http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Rama Komaragiri (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
-
Modeling and simulation of non-spiral coil for magnetic sensing applications
Krishnapriya S.,Rama S. Komaragiri,Suja K. J. 대한기계학회 2017 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.31 No.11
Microcoils are essential components in magnetic sensors made using Micro-electro-mechanical systems (MEMS) technology. Nonspiral microcoils have fabrication advantages over conventional spiral coils and can be effectively used in MEMS micro sensors for generation and detection of magnetic fields. In this work, an analytical model of peak flux density, sensitivity, and resolution for the nonspiral planar microcoil is reported for the first time. Self and mutual inductances of a non-spiral coil are used to calculate the flux density at the innermost turn of the coil. The model derived is compared with standard fabrication results, and it is found to be in good agreement with the experimental results.
-
Ashish Kumar,Rama Komaragiri,Manjeet Kumar 대한의용생체공학회 2019 Biomedical Engineering Letters (BMEL) Vol.9 No.3
A joint time–frequency localized three-band biorthogonal wavelet fi lter bank to compress Electrocardiogram signals is proposedin this work. Further, the use of adaptive thresholding and modifi ed run-length encoding resulted in maximum datavolume reduction while guaranteeing reconstructing quality. Using signal-to-noise ratio, compression ratio (C R ), maximumabsolute error (E MA ), quality score (Q s ), root mean square error, compression time (C T ) and percentage root mean squarediff erence the validity of the proposed approach is studied. The experimental results deduced that the performance of theproposed approach is better when compared to the two-band wavelet fi lter bank. The proposed compression method enablesloss-less data transmission of medical signals to remote locations for therapeutic usage.
-
Efficient QRS complex detection algorithm based on Fast Fourier Transform
Ashish Kumar,Ramana Ranganatham,Rama Komaragiri,Manjeet Kumar 대한의용생체공학회 2019 Biomedical Engineering Letters (BMEL) Vol.9 No.1
An ECG signal, generally filled with noise, when de-noised, enables a physician to effectively determine and predict thecondition and health of the heart. This paper aims to address the issue of denoising a noisy ECG signal using the FastFourier Transform based bandpass filter. Multi-stage adaptive peak detection is then applied to identify the R-peak in theQRS complex of the ECG signal. The result of test simulations using the MIT/BIH Arrhythmia database shows highsensitivity and positive predictivity (PP) of 99.98 and 99.96% respectively, confirming the accuracy and reliability ofproposed algorithm for detecting R-peaks in the ECG signal.
-
Pankaj,Ashish Kumar,Manjeet Kumar,Rama Komaragiri 대한의용생체공학회 2023 Biomedical Engineering Letters (BMEL) Vol.13 No.4
Appropriate blood pressure (BP) management through continuous monitoring and rapid diagnosis helps to take preventivecare against cardiovascular diseases (CVD). As hypertension is one of the leading causes of CVDs, keeping hypertensionunder control by a timely screening of subjects becomes lifesaving. This work proposes estimating BP from motion artifactaffectedphotoplethysmography signals (PPG) by applying signal processing techniques in realtime. This paper proposes adeep neural network-based methodology to accurately classify PPG signals using a Fourier theory-based time–frequency(TF) spectrogram. This work uses the Fourier decomposition method (FDM) to transform a PPG signal into a TF spectrogram. In the proposed work, the last three layers of the pre-trained deep neural network, namely, GoogleNet, DenseNet, andAlexNet, are modified and then used to classify the PPG signal into normotension, pre-hypertension, and hypertension. Theproposed framework is trained and tested using the MIMIC-III and PPG–BP databases using five-fold training and testing. Out of the three deep neural networks, the proposed framework with the DenseNet-201 network performs best, with a testaccuracy of 96.5%. The proposed work uses FDM to compute the TF spectrogram to accurately separate the motion artifactsand noise components from a noise-corrupted PPG signal. Capturing more frequency components that contain moreinformation from PPG signals makes the deep neural networks extract better and more meaningful features. Thus, training adeep neural network model with clean PPG signal features improves the generalized capability of a BP classification modelwhen tested in realtime.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
