A photonic-crystal optical antenna for extremely large local-field enhancement

We propose a novel design of an all-dielectric optical antenna based on photonic-band-gap confinement. Specifically, we have engineered the photonic-crystal dipole mode to have broad spectral response (Q ~70) and well-directed vertical-radiation by introducing a plane mirror below the cavity. Considerably large local electric-field intensity enhancement ~4,500 is expected from the proposed design for a normally incident planewave. Furthermore, an analytic model developed based on coupled-mode theory predicts that the electric-field intensity enhancement can easily be over 100,000 by employing reasonably high-Q (~10,000) resonators

Recital: Korea\u27s Kim Trio: Won-Mi Kim, Piano; Won-Joo Kim, Violin; Won-Hee Kim, Cello; September 3, 1974

Centennial East Recital HallTuesday EveningSeptember 3, 19748:15 p.m

COMPARATIVE ANALYSIS OF YANG HAK-SEON VAULT AND TSUKAHARA 1260? VAULT IN GYMNASTICS

The study was a single-subject study on the top-elite vaulter in the world. This study was purposed to compare kinematic differences between Yang Hak-Seon vault (Yang-1) and Tsukahara 1260? vault (Yang-2) performed by Yang Hak-Seon. Fourteen high-speed cameras were used to capture a whole body segment motion of Yang-2 vault during the practice session. Yang-1 vault showed faster CM vertical velocity until the vault table takeoff and faster CM horizontal velocity prior to the vault table touchdown. However, the trunk rotation angle and its angular velocity of Yang-2 vault exceeded Yang-1 vault significantly. This might be due to a half turn off the springboard onto the vault table of Yang-2 vault, which resulted in larger initial angular momentum at the vault table touchdown and further increase in angular velocity during the vault table contact

Improvisation of classification performance based on feature optimization for differentiation of Parkinson’s disease from other neurological diseases using gait characteristics

Most neurological disorders that include Parkinson’s disease (PD) as well as other neurological diseases such as Amyotrophic Lateral Sclerosis (ALS) and Huntington’s disease (HD) have some common abnormalities regarding the movement, vocal, and cognitive behaviors of sufferers. Variations in the manifestation of these types of abnormality help distinguish one disorder from another. In this study, differentiation was performed based on the gait characteristics of patients afflicted by different neurological disorders. In the recent past, many researchers have applied different machine learning and feature selection techniques to the classification of different groups of patients based on common abnormalities. However, in an era of modernization where the focus is on timely low-cost automatization and pattern recognition, such techniques require improvisation to provide high performance. We attempted to improve the performance of such techniques using different feature optimization methods, such as a genetic algorithm (GA) and principal component analysis (PCA), and applying different classification approaches, i.e., linear, nonlinear, and probabilistic classifiers. In this study, gait dynamics data of patients suffering with PD, ALS, and HD were collated from a public database, and a binary classification approach was used by taking PD as one group and adopting ALS+HD as another group. Performance comparison was achieved using different classification techniques that incorporated optimized feature sets obtained from GA and PCA. In comparison with other classifiers using different feature sets, the highest accuracy (97.87%) was obtained using random forest combined with GA-based feature sets. The results provide evidence that could assist medical practitioners in differentiating PD from other neurological diseases using gait characteristics