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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Proceeding of the Electrical Engineering Computer Science and Informatics

Adhi Susanto

Gadjah Mada University

Author-ID : 2377420

Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 4 Documents Claim Missing Document

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Articles

Dithering Analysis in an Orthogonal Frequency Division Multiplexing-Radio over Fiber Link Fakhriy Hario P; Adhi Susanto; I Wayan Mustika; Sevia M Idrus; Sholeh Hadi P
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 3: June 2016
Publisher : Institute of Advanced Engineering and Science

Nonlinearity is one major problem broadband communication faced on utilizing the high capacity of optical fibers. That is due to scattering phenomenon, which results in the deviations of wavelengths and energies. The dithering method is applied in the attempt to reduce those scatterings. In this paper, we propose the performance of a dithering technique based new system OFDM-RoF using two modulator scheme and coherent detection to alleviate the characteristics nonlinearity applied on the system. The dithering technique inputs signal externally to the signal processing systems to eliminate the effects of nonlinearity. Here, we report the performance of a dithering technique based on the OFDM-RoF, the results our experiment showed that the applied dithering with 16 QAM modulation can make the system more reliable and increases the power level 1.55% with 193.1 THz, 2% with 100 THz and 1.99% ~ 200 THz, the best condition are with fd < fc. However, all condition close proximity in the parameters OLP (optical launch power), BER and SER measurement. The result demonstrated a high efficiency and good power in which the OLP operated 6.396 dBm / 4.361 E-3 W~fd 200 THz, 3.578 dBm / 2.279 E-3 W~fd 193.1 THz and 6.420 dBm / 4.3384 E-3 W~100 THz. The best BER value is achieved at 0.33 and SER 0.78 at 5 km~fd 100 THz, 0.33 and 0.768 for 10 km~fd 193.1 THz, 0.478 and 0.92 for 50 km~fd 193.1 THz.

Multiple Processes for Least Mean Square Adaptive Algorithm on Roadway Noise Cancelling Sri Arttini Dwi Prasetyowati; Adhi Susanto
International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 2: April 2015
Publisher : Institute of Advanced Engineering and Science

Noise is a problem often found in daily life. Noise also make people could not concentrate to do their work. Efforts to reduce noise have been proposed, but, due to variety of the noise’s characteristics, every noise problem requires different solution. This research aim to cancel the vehicle’s noise while maintaining the information heard. These conditions happened in the hospitals classrooms, or work room near the roadway. The vehicle’s noise change very fast, so the adaptive system is the good solution candidate for solving this problem. On the beginning, the simulation process had the trouble with the iterations. Matlab software only can execute the certain range of iteration. It could not cancel the noise, even the information becomes criptic. The problem is how to cancell the vehicle’s noise with the restriction software and still manage the important information. This research will modify the LMS adaptive algorithm so that the iteration could be done by the system and the main goal of the research could be reached. The modification of the algorithm is based on the filter length (L) used to adapt with the noise. Therefore, this research conducted simulation of the Adaptive Noise Cancelling with two process steps. The output of the first adaptive process have the.same characteristics with the noise that would be cancelled, thus the first adaptive process have the error near to zero. The second adaptive process changes the input by the output of the first process and mix the information into the noise. Error occured in the final process is the information heard as the dominant output.

Classifying the EEG Signal through Stimulus of Motor Movement Using New Type of Wavelet Endro Yulianto; Adhi Susanto; Thomas Sri Widodo; Samekto Wibowo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 3: September 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (125.142 KB)

Brain Computer Interface (BCI) refers to a system designed to translate the brain signal in controlling a computer application. The most widely used brain signal is electroencephalograph (EEG) for using the non-invasive method, and having a quite good resolution and relatively affordable equipments. This research purposively is to obtain the characteristics of EEG signals using the motor movement of “turn right” and “turn left” that is by moving the simulation of steering wheel. The characteristic of signal obtained is subsequently used as a reference to create a new type of wavelet for classification. The signal processing, including a 4 – 20 Hz bandpass filter, signal segmentation in 1 to 2 seconds after stimuli and signal correlation, is used to obtain the characteristic of EEG signal; namely Event–Related Synchronization /Desynchronization (ERS/ERD). The result of test data classification to two new types of wavelet shows that each volunteer has a higher correlation value towards the new type of wavelet that has been designed with various wavelet scales for each individuals.DOI: http://dx.doi.org/10.11591/ij-ai.v1i3.843

Investigation of Diesel’s Residual Noise on Predictive Vehicles Noise Cancelling using LMS Adaptive Algorithm Sri Arttini Dwi Prasetyowati; Adhi Susanto; Ida Widihastuti
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 3: EECSI 2016
Publisher : IAES Indonesia Section

Every noise problems require different solution. In this research, the noise that must be cancelled comes from roadway. Least Mean Square (LMS) adaptive is one of the algorithm that can be used to cancel that noise. Residual noise always appears and could not be erased completely. This research aims to know the characteristic of residual noise from vehicle’s noise and analysis so that it is no longer appearing as a problem. LMS algorithm was used to predict the vehicle’s noise and minimize the error. The distribution of the residual noise could be observed to determine the specificity of the residual noise. The statistic of the residual noise close to normal distribution with = 0,0435, = 1,13 and the autocorrelation of the residual noise forming impulse. As a conclusion the residual noise is insignificant.

Co-Authors Endro Yulianto Fakhriy Hario P I Wayan Mustika Ida Widihastuti Samekto Wibowo Sevia M Idrus Sholeh Hadi P Sri Arttini Dwi Prasetyawati Thomas Sri Widodo

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