Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2
Search results for: U.Venu
2 Optimization of Quantization in Higher Order Modulations for LDPC-Coded Systems
Authors: M.Sushanth Babu, P.Krishna, U.Venu, M.Ranjith
Abstract:
In this paper, we evaluate the choice of suitable quantization characteristics for both the decoder messages and the received samples in Low Density Parity Check (LDPC) coded systems using M-QAM (Quadrature Amplitude Modulation) schemes. The analysis involves the demapper block that provides initial likelihood values for the decoder, by relating its quantization strategy of the decoder. A mapping strategy refers to the grouping of bits within a codeword, where each m-bit group is used to select a 2m-ary signal in accordance with the signal labels. Further we evaluate the system with mapping strategies like Consecutive-Bit (CB) and Bit-Reliability (BR). A new demapper version, based on approximate expressions, is also presented to yield a low complexity hardware implementation.Keywords: Low Density parity Check, Mapping, Demapping, Quantization, Quadrature Amplitude Modulation
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17361 Similarity Measure Functions for Strategy-Based Biometrics
Authors: Roman V. Yampolskiy, Venu Govindaraju
Abstract:
Functioning of a biometric system in large part depends on the performance of the similarity measure function. Frequently a generalized similarity distance measure function such as Euclidian distance or Mahalanobis distance is applied to the task of matching biometric feature vectors. However, often accuracy of a biometric system can be greatly improved by designing a customized matching algorithm optimized for a particular biometric application. In this paper we propose a tailored similarity measure function for behavioral biometric systems based on the expert knowledge of the feature level data in the domain. We compare performance of a proposed matching algorithm to that of other well known similarity distance functions and demonstrate its superiority with respect to the chosen domain.Keywords: Behavioral Biometrics, Euclidian Distance, Matching, Similarity Measure.
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