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INDONESIA
Jurnal Teknik Elektro
Published by Universitas Negeri Semarang
ISSN : 14110059     EISSN : 25491571     DOI : http://dx.doi.org/10.15294/jte
Core Subject : Engineering,
Electrical & Electronics Engineering
Arjuna Subject : -
Articles 482 Documents
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Sequential Detection under Correlated Observations using Recursive Method Suratman, Fiky Yosef; Istiqomah, Istiqomah; Rahmawati, Dien
Jurnal Teknik Elektro Vol 15, No 2 (2023): Jurnal Teknik Elektro
Publisher : Jurusan Teknik Elektro, Fakultas Teknik, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v15i2.46941

Abstract

Sequential analysis has been used in many cases when the decision is required to be made quickly, such as for signal detection in statistical signal processing, namely sequential detector. For identical error probabilities, a sequential detector needs a smaller average sample number (ASN) than its counterpart of a fixed sample number quadrature detector based on Neyman-Pearson criteria. The optimum sequential detector was derived based on the assumption that the observations are uncorrelated (independent). However, the assumption is commonly violated in realistic scenario, such as in radar. Using a sequential detector under correlated observations is sub-optimal and it poses a problem. It demands a high computational complexity since it needs to recalculate the inverse and the determinant of the signal covariance matrix for each new sample taken. This paper presents a technique for reducing computational complexity, which involves using recursive matrix inverse to calculate conditional probability density functions (pdf). This eliminates the need to recalculate the inverse and determinant, leading to a more reasonable solution in real-world scenarios. We evaluate the performance of the proposed (recursive) sequential detector using Monte-Carlo simulations and we use the conventional and non-recursive sequential detectors for comparisons. The results show that the recursive sequential detector has equal probabilities of false alarm and miss-detection with the conventional sequential detector and performs better than the non-recursive sequential detector. In terms of ASN, it maintains results comparable to those of the two conventional detectors. The recursive approach has reduced the computational complexity for matrix multiplication to O(n2) from O(n3) and has rendered the calculation of matrix determinants unnecessary. Therefore, by having a better probability of error and reduced computational complexities under correlated observations, the proposed recursive sequential detector may become a viable alternative to obtain a more agile detection system as required in future applications, such as radar and cognitive radio.
Leveraging Convolutional Neural Networks for Automated Detection and Grading of Diabetic Retinopathy from Fundus Images Yamani, Ibnu Uzail; Basari, Basari
Jurnal Teknik Elektro Vol 15, No 2 (2023): Jurnal Teknik Elektro
Publisher : Jurusan Teknik Elektro, Fakultas Teknik, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v15i2.48769

Abstract

This study addresses the critical challenge of Diabetic Retinopathy (DR) detection and severity grading, aiming to advance the field of medical image analysis. The research problem focuses on the need for an accurate and efficient model to discern DR conditions, thereby facilitating early diagnosis and intervention. Employing a Convolutional Neural Network (CNN), our methodology is developed to strike a balance between precision and computational efficiency, a pivotal aspect in the context of healthcare applications.  The research leverages the APTOS 2019 dataset, a comprehensive collection of fundus photographs, to evaluate the efficacy of our proposed model. The dataset allows for a thorough investigation into the model's performance in binary-class and multi-class classifications, providing a robust foundation for analysis.  The most important result of our study manifests in the achieved accuracy rates of 98.67% and 87.81% for binary-class and multi-class classifications, respectively. These outcomes underscore the model's reliability and innovation, surpassing established machine learning algorithms and affirming its potential as a valuable tool for early DR detection and severity assessment.  In conclusion, the study marks a significant advancement in leveraging deep learning for ophthalmic diagnoses, particularly in the nuanced landscape of DR. The implications of our findings extend to the broader realm of AI-driven healthcare solutions, presenting opportunities for enhanced clinical practices and early intervention strategies. Future research endeavors could explore further refinements to the model, considering additional datasets and collaborating with healthcare professionals for real-world validation, ensuring the continued progress of AI applications in the medical domain.

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