stochastic signal 中文意思是什麼

stochastic signal 解釋
隨機信號
  • stochastic : adj. 1. 機會的；有可能性的;隨便的。2. 【數學】隨機的。
  • signal : n 1 信號，暗號；信號器。2 動機，導火線 （for）。3 預兆，徵象。adj 1 暗號的，作信號用的。2 顯著的...
  1. Numerical simulation of stochastic resonance in bistable system for detecting weak signal

    隨機共振在微弱信號檢測中的數值模擬
  2. When the input noise is the single - peak gaussian noise, the noise can improve the signal correlation only for the sub - threshold signal, i. e. subthreshold stochastic resonance ( sr ) exists

    當輸入噪聲為單峰高斯噪聲時，輸入信號在閾下時噪聲才能改善信號的相關性，即隨機諧振現象存在。
  3. However, when the input noise is the double - peak gaussian mixture noise, the noise can improve the signal correlation for the subthreshold signal and the suprathreshold signal, i. e., both sr and suprathreshold stochastic resonance ( ssr ) often occurs

    而當輸入噪聲為雙峰高斯混合噪聲時，不僅輸入信號在閾下時隨機諧振現象有時存在，而且輸入信號在閾上時噪聲往往也能改善信號的相關性，即閾上隨機諧振現象存在。
  4. Xu bohou, li huafeng, duan fabing and li jianlong. the application of parameter - tuning stochastic resonance in signal processing, accepted by the fifth int. conf. on stochastic structural dynamics

    李華鋒，鮑榮浩，徐博侯.應用隨機共振進行海洋噪聲背景下的信號檢測.浙江大學學報（工學版） ，收錄
  5. Real time seafloor tracking technique is the critical technique to ensure smooth seafloor surveying with full coverage and high efficiency. after detailed investigation on stochastic features of seabed reverberation produced by mbss systems, the author presented an algorithm and a set of relevant key coefficients for real time seafloor tracking, taking into account of characteristics of signal processing and timing sequence of the real system and introducing theorems of terrain surveying. as an achievement, a mathematical model was established based on the technique of centered filtering

    海底地形實時跟蹤技術是保證多波束測深系統實現高效率全覆蓋水下地形測量的核心技術，作者通過對多波束測深系統海底回波信號統計特性的研究，依據地形測量理論，並結合實際系統信號處理的技術特點和處理時序，提出了實現海底跟蹤控制的關鍵參數及計算方法，建立了基於中值濾波技術的海底地形實時跟蹤數學模型，並開發出實時地形跟蹤專家系統，該系統經多次海上實驗驗證表明：理論正確、方案可行，取得良好效果。
  6. Considered the tiny quantity of the hydrogen, first we choose the gas chromatography technology to analyze the component of the work gas and to obtain the chromatography curve. then, treat the curve with the adaptive aperiodic stochastic resonance algorithm in order to eliminate the apparatus noise submerged in the tiny hydrogen signal. at last, calculate the quantity of the hydrogen based on that the quantity varies directly as a function of the area of the chromatography curve

    考慮到氫的含量非常微弱，在測量時採用氣相色譜法，並通過自適應非線性隨機共振演算法對色譜信號進行處理，以便提取出被儀器噪聲淹沒了的微弱氫信號的色譜曲線，最後根據氫含量與其色譜峰面積呈正比計算出工作氣體中的氫含量。
  7. Such organic combination of the determined signal analytical method for potential field analytical continuation with the stochastic signal analytical method for principal component analysis provides a new channel for the inversion of geophysical potential field

    這種將位場解析延拓的確定信號分析法與主成份分析的隨機信號分析法有機聯合，為解決地球物理位場反演問題提供了一種途徑。
  8. Stochastic resonance has attracted the attentions in many fields of science in recent decades, but it is a new method and theory in signal processing. in context of signal processing, for signal transmission by nonlinear systems, stochastic resonance is commonly described as an increase hi the signal - to - noise ratio ( snr ) at the output, which is obtained through an increase of noise level or tuning system parameters

    從信號處理的角度來講，隨機共振是在非線性系統信號處理中，輸入為強噪聲背景下的微弱信號，系統輸出以適宜的物理量來衡量，如信噪比，通過調節輸入噪聲強度或系統參數，都可使得系統輸出信噪比達到一個最大值，此時，稱信號、噪聲和非線性系統所產生的協同現象為隨機共振。
  9. A scheme to identify the stochastic signal and its modes by computer is introduced and using the method of the autocorrelation functions for recognizing the stochastic audio signal fleet has been applied

    摘要分析了利用自相關函數法實現快速識別隨機音頻信號，介紹一種用計算機實現隨機音頻信號處理與模式識別的硬體結構和程序設計。
  10. In the second, in allusion to non - stationary the characteristic of the signal, the author introduces to the method that using empirical mode decomposition to analysis the vibration signal so that the signal are made up of some intrinsic mode function, after this process, we can use stochastic subspace identification to identification the mode parameter of the structure and find the same work frequency

    其次，針對氣閥振動信號的非平穩特點，本文採用了經驗模式分解法（ empiricalmodedecomposition ）對振動信號進行分析處理，使之成為若干個基本模式函數imf （ intrinsicmodefunction ）和一個殘余量的線性組合。接著採用隨機子空間參數識別法對各個基本模式函數其進行結構參數識別，同時找出各種狀態的共同工作頻帶。
  11. In addition, this paper discusses the application of the parameter - induced stochastic resonance in the m - ary pam signal transmission, and explains the mechanism to stochastic resonance in a new view. we found that the single well is able to distinguish different signal levels, based on which the theory for m - ary pam signal transmission via parameter - induced stochastic resonance was briefly developed. the error code rate of m - ary pam signal was obtained

    此外，本文還初步探討了參數誘導的隨機共振在多進制數字調制信號傳輸中的應用，從不同的觀點解釋隨機共振形成的機理，充分認識到非線性系統單勢井的信號處理能力，並給出多進制信號誤碼率的理論公式，模擬實驗表明這個研究方向具有很好的研究前景。
  12. Traditional signal processing decomposes the signal into two poles : deterministic part and stochastic part

    傳統的信號處理認為信號由確定性信號和隨機信號這兩個對立的部分組成。
  13. As a statistics model, hidden markov model ( hmm ) have been widely used in pattern recognition and stochastic signal processing

    隱馬爾科夫模型（ hiddenmarkovmodel ，簡記為hmm ）作為一種統計模型，在模式識別與隨機信號處理中有著廣泛的應用。
  14. The deducing of the algorithms has very practical value in state estimation for systems under the complex environments. in the instance of complicated multi - channel system with multiplicative noise, the dissertation discusses the optimal estimation of state filtering and smoothing and the stochastic input signal with the technique of innovation and projection theorem of hilbert space. the main study of the dissertation is introduced as follows : 1 according to the practical requirement of complicated multi - channel system with multiplicative noise, the dissertation broadens rajasekaran filtering algorithm

    本文針對復雜多通道帶乘性噪聲系統，應用新息的方法和hilbert空間的投影定理，對狀態最優濾波和平滑估計、隨機輸入信號的最優估計等理論與應用方面的問題，進行了進一步的探討，著重完成了以下工作：第一，根據復雜多通道乘性噪聲系統問題的實際需要，推廣了rajasekaran濾波演算法。
  15. It works often in burst mode. the signal channel is complex and large. several protocols can be applied to the system : fixed addressing, committed addressing and stochastic addressing ( aloha ) protocols

    系統使用的多址協議可以是固定分配地址的、按需分配地址的或者是隨機接入的，這三種方式中，當網路用戶數量大而業務量小，用戶間歇工作時候，隨機接入多址技術簡單而高效。
  16. With the characteristics of large ambient noise, very narrow bandwidth, low carrier frequency, great propagation latency and time - space - frequency variant multipath effect ( mpe ), the stochastic ocean channel has demonstrated the greatest complexity and difficulty for underwater acoustic wireless communications. among them multipath effect ( mpe ) is the most difficult obstacle that results in signal fading and inter - symbol interference ( isi )

    但是噪聲高、帶寬窄、載波頻率低、傳輸時延大、多途徑效應隨時間?空間?頻率變化等通道特性都會給有效、可靠的水聲通信帶來很大的麻煩，其中多途徑效應是最主要的困擾因素，它會導致信號幅度衰落和碼間干擾。
  17. Parameter - tuning stochastic resonance ( psr ) is a more realistic way to handle the phenomenon of sr in a broad sense. based on the theory of psr, some key problems of using sr in signal processing are studied, these issues include : the application of intrawell sr in multi - frequency analog signal processing, recovery of the waveform distortion caused by the bistable system and other post treatments, measure of system performance with multi - frequency analog digital input and measurement of system performance of binary digital input. the results are used in signal detecting under the background of the real sea noise

    隨著參數調節隨機共振（ psr ）概念的提出，隨機共振在信號處理中的應用有著很大拓展的空間，本文就主要研究幾個目前隨機共振在信號處理中應用時經常碰到的問題，其中包括阱內隨機共振現象對多頻模擬信號處理的應用、雙穩態系統輸出的波形畸變的消除及其他后處理、適用於多頻信號的輸出性能衡量指標、二進制數字信號輸入情況下的系統輸出的性能衡量，並將所得的結果應用到了一個自然界的噪聲? ?海洋噪聲背景下的信號檢測中，這些研究對于隨機共振理論的進一步發展及其在非線性信號處理中的應用具有重要的意義。
  18. The stochastic resonance phenomenon in a bistable system under the simultaneous action of multiplicative and additive noise and periodic signal is studied by using the theory of signal - to - noise ( snr ) ratio in the adiabatic limit

    利用隨機共振理論，我們研究了在乘性和加性噪聲以及周期性驅動力共同作用下的雙穩系統的隨機共振現象。
  19. However there are some signals different from the two poles, named chaotic signals, which are generated by a determinate system but acting similar to stochastic signal and can not be forecasted in long term

    而混沌信號有別于這兩大類信號，它由確定性系統產生，但行為卻貌似隨機信號，具有長期不可預測性。
  20. Hidden markov models ( hmm ) have been widely used in pattern recognition and stochastic signal processing in recent years, and the best examples are speech recognition and character recognition

    近年來，隱馬爾可夫模型（ hiddenmarkovmodel ，簡記為hmm ）在模式識別與隨機信號處理中有著最廣泛的應用，最成功的例子如語音識別和文字識別。
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