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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Ala Walid, Md. Abul
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Electrical & Electronics Engineering

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An Adam based CNN and LSTM approach for sign language recognition in real time for deaf people Kumer Paul, Subrata; Ala Walid, Md. Abul; Rani Paul, Rakhi; Uddin, Md. Jamal; Rana, Md. Sohel; Kumar Devnath, Maloy; Rahman Dipu, Ishaat; Haque, Md. Momenul
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6059

Abstract

Hand gestures and sign language are crucial modes of communication for deaf individuals. Since most people can't understand sign language, it's hard for a mute and an average person to talk to each other. Because of technological progress, computer vision and deep learning can now be used to count. This paper shows two ways to use deep knowledge to recognize sign language. These methods help regular people understand sign language and improve their communication. Based on American sign language (ASL), two separate datasets have been constructed; the first has 26 signs, and the other contains three significant symbols with the crucial sequence of frames or videos for regular communication. This study looks at three different models: the improved ResNet-based convolutional neural network (CNN), the long short-term memory (LSTM), and the gated recurrent unit (GRU). The first dataset is used to fit and assess the CNN model. With the adaptive moment estimation (Adam) optimizer, CNN obtains an accuracy of 89.07%. In contrast, the second dataset is given to LSTM and GRU and a comparison has been conducted. LSTM does better than GRU in all classes. LSTM has a 94.3% accuracy, while GRU only manages 79.3%. Our preliminary models' real-time performance is also highlighted.
For S-band WLAN applications, a patch antenna design, simulation, and optimization Ahmed, Md. Eftiar; Pranto, Biprojitt Saha; Rana, Md. Sohel; Faruq Shakil, Md. Omar; Ala Walid, Md. Abul; Arin, Ifat; Mondal, Saikat; Chooyan, Samanta Mostafa
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1613-1623

Abstract

A rectangular microstrip patch antenna for 2.45 GHz is designed, tested, and analyzed in this study. It uses two substrate materials (design I and II) with different permittivity levels. RT5880 (design-I) and FR-4 (design-II) substrates have a thickness of 1.57 mm and 1.6 mm, respectively. Design-I and design-II substrates have relative permittivity of 2.2 and 4.3, respectively. Performance and efficiency are considered due to the substrate material's relative permittivity and thickness; return loss (S11), voltage standing wave ratio (VSWR), gain, directivity, surface current, and efficiency. Design II and design I have 3.25 dBi and 8.089 dBi gains, respectively, and 5.92 dBi and 8.64 dBi directivity, respectively. Design I had the best antenna efficiency, 93.64%, compared to design II, 54.96%. In contrast to the design I and design II, which had return losses (S11) of -53.29 dB and -51.38 dB, each of the suggested antennas had a return loss (S11) of more than -50 dB. The VSWR for design I is 1.0043, while the Design II material is 1.0054. This study aims to reduce return loss (S11) and close the VSWR to 1. This proposed design improves antenna gain, directivity, and efficiency for future wireless applications on wireless local area networks (WLANs).
Co-Authors Ahmed, Md. Eftiar Arin, Ifat Chooyan, Samanta Mostafa Faruq Shakil, Md. Omar Haque, Md. Momenul Kumar Devnath, Maloy Kumer Paul, Subrata Mondal, Saikat Pranto, Biprojitt Saha Rahman Dipu, Ishaat Rana, Md. Sohel Rani Paul, Rakhi Uddin, Md. Jamal