Volume-12 Issue-6, March 2024 – International Journal of Recent Technology and Engineering (IJRTE)

E798412050124

IJRTE Journal — Wed, 27 Mar 2024 10:05:23 +0000

Design and Analysis of Simple Compact MIMO Antenna for Ultra-Wide Band Application
Shivanand Konade¹, Manoj Dongre²

¹Shivanand Konade, Department of Electronics and Telecommunication, Ramrao Adik Institution of Technology, Dr. D. Y. Patil University Nerul, Navi Mumbai (M.H), India.

²Dr. Manoj Dongre, Department of Electronics and Telecommunication, Ramrao Adik Institution of Technology, Dr. D. Y. Patil University Nerul, Navi Mumbai (M.H), India.

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The proposed research presents a compact two-port Multiple Input Multiple Output (MIMO) antenna for Ultra-Wide Band (UWB) applications. The two symmetrical radiators consist of the presented antenna, developed on an FR4 substrate, with an overall size of 20 × 44.1 × 1.6 mm³. The microstrip line is fed with a 50Ω for the proposed antenna. The lower than isolation is < −25 dB from 3.1 to 10.6 GHz. The gain is 4.3dB, and the Envelope Correlation Coefficients (ECC) < 0.01. Radiators were used for better isolation in the F-shaped stub. The size of the vertical block of stub is 12.3 × 5 mm. Antenna parameters are evaluated for return loss, ECC, DG, gain, radiation pattern, and isolation.

Keywords: Compact, ECC, Isolation, MIMO, and UWB antenna
Scope of the Article: MIMO Antenna

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F801812060324

IJRTE Journal — Wed, 27 Mar 2024 09:59:20 +0000

Gibbs Haversine Reinforcement Learning Based Handover for 5g Enabled Seamless Mobility in Wireless Network
T. Vidhya¹, C. Chandrasekar²

¹T. Vidhya, Research Scholar, Periyar University, Salem.

²Dr. C. Chandrasekar, Professor, Periyar University, Salem.

Abstract: Seamless Mobility (SM) is crucial for bringing about better Quality of Service (QoS), like minimum handover latency with maximum throughput in 5G networks. In this work, a method called Jenkin Impulse Response Filtering and Reinforcement Learning-based Gibbs Haversine Distribution (JIRF-RLGHD) is designed for the optimal selection of target cells in the handover process to ensure seamless mobility. The JIRF-RLGHD method is split into two sections. They are predicting the signal quality of both serving and adjacent wireless nodes using the Box-Jenkins Impulse Response Filtering model. The second task involves applying a Reinforcement Learning-based Gibbs Haversine Distribution for the optimal selection of target cells during handover, ensuring seamless mobility in a wireless network. The overall proposed method was simulated on a Python programming interface. The simulation results reveal that the JIRF-RLGHD method offers a higher delivery rate and handover success with lower handover latency at a minimal packet loss rate. Numerical results show that the JIRF-RLGHD method performs better in terms of data delivery rate by 18% and handover latency by 33% compared to existing methods.

Keywords: Fifth Generation, Seamless Mobility, Quality of Service, Box Jenkin, Impulse Response Filter, Reinforcement Learning, Gibbs Haversine Distribution
Scope of the Article: Next Generation Internet & Web Architectures

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F801612060324

IJRTE Journal — Wed, 27 Mar 2024 09:52:05 +0000

Impact of Tire Shape on Localization Accuracy in Piping Inspection Robots
Hirofumi Maeda

Hirofumi Maeda, Department of Information Science and Technology, National Institute of Technology (KOSEN), Yuge College, Ehime Prefecture, Japan.

Abstract: The sewerage pipes laid in Japan are extensive, spanning approximately 470,000 km, with most of them constructed during the high economic growth period from around 1955 to around 1973. According to indicators from the Ministry of Land, Infrastructure, Transport, and Tourism, the service life of sewerage pipes is estimated at 50 years. This suggests that many sewer pipes installed across Japan over 50 years ago are becoming obsolete. Consequently, piping inspections using robots have commenced in Japan. Currently, stand-alone types that a single robot can inspect are garnering attention. Meanwhile, we have been conducting research and development to implement a small, portable, and stand-alone piping inspection robot. Furthermore, numerous stand-alone types have been employed to prevent falls by adjusting the tire shape or the distance between the axles. However, this hardware approach does not entirely prevent falls. Therefore, we have opted for a software approach to explore measures that prevent falls by controlling driving, aiming to achieve the advanced localisation required for this purpose. We are currently in the stage of verifying the localization. However, accurately measuring the robot’s position and orientation using general measuring instruments is challenging due to the curved piping. Hence, we developed a specialised three-dimensional position-measuring instrument for use with piping inspection robots. In this paper, we utilise the instrument to examine the influence of tire shape on localisation accuracy. Additionally, we demonstrate the effectiveness of the localization method in an environment where tire shape does not affect the outcome.

Keywords: Localization, Tire Shape, Estimation Error, Inspection Robot, Water Pipe
Scope of the Article: Robotics

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F801212060324

IJRTE Journal — Wed, 27 Mar 2024 09:45:36 +0000

Deep Learning for Real-time Affective Hand Gesture Recognition in EMASPEL
Mohamed Ben Ammar¹, Jihane Ben Slimane², Taoufik Saidani³, Refka Ghodhbani⁴

¹Dr. Mohamed Ben Ammar, Department of Information Systems, Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia.

²Jihane Ben Slimane, Department of Computer Sciences Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia, National Engineering School of Tunis, LR11ES20 Analysis Design and Control of Systems Laboratory, University of Tunis El Manar, Tunis, Tunisia.

³Taoufik Saidani, Department of Computer Sciences Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia.

⁴Refka Ghodhbani, Department of Computer Sciences Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia.

Abstract: This research marks a transformative leap in personalized learning through real-time affective hand gesture recognition in EMASPEL (Emotional Multi-Agents System for Peer-to-peer E-Learning), an educational platform. Our deep learning model, a meticulously crafted ensemble of convolutional and recurrent neural networks, deciphers the unspoken language of emotions embedded within student gestures, accurately capturing both spatial and temporal patterns. This detailed emotional map empowers EMASPEL to tailor its interactions with exquisite precision, addressing frustration, nurturing curiosity, and maximizing student engagement. The impact is profound: students flourish in personalized learning environments, experiencing enhanced outcomes and a newfound connection to their educational journey. Teachers, equipped with real-time emotional insights, provide targeted support and foster a more inclusive and responsive classroom. Beyond gestures, we envision a future enriched by multimodal data integration, encompassing facial expressions, voice analysis, and potentially physiological sensors, to paint even richer portraits of student emotions and cognitive states. Continuous refinement through rigorous longitudinal studies will pave the way for a deeper understanding and ensure responsible implementation. Ultimately, this research reimagines education as a dynamic ensemble of personalised learning, where technology serves as a bridge between teachers and students, unlocking not just academic success but a lifelong love of knowledge.

Keywords: Real-time Affective Hand Gesture Recognition, Deep Learning, EMASPEL
Scope of the Article: Deep Learning

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E798712050124

IJRTE Journal — Wed, 27 Mar 2024 09:37:49 +0000

Optimizing Classification Methods for Online Buyers’ Purchase Intentions in Bangladesh
Ikbal Ahmed¹, Md Mahmudul Hoque², Nayan banik³, Atiqur Rahman⁴, Mohammad Nur-E-Alam⁵, Mohammad Aminul Islam⁶

¹Ikbal Ahmed, Department of CSE, CCN University of Science and Technology, 3500 Cumilla, Bangladesh.

²Md Mahmudul Hoque, Department of CSE, CCN University of Science and Technology, 3500 Cumilla, Bangladesh.

³Nayan banik, Department of CSE, Comilla University, 3500 Cumilla, Bangladesh.

⁴Atiqur Rahman, School of Science Engineering, Chittagong Independent University, Jamal Khan, Bangladesh.

⁵Mohammad Nur-E-Alam, Institute of Sustainable Energy, Universiti Tenaga Nasional, Jalan IKRAM- UNITEN, 43000 Kajang, Selangor, Malaysia.

⁶Mohammad Aminul Islam, Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.

Abstract: The classification of online buyers’ purchasing intentions is of paramount importance, especially in the context of the period of the COVID-19/post-COVID-19 pandemic, as it carries significant implications for the business industry. However, effectively managing the diverse, ever-changing intentions of individual Internet customers remains a challenging task. This study aims to enhance the classification techniques used to categorise various types of online buyers’ purchasing intents in Bangladesh. A comprehensive analysis of different classification algorithms reveals that the Random Forest algorithm outperformed other methods, achieving exceptional accuracy rates of 99.9% in training and 89.7% in testing. Conversely, the Gaussian Naive Bayes algorithm demonstrated comparatively lower accuracy, with training and testing accuracies of 80% and 79%, respectively. This study contributes not only to a better understanding of online buyers’ purchase intentions in Bangladesh but also provides valuable insights into the business industry. Moreover, our work highlights the potential for future investigations in recognising Bangla numerals through gestures to enhance the accuracy of categorising online buyers’ intended purchases. This research serves as a stepping stone for further advancements in classifying and understanding online buyers’ purchase intentions, ultimately fostering more accurate decisionmaking in the realm of E-commerce in Bangladesh.

Keywords: Machine Learning, Online Purchase Intention, Random Forest, MLP Classifier, Decision Tree Classifier.
Scope of the Article: Classification

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F799812060324

IJRTE Journal — Wed, 27 Mar 2024 09:31:06 +0000

Exploring Investor Awareness and Perceptions of Cryptocurrency
Maheshwari Chikanal¹, Sowmya Sathish², Satish Chandra³

¹Maheshwari Chikanal, Department of Management Studies, Rajarajeswari College of Engineering Bangalore (Karnataka), India.

²Sowmya Sathish, Department of Management Studies, Don Bosco Institute of Technology, Bangalore (Karnataka), India.

³Satish Chandra, Department of Management Studies, Jnanavikas Institute of Technology, Bangalore (Karnataka), India.

Abstract: In recent years, cryptocurrency has emerged as a decentralized virtual asset operating across a vast network of computers, free from centralized governmental control. Proposed legislation aims to establish a framework for a digital currency issued by the Reserve Bank of India (RBI). This study examines the transformative impact of technology on payment systems and the level of awareness among investors regarding cryptocurrency. It examines the factors that influence this awareness, particularly following India’s surge in Bitcoin adoption, which has notably attracted the younger generation to cryptocurrency. Conducting this study involved gathering responses from 110 investors using a non-probability snowball sampling method. The majority of investors surveyed fell within the 18 to 24 age range. Findings indicate that awareness predominantly spreads through social circles and online platforms, with male respondents exhibiting higher awareness levels than their female counterparts. Moreover, higher educational qualifications are correlated with increased cryptocurrency awareness. Notably, a significant portion of respondents expressed interest in investing in cryptocurrency.

Keywords: RBI, Probability, Cryptocurrency, Bitcoin.
Scope of the Article: Industrial and Manufacturing Engineering

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F799912060324

IJRTE Journal — Wed, 27 Mar 2024 09:25:08 +0000

The Estimation of Battery State of Charge using Corny Network
Ismail¹, Firdaus², Rakiman³, Daddy Budiman⁴, Sardani⁵

¹Ismail, Department of Electrical Engineering Department, Politeknik Negeri Padang, Padang, Indonesia.

²Firdaus, Department of Electrical Engineering Department, Negeri Padang, Padang, Padang, Indonesia.

³Rakiman, Department of Mechanical Engineering, Politeknik Negeri Padang, Padang, Indonesia.

⁴Daddy Budiman, Department of Mechanical Engineering, Politeknik Negeri Padang, Padang, Indonesia.

⁵Sardani, Department of Electrical Engineering, Politeknik Negeri Padang, Padang, Indonesia.

Abstract: State of charge (SOC) estimation of lithium-ion batteries has been extensively studied, and the estimation accuracy was mainly investigated through the development of various battery models and dynamic estimation algorithms. All battery models, however, contain inherent model bias due to the simplifications and assumptions, which cannot be effectively addressed through the development of various conventional and intelligent computational methods. Consequently, some existing techniques for estimating battery state of charge (SOC) have not been very accurate in predicting battery SOC characteristics, using both traditional and intelligent computation methods. There are some drawbacks to employing deep learning to estimate SOC battery, such as the use of complicated algorithms or networks, overfitting, and others. The proposed method, the Corny architecture, has a narrow layer design. This design has low computational cost and prevents overfitting. The result shows that the accuracy of the method is very high. The predicted and targeted values are almost merged in a single line. The RMSE and MAX error indexes are very low. The accuracy of the model is acceptable; the electric vehicle battery can be expected to last longer and perform mobility tasks more reliably. Finally, this method also demonstrates that the accuracy of estimating the state of charge (SOC) of a battery in an electric vehicle can be improved by utilising narrow learning layers.

Keywords: Battery, Corny Architecture, Electric Vehicle, State of Charge.
Scope of the Article: Computer Architecture and VLSI

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F799712060324

IJRTE Journal — Wed, 27 Mar 2024 09:15:31 +0000

Multiplier Design Based on Booth and Sequential Algorithm
Vaishali Sharma¹, R. P. Agarwal²
¹Vaishali Sharma, Department of Electronics and Communication Engineering, Shobhit Deemed University, Meerut (Uttar Pradesh), India.

²R. P. Agarwal, Department of Electronics and Communication Engineering, Shobhit Deemed University, Meerut (Uttar Pradesh), India.

Abstract: Research on cellular networks and low-power electronic devices has ramped up in recent years, thanks to the proliferation of portable and mobile systems. Many portable applications in the present day require low power (smaller and more efficient batteries) and more milliampere-hours than before. As a result, designing low-power devices has become increasingly important as a performance criterion. Multipliers, arrangement (it is an organised set of adders), and delay make up the basic construction of an infinite impulse response filter. This paper presents an analytical procedure for performance evaluation to reduce time and RAM usage throughout the computation process. The Booth and Sequential multipliers have been used to simulate and implement in this article. Using the Sequential and Booth Algorithms, respectively. A comparative study is carried out using Xilinx 14.7 with the Virtex 5 family of devices for bit lengths of 4, 8, 16, and 32.

Keywords: FPGA, LUTs, Memory, Booth and Sequential Algorithms.
Scope of the Article: FPGAs

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