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2021 29th Signal Processing and Communications Applications Conference (SIU)最新文献

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Reinforcement Learning Algorithms Performance Comparison on the Game of DeepRTS deep - prts游戏中的强化学习算法性能比较
2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477938
S. O. Sahin, Veysel Yücesoy
{"title":"Reinforcement Learning Algorithms Performance Comparison on the Game of DeepRTS","authors":"S. O. Sahin, Veysel Yücesoy","doi":"10.1109/SIU53274.2021.9477938","DOIUrl":"https://doi.org/10.1109/SIU53274.2021.9477938","url":null,"abstract":"In this paper, i) we build a framework using macro actions on top of DeepRTS, which is specifically developed for learning purposes, and ii) we train a number of deep reinforcement learning based agents and then we conduct a detailed performance analysis on the performance of these agents. We make changes on the publicly available version of DeepRTS to make it parallel to real life planning problems and make it trainable with a decent hardware. Firstly, we create a set of macro actions based on human heuristics and we train our agent using only these actions. Secondly, similar to the real life planning problem, we change the problem such that we simultaneously take actions for all available unit at a time step. When we consider this situation together with duration of macro actions being different, multiple actions may start and multiple actions ends at any time step. Therefore, the problem draw apart from classical reinforcement learning problem in a certain amount. In addition, the credit assignment problem becomes more sophisticated since each action lasts a duration and at any time multiple actions are conducted. We create three rule based agents to use them as enemy players in training of our agents. We report the performance of the agents as player-1 and player-2 against each others.","PeriodicalId":160693,"journal":{"name":"2021 29th Signal Processing and Communications Applications Conference (SIU)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114388945","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
HQAM Analysis of MRC Diversity Systems over Fisher-Snedecor F Channels Fisher-Snedecor F频道MRC多样性系统的HQAM分析
2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477903
Caner Özen, M. Bilim
{"title":"HQAM Analysis of MRC Diversity Systems over Fisher-Snedecor F Channels","authors":"Caner Özen, M. Bilim","doi":"10.1109/SIU53274.2021.9477903","DOIUrl":"https://doi.org/10.1109/SIU53274.2021.9477903","url":null,"abstract":"With the acceleration of 5G and beyond studies and increasing research in this field, various modulation schemes, channel models, and diversity methods are being studied on factors such as power, bandwidth and efficiency. In recent years, it is seen that different mathematical derivations have been made for channel models and that models have been created that reveal wireless channel environments more successfully. In this paper, the maximal ratio combining technique with hexagonal quatrature amplitude modulation over the Fisher-Snedecor F channels is analyzed. As a result of the analysis, error expressions were derived and the accuracy of these expressions was shown with numerical results.","PeriodicalId":160693,"journal":{"name":"2021 29th Signal Processing and Communications Applications Conference (SIU)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128236076","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
TDOA Based Indoor Localization of Wearable Smart Devices 基于TDOA的可穿戴智能设备室内定位
2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477968
Buse Gültekin, Özge Taylan Moral, V. Kılıç
{"title":"TDOA Based Indoor Localization of Wearable Smart Devices","authors":"Buse Gültekin, Özge Taylan Moral, V. Kılıç","doi":"10.1109/SIU53274.2021.9477968","DOIUrl":"https://doi.org/10.1109/SIU53274.2021.9477968","url":null,"abstract":"The need for real-time and high-precision localization in indoor or outdoor areas has increased considerably recently. Real-time location systems offer solutions for this need with different technologies. One of the solutions is to determine the location using the time difference of arrival of the signal between anchor and tag. In this study, a new algorithm is proposed for indoor localization of wearable smart devices. The algorithm, which is based on the time difference of arrival of the signal from the tag to the anchor, allows the localization with submeter accuracy. Thus, it will be possible to track the location of patients, personnel or objects in military, health and commercial areas using real-time, accurate and precise location information via wearable smart devices.","PeriodicalId":160693,"journal":{"name":"2021 29th Signal Processing and Communications Applications Conference (SIU)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124697776","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
Uncertainty Components for RF Power Measurement between 110 GHz and 170 GHz 110 GHz和170 GHz之间射频功率测量的不确定度分量
2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9478019
Erkan Danaci, A. K. Doğan, Oguzhan Kizilbey
{"title":"Uncertainty Components for RF Power Measurement between 110 GHz and 170 GHz","authors":"Erkan Danaci, A. K. Doğan, Oguzhan Kizilbey","doi":"10.1109/SIU53274.2021.9478019","DOIUrl":"https://doi.org/10.1109/SIU53274.2021.9478019","url":null,"abstract":"Commercial power sensors operating above 110 GHz frequencies have begun to be supplied due to the development of communication systems. However, the characterization of these power sensors is still a problem to be solved. In this study, the measurements performed for the identification of RF power sensors operating between 110 GHz and 170 GHz according to the thermal principle and the studies of determining the parameters of the power measurement uncertainty calculations based on these measurements are included. It has been shown that the uncertainty calculations of power measurements preformed with power sensors operating according to the thermal principle used in high frequencies and long-term measurements should take into account the thermal stability and linearity of the power sensor.","PeriodicalId":160693,"journal":{"name":"2021 29th Signal Processing and Communications Applications Conference (SIU)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129289464","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
Near Field Focused Microstrip Patch Antenna Array Characteristics Enhancement With Parasitic Patch Elements 利用寄生贴片元件增强近场聚焦微带贴片天线阵列特性
2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477974
Muhammad Sohail, R. Uyguroglu
{"title":"Near Field Focused Microstrip Patch Antenna Array Characteristics Enhancement With Parasitic Patch Elements","authors":"Muhammad Sohail, R. Uyguroglu","doi":"10.1109/SIU53274.2021.9477974","DOIUrl":"https://doi.org/10.1109/SIU53274.2021.9477974","url":null,"abstract":"Several applications require a limited spot size and highly concentrated radiated electromagnetic waves from antenna arrays. In this study, we designed a near field focused rectangular microstrip patch antenna array with stacked parasitic patch elements to improve the focusing characteristics. The parasitic elements are driven by the conventional near field focused array and the radiated fields are intensified at the focal point, while preserving the basic focusing characteristics of patch antenna array. The designed parasitic patch antenna array raises the power level at the focal point by about 4.2 dB as compared to conventional near field focused patch antenna array. The designed antenna array provides a focused high power radiated fields to be used for different applications such as biomedical, industrial and short range wireless power transfer.","PeriodicalId":160693,"journal":{"name":"2021 29th Signal Processing and Communications Applications Conference (SIU)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116265642","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: 3
Semantic Segmentation on Multi-Spectral Images 多光谱图像的语义分割
2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477978
V. Aslantaş, Ahmet Nusret Toprak, Mehmet Elmaci
{"title":"Semantic Segmentation on Multi-Spectral Images","authors":"V. Aslantaş, Ahmet Nusret Toprak, Mehmet Elmaci","doi":"10.1109/SIU53274.2021.9477978","DOIUrl":"https://doi.org/10.1109/SIU53274.2021.9477978","url":null,"abstract":"Semantic segmentation is considered to be one of the basic steps in understanding image content. For semantic segmentation, if multi-spectral images are used together with color images, more successful results are obtained due to complementary information obtained from multi-spectral images. In this paper, a semantic segmentation method was developed in which the images obtained from CCD and thermal sensors were used together. In the proposed method, convolutional neural networks were used in encoder-decoder architecture. The experiments carried out show that the developed method produces better numerical and visual results than the works published in the literature.","PeriodicalId":160693,"journal":{"name":"2021 29th Signal Processing and Communications Applications Conference (SIU)","volume":"1 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113976328","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
A Lightweight Deep Model for Brain Tumor Segmentation 一种用于脑肿瘤分割的轻量级深度模型
2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477794
Coşku Öksüz, O. Urhan, M. Güllü
{"title":"A Lightweight Deep Model for Brain Tumor Segmentation","authors":"Coşku Öksüz, O. Urhan, M. Güllü","doi":"10.1109/SIU53274.2021.9477794","DOIUrl":"https://doi.org/10.1109/SIU53274.2021.9477794","url":null,"abstract":"Brain tumors are one of the major causes of increasing deaths worldwide. It is important to correctly identify cancerous tissues by experts in order to make correct treatment planning and to increase patient survival rates. However, manually tracking and segmentation of cancerous tissues in many sections of volumetric MR data is an error-prone and time-consuming process. Developments in the field of deep learning in recent years allow the tasks performed by humans to be performed with higher accuracy and speeds through the developed automatic systems. In this study, a deep learning-based light-weighted model with 6.78M parameters is proposed for the classification of cancerous tissues in the brain. Cross-validation of the proposed method on a public data set results in 84.61%, 82.54%, and 87.15% Boundary F1, mean IoU, and mean accuracy, respectively, shows the robustness of the proposed model.","PeriodicalId":160693,"journal":{"name":"2021 29th Signal Processing and Communications Applications Conference (SIU)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114788297","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
The Application of Matrix Pencil Method for Direction Finding Using a Linear Antenna Array 矩阵铅笔法在使用线性天线阵列寻向中的应用
2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9478050
Nilay Aytas, E. Afacan, N. Inanç
{"title":"The Application of Matrix Pencil Method for Direction Finding Using a Linear Antenna Array","authors":"Nilay Aytas, E. Afacan, N. Inanç","doi":"10.1109/SIU53274.2021.9478050","DOIUrl":"https://doi.org/10.1109/SIU53274.2021.9478050","url":null,"abstract":"The estimation of the direction of signal sources is used especially in military applications such as radar systems, locating systems, military warfare systems, and in civil applications such as mobile communication systems. The systems that used to only determine the location of a single signal source have obtained the ability to simultaneously detect the direction and location of signals from multiple signal sources following the development of subspace techniques. Matrix Pencil method is a method used in subspace analysis of the signal, in this study, it is aimed to estimate the direction of arrival of the signals impinging from one or more signal sources to the antenna array using this method. The analysis showed that high accuracy results have been obtained in the estimation of the direction of arrival the signals impinging to the antenna array by Matrix Pencil method.","PeriodicalId":160693,"journal":{"name":"2021 29th Signal Processing and Communications Applications Conference (SIU)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126339041","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
Use of Synthetic Data on Object Detection Applications 合成数据在目标检测应用中的应用
2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477977
Oguzhan Can, Özlem Er, Yusuf Kunt
{"title":"Use of Synthetic Data on Object Detection Applications","authors":"Oguzhan Can, Özlem Er, Yusuf Kunt","doi":"10.1109/SIU53274.2021.9477977","DOIUrl":"https://doi.org/10.1109/SIU53274.2021.9477977","url":null,"abstract":"It is not always possible to find enough data in Deep Learning applications, where huge amounts of data is needed. One may face difficulties while trying to perform qualified works, where data cannot be collected in rare occuring events and situations involving privacy concerns. To overcome this issue, we create a military vehicle dataset in which great amounts of Synthetic Dataset can be found. Thus, with this dataset, we bring a solution to the problem of detecting military vehicles, which is an area where collecting sufficient data is a problem. In a training with 5, 10 and 50 epochs we raise the mAP score by 0.09, 0.03, and 0.02, respectively.","PeriodicalId":160693,"journal":{"name":"2021 29th Signal Processing and Communications Applications Conference (SIU)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126424705","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
Comparison of Different Machine and Deep Learning Techniques to Predict Air Quality Index: A Case of Kocaeli Province 不同机器和深度学习技术预测空气质量指数的比较:以科采里省为例
2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477936
Zeynep Bilen, Ferhat Bozkurt
{"title":"Comparison of Different Machine and Deep Learning Techniques to Predict Air Quality Index: A Case of Kocaeli Province","authors":"Zeynep Bilen, Ferhat Bozkurt","doi":"10.1109/SIU53274.2021.9477936","DOIUrl":"https://doi.org/10.1109/SIU53274.2021.9477936","url":null,"abstract":"Air pollution is increasing day by day with the increase of urbanization and industrialization. Increased air pollution adversely affects our health. Air quality index is used to determine to what extent it affects our health. The air quality index is used to classify the quality of the air. In this study, Kocaeli province, where urbanization and industrialization is high, is selected. The data used in the study has been obtained from the Online Monitoring Center established by the Ministry of Environment and Urbanization to monitor air quality. Air quality index was calculated with the report containing the measurement values of the pollutant gases belonging to Kocaeli, and labeled by separating them into their classes. In order to predict the air quality on the prepared data set, the comparison of different machine and deep learning techniques is conducted. These techniques are k-Nearest Neighbor, Naive Bayes, Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), Recurrent Neural Networks (RNN), and Long-Short Term Memory (LSTM). According to experimental results, by considering the accuracy and AUC parameter used in the performance evaluation of the classification techniques, the highest accuracy value was observed as 94% with the Decision Trees and the highest AUC value was reported as 98% with the LSTM model.","PeriodicalId":160693,"journal":{"name":"2021 29th Signal Processing and Communications Applications Conference (SIU)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128200900","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
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