Dominik Birtić, M. Vranješ, Ž. Lukač, Gordana S. Velikic
{"title":"Graphic Environment for Generating Automated Tests and Documentation for ADAS","authors":"Dominik Birtić, M. Vranješ, Ž. Lukač, Gordana S. Velikic","doi":"10.1109/ZINC50678.2020.9161442","DOIUrl":"https://doi.org/10.1109/ZINC50678.2020.9161442","url":null,"abstract":"Advanced driver-assistance systems (ADAS) are primarily intended to help drivers in traffic and to increase driving safety. Today a large number of engineers are developing different algorithms for ADAS. This results in large quantity of written program codes on a daily basis, which has to be tested. Note that a manual testing and writing a code for the manual testing is an arduous task. To satisfy the increasing testing needs, and to accelerate the testing process, we developed a graphical environment that allows users to create automated tests quickly and efficiently by a simple drag and drop method. The environment was created using a Node.js server, MongoDB and Blockly. Blockly was used to create blocks and to combine blocks to make scripts, i.e., automated tests for ADAS. Node.js was used to save blocks into the MongoDB database and to load existing blocks into the environment. The environment enables users to use existing blocks to create Python scripts and their own blocks which can be used to create Python scripts, and to edit or remove existing blocks. The environment was tested manually. Testing results showed that all functionalities work properly and that the environment enables users to generate different scripts for automated testing in an order of seconds.","PeriodicalId":6731,"journal":{"name":"2020 Zooming Innovation in Consumer Technologies Conference (ZINC)","volume":"63 1","pages":"301-306"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79691668","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}
{"title":"Investigation of The Focusing Effect on The Reconstructed Image Quality in Digital Holographic Microscopy","authors":"Gülhan Ustabas Kaya, T. Onur","doi":"10.1109/ZINC50678.2020.9161444","DOIUrl":"https://doi.org/10.1109/ZINC50678.2020.9161444","url":null,"abstract":"Holography has been used as an imaging method in various fields of science for many years. However, practical applications, challenging procedures and equipment requirements cause restrictions on the use of traditional holography. These requirements and constraints have led to the development of digital holography and digital holographic microscopy. Digital holographic microscopy (DHM) imaging method allows to identify the three dimensional profiles of very small biological samples and transparent objects optically. In digital holographic microscopy, there are substantial advantages in many respects such as obtaining holograms quickly, having complete amplitude and phase information and applying versatile image processing techniques. Although digital holographic microscopy enables the application of many image processing techniques, it is important for the reconstruction process that the recorded image is in the focus of the imaging systems. This paper deals the study of the focusing effects on the reconstructed image obtained from recorded hologram of clear and blurred images with simulation using MATLAB software. The results obtained show that the focusing process in the recording stage is an important factor affecting the construction of holograms and therefore reconstruction performance","PeriodicalId":6731,"journal":{"name":"2020 Zooming Innovation in Consumer Technologies Conference (ZINC)","volume":"19 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82931165","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}
{"title":"Power Control of Grid-Connected Inverters Using One-Cycle Control Method for PV Systems","authors":"Amir Masuod Kianmanesh, A. Akhavan","doi":"10.1109/ZINC50678.2020.9161434","DOIUrl":"https://doi.org/10.1109/ZINC50678.2020.9161434","url":null,"abstract":"In this paper, one-cycle control (OCC) method as an appropriate control scheme is applied and developed to control grid-connected inverters with the LCL filter for VAR compensation and injecting power to the grid. Also, particle swarm optimization (PSO) algorithm is employed to find the optimal LCL filter parameters by minimizing the total harmonic distortion (THD) of AC-side current as a cost function. LCL filters are generally applied in renewable power generation systems to smooth the injected current into the network because of their better attenuation capability and smaller elements than other filters. Although an LCL filter attenuates harmonics created by switches more effectively, due to its inherent resonance, it may cause stability issues. To create stable performance, either an active damping algorithm or a physical damping resistor should be used. However, a physical resistor causes additional power loss and slower dynamic response, while by adding a digital filter as a conventional virtual resistor and modifying the control scheme, the above-mentioned problems could be solved.","PeriodicalId":6731,"journal":{"name":"2020 Zooming Innovation in Consumer Technologies Conference (ZINC)","volume":"3 1","pages":"253-258"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79130287","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}
Andrija Mihalj, R. Grbić, N. Lukic, Zvonimir Kaprocki
{"title":"Code Generator for ADAS Software Testing","authors":"Andrija Mihalj, R. Grbić, N. Lukic, Zvonimir Kaprocki","doi":"10.1109/ZINC50678.2020.9161801","DOIUrl":"https://doi.org/10.1109/ZINC50678.2020.9161801","url":null,"abstract":"Modern cars use advanced electronic systems that help the driver with the driving process - so-called Advanced Driver-Assistance Systems (ADAS). ADAS systems are used to automate, customize and improve systems within a vehicle for greater safety and better driving experience. Since ADAS systems as such can have a significant impact on the driving process, the vehicle and the driver, they must be thoroughly tested and developed within many industry standards. The key factor in their work is communication between individual system components. This standardized communication is necessary to test, which is usually performed by developing AUTomotive Open System Architecture (AUTOSAR) communication tests. Since ADAS testing can be quite a complex and time-consuming process, automated testing is performed in an appropriate testing environment. In this paper, existing ADAS environment testing systems is presented, which generates a test environment for the simulation of communication in the middle layer (Middleware) of AUTOSAR architecture. Test Environment Generator (TEG) is a Python program for processing ARXML test files based on which it generates a test environment model in the form of separate components in the C programming language. The program consists of input data parsing, parsed data storing and components generation that build the test environment. Based on the detected disadvantages of the existing TEG, several modifications are proposed in order to accelerate its execution time and to introduce more robust and stable data storage methods in database form.","PeriodicalId":6731,"journal":{"name":"2020 Zooming Innovation in Consumer Technologies Conference (ZINC)","volume":"47 1","pages":"184-189"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91183142","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}
Geerish Suddul, K. Dookhitram, Girish Bekaroo, Nikhilesh Shankhur
{"title":"An Evolutionary MultiLayer Perceptron Algorithm for Real Time River Flood Prediction","authors":"Geerish Suddul, K. Dookhitram, Girish Bekaroo, Nikhilesh Shankhur","doi":"10.1109/ZINC50678.2020.9161824","DOIUrl":"https://doi.org/10.1109/ZINC50678.2020.9161824","url":null,"abstract":"Severe flash flood events give very little opportunity for issuing warnings. In this paper, we approach the automated and real time prediction of river flooding by proposing and evaluating different variations of the conventional Multilayer Perceptron (MLP) machine learning algorithm. Our first approach follows a trial and error attempt to optimize the MLP architecture. The second and third approaches are based on the application of nature inspired evolutionary techniques, namely the Genetic Algorithm (MLP-GA) and the Bat Algorithm (MLP-BA) respectively. The MLP-GA generates an improved MLP configuration and MLP-BA enhances the training method. Our fourth, novel approach (MLP-BA-GA) is based on the application of GA to further optimize both the BA and MLP architecture. When compared with previous work, experiments show improvement in the accuracy of river flood prediction, with significant results for the MLP-BA-GA.","PeriodicalId":6731,"journal":{"name":"2020 Zooming Innovation in Consumer Technologies Conference (ZINC)","volume":"42 1","pages":"109-112"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91183280","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}
Phavish Babajee, Geerish Suddul, S. Armoogum, Ravi Foogooa
{"title":"Identifying Human Emotions from Facial Expressions with Deep Learning","authors":"Phavish Babajee, Geerish Suddul, S. Armoogum, Ravi Foogooa","doi":"10.1109/ZINC50678.2020.9161445","DOIUrl":"https://doi.org/10.1109/ZINC50678.2020.9161445","url":null,"abstract":"The identification of facial expressions that reveal human emotions can help computers to better assess the human state of mind, so as to provide a more customized interaction. We explore the recognition of human facial expressions through a deep learning approach using a Convolutional Neural Network (CNN) algorithm. The system uses a labelled data set containing around 32,298 images with multiple facial expressions for training and testing. The pre-training phase involves a face detection subsystem with noise removal, including feature extraction. The generated classification model used for prediction can identify seven emotions of the Facial Action Coding System (FACS). Results of our work in progress demonstrate an accuracy of 79.8% for the recognition of all basic seven human emotions, without the application of optimization techniques.","PeriodicalId":6731,"journal":{"name":"2020 Zooming Innovation in Consumer Technologies Conference (ZINC)","volume":"8 4 1","pages":"36-39"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74904780","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}
{"title":"Optimization of Application Deployment Delay with Efficient Task Scheduling in Cloud-Based Smart Home Platform","authors":"J. Rajkumar, Chuan Pham, K. Nguyen, M. Cheriet","doi":"10.1109/ZINC50678.2020.9161796","DOIUrl":"https://doi.org/10.1109/ZINC50678.2020.9161796","url":null,"abstract":"Smart home platform is an incarnation of Internet of Things (IoT) system. In such a platform, home applications are deployed using Software as a Service (SaaS) deployment model, a new way of software service provisioning for quick application deployment. However, this deployment model still has deployment performance issues due to the high degree of coordination and mutual dependencies of distributed services built on heterogeneous technologies. In a large scale deployment setup with more number of services, inter and intra-communication links between the coordinated services increase thereby introducing execution delays at service computation, and inter-service communications. Therefore, in this paper, we propose a smart home platform architecture based on Platform as a Service (PaaS) model supporting the SaaS deployment model. Based on the designed architecture, we model an optimization problem named as optimized IoT Application Deployment (OIAD) to minimize application deployment time (total execution time). To solve the OIAD problem, this paper proposes a heuristic algorithm to find a near-optimal deployment time. The results of our simulation show an improvement in comparison with FCFS (First Come First Serve) and Random execution algorithm under various deployment scenarios and strategies.","PeriodicalId":6731,"journal":{"name":"2020 Zooming Innovation in Consumer Technologies Conference (ZINC)","volume":"37 1","pages":"67-72"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80191632","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}
{"title":"An Efficient Analysis Scheme for Intelligent ECG Monitoring Devices","authors":"S. Raj","doi":"10.1109/ZINC50678.2020.9161780","DOIUrl":"https://doi.org/10.1109/ZINC50678.2020.9161780","url":null,"abstract":"In the last few decades, consumer electronics in the field of biomedical devices has sought significant growth. With the advancement in artificial intelligence (AI), the biomedical devices such as electrocardiography (ECG) monitoring systems are now capable of performing automated analysis. However, there is significant room for enhancement in the efficiency of the available devices. This paper presents an efficient methodology for automated recognition of ECG signals. Here, the double density complex wavelet transform (DDCWT) method is employed for capturing the time-frequency (TF) information from the ECG signals. Different features from the output coefficients are captured and concatenated with the heart-rate variability information between ECG signals. This resulting vector carry sufficient information of each heartbeat and is classified using twin support vector machine (TSVM) scheme to classify into five categories. The classifier metrics are chosen by employing artificial bee colony (ABC) algorithm to enhance its performance. The experiments are conducted on the MIT-BIH data under subject oriented scheme where an accuracy of 97.20% is reported. The microcontroller-based implementation of the proposed methodology will result in the development an efficient monitoring system for consumers targeted for mass market.","PeriodicalId":6731,"journal":{"name":"2020 Zooming Innovation in Consumer Technologies Conference (ZINC)","volume":"23 1","pages":"207-212"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79838755","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}
{"title":"Discovery of Elsagate: Detection of Sparse Inappropriate Content from Kids Videos","authors":"Wenlin Han, Madhura Ansingkar","doi":"10.1109/ZINC50678.2020.9161808","DOIUrl":"https://doi.org/10.1109/ZINC50678.2020.9161808","url":null,"abstract":"Elsagate refers to kids videos containing inappropriate content for children but difficult to filter thus often shown up on kids channels. Most of the mainstream kids channels have powerful filters. However, when the inappropriate content is sparse, the filters often fail to detect the inappropriateness. In this paper, we introduce our work in progress, a scheme to detect Elsagate videos based on Sparse Linear Discrimination (SLD), an effective way to help detect and classify these kinds of unsafe videos and enrich better user experience.","PeriodicalId":6731,"journal":{"name":"2020 Zooming Innovation in Consumer Technologies Conference (ZINC)","volume":"13 1","pages":"46-47"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87842191","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}