cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 65 Documents
 3   4   5   6   7 
Search results for , issue "Vol 25, No 1: January 2022" : 65 Documents clear
Lightweight hardware fingerprinting solution using inherent memory in off-the-shelf commodity devices Mohd Syafiq Mispan; Aiman Zakwan Jidin; Muhammad Raihaan Kamarudin; Haslinah Mohd Nasir
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp105-112

Abstract

An emerging technology known as Physical unclonable function (PUF) can provide a hardware root-of-trust in building the trusted computing system. PUF exploits the intrinsic process variations during the integrated circuit (IC) fabrication to generate a unique response. This unique response differs from one PUF to the other similar type of PUFs. Static random-access memory PUF (SRAM-PUF) is one of the memory-based PUFs in which the response is generated during the memory power-up process. Non-volatile memory (NVM) architecture like SRAM is available in off-the-shelf microcontroller devices. Exploiting the inherent SRAM as PUF could wide-spread the adoption of PUF. Therefore, in this study, we evaluate the suitability of inherent SRAM available in ATMega2560 microcontroller on Arduino platform as PUF that can provide a unique fingerprint. First, we analyze the start-up values (SUVs) of memory cells and select only the cells that show random values after the power-up process. Subsequently, we statistically analyze the characteristic of fifteen SRAM-PUFs which include uniqueness, reliability, and uniformity. Based on our findings, the SUVs of fifteen on-chip SRAMs achieve 42.64% uniqueness, 97.28% reliability, and 69.16% uniformity. Therefore, we concluded that the available SRAM in off-the-shelf commodity hardware has good quality to be used as PUF.
Optimal design of CMOS current mode instrumentation amplifier using bio-inspired method for biomedical applications Issa Sabiri; Hamid Bouyghf; Abdelhadi Raihani; Brahim Ouacha
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp120-129

Abstract

Analog integrated circuits for biomedical applications require good performance. This paper presents an instrumentation amplifier (IA) design based on three complementary metal oxide semiconductor (CMOS) conveyors with an active resistor. This circuit offers the possibility to control the gain by voltage and current. We have designed the IA to minimize the parasitic resistance (Rx) with large bandwidth and high common mode rejection ratio (CMRR) using the artificial bee colony algorithm (ABC). The topology is simulated using 0.35µm CMOS technology parameters. The optimization problem is represented by an objective function that will be implemented using MATLAB script. The results were approved by the simulation using the advanced design system (ADS) tool. The simulation results were compared to the characteristics of some other instrumentation amplifiers exsisting in the literature. The circuit has a higher CMRR than other topologies.
Sliding mode control with observer for permanent magnet synchronous machine drives Muhammad Haziq Nashren Razali; Jurifa Mat Lazi; Zulkifilie Ibrahim; Md Hairul Nizam Talib; Fizatul Aini Patakor
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp89-97

Abstract

This paper aims to develop the sliding mode control (SMC) scheme in sensorless permanent magnet synchronous machine (PMSM) drives to replace conventional proportional integral (PI) speed control. The SMC is formulated based on the integral sliding surface of the speed error. And the error is corrected based on the concept of Lyapunov stability. The SMC is designed with the load torque observer so that the disturbance can be estimated as feedback to the controller. The vector control technique which is also known as field-oriented control (FOC) is also used to split the stator current into the magnetic field generating part which is the direct axis and the torque generating part which is the quadrature axis. This can be done by using Park and Clarke transformations. The performance of the proposed SMC is tested under changes in load-torque and without load for different speed commands. The results prove that the SMC produces robust performances under variations of speeds and load disturbances. The effectiveness of the proposed method is verified and simulated by using MATLAB/SIMULINK software.
A weighted group shuffled decoding for low-density parity-check codes Fatima Zahrae Zenkouar; Mustapha El Alaoui; Said Najah
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp375-381

Abstract

In this paper, we have developed several concepts such as the tree concept, the short cycle concept and the group shuffling concept of a propagation cycle to decrypt low-density parity-check (LDPC) codes. Thus, we proposed an algorithm based on group shuffling propagation where the probability of occurrence takes exponential form exponential factor appearance probability belief propagation-group shuffled belief propagation (EFAP-GSBP). This algorithm is used for wireless communication applications by providing improved decryption performance with low latency. To demonstrate the effectiveness of our suggested technique EFAP-GSBP, we ran numerous simulations that demonstrated that our algorithm is superior to the traditional BP/GSBP algorithm for decrypting LPDC codes in both regular and non-regular forms
Elliptical curve cryptography image encryption scheme with aid of optimization technique using gravitational search algorithm Ramireddy Navatejareddy; Muthukuru Jayabhaskar; Bachala Sathyanarayana
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp247-255

Abstract

Image encryption enables users to safely transmit digital photographs via a wireless medium while maintaining enhanced anonymity and validity. Numerous studies are being conducted to strengthen picture encryption systems. Elliptical curve cryptography (ECC) is an effective tool for safely transferring images and recovering them at the receiver end in asymmetric cryptosystems. This method's key generation generates a public and private key pair that is used to encrypt and decrypt a picture. They use a public key to encrypt the picture before sending it to the intended user. When the receiver receives the image, they use their private key to decrypt it. This paper proposes an ECC-dependent image encryption scheme utilizing an enhancement strategy based on the gravitational search algorithm (GSA) algorithm. The private key generation step of the ECC system uses a GSA-based optimization process to boost the efficiency of picture encryption. The image's output is used as a health attribute in the optimization phase, such as the peak signal to noise ratio (PSNR) value, which demonstrates the efficacy of the proposed approach. As comparison to the ECC method, it has been discovered that the suggested encryption scheme offers better optimal PSNR values.
Development of smart machine for sorting of deceased onions Kokate Mahadeo Digamber; Wankhede Vishal Ashok; Pawar Dhananjay Jagdish
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp191-199

Abstract

Today, we are thinking to raise Farmer’s income through various means and measures. Implementation of new crop patterns, technology inclusion and promoting the eshtablishment of numerous agro processing industries will play a major role in agriculture sector. The labour issue is also one of the main concerns in many of the agricultural activities. In this paper we propose a technological evolvement in onion detection process, where we apply image processing and sensory mechanism to identify sprouted and rotten onions respectively. This will yield to quick, accurate and prompt supply of goods to the market, irrespective of lack of consistent but costly manpower. The efficiency of this prototype in identifying the sprouted onions with the help of camera is observed to be upto 87% and also the response of Gas sensing system in detecting rooten onions under prescribed chamber dimensions is analysed and obtained encouraging results.
Design of multi-band millimeter wave antenna for 5G smartphones Oras Ahmed Shareef; Ahmed Mohammed Ahmed Sabaawi; Karrar Shakir Muttair; Mahmood Farhan Mosleh; Mohammad Bashir Almashhdany
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp382-387

Abstract

The design of a millimeter wave (mmW) antenna for the 5G mobile applications is presented in this paper. The designed antenna has dimensions of 10×10×0.245 mm3. This includes the copper ground plane. The resonance of the proposed mmW antenna lies within the range of 33 GHz and 43 GHz. These frequency bands are covering the 5G proposed band in terms of the signal speed, data transmission, and high spectral efficiencies. Computer simulation technology (CST) software is used to simulate the proposed 5G antenna including the characteristics of S-parameters, gain, and radiation pattern. Simulation results show that the return loss at resonant frequencies goes -22 dB, which satisfies the requirements of 5G mobile technology.
Multi-scale 3D-convolutional neural network for hyperspectral image classification Murali Kanthi; Thogarcheti Hitendra Sarma; Chigarapalle Shoba Bindu
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp307-316

Abstract

Deep Learning methods are state-of-the-art approaches for pixel-based hyperspectral images (HSI) classification. High classification accuracy has been achieved by extracting deep features from both spatial-spectral channels. However, the efficiency of such spatial-spectral approaches depends on the spatial dimension of each patch and there is no theoretically valid approach to find the optimum spatial dimension to be considered. It is more valid to extract spatial features by considering varying neighborhood scales in spatial dimensions. In this regard, this article proposes a deep convolutional neural network (CNN) model wherein three different multi-scale spatial-spectral patches are used to extract the features in both the spatial and spectral channels. In order to extract these potential features, the proposed deep learning architecture takes three patches various scales in spatial dimension. 3D convolution is performed on each selected patch and the process runs through entire image. The proposed is named as multi-scale three-dimensional convolutional neural network (MS-3DCNN). The efficiency of the proposed model is being verified through the experimental studies on three publicly available benchmark datasets including Pavia University, Indian Pines, and Salinas. It is empirically proved that the classification accuracy of the proposed model is improved when compared with the remaining state-of-the-art methods.
A novel salp swarm clustering algorithm for prediction of the heart diseases Nitesh Sureja; Bharat Chawda; Avani Vasant
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp265-272

Abstract

Heart diseases have a severe impact on human life and health. Cardiovascular deaths and diseases have increased at a fast rate worldwide. The early prediction of these diseases is necessary to prevent deaths. Now a day; a considerable amount of medical information is available and collected as databases. An efficient technique is required to analyse this data and predict the disease. Clustering can help medical practitioners in diagnosis by classifying the patient’s data collected for a disease. Clustering techniques can analyse such data based on each patient-generated and predict disease. A new prediction model based on salp swarm algorithm and support vector machine is proposed in this research for predicting heart diseases. Salp swarm algorithm is used to select the useful features from the database. Support vector machine classifier is used to predict heart diseases. Results obtained are compared with the other algorithms available in the literature. It is observed that the proposed approach produces better results with accuracy 98.75% and 98.46% with the dataset 1 and 2, respectively. In addition to this, the algorithm converges in significantly less time in comparison to other algorithms. This algorithm might become a perfect supporting tool for medical practitioners.
An intelligent irrigation system based on internet of things (IoT) to minimize water loss Samar Amassmir; Said Tkatek; Otman Abdoun; Jaafar Abouchabaka
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp504-510

Abstract

This paper proposes a comparison of three machine learning algorithms for a better intelligent irrigation system based on internet of things (IoT) for differents products. This work's major contribution is to specify the most accurate algorithm among the three machine learning algorithms (k-nearest neighbors (KNN), support vector machine (SVM), artificial neural network (ANN)). This is achieved by collecting irrigation data of a specific products and split it into training data and test data then compare the accuracy of the three algorithms. To evaluate the performance of our algorithm we built a system of IoT devices. The temperature and humidity sensors are installed in the field interact with the Arduino microcontroller. The Arduino is connected to Raspberry Pi3, which holds the machine learning algorithm. It turned out to be ANN algorithm is the most accurate for such system of irrigation. The ANN algorithm is the best choice for an intelligent system to minimize water loss for some products.

Page 6 of 7 | Total Record : 65

 3   4   5   6   7 

Filter by Year

2022 2022


Reset
Filter By Issues
All Issue Vol 41, No 2: February 2026 Vol 41, No 1: January 2026 Vol 40, No 3: December 2025 Vol 40, No 2: November 2025 Vol 40, No 1: October 2025 Vol 39, No 3: September 2025 Vol 39, No 2: August 2025 Vol 39, No 1: July 2025 Vol 38, No 3: June 2025 Vol 38, No 2: May 2025 Vol 38, No 1: April 2025 Vol 37, No 3: March 2025 Vol 37, No 2: February 2025 Vol 37, No 1: January 2025 Vol 36, No 3: December 2024 Vol 36, No 2: November 2024 Vol 36, No 1: October 2024 Vol 35, No 3: September 2024 Vol 35, No 2: August 2024 Vol 35, No 1: July 2024 Vol 34, No 3: June 2024 Vol 34, No 2: May 2024 Vol 34, No 1: April 2024 Vol 33, No 3: March 2024 Vol 33, No 2: February 2024 Vol 33, No 1: January 2024 Vol 32, No 3: December 2023 Vol 32, No 1: October 2023 Vol 31, No 3: September 2023 Vol 31, No 2: August 2023 Vol 31, No 1: July 2023 Vol 30, No 3: June 2023 Vol 30, No 2: May 2023 Vol 30, No 1: April 2023 Vol 29, No 3: March 2023 Vol 29, No 2: February 2023 Vol 29, No 1: January 2023 Vol 28, No 3: December 2022 Vol 28, No 2: November 2022 Vol 28, No 1: October 2022 Vol 27, No 3: September 2022 Vol 27, No 2: August 2022 Vol 27, No 1: July 2022 Vol 26, No 3: June 2022 Vol 26, No 2: May 2022 Vol 26, No 1: April 2022 Vol 25, No 3: March 2022 Vol 25, No 2: February 2022 Vol 25, No 1: January 2022 Vol 24, No 3: December 2021 Vol 24, No 2: November 2021 Vol 24, No 1: October 2021 Vol 23, No 3: September 2021 Vol 23, No 2: August 2021 Vol 23, No 1: July 2021 Vol 22, No 3: June 2021 Vol 22, No 2: May 2021 Vol 22, No 1: April 2021 Vol 21, No 3: March 2021 Vol 21, No 2: February 2021 Vol 21, No 1: January 2021 Vol 20, No 3: December 2020 Vol 20, No 2: November 2020 Vol 20, No 1: October 2020 Vol 19, No 3: September 2020 Vol 19, No 2: August 2020 Vol 19, No 1: July 2020 Vol 18, No 3: June 2020 Vol 18, No 2: May 2020 Vol 18, No 1: April 2020 Vol 17, No 3: March 2020 Vol 17, No 2: February 2020 Vol 17, No 1: January 2020 Vol 16, No 3: December 2019 Vol 16, No 2: November 2019 Vol 16, No 1: October 2019 Vol 15, No 3: September 2019 Vol 15, No 2: August 2019 Vol 15, No 1: July 2019 Vol 14, No 3: June 2019 Vol 14, No 2: May 2019 Vol 14, No 1: April 2019 Vol 13, No 3: March 2019 Vol 13, No 2: February 2019 Vol 13, No 1: January 2019 Vol 12, No 3: December 2018 Vol 12, No 2: November 2018 Vol 12, No 1: October 2018 Vol 11, No 3: September 2018 Vol 11, No 2: August 2018 Vol 11, No 1: July 2018 Vol 10, No 3: June 2018 Vol 10, No 2: May 2018 Vol 10, No 1: April 2018 Vol 9, No 3: March 2018 Vol 9, No 2: February 2018 Vol 9, No 1: January 2018 Vol 8, No 3: December 2017 Vol 8, No 2: November 2017 Vol 8, No 1: October 2017 Vol 7, No 3: September 2017 Vol 7, No 2: August 2017 Vol 7, No 1: July 2017 Vol 6, No 3: June 2017 Vol 6, No 2: May 2017 Vol 6, No 1: April 2017 Vol 5, No 3: March 2017 Vol 5, No 2: February 2017 Vol 5, No 1: January 2017 Vol 4, No 3: December 2016 Vol 4, No 2: November 2016 Vol 4, No 1: October 2016 Vol 3, No 3: September 2016 Vol 3, No 2: August 2016 Vol 3, No 1: July 2016 Vol 2, No 3: June 2016 Vol 2, No 2: May 2016 Vol 2, No 1: April 2016 Vol 1, No 3: March 2016 Vol 1, No 2: February 2016 Vol 1, No 1: January 2016 Vol 16, No 3: December 2015 Vol 16, No 2: November 2015 Vol 16, No 1: October 2015 Vol 15, No 3: September 2015 Vol 15, No 2: August 2015 Vol 15, No 1: July 2015 Vol 14, No 3: June 2015 Vol 14, No 2: May 2015 Vol 14, No 1: April 2015 Vol 13, No 3: March 2015 Vol 13, No 2: February 2015 Vol 13, No 1: January 2015 Vol 12, No 12: December 2014 Vol 12, No 11: November 2014 Vol 12, No 10: October 2014 Vol 12, No 9: September 2014 Vol 12, No 8: August 2014 Vol 12, No 7: July 2014 Vol 12, No 6: June 2014 Vol 12, No 5: May 2014 Vol 12, No 4: April 2014 Vol 12, No 3: March 2014 Vol 12, No 2: February 2014 Vol 12, No 1: January 2014 Vol 11, No 12: December 2013 Vol 11, No 11: November 2013 Vol 11, No 10: October 2013 Vol 11, No 9: September 2013 Vol 11, No 8: August 2013 Vol 11, No 7: July 2013 Vol 11, No 6: June 2013 Vol 11, No 5: May 2013 Vol 11, No 4: April 2013 Vol 11, No 3: March 2013 Vol 11, No 2: February 2013 Vol 11, No 1: January 2013 Vol 10, No 8: December 2012 Vol 10, No 7: November 2012 Vol 10, No 6: October 2012 Vol 10, No 5: September 2012 Vol 10, No 4: August 2012 Vol 10, No 3: July 2012 More Issue