Department of Modeling and Design Engineering Systems
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Browsing Department of Modeling and Design Engineering Systems by Subject "electrical & electronics engineering"
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Item ASSESSMENT OF FEATURES AND CLASSIFIERS FOR BLUETOOTH RF FINGERPRINTING(2022-02-14) Ali, Aysha; Kara, AliIn this thesis, we introduced a novel technique to enhance the security at physical layer of wireless networks. This is based on the use of radio freqency (RF) fingerprinting for Bluetooth (BT) signals. BT signal records are acquired from twenty different cell phone brands, models, and serial numbers. One hundred fifty records are collected from each device. For the first time, Hilbert Huang Transform (HHT) are used for the BT device identification with such huge data set. By means of the signals’ energy envelopes with some improvements, the transient signals are detected accurately. Through the Empirical mode decomposition (EMD) and Hilbert Transform (HT), the HHT is implemented to obtain Time Frequency Energy Distributions (TFED) of the detected transients. Thirteen features are extracted from the signals’ transients and their TFEDs. The extracted features are pre-processed to enhance their usability. Different classifiers are employed with the extracted features for device identification, and comparative analysis of the classifiers is also provided. The classifier performance is examined for different SNR levels from 8 dB to 35+ dB . The identification performance demonstrates the feasibility of the method.Item Development, Verification and Validation of an Industrial Communication Suite for Usage on an Oil Rig Environment(2022-01-17) Yücesan, Ongun; Özkil, AltanThe modern computer based supervisory, control and data acquisition techniques find more and more interest as Industrie 4.0, Industrial Internet of Things, Plug and Pro duce like concepts comes to life. Sometimes these applications on mechanical activ ities, involve human workers cooperations. Their safety and successful operation can be achieved by verification and validation of the software and hardware suites they rely on. A testing activity requires a repeatable series of actions. For this purpose a test suite is prepared either as a software, or a manual set of instruction for humans. Given a test suite, it is found that conducting a test based on controlled experiments is generally faster than completely running it to achieve a reliability level. However, complete runs have advantages like providing statistics such as the number of failures in a software. Both results of mathematical analysis and Monte Carlo Simulations results do not support complete rejection of either technique. Conducting controlled experiments, while time to time making full runs is found to be best course of action by the study. These ideas were implemented in our Industrial Control applications. Effort to identify means for the interarrival times, confidence intervals on them and underlying statistical distributions proved beneficient. Some observations on rare con ditions took place giving better view of the data at hand. Also statistical tests on the data, based on independence and identicalness of results indicated possible usage of statistical models. Such a result removes a cloud of uncertainty about the underlying conditions. Since data is found according with a random variable, the outcomes can be expressed more dependably.Item FEEDBACK CONTROL OF SYNAPTICALLY COUPLED HODGKIN-HUXLEY NEURONS(2022-01-18) Abobakar, Zargoun; Doruk, Reşat ÖzgürThrough a gap junction (electrical synapse) a pair of identical Hodgkin-Huxley neuron models are coupled together. These neurons are excited by an external current. The system we have represented is a nonlinear electrical circuit and the gap is a synaptic conductance. The complete system is of nonlinear multi-input, multi output (MIMO) type system. By using the MATLAB based software package called MATCONT and the bifurcation theory we tracked the neuron parameters that lead to bifurcation conditions. In addition, we studied the couple of the Hodgkin-Huxley model by selected different values of the synaptic conductance. For each value of the synaptic conductance we analyzed the bifurcations for the parameters of the neurons one-by-one using MATCONT. After that, we designed a controller to stabilize oscillation in the membrane potential caused by the change the parametersof the neurons. A washout filter controller of the second order type is used. This controller provides an electrical current injection to control the unwanted behaviour of the neurons due to parametric bifurcations. Linear Quadratic Regulator (LQR) supported by projective control theory, serves as the reference method in the design of the controller. The washout filter processes the membrane potentials only and projective control generates a gain to transform the filtered output to a current injection to the slave neuron.Item MODELING AND CHARACTERIZATION OF HUMAN BODY SHADOWING AT MILLIMETER WAVES(2022-01-26) Bin-Alabish, Ahmed H. A.; Kara, Ali; Dalveren, YaserAs 5G communication may use Millimetre waves (mmWave) bands, it is essential to estimate short range indoor links from blockage point of view. This study presents some initial studies for characterizing effects of human body movement on short range link. To the best of our knowledge, this study is the first to experimentally examine the effects of human body movement at this band. This study also presents a simple approach to characterize the effects of scattering objects around indoor links at 28 GHz while the link is fully blocked by human body. The effects of scattering objects close to the link were carried out by performing measurements with a metallic reflector and human body. Here, fundamental mechanisms of wave propagation such as reflection and diffraction were accounted for each scattering object. To predict the attenuation produced by metallic reflector, specular reflection model was used in reflection modelling. In diffraction modelling, on the other hand, the double knife-edge diffraction (DKED) model was exploited to predict the attenuation by human body. Simulations were then compared with measurements to estimate the prediction accuracy of the models. Results indicate that presented simple models work well for indoor links. Therefore, the results of this study could be extended to model multiple human body near the indoor links of fifth generation (5G) systems.Item NEURAL NETWORK BASED FEATURE EXTRACTION FOR HANDWRITTEN DIGIT RECOGNITION(2017-01-07) Günler Pirim, Mine Altınay; Tora, Hakan; Öztoprak, KasımIn this dissertation, it is proposed that hidden layer output weights of semi-trained neural network to be used as feature vectors. In pattern recognition neural network is a training algorithm which provides classification. In this thesis in addition to this fact, it has been shown that semi-trained neural network can be used as a tool to extract hidden layer output vectors that are used as features of the image. The system is mainly composed of three steps: preprocessor, feature extractor, and classifier. Only the classifier layer differs for each experiment, the other two layers are used as default for all experiments. Support vector machine, neural network, and Euclidean distance classifiers are utilized. The experiments were conducted on MNIST and USPS benchmark datasets to evaluate the performance of the proposed approach.Item PERFORMANCE ANALYSIS OF SIGNAL DE-NOISING TECHNIQUES IN DISTRIBUTED ACOUSTIC SENSING SYSTEMS(2022-01-21) Abufana, Saleh; Kara, AliIn this thesis, it is aimed to propose a novel method to detect and classify the threats for fiber optic distributed acoustic sensing (DAS) systems based on phase OTDR. In the first stage of the proposed method, Wavelet de-noising method is applied to remove the noise from the measured backscattered signal, and difference in time domain approach is used to perform high-pass filtering. In this stage, autocorrelation is also used for improving interferometric visibility of the events in all range bins. Further, the power of the correlated signals at each bin is calculated and sorted. Hence, the maximum valued bins are considered to be the event signal. In the second stage, the detected event signals are decomposed into a series of band-limited modes by using VMD technique, and from these modes, the enhanced event signals are reconstructed. Moreover, from the reconstructed event signals, higher order statistical (HOS) features are extracted. In the last stage, Linear Support Vector Machine (LSVM)-based classification approach is implemented to the extracted features for discriminating the threats. In order to measure the effects of the proposed method on the classification performance, different types of activities collected from various points of a fiber optic cable have been used under different SNR levels. The results show the effectiveness of the proposed method in threat detection and classification.Item STRUCTURE ANALYSES OF THIN FILMS ON BASIS OF MATLAB BY LASER INDUCED BREAKDOWN SPECTROSCOPY(LIBS)(2022-01-17) Azouz , Omaro A.; Eseller, Kemal EfeIn this thesis, we work with Laser induced breakdown Spectroscopy (LIBS), which is a type of atomic emission technique that used the laser shot on specific area of the sample to atomization-cum-excitation process , which released photon (light) at different wavelengths,which compared with the NIST data ,which can be after that used to determine elements in each sample and its concentration, in our work we develop a MATLAB code to determine the peaks and the area under it with using Lorentzian fitting, so that we can calculate the Area percentage under each peak and we compare the results with other methods Origin software and Excel , so that we have the ability to calculate the RSD ,LOD and SNR and the noise Fluctuation for each shot that we had, the sample was prepared throught CVD and we used the LIBS Type ((LPS-1064-A 50mJ) , Start Wavelength 186.263 nm, End Wavelength: 888.604 nm, Gate Delay: 0.350 ,Gate Width (ms) :1.050, the calculation by using certain program showed identic results regards the Area -percentage under each peaks and abnormal response on the first and second shot at each wavelength after that the photon emission effect goes ordinary . The sample was continued: Sn, Mg, Fe, Al, Cu, S, Si, Zn .Item VARIATIONAL MODE DECOMPOSITION BASED RADIO FREQUENCY FINGERPRINTING OF BLUETOOTH DEVICES(2022-01-26) Aghnaiya, Alghannai; Kara, AliIn this thesis, we evaluated the performance of RF fingerprinting method based on variational mode decomposition (VMD). Radio frequency fingerprinting (RFF) is based on identification of unique features of RF transient signals emitted by radio devices. RF transient signals of radio devices are short in duration, non-stationary and nonlinear time series. For this purpose, VMD is used to decompose Bluetooth (BT) transient signals into a series of band-limited modes, and then, the transient signal is reconstructed from the modes. Higher order statistical (HOS) features are extracted from the complex form of VMD-reconstructed transients and VMD-modes. Then, Linear Support Vector Machine (LVM) classifier is used to identify BT devices. The method has been tested experimentally with BT devices of different brands, models and series. The classification performance shows that VMD reconstructed transients method achieves better performance (at least 8% higher) than time-frequency-energy (TFED) distribution based methods such as Hilbert Huang Transform. This is demonstrated with the same dataset but with smaller number of features (nine features) and slightly lowers (2-3 dB) SNR levels. For the same dataset the classification performance demonstrates that VMD-modes method achieves better performance (4% higher) than VMD-reconstructed transient method.