Traditional speech detection methods regard the noise as a jamming signal to filter, but under the strong noise background, these methods lost part of the original speech signal while eliminating noise. Stochastic resonance can use noise energy to amplify the weak signal and suppress the noise. According to stochastic resonance theory, a new method based on adaptive stochastic resonance to extract weak speech signals is proposed. This method, combined with twice sampling, realizes the detection of weak speech signals from strong noise. The parameters of the system a, b are adjusted adaptively by evaluating the signal-to-noise ratio of the output signal, and then the weak speech signal is optimally detected. Experimental simulation analysis showed that under the background of strong noise, the output signal-to-noise ratio increased from the initial value-7 dB to about 0.86 dB, with the gain of signal-to-noise ratio is 7.86 dB. This method obviously raises the signal-to-noise ratio of the output speech signals, which gives a new idea to detect the weak speech signals in strong noise environment.
Clinical studies had demonstrated that early diagnosis of lesion could significantly reduce the risk of cancer. Magneto-acoustic-electrical tomography (MAET) is expected to become a new detection method due to its advantages of high resolution and high contrast. Based on thinking of modular design, a low-cost, digital magneto-acoustic conductivity detection system was designed and implemented in this study. The theory of MAET using chirp continuous wave excitation was introduced. The results of homogeneous phantom experiment with 0.5% NaCl clearly showed that the conductivity curve of homogeneous phantom was highly consistent with the actual physical size, which indicated that the chirp excitation theory in our proposed system was correct and feasible. Besides, the resolution obtained by 1 000 μs sweep time was better than that obtained by 500 μs and 1 500 μs, which means that sweep time is an important factor affecting the detection resolution of the conductivity. The same result was obtained in the experiments carried out on homogeneous phantoms with different concentrations of NaCl, which demonstrated the repeatability of our proposed MAET system.
Medical magnetic nanoparticles are nano-medical materials with superparamagnetism, which can be collected in the tumor tissue through blood circulation, and magnetic particle imaging technology can be used to visualize the concentration of magnetic nanoparticles in the living body to achieve the purpose of tumor imaging. Based on the nonlinear magnetization characteristics of magnetic particles and the frequency characteristics of their magnetization, a differential detection method for the third harmonic of magnetic particle detection signals is proposed. It was modeled and analyzed, to study the nonlinear magnetization response characteristics of magnetic particles under alternating field, and the spectral characteristics of magnetic particle signals. At the same time, the relationship between each harmonic and the amount of medical magnetic nanoparticle samples was studied. On this basis, a signal detection experimental system was built to analyze the spectral characteristics and power spectral density of the detected signal, and to study the relationship between the signal and the excitation frequency. The signal detection experiment was carried out by the above method. The experimental results showed that under the alternating excitation field, the medical magnetic nanoparticles would generate a spike signal higher than the background sensing signal, and the magnetic particle signal existed in the odd harmonics of the detected signal spectrum. And the spectral energy was concentrated at the third harmonic, that is, the third harmonic magnetic particle signal detection that meets the medical detection requirement could be realized. In addition, the relationship between each harmonic and the particle sample volume had a positive growth relationship, and the detected medical magnetic nanoparticle sample volume could be determined according to the relationship. At the same time, the selection of the excitation frequency was limited by the sensitivity of the system, and the detection peak of the third harmonic of the detection signal was reached at the excitation frequency of 1 kHz. It provides theoretical and technical support for the detection of medical magnetic nanoparticle imaging signals in magnetic particle imaging research.
近十年，在药品不良反应监测领域，基于医疗保健数据库的安全信号检测方法受到越来越多的关注，已成为弥补自发报告固有局限性的重要手段。目前数据挖掘方法主要基于比值失衡分析法（disproportionality analysis）、传统药物流行病学设计（如自身对照设计）、序列对称分析（sequence symmetry analysis，SSA）、序贯统计检验（sequential statistical testing）、时序关联规则（temporal association rules）、监督机器学习（supervised machine learning，SML）、树状扫描统计量方法（tree-based scan statistic）等。本文从应用场景和实用性角度对医疗保健数据库中安全信号检测方法及其性能进行介绍。