Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to dete...Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or pathologic.Various cardiotocography measures infer wrongly and give wrong predictions because of human error.The traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as well.Fetal condition is very important to measure at numerous stages and give proper medications to the fetus for its well-being.In the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional results.ML techniques play a pivotal role in detecting fetal disease in its early stages.This research article uses Federated machine learning(FML)and ML techniques to classify the condition of the fetus.This study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the data.So,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,respectively.So,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.展开更多
Recently,real-time processing systems for bio-signal of the muscles generated by the movement of the user have been developed.Finite impulse response(FIR)filter for bio-signal processing in bio-signal process systems ...Recently,real-time processing systems for bio-signal of the muscles generated by the movement of the user have been developed.Finite impulse response(FIR)filter for bio-signal processing in bio-signal process systems is composed of multiple multiplier and adder of high-area.This makes the chip area increase significantly.To solve this problem,a low-area digital FIR filter is proposed in this paper,which can reduce the chip area.展开更多
从自然界中动物的正常生存到工业中机器的安全运作,碰撞感知能力始终至关重要.受蝗虫视觉神经元LGMD(Lobula Giant Movement Detector)的启发,许多仿生的计算模型已经被用于实时可靠的碰撞感知.然而,受限于二维单目的输入信号,目前的方...从自然界中动物的正常生存到工业中机器的安全运作,碰撞感知能力始终至关重要.受蝗虫视觉神经元LGMD(Lobula Giant Movement Detector)的启发,许多仿生的计算模型已经被用于实时可靠的碰撞感知.然而,受限于二维单目的输入信号,目前的方法难以捕捉运动目标的深度特征,进而无法满足在复杂的真实动态场景下进行迫近感知的需求.因此,本研究提出一种融合生物似然性运动通路和视差通路的三维迫近感知模型.在突触前神经网络,通过对2种视觉通路从时空维度上进行实时神经信号整合,所提出的模型不仅能够有效排除大范围的背景杂波干扰,而且可以明显抑制前景非迫近运动所产生的视觉刺激,降低了对突然出现在感受野目标的关注度,进一步提高在未知现实环境中对迫近运动的选择.真实场景数据集的离线测试,以及在线机器人测试的实验结果显示,与目前最先进的方法相比,我们的模型在时间复杂度降低了一个数量级的前提下,准确率提升至96.09%,且能够协助移动机器人在自主导航时实时稳健检测,避免潜在的碰撞威胁.研究综合揭示出迫近感知神经网络对于运动通路的高效性以及视差通路的可靠性,具备显著的协同能力.展开更多
文摘Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or pathologic.Various cardiotocography measures infer wrongly and give wrong predictions because of human error.The traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as well.Fetal condition is very important to measure at numerous stages and give proper medications to the fetus for its well-being.In the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional results.ML techniques play a pivotal role in detecting fetal disease in its early stages.This research article uses Federated machine learning(FML)and ML techniques to classify the condition of the fetus.This study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the data.So,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,respectively.So,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.
基金The MKE(the Ministry of Knowledge Economy),Korea,under the ITRC(Information Technology Research Center)support program supervised by the NIPA(National IT Industry Promotion Agency) (NIPA-2012-H0301-12-2006)the Seoul Metropolitan Government,under the Seoul R & BD Program supervised by Seoul Business Agency(ST110039)
文摘Recently,real-time processing systems for bio-signal of the muscles generated by the movement of the user have been developed.Finite impulse response(FIR)filter for bio-signal processing in bio-signal process systems is composed of multiple multiplier and adder of high-area.This makes the chip area increase significantly.To solve this problem,a low-area digital FIR filter is proposed in this paper,which can reduce the chip area.
文摘从自然界中动物的正常生存到工业中机器的安全运作,碰撞感知能力始终至关重要.受蝗虫视觉神经元LGMD(Lobula Giant Movement Detector)的启发,许多仿生的计算模型已经被用于实时可靠的碰撞感知.然而,受限于二维单目的输入信号,目前的方法难以捕捉运动目标的深度特征,进而无法满足在复杂的真实动态场景下进行迫近感知的需求.因此,本研究提出一种融合生物似然性运动通路和视差通路的三维迫近感知模型.在突触前神经网络,通过对2种视觉通路从时空维度上进行实时神经信号整合,所提出的模型不仅能够有效排除大范围的背景杂波干扰,而且可以明显抑制前景非迫近运动所产生的视觉刺激,降低了对突然出现在感受野目标的关注度,进一步提高在未知现实环境中对迫近运动的选择.真实场景数据集的离线测试,以及在线机器人测试的实验结果显示,与目前最先进的方法相比,我们的模型在时间复杂度降低了一个数量级的前提下,准确率提升至96.09%,且能够协助移动机器人在自主导航时实时稳健检测,避免潜在的碰撞威胁.研究综合揭示出迫近感知神经网络对于运动通路的高效性以及视差通路的可靠性,具备显著的协同能力.
