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Signal and Image Processing Letters
Published by Association For Scientific Computing Electrical and Engineering
ISSN : 27146669     EISSN : 27146677     DOI : 10.31763/simple
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering
The journal invites original, significant, and rigorous inquiry into all subjects within or across disciplines related to signal processing and image processing. It encourages debate and cross-disciplinary exchange across a broad range of approaches.
Arjuna Subject : Ilmu Komputer (semua kategori) - Pemrosesan Sinyal
Articles 5 Documents
 1 
Search results for , issue "Vol 1, No 3 (2019)" : 5 Documents clear
Lung Cancer Prediction and Detection Using Image Processing Mechanisms: An Overview Ahmed, Bakhan Tofiq
Signal and Image Processing Letters Vol 1, No 3 (2019)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v1i3.11

Abstract

Nowadays, cancer has counted as a hazardous disease that many people suffered from especially Lung-Cancer. Cancer is the disease that cell has grown rapidly and abnormally that is why treating it is somehow tough in some cases but it can be controlled if it is detected in the initial stage. Image Processing Mechanisms have a vital role in predicting and recognizing both benign and malignant cells with the help of classifier mechanisms such as Decision-Tree (D-Tree), A-NN, Support-Vector-Machine, and Naïve-Bayes classifier which are widely utilized in the biomedical field. These classifiers are available to classify the usual and unusual cells. This study aims to review the most well-known Image Processing Mechanisms for Lung-Cancer Detection and Prediction. Brief information about the main steps of proposing an effective system by using Image Processing stages like Image Acquisition, Pre-processing of the image which includes noise elimination and enhancement, Segmentation, Extracting Feature, and Binarization had been demonstrated. In the literature, several researchers' work had been reviewed. A comparison had been done among various reviewed research papers that proposed various models for recognizing and estimating the Lung-Cancer nodule. The comparison based on the Image Processing Mechanisms, accuracy, and classifier used in each reviewed research paper.
AD9850 based function generator Yusuf Fernades, Rudi; Sapto Aji, Wahyu
Signal and Image Processing Letters Vol 1, No 3 (2019)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v1i3.18

Abstract

Function generator consists of the main generator and a modulation generator. The main generator provides sine, square, or triangular wave output with a frequency range of 0.01Hz to 13MHz. The modulation generator generates sine, square, and triangle waveforms with a frequency range of 0.01Hz to 10kHz. The input signal generator can be used as Amplitude Modulation (AM) or Frequency Modulation (FM). The AM envelope can be adjusted from 0% to 100%, while the carrier frequency of FM can be set up to± 5%. Function generator generally produces frequencies in the range of 0.5Hz to 20MHz or more depending on the manufacturer's design. The resulting frequency can be selected by rotating the frequency range knob. The amplitude signal can be adjusted within a range from 0.1V to 20Vpp (peak-to-peak voltage) at no-load conditions and 0.1V to 10Vpp with a load of 50ohms. The main output is specified by SYNC Output. This research makes a wave generator and its frequency, as well as DDS AD9850 as a wave reader sensor on the oscilloscope using Arduino Uno to generate waves and a rotary encoder as a frequency regulator. Based on the experiment by varying frequency, peak-to-peak voltage and period are produced as follows: if the frequency at 50Hz, then the peak-to-peak voltage is 1.2Vpp and period (T) is 0.006s if the frequency at 100Hz, then the peak-to-peak voltage is 1.2Vpp and period is 0.005s if the frequency at 150Hz then the peak-to-peak voltage is 1.2Vpp and period is 0.034s if the frequency at 1KHz then the peak-to-peak voltage is 1.2Vpp and period is 0.0006s if the frequency at 1.5KHz then the peak-to-peak voltage is 1.2Vpp and period is 0.0004s, and finally if the frequency at 2KHz then 1.2Vpp and period are 0.000225s.
Classification of concentration or focus by signal Electroencephalography (EEG) and addiction Watching K-Dramas Using Algoritma K-Nearest Neighbor Azhari, Ahmad; Ramadan, Rizky
Signal and Image Processing Letters Vol 1, No 3 (2019)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v1i3.26

Abstract

K-drama or drakor is currently being enjoyed in Indonesia when the Covid-19 pandemic hits, especially by the fair sex. From the sources obtained, the number of k-dramas or dramas also increased during the covid-19 pandemic from the previous 2.7 hours a day to 4.6 hours a day. The issue raised by the authors in this study is whether the impressions of drakor will later affect the concentration of an individual. Data acquisition was carried out using the NeuroSky Mindwave Mobile 2 tool to retrieve EEG data.  After the data acquisition is completed, the next process is preprocessing, which is to perform feature extraction using the Fast Furious Transform method to find the average values of the highest and lowest peaks. After the preprocessing is completed go to the classification stage. The classification used is K-Nearest Neighbor with a value of k=9.  For evaluation using confusion matrix to determine the accuracy value of the built KNN model. This study used 100 respondents who were37 people who were addicted to drakor. A total of 24 people out of the 37 or about 64.87% turned out to have a lack of concentration level when taking concentration tests. This is enough to prove that drama impressions can reduce the concentration or focus of a person, especially women. For the classification process to have an accuracy of 80% and for variable correlation testing, it turns out that independent variables do have a simultaneous effect on the dependent variables with a calculated f value of 35.642 and a sig value of 0.000b.
Mangrove Forest Classification in Drone Images Using HSV Color Moment and Haralick Features Extraction with K-Nearest Neighbor Widodo, Agus Wahyu; Hernando, Deo; Mahmudy, Wayan Firdaus
Signal and Image Processing Letters Vol 1, No 3 (2019)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v1i3.6

Abstract

Due to the problems with uncontrolled changes in mangrove forests, a forest function management and supervision is required. The form of mangrove forest management carried out in this study is to measure the area of mangrove forests by observing the forests using drones or crewless aircraft. Drones are used to take photos because they can capture vast mangrove forests with high resolution. The drone was flown over above the mangrove forest and took several photos. The method used in this study is extracting color features using mean values, standard deviations, and skewness in the HSV color space and texture feature extraction with Haralick features. The classification method used is the k-nearest neighbor method. This study conducted three tests, namely testing the accuracy of the system, testing the distance method used in the k-nearest neighbor classification method, and testing the k value. Based on the results of the three tests above, three conclusions obtained. The first conclusion is that the classification system produces an accuracy of 84%. The second conclusion is that the distance method used in the k-nearest neighbor classification method influences the accuracy of the system. The distance method that produces the highest accuracy is the Euclidean distance method with an accuracy of 84%. The third conclusion is that the k value used in the k-nearest neighbor classification method influences the accuracy of the system. The k-value that produces the highest accuracy is k = 3, with an accuracy of 84%.
Object-Moving Robot Arm based on Color Sengsalong, Areepen; Satya Widodo, Nuryono
Signal and Image Processing Letters Vol 1, No 3 (2019)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v1i3.7

Abstract

The aim of this research is to make a robot arm moving objects based on color using 2 servo motors and 6 light photodiode sensors integrated with the Arduino Mega 2560 microcontroller.  The light photodiode sensor is used to detect red, green and blue colors. This system is equipped with an LCD to display the output of the Arduino Mega 2560 which is a notice of the color detected. The process of moving objects based on color is simulated using 3 colored objects namely red, green, and blue. The robot arm gripper will move to pick and move objects based on color, when the light photodiode sensor detects a color input. Based on system testing, overall the robot arm is functioning properly, i.e. it shows that the robot arm is able to move objects automatically with large test results obtained by 0°, 40°, 60°, 90°, and 120°. Whereas for sensor testing the value of red is 400, the value of green is 150, and the value of blue is 600.

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