Browsing Elektrik-Elektronik Mühendisliği / Electrical - Electronics Engineering by Title
Now showing items 304-323 of 330
-
The effect of channel models on compressed sensing based UWB channel estimation
Authors:
Publisher and Date:(2011)Ultra-wideband (UWB) multipath channels are assumed to have a sparse structure as the received consecutive pulses arrive with a considerable time delay and can be resolved individually at the receiver. Due to this sparse structure there has been a significant amount of interest in applying the compressive sensing (CS) theory to UWB channel estimation. There are various implementations of the CS theory for the UWB channel estimation based on the assumption that the UWB channels are sparse. However ...
-
The Effect of Channel Models on Compressed Sensing Based UWB Channel Estimation
Authors:
Publisher and Date:(IEEE, 2011)Ultra-wideband (UWB) multipath channels are assumed to have a sparse structure as the received consecutive pulses arrive with a considerable time delay and can be resolved individually at the receiver. Due to this sparse structure there has been a significant amount of interest in applying the compressive sensing (CS) theory to UWB channel estimation. There are various implementations of the CS theory for the UWB channel estimation based on the assumption that the UWB channels are sparse. However ...
-
The effect of channel resolution on compressed sensing based Ultra-Wideband channel estimation [Kanal çözünürlüǧünün sıkıştırılmış algılama tabanlı ultra geni̇ş bantlı kanal kesti̇ri̇mi̇ne etki̇si̇]
Authors:
Publisher and Date:(2011)Ultra short pulses are transmitted in Ultra-Wideband (UWB) communications. As a result of this pulses at the receiver can be resolved individually due to the sparse structure of the channel. Due to these properties the UWB channel can be estimated by using a compressed sensing based channel estimation method. The performance of channel estimation depends on the sparsity of the channel. In this work the effect of channel resolution which directly affects the sparsity is examined. It is shown that ...
-
The Effect of Primary User Bandwidth on Bayesian Compressive Sensing Based Spectrum Sensing
Authors:
Publisher and Date:(IEEE Computer Society, 2016)The application of compressive sensing (CS) theory has found great interest in wideband spectrum sensing. Although most studies have considered perfect reconstruction of the primary user signal it is actually more important to assess the presence or absence of the signal. Among CS based methods Bayesian CS (BCS) takes into consideration the prior information of signal coefficients to be estimated which improves signal reconstruction performance. On the other hand the sparsity level of the signal ...
-
The Effect of Primary User Bandwidth on Bayesian Compressive Sensing Based Spectrum Sensing
Authors:
Publisher and Date:(IEEE, 2015)The application of compressive sensing (CS) theory has found great interest in wideband spectrum sensing. Although most studies have considered perfect reconstruction of the primary user signal it is actually more important to assess the presence or absence of the signal. Among CS based methods Bayesian CS (BCS) takes into consideration the prior information of signal coefficients to be estimated which improves signal reconstruction performance. On the other hand the sparsity level of the signal ...
-
The effect of secondary user locations on the cooperative detection performance [İki̇nci̇l kullanıcı konumlarının i̇şbi̇rli̇kli̇ algılama yöntemi̇ performansına etki̇si̇]
Authors:
Publisher and Date:(IEEE, 2012)In conventional cooperative detection a fusion center decides on the presence or absence of the primary user by gathering all the information from secondary users and conveys this decision to all users. This approach does not take into account the locations of the secondary users where a user far from the primary user may also have to keep silent. In a recent work there has been a new detection method based on combining received signals from more than two users obtaining decision tables and deciding ...
-
Throughput Maximization for Full Duplex Wireless Powered Communication Networks
Authors:
Publisher and Date:(Institute of Electrical and Electronics Engineers Inc., 2020)In this paper, we consider a full duplex wireless powered communication network where multiple users with RF energy harvesting capabilities communicate to a hybrid energy and information access point. An optimization framework is proposed with the objective of maximizing the sum throughput of the users subject to energy causality and maximum transmit power constraints considering a realistic energy harvesting model incorporating initial battery levels of the users. The joint optimization of power ...
-
Throughput maximization in discrete rate based full duplex wireless powered communication networks
Authors:
Publisher and Date:(John Wıley & Sons Ltd, 2020)In this study, we consider a discrete rate full-duplex wireless powered communication network. We characterize a novel optimization framework for sum throughput maximization to determine the rate adaptation and transmission schedule subject to energy causality and user transmit power. We first formulate the problem as a mixed integer nonlinear programming problem, which is hard to solve for a global optimum in polynomial-time. Then, we investigate the characteristics of the solution and propose a ...
-
Thyroid and breast cancer disease diagnosis using Fuzzy-neural networks
Authors:
Publisher and Date:(2009)In this paper a new hybrid structure in which Neural Network and Fuzzy Logic are combined is proposed and its algorithm is developed. Fuzzy-CSFNN Fuzzy-MLP and Fuzzy-RBF structures are constituted and their performances are compared. Conic Section Function Neural Network (CSFNN) unifies the propagation rules of the Multilayer Perceptron (MLP) and the Radial Basis Function (RBF) networks at a unique network by its distinctive propagation rules. That means CSFNNs accommodate MLPs and RBFs in its own ...
-
Transfer matrix factorization based synthesis of resistively terminated LC ladder networks
Authors:
Publisher and Date:(2009)In this paper a transfer matrix factorization based synthesis algorithm for resistively terminated low-pass LC ladder networks is presented. In the algorithm component value of the extracted element and the reflection factor of the remaining network are well formulated in terms of reflection factor coefficients of the whole network. An example is presented to exhibit the application of the proposed synthesis algorithm.
-
Transitional Butterworth-Chebyshev Filters
Authors:
Publisher and Date:(IEEE, 2018)Filters which are studied in this paper have properties that lie between those of the Butterworth and Chebyshev filters. So it is appropriate to call this kind of filters as transitional Butterworth-Chebyshev filters. In this work different 6th order transitional filters are designed and their responses are compared with each other.
-
Transitional Butterworth-Chebyshev Filters
Authors:
Publisher and Date:(IEEE, 2019)Filters which are studied in this paper have properties that lie between those of the Butterworth and Chebyshev filters. So it is appropriate to call this kind of filters as transitional Butterworth-Chebyshev filters. In this work different 6 th order transitional filters are designed and their responses are compared with each other. © 2018 IEEE.
-
Transmitter source location estimation using crowd data
Authors:
Publisher and Date:(Pergamon-Elsevier Science Ltd, 2018)The problem of transmitter source localization in a dense urban area has been investigated where a supervised learning approach utilizing neural networks has been adopted. The cellular phone network cells and signals have been used as the test bed where data are collected by means of a smart phone. Location and signal strength data are obtained by random navigation and this information is used to develop a learning system for cells with known base station location. The model is applied to data ...
-
Trellis code design for spatial modulation
Authors:
Publisher and Date:(2011)In this paper we propose a novel MIMO transmission scheme called trellis coded spatial modulation (TC-SM) in which the trellis (convolutional) encoder and the spatial modulation (SM) mapper are jointly designed similar to the conventional trellis coded modulation (TCM). A soft decision Viterbi decoder which is fed with the soft information supplied by the optimal SM decoder is used at the receiver. We derive the pairwise error probability (PEP) upper bound for the TC-SM scheme in uncorrelated ...
-
Trellis Code Design for Spatial Modulation
Authors:
Publisher and Date:(IEEE, 2011)In this paper we propose a novel multiple-input multiple-output (MIMO) transmission scheme called trellis coded spatial modulation (TC-SM) in which a trellis (convolutional) encoder and a spatial modulation (SM) mapper are jointly designed similar to the conventional trellis coded modulation (TCM). A soft decision Viterbi decoder which is fed with the soft information supplied by the optimal SM decoder is used at the receiver. The pairwise error probability (PEP) upper bound is derived for the ...
-
Trellis coding for spatial modulation [Uzaysal modulasyon i̇çi̇n kafes kodlama]
Authors:
Publisher and Date:(2011)In this study by combining trellis coding with spatial modulation (SM) a new multiple-input multiple output (MIMO) communication scheme called trellis coded spatial modulation (TC-SM) is proposed. For uncorrelated Rayleigh fading channels code design criteria are given by deriving pairwise error probability (PEP) of this system in which a trellis encoder and SM mapper are jointly designed. These criteria are then used to obtain 4 8 and 16-states TC-SM schemes. It is shown via computer simulations ...
-
UNIFAC application to water-1-propanol-n-amyl alcohol and n-amyl acetate ternaries
Authors:
Publisher and Date:(2006)Liquid-liquid equilibrium (LLE) data for water-1-propanol-n-amyl alcohol and water-1-propanol-n-amyl acetate ternaries were measured at T=298.2 K. The UNIFAC model was used to correlate the experimental data. A comparison of the extracting capabilities of the solvents was made with respect to distribution coefficients and separation factors.
-
Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in Italy
Authors:
Publisher and Date:(Ieee-Inst Electrıcal Electronıcs Engıneers Inc, 2020)Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most real-world time series data have a spatial dimension as an additional context which is often expressed in terms of coordinates of the region of interest (such as latitude - longitude information). However, existing techniques are limited to handle spatial and temporal contextual attributes in an integrated ...
-
VLC Sparse Channel Estimation in the Presence of Non-Gaussian Clipping Noise
Authors:
Publisher and Date:(IEEE, 2016)In this paper a new computationally efficient and high performance channel estimation algorithm is proposed for the indoor visible light communication (VLC) sparse channels in the presence of a clipping noise. The clipping noise is modelled as a Gaussian mixture and a first time in the literature the matching pursuit (MP) and the space-alternating expectation-maximization (SAGE) algorithms are combined into the new estimation called the SAGE-MP algorithm for iteratively estimating the sparse channel ...
-
VLC Sparse Channel Estimation in the Presence of Non-Gaussian Clipping Noise [Gauss Olmayan Kırpma Gürültüsü Altında VLC Seyrek Kanal Kestirimi]
Authors:
Publisher and Date:(Institute of Electrical and Electronics Engineers Inc., 2016)In this paper a new computationally efficient and high performance channel estimation algorithm is proposed for the indoor visible light communication (VLC) sparse channels in the presence of a clipping noise. The clipping noise is modelled as a Gaussian mixture and a first time in the literature the matching pursuit (MP) and the space-alternating expectation- maximization (SAGE) algorithms are combined into the new estimation called the SAGE-MP algorithm for iteratively estimating the sparse ...