A Deep Learning-Based Approach to Forecast Ionospheric Delays for GPS Signals

Srivani, I.; Prasad, G. Siva Vara; Ratnam, D. Venkata

doi:10.1109/lgrs.2019.2895112

Cited by 96 publications

(56 citation statements)

References 22 publications

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“…Dari gambar 4 desain hardware di atas, BEWARE akan bekerja dengan andal bila kamera Mtek Vision bertugas sebagai pengambil gambar untuk database dan sebagai monitoring kondisi pengemudi. Modul Gps NEO6M mampu menjalankan tugasnya sebagai penanda lokasi BEWARE berada (Srivani, Prasad, & Ratnam, 2019). Mini Pc Odroid bekerja sebagai pemrosesan pertama setelah data citra dikirmkan oleh kamera Mtek vision dimana jika data citra tersebut telah diproses (diklasifikasikan) maka akan dikirimkan ke Arduino Promini dan modul relay.…”

Section: Metodologi Penelitianunclassified

Beware Alat Pendeteksi Kelelahan Berbasis Kecerdasan Buatan dengan Metode Pengolahan Citra untuk Mencegah Kecelakaan Berkendara

Habibi

Fariqi

Anggriawan

2020

JPTD

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Masalah terbesar yang di hadapi Indonesia saat ini yaitu kecelakaan lalu lintas. Kerugian yang didapatkan dari kecelakaan lalu lintas diantaranya menimbulkan korban jiwa maupun kerugian material. Korban yang paling banyak berasal dari kendaraan roda 4 atau lebih yang dimana korbannya ada yang terluka maupun ada yang meninggal dunia. Penyebab tingginya jumlah kecelakaan lalu lintas adalah disebabkan oleh faktor manusia itu sendiri seperti mengantuk ketika berkendara, dibawah pengaruh alkohol, menggunakan ponsel ketika mengemudi dan melanggar rambu lalu lintas. Oleh karena itu, BEWARE hadir untuk mengatasi masalah ini. BEWARE dapat mendeteksi pengemudi yang mengantuk dengan mengamati gerakan kelopak mata pengemudi dengan menggunakan metode pengolahan citra yang berbasis kecerdasan buatan. BEWARE menggunakan daya rendah hanya 18watt dan BEWARE juga memiliki fungsi dalam pencahayaan yang memadai ataupun dalam pencahayaan yang buruk (gelap gulita). BEWARE sendiri sudah dilengkapi dengan keamanan bertingkat seperti sirine, alarm, aktivasi lampu tanda darurat secara otomatis,dan akan menonaktifkan mesin mobil jika dalam waktu lebih dari 5 detik pengemudi tidak bangun dari tidur. Selain itu, BEWARE sudah terintegrasi dengan aplikasi yang dapat menampilkan informasi pengemudi pada ponsel yang nantinya informasi tersebut akan dikirim ke ponsel saudara terdekat dengan pemberitahuan khusus (Notification Real Time). Alat kami memberikan kenyamanan dan kemudahan bagi pemakainya dengan menempatkan kamera di Dashboard mobil dan menghadap ke wajah pengemudi maksimal 1 meter.

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Section: Metodologi Penelitianunclassified

Beware Alat Pendeteksi Kelelahan Berbasis Kecerdasan Buatan dengan Metode Pengolahan Citra untuk Mencegah Kecelakaan Berkendara

Habibi

Fariqi

Anggriawan

2020

JPTD

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“…Then we get (24) For the above equation, trace can be taken on both sides, then by using below relation: (25) Then MSD dynamic equation is derived as (26) By using trace properties, inequalities for in ( 26) is obtained as (27) Then by substituting ( 27) into ( 26) gives (28) It has to satisfy that , it exits as positive constant so that (29) Value of is set to one because input noise covariance matrix is conditioned well for white Gaussian input noise vector, so equation ( 29) for MSD is shown as (30) Then by substituting equation ( 15) results with conditional variance we get the above equation. After substituting ( 31) into (30), MSD dynamic equations is represented as equation (19).…”

Section: B Mean Square Deviation Analysismentioning

confidence: 99%

“…While measuring impedance cardiography using TEB, respiratory artifacts are occurred. By notifying the physiological and non-physiological artifacts from wireless telemetry measurements, they are reduced by using adaptive algorithm along with TEB module, also studied various signal analyzing techniques in [17]- [29]. There are mathematical difficulties are also there while measuring stroke volume in remote health care monitoring systems.…”

Section: Introductionmentioning

confidence: 99%

Artifact Elimination in Thoracic electrical bioimpedance signals using new Normalized Adaptive Filters

2020

IJETER

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Thoracic electrical bioimpedance (TEB) used for facilitating stroke volume from sudden cardiac arrest signals. It is a non -invasive method used monitoring cardiac outputs, measuring stroke volumes and to observe changes in hemodynamic parameters of volume of blood. While measuring volume of blood, TEB signal is contaminated with physiological and non-physiological signal artifacts. For avoiding these artifacts in this paper proposed an adaptive filter method for enhancing TEB Signals. Least Mean Square (LMS) algorithm is a basic adaptive method, but it is non stationary in nature and it has low convergence rate problems. Hence, Bias compensated Normalized Least Mean Square (BC NLMS) algorithm is proposed, then it check initially for stability in terms of mean deviation analysis and mean square deviation analysis. Depending in this analysis, noisy input variance estimation and variable step size are taken into consideration for better performance in terms of reduced steady error rate, good stability and convergence improvement. In this paper we present various adaptive noise cancellers (ANCs) for the elimination of artifacts from TEB signals. Also, in simulation results, artifacts are eliminated from noisy input signal and it performs well when compared to exiting methods.

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“…This spectrum sensing concept is used in medical telemetry networks by allocating frequency bands to every patient and then data is considered from patient and further treatment is given to patient by allocating doctor to that patient. It gives poor results for if threshold point and signal to noise ratio (SNR) values are not considered well, then it may lead to give interference problems for primary users and their noise performance [26]- [29] will limit performance.…”

Section: Spectrum Sensing Using Nm Lmfmentioning

confidence: 99%

Spectrum Sensing using NMLMF algorithm in Cognitive Radio Networks for Health Care Monitoring Applications

2020

IJATCSE

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To use unlicensed version of cognitive radio spectrum without any interference spectrum sensing method is used. Energy detection is mostly used technique for sensing spectrum because it does not need any prior information of primary input. Spectrum sensing performance is good by using multiple antennas. Performance is low for noise uncertainty values at selecting threshold point and for low SNR values. Detection probability is improved by using dynamic threshold point, but there is noise present at the receiver output. To avoid this noisy output proposed an adaptive filter algorithm i.e., Maximum Normalized Least Mean Fourth (MNLMF) algorithm. By the proposed algorithm, noisy outputs are decreased, also variable step size considered in weight update equation of NMLMF for better estimation of error estimates. Then we get better probability detection performance for decreased false alarm probability and for various values of signal to noise ratio values. Performance is measured in terms of convergence, get the better results for sign regressor based algorithm when compared to other two variants of MN LMF algorithm.

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A Deep Learning-Based Approach to Forecast Ionospheric Delays for GPS Signals

Cited by 96 publications

References 22 publications

Beware Alat Pendeteksi Kelelahan Berbasis Kecerdasan Buatan dengan Metode Pengolahan Citra untuk Mencegah Kecelakaan Berkendara

Beware Alat Pendeteksi Kelelahan Berbasis Kecerdasan Buatan dengan Metode Pengolahan Citra untuk Mencegah Kecelakaan Berkendara

Artifact Elimination in Thoracic electrical bioimpedance signals using new Normalized Adaptive Filters

Spectrum Sensing using NMLMF algorithm in Cognitive Radio Networks for Health Care Monitoring Applications

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