{"title":"Celsius Tracker: A Mobile Application to Ease Movement Control Order Procedure During Covid-19 Pandemic","authors":"M. Sayuti, Nuraidah Norahim","doi":"10.1109/ICOSST53930.2021.9683810","DOIUrl":"https://doi.org/10.1109/ICOSST53930.2021.9683810","url":null,"abstract":"Since the worldwide emergence of Covid-19 pandemic, several rules are imposed to curb its spreading. Logging visitor body temperatures before entering a premise is one of the rules enforced on every store in Malaysia. Following the rule requires a non-contact thermometer and a logbook prepared by storekeepers to record their customer body temperatures. For a large shopping mall where many small stores exist, the body temperature recording procedure is repeated at every store. For many customers, repetitive procedure could turn into a hassle, causing long waiting queues, and at the same time difficult for people with disabilities. Furthermore, maintaining this procedure adds additional tasks on the employed staffs for ensuring customers abide with the rule. The proposed mobile phone application is aimed towards improving the body temperature recording procedure by using QR code. The application consists of three main modules, by which a user can record body temperature and combined with the user's personal identification, it timestamps the information as a QR code. From the generated QR code, storekeepers could verify the information easily and if the scanner function is integrated with a fully-automated door access, an automatic entry can be enabled as well, which could help reducing the waiting queue at the store. From the users point of view, majority of them agree that the application is convenient and suitable for its purpose.","PeriodicalId":325357,"journal":{"name":"2021 15th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"254 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124183772","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}
J. Hussain, Muhammad Usman, R. Ramzan, Hassan Saif
{"title":"12-bit Sigma-Delta Modulator for Biomedical Wireless Applications","authors":"J. Hussain, Muhammad Usman, R. Ramzan, Hassan Saif","doi":"10.1109/ICOSST53930.2021.9683923","DOIUrl":"https://doi.org/10.1109/ICOSST53930.2021.9683923","url":null,"abstract":"A high-resolution, low-voltage sigma-delta ($Sigma Delta$) analog-to-digital converter (ADC) is required for the bio-medical application. A high-resolution first-order $Sigma Delta$ modulator is proposed in this paper. The operational amplifier of the proposed modulator is implemented by folded cascode common source (FC-CS) approach, which achieves the gain of 63 dB with a bandwidth of 1.3 MHz. The modulator is implemented with an OSR of 20, signal bandwidth of 2 MHz, and 80 MHz of sampling frequency. This designed modulator is appropriate for high-resolution medical applications in the 2.4 GHz ISM frequency band for IEEE 802.15.6/Bluetooth standard. The proposed modulator is designed in 65 nm bulk CMOS, providing the peak SNDR of 70 dB, ENOB of 12, bandwidth 2 MHz, and power consumption of 2.12 mW at 1.2 V supply voltage.","PeriodicalId":325357,"journal":{"name":"2021 15th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130226783","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}
Sidra Jawad, H. Munsif, Arsal Azam, Arham Hasib Ilahi, S. Zafar
{"title":"Internet of Things-based Vehicle Tracking and Monitoring System","authors":"Sidra Jawad, H. Munsif, Arsal Azam, Arham Hasib Ilahi, S. Zafar","doi":"10.1109/ICOSST53930.2021.9683883","DOIUrl":"https://doi.org/10.1109/ICOSST53930.2021.9683883","url":null,"abstract":"Vehicles play an integral part in the life of a human being by facilitating in everyday tasks. The major concern that arises with this fact is that the rate of vehicle thefts have increased exponentially and retrieving them becomes almost impossible as the responsible party completely alters the stolen vehicles, leaving them untraceable. Ultimately, tracking and monitoring of vehicles using on-vehicle sensors is a promising and an efficient solution. The Internet of Things (IoT) is expected to play a vital role in revolutionizing the Security and Safety industry through a system of sensor networks by periodically sending the data from the sensors to the cloud for storage, from where it can be accessed to view or take any necessary actions (if required). The main contributions of this paper are the implementation and results of the prototype of a vehicle tracking and monitoring system. The system comprises of an Arduino UNO board connected to the Global Positioning System (GPS) module, Neo-6M, which senses the exact location of the vehicle in the form of latitude and longitude, and the ESP8266 Wi-Fi module, which sends the data to the Application Programming Interface (API) Cloud service, ThingSpeak, for storage and analyzing. An Android based mobile application is developed that utilizes the stored data from the Cloud and presents the user with the findings. Results show that the prototype is not only simple and cost effective, but also efficient and can be readily used by everyone from all walks of life to protect their vehicles.","PeriodicalId":325357,"journal":{"name":"2021 15th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134485999","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}
A. Ikram, H. Imran, Ahmed Jamal Ikram, Kiran Hamza, Khawaja Usman Riaz Sehgal
{"title":"IoT based Smart Fan Dimmer with suppressed Humming Sound and Nonlinear Effect of Inverter","authors":"A. Ikram, H. Imran, Ahmed Jamal Ikram, Kiran Hamza, Khawaja Usman Riaz Sehgal","doi":"10.1109/ICOSST53930.2021.9683877","DOIUrl":"https://doi.org/10.1109/ICOSST53930.2021.9683877","url":null,"abstract":"To address power shortages in third-world countries, other energy sources such as uninterrupted power supplies that charge batteries while line voltage is available are employed. Inverters on the market contain output voltage waveforms that do not provide pure sine waves, and these modified sine waves are non-linear and high-frequency components that impair connected appliances such as fan dimmers. With the rising smart home and IoT industries in mind, this article presents a revised fan dimmer that does not generate humming noises, is resilient, and can be connected to the internet via WiFi using the ESP8266 board. This allows the user to control the speed of the fan from a smartphone via voice command or user-interface","PeriodicalId":325357,"journal":{"name":"2021 15th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121332703","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":"A GAN Based Malware Adversaries Detection Model","authors":"Muhammad Umer, Y. Saleem, M. Saleem, Naqqash Aman","doi":"10.1109/ICOSST53930.2021.9683863","DOIUrl":"https://doi.org/10.1109/ICOSST53930.2021.9683863","url":null,"abstract":"Deep Learning algorithms are effectively working for detection and classification in real-time systems. It surpasses human-level accuracy in image detection, disease classification, and many other fields. But recent studies show how deep learning detection systems are vulnerable to adversarial attacks. GANs are used to generate zero-day adversarial attacks by training the generator and discriminator network on a malware dataset. This study aims to provide a method to detect the malware adversaries generated by GAN. Firstly, we acquired a malware dataset from an online source. Secondly, a discriminator and generator network were selected to generate the adversarial data for testing purposes. In the end, we developed a novel deep neural network model and trained it using the augmented dataset. Our proposed model achieved an 84 % accuracy level in case of an adversary attack, and it forces the GAN network-based attack to create adversarial deformed samples. Our proposed model protects against deep learning-based adversarial attacks and helps in the detection of zero-day malware attacks.","PeriodicalId":325357,"journal":{"name":"2021 15th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126920036","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}
Muhammad Adeel Asghar, Mehak Sheikh, Saqlain Razzaq, Muhammad Noman Malik
{"title":"Real-time EEG-based Driver's Fatigue Detection System using Deep Neural Network","authors":"Muhammad Adeel Asghar, Mehak Sheikh, Saqlain Razzaq, Muhammad Noman Malik","doi":"10.1109/ICOSST53930.2021.9683896","DOIUrl":"https://doi.org/10.1109/ICOSST53930.2021.9683896","url":null,"abstract":"Driver fatigne is often the direct cause of many road accidents. Therefore, developing an accurate system that senses and informs the driver of his inadequate psychophysical situation is necessary. The Electroencepbalogram (EEG)-based two-channel real-time test was conducted on 30 healthy subjects on a driving simulator to monitor the significant change in the subject's EEG signal, which happens during the transition of the subject's normal state to a normal drowsiness state. To discriminate the correct parts of the signal from the acquired EEG signal, we use preprocessing. The features were extracted using deep neural networks and defining an optimal set of characteristics and representing the signal in the frequency-time domain. The k-NN (k-Nearest neighbor), and SVM (Support vector machine) classification methods are used to achieve high classification performance, considering the differences between the controllers. The algorithm we propose in this paper uses a discrete wavelet transform to eliminate noise in the data. The classification accuracy achieved was 79.7% using the proposed system. Hence, there is a possibility to identify and distinguish the state of drowsiness of the driver. This advantage contributes towards safe and more relaxed driving.","PeriodicalId":325357,"journal":{"name":"2021 15th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132261236","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":"AGSS: An Airline Ground Service Supervising System Based on Blockchain","authors":"Xuhan Liao, Weina Niu, Jing Li, Hang Zhu, Yanping Wang, Xiaosong Zhang","doi":"10.1109/ICOSST53930.2021.9683885","DOIUrl":"https://doi.org/10.1109/ICOSST53930.2021.9683885","url":null,"abstract":"Air-ground service systems can effectively dispatch vehicles, thus improving the quality and efficiency of air service and avoiding flight delays. However, the current system lacks oversight and traceability. To be specific, when a dispatching accident happened, it is difficult to find out the root cause of the accident and the one in charge of the accident. Therefore, we construct an airline ground service supervising system based on blockchain named AGSS. In AGSS, each step requires agreements of most organizations. The event and the organizations in charge are recorded in blockchain immutably. It's easy to know what happened and the one responsible when needed. First, AGSS is easy to use with a layered design. Users only need to know the application layer, and other layers are available for the developer to modify low-level code. Then Hyperledger Fabric — a permissioned blockchain framework is adopted by us to implement AGSS. In addition, we carried out experiments about the time cost and throughput. The results show that AGSS can supervise and trace events with low overhead.","PeriodicalId":325357,"journal":{"name":"2021 15th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130155096","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":"Detection of Freezing of Gait in Parkinson's Disease by Squeeze-and-Excitation Convolutional Neural Network with Wearable Sensors","authors":"S. Mekruksavanich, A. Jitpattanakul","doi":"10.1109/ICOSST53930.2021.9683890","DOIUrl":"https://doi.org/10.1109/ICOSST53930.2021.9683890","url":null,"abstract":"It is one of the most severe motor indications of Parkinson's disease that one's stride becomes freezing of gait (FOG). Patients' quality of life is negatively impacted by FOG, which may lead to falls. Typically, questionnaires have been used to diagnose FOG; however, this method is subjective and may not correctly represent the severity of this disorder. It is possible to monitor symptoms using sensor-based devices, which can provide reliable and objective data. In this paper, the SE-DeepConvNet, a compact deep convolutional neural network including squeeze-and-excite components for fog detection, was proposed. In conducted to evaluate SE-DeepConvNet, we employed Daphnet, a publicly accessible benchmark FOG dataset. In terms of effectiveness, the SE-DeepConvNet excels most traditional deep learning models, receiving a score of 95.66% on the accuracy evaluation.","PeriodicalId":325357,"journal":{"name":"2021 15th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121659613","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":"Violent Views Detection in Urdu Tweets","authors":"Muhammad Hammad Akram, Khurram Shahzad","doi":"10.1109/ICOSST53930.2021.9683934","DOIUrl":"https://doi.org/10.1109/ICOSST53930.2021.9683934","url":null,"abstract":"The widespread use of social media has led to substantial increase in the global connectivity. Consequently, the content shared on social media has the potential to become viral in a short span of time. While some content is desired to become viral, there is a high risk that the inappropriate messages can also become viral which could be disastrous for the society. For instance, spreading violent views may lead to riots and unrest in the society. Therefore, it is desired to detect violent views to ensure stopping them from spreading. To that end, this study has scrapped Twitter to develop and publicly release the first-ever Violent Views Detection corpus for Urdu (VVD-21). The corpus is composed of 3297 Urdu tweets which are manually classified into Violent and Non-Violent views. Furthermore, experiments are performed using six traditional and two deep learning techniques to evaluate their effectiveness of these techniques for automatically detecting violent views in Urdu text. The results of the experiments show that Logistic Regression is the most effective technique as it achieved the highest F1 score of 0.881.","PeriodicalId":325357,"journal":{"name":"2021 15th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129240615","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}
Muhammad Aslam, Hashir Irfan, M. arshad, Syed Ansab Waqar Gillani
{"title":"A Study to Distribute the Data of Blockchain in a DHT-Based System for DNS","authors":"Muhammad Aslam, Hashir Irfan, M. arshad, Syed Ansab Waqar Gillani","doi":"10.1109/ICOSST53930.2021.9683861","DOIUrl":"https://doi.org/10.1109/ICOSST53930.2021.9683861","url":null,"abstract":"The current Domain Name System (DNS) has a number of issues such as cache poisoning. A number of decentralized Domain Name Systems have been proposed using a blockchain system. However, a number of problems are present in these proposed systems. We attempt to decentralize the Domain Name System using Blockchain while using Distributed Hash Tables (DHTs) to reduce the data storage needed to store by each node while improving on the previous attempts at a DHT based DNS system by the addition of an accounts list that removes the need of previous blocks while significantly improving on the time required for transaction verification.","PeriodicalId":325357,"journal":{"name":"2021 15th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"300 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131569149","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}