AC) distribution for experimentation. To evaluate the overall nanogrid efficiency and optimize its lifetime, a method to measure the global efficiency of the system in AC or DC distribution for an identical final load and section cable is presented. Results have shown a total efficiency gain of 18% in DC compared to AC distribution, for certain conditions described in the paper. Voltage drop and battery capacity during discharge have also been measured to complete the results.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121559090","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

Hydro-Meteorological Flood Data Sensing, Prediction and Classification using Internet of Things 基于物联网的水文气象洪水数据感知、预测与分类

2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223648

引用次数: 0

A 1-Bit Electronically Reconfigurable Unit Cell Using PIN Diode for Reflectarray Antenna 用于反射天线的PIN二极管1位电子可重构单元

2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223614

Ji-Yeon Ha, Dong-Wook Seo

引用次数: 0