A Pd-24 Pregnant Molecular Nanoball: Self-Templated Stellation by Precise Mapping of Coordination Sites

We found that Pd(II) ion (M) and the smallest 120 bidentate donor pyrimidine (L-a) self-assemble into a mononuclear M(L-a)(4) complex (1a) instead of the expected smallest M-12(L-a)(24) molecular ball (1), presumably due to the weak coordination nature of the pyrimidine. To construct such a pyrimidine bridged nanoball, we employed a new donor tris(4-(pyrimidin-5-yl)phenyl)amine (L); which upon selective complexation with Pd(II) ions resulted in the formation of a pregnant M24L24 molecular nanoball (2) consisting of a pyrimidine-bridged Pd-12 baby-ball supported by a Pd-12 larger mother-ball. The formation of the baby-ball was not successful without the support of the mother-ball. Thus, we created an example of a self-assembly where the inner baby-ball resembling to the predicted M-12(L-a)(24) ball (1) was incarcerated by the giant outer mother-ball by means of geometrical constraints. Facile conversion of the pregnant ball 2 to a smaller M-12(L-b)(24) ball 3 with dipyridyl donor was achieved in a single step.

Anomaly detection in compressed H.264/AVC video

Real time anomaly detection is the need of the hour for any security applications. In this article, we have proposed a real time anomaly detection for H.264 compressed video streams utilizing pre-encoded motion vectors (MVs). The proposed work is principally motivated by the observation that MVs have distinct characteristics during anomaly than usual. Our observation shows that H.264 MV magnitude and orientation contain relevant information which can be used to model the usual behavior (UB) effectively. This is subsequently extended to detect abnormality/anomaly based on the probability of occurrence of a behavior. The performance of the proposed algorithm was evaluated and bench-marked on UMN and Ped anomaly detection video datasets, with a detection rate of 70 frames per sec resulting in 90x and 250x speedup, along with on-par detection accuracy compared to the state-of-the-art algorithms.