Pub Date : 2022-12-17DOI: 10.1109/ICSPC55597.2022.10001788
Anes Kelić, A. Almisreb, N. Md. Tahir, Jamil Bakri
Management accounting is the central piece that controls organisations’ decision-making based on management accounting, yet little research focuses on this area. Hence this study, object classification, descriptive analysis and pattern recognition are implemented to investigate the correlation between decision-making and business intelligence. The data acquisition is from organisations in Bosnia and Herzegovina, specifically from a Structured Query Language Database and quantitative approach. Further, the statistical data generated patterns of sales and expenses that can be recognised, marking down the organisation might have to take a different approach which is based on 55000 transactions accumulated over the past six years. These transactions are then normalised into a standardised format and imported into the Atoti BI with Python. Initial findings showed that based on numerical analysis, the revenue and total revenue increased by 10% in the product price.
{"title":"Big Data and Business Intelligence - A Data Driven Strategy for Business in Bosnia Herzegovina","authors":"Anes Kelić, A. Almisreb, N. Md. Tahir, Jamil Bakri","doi":"10.1109/ICSPC55597.2022.10001788","DOIUrl":"https://doi.org/10.1109/ICSPC55597.2022.10001788","url":null,"abstract":"Management accounting is the central piece that controls organisations’ decision-making based on management accounting, yet little research focuses on this area. Hence this study, object classification, descriptive analysis and pattern recognition are implemented to investigate the correlation between decision-making and business intelligence. The data acquisition is from organisations in Bosnia and Herzegovina, specifically from a Structured Query Language Database and quantitative approach. Further, the statistical data generated patterns of sales and expenses that can be recognised, marking down the organisation might have to take a different approach which is based on 55000 transactions accumulated over the past six years. These transactions are then normalised into a standardised format and imported into the Atoti BI with Python. Initial findings showed that based on numerical analysis, the revenue and total revenue increased by 10% in the product price.","PeriodicalId":334831,"journal":{"name":"2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129837488","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 : 2022-12-17DOI: 10.1109/ICSPC55597.2022.10001741
Azlan Salim, I. Yassin, M. K. A. Mahmood, Z. I. Khan, M. Ali, Khairul Khaizi Mohd Shariff, F. N. Osman, Adizul Ahmad, F. Eskandari
This research describes an intelligent method for differentiating coffee roasting levels based on Microwave Non- Destructive Testing (MNDT) data. The MNDT method collects s-parameter readings from several types of coffee (dark, medium, and light roast) by passing microwaves through them. Error-Correcting Output Coding Support Vector Machine (ECOC-SVM) was fed a multi-layer perceptron neural network to assess the degree of different coffee roasts. With a small number of hidden units, the ECOC-SVM could identify between the various roasts (with 6,400 data points per sample).
{"title":"ECOC-SVM Classification of Coffee Roast Levels based on MNDT s-Parameters","authors":"Azlan Salim, I. Yassin, M. K. A. Mahmood, Z. I. Khan, M. Ali, Khairul Khaizi Mohd Shariff, F. N. Osman, Adizul Ahmad, F. Eskandari","doi":"10.1109/ICSPC55597.2022.10001741","DOIUrl":"https://doi.org/10.1109/ICSPC55597.2022.10001741","url":null,"abstract":"This research describes an intelligent method for differentiating coffee roasting levels based on Microwave Non- Destructive Testing (MNDT) data. The MNDT method collects s-parameter readings from several types of coffee (dark, medium, and light roast) by passing microwaves through them. Error-Correcting Output Coding Support Vector Machine (ECOC-SVM) was fed a multi-layer perceptron neural network to assess the degree of different coffee roasts. With a small number of hidden units, the ECOC-SVM could identify between the various roasts (with 6,400 data points per sample).","PeriodicalId":334831,"journal":{"name":"2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)","volume":"26 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134506203","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 : 2022-12-17DOI: 10.1109/ICSPC55597.2022.10001803
Cherng Liin Yong, Ban Hoe Kwan, D. Ng, Hong Seng Sim
Service robot technology is rapidly improving to give rise to robust and reliable machines operating alongside humans. This paper presents a human-following system that can identify a target human in a crowded environment and track the person’s motion, simultaneously avoiding obstacles while navigating through the environment. We implement the system on a mobile service robot platform with light detection and ranging (LIDAR) and RGBD sensors. The system uses a Discriminative Generative network (DG-net) for human detection. After detection, the localization module will locate the target person’s position in the environment. The navigation module generates a cost map of the surroundings for path planning. It allows the robot to navigate the changing environment avoiding obstacles while tracking the target person. Experimental results showed that the robot could identify and follow the target person reliably. At the same time, the robot navigates the crowded environment safely, avoiding other people and obstacles in the environment. Despite all that, the recovery module could not recover reliably after losing the target person. The demonstration video is available at https://github.com/LeoYong95/human_following.git
{"title":"Human Tracking and Following using Machine Vision on a Mobile Service Robot","authors":"Cherng Liin Yong, Ban Hoe Kwan, D. Ng, Hong Seng Sim","doi":"10.1109/ICSPC55597.2022.10001803","DOIUrl":"https://doi.org/10.1109/ICSPC55597.2022.10001803","url":null,"abstract":"Service robot technology is rapidly improving to give rise to robust and reliable machines operating alongside humans. This paper presents a human-following system that can identify a target human in a crowded environment and track the person’s motion, simultaneously avoiding obstacles while navigating through the environment. We implement the system on a mobile service robot platform with light detection and ranging (LIDAR) and RGBD sensors. The system uses a Discriminative Generative network (DG-net) for human detection. After detection, the localization module will locate the target person’s position in the environment. The navigation module generates a cost map of the surroundings for path planning. It allows the robot to navigate the changing environment avoiding obstacles while tracking the target person. Experimental results showed that the robot could identify and follow the target person reliably. At the same time, the robot navigates the crowded environment safely, avoiding other people and obstacles in the environment. Despite all that, the recovery module could not recover reliably after losing the target person. The demonstration video is available at https://github.com/LeoYong95/human_following.git","PeriodicalId":334831,"journal":{"name":"2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129237815","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 : 2022-12-17DOI: 10.1109/ICSPC55597.2022.10001794
M. R. Ghazali, Mohd Ashraf Ahmad, Yoong Tai Hui, Nor Aimi Najwa Shamsudin, W. I. Ibrahim
Cupping therapy represents an unconventional remedy which utilized combination of traditional and contemporary Islamic medications to expedite wellness restoration through stimulated blood and air extractions by application of vacuum on the human skin. A number of methods enclosing immediate engulfing of bitten wounds through the human’s mouth, blood withdrawal through the assistance of leeches, as well as the operationalizing of heat or pump-based techniques through utilization of venerable instruments like organic horns and modernized approach like bamboo, plastic and glassware to develop the suction effect. Modern cupping mechanism especially recognized uncontrollable discharge of pressure amid the engulfing process following air dispersal through gaps in the hair. Such predicament then drove cupping practitioners to usage of the phlegm suction machine available within the market for engulfment on hairy areas of the human body. However, several disadvantages surfaced from its enormous size, inability for simultaneous suctions, as well as extended cupping interval and skilful operational requirement from manually administered suction power control for blister and skin damage preventions. Resolution to the aforementioned problems is proposed through the current paper by development of a cupping suction system as equipped with an automatic suction control and simultaneous suction outputs. The system additionally included the attributes of interval selection and alarm mechanism with time display for clarified interval indication towards better remedial outcomes. The system’s installation of fuzzy control as the intelligent controller further works to ensure lowered power consumption and better engulfment control on the skin. Demonstrated results ultimately confirmed the proposed system as an efficient suction structure for reduced negative effect of cupping therapy on patients’ skin and eased adoption among cupping practitioner.
{"title":"Cupping Suction System with Fuzzy Logic Controller Design","authors":"M. R. Ghazali, Mohd Ashraf Ahmad, Yoong Tai Hui, Nor Aimi Najwa Shamsudin, W. I. Ibrahim","doi":"10.1109/ICSPC55597.2022.10001794","DOIUrl":"https://doi.org/10.1109/ICSPC55597.2022.10001794","url":null,"abstract":"Cupping therapy represents an unconventional remedy which utilized combination of traditional and contemporary Islamic medications to expedite wellness restoration through stimulated blood and air extractions by application of vacuum on the human skin. A number of methods enclosing immediate engulfing of bitten wounds through the human’s mouth, blood withdrawal through the assistance of leeches, as well as the operationalizing of heat or pump-based techniques through utilization of venerable instruments like organic horns and modernized approach like bamboo, plastic and glassware to develop the suction effect. Modern cupping mechanism especially recognized uncontrollable discharge of pressure amid the engulfing process following air dispersal through gaps in the hair. Such predicament then drove cupping practitioners to usage of the phlegm suction machine available within the market for engulfment on hairy areas of the human body. However, several disadvantages surfaced from its enormous size, inability for simultaneous suctions, as well as extended cupping interval and skilful operational requirement from manually administered suction power control for blister and skin damage preventions. Resolution to the aforementioned problems is proposed through the current paper by development of a cupping suction system as equipped with an automatic suction control and simultaneous suction outputs. The system additionally included the attributes of interval selection and alarm mechanism with time display for clarified interval indication towards better remedial outcomes. The system’s installation of fuzzy control as the intelligent controller further works to ensure lowered power consumption and better engulfment control on the skin. Demonstrated results ultimately confirmed the proposed system as an efficient suction structure for reduced negative effect of cupping therapy on patients’ skin and eased adoption among cupping practitioner.","PeriodicalId":334831,"journal":{"name":"2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129741915","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 : 2022-12-17DOI: 10.1109/ICSPC55597.2022.10001743
N. S. M. Zamani, W. Zaki, A. B. Huddin, Z. Hamid
Image classification using deep learning has been widely implemented, primarily in medical imaging. However, features and focus regions are extracted by the network, becomes a black box mystery in the feature extraction layer during network training, unlike conventional feature extraction approaches, where various methods can extract image features. Regardless, traditional image feature extraction is laborious to find the most suitable algorithm. It takes time to meet the significant image features before classification and final image localisation, especially for the microscopic images. Therefore, a method to localise the region of interest (ROI) in vitro of the colony-formation unit (CFU) of hematopoietic stem/progenitor cell (HSPC) using gradCAM through deep learning, approaches have been proposed. This work comprises three main phases: CFU data preparation, convolutional neural network (CNN) pre-trained networks and localisation of the ROI. The proposed method has successfully localised the ROI of the CFU HSPC using gradCAM through a deep neural network with 87.5% sensitivity performed by DarkNet19. The finding of this work can be used as a baseline for future CFU HSPC classification that focuses on the CFU region.
{"title":"Region of Interest Localisation of Hematopoietic Stem/Progenitor Cell Images","authors":"N. S. M. Zamani, W. Zaki, A. B. Huddin, Z. Hamid","doi":"10.1109/ICSPC55597.2022.10001743","DOIUrl":"https://doi.org/10.1109/ICSPC55597.2022.10001743","url":null,"abstract":"Image classification using deep learning has been widely implemented, primarily in medical imaging. However, features and focus regions are extracted by the network, becomes a black box mystery in the feature extraction layer during network training, unlike conventional feature extraction approaches, where various methods can extract image features. Regardless, traditional image feature extraction is laborious to find the most suitable algorithm. It takes time to meet the significant image features before classification and final image localisation, especially for the microscopic images. Therefore, a method to localise the region of interest (ROI) in vitro of the colony-formation unit (CFU) of hematopoietic stem/progenitor cell (HSPC) using gradCAM through deep learning, approaches have been proposed. This work comprises three main phases: CFU data preparation, convolutional neural network (CNN) pre-trained networks and localisation of the ROI. The proposed method has successfully localised the ROI of the CFU HSPC using gradCAM through a deep neural network with 87.5% sensitivity performed by DarkNet19. The finding of this work can be used as a baseline for future CFU HSPC classification that focuses on the CFU region.","PeriodicalId":334831,"journal":{"name":"2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128286001","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 : 2022-12-17DOI: 10.1109/ICSPC55597.2022.10001807
Ashruti Upadhyaya, C. Mahanta
Velocity prediction is an integral part for the development of robust Energy Management System (EMS) of an Electric Vehicle (EV) which essentially enhances the performance and life cycle of the vehicle. In this paper an ANN based approach combining Back-propagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN) is used to forecast velocity on different prediction horizons. These methods are tested on two conventional driving cycles viz. Manhattan and WYU driving cycle and one mixed cycle which is created by combining different random driving cycles. The results are studied in terms of Root Mean Square Error (RMSE) where the proposed network yields the least value in all the cases as compared to conventional BPNN method. The results proved the robustness and adaptability of the proposed method which can be used in practical applications.
{"title":"Improving Velocity Prediction in Electric Vehicles using Hybrid Artificial Neural Network (ANN)","authors":"Ashruti Upadhyaya, C. Mahanta","doi":"10.1109/ICSPC55597.2022.10001807","DOIUrl":"https://doi.org/10.1109/ICSPC55597.2022.10001807","url":null,"abstract":"Velocity prediction is an integral part for the development of robust Energy Management System (EMS) of an Electric Vehicle (EV) which essentially enhances the performance and life cycle of the vehicle. In this paper an ANN based approach combining Back-propagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN) is used to forecast velocity on different prediction horizons. These methods are tested on two conventional driving cycles viz. Manhattan and WYU driving cycle and one mixed cycle which is created by combining different random driving cycles. The results are studied in terms of Root Mean Square Error (RMSE) where the proposed network yields the least value in all the cases as compared to conventional BPNN method. The results proved the robustness and adaptability of the proposed method which can be used in practical applications.","PeriodicalId":334831,"journal":{"name":"2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125565556","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 : 2022-12-17DOI: 10.1109/ICSPC55597.2022.10001816
R. A. Karim, Muhamad Hamizi Zaidi Bin Mohd Jonhanis, Wan Nur Azhani Binti W. Samsudin, N. W. Arshad, N. F. Zakaria
Communication over the internet is a common practice among computer users. A pointer is an essential tool for effective communication, pointing to a landmark or an intended object. Telepointer have become an important gadget for telemedicine to pinpoint the exact location of lesions, especially for endoscopic images. The endoscopic image will be displayed on the monitor at the surgeon's site, and the same view will be displayed at the remote expert site. However, the challenges for endoscopic images are the unconscious movement of the tissues in the endoscopic images, uniform texture, and varied illumination, which make it hard to keep track of the intended object. In this paper, a comparative study to detect the cursor over the endoscopic images was explored. RGB color space and HSV color space were used for comparative study. Experimental results revealed that HSV color space works well for cursor detection with an accuracy of 99.59%.
{"title":"Comparative Study for Cursor Detection at Endoscopic Images for Telepointer","authors":"R. A. Karim, Muhamad Hamizi Zaidi Bin Mohd Jonhanis, Wan Nur Azhani Binti W. Samsudin, N. W. Arshad, N. F. Zakaria","doi":"10.1109/ICSPC55597.2022.10001816","DOIUrl":"https://doi.org/10.1109/ICSPC55597.2022.10001816","url":null,"abstract":"Communication over the internet is a common practice among computer users. A pointer is an essential tool for effective communication, pointing to a landmark or an intended object. Telepointer have become an important gadget for telemedicine to pinpoint the exact location of lesions, especially for endoscopic images. The endoscopic image will be displayed on the monitor at the surgeon's site, and the same view will be displayed at the remote expert site. However, the challenges for endoscopic images are the unconscious movement of the tissues in the endoscopic images, uniform texture, and varied illumination, which make it hard to keep track of the intended object. In this paper, a comparative study to detect the cursor over the endoscopic images was explored. RGB color space and HSV color space were used for comparative study. Experimental results revealed that HSV color space works well for cursor detection with an accuracy of 99.59%.","PeriodicalId":334831,"journal":{"name":"2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124131944","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 : 2022-12-17DOI: 10.1109/ICSPC55597.2022.10001789
Mohd Ashraf Ahmad, M. Tumari, M. R. Ghazali, M. H. Suid
This paper exclusively endorses the optimization of self-tuned PID using Safe Experimentation Spiral Dynamic Algorithm (SESDA) for elastic joint handling. SESDA is hereby devised by adoption of spiral function to a standard Safe Experimentation Dynamics Algorithm (SEDA). Such modification is implemented to exploit the ability of spiral function in enhancing both the algorithm’s exploration competency and convergence accuracy. Rotating angle tracking and vibration were then commanded by employing a pair of self-tuned PID controllers to the elastic joint system in appraising the optimization efficacy of SESDA. Performance of the updated self-tuned PID controller was further assessed in accordance to the recorded outputs on angular motion trajectory tracking, vibration suppression and statistical evaluations centering its pre-established control fitness function. The proposed SESDA produced 6.51 %, 5.54 % and 8.51 % improvement of fitness function, tracking error and control input energy, respectively, as compared with the standard SEDA. Acquired results ultimately confirmed the excellence of SESDA towards self-tuned PID’s superior regulatory precision against the standard SEDA as well as its variants.
{"title":"Implementation of Safe Experimentation Spiral Dynamics Algorithm for Self-Tuning of PID Controller in Elastic Joint Manipulator","authors":"Mohd Ashraf Ahmad, M. Tumari, M. R. Ghazali, M. H. Suid","doi":"10.1109/ICSPC55597.2022.10001789","DOIUrl":"https://doi.org/10.1109/ICSPC55597.2022.10001789","url":null,"abstract":"This paper exclusively endorses the optimization of self-tuned PID using Safe Experimentation Spiral Dynamic Algorithm (SESDA) for elastic joint handling. SESDA is hereby devised by adoption of spiral function to a standard Safe Experimentation Dynamics Algorithm (SEDA). Such modification is implemented to exploit the ability of spiral function in enhancing both the algorithm’s exploration competency and convergence accuracy. Rotating angle tracking and vibration were then commanded by employing a pair of self-tuned PID controllers to the elastic joint system in appraising the optimization efficacy of SESDA. Performance of the updated self-tuned PID controller was further assessed in accordance to the recorded outputs on angular motion trajectory tracking, vibration suppression and statistical evaluations centering its pre-established control fitness function. The proposed SESDA produced 6.51 %, 5.54 % and 8.51 % improvement of fitness function, tracking error and control input energy, respectively, as compared with the standard SEDA. Acquired results ultimately confirmed the excellence of SESDA towards self-tuned PID’s superior regulatory precision against the standard SEDA as well as its variants.","PeriodicalId":334831,"journal":{"name":"2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)","volume":"26 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120858033","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 : 2022-12-17DOI: 10.1109/ICSPC55597.2022.10001801
Noorfadzli bin Abdul Razak, Muhammad Zaim bin Mazlan, J. Johari, Syahrul Afzal Bin Che Abdullah, N. K. Mun
Lane detection and tracking technique are commonly used for a vehicle to navigate autonomously on the road. Various techniques have been developed by researchers and it seems image processing from vision sensors appears to be a popular approach. Hence, seeing the relevance of the technique, this research intends to develop the road lane detection technique which comprises OpenCV, Gaussian Blur, Masking, Canny Edge, and the Hough Transform methods. The technique was set to run using an embedded controller that is connected to a vision sensor. They were installed on the dashboard of the car to perform the detection of the two-lane road at different times. Several videos were recorded in real-time with 3-hour intervals starting at 10 am. During the recording, the technique analyzes and segmentizes the images from the video so that the white lanes on the road can be detected and tracked. To observe the performance of the technique, the images of the detected lane were converted to a histogram. Via the histogram value, it shows the best time to attain optimal performance of the lane detection technique. According to the outcomes of the experiment, it appears that at 1 pm., the technique works very well to perform the detection compared to other times. At present, we established a two-road lane detection and tracking technique that can be applied for autonomous navigation. However, there is still improvement that can be made to enhance the technique to carry out lane detection in the presence of shadows and perform at night.
{"title":"A Lane Detection Using Image Processing Technique for Two-Lane Road","authors":"Noorfadzli bin Abdul Razak, Muhammad Zaim bin Mazlan, J. Johari, Syahrul Afzal Bin Che Abdullah, N. K. Mun","doi":"10.1109/ICSPC55597.2022.10001801","DOIUrl":"https://doi.org/10.1109/ICSPC55597.2022.10001801","url":null,"abstract":"Lane detection and tracking technique are commonly used for a vehicle to navigate autonomously on the road. Various techniques have been developed by researchers and it seems image processing from vision sensors appears to be a popular approach. Hence, seeing the relevance of the technique, this research intends to develop the road lane detection technique which comprises OpenCV, Gaussian Blur, Masking, Canny Edge, and the Hough Transform methods. The technique was set to run using an embedded controller that is connected to a vision sensor. They were installed on the dashboard of the car to perform the detection of the two-lane road at different times. Several videos were recorded in real-time with 3-hour intervals starting at 10 am. During the recording, the technique analyzes and segmentizes the images from the video so that the white lanes on the road can be detected and tracked. To observe the performance of the technique, the images of the detected lane were converted to a histogram. Via the histogram value, it shows the best time to attain optimal performance of the lane detection technique. According to the outcomes of the experiment, it appears that at 1 pm., the technique works very well to perform the detection compared to other times. At present, we established a two-road lane detection and tracking technique that can be applied for autonomous navigation. However, there is still improvement that can be made to enhance the technique to carry out lane detection in the presence of shadows and perform at night.","PeriodicalId":334831,"journal":{"name":"2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)","volume":"56 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132153894","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 : 2022-12-17DOI: 10.1109/ICSPC55597.2022.10001802
S. S. Salleh, Wan Aryati Wan Ghani, J. Kamaroddin
Micro-transit in the school children's and parents' community is rarely studied. e-hailing can be used to transport school children to and from school, helping parents manage their children's mobility. However, deeper research is needed to discover regulatory facts and concerns about using e-hailing for school children. Adopting micro-transit or ride sourcing for school children has not been well-analysed and may not be safe. Thus, this research seeks to gather and analyse three key micro-transit studies, find associated regularity components, and design an initial micro-transit schematic in connection to safety. This study involves three stages: (i) data collection and knowledge acquisition, (ii) data assessment and selection, and (iii) schematic construction and elements identification. A focus group discussion was conducted among transportation and safety agencies with four parents who send their children to school using public transportation to discuss roles of agencies in supporting parents’ concerns regarding their children's safety. The finding shows there is a knowledge gap about e-hailing users' needs; insufficient cyber security and safety measures, and minimum awareness from the public towards the e-hailing apps services vulnerabilities. Most parents who drive their children to and from school are concerned about safety i.e., crime and traffic. In the development of e-hailing or ride-sourcing apps, safety and security features shall align with the requirements of the components linked in the focus group schematic as shown in the result and discussion section. Literature analysis shows limited examples of safety and security features, such as applying real-time passenger information during the ride. Therefore, this limitation needs to be explored. In addition, this paper helps to identify the outlook of a workable micro-transit application domain with proper agencies and people to help find technology solutions for real-time safety measures.
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