Browsing by Subject "Signal processing--Digital techniques."
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Item Open Access Computationally efficient voice dialing system(Bilkent University, 1998) Solmaz, Mustafa HakanSubband based feature parameters are becoming widely used for speech recognition purposes. In this thesis a subband-based, small-vocabulary, speaker-dependent, isolated-word recognition system is proposed. The most distinctive property of the proposed system is its low computational cost which enables it to run at real-time on a simple microcontroller. The system is used as the core of a voice dialer which is designed to work together with Karel switchboxes. In training section first, an energy-based endpoint (startingpoint) detection method is applied for speech detection. Then feature extraction is applied on a fixed length, pcm-quantized (a-law) speech long enough to cover a single word. In recognition section template matching is used to find the most likely vocabulary element. A recognition rate of 93% is obtained in the simulations.Item Open Access Cutoff rate for fixed-composition coding over energy constrained AWGN channels(Bilkent University, 1990) Oğuz, Nihat CemShannon showed that, under an energy constraint, the ensemble of shell constrained codes optimizes the cutoff rate for AVVGN channels. Unfortunately, this ensemble is not very practical since its input alphabet is the entire real line. In this thesis, we consider the ensemble of fixed-composition codes which satisfy the shell constraint and have a finite input alphabet. For a certain four-letter symrnetric input alphabet, the cutoff rates for ensembles of fixed-composition codes of blocklengths ii]) to 10 are compnti'd for tlie AVVGN channel at various signal-to-noise ratios. Also an asymptotic analysis of these cutoff rates is carried out a.s blocklenghth tends to infinity. These results are compared with the cutoff rates optimized over the independentletters code ensemble, which is the ensemble ordinarily used in [>ractice. The results of this comparison show that, for relatively moderate signal-to-noise ratios, it is possible to achieve cutoff rates within 1-2% of the optimum value by using fixed-composition codes; whereas, with iudepeiideiit-letters codes, one can get at most within 9-10% of the optimum value. Thus, fixed-composition codes can provide significant improvements in cutoff rate in practice, cispiicially for moderate to high signal-to-noise ratios.Item Open Access Cutoff rate for fixed-composition on-off keying over direct detection photon channels(Bilkent University, 1990) Toygar, M. Şenol.In this thesis, we consider direct detection photon channel with peak and average power constraints. This channel is modelled as a binary input discrete memoryless channel. We study the cutoff rate for different modulation formats on this channel since it is a measure of decoding complexity when sequential decoding is used and also, it gives an upper bound for the probability of error which decreases exponentially with the constraint length of convolutional code. Cutoff rates for the ensembles of fixed-composition and independent-letters codes along with ON-OFF keying are computed numerically and also some bounds are given. Cutoff rates versus signal-to-noise ratio or peak power are plotted for blocklengths of N=40,100 and for both ensembles. Comparison of cutoff rates for these two ensembles shows that for the direct detection photon channel the cutoff rate of fixed-composition ensemble is significantly greater than that of independent-letters ensemble for small values of signal-to-noise ratio and when the average power is a small fraction of peak power, say, 5-30%. In an uncoded system, for achieving a probability of error P(E)=(10 to the power -9), we should send 10 photons/slot with rate R=1 bit/slot, resulting in an efficiency of 0.1 bits/photon.However, using coding we can make probability of error arbitrarily small achieving an efficiency of 1 bit/photon. Also, some remarks on the implementation of fixed-composition trellis codes and on multi-level signalling instead of ON-OFF keying are given in conclusions.Item Open Access Design and implementation of a dsp based controller for brushless dc motors(Bilkent University, 1996) Ergün, ArmağanThis work presents development of a high speed digital signal processor based controller for Brushless DC Motors. A working model of the plant as motor and amplifier is introduced and verified through experiment. Design and implementation of the hardware using wire-wrap board is accomplished and a PID control algorithm is developed. A user interface is designed for easy performance testing of the control system.Item Open Access The design of finite-state machines for quantization using simulated annealing(Bilkent University, 1993) Kuruoğlu, Ercan EnginIn this thesis, the combinatorial optimization algorithm known as simulated annealing (SA) is applied to the solution of the next-state map design problem of data compression systems based on finite-state machine decoders. These data compression systems which include finite-state vector ciuantization (FSVQ), trellis waveform coding (TWC), predictive trellis waveform coding (PTWC), and trellis coded quantization (TCQ) are studied in depth. Incorporating generalized Lloyd algorithm for the optimization of output map to SA, a finite-state machine decoder design algorithm for the joint optimization of output map and next-state map is constructed. Simulation results on several discrete-time sources for FSVQ, TWC and PTWC show that decoders with higher performance are obtained by the SA-I-CLA algorithm, when compared to other related work in the literature. In TCQ, simulation results are obtained for sources with memory and new observations are made.Item Open Access Efficient parallel digital signal processing algorithms for hypercube-connected multicomputers(Bilkent University, 1992) Derviş, ArgunIn this thesis, efficient parallelization of Digital Signal Processing (DSP) algorithms, (FFT, FHT and FCT), on multicomputers implementing the hypercube interconnection topology are investigated. The proposed algorithms, maintain perfect load-balance, minimize communication overhead, can overlap communications with computations and achieve regular computational patterns. The proposed parallel algorithms are implemented on Intel’s iPSC/2^ hypercube multicomputer with 32 processors. High efficiency and almost linear speedup values are obtained for even small size problems.Item Open Access Heart motion prediction based on adaptive estimation aşgorithms fo robotic-assisted beating heart surgery(Bilkent University, 2011) Tuna, Eser ErdemRobotic assisted beating heart surgery aims to allow surgeons to operate on a beating heart without stabilizers as if the heart is stationary. The robot actively cancels heart motion by closely following a point of interest (POI) on the heart surface—a process called Active Relative Motion Canceling (ARMC). Due to the high bandwidth of the POI motion, it is necessary to supply the controller with an estimate of the immediate future of the POI motion over a prediction horizon in order to achieve sufficient tracking accuracy. In this thesis two prediction algorithms, using an adaptive filter to generate future position estimates, are studied. In addition, the variation in heart rate on tracking performance is studied and the prediction algorithms are evaluated using a 3 degrees of freedom test-bed with prerecorded heart motion data. Besides this, a probabilistic robotics approach is followed to model and characterize noise of the sensor system that collects heart motion data used in this study. The generated model is employed to filter and clean the noisy measurements collected from the sensor system. Then, the filtered sensor data is used to localize POI on the heart surface accurately. Finally, estimates obtained from the adaptive prediction algorithms are integrated to the generated measurement model with the aim of improving the performance of the presented approach.Item Open Access Multi-sensor based ambient assisted living system(Bilkent University, 2013) Yazar, AhmetAn important goal of Ambient Assisted Living (AAL) research is to contribute to the quality of life of the elderly and handicapped people and help them to maintain an independent lifestyle with the use of sensors, signal processing and the available telecommunications infrastructure. From this perspective, detection of unusual human activities such as falling person detection has practical applications. In this thesis, a low-cost AAL system using vibration and passive infrared (PIR) sensors is proposed for falling person detection, human footstep detection, human motion detection, unusual inactivity detection, and indoor flooding detection applications. For the vibration sensor signal processing, various frequency analysis methods which consist of the discrete Fourier transform (DFT), mel-frequency cepstral coefficients (MFCC), discrete wavelet transform (DWT) with different filter-banks, dual-tree complex wavelet transform (DT-CWT), and single-tree complex wavelet transform (ST-CWT) are compared to each other to obtain the best possible classification result in our dataset. Adaptive-threshold based Markov model (MM) classifier is preferred for the human footstep detection. Vibration sensor based falling person detection system employs Euclidean distance and support vector machine (SVM) classifiers and these classifiers are compared to each other. PIR sensors are also used for falling person detection and this system employs two PIR sensors. To achieve the most reliable system, a multi-sensor based falling person detection system which employs one vibration and two PIR sensors is developed. PIR sensor based system has also the capability of detecting uncontrolled flames and this system is integrated to the overall system. The proposed AAL system works in real-time on a standard personal computer or chipKIT Uno32 microprocessors without computers. A network is setup for the communication of the Uno32 boards which are connected to different sensors. The main processor gives final decisions and emergency alarms are transmitted to outside of the smart home using the auto-dial alarm system via telephone lines. The resulting AAL system is a low-cost and privacy-friendly system thanks to the types of sensors used.Item Open Access Nearest-neighbor based metric functions for indoor scene recognition(Bilkent University, 2011) Çakır, FatihIndoor scene recognition is a challenging problem in the classical scene recognition domain due to the severe intra-class variations and inter-class similarities of man-made indoor structures. State-of-the-art scene recognition techniques such as capturing holistic representations of an image demonstrate low performance on indoor scenes. Other methods that introduce intermediate steps such as identifying objects and associating them with scenes have the handicap of successfully localizing and recognizing the objects in a highly cluttered and sophisticated environment. We propose a classi cation method that can handle such di culties of the problem domain by employing a metric function based on the nearest-neighbor classi cation procedure using the bag-of-visual words scheme, the so-called codebooks. Considering the codebook construction as a Voronoi tessellation of the feature space, we have observed that, given an image, a learned weighted distance of the extracted feature vectors to the center of the Voronoi cells gives a strong indication of the image's category. Our method outperforms state-of-the-art approaches on an indoor scene recognition benchmark and achieves competitive results on a general scene dataset, using a single type of descriptor. In this study although our primary focus is indoor scene categorization, we also employ the proposed metric function to create a baseline implementation for the auto-annotation problem. With the growing amount of digital media, the problem of auto-annotating images with semantic labels has received signi cant interest from researches in the last decade. Traditional approaches where such content is manually tagged has been found to be too tedious and a time-consuming process. Hence, succesfully labeling images with keywords describing the semantics is a crucial task yet to be accomplished.Item Open Access Novel methods for SAR imaging problems(Bilkent University, 2013) Uğur, SalihSynthetic Aperture Radar (SAR) provides high resolution images of terrain reflectivity. SAR systems are indispensable in many remote sensing applications. High resolution imaging of terrain requires precise position information of the radar platform on its flight path. In target detection and identification applications, imaging of sparse reflectivity scenes is a requirement. In this thesis, novel SAR image reconstruction techniques for sparse target scenes are developed. These techniques differ from earlier approaches in their ability of simultaneous image reconstruction and motion compensation. It is shown that if the residual phase error after INS/GPS corrected platform motion is captured in the signal model, then the optimal autofocused image formation can be formulated as a sparse reconstruction problem. In the first proposed technique, Non-Linear Conjugate Gradient Descent algorithm is used to obtain the optimum reconstruction. To increase robustness in the reconstruction, Total Variation penalty is introduced into the cost function of the optimization. To reduce the rate of A/D conversion and memory requirements, a specific under sampling pattern is introduced. In the second proposed technique, Expectation Maximization Based Matching Pursuit (EMMP) algorithm is utilized to obtain the optimum sparse SAR reconstruction. EMMP algorithm is greedy and computationally less complex resulting in fast SAR image reconstructions. Based on a variety of metrics, performances of the proposed techniques are compared. It is observed that the EMMP algorithm has an additional advantage of reconstructing off-grid targets by perturbing on-grid basis vectors on a finer grid.Item Open Access Online ECG signal orthogonalization based on singular value decomposition(Bilkent University, 1996) Acar, BurakElectrocardiogram (ECG) is the measurement of potential differences occurring on the body due to the currents that flow on the heart during diastole and systole. Cardiac abnormalities cause uncommon current flows, leading to strange waveform morphologies in the recorded ECG. Since some abnormalities become visible in ECG only during activity, exercise ECG tests are conducted. The sources of noise during an exercise test are electro myogram (EMG) due to increased muscle activity and baseline wander (BW) due to mechanical motion. Frequency band filtering, used to eliminate noise, is not an efficient method for filtering noise because usually frequency spectra of the interference and the ECG overlap. Rather, a fast morphological filter is required. This thesis is focused on an online filtering approach which separates noise and ECG signals without changing the morphology. The redundancy present in standard 12 lead ECG records is made operational by a Singular Value Decomposition based orthogonalization of the input signals. ECG is represented in a minimum dimensional space whose orthogonal complement takes on noise. The signals in this low dimensional space are used to reconstruct the input signals without noise. Noise elimination also improves data compression. A comparative study of the ST analysis of original and reconstructed signals is presented at the end.Item Open Access Output regulation for all-pole and minimum phase LTI(Bilkent University, 2010) Saldı, NaciIn this thesis, the problem of enabling the output of a system to track the reference signals and reject the disturbances created by the same exogenous system is considered. This problem is widely known as Output Regulation Problem. Firstly, we propose a method for all-pole LTI systems by using relative degree property and then we apply the same method for minimum phase LTI systems along with some modifications. In order to obtain controllers for a minimum phase LTI case, the system is converted into an all-pole system by employing the inverse system as the first part of the controller. Then using the method that we used in all-pole cases, we obtain the second part of the controller. Combining these two controllers gives us an overall controller which solves the output regulation problem. This method for LTI systems is then extended to all-pole and minimum phase LTV systems. However, in order to apply the same methodology we have to make some assumptions on LTV systems. For minimum phase cases, the normal form is obtained by applying certain Lyapunov transformations and then minimum phaseness is defined in accordance with the normal form. Furthermore we show that, similar to minimum phase LTI cases, pole / zero cancelations occur between the inverse system and the original system in minimum phase LTV cases. The method that we develop depends on analytical calculation of the controller and gives a certain degree of freedom to change the transient behavior of the system by only changing some controller parameters.Item Open Access Signal and image processing algorithms using interval convex programming and sparsity(Bilkent University, 2012) Köse, KıvançIn this thesis, signal and image processing algorithms based on sparsity and interval convex programming are developed for inverse problems. Inverse signal processing problems are solved by minimizing the ℓ1 norm or the Total Variation (TV) based cost functions in the literature. A modified entropy functional approximating the absolute value function is defined. This functional is also used to approximate the ℓ1 norm, which is the most widely used cost function in sparse signal processing problems. The modified entropy functional is continuously differentiable, and convex. As a result, it is possible to develop iterative, globally convergent algorithms for compressive sensing, denoising and restoration problems using the modified entropy functional. Iterative interval convex programming algorithms are constructed using Bregman’s D-Projection operator. In sparse signal processing, it is assumed that the signal can be represented using a sparse set of coefficients in some transform domain. Therefore, by minimizing the total variation of the signal, it is expected to realize sparse representations of signals. Another cost function that is introduced for inverse problems is the Filtered Variation (FV) function, which is the generalized version of the Total Variation (VR) function. The TV function uses the differences between the pixels of an image or samples of a signal. This is essentially simple Haar filtering. In FV, high-pass filter outputs are used instead of differences. This leads to flexibility in algorithm design adapting to the local variations of the signal. Extensive simulation studies using the new cost functions are carried out. Better experimental restoration, and reconstructions results are obtained compared to the algorithms in the literatureItem Open Access Spatial decoding of oscillatory neural activity for brain computer interfacing(Bilkent University, 2013) Onaran, İbrahimNeuroprosthetics (NP) aim to restore communication between people with debilitating motor impairments and their environments. To provide such a communication channel, signal processing techniques converting neurophysiological signals into neuroprosthetic commands are required. In this thesis, we develop robust systems that use the electrocorticogram (ECoG) signals of individuated finger movements and electroencephalogram (EEG) signals of hand and foot movement imageries. We first develop a hybrid state detection algorithm for the estimation of baseline (resting) and movement states of the finger movements which can be used to trigger a free paced neuroprosthetic using the ECoG signals. The hybrid model is constructed by fusing a multiclass support vector machine (SVM) with a hidden Markov model (HMM), in which the internal hidden state observation probabilities are represented by the discriminative output of the SVM. We observe that the SVM based movement decoder improves accuracy for both large and small numbers of training dataset. Next, we tackle the problem of classifying multichannel ECoG related to individual finger movements for a brain machine interface (BMI). For this particular problem we use common spatial pattern (CSP) method which is a popular method in BMI applications, to extract features from the multichannel neural activity through a set of spatial projections. Since we try to classify more than two classes, our algorithm extends the binary CSP algorithm to multiclass problem by constructing a redundant set of spatial projections that are tuned for paired and group-wise discrimination of finger movements. The groupings are constructed by merging the data of adjacent fingers and contrasting them to the rest, such as the first two fingers (thumb and index) vs. the others (middle, ring and little). In the remaining parts of the thesis, we investigate the problems of CSP method and propose techniques to overcome these problems. The CSP method generally overfits the data when the number of training trials is not sufficiently large and it is sensitive to daily variation of multichannel electrode placement, which limits its applicability for everyday use in BMI systems. The amount of channels used in projections should be limited to some adequate number to overcome these problems. We introduce a spatially sparse projection (SSP) method, taking advantage of the unconstrained minimization of a new objective function with approximated `1 penalty. Furthermore, we investigate the greedy `0 norm based channel selection algorithms and propose oscillating search (OS) method to reduce the number of channels. OS is a greedy search technique that uses backward elimination (BE), forward selection (FS) and recursive weight elimination (RWE) techniques to improve the classification accuracy and computational complexity of the algorithm in case of small amount of training data. Finally, we fuse the discriminative and the representative characteristic of the data using a baseline regularization to improve the classification accuracy of the spatial projection methods.