The 2024 2nd International Conference on Information and Communication Technology (ICICT) proudly announces the following 3 papers (ordered by Paper ID) for best paper awards.
#33 A Compact Wideband Patch Antenna for Ka-band and 5G mm-wavelength Applications
Md. Shajib Hossain (Hajee Mohammad Danesh Science and Technology University ); MD. Fazle Hasan Mihad (Hajee Mohammad Danesh Science and Technology University); Mahabuba Khatun (Hajee Mohammad Danesh Science and Technology University); Jannatun Ferdous ( Hajee Mohammad Danesh Science and Technology University); Mahfujur Rahman (Hajee Mohammad Danesh Science and Technology University )*
Abstract: In the current era, 5G technology is crucial for fast and secure data communication. A wide bandwidth allows high data rates and the ability to simultaneously manage large amounts of information. The recommended antenna enables a wide bandwidth of 9.94 GHz, covering frequencies from 27.92 GHz to 37.86 GHz, ideal for Ka-band and 5G applications. This compact antenna, with dimensions of 7×8×0.8 mm3, employs Rogers RO4350 (lossy) material as the substrate, featuring a dielectric constant (ϵr) of 3.66 and a loss tangent (δ) of 0.0037. It achieves a peak gain of 7 dBi and a directivity of 7.99 dBi at 37.86 GHz, along with a radiation efficiency of 80%. The antenna also demonstrates a minimum return loss of -27.76 dB at 35.92 GHz and maintains VSWR within the acceptable range across the entire frequency band. These outcomes affirm that the antenna is highly suitable for 5G mobile communication and various Ka-band applications.
#104 An Autoencoder-based Approach of Automatic Writer Identification from Biscriptual Characters
Rifa Tabassum Mim (RUET)*; Shah Ariful Hoque Chowdhury (RUET); Al Nahian Mugdho (Rajshahi University of Engineering & Technology)
Abstract: In the last decade, automatic writer identification using a convolutional neural network (CNN) has been well studied. For further performance improvement of the writer identification task, a generative adversarial network-based approach is proposed in this paper to find out the corresponding writer of the given handwriting. This is done using an autoencoder model as the generator and a CNN-based classifier model as the discriminator. The autoencoder network generates synthetic images from the input real images which are then fed to the discriminator along with input real images. The synthetic images provide crucial features to the classifier network. Thus, the autoencoder is used as a feature extractor to aid the discriminator for classification. In this work, autoencoder and discriminator models are trained simultaneously on a self-captured dataset containing handwritten characters from two scripts: Bangla and English written by 24 writers three times. Writer identification accuracy of the proposed autoencoder-based approach is 94.24% with a relative improvement of 2.54% over the accuracy of 91.85% obtained using the same discriminator without the autoencoder. The proposed classifier also outperforms prior models where the accuracy of the second-best model is 90.07%.
#120 A robust framework to secure IoT sensor data using Elliptic Curve Cryptography based CP-ABE and Secret sharing scheme
Anika Bushra (Rajshahi University of Engineering & Technology)*; Mahit Kumar Paul (Rajshahi University of Engineering & Technology)
Abstract: The growth of the Internet of Things (IoT) sector has ushered in an unprecedented era of connectedness, enabling a profusion of applications ranging from smart homes to industrial automation. Researchers predict that by 2030, there will be about 50 billion IoT devices and gadgets globally, completely changing how we interact with our environment. However, because these interconnected systems are vulnerable to many cyber attacks, the rise of IoT devices has also raised worries about data security and privacy. This paper investigates the potential of cryptographic approaches to mitigate IoT data security issues. It proposes an all-encompassing framework to protect IoT data from various threats and vulnerabilities. The framework leverages the robustness of the Advanced Encryption Standard (AES) as the primary data encryption technology. Additionally, it integrates ciphertext-policy attribute-based encryption (CP-ABE) in conjunction with Elliptic Curve Cryptography to enable fine-grained access control, ensuring that only authorized entities can access specific IoT data. Furthermore, to fortify the encryption process, the framework implements a secret sharing method for the encryption key, distributing the key across multiple entities, reducing the risk of a single point of failure. The suggested framework seeks to improve the security posture of IoT systems by integrating these cryptographic algorithms and guaranteeing the confidentiality, integrity, and accessibility of data in IoT contexts.