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A tetranuclear mixed ligand copper(II) complex of a pyrazole containing Schiff base and a hydroxyhexahydropyrimidylpyrazole and copper(II) and nickel(II) complexes of the Schiff base having N-donor atoms have been investigated. A 2 equiv amount of 5-methyl-3-formylpyrazole (MPA) and 2 equiv of 1,3-diamino-2-propanol (1,3-DAP) on reaction with 1 equiv of copper(II) nitrate produce an unusual tetranuclear mixed ligand complex [Cu4(L1)2(L2)2(NO3)2] (1), where H2L1 = 1,3-bis(5-methyl-3-formylpyrazolylmethinimino)propane-2-ol and HL2 = 5-methyl-3-(5-hydroxyhexahydro-2-pyrimidyl)pyrazole. In contrast, a similar reaction with nickel(II) nitrate leads to the formation of a hygroscopic intractable material. On the other hand, the reaction involving 2 equiv of MPA and 1 equiv each of 1,3-DAP and various copper(II) salts gives rise to two types of products, viz. [Cu(T3-porphyrinogen)(H2O)]X2 (X = ClO4, NO3, BF4 (2)) (T3-porphyrinogen = 1,6,11,16-tetraza-5,10,15,20-tetrahydroxy-2,7,12,17-tetramethylporphyrinogen) and [Cu(H2L1)X]X x H2O (X = Cl (3), Br (4)). The same reaction carried out with nickel(II) salts also produces two types of compounds [Ni(H2L1)(H2O)2]X2 [X = ClO4 (5), NO3 (6), BF4 (7)] and [Ni(H2L1)X2] x H2O [X = Cl (8), Br (9)]. Among the above species 1, 3, and 5 are crystallographically characterized. In 1, all four copper atoms are in distorted square pyramidal geometry with N4O chromophore around two terminal copper atoms and N5 chromophore around two inner copper atoms. In 3, the copper atom is also in distorted square pyramidal geometry with N4Cl chromophore. The nickel atom in 5 is in a distorted octahedral geometry with N4O2 chromophore, where the metal atom is slightly pulled toward one of the axial coordinated water molecules. Variable-temperature (300 to 2 K) magnetic susceptibility measurements have been carried out for complex 1. The separations between the metal centers, viz., Cu(1)...Cu(2), Cu(2)...Cu(2)A, and Cu(2)A...Cu(1)A are 3.858, 3.89, and 3.858 A, respectively. The overall magnetic behavior is consistent with strong antiferromagnetic interactions between the spin centers. The exchange coupling constants between Cu(1)...Cu(2) and Cu(2)...Cu(2A) centers have turned out to be -305.3 and -400.7 cm(-1), respectively, resulting in a S = 1/2 ground state. The complexes are further characterized by UV-vis, IR, electron paramagnetic resonance, and electrochemical studies.

The reactions of [VO(acac)2] with bis(salicylaldehyde)-oxaloyldihydrazone (H4L) and an alkali metal carbonate M2CO3 (M = K, Na, Cs), in EtOH/H2O medium upon reflux, resulted in the generation of three new heterometallic V(V)/M materials, namely the 1D [(VO2)2(μ4-L){Na2(μ-H2O)2(H2O)2}]n (1), 2D [{V(μ-O)2}2(μ4-L){K2(μ-H2O)2(H2O)2}]n (2), and 3D [{V(μ-O)(μ3-O)}2(μ8-L){Cs2(μ-H2O)2(H2O)2}]n (3) coordination polymers. They were isolated as air-stable solids and fully characterized by IR, UV-vis, (1)H, and (51)V NMR spectroscopy, ESI-MS(±), elemental, thermal, and single-crystal X-ray diffraction analyses, the latter showing that 1-3 are constructed from the resembling [(VO2)2(μ(4/8)-L)](2-) blocks assembled by the differently bound aqua-metal [M2(μ-H2O)2(H2O)2](2+) moieties (M = Na, K, Cs). The main distinctive features of 1-3 arise from the different coordination numbers of Na (5), K (7), and Cs (9) atoms, thus increasing the complexity of the resulting networks from the ladder-like 1D chains in 1 to double 2D layers in 2, and layer-pillared 3D framework in 3. The topological analysis of 2 disclosed a uninodal 4-connected underlying net with a rare kgm [Shubnikov plane net (3.6.3.6)/kagome pattern] topology, while 3 features a trinodal 4,7,8-connected underlying net with an unprecedented topology. Compounds 1-3 also show solubility in water (S(25 °C) ≈ 4-7 mg mL(-1)) and were applied as efficient precatalysts for the homogeneous oxidation of cyclohexane by aqueous H2O2, under mild conditions (50 °C) in MeCN/H2O medium and in the presence of an acid promoter. Total yields (based on substrate) of cyclohexanol and cyclohexanone up to 36% and turnover numbers (TONs) up to 5700 were achieved.

The appropriate selection of distinctive keyframes to represent the salient contents of a video is a critical task in video processing applications that rely on content analysis or information retrieval. Although many of the existing keyframe selection techniques perform satisfactorily in capturing salient visual contents, they often fail to adequately highlight the changes in visual information brought about by motion of objects between frames. In this paper, we propose a technique for keyframe selection by formulating the dissimilarity between the frames of a video shot in terms of the change in orientations that the corresponding objects of the two frames have undergone due to motion. This is accomplished by steerable filtering of the frames in order to extract the information about the local orientation of pixels within each frame. The frame to frame dissimilarity is adaptively thresholded over a group of frames in order to select the keyframes. In essence, keyframes are selected at the temporal instances where the change in orientation attains local maxima. Our keyframe selection methodology is specifically relevant to video colourization due to the fact that the keyframes that are to be employed for colourization must be chosen such that they capture all orientational changes effectively, while ensuring adequate content coverage.

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ABSTRACT:Mobile augmented reality system is the next generation technology to visualise 3D real world intelligently. The technology is expanding at a fast pace to upgrade the status of a smart phone to an intelligent device. The research problem identified and presented in the current work is to view actual dimensions of various objects that are captured by a smart phone in real time. The methodology proposed first establishes correspondence between LiDAR point cloud, that are stored in a server, and the image t hat is captured by a mobile. This correspondence is established using the exterior and interior orientation parameters of the mobile camera and the coordinates of LiDAR data points which lie in the viewshed of the mobile camera. A pseudo intensity image is generated using LiDAR points and their intensity. Mobile image and pseudo intensity image are then registered using image registration method SIFT thereby generating a pipeline to locate a point in point cloud corresponding to a point (pixel) on t he mobile image. The second part of the method uses point cloud data for computing dimensional information corresponding to the pairs of points selected on mobile image and fetch the dimensions on top of the image. This paper describes all steps of the proposed method. The paper uses an experimental setup to mimic the mobile phone and server system and presents some initial but encouraging results.