Browsing by Author "Kornkamol Thakulsukannant"
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ItemAdaptive Two-stage Spectrum Sensing under Noise Uncertainty in Cognitive Radio NetworksTo utilize licensed spectrum bands efficiently, spectrum sensing needs to be accurate and fast. The occurrence of noise uncertainty and the lower in received PU signal power due to the distance between the transmitter and the receiver, path loss, are the main challenges that has a great impact on the accuracy of spectrum sensing. In this paper, we propose a new scheme of two-stage spectrum sensing, “Adaptive Two-stage Spectrum Sensing (ATSS)”, under noise uncertainty environment. ATSS is a modified of a conventional two-stage spectrum sensing where the decision threshold of both stages are adapted on the distance, estimated noise variance and calculated noise uncertainty interval. Therefore, ATSS improves the detection performance of the existing spectrum sensing and is robust to noise uncertainty. The contribution of this paper is three-fold. First, an unreliable detection and wasted stage activation of a conventional two-stage spectrum sensing are reduced. Second, noise uncertainty is addressed. Third, a new parameter, critical distance ( ), is proposed in order to reduce computational burden and sensing time of the first stage.
ItemAn Alternative Single-Image Super Resolution Framework Employing High Frequency Prediction Using A Robust Huber Rational FunctionIn general prospective, SI-SR or Single-Image Super-Resolution, which is one of the most useful algorithms of Super Resolution-Reconstruction (SRR) algorithms, is a mathematical procedure for acquiring a high-resolution image from only one coarse-resolution image, which is usually computed by Digital Image Processing (DIP). Even thought there have been substantially researched during the last decade, Single - Image Super-Resolution for applying on real implementations still keeps throw down the gauntlet. One of the practical Single- Image Super-Resolution is the resolution enhancement using prediction of the high-frequency image because of its high performance and its less comple xity however the rational function C(x, y) of high-frequency image prediction process of this technique is depend upon three parameters (b, h, k) therefore the parameter turning is difficult for maximizing its performance. From this problem prospective, this paper presents the alternative SI-SR framework employing robust rational function based on Huber function, which is depend upon only one parameter (T), instead of three parameters like the rational function C(x,y). Using up to fourteen standard images, which are crooked by varied noise models, in analysis testing section, the proposed SI-SR is demonstrated to be somewhat simper than the original SI-SR with equivalent efficiency because the saving in parameter turning time will be very important for SI-SR in real implementations.
ItemPractical Programming Tutorial of Two Dimensional Discrete Fourier Transform (DFT) Based on MATLAB® for Both 2D Signals and ImagesThe two-dimensional (2-D) Discrete Fourier Transform (DFT) and Inverse Discrete Fourier Transform (IDFT) represent mathematical models for 2-D signals (such as digital images and digital videos) in the frequency and spatial domains, respectively. Digital Image Processing (DIP) has been implemented globally over the past two decades. Thus, 2-D Discrete Fourier Transform (2-D DFT) is essential in terms of representing mathematical models and analyzing 2-D signals and systems. In light of its importance, this article presents a tutorial for 2-D DFT utilizing MATLAB® for both 2-D signals and images. The analysis of the discrete signals are based on both spatial and frequency domains. The theoretical basic of 2-D DFT is presented, followed by a tutorial based on synthetic and real examples using MATLAB®.
ItemTutorial of One Dimensional Discrete Fourier Transform (DFT): Theory, Implementation and MATLAB® programmingThe Discrete Fourier Transform (DFT) and Inverse Discrete Fourier Transform (IDFT) are classical approaches to mathematically model signals and systems in the frequency and spatial (or temporal) domains, respectively. Due to worldwide implementation of Digital Signal Processing (DSP) during the last two decades, Discrete Fourier analysis has become one of the most useful mathematical techniques for analyzing digital signals and systems. Consequently, this article provides a tutorial for the Discrete Fourier Transform (DFT) on 1- dimensional (1-D) signals employing MATLAB®. While the Discrete Fourier analysis provides information for both spatial and frequency domains, this paper focuses on the frequency domain of the discrete signal.