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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknologi Informasi dan Ilmu Komputer Indonesian Journal of Electrical Engineering and Computer Science
Suryadi Satyawan, Arief
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Robust automotive radar interference mitigation using multiplicative-adaptive filtering and Hilbert transform Asmaur Rohman, Budiman Putra; Suryadi Satyawan, Arief; Kurniawan, Dayat; Indrawijaya, Ratna; Bin Ali Wael, Chaeriah; Armi, Nasrullah
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp326-336

Abstract

Radar is one of the sensors that have significant attention to be implemented in an autonomous vehicle since its robustness under many possible environmental conditions such as fog, rain, and poor light. However, the implementation risks interference because of transmitting and/or receiving radar signals from/to other vehicles. This interference will increase the floor noise that can mask the target signal. This paper proposes multiplicative-adaptive filtering and Hilbert transform to mitigate the interference effect and maintain the target signal detectability. The method exploited the trade-off between the step-size and sidelobe effect on the least mean square-based adaptive filtering to improve the target detection accuracy, especially in the long-range case. The numerical analysis on the millimeter-wave frequency modulated continuous wave radar with multiple interferers concluded that the proposed method could maintain and enhance the target signal even if the target range is relatively far from the victim radar.
Model Deep Learning Untuk Klasifikasi Objek Pada Gambar Fisheye Putri, Riza Ayu; Suryadi Satyawan, Arief; Prihantono, Johanes Adi; Linggi, Rinda Safana; Paramita, I Gusti Ayu Putri Surya; Iswarawati, Ni Kadek Emy; Akbar, Fabian; Utomo, Prio Adjie
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 3: Juni 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.938047

Abstract

Pengenalan suatu objek secara otomatis adalah suatu pekerjaan yang sangat penting seperti halnya untuk mengidentifikasi sebuah objek tertentu. Jika hal ini dilakukan oleh manusia maka akan sulit untuk mendapatkan hasil yang baik dengan konsisten, oleh sebab itu digunakan komputer. Komputer dapat mengenali objek selayaknya kemampuan manusia dalam mengenali objek, dengan cara mengamati gambar yang diperoleh dari kamera, dan menerapkan metode pengenalan pada gambar tersebut. Pada penelitian ini metode pengenalan objek akan dikembangkan dengan menggunakan kamera fisheye yang memiliki luas tangkap empat kali kamera konvensional. Metode pengenalan objek yang digunakan yaitu deep learning dengan arsitektur CNN (Convolution Neural Network). CNN memiliki kemampuan untuk mengenali objek dalam gambar. Model CNN yang digunakan terdiri dari 1 layer, 2 layer, 3 layer, dan 7 layer. Sedangkan untuk melatih dan memvalidasi model tersebut digunakan  900 gambar dataset. Hasil pengujian pada penelitian Skripsi ini menunjukan bahwa pada 7 layer CNN menghasilkan nilai presisi, recall dan akurasi tertinggi dengan komposisi nilai presisi 98,56%, recall 98,5% dan akurasi 98,59%. Nilai tersebut menunjukan bahwa hasil klasifikasi terhadap ketiga klasifikasi objek gambar manusia pada gambar fisheye dapat dilakukan dengan sangat baik.
SRCNN-based image transmission for autonomous vehicles in limited network areas Afina Carmelya, Anindya; Suryadi Satyawan, Arief; Muhammad Suranegara, Galura; Mirza Etnisa Haqiqi, Mokhamamad; Susilawati, Helfy; Alam Hamdani, Nizar; Dani Prasetyo Adi, Puput
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp903-912

Abstract

High-quality images are crucial for navigation, obstacle detection, and environmental understanding, but transmitting high-resolution images over constrained networks presents significant challenges. This study introduces an image transmission system using super-resolution convolutional neural networks (SRCNN) to enhance image quality without increasing bandwidth requirements by transmitting low-resolution images and upscaling them with SRCNN. The first phase of the research involved data collection, in which information was acquired directly from an appropriate locus to produce training, validation, and testing datasets. The second, three SRCNN models (915, 935, and 955) were trained using such a training dataset. The last was an evaluation, in which model 915 showed quick learning and stable performance with initial high loss, while model 935 had rapid convergence but potential overfitting. Model 955 achieved high initial performance. Three SRCNN model configurations were tailored to the specific needs of autonomous electric vehicles operating in limited areas, such as the locus. Input image resolution ranged from 128×128 pixels to 256×256 pixels, while output resolution varied from 256×256 pixels to 512×512 pixels. These resolutions can be acceptable for efficient image transmission over IEEE 802.11ac, but on the long range (LoRa) network, it still produces some delay.
Co-Authors Afina Carmelya, Anindya Akbar, Fabian Bin Ali Wael, Chaeriah Budiman Putra Asmaur Rohman Dani Prasetyo Adi, Puput Dayat Kurniawan Galura Muhammad Suranegara Hamdani, Nizar Alam Helfy Susilawati Indrawijaya, Ratna Iswarawati, Ni Kadek Emy Linggi, Rinda Safana Mirza Etnisa Haqiqi, Mokhamamad Nasrullah Armi Paramita, I Gusti Ayu Putri Surya Prihantono, Johanes Adi Putri, Riza Ayu Utomo, Prio Adjie