Elektrik - Elektronik Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://gcris.khas.edu.tr/handle/20.500.12469/47

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Now showing 1 - 5 of 5

Citation Count: 4
Broadband Equalizer Design with Commensurate Transmission Lines via Reflectance Modeling
(IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG, 2008) Şengül, Metin Y.; Yarman, Sıddık Binboğa
In this paper an alternative approach is presented to design equalizers (or matching networks) with commensurate (or equal length) transmission lines. The new method automatically yields the matching network topology with characteristic impedances of the commensurate lines. In the implementation process of the new technique first the driving point impedance data of the matching network is generated by tracing a pre-selected transducer power gain shape without optimization. Then it is modelled its it realizable bounded-real input reflection coefficient in Richard domain which ill turn yields the desired equalizer topology with line characteristic impedances. This process results in an excellent initial design for the commercially available computer aided design (CAD) packages to generate final circuit layout for fabrication. An example is given to illustrate the utilization of the new method. It is expected that the proposed design technique is employed as it front-end to commercially available computer aided design (CAD) packages which generate the actual equalizer circuit layout with physical dimensions for mass production.
Citation Count: 4
Design and implementation of a negative feedback oscillator circuit based on a Cellular Neural Network with an Opposite Sign Template
(2010) Özmen, Atilla; Tander, Baran; Özçelep, Yasin
In this paper explicit amplitude and frequency expressions for a Cellular Neural Network with an Opposite-Sign Template (CNN-OST) under oscillation condition are derived and a novel inductorless oscillator circuit with negative feedbacks based on this simple structure is designed and implemented. The system is capable of generating quasi-sine signals with tuneable amplitude and frequency which can't be provided at the same time in the classical oscillator circuits.
Citation Count: 1
Fuzzy-neural networks for medical diagnosis
(2010) Şenol, Canan; Yıldırım, Tülay
In this paper a novel fuzzy-neural network architecture is proposed and the algorithm is developed. Using this new architecture fuzzy-CSFNN fuzzy-MLP and fuzzy-RBF configurations were constituted and their performances have been compared on medical diagnosis problems. Here conic section function neural network (CSFNN) is also a hybrid neural network structure that unifies the propagation rules of multilayer perceptron (MLP) and radial basis function (RBF) neural networks at a unique network by its distinctive propagation rules. That means CSFNNs accommodate MLPs and RBFs in its own self-network structure. The proposed hybrid fuzzy-neural networks were implemented in a well-known benchmark medical problems with real clinical data for thyroid disorders breast cancer and diabetes disease diagnosis. Simulation results show that proposed hybrid structures outperform both MATLAB-ANFIS and non-hybrid structures. © 2010 Inderscience Enterprises Ltd.
Citation Count: 0
Joint data detection and channel estimation for uplink MC-CDMA systems over frequency selective channels
(Springer, 2007) Panayırcı, Erdal; Doğan, Hakan; Çırpan, Hakan Ali; Fleury, Bernard Henri
This paper is concerned with joint channel estimation and data detection for uplink multicarrier code-division multiple-access (MC-CDMA) systems ill the presence of frequency fading channel. The detection and estimation implemented at the receiver are based on a version of the expectation maximization (EM) algorithm which is very suitable for the the multicarrier signal formats. Application of the EM-based algorithm to the problem of iterative data detection and channel estimation leads to a receiver structure that also incorporates a partial interference cancelation. Computer simulations show that the proposed algorithm has excellent BER end estimation performance.
Citation Count: 0
Rapidly Varying Sparse Channel Tracking with Hybrid Kalman-OMP Algorithm
(Springer, 2019) Şenol, Habib; Erküçük, Serhat; Erküçük, Serhat; Cirpan, Hakan Ali
It is expected from future communication standards that channel estimation algorithms should be able to operate over very fast varying frequency selective channel models. Therefore in this study autoregressive (AR) modeled fast varying channel has been considered and tracked with Kalman filter over one orthogonal frequency division multiplexing (OFDM) symbol. Channel sparsity is exploited which decreases the complexity requirements of the Kalman algorithm. Since Kalman filter is not directly applicable to sparse channels orthogonal matching pursuit (OMP) algorithm is modified for AR modeled sparse signal estimation. Also by using windows sparsity detection errors have been decreased. The simulation results showed that sparse fast varying channel can be tracked with the proposed hybrid Kalman-OMP algorithm and windowing method offers improved MSE results.

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