Elektronik ve Haberleşme Mühendisliği

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  • Öge
    Embedded vision system designed on a heterogeneous computing platform and applied to semen analysis
    (Lisansüstü Eğitim Enstitüsü, 2021) Şavkay, Osman Levent ; Yalçın, Müştak Erhan ; 709922 ; Elektronik ve Haberleşme Mühendisliği
    Image and video inspections in the medical field require intelligent systems in addition to microscopes for more accurate and detailed analyses. In this study, a reliable, reconfigurable, and compact intelligent analyzer system is proposed using a hybrid platform comprising a field programmable gate array (FPGA) and central processing unit (CPU) as the embedded video processing system. Computer assisted semen analysis (CASA) is then developed as an application on the proposed system. The developed system architecture can also be utilized in biological imaging and video applications, which require accurate and detailed analysis. Relevant semen analysis algorithms were constructed specially for the proposed architecture; however, the algorithms may also be used with similar existing embedded system platforms and newly developed hardware. As the speeds and performances of CPUs have approached their technological limits, a newer trend has evolved toward parallel computing; more recently, the use of heterogeneous computing platforms have also increased as they have attracted the interest of scientists and system developers owing to their operating performances. They surpass conventional general-purpose CPU-based architectures in terms of capacity, speed, and performance characteristics. The proposed system utilizes a heterogeneous computing platform combining the CPU and FPGA, thereby providing enhanced performance compared to conventional systems. Using a high-definition camera, actual sperm movements could be traced with high fidelity. The FPGA provides very high execution speeds necessary for recursive and expensive image preprocessing tasks. The CPU connects the camera to the devised system and is also responsible for performing calculations. The processing algorithms are executed mainly on the CPU, and the embedded system provides compact structure; moreover, the physical footprint of the system is smaller, which is advantageous in laboratory environments. Consequently, the proposed architecture is considered to be a step ahead of third-generation CASA systems, which is the primary novelty of this work. The proposed semen analysis system is implemented on a real-time operating system (RTOS) that also manages the embedded system with reconfigurable hardware for preprocessing and CPU for processing the executable parts of the algorithm. For sperm detection and tracking, the multiple moving object tracking system (MMOTS) algorithm is developed, which is the second novelty of this work. In terms of object detection, the background subtraction method is used for segmentation in MMOTS. The MMOTS approach is suitable for the selected architecture and imposes less computing load to achieve fast responses, which are crucial for real-time processing. Cellular neural network templates are used and experimented in the preprocessing part of the proposed system, where image-processing steps such as convolution, segmentation, and point representation are applied. This thesis begins with a brief introduction to sperm biology, semen analysis, sperm morphology and motility, and evolution of CASA systems. While the proposed work addresses motility analysis, the developed software also allows segmentation of static images to specify the head, nucleus, mitochondrial part, head shape, and size of the sperm as medical parameters. The algorithms, which are designed by considering computations on heterogeneous computing platforms, was next implemented on recent and state-of-the-art reconfigurable hardware. These hardware are commercially available and contain up-to-date components with reasonable prices. The complete system was realized on an NI CompactRIO 9030 hardware integrated with a microscope and available for medical tests as a prototype. The experimental work on human sperm samples were conducted at the Ege University Faculty of Medicine, İzmir, Turkey and Gelişim Tıp Laboratory, İstanbul, Turkey. The existing CASA system in Gelişim Tıp Laboratory was examined, and some experiments were carried out; the videos of these samples were obtained and examined in our preliminary and simulation studies. With respect to Ege University, the results of the proposed system are compared with those from manual inspections. In the manual assessments, the classifications were performed using the Makler camara (counting chamber), and the results of the proposed system were found to be 100% similar to those from manual assessments. The motility parameters, such as average progressive velocity VAP and curvilinear velocity VCL, can only be estimated roughly from manual assessments, but the results from the proposed system were deemed to be acceptable by the biologists in the laboratory. Experimental investigations were also performed on animal sperm samples at İstanbul University's Faculty of Veterinary Medicine, and the results of the proposed system were found to be approximately 80% compliant with those of the existing system in the laboratory. These variations are attributed to parameter settings; the parameters were roughly set to enable inspection of the effects of parameter values on the results, besides test result comparisons. The proposed system is flexible and hardware-based, so that different biological imaging applications could be deployed on it in the future. Eventually, the goal of this study is to achieve an intelligent biological analysis system.
  • Öge
    Microwave dielectric property characterization with open-ended coaxial probe and sensing depth analysis of the probes for biological tissues
    (Lisansüstü Eğitim Enstitüsü, 2021) Aydınalp, Cemanur ; Abdolsaheb, Tuba Yılmaz ; 725335 ; Elektronik ve Haberleşme Mühendisliği
    Inherent dielectric property discrepancy at microwave frequencies between the healthy and malignant tissues enabled many different microwave diagnostic technologies among these microwave breast cancer imaging, microwave hyperthermia, and microwave ablation are popular research topics. To develop and test such technologies the dielectric properties of the biological tissues must be quantified. This is mostly done with the open-ended coaxial probes in the laboratory environment due to advantages of the technique including but not limited to minimal sample preparation requirements, commercial availability and broadband measurement capabilities. Despite being commercially available, the technique suffers from high error rates and remains overlooked as a potential diagnostic technology. The error sources can be categorized as the sample and equipment related complications. The sample related error sources can be mitigated via the selection of an appropriate probe for dielectric property characterization. Particularly, biological tissues are known to be heterogeneous contributing to the high measurement error due to sample. Hence, it is important to analyze the sensing depth of the probes under different conditions including using samples with varying dielectric properties and probes with different aperture diameters. Next, the equipment related errors mostly due to the mathematical approach which can potentially be diminished via the introduction of new retrieval methodologies. Towards this end, in an attempt to enable diagnostics applications of the open-ended coaxial probe technique, this thesis focuses on the improvement of the two shortcomings by sensing depth characterization and introducing a deep learning based model for dielectric property retrieval. In the first part of the thesis, sensing depth analysis of the 2.2 mm diameter open-ended coaxial probe was performed using two different double-layered configurations to mimic the tissue heterogeneity. The double-layered configurations are used to mimic the heterogeneous skin tissue in order to establish the potential use of the open-ended coaxial probe method for skin cancer diagnosis. To this end, the sensing depth analysis was performed via simulations and measurements. The double-layered sample configurations are composed using skin-mimicking phantom and olive oil or triton X-100 liquids. In addition, the experiments were carried out by following a newly proposed measurement protocol, which can be easily applied to any tissue type. The results show that the sensing depth was independent of the frequency of operation (0.5-6 GHz) and was affected by the following conditions: by the material located immediately at the probe tip, and by the dielectric property contrast between the two layers. Thus, in order to accurately obtain dielectric property measurement results using the open-ended coaxial probe method, there is a need to establish a pre-measurement protocol to minimize the error due to the skin tissue diversity. The second part of thesis reports the sensing depth analysis of the open-ended coaxial probe for ex vivo experiments on real heterogeneous tissue. The knowledge on the sensing depth of the probe can help eliminate the errors due to tissue heterogeneity. Accurate classification of tissues with similar dielectric properties can be obtained by minimizing the measurement errors. Therefore, this method can be applied in practical applications, such as microwave biopsy. In this work, double-layered sample configuration consisting of an ex vivo rat's breast or wet skin as first layer and pure liquids olive oil or triton X-100 as second layer was utilized to perform the sensing depth analysis of the probe from 0.5 to 6 GHz frequency range. A straight forward, adoptable experimental protocol was established and employed in this study. The analysis was performed by determining five different the percent change in measured dielectric property values. The results indicate a discrepancy of 52%-84% of the measured dielectric property when a membrane layer (between 0.4-0.8 mm thickness) was present on the wet skin tissue and breast tissue. The aim of the third part of this thesis is to analyze and to specify the sensing depth of the open-ended coaxial probe in order to employ the appropriate probe aperture dimension for any given measurement set-up. The proposed method has the potential to reduce the errors due to tissue heterogeneity for skin cancer diagnosis. This work presents the sensing depth comparison of three different probes with different aperture sizes. Simulations of the probes with 0.5, 0.9 and 2.2 mm-diameters terminated with a double-layered skin tissue and olive oil sample configuration were performed. It should be noted that probes with different aperture diameters were investigated in the literature but no information was reported on probes with small aperture sizes. An experimental validation of the simulated scenario was performed with the 2.2 mm-diameter probe and the fully developed double-layered configuration. The acquired simulations and experimental results indicate a proportional relation between the sensing depth and the aperture of the probe. From this relation, it can be concluded that probes with smaller aperture size can possibly help to obtain more precise results from the heterogeneous tissues which can lead to the accurate characterization of thin skin tissue layers. In order to obtained more accurate results especially for tissues with multi-layered structures or membrane-like layers, it is recommended to a establish measurement protocols to prepare the surface of the tissue. In the fourth and last part of the thesis, a novel approach for the determination of material dielectric properties from the reflection coefficient response of the open-ended coaxial probe is proposed. This technique retrieves the Debye parameters of the material under test using a deep learning model which is trained with numerically generated data. The ability to train the deep learning model with synthetic data provide the advantage of rapid generation of a large variety of materials as a dataset. Additionally, the presented method can be easily adapted to any type of probe with desired dimensions and materials. An experimental verification of the trained deep learning model was performed by testing the network with measured reflection coefficients obtained from five different standard liquids, four mixtures, and a gel-like material. A comparison of the acquired results from the deep learning model with literature values is also performed. Finally, a large-scale statistical verification of the retrieved dielectric property from the proposed technique is presented.
  • Öge
    Design and implementation of high power GaN amplifiers with nonlinear optimization techniques
    (Lisansüstü Eğitim Enstitüsü, 2021) Kouhalvandi, Lida ; Özoğuz, İsmail Serdar ; 671561 ; Elektronik ve Haberleşme Mühendisliği
    In this thesis new and novel optimization methodologies will be prepared to design nonlinear circuits operating at high frequencies. These novel methods can be used to optimize circuits with realistic models and process design kits (PDKs). Thus, nonlinear and complex power amplifiers with wide-band and high-efficient specifications can be designed by applying the proposed optimization algorithms. The developed script runs on a computer and manages the nonlinear simulator and numerical analyzer to optimize the challenging nonlinear circuit design problems regarding the design rules and conditions set by the designer and requirements. The proposed optimization algorithms are implemented in an automated environment with the combinations of electronic design automation (EDA) tool such as ADS and numeric analyzer as MATLAB. This process decreases the dependency to any designer's experience and without any human interruption all the optimization process is performed automatically. Power amplifiers (PAs) consisting of Gallium Nitride (GaN) high electron mobility transistors (HEMTs) will be designed with the proposed optimization algorithms. The PAs' efficiency, gain response, linearity, and bandwidth will be optimized to achieve high performance results regarding the reported studies. The PAs operate at saturation mode and nonlinear region; hence, high dimensions of variables are achieved. EDA tools such as ADS, AWR, etc. include nonlinear optimizations and are successful tools in optimizing circuits; however, additional powerful optimizations are required to deal with large amount of data. Also, the commercial EDA tools face with the problems when the unreliable nonlinear models are used during the optimization process.Therefore, a need for the new simulation environment that is the combination of the EDA tool and numerical analyzer becomes essential. Regarding to the difficulties in commercial EDA tools, it becomes necessary to propose an optimization strategy suitable for nonlinear circuits to be reliable for simulating nonlinear models and also able to challenge many trade-offs of high power amplifiers (HPAs) such as efficiency, linearity and gain flatness. The scope of the proposed methods are based on scrip development for two processes: i) a scrip to control nonlinear simulator (ADS) and numerical analyzer (MATLAB), ii) algorithms to optimize the circuit parameters. These algorithms result in high performance PAs in terms of efficiency, output power, gain, and linearity. The used transistor model is GaN technology due to the several advantages for radio frequency (RF) circuits such as high power density, high thermal conductivity, large breakdown voltage, and good reliability. This technology is suitable for future applications of radar and fifth generation (5G) systems. The importance of this work is divided in to four sections: 1) Providing an automated environment that is a reliable simultaneous co-operation of EDA tool (ADS) and mathematical analyzer (MATLAB); 2) Proposing novel optimization strategy based on intelligent algorithms for RF nonlinear circuits results in best high performance; 3) Substituting the proposed novel optimization technique to the automated environment; and 4) Optimizing the whole PA designs automatically and comparing the results of fabricated PAs with the simulation outcomes.
  • Öge
    Design and implementation of high power GaN amplifiers with nonlinear optimization techniques
    (Lisansüstü Eğitim Enstitüsü, 2021) Kouhalvandi, Lida ; Özoğuz, İsmail Serdar ; 671561 ; Elektronik ve Haberleşme Mühendisliği
    In this thesis new and novel optimization methodologies will be prepared to design nonlinear circuits operating at high frequencies. These novel methods can be used to optimize circuits with realistic models and process design kits (PDKs). Thus, nonlinear and complex power amplifiers with wide-band and high-efficient specifications can be designed by applying the proposed optimization algorithms. The developed script runs on a computer and manages the nonlinear simulator and numerical analyzer to optimize the challenging nonlinear circuit design problems regarding the design rules and conditions set by the designer and requirements. The proposed optimization algorithms are implemented in an automated environment with the combinations of electronic design automation (EDA) tool such as ADS and numeric analyzer as MATLAB. This process decreases the dependency to any designer's experience and without any human interruption all the optimization process is performed automatically. Power amplifiers (PAs) consisting of Gallium Nitride (GaN) high electron mobility transistors (HEMTs) will be designed with the proposed optimization algorithms. The PAs' efficiency, gain response, linearity, and bandwidth will be optimized to achieve high performance results regarding the reported studies. The PAs operate at saturation mode and nonlinear region; hence, high dimensions of variables are achieved. EDA tools such as ADS, AWR, etc. include nonlinear optimizations and are successful tools in optimizing circuits; however, additional powerful optimizations are required to deal with large amount of data. Also, the commercial EDA tools face with the problems when the unreliable nonlinear models are used during the optimization process.Therefore, a need for the new simulation environment that is the combination of the EDA tool and numerical analyzer becomes essential. Regarding to the difficulties in commercial EDA tools, it becomes necessary to propose an optimization strategy suitable for nonlinear circuits to be reliable for simulating nonlinear models and also able to challenge many trade-offs of high power amplifiers (HPAs) such as efficiency, linearity and gain flatness. The scope of the proposed methods are based on scrip development for two processes: i) a scrip to control nonlinear simulator (ADS) and numerical analyzer (MATLAB), ii) algorithms to optimize the circuit parameters. These algorithms result in high performance PAs in terms of efficiency, output power, gain, and linearity. The used transistor model is GaN technology due to the several advantages for radio frequency (RF) circuits such as high power density, high thermal conductivity, large breakdown voltage, and good reliability. This technology is suitable for future applications of radar and fifth generation (5G) systems. The importance of this work is divided in to four sections: 1) Providing an automated environment that is a reliable simultaneous co-operation of EDA tool (ADS) and mathematical analyzer (MATLAB); 2) Proposing novel optimization strategy based on intelligent algorithms for RF nonlinear circuits results in best high performance; 3) Substituting the proposed novel optimization technique to the automated environment; and 4) Optimizing the whole PA designs automatically and comparing the results of fabricated PAs with the simulation outcomes.
  • Öge
    Derin obje sezicilerle tümleştirilmiş bayesçi filtreleme ile videoda obje izleme
    (Lisansüstü Eğitim Enstitüsü, 2021) Gürkan Gölcük, Filiz ; Günsel Kalyoncu, Bilge ; 691735 ; Elektronik ve Haberleşme Mühendisliği
    Güvenlik, hareket ve aktivite tanıma, robotik uygulamaları ve daha birçok uygulamada gerek duyulan obje izleme, belirlenen bir veya daha fazla hedef objenin konumunun video boyunca kestirilmesi olarak tanımlanır. Uzun yıllardır bu alanda yapılan çalışmalar, izleme başarımını arttırmanın yanı sıra, örtüşme, deformasyon, ölçek ve görünüm değişimi gibi izlemeyi zorlaştırıcı etkilere karşı gürbüz algoritmalar geliştirmeyi amaçlamaktadır. Tez çalışması kapsamında, üretici ve ayırıcı yöntemlerin entegre edilmesine olanak sağlayan obje-sezme-ile-obje-izleme (tracking-by- detection - TBD) yaklaşımı altında, IDPF-RP, L1DPF-M ve TDIOT olarak adlandırılan, üç farklı obje izleyici önerilmiştir. Önerilen tüm izleyicilerde, tek obje izleme problemi üzerinde yoğunlaşılmakta ve objenin son konumu, Bayesci filtreleme tabanlı bir obje izleyici bir derin obje sezici ile tümleştirilerek kestirilmektedir. Derin obje sezici olarak yüksek lokalizayon doğruluğuna sahip Mask R-CNN sezici kullanılırken, Bayesçi filtrelemede hedef obje modellemede başarılı olduğu gösterilen renk-tabanlı parçacık filtreleme ve seyrek parçacık filtreleme kullanılmaktadır. İzleyicilerin bir diğer ortak noktası, kullanılan derin obje sezicinin izleme amacıyla yeniden eğitilmesini, ya da izleyicinin uçtan-uca yeniden eğitimini gerektirmemeleridir. Böylelikle etiketlenmiş izleme verisi olmaması durumunda çalışabilen, çevrim dışı eğitim yükünü en aza indiren, çok farklı derin obje sezicilerin farklı omurga mimarileri ile tümleştirilebilmesine olanak sağlayan obje izleyicilerin gerçeklenmesi hedeflenmiştir. IDPF-RP (Interleaving deep learning and particle filtering by region proposal suppression), VRCPF obje izleyici ile senkronize çalışan Mask R-CNN obje sezici kararlarını tümleştiren yeni bir karar tümleştirme mekanizması sunmakta, bu sayede objenin son konumu, izleyici ve sezici arasındaki karar birliğini enbüyükleyecek şekilde belirlenmektedir. IDPF-RP lokalizasyon hizalama katmanı (LH), Mask R-CNN sezicinin ölçek değişimlerine uyumluluk ve lokalizasyon doğruluğu avantajı ile VRCPF izleyicinin hedefe lokalize olma özelliğinden yararlanan bir tümleştirme gerçekler. Bu sayede izleme performansını doğrudan etkileyen aday obje BB'lerinin, içeriğe bağlı olarak değişen sayıda ve yüksek lokalizasyon doğruluğu ile örneklenmesi sağlanabilmekte, böylelikle izleme sürekliliği arttırılmaktadır. IDPF-RP, derin obje seziciden alınan geri besleme ile, hedef obje modelini güncelleyerek ölçek, ışıklılık ve görünüm değişimleri gibi obje izlemeyi zorlaştıran problemlere karşı gürbüzlüğü arttırmaktadır. Tez kapsamında önerilen bir diğer obje izleyici, L1DPF-M, Mask R-CNN derin obje sezici ve seyrek parçacık filtresini TBD yaklaşımı altında entegre eden yeni bir model sunmaktadır. Hedef obje modellemede kullanılan seyrek gösterim, derin obje sezicinin kılavuzluğunda güncellenerek, örtüşme, bakış-açısı değişimi gibi etmenlerden kaynaklanan obje görünüm değişikliklerine karşı gürbüzlük arttırılmaktadır. L1DPF-M, önerilen yeni PF gözlem modeli sayesinde, sezici ve izleyici arasında fikir birliğini ön plana çıkararak hedef objenin son konumunun daha doğru kestirilmesine olanak tanımaktadır. Bunun yanı sıra, L1DPF-M kapsamında önerilen yeni durum vektörü ile, obje hareketinin öteleme, dönme, ölçekleme ve kırpma olarak farklı komponentlerle modellenebilmesi bu sayede obje sınırlarının deforme BB'ler ile izlenebilmesi ve lokalizasyon doğruluğunun arttırılması sağlanmıştır. L1DPF-M, Mask R-CNN çıkışında elde edilen ve objeye piksel bazında erişim sağlayan bölütleme maskelerini kullanarak, izlemenin afin dönüşümlere gürbüzlüğünü arttırmaktadır. Tez kapsamında geliştirilen üçüncü obje izleyici, TDIOT, videodaki zamansal bilginin 3B CNN, LSTM ve benzeri mimariler kullanılmaksızın, işlem yükü çok arttırılmadan modele katılmasını amaçlamaktadır. IDPF-RP ve L1DPF-M den farklı olarak mimarisinde yalnızca PF izleyicinin parçacık örnekleme modülünü içerir ve hedefin son konumunun kestiriminde derin obje seziciye öncelik verir. Literatürdeki birçok derin izleyiciden farklı olarak, sezici eğitiminde kullanılan mimarinin izleme amacıyla transfer öğrenme ile yeniden eğitilmesini, ya da uçtan-uca yeniden eğitimini gerektirmez. Önerilen çıkarım mimarisinde Mask R-CNN aday bölge öneri katmanına eklenen parçacık örnekleyici, objenin geçmiş çerçevelerdeki konum bilgisini kullanarak, objeye uyumlu ölçek ve boyutlarda aday obje bölgelerinin önerilmesine olanak vermektedir. Öte yandan tepe katmanına eklenen "Benzerlik Eşleme" ve "Yerel Arama ve Eşleme" katmanları ile siyam benzerlik kriterine dayalı veri ilişkilendirme gerçeklenir. TDIOT obje izleyicinin obje giriş çıkışlarının da olduğu uzun süreli izleme isterlerini karşılaması amacıyla, yerel ikili örüntü tabanlı bir hedef-obje-doğrulama katmanı izleme mimarisine eklenmiş, uzun süreli izleme başarımının arttırıldığı gösterilmiştir. TDIOT doğrulama katmanının, insan, araba ve benzeri belirli objeler için eğitilmiş yeniden yakalama ağları ile değiştirilmesiyle, daha yüksek işlemsel karmaşıklığa karşın, obje doğrulama başarımının arttırılması olanaklıdır. Önerilen yöntemlerin başarımı literatürde sıklıkla kullanılan VOT ve VOT-LT veri tabanlarına ait videolar üzerinde raporlanmaktadır. Her üç yöntem için güncel izleyiciler ile karşılaştırmalı olarak sunulan izleme performansları, önerilen izleyicilerin lokalizasyon doğruluğunu önemli ölçüde arttırdığını göstermektedir. VOT2016 veri setine ait videolarda yapılan performans raporlamaları, IDPF-RP ve L1DPF-M ile güncel izleyicilere kıyasla sırasıyla \%7 ve \%6 daha yüksek başarım oranına (IoU-th=0.5) ulaşıldığını göstermektedir. Ayrıca, TDIOT ile lokalizasyon doğruluğunun (accuracy), VOT2016'nın en yüksek başarımlı izleyicisine göre \%3 oranında arttırıldığı, TDIOT-LT ile uzun süreli videolarda, literatür ile karşılaştırılabilir izleme performansına ulaşıldığı raporlanmaktadır. Bunun yanı sıra, izleme performansı her bir zorluk kategorisi için ayrı olarak analiz edilmiş ve önerilen izleyicilerin birçok zorluk durumunda izleme performansını arttırdığı gösterilmiştir. VOT2018 veri setine ait videolarda yapılan testler, IDPF-RP izleyicinin, ölçek değişimi içeren videolarda başarım oranını \%4, L1DPF-M izleyicinin, ışıklılık değişimi içeren videolarda başarım oranını \%5 oranında arttırdığını göstermektedir (IoU-th=0.5). Öte yandan, TDIOT obje izleyici, özellikle ışıklılık ve ölçek değişimine karşı gürbüzlüğü arttırarak, izleme doğruluğunu sırasıyla \%4 ve \%2 oranında iyileştirmektedir. TDIOT-LT ise bakış açısı değişiminin olduğu uzun süreli videolarda en yüksek izleme başarımına ulaşmaktadır.