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Journal : TELKOMNIKA (Telecommunication Computing Electronics and Control)

On-chip debugging for microprocessor design Fajar Suryawan; Bana Handaga; Abdul Basith
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.13174

Abstract

This article proposes a closer-to-metal approach of RTL inspection in microprocessor design for use in education, engineering, and research. Signals of interest are tapped throughout the microprocessor hierarchical design and are then output to the top-level entity and finally displayed to a VGA monitor. Input clock signal can be fed as slow as one wish to trace or debug the microprocessor being designed. An FPGA development board, along with its accompanying software package, is used as the design and test platform. The use of VHDL commands ’type’ and ’record’ in the hierarchy provides key ingredients in the overall design, since this allows simple, clean, and tractable code. The method is tested on MIPS single-cycle microprocessor blueprint. The result shows that the technique produces more consistent display of the true contents of registers, ALU input/output signals, and other wires – compared to the standard, widely-used simulation method. This approach is expected to increase confidence in students and designers since the reported signals’ values are the true values. Its use is not limited to the development of microprocessors; every FPGAbased digital design can benefit from it.
Lung diseases detection caused by smoking using support vector machine Sri Widodo; Ratnasari Nur Rohmah; Bana Handaga; Liss Dyah Dewi Arini
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 3: June 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i3.9799

Abstract

Type of lung disease is very much manifold, but type of lung disease caused by smoking there are only 4, namely Bronchitis, Pneumonia, Emphysema and Lung Cancer. Doctors usually diagnose lung disease from CT scans using the naked eye, then interpret data one by one.This procedure is not effective. The aim of this research is improvement accuracy of lung diseases detection caused by smoking using support vector machine on computed tomography scan (CT scan) images. This study includes 4 (four) main points. First is the development of software for segmentation of lung organ automatically using Active Shape Model (ASM) method. Second is the segmentation of candidates who are considered illness by using Morphology Mathematics. The third process of lung disease detection using Support Vector Machine (SVM). Fourth is visualization of disease or lung disorder using Volume Rendering.
A statistical approach on pulmonary tuberculosis detection system based on X-ray image Ratnasari Nur Rohmah; Bana Handaga; Nurokhim Nurokhim; Indah Soesanti
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 3: June 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i3.10546

Abstract

This paper presented the research result on the design of pulmonary TB (Tuberculosis) detection systems using a statistical approach. The study aimed to address two problems in detecting pulmonary TB by doctors, especially in remote areas of Indonesia, namely the long waiting time for patients to get the doctor's diagnosis and the doctor's subjectivity. We used hundreds of X-ray images from radiology department of Sardjito Hospital, Yogyakarta, as primary data and thirty data from various sources on the internet as secondary data. Using statistical approach, we exploited statistical image feature from image histogram, examined two statistical methods of PCA and LDA transformation for feature extraction, and two minimum distance classifier in image classification. We also used histogram equalization in the image enhancement process and bicubic interpolation in image segmentation and template making. Test results on primary and secondary data images show the identification accuracy of 94% and 83.3%, respectively.