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EMITTER International Journal of Engineering Technology
Published by Politeknik Elektronika Negeri Surabaya
ISSN : 2355391x     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
EMITTER International Journal of Engineering Technology is a BI-ANNUAL journal published by Politeknik Elektronika Negeri Surabaya (PENS). It aims to encourage initiatives, to share new ideas, and to publish high-quality articles in the field of engineering technology and available to everybody at no cost. It stimulates researchers to explore their ideas and enhance their innovations in the scientific publication on engineering technology. EMITTER International Journal of Engineering Technology primarily focuses on analyzing, applying, implementing and improving existing and emerging technologies and is aimed to the application of engineering principles and the implementation of technological advances for the benefit of humanity.
Arjuna Subject : -
Articles 445 Documents
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Towards Robust Recognition of Handwritten Arabic Characters with Diacritics Using an Incremental Learning Approach Based on CNNs Shugaba, Fatima Aliyu; Ullah Sheikh, Usman; Othman, Mohd Afzan; Khamis, Nurulaqilla; Abdulfattah, Muhammad Habibullah
EMITTER International Journal of Engineering Technology Vol 13 No 2 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i2.982

Abstract

Handwritten Arabic text recognition (HATR) presents unique challenges due to complex character shapes, contextual variations, cursive connections, and the presence of diacritical marks. This study introduces AHAD (Arabic Handwritten Alphabet with Diacritics), a novel benchmark dataset of 71,061 handwritten Arabic character images annotated with five primary vowel diacritics; Fathah, Kasrah, Dammah, Shaddah, and Sukoon, covering 492 distinct classes that combine character identity, contextual form, and diacritic. Leveraging this dataset, we propose an incremental learning framework based on Convolutional Neural Networks (CNNs) to address fine-grained recognition of handwritten Arabic characters with its corresponding diacritics. The model was initially trained on a 114-class dataset of handwritten Arabic characters (in all contextual forms) of non-diacritic characters and fine-tuned in two phases using the AHAD dataset. The two-phase strategy includes output layer expansion, learning rate adjustment, and gradual unfreezing of deeper layers to enhance knowledge retention and prevent catastrophic forgetting. The proposed method achieved a validation accuracy of 92.96% and a test accuracy of 93.26%. Our findings demonstrate the effectiveness of incremental learning for diacritic-aware Arabic handwriting recognition and establish AHAD as a strong baseline for future research in this field.
Design of Optimized Hardware Architecture for Discrete Cosine Transform using Loeffler’s Algorithm S V, CHINMAYI; M B, VEENA
EMITTER International Journal of Engineering Technology Vol 13 No 2 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i2.819

Abstract

The Discrete Cosine Transform (DCT) is the most commonly used transformation technique in image processing applications for data compression. It transforms a finite set of data points or pixels into frequency domain in terms of sum of cosine coefficients of various frequencies. This paper proposes an optimized hardware architecture for 2D 8x8 Discrete and Inverse discrete cosine transforms (DCT and IDCT) using Loeffler’s algorithm. The hardware architecture is optimized using efficient adders and multipliers. Modified carry select adders (CSLA) are used to boost the speed, wherein Booth multipliers improve overall performance of the design. The Loeffler’s algorithm consisting of 8-stage pipelined architecture reduces the arithmetic operations per cycle and improves processor efficiency. The front-end RTL design of the proposed architecture is implemented on Virtex-7 FPGA, while the backend design is implemented in Cadence using 45nm CMOS technology. The proposed design possesses 24% lesser area, 25% lesser leakage power and 8.8% lesser delay than the existing designs.
Sentiment Analysis Design and Development for Low Resource Languages in the Case of Telugu Srinivasu Badugu; Suneetha Chittineni; G.L. Anand Babu; G. Sekhar Reddy; S. Vijaykumar; N. Nagalakshmi
EMITTER International Journal of Engineering Technology Vol 13 No 2 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i2.861

Abstract

The use of sentiment analysis has become more widespread because it is necessary to filter and analyze information on the internet. It has a wide range of applications, including monitoring social media market research and opinion mining. Still, this development is restricted to few languages with enough resources. The Telugu language lags behind in this field of study, even though it is the fourth most spoken language in India and generates a vast quantity of data every day. In this research paper, we develop a trustworthy source for sentiment analysis in Telugu. To use in sentiment analysis, the data is annotated with Telugu movie reviews. We extracted 1844 sentences from 100 film reviews. We annotated the data with two annotators and calculated the kappa coefficient to determine the annotators' inter-rater reliability. We obtained a kappa value of 0.90 for 1844 sentences, indicating nearly perfect agreement. After the annotators' disagreements and discrepancies were resolved, 1807 sentences were chosen. For feature extraction, we used two vectorization methods: TF-IDF and Count vectorization. Using the two vectorization methods, we used SVM and Logistic regression. We used two vectorization approaches to test different split ratios such as 80-20%, 70-30%, and 60-40% on SVM and Logistic regression. The outcomes of the various combinations are compared. We discovered that combining TF-IDF with SVM for a 70-30% ratio yields the highest accuracy among the combinations tested on our dataset.
Machine Learning Approaches for Subcluster in IoT Sensor Networks with Hierarchical Clustering and Dendrograms Bajaber, Fuad
EMITTER International Journal of Engineering Technology Vol 13 No 2 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i2.906

Abstract

This research focuses on optimizing IoT Sensor Networks (ISNs) by implementing hierarchical clustering algorithms. Traditional clustering methods often lead to imbalanced energy consumption, impacting network lifetime and performance. Our approach leverages hierarchical clustering to partition the network into a set of clusters. Each cluster has a cluster head and a set of sensor nodes. To enhance data aggregation and energy efficiency, we introduce subclustering within clusters using dendrograms. We assessed performance metrics using simulation, including energy consumption and scalability. The proposed hierarchical clustering methodology significantly improves network lifetime, energy efficiency, and data aggregation.
The Impact of Social Force Model Parameters On Frontier-Based Exploration Performance Irsyad, Asyam; Dewantara, Bima Sena Bayu Dewantara; Setiawardhana
EMITTER International Journal of Engineering Technology Vol 13 No 2 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i2.994

Abstract

Autonomous exploration is one of the most challenging tasks in mobile robotics, particularly in environments that contain dynamic obstacles and require fully autonomous mapping without human intervention. This study addresses the dual problem of enabling navigation in the presence of potential static obstacles and achieving autonomous map building. To solve this, we utilize the Social Force Model (SFM), which offers a behavior-based approach suitable for dynamic and uncertain environments. The objective of this research is to investigate how different SFM parameters—Gain (ks), Radius (rR), and Effective range (ψs)—influence the effectiveness of autonomous exploration. Experiments were conducted using a TurtleBot3 robot in a simulated 155 m² environment, where various parameter combinations were tested. Evaluation metrics included mapping completion, failure types, travel distance, and exploration duration. Results indicate that tuning the SFM parameters significantly affects the robot's ability to explore autonomously and avoid obstacles. Extremely low parameter values led to collisions, while excessively high values caused unstable or inefficient behavior. The Radius parameter had a major impact on spatial awareness, and moderate effective range values contributed to stable tracking. Furthermore, higher frontier sensing latency resulted in longer exploration times. This study provides practical insights into the sensitivity of SFM parameters and offers guidance for optimizing navigation systems for fully autonomous exploration in both simulated and real-world settings.

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