2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)最新文献

An End-to-end Anchorless Approach to Recognize Hand Gestures using CenterNet 基于CenterNet的端到端无锚点手势识别方法

2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2023-07-13 DOI: 10.1109/IAICT59002.2023.10205726

H. Dutta, K. Manivas, Marjana Bhuyan, M. Bhuyan

{"title":"An End-to-end Anchorless Approach to Recognize Hand Gestures using CenterNet","authors":"H. Dutta, K. Manivas, Marjana Bhuyan, M. Bhuyan","doi":"10.1109/IAICT59002.2023.10205726","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205726","url":null,"abstract":"Hand gesture recognition is one of the interesting problems of Computer Vision. It has a wide range of applications in the fields of Human-Computer Interaction, Robotics, Sign language interpretation, Augmented Reality, etc. Most of the existing deep learning methods detect hand gestures in two stages. The hand is located in the first stage, and classification is performed on the hand portion in the second stage to estimate the hand pose. Although these methods are accurate, they are slow and cant be used for real-time applications. Few existing literature even explored one-stage approaches, like YOLO, SSD, etc., for hand gesture recognition as they have less inference time. But they place many anchor boxes over an image of which only a small percentage are positive. This leads to a huge imbalance between positive and negative anchor boxes and slows the training process. In this paper, we have used an end-to-end, one-stage hand detection-based approach, namely, CenterNet, for hand gesture recognition. It detects the object as a point, i.e., the center point of the bounding box encompassing the object, and regresses to the object size. This eliminates the need for anchor boxes in CenterNet. We have added Dual Attention Network to the CenterNet architecture to improve the performance. Our model achieves a mean F1-score of 84.40% and 98.83% on Ouhands and NUS hand pose datasets, respectively. Results show that our model can perform well even under complex backgrounds and varying illumination conditions, and the F1-scores obtained are close to benchmark values.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114379220","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

Snacks Detection Under Overlapped Conditions Using Computer Vision 基于计算机视觉的重叠条件下零食检测

2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2023-07-13 DOI: 10.1109/IAICT59002.2023.10205599

Laode Muh, AM Armadi, Indrabayu, I. Nurtanio

引用次数: 0

Performance Evaluation of LoRa 915 MHz for IoT Communication System on Indonesian Railway Tracks with Environmental Factor Propagation Analysis 基于环境因素传播分析的印尼轨道物联网通信系统LoRa 915mhz性能评价

2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2023-07-13 DOI: 10.1109/IAICT59002.2023.10205909

A. Suharjono, M. Mukhlisin, E. Wardihani, Muhlasah Novitasari, Efrilia M Khusna, Dara Aulia Feryando, W. Adi, S. Pramono, R. Apriantoro, Irfan Mujahidin

{"title":"Performance Evaluation of LoRa 915 MHz for IoT Communication System on Indonesian Railway Tracks with Environmental Factor Propagation Analysis","authors":"A. Suharjono, M. Mukhlisin, E. Wardihani, Muhlasah Novitasari, Efrilia M Khusna, Dara Aulia Feryando, W. Adi, S. Pramono, R. Apriantoro, Irfan Mujahidin","doi":"10.1109/IAICT59002.2023.10205909","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205909","url":null,"abstract":"Propagation along railway crossings exhibits different propagation characteristics compared to the general environment, especially in Indonesia. The environmental conditions along the railway crossings vary, including straight tracks, track turns, track bridges, and tunnel tracks, resulting in pathloss that originates from the material structure of the railroad tracks. The purpose of this research is to determine the path loss coefficient values (n) under railway crossing conditions using LoRa system transmission performance with a sample area in Indonesia. The method involves measuring the RSSI (Signal Strength parameter indicator) values of LoRa nodes under various environmental conditions, including straight tracks, track turns, track bridges, and tunnel tracks. Based on the results and analysis, the value of n for RSSI under straight track conditions with no railway crossing was found to be 1.948, while it increased to 2.4929 when trains passed through. Under turn track conditions with no trains passing, the value of n was found to be 1.8646.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124127957","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

ECSM: An Ensembled Client Selection Mechanism for Efficient Federated Learning ECSM:高效联邦学习的集成客户端选择机制

2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2023-07-13 DOI: 10.1109/IAICT59002.2023.10205864

Made Adi Paramartha Putra, G. Sampedro, Dong‐Seong Kim, Jae-Min Lee

{"title":"ECSM: An Ensembled Client Selection Mechanism for Efficient Federated Learning","authors":"Made Adi Paramartha Putra, G. Sampedro, Dong‐Seong Kim, Jae-Min Lee","doi":"10.1109/IAICT59002.2023.10205864","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205864","url":null,"abstract":"This research paper proposes a multi-criteria client selection approach to enhance the efficiency of Federated Learning (FL). While the state-of-the-art client selection in FL mainly focuses on a single characteristic to determine a suitable client for the training process, a multi-criteria selection is needed to provide a more efficient FL system. We introduce the Ensembled Client Selection Mechanism (ECSM) as a novel approach to address this issue. The proposed approach takes into account client accuracy, reputation, and randomness to improve accuracy during the lower communication period. The study employs random client selection to prevent repetitive training and ensure model generalization. The results indicate that the proposed ECSM mechanism can improve FL performance by achieving the desired accuracy with fewer communication rounds. Specifically, the approach improves FL efficiency by 56% when tested on the FMNIST dataset compared to the baseline approach. These findings suggest that the ECSM mechanism can significantly enhance the efficiency of the FL process.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"8 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131696330","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

Online Video Stabilization using Mesh Flow with Minimum Latency 在线视频稳定使用网格流与最小延迟

2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2023-07-13 DOI: 10.1109/IAICT59002.2023.10205853

Devaguptam Sreegeethi, Kogatam Thanmai, Lakshmi S Raj, D. Naik, Ranjit P. Kolkar

{"title":"Online Video Stabilization using Mesh Flow with Minimum Latency","authors":"Devaguptam Sreegeethi, Kogatam Thanmai, Lakshmi S Raj, D. Naik, Ranjit P. Kolkar","doi":"10.1109/IAICT59002.2023.10205853","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205853","url":null,"abstract":"Most existing video stabilization techniques are used for post-processing, where previously recorded videos are given to the model to obtain stabilized versions. Online video stabilization usually relies on sensors like gyroscopes or assumes constant motion, which is not suitable for videos with changing motions. This work introduces a video stabilization technique with just one-frame latency. The algorithm operates at the spatial level in the infrequent domain, tracking the motion of mesh vertices. Motion tracks of feature marks are combined with the nearest mesh vertex using two median gauges, assigning each vertex a smooth motion track. The proposed approach, called anticipated foster track leveling, smoothes the motion profiles by utilizing previous motions and adapting accordingly for smoother results. This method can handle changes in movement in space and time and works in real-time, allowing applications in security systems, robotics, and unmanned aerial vehicles (UAVs). When evaluated against other models, MeshFlow gives an overall good performance in all comparison metrics evaluated. Hence MeshFlow can be used as a reliable low-latency technique for real-time video stabilization in remote devices.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123468210","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 Method for Improving AlexNet’s Performance in The Area of Facial Expressions Recognition 一种提高AlexNet面部表情识别性能的方法

2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2023-07-13 DOI: 10.1109/IAICT59002.2023.10205951

Akhmad Sarif, D. Gunawan

{"title":"A Method for Improving AlexNet’s Performance in The Area of Facial Expressions Recognition","authors":"Akhmad Sarif, D. Gunawan","doi":"10.1109/IAICT59002.2023.10205951","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205951","url":null,"abstract":"Facial Expression Recognition (FER) through digital images has undergone significant development in line with the development of computer vision technology and artificial intelligence. Facial expression recognition that has utilized deep learning shows promising results. By using deep learning, classifying millions of digital images can be easier and more accurate. However, misclassification of facial expressions sometimes still occurs. This paper proposes a method for improving the AlexNet model for application in the FER area. Some pre-processing procedures were performed on the image dataset, including resizing the image size to 227x227, converting the image to RGB (Red Blue Green) format, adjusting the contrast level of the image using CLAHE (Contrast Limited Adaptive Histogram Equalization), and augmenting by cropping the dataset image. Meanwhile, fine-tuning the AlexNet model was done by changing the ReLU activation function to Leaky ReLU, input normalization from cross channel to batch normalization, and two dropout values (from 0.5 to 0.3 and 0), and changing the number of output classifications from 1000 to 7. The experimental results show that the proposed method enhances standard AlexNet’s performance by improving its accuracy to 24.82% on the CK+ dataset and 20.05% on the KDEF dataset. There is no misclassification of facial expressions when using the proposed method, as it occurs when using the standard AlexNet model.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123509878","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

Comparison Analysis of SVM and KNN Algorithm For IoT-Based Home Fire Detection System 基于物联网的家庭火灾探测系统中SVM与KNN算法的比较分析

2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2023-07-13 DOI: 10.1109/IAICT59002.2023.10205837

R. Wibowo, Istikmal, A. Irawan

{"title":"Comparison Analysis of SVM and KNN Algorithm For IoT-Based Home Fire Detection System","authors":"R. Wibowo, Istikmal, A. Irawan","doi":"10.1109/IAICT59002.2023.10205837","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205837","url":null,"abstract":"Internet of Things (IoT) is a network that connects various integrated objects. One application of IoT is a fire detection system to provide remote warnings. In this study, IoT deployments were performed using SVM (Support Vector Machine) algorithm and KNN (K-Nearest Neighbor) algorithm. The algorithm is attached to the ESP32 microcontroller for data classification. The sensors used include temperature, humidity, fire, and smoke sensors. In case of fire a warning will be sent to Telegram. Classification results were tested with Quality of Service (QoS) parameters on throughput, delay, and jitter values, as well as with the confusion matrix with 3 simulation variations. The test outcomes display that the system is in the correct category with an average throughput value of 1.848 bps and the best value of 1.858 bps, an average delay of 593.045 ms, and a jitter of 594.188 ms. The highest accuracy was obtained in simulation 2, namely 100% for SVM and 97.5% for KNN with K=1 in KNN. Meanwhile, in simulation 1 KNN has an accuracy of 95% and SVM 98%, simulation 3 KNN 97% and SVM 100%. Thus, the SVM algorithm can classify the system better than the KNN algorithm.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124417650","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

Classification Of Fertile And Infertile Eggs Using Thermal Camera Image And Histogram Analysis: Technology Application In Poultry Farming Industry 利用热像仪图像和直方图分析分类可育蛋和不育蛋:技术在家禽养殖业中的应用

2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2023-07-13 DOI: 10.1109/IAICT59002.2023.10205610

Rahmat, Z. Zainuddin, A. Achmad

{"title":"Classification Of Fertile And Infertile Eggs Using Thermal Camera Image And Histogram Analysis: Technology Application In Poultry Farming Industry","authors":"Rahmat, Z. Zainuddin, A. Achmad","doi":"10.1109/IAICT59002.2023.10205610","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205610","url":null,"abstract":"Thermal imaging is a technology that utilizes heat radiation from objects, including duck eggs and has been widely used in the field of thermography. This study discusses a classification system of eggs using thermal camera images to differentiate between fertile and infertile eggs. The image processing methods used in this study are the histogram analysis and ROI method to identify the thermal characteristics of different eggs. The results of this study show that this method can distinguish between fertile and infertile eggs with high accuracy. This study can help farmers improve the efficiency of chicken reproduction and produce better-quality eggs. Therefore, this article has the potential to provide benefits for the livestock and food industries. In egg image processing, the ROI method increases analysis accuracy and classifies objects in the image. The histogram analysis method is used to provide accurate information. Testing with single and group egg images resulted in 93.7% accuracy in determining fertile and infertile eggs on the 9th day of incubation.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124668496","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

Distributed Compressive Power Spectrum Sensing for Cognitive Radio 认知无线电的分布式压缩功率谱感知

2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2023-07-13 DOI: 10.1109/IAICT59002.2023.10205543

I. M. A. Wiryawan, D. D. Ariananda, S. Wibowo

{"title":"Distributed Compressive Power Spectrum Sensing for Cognitive Radio","authors":"I. M. A. Wiryawan, D. D. Ariananda, S. Wibowo","doi":"10.1109/IAICT59002.2023.10205543","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205543","url":null,"abstract":"In cognitive radio (CR) networks, secondary users (SUs) might be required to gauge a wide frequency band to find frequency holes that they can use for signal transmission. When this spectrum sensing process is conducted digitally, a high sampling rate might be needed to satisfy the Nyquist rate. However, the existence of the frequency holes can be concluded by simply constructing the power spectral density (PSD) instead of the original signal. In fact, the Nyquist criterion is not applicable when we aim to reconstruct the PSD (and not the original analog signal). This paper introduces a distributed wideband power spectrum sensing using multiple SUs to first estimate the power spectrum of signals received from sources in a collaborative manner. Each SU samples the received signal at sub-Nyquist rate and reconstructs the local PSD estimate based on the received digital samples. The local PSD estimate is then exchanged between SUs based on the consensus approach without fusion center. Once convergence on the PSD is reached, the detection on the existence of PUs is conducted. We found that for a PU signal power of 4 mW, noise power of 1 mW, and Rayleigh fading with the variance of -1 dB, the probability of detection can be at least 0.9 for the probability of a false alarm of 0.1 if the number of SUs is at least 40 or the compression rate is at least 0.4.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130532033","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

UE Clustering Based on Grid Affinity Propagation for mmWave D2D in Virtual Small Cells 基于网格亲和传播的虚拟小蜂窝毫米波D2D UE聚类

2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2023-07-13 DOI: 10.1109/IAICT59002.2023.10205374

Achmad Rizal Danisya, G. Hendrantoro, P. Handayani

引用次数: 0