Browsing by Author "Wilaiporn Lee"
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    Adaptive Two-stage Spectrum Sensing under Noise Uncertainty in Cognitive Radio Networks
    ( 2016) Kornkamol Thakulsukannant ; Wilaiporn Lee ; Akara Prayote ; Kanabadee Srisomboon
    To 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.
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    The Switching Bilateral Denoising Performance Influence of Spatial and Radiometric Variance
    ( 2016-06) Kriengkri Langampol ; Wilaiporn Lee ; Vorapoj Patanavijit
    In this paper, we investigate the performance of switching bilateral filter (SBF) influenced by two parameters — radiometric variance ( R σ ) and spatial variance ( S σ ). The SBF can be used to filter Gaussian noise and impulse noise at the same time. For SBF, R σ and S σ are the two most important factors that affect increasing /decreasing the performance of SBF. Then, we investigate the influence of two parameters by varying their values from 1 to 100 and evaluate the performance of the de-noising for seven types of image under Gaussian noise environment. The simulation results show that the optimal values of R σ and S σ are depended on image type. Therefore, we propose the proportional of R σ and S σ that can be applied for all image type.