http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Decoding Brain Patterns for Colored and Grayscale Images using Multivariate Pattern Analysis
( Raheel Zafar ),( Muhammad Noman Malik ),( Huma Hayat ),( Aamir Saeed Malik ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.4
Taxonomy of human brain activity is a complicated rather challenging procedure. Due to its multifaceted aspects, including experiment design, stimuli selection and presentation of images other than feature extraction and selection techniques, foster its challenging nature. Although, researchers have focused various methods to create taxonomy of human brain activity, however use of multivariate pattern analysis (MVPA) for image recognition to catalog the human brain activities is scarce. Moreover, experiment design is a complex procedure and selection of image type, color and order is challenging too. Thus, this research bridge the gap by using MVPA to create taxonomy of human brain activity for different categories of images, both colored and gray scale. In this regard, experiment is conducted through EEG testing technique, with feature extraction, selection and classification approaches to collect data from prequalified criteria of 25 graduates of University Technology PETRONAS (UTP). These participants are shown both colored and gray scale images to record accuracy and reaction time. The results showed that colored images produces better end result in terms of accuracy and response time using wavelet transform, t-test and support vector machine. This research resulted that MVPA is a better approach for the analysis of EEG data as more useful information can be extracted from the brain using colored images. This research discusses a detail behavior of human brain based on the color and gray scale images for the specific and unique task. This research contributes to further improve the decoding of human brain with increased accuracy. Besides, such experiment settings can be implemented and contribute to other areas of medical, military, business, lie detection and many others.
Maria Yousaf Lodhi,Khalid Mahmood,Azhar Mahmood,Huma Malik,Muhammad Farooq Warsi,Imran Shakir,M. Asghar,Muhammad Azhar Khan 한국물리학회 2014 Current Applied Physics Vol.14 No.5
In this work cobalt substituted magnesium zinc nanocrystalline spinel ferrites having general formula Mg0.5CoxZn0.5xFe2O4 where x ¼ 0.1, 0.2, 0.3, 0.4, 0.5 were synthesized using micro-emulsion technique. The Co substituted samples annealed at 700 C and characterized by various characterization techniques, such as X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), dielectric measurements and vibrating sample magnetometer (VSM). XRD analysis confirmed single phase spinel structure and the crystalline size calculated by Scherrer’s formula found to be in 21.38e45.5 nm range. The lattice constant decreases as substitution of Co is increased. The decrease in lattice constant is attributed to the smaller ionic radius of cobalt as compared to zinc ion. The FTIR spectra reveled two prominent frequency bands in the wave number range 400e600 cm1 which confirm the cubic spinel structure and completion of chemical reaction. The dielectric parameters were observed to decrease with the increased Co contents. The peaking behavior was observed beyond 1.8 GHz. The frequency dependent dielectric properties of all these nanomaterials have been explained qualitatively in accordance with Koop’s phenomenological theory. Magnetic studies revealed that the coercivity (Hc) attains maximum value of 818 Oe at w21 nm. The increasing trend of magnetic parameters (coercivity and retentivity) is consistent with crystallinity. The crystallite size is small enough to attain considerable signal to noise ratio in high density recording media. The optimized magnetic parameters suggest that the material with composition Mg0.5Co0.5Fe2O4 may have potential applications in high density recording media.