Pub Date : 2020-10-05DOI: 10.1109/SIU49456.2020.9302121
Süreyya Atasever, Ilker Özçelik, Ş. Sağiroğlu
Many detection approaches have been proposed to address growing threat of Distributed Denial of Service (DDoS) attacks on the Internet. The attack detection is the initial step in most of the mitigation systems. This study examined the methods used to detect DDoS attacks with the focus on learning based approaches. These approaches were compared based on their efficiency, operating load and scalability. Finally, it is discussed in details.
{"title":"An Overview of Machine Learning Based Approaches in DDoS Detection","authors":"Süreyya Atasever, Ilker Özçelik, Ş. Sağiroğlu","doi":"10.1109/SIU49456.2020.9302121","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302121","url":null,"abstract":"Many detection approaches have been proposed to address growing threat of Distributed Denial of Service (DDoS) attacks on the Internet. The attack detection is the initial step in most of the mitigation systems. This study examined the methods used to detect DDoS attacks with the focus on learning based approaches. These approaches were compared based on their efficiency, operating load and scalability. Finally, it is discussed in details.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127692055","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302325
Mahmut Yurt, Tolga Cukurmm
Özetçe —Yüksek çözünürlüklü manyetik rezonans görüntülerinin (MRG) farklı kontrastlar altında edinimi klinik tanıda gerekli olan teşhis bilgisini artırır. Ancak, artan gürültü oranı, uzun tarama süreleri ve donanım maliyetlerinden ötürü yüksek çözünürlüklü görüntülerin edinimi pratikte mümkün olmayabilir. Bu durumlarda, düşük çözünürlüklü görüntülerden yüksek çözünürlüklü görüntülerin üretilebilmesi alternatif bir çözüm olabilir. Yaygın yöntemler tek bir görüntünün süper çözünürlüğünü yapar. Ancak, çok kontrastlı MRG’de, tek bir kontrastın düşük çözünürlüklü görüntüsü başarılı bir netleştirme için gerekli ön bilgiyi içermez. Gerekli bilgiyi zenginleştirebilmek için, farklı kontrastlardaki tamamlayıcı ön bilgiler kullanılabilir. Bu sebeple, bu çalışmada birden çok kontrasta ait görüntüleri eşzamanlı olarak netleştiren bir çoklu kontrast MRG süper çözünürlük yöntemi önerilmiştir. Önerilen yöntem yüksek frekans detaylarını daha iyi kurtararak olabildiğince gerçekçi hedef görüntüler üretebilen koşullu çekişmeli üretici ağlara dayanmaktadır. Çoklu kontrast MR görüntüleri içeren veri setinde yapılan sayısal ve görsel değerlendirmeler, önerilen yöntemin alternatif tekli görüntü MRG süper çözünürlük yönteminden daha üstün performans gösterdiğini ortaya koymuştur.
{"title":"Multi-Image Super Resolution in Multi-Contrast MRI","authors":"Mahmut Yurt, Tolga Cukurmm","doi":"10.1109/SIU49456.2020.9302325","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302325","url":null,"abstract":"Özetçe —Yüksek çözünürlüklü manyetik rezonans görüntülerinin (MRG) farklı kontrastlar altında edinimi klinik tanıda gerekli olan teşhis bilgisini artırır. Ancak, artan gürültü oranı, uzun tarama süreleri ve donanım maliyetlerinden ötürü yüksek çözünürlüklü görüntülerin edinimi pratikte mümkün olmayabilir. Bu durumlarda, düşük çözünürlüklü görüntülerden yüksek çözünürlüklü görüntülerin üretilebilmesi alternatif bir çözüm olabilir. Yaygın yöntemler tek bir görüntünün süper çözünürlüğünü yapar. Ancak, çok kontrastlı MRG’de, tek bir kontrastın düşük çözünürlüklü görüntüsü başarılı bir netleştirme için gerekli ön bilgiyi içermez. Gerekli bilgiyi zenginleştirebilmek için, farklı kontrastlardaki tamamlayıcı ön bilgiler kullanılabilir. Bu sebeple, bu çalışmada birden çok kontrasta ait görüntüleri eşzamanlı olarak netleştiren bir çoklu kontrast MRG süper çözünürlük yöntemi önerilmiştir. Önerilen yöntem yüksek frekans detaylarını daha iyi kurtararak olabildiğince gerçekçi hedef görüntüler üretebilen koşullu çekişmeli üretici ağlara dayanmaktadır. Çoklu kontrast MR görüntüleri içeren veri setinde yapılan sayısal ve görsel değerlendirmeler, önerilen yöntemin alternatif tekli görüntü MRG süper çözünürlük yönteminden daha üstün performans gösterdiğini ortaya koymuştur.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128145044","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302305
E. Arslan, A. T. Dogukan, E. Başar
Ultra-reliable and low-latency communications (URLLC) partake a major role in future communication systems. A possible strong candidate for future URLLC networks is sparse vector coding (SVC), which enables a superior performance in terms of bit error rate (BER). In SVC, virtual digital domain (VDD) and compressed sensing (CS) algorithms are used to encode and decode information. In this paper, orthogonal frequency division multiplexing (OFDM)-based a novel system called orthogonal frequency division multiplexing with codebook index modulation (OFDM-CIM) and which can meet the needs of URLLC systems has been proposed. In OFDM-CIM, information bits are transmitted via both active subcarrier indices and codebook indices. As a result of computer simulations, OFDMCIM is presented as a strong candidate for next generation communication systems.
{"title":"Orthogonal Frequency Division Multiplexing with Codebook Index Modulation","authors":"E. Arslan, A. T. Dogukan, E. Başar","doi":"10.1109/SIU49456.2020.9302305","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302305","url":null,"abstract":"Ultra-reliable and low-latency communications (URLLC) partake a major role in future communication systems. A possible strong candidate for future URLLC networks is sparse vector coding (SVC), which enables a superior performance in terms of bit error rate (BER). In SVC, virtual digital domain (VDD) and compressed sensing (CS) algorithms are used to encode and decode information. In this paper, orthogonal frequency division multiplexing (OFDM)-based a novel system called orthogonal frequency division multiplexing with codebook index modulation (OFDM-CIM) and which can meet the needs of URLLC systems has been proposed. In OFDM-CIM, information bits are transmitted via both active subcarrier indices and codebook indices. As a result of computer simulations, OFDMCIM is presented as a strong candidate for next generation communication systems.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132537658","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302468
Alperen Erdoğan, S. Guney
Nowadays, one of the most important illness is heart disease which cause of mostly patients dead. Medical diagnosis of heart diseases is very difficult. While heart diseases are diagnosed medically, they can be confused with other diseases that show same symptoms such as chest pain, shortness of breath, palpitations and nausea. This makes it difficult to diagnose heart diseases medically. In this study, the presence of heart diseases was determined by using machine learning algorithms. In this study, the data obtained from the patients were weighted according to their effects on the success rate. In this study, a method is proposed for determine weight coefficient. According to proposed method's results, 86,90% success was achieved with 13 different features obtained from the patients.
{"title":"Heart Disease Prediction by Using Machine Learning Algorithms","authors":"Alperen Erdoğan, S. Guney","doi":"10.1109/SIU49456.2020.9302468","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302468","url":null,"abstract":"Nowadays, one of the most important illness is heart disease which cause of mostly patients dead. Medical diagnosis of heart diseases is very difficult. While heart diseases are diagnosed medically, they can be confused with other diseases that show same symptoms such as chest pain, shortness of breath, palpitations and nausea. This makes it difficult to diagnose heart diseases medically. In this study, the presence of heart diseases was determined by using machine learning algorithms. In this study, the data obtained from the patients were weighted according to their effects on the success rate. In this study, a method is proposed for determine weight coefficient. According to proposed method's results, 86,90% success was achieved with 13 different features obtained from the patients.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"E-29 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132693793","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302370
Fatih Yazıcı, Ayhan Sefa Yıldız, Alper Yazar, E. G. Schmidt
In this paper, we propose a scalable on-chip packet switch architecture for hardware accelerated cloud computing systems. Our proposed switch architecture is implemented on the FPGA and interconnects reconfigurable regions, 40 Gbps Ethernet interfaces and a PCIe interface. The switch fabric operates at line speed to achieve scalability. We propose a new algorithm that grants access to the fabric according to the allocated prioritization to input-output port pairs. The switch is implemented on Xilinx Zynq 7000-SoC and can work at 40 Gbps rate. Our simulation results show that our proposed algorithm achieves desired prioritization without degrading the throughput. Keywords—cloud computing, on-chip switch, switch fabric arbitration.
{"title":"An On-chip Switch Architecture for Hardware Accelerated Cloud Computing Systems","authors":"Fatih Yazıcı, Ayhan Sefa Yıldız, Alper Yazar, E. G. Schmidt","doi":"10.1109/SIU49456.2020.9302370","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302370","url":null,"abstract":"In this paper, we propose a scalable on-chip packet switch architecture for hardware accelerated cloud computing systems. Our proposed switch architecture is implemented on the FPGA and interconnects reconfigurable regions, 40 Gbps Ethernet interfaces and a PCIe interface. The switch fabric operates at line speed to achieve scalability. We propose a new algorithm that grants access to the fabric according to the allocated prioritization to input-output port pairs. The switch is implemented on Xilinx Zynq 7000-SoC and can work at 40 Gbps rate. Our simulation results show that our proposed algorithm achieves desired prioritization without degrading the throughput. Keywords—cloud computing, on-chip switch, switch fabric arbitration.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133082430","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302090
Cahfer Güngen, Özlem Polat, R. Karakis
The classification of brain tumors has great importance in medical applications that benefit from computer-assisted diagnosis. Misdiagnosis of brain tumor types, both prevents the patient's response to treatment effectively and reduce the chance of survival. This study proposes a solution for the classification of brain tumors using MR images. The most common brain tumors, glioma, meningioma and pituitary, are detected using convolutional neural networks. The convolutional network is trained and tested on an accessible Figshare dataset containing 3064 MR images using four different optimizers. AUC, sensitivity, specificity and accuracy are used as performance measure. The proposed method is comparable to the literature and classifies brain tumors with an average accuracy of 96.84% and a maximum accuracy of 97.75%.
{"title":"Classification of Brain Tumors using Convolutional Neural Network from MR Images","authors":"Cahfer Güngen, Özlem Polat, R. Karakis","doi":"10.1109/SIU49456.2020.9302090","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302090","url":null,"abstract":"The classification of brain tumors has great importance in medical applications that benefit from computer-assisted diagnosis. Misdiagnosis of brain tumor types, both prevents the patient's response to treatment effectively and reduce the chance of survival. This study proposes a solution for the classification of brain tumors using MR images. The most common brain tumors, glioma, meningioma and pituitary, are detected using convolutional neural networks. The convolutional network is trained and tested on an accessible Figshare dataset containing 3064 MR images using four different optimizers. AUC, sensitivity, specificity and accuracy are used as performance measure. The proposed method is comparable to the literature and classifies brain tumors with an average accuracy of 96.84% and a maximum accuracy of 97.75%.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132120474","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302125
Sara Atito Ali Ahmed, B. Yanikoglu, Özgü Göksu, E. Aptoula
Early detection of skin cancer is vital when treatment is most likely to be successful. However, diagnosis of skin lesions is a very challenging task due to the similarities between lesions in terms of appearance, location, color, and size. We present a deep learning method for skin lesion classification by fusing and fine-tuning three pre-trained deep learning architectures (Xception, Inception-ResNet-V2, and NasNetLarge) using training images provided by ISIC2019 organizers. Additionally, the outliers and the heavy class imbalance are addressed to further enhance the classification of the lesion. The experimental results show that the proposed framework obtained promising results that are comparable with the ISIC2019 challenge leader board.
{"title":"Skin Lesion Classification With Deep CNN Ensembles","authors":"Sara Atito Ali Ahmed, B. Yanikoglu, Özgü Göksu, E. Aptoula","doi":"10.1109/SIU49456.2020.9302125","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302125","url":null,"abstract":"Early detection of skin cancer is vital when treatment is most likely to be successful. However, diagnosis of skin lesions is a very challenging task due to the similarities between lesions in terms of appearance, location, color, and size. We present a deep learning method for skin lesion classification by fusing and fine-tuning three pre-trained deep learning architectures (Xception, Inception-ResNet-V2, and NasNetLarge) using training images provided by ISIC2019 organizers. Additionally, the outliers and the heavy class imbalance are addressed to further enhance the classification of the lesion. The experimental results show that the proposed framework obtained promising results that are comparable with the ISIC2019 challenge leader board.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114025575","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302089
E. Aptoula, F. Kahraman, Gökhan Özbulak, S. Aydemir, M. Imamoglu, A. Sofu, Ismail Yilmaz
Segmentation networks have proven to be popular tools for large scale pixel-wise remote sensing image classification as they can deal with wide spatial areas efficiently, as opposed to convolutional neural networks trained with pixel centered patches. However, they are often criticized in terms of spatial consistency. As such, they have received various extensions through the last few years, in the form of dilated convolutions and skip connections and more. In this paper, we address the same issue by feeding attribute filtered images, that contain inherently a multiscale hierarchical representation of the underlying image, as input to a segmentation network, in an effort to both accelerate convergence and render easier the feature learning task of the bottom layers. We validate our approach through the production of land-use and land-cover maps for a large area of Turkey using Sentinel 2 multispectral images and ground truth from the Copernicus Land Monitoring Service.
{"title":"Segmentation networks reinforced with attribute profiles for large scale land-cover map production","authors":"E. Aptoula, F. Kahraman, Gökhan Özbulak, S. Aydemir, M. Imamoglu, A. Sofu, Ismail Yilmaz","doi":"10.1109/SIU49456.2020.9302089","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302089","url":null,"abstract":"Segmentation networks have proven to be popular tools for large scale pixel-wise remote sensing image classification as they can deal with wide spatial areas efficiently, as opposed to convolutional neural networks trained with pixel centered patches. However, they are often criticized in terms of spatial consistency. As such, they have received various extensions through the last few years, in the form of dilated convolutions and skip connections and more. In this paper, we address the same issue by feeding attribute filtered images, that contain inherently a multiscale hierarchical representation of the underlying image, as input to a segmentation network, in an effort to both accelerate convergence and render easier the feature learning task of the bottom layers. We validate our approach through the production of land-use and land-cover maps for a large area of Turkey using Sentinel 2 multispectral images and ground truth from the Copernicus Land Monitoring Service.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114195087","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302357
Serhat Sert, Ahmet Halit Akilli, Ozcan Bilbay, C. Cengiz, A. Ozen
Optical radio communication systems and its application, visible light communication (VLC), provide very important technical and operational advantages in applications. In this study, it is recommended to use optical single carrier frequency domain channel equalizer (OSC-FDCE) to repair corrupted data of single carrier VLC systems in multipath optical channel environment. Computer simulation studies are performed to test the performance of OSC-FDCE method in 5, 6 and 10 branch optical channel environment over the Bit Error Rate (BER) performance criterion. From the obtained simulation results, it is seen that OSC-FDCE method has better performance than VLC-OFDM-FDE method.
{"title":"Optical Single Carrier Frequency Domain Channel Equalizer for Visible Light Communication","authors":"Serhat Sert, Ahmet Halit Akilli, Ozcan Bilbay, C. Cengiz, A. Ozen","doi":"10.1109/SIU49456.2020.9302357","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302357","url":null,"abstract":"Optical radio communication systems and its application, visible light communication (VLC), provide very important technical and operational advantages in applications. In this study, it is recommended to use optical single carrier frequency domain channel equalizer (OSC-FDCE) to repair corrupted data of single carrier VLC systems in multipath optical channel environment. Computer simulation studies are performed to test the performance of OSC-FDCE method in 5, 6 and 10 branch optical channel environment over the Bit Error Rate (BER) performance criterion. From the obtained simulation results, it is seen that OSC-FDCE method has better performance than VLC-OFDM-FDE method.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114694579","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302093
Ahmet Ketenci, Ebru Aydin Gol
In this paper, a backpropagation based algorithm is presented to learn parameters of past time Signal Temporal Logic (ptSTL) formulas. A differentiable weight matrix over the parameter values and a loss function based on the mismatch value of the corresponding formulas over the labeled dataset are used in the algorithm. Analysis over a sample dataset shows that the algorithm solves the ptSTL parameter synthesis problem in an efficient way.
{"title":"Learning Parameters of ptSTL Formulas with Backpropagation","authors":"Ahmet Ketenci, Ebru Aydin Gol","doi":"10.1109/SIU49456.2020.9302093","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302093","url":null,"abstract":"In this paper, a backpropagation based algorithm is presented to learn parameters of past time Signal Temporal Logic (ptSTL) formulas. A differentiable weight matrix over the parameter values and a loss function based on the mismatch value of the corresponding formulas over the labeled dataset are used in the algorithm. Analysis over a sample dataset shows that the algorithm solves the ptSTL parameter synthesis problem in an efficient way.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114714411","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}
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