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All Journal Journal of Robotics and Control (JRC)
Alzubi, Maen
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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EI-FRI: Extended Incircle Fuzzy Rule Interpolation for Multidimensional Antecedents, Multiple Fuzzy Rules, and Extrapolation Using Total Weight Measurement and Shift Ratio Alzubi, Maen; Almseidin, Mohammad; Kovacs, Szilveszter; Al-Sawwa, Jamil; Alkasassbeh, Mouhammd
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.20515

Abstract

Traditional fuzzy reasoning techniques demand a condensed fuzzy rule base to conclude a result. Still, due to incomplete data or a deficiency of expertise and knowledge, dense rule bases are not always available. Fuzzy interpolation methods have been widely explored to reasonably allow the interpolation of a fuzzy result using the closest current rules. Fuzzy rule interpolation is a type of fuzzy inference system in which conclusions can be obtained even with a few fuzzy rules. This benefit could be used to adapt the FRI to different application areas that suffer from a lack of knowledge. Alzubi et al. [17] offered a novel interpolative method that uses a weighted average based on the center point of the Incircle of the fuzzy sets. Nevertheless, the interpolated observation does not completely define the actual observation that is provided. In our offered extension to this method, a modification weight measure calculation and a shift technique are included to guarantee that the center point of the observation and the interpolated observation are mapped together. This weight measure calculation and shift technique enabled the capability of extrapolation to be conducted implicitly, which is also improves the performance results of the algorithm in the presence of multiple fuzzy rules and multidimensional priors.
LICA-CS: Efficient Lossless Image Compression Algorithm via Column Subtraction Model Al Qerom, Mahmoud; Otair, Mohammad; Meziane, Farid; AbdulRahman, Sawsan; Alzubi, Maen
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.21834

Abstract

Driven by the unprecedented amount of data generated in the last few decades, data storage and communication are becoming more challenging. Although many approaches in data compression have been developed to alleviate these challenges, more efforts are still needed, especially for lossless image compression, which is a promising technique when critical information loss is not allowed. In this paper, we propose a new algorithm called the Lossless Image Compression Algorithm using a Column Subtraction model (LICA-CS). LICA-CS is efficient, low in complexity, decreases the image bit-depth, and enhances state-of-the-art performance. LICA-CS first implements a color transformation method as a pre-processing phase, which strategically minimizes inter-channel correlations to optimize compression outcomes. After that, a novel subtraction method was developed to compress the image data column-wise. We tackle the similarity and proximity of pixel values within adjacent columns, which offers a distinct advantage in reducing image size observing a significant size reduction of 71%. This is achieved through the subtraction of neighboring columns. The conducted experiments on colored images show that LICA-CS outperforms existing algorithms in terms of compression rate. Moreover, our method exhibited remarkable enhancements in execution time, with compression and decompression processes averaging 1.93 seconds. LICA-CS advances the state-of-the-art in lossless image compression, promising enhanced efficiency and effectiveness in image compression technologies.
LICA-CS: Efficient Lossless Image Compression Algorithm via Column Subtraction Model Al Qerom, Mahmoud; Otair, Mohammad; Meziane, Farid; AbdulRahman, Sawsan; Alzubi, Maen
Journal of Robotics and Control (JRC) Vol. 5 No. 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.21834

Abstract

Driven by the unprecedented amount of data generated in the last few decades, data storage and communication are becoming more challenging. Although many approaches in data compression have been developed to alleviate these challenges, more efforts are still needed, especially for lossless image compression, which is a promising technique when critical information loss is not allowed. In this paper, we propose a new algorithm called the Lossless Image Compression Algorithm using a Column Subtraction model (LICA-CS). LICA-CS is efficient, low in complexity, decreases the image bit-depth, and enhances state-of-the-art performance. LICA-CS first implements a color transformation method as a pre-processing phase, which strategically minimizes inter-channel correlations to optimize compression outcomes. After that, a novel subtraction method was developed to compress the image data column-wise. We tackle the similarity and proximity of pixel values within adjacent columns, which offers a distinct advantage in reducing image size observing a significant size reduction of 71%. This is achieved through the subtraction of neighboring columns. The conducted experiments on colored images show that LICA-CS outperforms existing algorithms in terms of compression rate. Moreover, our method exhibited remarkable enhancements in execution time, with compression and decompression processes averaging 1.93 seconds. LICA-CS advances the state-of-the-art in lossless image compression, promising enhanced efficiency and effectiveness in image compression technologies.
Co-Authors AbdulRahman, Sawsan Al Qerom, Mahmoud Al-Sawwa, Jamil AlKasassbeh, Mouhammd Almseidin, Mohammad Kovacs, Szilveszter Meziane, Farid Otair, Mohammad