Hashing and Trie tree data structures are among the preeminent data mining techniques considered for the ideal search. Hashing techniques have the amortized time complexity of O(1). Although in worst case, searching a...Hashing and Trie tree data structures are among the preeminent data mining techniques considered for the ideal search. Hashing techniques have the amortized time complexity of O(1). Although in worst case, searching a hash table can take as much as θ(n) time [1]. On the other hand, Trie tree data structure is also well renowned data structure. The ideal lookup time for searching a string of length m in database of n strings using Trie data structure is O(m) [2]. In the present study, we have proposed a novel Prime Box parallel search algorithm for searching a string of length m in a dictionary of dynamically increasing size, with a worst case search time complexity of O(log2m). We have exploited parallel techniques over this novel algorithm to achieve this search time complexity. Also this prime Box search is independent of the total words present in the dictionary, which makes it more suitable for dynamic dictionaries with increasing size.展开更多
To improve the performance of sound source localization based on distributed microphone arrays in noisy and reverberant environments,a sound source localization method was proposed.This method exploited the inherent s...To improve the performance of sound source localization based on distributed microphone arrays in noisy and reverberant environments,a sound source localization method was proposed.This method exploited the inherent spatial sparsity to convert the localization problem into a sparse recovery problem based on the compressive sensing（CS） theory.In this method two-step discrete cosine transform（DCT）-based feature extraction was utilized to cover both short-time and long-time properties of the signal and reduce the dimensions of the sparse model.Moreover,an online dictionary learning（DL） method was used to dynamically adjust the dictionary for matching the changes of audio signals,and then the sparse solution could better represent location estimations.In addition,we proposed an improved approximate l_0norm minimization algorithm to enhance reconstruction performance for sparse signals in low signal-noise ratio（SNR）.The effectiveness of the proposed scheme is demonstrated by simulation results where the locations of multiple sources can be obtained in the noisy and reverberant conditions.展开更多
文摘Hashing and Trie tree data structures are among the preeminent data mining techniques considered for the ideal search. Hashing techniques have the amortized time complexity of O(1). Although in worst case, searching a hash table can take as much as θ(n) time [1]. On the other hand, Trie tree data structure is also well renowned data structure. The ideal lookup time for searching a string of length m in database of n strings using Trie data structure is O(m) [2]. In the present study, we have proposed a novel Prime Box parallel search algorithm for searching a string of length m in a dictionary of dynamically increasing size, with a worst case search time complexity of O(log2m). We have exploited parallel techniques over this novel algorithm to achieve this search time complexity. Also this prime Box search is independent of the total words present in the dictionary, which makes it more suitable for dynamic dictionaries with increasing size.
基金supported by the Doctoral Program of Higher Education of China(20133207120007)the National Natural Science Foundation of China(61405094)+1 种基金the Open Research Fund of Jiangsu Key Laboratory of Meteorological Observation and Information Processing(KDXS1408)the Science and Technology Support Project of Jiangsu Province-Industry(BE2014139)
文摘To improve the performance of sound source localization based on distributed microphone arrays in noisy and reverberant environments,a sound source localization method was proposed.This method exploited the inherent spatial sparsity to convert the localization problem into a sparse recovery problem based on the compressive sensing（CS） theory.In this method two-step discrete cosine transform（DCT）-based feature extraction was utilized to cover both short-time and long-time properties of the signal and reduce the dimensions of the sparse model.Moreover,an online dictionary learning（DL） method was used to dynamically adjust the dictionary for matching the changes of audio signals,and then the sparse solution could better represent location estimations.In addition,we proposed an improved approximate l_0norm minimization algorithm to enhance reconstruction performance for sparse signals in low signal-noise ratio（SNR）.The effectiveness of the proposed scheme is demonstrated by simulation results where the locations of multiple sources can be obtained in the noisy and reverberant conditions.
