cover
Contact Name
Ahmad Azhari
Contact Email
ahmad.azhari@tif.uad.ac.id
Phone
+6281294055949
Journal Mail Official
simple@ascee.org
Editorial Address
UAD 4rd Campus, Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Daerah Istimewa Yogyakarta 55191
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
Signal and Image Processing Letters
ISSN : 27146669     EISSN : 27146677     DOI : https://doi.org/10.31763/simple
The Signal and Image Processing Letters (SIMPLE) is an international, peer-reviewed, open access, online journal of applied research in the field of Signal and Image Processing. It is designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in the signal, image, speech, language and audio processing. It is published three times a year, published in March, July, and November in electronic format with free online access
Articles 5 Documents
Real-time Facial Expression Recognition to Track Non-verbal Behaviors as Lie Indicators During Interview Setiawan, Arif Budi; Anwar, Kaspul; Azizah, Laelatul; Prahara, Adhi
Signal and Image Processing Letters Vol 1 No 1 (2019)
Publisher : Association for Scientic Computing and Electronics, Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v1i1.144

Abstract

During interview, a psychologist should pay attention to every gesture and response, both verbal and nonverbal language/behaviors, made by the client. Psychologist certainly has limitation in recognizing every gesture and response that indicates a lie, especially in interpreting nonverbal behaviors that usually occurs in a short time. In this research, a real time facial expression recognition is proposed to track nonverbal behaviors to help psychologist keep informed about the change of facial expression that indicate a lie. The method tracks eye gaze, wrinkles on the forehead, and false smile using combination of face detection and facial landmark recognition to find the facial features and image processing method to track the nonverbal behaviors in facial features. Every nonverbal behavior is recorded and logged according to the video timeline to assist the psychologist analyze the behavior of the client. The result of tracking nonverbal behaviors of face is accurate and expected to be useful assistant for the psychologists.
Gender Classification using Fisherface and Support Vector Machine on Face Image Fatkhannudin, Muhammad Noor; Prahara, Adhi
Signal and Image Processing Letters Vol 1 No 1 (2019)
Publisher : Association for Scientic Computing and Electronics, Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v1i1.147

Abstract

Computer vision technology has been widely used in many applications and devices that involves biometric recognition. One of them is gender classification which has notable challenges when dealing with unique facial characteristics of human races. Not to mention the challenges from various poses of face and the lighting conditions. To perform gender classification, we resize and convert the face image into grayscale then extract its features using Fisherface. The features are reduced into 100 components using Principal Component Analysis (PCA) then classified into male and female category using linear Support Vector Machine (SVM). The test that conducted on 1014 face images from various human races resulted in 86% of accuracy using standard k-NN classifier while our proposed method shows better result with 88% of accuracy.
PID Control for Temperature and Motor Speed Based on PLC Al Andzar, Muhammad Faqihuddin; Puriyanto, Riky Dwi
Signal and Image Processing Letters Vol 1 No 1 (2019)
Publisher : Association for Scientic Computing and Electronics, Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v1i1.150

Abstract

Transesterification process of used cooking oil to biodiesel need heating and mixing of ingredients and catalyst at temperature of 30-65oC and stirring speed of 700 rpm for 60 minutes. This research builds a prototype of biodiesel reactor control system to control those process automatically. The system is built using heater element, LM35DZ temperature sensor, DC motor to drive the stirrer, and rotary encoder sensor. PLC OMRON CP1E NA20DR-A is used as system controller by using PID algorithm. The results of this research shows that this system works well as expected. Test results of motor speed control shows, at 700 rpm set point this system gives stable response at 100 % Proportional band, 1,6 s Integral, and 0,2 derivative PID parameters, the system at this setting gives fast rise time and have small overshoot. Test result of temperature control shows, at 60oC set point this system works well at 1% proportional band, 400 s integral, and 0 s derivative PID parameters, the system at this setting gives fast rise time and stable steady state.
Characteristics Study of Wireless Power Transfer with Series-series Inductive Magnetic Coupled Principle Cahya, Ahmad Raditya
Signal and Image Processing Letters Vol 1 No 1 (2019)
Publisher : Association for Scientic Computing and Electronics, Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v1i1.164

Abstract

The wireless power transfer system using series-series inductive coupled magnetic resonance is studied in this work. The research is conducted using two separated circular coil facing each other serving as transmitter and reciver coil respectively. The effect of distance variation between two coils as well as loading variation to power efficiency and other electrical properties such as current, voltage, active power, and efficiency are observed. The coil's number of turn, transmitter input voltage, coil's attitude, and electrical frequency of the system are kept constant. The results show that the inter-coil distance value affect the overall performance of wireless power transfer system and match the theoretical prediction.
Classification of Concentration Levels in Adult-Early Phase using Brainwave Signals by Applying K-Nearest Neighbor Azhari, Ahmad; Ammatulloh, Fathia Irbati
Signal and Image Processing Letters Vol 1 No 1 (2019)
Publisher : Association for Scientic Computing and Electronics, Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v1i1.170

Abstract

The brain controls the center of human life. Through the brain, all activities of living can be done. One of them is cognitive activity. Brain performance is influenced by mental conditions, lifestyle, and age. Cognitive activity is an observation of mental action, so it includes psychological symptoms that involve memory in the brain's memory, information processing, and future planning. In this study, the concentration level was measured at the age of the adult-early phase (18-30 years) because in this phase, the brain thinks more abstractly and mental conditions influence it. The purpose of this study was to see the level of concentration in the adult-early phase with a stimulus in the form of cognitive activity using IQ tests with the type of Standard Progressive Matrices (SPM) tests. To find out the IQ test results require a long time, so in this study, a recording was done to get brain waves so that the results of the concentration level can be obtained quickly.EEG data was taken using an Electroencephalogram (EEG) by applying the SPM test as a stimulus. The acquisition takes three times for each respondent, with a total of 10 respondents. The method implemented in this study is a classification with the k-Nearest Neighbor (kNN) algorithm. Before using this method, preprocessing is done first by reducing the signal and filtering the beta signal (13-30 Hz).The results of the data taken will be extracted first to get the right features, feature extraction in this study using first-order statistical characteristics that aim to find out the typical information from the signals obtained. The results of this study are the classification of concentration levels in the categories of high, medium, and low. Finally, the results of this study show an accuracy rate of 70%.

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