The steps leading to the development of a Cyrillic OPAC at Queens Borough Public Library are presented by the authors. Issues covered include Cyrillic character display, font requirements, searching strategies, web br...
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Over the decades, many studies have been done for the prediction of the protein structure. Since the protein secondary structure is closely related to the protein tertiary structure, many approaches begin with the pre...
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ISBN:
(纸本)0819453560
Over the decades, many studies have been done for the prediction of the protein structure. Since the protein secondary structure is closely related to the protein tertiary structure, many approaches begin with the prediction of secondary structure and apply the results to predict the tertiary structure. The recent trend of secondary structure prediction studies is mostly based on the neural network or the support vector machine (SVM). In this study, SVM is used as a machine learning tool for the prediction of secondary structure and several new encoding schemes, including orthogonal matrix, hydrophobicity matrix, BLOSUM62 substitution matrix and combined matrix of these, are developed and optimized to improve the prediction accuracy. Based on the best encoding scheme, each protein sequence is expressed as consecutive sliding windows and each amino acid inside a window is represented with 20 different matrix values. Once the optimal window length for six SVM binary classifiers is chosen to be 13 through many experiments, the new encoding scheme is tested based on this optimal window size with the 7-fold cross validation tests. The results show 2% increase in the accuracy of the binary classifiers when compared with the instances in which the classical orthogonal matrix is used. For the training and testing of the SVM binary classifiers, RS126 data sets is used since this is the common set adopted by the previous research groups. Finally, to combine the results of the six SVM binary classifiers, several existing tertiary classifiers are applied and the efficiency of each tertiary classifier is compared.
Protein secondary structure prediction is very important for drug design, protein engineering and immunological studies. This research uses fully connected multilayer perceptron (MLP) neural network with one, two and ...
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ISBN:
(纸本)0819453560
Protein secondary structure prediction is very important for drug design, protein engineering and immunological studies. This research uses fully connected multilayer perceptron (MLP) neural network with one, two and three hidden layers to predict protein secondary structure. Orthogonal matrix, BLOSUM62 matrix and hydrophobicity matrix are used for input profiles. To increase the input information for neural networks, the combined matrix from BLOSLTM62 and orthogonal matrix and the combined matrix from BLOSUM62 and hydrophobicity matrix are also experimented. Binary classifiers indicate test accuracy of one hidden layer is better than that of two and three hidden layers. This may indicate that increasing complexity of architecture may not help neural network to recognize structural pattern of protein sequence more accurately. The results also show that the combined input profile of BLOSUM62 matrix and orthogonal matrix is the best one among five encoding schemes. While accuracy of the tertiary classifier reaches 63.20%, binary classifier for H/similar toH is 78.70%, which is comparable to other researchers' results.
A high-performance and low-power 32-bit multiply-accumulate unit (MAC) is described in this paper. The fast mixed-length encoding scheme used in the MAC leverages the advantage of a 16-bit encoding scheme without addi...
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A high-performance and low-power 32-bit multiply-accumulate unit (MAC) is described in this paper. The fast mixed-length encoding scheme used in the MAC leverages the advantage of a 16-bit encoding scheme without adding extra delay to the faster four-stage Wallace tree of a 12-bit encoding scheme. With this new encoding scheme, one-cycle throughput for 16-bit x 16-bit and 32-bit x 16-bit MAC instructions was achieved at very high frequencies. To handle media streams more efficiently, the single-instruction-multiple-data (SIMD) and the multiply-with-implicit-accumulate (MIA) features were added. A mixture of static CMOS logic and complementary pass-gate logic (CPL) was used to achieve the high-speed and low-power goals. Several power-saving techniques were also implemented in this MAC.
In this paper I intend to present the concept of superframes and its use, primarily, in multiplexing techniques. The signals are supposed band limited and three multiplexing schemes are considered: Time Division Multi...
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ISBN:
(纸本)0819437646
In this paper I intend to present the concept of superframes and its use, primarily, in multiplexing techniques. The signals are supposed band limited and three multiplexing schemes are considered: Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA) and Frequency Hoping Multiple Access (FHMA). The first two schemes give rise to tight superframes, whereas for FHMA, the associated superframes are more complex. For some such superframes the dual superframe is obtained in closed form. An example of a FHMA scheme is also presented.
A phase-noise-cancelled coherent subcarrier fiber-optic communication system that uses integrated-optic Mach-Zehnder waveguide modulators for frequency-pair encoding is introduced. Optical double-sideband suppressed-c...
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A phase-noise-cancelled coherent subcarrier fiber-optic communication system that uses integrated-optic Mach-Zehnder waveguide modulators for frequency-pair encoding is introduced. Optical double-sideband suppressed-carrier signals with the same random phase noise and information encoded in the frequency separation of the two sidebands are generated by properly biasing the modulator. Phase noise is eliminated at the receiver by a nonlinear operation on the two sidebands. System performance analysis with numerical examples, taking into account the modulation index and laser normalized linewidth, is presented.
A novel encoding scheme of a bidirectional associative memory (BAM) incorporating high-order nonlinearity is proposed. This method significantly improves the storage capacity and error-correcting capability of the BAM.
A novel encoding scheme of a bidirectional associative memory (BAM) incorporating high-order nonlinearity is proposed. This method significantly improves the storage capacity and error-correcting capability of the BAM.
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