Indonesian Vowel Recognition Using Artificial Neural Network Based On the Wavelet Features

There are six vowels in Indonesian language, i.e. /a/, /i/, /u/, /e/, /ə/ and /o/. This paper presents Indonesian vowel recognition using artificial neural network (ANN) based on the wavelet features. The wavelet features were the wavelet coefficients of vowel signal which were extracted by using discrete wavelet transform (DWT). Vowel samples were recorded from native Indonesian speakers, 10 males and 10 females. Db4 and sym4 were used as the mother wavelet, and decomposition level 2, 4 and 6 were implemented for each vowel sample. Minimum, maximum, mean and standard deviation value of the wavelet coefficients then were used as input vectors of ANN with 2 hidden layers. Backpropagation algorithm was used to training the ANN. From the experimental results, an overall recognition rate of 70.83% could be achieved. In case of male speakers the highest recognition rate is 90% and in case of female speakers the highest recognition rate is 80%.DOI:http://dx.doi.org/10.11591/ijece.v3i2.232

Monte Carlo Simulation to Test the Effectiveness of Crystal Detector Length for PHITS-Based PET Modality

PET (Positron-emission tomography) is used to determine physiological and metabolic functions in the body. Monte Carlo simulation is an important part of PET imaging, and the Particle Heavy Ion Transport code System (PHITS) is a simulation platform that can be used to perform Monte Carlo simulations. This study uses a Monte Carlo simulation based on PHITS to determine the range of gamma absorption with an energy of 511 keV in a scintillation detector crystal material. The gamma absorption range determines the effective crystal length in the PET modality. The simulation process is carried out by shooting Gamma at various types of materials, which are the materials used in PET scintillation crystals. The materials used in this simulation are NaI (Sodium Iodide), BaF2 (Barium Florida), BGO (Bismuth Germanate), and GSO (Gadolinium Oxyorthosilicate), considering their atomic number and crystal density. The crystal material is capable of absorbing gamma radiation with an energy of 511 keV with detailed crystal lengths for each NaI crystal of 0.26 cm; 0.25 cm BaF2 crystals; 0.1cm BGO crystals; and 0.18 cm GSO crystals. The crystal length from this simulation is smaller than the commercially available crystal length (range 1-3 cm). Based on the crystal length data, the most effective crystal for absorbing gamma radiation is the BGO crystal

Monte Carlo Simulation to Test the Effectiveness of Crystal Detector Length for PHITS-Based PET Modality

PET (Positron-emission tomography) is used to determine physiological and metabolic functions in the body. Monte Carlo simulation is an important part of PET imaging, and the Particle Heavy Ion Transport code System (PHITS) is a simulation platform that can be used to perform Monte Carlo simulations. This study uses a Monte Carlo simulation based on PHITS to determine the range of gamma absorption with an energy of 511 keV in a scintillation detector crystal material. The gamma absorption range determines the effective crystal length in the PET modality. The simulation process is carried out by shooting Gamma at various types of materials, which are the materials used in PET scintillation crystals. The materials used in this simulation are NaI (Sodium Iodide), BaF2 (Barium Florida), BGO (Bismuth Germanate), and GSO (Gadolinium Oxyorthosilicate), considering their atomic number and crystal density. The crystal material is capable of absorbing gamma radiation with an energy of 511 keV with detailed crystal lengths for each NaI crystal of 0.26 cm; 0.25 cm BaF2 crystals; 0.1cm BGO crystals; and 0.18 cm GSO crystals. The crystal length from this simulation is smaller than the commercially available crystal length (range 1-3 cm). Based on the crystal length data, the most effective crystal for absorbing gamma radiation is the BGO crystal

Interactive GPU active contours for segmenting inhomogeneous objects

We present a segmentation software package primarily targeting medical and biological applications, with a high level of visual feedback and several usability enhancements over existing packages. Specifically, we provide a substantially faster GPU implementation of the local Gaussian distribution fitting energy model, which can segment inhomogeneous objects with poorly defined boundaries as often encountered in biomedical images. We also provide interactive brushes to guide the segmentation process in a semiautomated framework. The speed of our implementation allows us to visualize the active surface in real time with a built-in ray tracer, where users may halt evolution at any time step to correct implausible segmentation by painting new blocking regions or new seeds. Quantitative and qualitative validation is presented, demonstrating the practical efficacy of our interactive elements for a variety of real-world datasets