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The Dean’s Office The Education Department is a core unit within the faculty, responsible for planning, organizing, and overseeing educational activities. It works closely with academic staff to design and update course curricula, coordinate class schedules, and enhance the overall quality of teaching. The department aims to provide a supportive environment for effective learning and the academic development of students. It also plays a key role in academic advising, addressing educational concerns, and organizing consultation sessions. By applying modern teaching methods and responding to current educational needs, the Education Department strives to improve the learning process and contribute to student success.
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Publication Date: 1992
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (15206149) 5pp. 309-312
This paper introduces an efficient algorithm that jointly estimates differential time delays and frequency offsets between two signals is introduced. The approach is a two-step procedure. First, the differential frequency offsets are estimated from measurement of the autocorrelation functions of the received and transmitted signals. The time delays are estimated from estimates of the higher-order statistics of the two signals involved. The major advantage of the approach is its remarkably reduced computational complexity over traditional approaches. The experimental results indicate that the algorithm performs better than the traditional methods in most cases of interest in spite of its reduced computational complexity. © 1992 IEEE.
Publication Date: 2006
pp. 459-462
In this paper we propose a new deinterlacing algorithm using motion compensation and directional interpolation. To limit the propagation error that is a major drawback of conventional motion compensated methods, motion estimation is performed using original lines only, for same and opposite parity fields. In addition, a threshold value is used during the search to recognize situations where the motion estimator fails to find an optimal matching block. Enhanced edge-based line average with median filtering is used in these situations. Experimental results show that the proposed method performs better than the traditional motion compensated method, based on objective and subjective criteria. © 2006 IEEE.
Publication Date: 2006
Electronic Transactions on Numerical Analysis (10689613) 23pp. 251-262
A new and efficient sine-convolution algorithm is introduced for the numerical solution of the radiosity equation. This equation has many applications including the production of photorealistic images. The method of sine-convolution is based on using collocation to replace multi-dimensional convolution-type integrals - such as two dimensional radiosity integral equations - by a system of algebraic equations. The developed algorithm solves for the illumination of a surface or a set of surfaces when both reflectivity and emissivity of those surfaces are known. It separates the radiosity equation's variables to approximate its solution. The separation of variables allows the elimination of the formulation of huge full matrices and therefore reduces required storage, as well as computational complexity, as compared with classical approaches. Also, the highly singular nature of the kernel, which results in great difficulties using classical numerical methods, poses absolutely no difficulties using sine-convolution. In addition, the new algorithm can be readily adapted for parallel computation for an even faster computational speed. The results show that the developed algorithm clearly reveals the color bleeding phenomenon which is a natural phenomenon not revealed by many other methods. These advantages should make real-time photorealistic image production feasible. Copyright © 2006, Kent State University.
Publication Date: 2006
pp. 25-28
This paper proposes optimization techniques to accelerate the enhanced edge-based line average (ELA) deinterlacing method. ELA is based on edge detection and directional interpolation as well as median filtering. The techniques are first based on low-level software optimizations to accelerate loops and arithmetic operations. Specialized hardware structures and corresponding new instructions are then defined for the Xtensa reconfigurable processor to accelerate ELA-specific operations. The combined software and hardware techniques result in a speed-up of 67× when compared to a base case. This accelerates the processing time from 25 times slower than real time to 2.7 times faster for a NTSC frame rate. A parallel processing version of ELA is also discussed. © 2006 IEEE.
Publication Date: 2007
IEEE Transactions on Consumer Electronics (00983063) 53(3)pp. 1117-1124
A new motion compensated deinterlacing method using forward and backward motion estimation is proposed in this paper. Bi-directional motion estimation is performed using two previous and two subsequent fields. The motion estimator uses pre-filtering prior to motion estimation for the current and the subsequent two fields. The motion estimator finds a single optimal matching block in the same or opposite parity reference fields. Motion compensation is done according to the amount of vertical motion within the reference fields to achieve the highest vertical resolution improvement. A novel technique to prevent the appearance of visual artifacts in the presence of fast-moving objects is proposed. Experimental results show that the proposed method performs better than the conventional deinterlacing methods, based on objective and subjective criteria. © 2007 IEEE.
Publication Date: 2008
pp. 941-945
Block matching has been widely used for block motion estimation; however most of the block matching algorithms impose heavy computational load to the system, and require much time for execution. This problem prevents using them in time critical applications. In this paper, a new approach to block matching technique is presented, which has small computational complexity as well as high accuracy. The main assumption of the algorithm is that, all the pixels of a block move equally by a linear motion. Experimental results show the feasibility and effectiveness of the proposed algorithm. © 2008 IEEE.
Publication Date: 2008
2pp. 539-544
In this paper, a new robust digital image watermarking algorithm based on Joint DWT-DCT Transformation is proposed. The imperceptibility is provided as well as higher robustness against common signal processing attacks. A binary watermarked image is embedded in certain sub-bands of a 3-level DWT transformed of a host image. Then, DCT transform of each selected DWT sub-band is computed and the PN-sequences of the watermark bits are embedded in the coefficients of the corresponding DCT middle frequencies. In extraction stages, the watermarked image, which maybe attacked, is first preprocessed by sharpening and Laplassian of Gaussian filters. Then, the same approach as the embedding process is used to extract the DCT middle frequencies of each sub-band. Finally, correlation between mid-band coefficients and PN-sequences is calculated to determine watermarked bits. Experimental results show that the proposed method improved the performance of the watermarking algorithms which are based on the joint of DWT-DCT. © 2008 IEEE.
This paper presents a new robust digital image watermarking technique based on Discrete Cosine Transform (DCT) and neural network. The neural network is Full Counter propagation Neural Network (FCNN). FCNN has been used to simulate the perceptual and visual characteristics of the original image. The perceptual features of the original image have been used to determine the highest changeable threshold values of DCT coefficients. The highest changeable threshold values have been used to embed the watermark in DCT coefficients of the original image. The watermark is a binary image. The pixel values of this image are inserted as zero and one values in the DCT coefficients of the image. The implementation results have shown that this watermarking algorithm has an acceptable robustness versus different kinds of watermarking attacks. © 2008 IEEE.
Publication Date: 2008
pp. 623-628
Background estimation is one of the most challenging phases in extracting foreground objects from video sequences. In this paper we present a background modeling approach that uses the similarity of frames to extract background areas from the video sequence. We use a window over the frames history and compute the similarity between the selected frames of this window as a similarity window. The properties of similarity window depend on the characteristics of the scene and can be adjusted parametrically. Our primary results show that if proper parameters are chosen, this method can give a good approximation of the background model. ©2008 IEEE.
Publication Date: 2008
Eurasip Journal on Advances in Signal Processing (16876172) 2008
An electrocardiogram (ECG) beat classification scheme based on multiple signal classification (MUSIC) algorithm, morphological descriptors, and neural networks is proposed for discriminating nine ECG beat types. These are normal, fusion of ventricular and normal, fusion of paced and normal, left bundle branch block, right bundle branch block, premature ventricular concentration, atrial premature contraction, paced beat, and ventricular flutter. ECG signal samples from MIT-BIH arrhythmia database are used to evaluate the scheme. MUSIC algorithm is used to calculate pseudospectrum of ECG signals. The low-frequency samples are picked to have the most valuable heartbeat information. These samples along with two morphological descriptors, which deliver the characteristics and features of all parts of the heart, form an input feature vector. This vector is used for the initial training of a classifier neural network. The neural network is designed to have nine sample outputs which constitute the nine beat types. Two neural network schemes, namely multilayered perceptron (MLP) neural network and a probabilistic neural network (PNN), are employed. The experimental results achieved a promising accuracy of 99.03 for classifying the beat types using MLP neural network. In addition, our scheme recognizes NORMAL class with 100 accuracy and never misclassifies any other classes as NORMAL.
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