2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA)最新文献_第4页

Automatic Tuning of MPC using Genetic Algorithm with Historic Process Data 基于历史过程数据的MPC遗传算法自动调优

2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA) Pub Date : 2022-05-12 DOI: 10.1109/CSPA55076.2022.9782011

Yusuf Abuabakar Sha’aban

引用次数: 2

Automatic Spike Removal Technique for Airborne Magnetic Data 航空磁数据自动去钉技术

2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA) Pub Date : 2022-05-12 DOI: 10.1109/CSPA55076.2022.9781981

Bassant Abdelhamid, M. Elkattan

引用次数: 0

Istiqamah App: A Mobile Application for Sunnah and Hadith reminder using Flutter framework Istiqamah App:一个使用Flutter框架的圣训和圣训提醒移动应用程序

2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA) Pub Date : 2022-05-12 DOI: 10.1109/CSPA55076.2022.9782052

Raihah Aminuddin, Nor-Ashikin Mohamed Noor Khan, Mohamad Hakimi Mohamad Noor, Ihsan Ismail, Mohd Ali Mohd Isa, Siti Soraya Mohd Elias, Syafiqah Johan Amir Johan, Yuhaniza Shafinie Kamsani, N. F. Ilias

{"title":"Istiqamah App: A Mobile Application for Sunnah and Hadith reminder using Flutter framework","authors":"Raihah Aminuddin, Nor-Ashikin Mohamed Noor Khan, Mohamad Hakimi Mohamad Noor, Ihsan Ismail, Mohd Ali Mohd Isa, Siti Soraya Mohd Elias, Syafiqah Johan Amir Johan, Yuhaniza Shafinie Kamsani, N. F. Ilias","doi":"10.1109/CSPA55076.2022.9782052","DOIUrl":"https://doi.org/10.1109/CSPA55076.2022.9782052","url":null,"abstract":"Istiqamah App is a mobile application that utilizes the feature of notification to keep reminding users to perform sunnah activities daily. Besides, it also comes together with hadith and Quran quotations related to each sunnah activity. Five categories of notifications related to sunnah activities such eating Habbatus Sauda and honey, performing Tahajjud prayer, performing Sadaqah, maintaining water intake based on Body Mass Index, and taking medicines. Each time a user receives the notification, the Istiqamah App will display hadith or Quran quotations related to the sunnah activities. Users can choose one from the three actions available: 1) Skip the reminder; 2) Perform the sunnah and mark it as taken or complete; or 3) Remind them again five minutes later. The application also enables users to keep track of their progress. The project used the Rapid Application Development methodology to develop the mobile application. The mobile application is developed using Flutter framework, Dart programming language, and Firebase database.","PeriodicalId":174315,"journal":{"name":"2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA)","volume":"396 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121254051","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: 1

Collision Avoidance Concept of UAV During Fire Related Incident for SAR Missions 无人机在SAR火灾事件中的避碰概念

2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA) Pub Date : 2022-05-12 DOI: 10.1109/CSPA55076.2022.9782034

Nurulhani Adlina Madzan, S. F. S. Dardin, Zuhari Abdul Rashid, Muhammad Afiq Alimi, Nur Shafa’ Darmawan

{"title":"Collision Avoidance Concept of UAV During Fire Related Incident for SAR Missions","authors":"Nurulhani Adlina Madzan, S. F. S. Dardin, Zuhari Abdul Rashid, Muhammad Afiq Alimi, Nur Shafa’ Darmawan","doi":"10.1109/CSPA55076.2022.9782034","DOIUrl":"https://doi.org/10.1109/CSPA55076.2022.9782034","url":null,"abstract":"Current trend in Search and Rescue mission begins to incorporate the use of Unmanned Aerial Vehicle or drone which means that for each type of catastrophes, a specific payload is required. The main target for this project is to develop a new payload which is an active solution for flame-based collision avoidance system (CAS) concept so that a drone can manoeuvre very closely in a flammable related area without harming itself. A hex-copter drone type will be used as a platform for this proposed system. The proposed payload is developed using off-the-shelf sensors, integrated using Arduino with Pixhawk Cube is used as the flight control system (FCS) and remotely control by using 8-channels remote controller (RC). Static test (propeller is not connected) was performed to evaluate the response of all 6 motors in order to see it responses when the payload is being triggered. The preliminary result shows that the ground control RC command can be bypass successfully by maintaining some level of control of the motor. Extensive study will be carried out later in the phase to ensure the correct response before actual flight test can be carry out. In conclusion, this flame-CAS payload shows a promising result that can be implemented as an alternative manoeuvring aid to ensure safety of the drone deployed during fire related SAR operation provided an efficient control scheme can be developed.","PeriodicalId":174315,"journal":{"name":"2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122795448","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

Building a Logistic Optimization Model for Mekong Delta in Vietnam 越南湄公河三角洲物流优化模型构建

2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA) Pub Date : 2022-05-12 DOI: 10.1109/CSPA55076.2022.9781969

Dat Huynh Ba, Nhien Huynh Thi, Xuan Truong Thi Thanh, Cuong Pham Nhat, Khoi Nguyen Dinh, Sang Luu Phuoc, Bay Huynh Van, Lan Le Thi Thu, Luyl-Da Quach

引用次数: 0

Distracted Driver Detection Using Deep Learning 基于深度学习的分心驾驶员检测

2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA) Pub Date : 2022-05-12 DOI: 10.1109/CSPA55076.2022.9781938

Muhammad Saiful Haqem Saiful Bahari, Lucyantie Mazalan

{"title":"Distracted Driver Detection Using Deep Learning","authors":"Muhammad Saiful Haqem Saiful Bahari, Lucyantie Mazalan","doi":"10.1109/CSPA55076.2022.9781938","DOIUrl":"https://doi.org/10.1109/CSPA55076.2022.9781938","url":null,"abstract":"The increasing number of vehicle road accident is worrying with more than 1.35 million people died on the highways globally in 2019. Prevention must be taken to reduce road accidents by controlling one of the factors contributed to the increasing number of the cases, i.e., distracted drivers. Previous researches have used Deep Learning classification-based technology in detecting a distracted driver in a vehicle. However, there is potential for improvement in the investigation and development of detecting an action of a distracted driver focusing on looking elsewhere. This study aims to detect a distracted driver who is looking elsewhere using Deep Learning-based classification. The method proposed uses Jupyter Notebook and Python to program and run ResNet 50 network. The State Farm dataset, which consists of 10 types of driving behavior is also used in this study. The model has been evaluated based on confusion metrics, accuracy, precision, recall, and F1 score criterion. As a result, the model achieved 94% of accuracy in the classification of distracted driver looking elsewhere. The images of distracted driver were identified, and a notification will appear at the video that contains a distracted driver.","PeriodicalId":174315,"journal":{"name":"2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116928225","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: 2

Image Steganalysis based on Pretrained Convolutional Neural Networks 基于预训练卷积神经网络的图像隐写分析

2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA) Pub Date : 2022-05-12 DOI: 10.1109/CSPA55076.2022.9782061

Ismail Taha Ahmed, Baraa Tareq Hammad, N. Jamil

{"title":"Image Steganalysis based on Pretrained Convolutional Neural Networks","authors":"Ismail Taha Ahmed, Baraa Tareq Hammad, N. Jamil","doi":"10.1109/CSPA55076.2022.9782061","DOIUrl":"https://doi.org/10.1109/CSPA55076.2022.9782061","url":null,"abstract":"the process of identifying the presence of secret information in cover images is known as image steganalysis. As a result, classifying an image as a cover image or a stego image might be considered a classification task. The majority of steganalysis approaches that rely on deep learning are effective. Deep learning technology can identify and extract features mechanically using deep networks, allowing steganalysis technology to eliminate the need for specialist knowledge. However, Deep learning model training is tough and takes a large amount of processing time and information. Therefore, pre-trained CNN such as AlexNet model were used as feature extractors to save time during training. Therefore, this research presented an image steganalysis method based on AlexNet CNN Model. There are 3 steps make up the proposed image steganalysis method: Firstly, Data collection and preparation. Secondly, AlexNet model are used for extract Distinctive features. Lastly, the feature vector is then utilized to train the Random forest (RF) classifier in order to detect the binary classification (Cover/Stego). The experimental results under IStego100K database show that the proposed method accuracy is 99%. The properties of AlexNet models can be deduced to be useful and concise to classify using RF. In compared to previous techniques, the presented method outperformed them.","PeriodicalId":174315,"journal":{"name":"2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114648491","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: 1

Driving Behavior Recognition using Multiple Deep Learning Models 使用多个深度学习模型的驾驶行为识别

2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA) Pub Date : 2022-05-12 DOI: 10.1109/CSPA55076.2022.9781995

Muhammad Hafizin Zarif Mohd Fodli, Fadhlan Hafizhelmi Kamaru Zaman, N. K. Mun, Lucyantie Mazalan

{"title":"Driving Behavior Recognition using Multiple Deep Learning Models","authors":"Muhammad Hafizin Zarif Mohd Fodli, Fadhlan Hafizhelmi Kamaru Zaman, N. K. Mun, Lucyantie Mazalan","doi":"10.1109/CSPA55076.2022.9781995","DOIUrl":"https://doi.org/10.1109/CSPA55076.2022.9781995","url":null,"abstract":"Malaysia has one of the highest traffic fatality rates in the world. The main cause towards the increment of annual rate on traffic accident in Malaysia is due to distracted driver on wheel. Due to the advancement and integration of technologies within the society, drivers tend to get distracted either by their devices or infotainment that is build-in with the vehicles. This paper presents the application of deep learning to classify driver’s distracted behavior behind the wheel. This paper implements deep convolution neural network to classify driver’s distracted behavior behind the wheel. The experiment was conducted to classify drowsiness dataset of 10 classes from State Farm and 2 classes from National Tsing Hua University (NTHU). Fast and accurate models for driving behavior classification are desired for real-world deployment and application in vehicle system. The results of this investigation show that MobileNetV2 outperforms other models, presenting a good balance between accuracy and processing runtime for real-world deployment.","PeriodicalId":174315,"journal":{"name":"2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124207749","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: 1

Power Quality Disturbances Classification Analysis Using Residual Neural Network 基于残差神经网络的电能质量扰动分类分析

2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA) Pub Date : 2022-05-12 DOI: 10.1109/CSPA55076.2022.9782013

Nurul Usni Iman Abd Jamlus, S. Shahbudin, Murizah Kassim

{"title":"Power Quality Disturbances Classification Analysis Using Residual Neural Network","authors":"Nurul Usni Iman Abd Jamlus, S. Shahbudin, Murizah Kassim","doi":"10.1109/CSPA55076.2022.9782013","DOIUrl":"https://doi.org/10.1109/CSPA55076.2022.9782013","url":null,"abstract":"Along with the Power Quality Disturbances (PQD) such as normal, harmonics, notch, transient, sag and swell that are due to load or electrical appliances continuously occurring in a power system, the supervised detection, and classification method is still in development progress to gain the ideal PQD classification method in order to improve the low power quality in a power system. Automatic detection and classification techniques such as deep learning algorithms are frequently preferred nowadays. Many researchers implement deep learning algorithms especially Convolutional Neural Network (CNN) architecture as a multiple PQD analysis using advanced CNN architecture namely Residual Neural Network (ResNet). To identify which ResNet architecture gives the best performance, two types of ResNet architecture; ResNet-18 and ResNet-50 are implemented. The results obtained and then compared with other CNN architectures such as basic CNN, Deep CNN (DCNN) and GoogLeNet. The results show that ResNet-18 outperforms other CNN architectures with achieved the best performance in terms of accuracy (95.77%), precision (73.73%), sensitivity (67.37%), specificity (97.29%) and F1-score (70.14%).","PeriodicalId":174315,"journal":{"name":"2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116923161","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: 4

The Grading of Agarwood Oil Quality Based on Multiclass Support Vector Machine (MSVM) Model 基于多类支持向量机(MSVM)模型的沉香油质量分级

2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA) Pub Date : 2022-05-12 DOI: 10.1109/CSPA55076.2022.9782021

Aqib Fawwaz Mohd Amidon, Noratikah Zawani Mahabob, Siti Mariatul Hazwa Mohd Huzir, Z. Mohd Yusoff, N. Ismail, M. Taib

{"title":"The Grading of Agarwood Oil Quality Based on Multiclass Support Vector Machine (MSVM) Model","authors":"Aqib Fawwaz Mohd Amidon, Noratikah Zawani Mahabob, Siti Mariatul Hazwa Mohd Huzir, Z. Mohd Yusoff, N. Ismail, M. Taib","doi":"10.1109/CSPA55076.2022.9782021","DOIUrl":"https://doi.org/10.1109/CSPA55076.2022.9782021","url":null,"abstract":"Agarwood oil is one of the most valued oils among the world's peoples, which contributes to its ever-increasing demand. It has a variety of advantages and applications, including the manufacturing of incense and fragrances, and also is employed in traditional medicine. However, without a standard grading model for agarwood oil has resulted in certain flaws in the grading procedure. To address these flaws, a standard grading model must be developed and deployed as soon as possible. By continuing the research study of standard grading model development, intelligent algorithm must be implemented as main function to establishment of this standard to ensure that the model’s capability is entirely unquestioned. One of classification algorithm which is Support Vector Machine algorithm has been chosen and multiclass classifier algorithm has been used as supporter to SVM. One of multiclass classifier strategies which is One versus All strategy has been implemented to improve the ability of SVM. By combining both intelligent techniques, the model was able to be function as multiclass classification model, known as Multiclass Support Vector Machine (MSVM) model. In MSVM model, percentage of abundance chemical compounds have been used as input and quality (low, medium low, medium high and high) was used as output. The Matlab software version r2020a was used in this research work to train and test the model's performance. The results revealed that the model passed the performance requirements standard while employing the multiclass function. The findings of this study will undoubtedly be useful in future agarwood oil research, particularly in quality categorization.","PeriodicalId":174315,"journal":{"name":"2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130053190","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