n. 名詞 〈蘇格蘭語〉冷海霧〈伴隨有毛毛雨的濕冷海霧〉。


    1. Application of haar wavelet on analysis of vibration signal of rotating machinery in fast run - up state

    2. The martingale analysis in the multiresolution analysis under the haar scaling function and its application in the signal processing

    3. Its basic thought is that using amass of simple classifier which has common classified ability and through thecertain method , at last , constitutes a very strong classifier which has strongclassified ability carries on many times with this strong classifier to the goalpictures , finally confirmed the pictures whether includes the human face andits the general position this algorithm uses a characteristic which called haar characteristic thischaracteristic is one kind of simple rectangular characteristic , because it issimilar with the haar wavelet , so called haar characteristic this kind ofcharacteristic is composed of two or many rectangles that are congruent andneighboring there are white and black kinds of rectangles in the characteristictemplate, and defines this characteristic template characteristic value as thewhite rectangle this characteristic value is that the difference between white

    4. This thesis makes a fairly in - depth study on the basic techniques about the current svc algorithms by reading a lot of relevant references, including the svc system schemes and the techniques of various scalabilities. based on these knowledge, motion compensated temporal filtering ( mctf ) which is an effective method to eliminate temporal redundancy is researched, and mctf based on db2 wavelet is presented by studying the method of mctf using haar wavelet. the simulation proves that the low - pass frame is better than the haar one, it means that the temporal scalability is improved

      在此基礎上,本文研究了在時間可伸縮性中採用的一種有效方法:運動補償時域濾波( mctf )技術,並借鑒haar小波進行運動補償時域的方法,研究並實現了基於db2小波的運動補償時域濾波方法,模擬結果表明該方法比基於haar小波的方法得到了更高質量的低通幀,這也意味著在時間可伸縮性的處理上有了進一步的改善。
    5. At last, combining the related knowledge of wavelet theory and hidden markov models, we introduce wavelet transformation for nonparametric estimation of hmm ' s and discuss how to choose resolving scale of haar - wavelet orthogonal series " estimation