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ItemComputational Tutorial of Steepest Descent Method and Its Implementation in Digital Image Processing( 2013) Vorapoj PatanavijitIn the last decade, optimization techniques have extensively come up as one of principal signal processing techniques, which are used for solving many previous intractable problems in both digital signal processing (DSP) problems and digital image processing (DIP) problem. Due to its low computational complexity and uncomplicated implementation, the Gradient Descent (GD) method [1] is one of the most popular optimization methods for problems, which can be formulated as a differentiable multivariable functions. The GD method is ubiquitously used from basic to advanced researches. First, this paper presents the concept of GD method and its implementations for general mathematical problems. Next, the computation of GD processes is shown step by step with the aim to understand the effect of important parameters (such as its initial value and step size) to the performance of GD. Later, the computational concept of GD method for DIP problems [2-5] is formulated and the computation of GD is demonstrated step by step. The effect of the initial value and the step size to the performance of GD method in DIP is also presented.
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ItemPolicies for Channel Allocation in Cognitive Radio Networks using Game Theory( 2016) Bhattarai, AmulyaCognitive radio networks evolve according to the actions of sel sh users who act independently. The eventual state reached in most of the cases by such a system is a Nash Equilibrium (NE). Multiple NE with di erent qualities may exist. In this work we consider the problem of allocating channels to multiple transceivers. Based on policies derived from various network metrics we develop a way to initialize the allocation and choose the appropriate channel for each user to push the system towards a higher normalized cumulative total throughput of the CRN. A novel equation to allocate channels is also derived. The improvement is con rmed by our simulation results.
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ItemStudies on Inter-component Communication Latency based on Variation Number of Components and Packet size in SDR-SCA Waveform Application( 2016) Pasd Putthapipat ; Andrian, Jean H. ; Liu, ChenThe ability to use software defined radio (SDR) in the civilian mobile applications will open up the next generation of mobile devices to reality such as multi-standard personal wireless devices or ubiquitous wireless devices. Many challenges in commercialising SDR are still the subject of interest in the software radio research community. Four main issues that have been already addressed are performance, size, weight, and power. The investigation presents in-depth study of SDR inter-components communications in terms of total link delay related to the number of components and packet sizes in software communication architecture (SCA)-based systems. The study is based on the investigation of the controlled environment platform. Results indicate the total link delay did not linearly increase with the number of components and the packet sizes. The closed form expression of the delay was modelled using the logistic function in terms of the number of components and packet sizes. The model performed very well when the number of components was large.
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ItemA Modified- Fast Spectrum Sensing with Coordinate System under Path Loss Effect and Noise Uncertainty for Cognitive Radio Networks( 2015) Vorapoj PatanavijitSpectrum sensing is a critical function for cognitive radio which is used to continually monitor the activities of PU in the licensed spectrum band. In this paper, we first re-derive some parameters of FSC algorithm, which is called Modified- fast spectrum sensing with coordinate system (MFSC), to perform spectrum sensing under path loss effect and noise uncertainty. Then, performance of three knowledgebased spectrum sensing techniques — MFD, LED and MFSC — are evaluated under path loss effect and noise uncertainty. From the simulation results, we found that MFD gives the highest probability of detection when noise uncertainty does not exist. However its detection performance greatly degrades due to the occurrence of noise uncertainty. On the other hand, the effect of noise uncertainty does not cause any degradation to the detection performance of LED, however the detection performance of LED is worse than MFSC. When both factors — path loss and noise uncertainty — are both taken in the account, MFSC algorithm is the most achievable of spectrum sensing requirement since it gives high detection performance while consumes the least average sensing time.
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ItemAn Adaptive And Statistical Efficiency Myriad Filter For A Recursive Image Reconstruction Using a Multi-Frame SRR Algorithm With A Stochastic Regularization For Video Sequences( 2015) Vorapoj PatanavijitIn real applied implementations, a collection of classical linear filtering theories, for example, a Median filter and a Mean filter, can be only applied to the Gaussian noise environments due to the fact that these linear filters usually gives the poor performance under the presence of non-Gaussian noise environments. Because of the motion estimation blunder and observation process error, which are usually caused from real electronic noise, non-accurate optical devices or mathematical simplification models of observed process systems, the SRR (Super Resolution Reconstruction) algorithms using classical linear filters (a Median filter and a Mean filter) should possibly demote the quality of a reconstructed image rather than improve its quality. Under non-Gaussian environments, a class of flexible filters with high statistical efficiency, so called Myriad filter, has been proposed and its solid theoretical principle was analyzed for indicating that the Myriad filter is usually more powerful than a class of linear filters, especially for non-Gaussian environments. This paper proposes an adaptive and statistical efficiency myriad filter for a recursive image reconstruction for applied implementing on real video sequences, which are contaminated by both Gaussian and non-Gaussian noise environments. Thus, the Myriad filter, which is implemented for getting rid of noise in an expected image and for valuing the contrast between the back-propagated expecting of the reconstructed high resolution image and a group of low resolution images, is involved in this stochastic regularization SRR framework for the elimination proposing of Gaussian and non-Gaussian noise. Because of an ill-pose condition of the SRR algorithm, Tikhonov regularization methodology is mathematically required for getting rid of deformation from the reconstructed high resolution image and reforming the calculated time of its convergence. Under a lot of noisy corrupted environments (Noiseless, AWGN, Impulsive Noise, Poisson Noise and Speckle Noise at unequal noise power), the performance from the both PSNR and virtual quality prospect of the proposed SRR algorithm using Myriad filter, which is compared with SRR algorithms using classical linear filters (a Median filter and a Mean filter) are depicted and the proposed SRR algorithm gives the superior visually quality and, thus, superior PSNR than the SRR algorithms using classical linear filters.
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ItemComparative Experimental Exploration of Robust Norm Functions for Iterative Super Resolution Reconstructions under Noise Surrounding( 2015) Vorapoj PatanavijitIn DIP (Digital Image Processing) research society, the multi-frame SRR (Super Resolution Reconstruction) algorithm has grown to be the momentous theme in the last ten years because of its cost e ectiveness and its superior spectacle. Consequently, for a multi-frame SRR algorithm which is commonly comprised of a Bayesian ML (Maximum Likelihood) approach and a regularization technique into the unify SRR framework, numerous robust norm functions (which have both redescending and non-redescending in uence functions) have been commonly comprised in the unify SRR framework for increasingly against noise or outlier. First, this paper presents the mathematical model of several iterative SRR based on a Bayesian ML (Maximum Likelihood) approach and a regularization technique. Three groups of robust norm functions (a zero-redescending in uence function (Tukey's Biweight, Andrew's Sine and Hampel), a nonzero-redescending in uence function (Lorentzian, Leclerc, Geman&McClure, Myriad and Meridian) and a non-redescending in uence function (Huber)) are mathematically incorporated into the SRR framework. The close form solutions of the SRR framework based on these robust norm functions have been concluded. Later, the experimental section utilizes two standard images of Lena and Susie (40th) for pilot studies and fraudulent noise patterns of noiseless, AWGN, Poisson, Salt&Pepper, and Speckle of several magnitudes used to contaminate these two standard images. In order to acquire the maximum PSNR, the comparative experimental exploration has been done by comprehensively tailoring all experimental parameters such as step-size, regularization parameter, norm constant parameter.
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ItemA performance impact of Andrew’s Sine threshold for a robust regularized SRR based on ML framework( 2016) Vorapoj PatanavijitOne of the most successful Digital Image Reconstruction (DIR) techniques for increasing image resolution and improving image quality is the Super-Resolution Reconstruction (SRR), which is the procedure of integrating a collection of aliased low-resolution low-quality images to form a single high-resolution high-quality image. However, the mainstream SRR algorithms are too delicate to noisy environments because these mainstream SRR algorithms are often comprised by the ML (L1 or L2) estimation techniques thereby the new robust SRR algorithm, which is comprised by Andrew’s Sine norm, has been proposed for dealing with noisy environments. Because the performance of the new SRR algorithm heavily relies on this Andrew’s Sine norm soft-threshold parameter, resultantly, this paper aims to investigate the impact characteristic of this norm constant parameter on the novel SRR algorithm. In addition, multitudinous experiments (which are applied on two standard images: Lena image and Susie image) are simulated to make the extensive results under five noise models: noise free, additive Gaussian noise, multiplicative Gaussian noise, Poisson noise and Impulsive noise with several noise powers for demonstrating the relationship between the SRR performance (in PSNR) and Andrew’s Sine norm soft-threshold parameter under each noisy cases.
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ItemA Conceptual Framework of Super Resolution Reconstruction (SRR) Techniques( 2016) Vorapoj PatanavijitTypically, a spatial resolution (Pixel per Area) is an important factor used to define the image quality. Due to the dramatically advance of digital image processing in this decade, high resolution (HR) images are in demand because HR images give more detail and information that directly impact their application performance. Today, there are several techniques that can capture high resolution images such as resolution increment by reducing pixel side. Consequently this high resolution sensor is so expensive and do not proper for general applications. Moreover, due to reducing pixel side, the SNR of sensors decrease. From the signal processing theory, the alternative algorithm for increasing resolution of captured image is called “Super Resolution Reconstruction or SRR” that can solve this problem. Hence, the SRR refers that the process of increasing resolution and improving the quality of image to be higher resolution and better quality. This paper aim to review the ideal and concept of the SRR technique and its SRR observation model but this paper don’t review all SRR frameworks because there are so many proposed SRR techniques. Author hopes that the SRR ideal and concept framework reviewed in this paper will motivate the reader to conduct in this research areas.
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ItemConceptual Framework of Super Resolution Reconstruction Based on Frequency Domain From Aliased Multi-Low Resolution Images: Theory Part( 2016) Vorapoj PatanavijitTypically, a spatial resolution (Pixel per Area) is an important factor used to define the image quality. Due to the dramatically advance of digital image processing in this decade, high resolution (HR) images are in demand because HR images give more detail and information that directly impact their application performance. Today, there are several techniques that can capture high resolution images such as resolution increment by reducing pixel side. Consequently this high resolution sensor is so expensive and do not proper for general applications. Moreover, due to reducing pixel side, the SNR of sensors decrease. From the signal processing theory, the alternative algorithm for increasing resolution of captured image is called “Super Resolution Reconstruction or SRR” that can solve this problem. Hence, the SRR refers that the process of increasing resolution and improving the quality of image to be higher resolution and better quality. This paper aim to review the ideal and concept of the SRR technique and its SRR observation model but this paper don’t review all SRR frameworks because there are so many proposed SRR techniques. Author hopes that the SRR ideal and concept framework reviewed in this paper will motivate the reader to conduct in this research areas.
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ItemTracking of High-speed, Non-smooth and Microscale-amplitude Wave Trajectories( 2016) Jiradech KongthonIn this article, an inversion-based control approach is proposed and presented for tracking desired trajectories with high-speed (100Hz), non-smooth (triangle and sawtooth waves), and microscale-amplitude (10 micron) wave forms. The interesting challenge is that the tracking involves the trajectories that possess a high frequency, a microscale amplitude, sharp turnarounds at the corners. Two different types of wave trajectories, which are triangle and sawtooth waves, are investigated. The model, or the transfer function of a piezoactuator is obtained experimentally from the frequency response by using a dynamic signal analyzer. Under the inversion-based control scheme and the model obtained, the tracking is simulated in MATLAB. The main contributions of this work are to show that (1) the model and the controller achieve a good tracking performance measured by the root mean square error (RMSE) and the maximum error (Emax), (2) the maximum error occurs at the sharp corner of the trajectories, (3) tracking the sawtooth wave yields larger RMSE and Emax values,compared to tracking the triangle wave, and (4) in terms of robustness to modeling error or unmodeled dynamics, Emax is still less than 10% of the peak to peak amplitude of 20 micron if the increases in the natural frequency and the damping ratio are less than 5% for the triangle trajectory and Emax is still less than 10% of the peak to peak amplitude of 20 micron if the increases in the natural frequency and the damping ratio are less than 3.2 % for the sawtooth trajectory.
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ItemDamped Oscillator under Stokesian Realm and Added-Mass Effects( 2015) Jiradech KongthonThis article presents the modeling and simulations of a sphere that oscillates vertically in a high-viscosity liquid. The sphere is connected to a linear spring and given an initial displacement from the equilibrium position to allow free vibration and the sphere undergoes the inertia force, the spring force, the drag force, the buoyancy force, the gravity force, and the added-mass force. In general, the added-mass force is not considered in modeling an oscillator. In this article, the added-mass force is included in the modeling to reflect the reality and the effect of the added-mass force is investigated and discussed. The main contribution of this article is to model and simulate the system and to show that i) the natural frequency of oscillation is reduced by the added mass; ii) the damping ratio is also reduced by the added mass; iii) the return time to the original equilibrium is increased by the added mass; iv) furthermore, the difference on the densities between the liquid’s density and the sphere’s density dictates the degree of the added-mass effect; i.e., the effect due to the added mass is small if the difference on the densities is large.
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