Copper(II) complexes of N(4)-substituted thiosemicarbazones derived from pyridine-2-carbaldehyde: Crystal structure of a binuclear complex

Ten copper(II) complexes {[CuL1Cl] (1), [CuL1NO3]2 (2), [CuL1N3]2 · 2/3H2O (3), [CuL1]2(ClO4)2 · 2H2O (4), [CuL2Cl]2 (5), [CuL2N3] (6), [Cu(HL2)SO4]2 · 4H2O (7), [Cu(HL2)2] (ClO4)2 · 1/2EtOH (8), [CuL3Cl]2 (9), [CuL3NCS] · 1/2H2O (10)} of three NNS donor thiosemicarbazone ligands {pyridine-2-carbaldehyde-N(4)-p-methoxyphenyl thiosemicarbazone [HL1], pyridine-2-carbaldehyde-N(4)-2-phenethyl thiosemicarbazone [HL2] and pyridine-2-carbaldehyde N(4)-(methyl), N(4)-(phenyl) thiosemicarbazone [HL3]} were synthesized and physico-chemically characterized. The crystal structure of compound 9 has been determined by X-ray diffraction studies and is found that the dimer consists of two square pyramidal Cu(II) centers linked by two chlorine atoms.

Structural and spectral studies of novel Co(III) complexes of N(4)-substituted thiosemicarbazones derived from pyridine-2-carbaldehyde

Seven bis(ligand) Co(III) complexes {[CoL21] NO3 · H2 O (1), [CoL21] Cl · 2 H2 O (2),[CoL21] ClO4 (3), [CoL22] NO3 (4), [CoL22] Cl · 2 H2 O (5), [CoL23] Br · 2 H2 O (6), [CoL23] ClO4 · H2 O (7)} of three thiosemicarbazone ligands {pyridine-2-carbaldehyde-N(4)-p-methoxyphenyl thiosemicarbazone [HL1], pyridine-2-carbaldehyde-N(4)-2-phenylethyl thiosemicarbazone [HL2] and pyridine-2-carbaldehyde-N(4)-(methyl),N(4)-(phenyl) thiosemicarbazone [HL3]} were synthesized and physico-chemically characterized. All complexes are assigned octahedral geometries on the basis of spectral studies. The ligands deprotonate and coordinate by means of pyridine nitrogen, azomethine nitrogen, and thiolate sulfur atoms. The single crystal X-ray structures of HL3 and two nitrate compounds are discussed. The structural studies corroborate the spectral characterization.

Investigations On The Development Of An Ann Model & Visual Manipulation Approach For 2-D Dft Computation In Image Processing

This thesis is an outcome of the investigations carried out on the development of an Artificial Neural Network (ANN) model to implement 2-D DFT at high speed. A new definition of 2-D DFT relation is presented. This new definition enables DFT computation organized in stages involving only real addition except at the final stage of computation. The number of stages is always fixed at 4. Two different strategies are proposed. 1) A visual representation of 2-D DFT coefficients. 2) A neural network approach. The visual representation scheme can be used to compute, analyze and manipulate 2D signals such as images in the frequency domain in terms of symbols derived from 2x2 DFT. This, in turn, can be represented in terms of real data. This approach can help analyze signals in the frequency domain even without computing the DFT coefficients. A hierarchical neural network model is developed to implement 2-D DFT. Presently, this model is capable of implementing 2-D DFT for a particular order N such that ((N))4 = 2. The model can be developed into one that can implement the 2-D DFT for any order N upto a set maximum limited by the hardware constraints. The reported method shows a potential in implementing the 2-D DF T in hardware as a VLSI / ASIC