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In this paper, the sparse sensor placement problem for least-squares estimation is considered, and the previous novel approach of the sparse sensor selection algorithm is extended. The maximization of the determinant of the matrix which appears in pseudo-inverse matrix operations is employed as an objective function of the problem in the present extended approach. The procedure for the maximization of the determinant of the corresponding matrix is proved to be mathematically the same as that of the previously proposed QR method when the number of sensors is less than that of state variables (undersampling). On the other hand, the authors have developed a new algorithm for when the number of sensors is greater than that of state variables (oversampling). Then, a unified formulation of the two algorithms is derived, and the lower bound of the objective function given by this algorithm is shown using the monotone submodularity of the objective function. The effectiveness of the proposed algorithm on the problem using real datasets is demonstrated by comparing with the results of other algorithms. The numerical results show that the proposed algorithm improves the estimation error by approximately 10% compared with the conventional methods in the oversampling case, where the estimation error is defined as the ratio of the difference between the reconstructed data and the full observation data to the full observation. For the NOAA-SST sensor problem, which has more than ten thousand sensor candidate points, the proposed algorithm selects the sensor positions in few seconds, which required several hours with the other algorithms in the oversampling case on a 3.40 GHz computer.

Arterial wall enhancement is related to aging and is probably due to neovascularity in association with atherosclerotic plaques. This finding may permit assessment of intracranial atherosclerosis and other vascular diseases.

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From lysozyme digests of N-acetylated cell walls of Bacillus cereus AHU 1030, two acidic polymer fractions with molecular weights of about 24000 and 45000 were isolated by ion-exchange chromatography and gel chromatography. These polymer fractions, containing glycerol, phosphorus and glucose in a molar ratio of 1.00 : 1.00 :0.85 together with small amounts of glycopeptide components and mannosamine, were characterized as teichoic-acidglycopeptide complexes with one and two teichoic acid chains made of 60 -65 repeating glycerol phosphate units that were mostly glucosylated. Mild alkali treatment of the complexes yielded a disaccharide-linked glycopeptide. The disaccharide was liberated from the glycopeptide by mild acid treatment and identified as N-acetylmannosaminyl(P 1 +4jN-acetylglucosamine. On the other hand, the same disaccharide linked to the teichoic acid chain was obtained by direct heating of the cell walls at pH 2.5. These results lead to a conclusion that in the cell walls of this strain the glycerol teichoic acid chain is attached to the glycan chain of peptidoglycan through this disaccharide unit. The disaccharide is linked at its reducing and nonreducing ends to the glycan chain and the teichoic acid chain, respectively, through phosphodiester bridges.