The amide A band of protein is sensitive to the hydrogen bands of amide groups of proteins. However, it is hard to distinguish the amide A band of aqueous protein in situ directly, since it overlaps with O-H stretchin...The amide A band of protein is sensitive to the hydrogen bands of amide groups of proteins. However, it is hard to distinguish the amide A band of aqueous protein in situ directly, since it overlaps with O-H stretching vibration of water. In this work, we presented a new analytical method of Raman ratio spectrum, which can extract the amide A band of proteins in water. To obtain the Raman ratio spectrum, the Raman spectrum of aqueous protein was divided by that of pure water. A mathematical simulation was employed to examine whether Raman ratio spectrum is effective. Two kinds of protein, lysozyme and （^-chymotrypsin were employed. The amide A bands of them in water were extracted from Raman ratio spectra. Additionally, the process of thermal denaturation of lysozyme was detected from Raman ratio spectrum. These results demonstrated the Raman ratio spectra could be employed to study the amide A modes of proteins in water.展开更多
Spectrum sensing is the fundamental task for Cognitive Radio (CR). To overcome the challenge of high sampling rate in traditional spectral estimation methods, Compressed Sensing (CS) theory is developed. A sparsity an...Spectrum sensing is the fundamental task for Cognitive Radio (CR). To overcome the challenge of high sampling rate in traditional spectral estimation methods, Compressed Sensing (CS) theory is developed. A sparsity and compression ratio joint adjustment algorithm for compressed spectrum sensing in CR network is investigated, with the hypothesis that the sparsity level is unknown as priori knowledge at CR terminals. As perfect spectrum reconstruction is not necessarily required during spectrum detection process, the proposed algorithm only performs a rough estimate of sparsity level. Meanwhile, in order to further reduce the sensing measurement, different compression ratios for CR terminals with varying Signal-to-Noise Ratio (SNR) are considered. The proposed algorithm, which optimizes the compression ratio as well as the estimated sparsity level, can greatly reduce the sensing measurement without degrading the detection performance. It also requires less steps of iteration for convergence. Corroborating simulation results are presented to testify the effectiveness of the proposed algorithm for collaborative spectrum sensing.展开更多
基金This work was supported by the National Natural Science Foundation of China （No.91127042, No.21103158, No.21273211, No.21473171）, the National Key Basic Research Special Foundation （No.2013CB834602 and No.2010CB923300）, the Fundamental Research Funds for the Central Universities （No.7215623603）, and the Hua-shan Mountain Scholar Program. We also thank Doctor Kang-zhen Tian and Professor Shu-ji Ye for the measurement of IR spectra of aqueous lysozyme.
文摘The amide A band of protein is sensitive to the hydrogen bands of amide groups of proteins. However, it is hard to distinguish the amide A band of aqueous protein in situ directly, since it overlaps with O-H stretching vibration of water. In this work, we presented a new analytical method of Raman ratio spectrum, which can extract the amide A band of proteins in water. To obtain the Raman ratio spectrum, the Raman spectrum of aqueous protein was divided by that of pure water. A mathematical simulation was employed to examine whether Raman ratio spectrum is effective. Two kinds of protein, lysozyme and （^-chymotrypsin were employed. The amide A bands of them in water were extracted from Raman ratio spectra. Additionally, the process of thermal denaturation of lysozyme was detected from Raman ratio spectrum. These results demonstrated the Raman ratio spectra could be employed to study the amide A modes of proteins in water.
基金Supported by the National Natural Science Foundation of China (No. 61102066)China Postdoctoral Science Foundation (No. 2012M511365)the Scientific Research Project of Zhejiang Provincial Education Department (No.Y201119890)
文摘Spectrum sensing is the fundamental task for Cognitive Radio (CR). To overcome the challenge of high sampling rate in traditional spectral estimation methods, Compressed Sensing (CS) theory is developed. A sparsity and compression ratio joint adjustment algorithm for compressed spectrum sensing in CR network is investigated, with the hypothesis that the sparsity level is unknown as priori knowledge at CR terminals. As perfect spectrum reconstruction is not necessarily required during spectrum detection process, the proposed algorithm only performs a rough estimate of sparsity level. Meanwhile, in order to further reduce the sensing measurement, different compression ratios for CR terminals with varying Signal-to-Noise Ratio (SNR) are considered. The proposed algorithm, which optimizes the compression ratio as well as the estimated sparsity level, can greatly reduce the sensing measurement without degrading the detection performance. It also requires less steps of iteration for convergence. Corroborating simulation results are presented to testify the effectiveness of the proposed algorithm for collaborative spectrum sensing.
