The results showed that the universal probe could detect all the phytoplasmas used, while no signal was detected for bacteria.
结果表明，用广谱探针可检测到所有植原体产生荧光信号，而细菌不产生荧光信号。
The states of Viterbi decoding were reduced through the state combination. The signal was detected combined with the decision feedback.
在序列检测阶段通过状态融合减少维特比译码的状态数，然后结合判决反馈进行检测。
Finally after more than a month, a signal was detected coming from the original radio source on Ganymede and incredibly it was in Morse Code.
最后经过一个月多，信号检测来自木卫三的原始来源电台和令人难以置信的是它的代码在莫尔斯。
A thermometer in the office detected when it was getting too cold and sent a signal to the network requesting that calculations be diverted to the servers in the office.
办公室里有一个温度计，当温度太低时，它发送请求信号到网上，服务器开始进行计算。
Sadly, the Wow! Signal was never again to be detected.
可惜的是，这一喔信号之后再也没有被探测到过。
The real-time RAM results also showed that as many as 10 bacteria can be detected and the time of appearance of detectable signal was depended on the target concentration.
瞬时RAM结果也表明最低能检测10个细菌，检测信号的出现依赖于样品的浓度。
Differential rank order impulse detector was improved for the impulse noise's detection. The impulse noise was detected more exactly and signal details were preserved.
在检测脉冲噪声时，本文改进了差分排序脉冲噪声检测法，改进后的方法提高了脉冲噪声检测的准确率，保护了信号的细节信息。
Basing on the signal space model, the harmonics in power system was detected by combining the subspace rotational invariance and least squares method.
该方法以信号空间模型为基础，结合子空间旋转不变性和最小二乘法实现了电力系统的谐波检测。
Transforming the output of frequency to the magnetic field through the feedback of Lorentz force, this device was capacitively excited and the signal was capacitively detected.
采用静电激励和静电检测，通过罗伦兹力的反馈将磁场的大小转变为频率输出。
Even better, the LABS detected a second round of ripples a few months later, confirming that the signal was no fluke.
还有更好的消息，几个月后实验室又探测到了第二轮引力波，证明了波动信号并非偶然。
Results: Positive SARS samples were detected by oligonucleotide array and fluorescence signal intensity was highly related to the probe's second structure.
结果：SARS阳性样品与寡核苷酸芯片杂交后出现阳性杂交信号，具有不同二级结构的寡核苷酸探针杂交信号强度不一。
It was applied in the control system of prosthesis hand, the detected signal was recognized by neural network, 6 action patterns can be classified, the success rate is above 95%.
将其应用于假肢手的控制系统中，通过神经网络进行动作模式识别，共识别了6个手部动作模式，识别成功率在95%以上。
It was applied in the control system of prosthesis hand, the detected signal was recognized by neural network, 6 action patterns can be classified, the success rate is above 95%.
将其应用于假肢手的控制系统中，通过神经网络进行动作模式识别，共识别了6个手部动作模式，识别成功率在95%以上。
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