{"title":"FIBER OPTICAL SENSORS FOR IOT FACILITIES","authors":"Yashar Hajiyev Yashar Hajiyev","doi":"10.36962/piretc24032023-100","DOIUrl":"https://doi.org/10.36962/piretc24032023-100","url":null,"abstract":"Mass development of the latest applications for the famous 19 services of the Internet of things (IOT) becomes dominant a factor in the intellectually intensive segments of automation of technological systems. IoT is supported by regulatory switching of IoT components and devices to the Internet for information exchange with these facilities in order to control and regulate their functional modes. Our article prescribes the use of fiber optic sensors devices that can be equipped with technological and industrial installations in order to monitor them and detect deviations from operating modes. The development of fiber optic communication and monitoring devices makes IOT systems easily manageable and remotely adjustable. \u0000The integration of devices for reading technological parameters and their further transmission over a fiber optic network gives rise to several problems that were analyzed in detail in the article. In this regard, it can be established that fiber optic IOT has a broad development perspective. This article describes the current capabilities of the Internet of Things, and the constituent components of the Internet of Things, and also presents the theoretical premises that underlie the Internet of Things.\u0000Keywords: Internet of things, fiber-optical sensor, fiber network, signal processing, home safety monitoring, technological parameters acquisition.","PeriodicalId":107886,"journal":{"name":"PIRETC-Proceeding of The International Research Education & Training Centre","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126205142","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":"DETECTİON OF HAND MOVEMENTS BY ANALYZING EEG SIGNALS USING CNN","authors":"Aynur Jabiyeva, Mardaxay Ravinov Aynur Jabiyeva, Mardaxay Ravinov","doi":"10.36962/piretc24032023-117","DOIUrl":"https://doi.org/10.36962/piretc24032023-117","url":null,"abstract":"In this paper a method for detecting six specific hand movement events in a 32-component brain EEG signal by using an ensemble of convolutional neural networks (CN) as a multiclass classifier is considered. The paper proposes and empirically evaluates several options for the architecture of convolutional neural networks, as well as an ensemble that combines the proposed options for the architecture of convolutional neural networks, using a blending algorithm and a final classifier based on logistic regression. The advantages of the chosen classification method for the problem being solved are shown. The results obtained make it possible to say that the use of a classifier in the form of an ensemble of several models of convolutional neural networks allows one to effectively identify characteristic features in the initial EEG signals, and at the output of the classifier to obtain the probabilities that the input signal belongs to one of the given classes of hand movements. The use of the blending algorithm makes it possible to obtain optimal classification results by integrating the best estimates of several models, which individually on the entire test set may give a non-optimal result.","PeriodicalId":107886,"journal":{"name":"PIRETC-Proceeding of The International Research Education & Training Centre","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126973648","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":"RESEARCH NEW GENERATION ULTRASOUND TECHNOLOGIES IN BLOOD FLOW\u0000MONITORING","authors":"Laman Niftaliyeva Laman Niftaliyeva","doi":"10.36962/piretc24032023-82","DOIUrl":"https://doi.org/10.36962/piretc24032023-82","url":null,"abstract":"Currently, ultrasound machines are widely used in hospitals for the first diagnosis of various pathologies. There is also an ultrasound Doppler method to determine and monitor blood flow. Through this method, it is possible to get information about the general condition of the veins and whether there are any problems during the examination of the veins in the clinic. But, it does not have the ability to continuously monitor the condition of the veins. Continuous monitoring of blood flow rate will facilitate the work of doctors during post-operative monitoring or diagnosis of the patient's condition. At the same time, traditional ultrasound transducers may not be comfortable for post-operative examinations. In this article, a number of difficulties encountered during examinations conducted using a conventional ultrasound machine were investigated. At the same time, as a solution to these difficulties, one of the newest technologies of the modern era, the new generation ultrasound machine “USM patch”, its main features and advantages were discussed. This device, based on the working principle of the Doppler effect, is suitable for continuous monitoring of the absolute speed of blood flow in the arteries of the deep layers. It is lightweight, small in size, and has the potential to increase the accuracy and quality of the examination.\u0000Keywords: Blood Flow Sensor, movement of red blood cells, Doppler effect, ultrasound machine, Doppler ultrasound patch, biodegradable sensor, automatic","PeriodicalId":107886,"journal":{"name":"PIRETC-Proceeding of The International Research Education & Training Centre","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127005510","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}