Elektronik ve Haberleşme Mühendisliği

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  • Öge
    Microwave spectroscopy based classification of rat hepatic tissues: On the significance of dataset
    (BAJECE, 2020-10) 0000-0003-3052-2945 ; Elektronik ve Haberleşme Mühendisliği
    With the advancements in machine learning (ML) algorithms, microwave dielectric spectroscopy emerged as a potential new technology for biological tissue and material categorization. Recent studies reported the successful utilization of dielectric properties and Cole-Cole parameters. However, the role of the dataset was not investigated. Particularly, both dielectric properties and Cole-Cole parameters are derived from the S parameter response. This work investigates the possibility of using S parameters as a dataset to categorize the rat hepatic tissues into cirrhosis, malignant, and healthy categories. Using S parameters can potentially remove the need to derive the dielectric properties and enable the utilization of microwave structures such as narrow or wideband antennas or resonators. To this end, in vivo dielectric properties and S parameters collected from hepatic tissues were classified using logistic regression (LR) and adaptive boosting (AdaBoost) algorithms. Cole-Cole parameters and a reproduced dielectric property data set were also investigated. Data preprocessing is performed by using standardization a principal component analysis (PCA). Using the AdaBoost algorithm over 93% and 88% accuracy is obtained for dielectric properties and S parameters, respectively. These results indicate that the classification can be performed with a 5% accuracy decrease indicating that S parameters can be an alternative dataset for tissue classification.
  • Öge
    Multiclass classification of hepatic anomalies with dielectric properties: From phantom materials to rat hepatic tissues
    (MDPI, 2020-01) Yılmaz, Tuba ; Elektronik ve Haberleşme Mühendisliği ; Electronics and Communication Engineering
    Open-ended coaxial probes can be used as tissue characterization devices. However, the technique suffers from a high error rate. To improve this technology, there is a need to decrease the measurement error which is reported to be more than 30% for an in vivo measurement setting. This work investigates the machine learning (ML) algorithms’ ability to decrease the measurement error of open-ended coaxial probe techniques to enable tissue characterization devices. To explore the potential of this technique as a tissue characterization device, performances of multiclass ML algorithms on collected in vivo rat hepatic tissue and phantom dielectric property data were evaluated. Phantoms were used for investigating the potential of proliferating the data set due to difficulty of in vivo data collection from tissues. The dielectric property measurements were collected from 16 rats with hepatic anomalies, 8 rats with healthy hepatic tissues, and in house phantoms. Three ML algorithms, k-nearest neighbors (kNN), logistic regression (LR), and random forests (RF) were used to classify the collected data. The best performance for the classification of hepatic tissues was obtained with 76% accuracy using the LR algorithm. The LR algorithm performed classification with over 98% accuracy within the phantom data and the model generalized to in vivo dielectric property data with 48% accuracy. These findings indicate first, linear models, such as logistic regression, perform better on dielectric property data sets. Second, ML models fitted to the data collected from phantom materials can partly generalize to in vivo dielectric property data due to the discrepancy between dielectric property variability.
  • Öge
    In vivo dielectric properties of healthy and benign rat mammary tissues from 500 MHz to 18 GHz
    (MDPI, 2020-04) Yılmaz, Tuba ; Ateş Alkan, Fatma ; Elektronik ve Haberleşme Mühendisliği ; Electronics and Communication Engineering
    This work investigates the in vivo dielectric properties of healthy and benign rat mammary tissues in an attempt to expand the dielectric property knowledge of animal models. The outcomes of this study can enable testing of microwave medical technologies on animal models and interpretation of tissue alteration-dependent in vivo dielectric properties of mammary tissues. Towards this end, in vivo dielectric properties of healthy rat mammary tissues and chemically induced benign rat mammary tumors including low-grade adenosis, sclerosing adenosis, and adenosis were collected with open-ended coaxial probes from 500 MHz to 18 GHz. The in vivo measurements revealed that the dielectric properties of benign rat mammary tumors are higher than the healthy rat mammary tissues by 9.3% to 35.5% and 19.6% to 48.7% for relative permittivity and conductivity, respectively. Furthermore, to our surprise, we found that the grade of the benign tissue affects the dielectric properties for this study. Finally, a comparison with ex vivo healthy human mammary tissue dielectric properties revealed that the healthy rat mammary tissues best replicate the dielectric properties of healthy medium density human samples.
  • Öge
    Analog neural network based on memristor crossbar arrays
    (İstanbul Teknik Üniversitesi, 2019) Yıldız, Hacer A. ; Altun, Mustafa ; Güngördü, Doğuş ; Stan, Mircea ; Elektronik ve Haberleşme Mühendisliği ; Electronics and Communications Engineering
    In this paper, a new feed forward analog neural network is designed using a memristor based crossbar array architecture. This structure consists of positive and negative polarity connection matrices. In order to show the performance and usefulness of the proposed circuit, it is considered a sample application of iris data recognition. The proposed neural network implementation is approved by the simulation in Cadence design environment using 0.35µm CMOS technology. The results obtained are promising for the implementation of high density neural network.
  • Öge
    Novel methods for efficient realization of logic functions using switching lattices
    (IEEE Transactions on Computers, 2019) Aksoy, Levent ; Altun, Mustafa ; Elektronik ve Haberleşme Mühendisliği ; Electronics and Communications Engineering
    Two-dimensional switching lattices including four-terminal switches are introduced as alternative structures to realize logic functions, aiming to outperform the designs consisting of one-dimensional two-terminal switches. Exact and approximate algorithms have been proposed for the problem of finding a switching lattice which implements a given logic function and has the minimum size,i.e., a minimum number of switches. In this article, we present an approximate algorithm, called JANUS, that explores the search space in a dichotomic search manner. It iteratively checks if the target function can be realized using a given lattice candidate, which is formalized as a satisfiability (SAT) problem. As the lattice size and the number of literals and products in the given target function increase, the size of a SAT problem grows dramatically, increasing the run-time of a SAT solver. To handle the instances that JANUS cannot cope with, we introduce a divide and conquer method called MEDEA. It partitions the target function into smaller sub-functions,finds the realizations of these sub-functions on switching lattices using JANUS, and explores alternative realizations of these sub-functions which may reduce the size of the final lattice. Moreover, we describe the realization of multiple functions in a single lattice. Experimental results show that JANUS can find better solutions than the existing approximate algorithms, even than the exact algorithm which cannot determine a minimum solution in a given time limit. On the other hand, MEDEA can find better solutions on relatively large size instances using a little computational effort when compared to the previously proposed algorithms. Moreover, on instances that the existing methods cannot handle, MEDEA can easily find a solution which is significantly better than the available solutions.