This manuscript presents the design and evaluation of a flexible, optically transparent monopole antenna tailored for sub-6 GHz 5G applications. The antenna utilizes a polyimide substrate and a meshed radiating structure to balance electromagnetic performance with high optical transparency. Three variations, non-meshed, circularly-meshed, and square-meshed, were designed and analyzed through CST Microwave Studio simulations. The square-meshed design achieved the best tradeoff, offering a transparency of $79.89 \%$, a peak realized gain of 3.39 dBi, and stable performance under 30° mechanical bending. The antenna demonstrates omnidirectional radiation patterns, wide impedance bandwidth ($2.83-3.8 \mathrm{GHz}$), and strong resilience to deformation, confirming its suitability for integration into wearable, transparent, and conformal devices in next-generation 5G systems.

With the rapid advancements in quantum computing, traditional cryptographic schemes like Rivest-Shamir-Adleman (RSA) and elliptic curve cryptography (ECC) are becoming vulnerable, necessitating the development of quantum-resistant algorithms. The National Institute of Standards and Technology (NIST) has initiated a standardization process for PQC algorithms, and several candidates, including CRYSTALS-Kyber and McEliece, have reached the final stages. This paper first provides a comprehensive analysis of the hardware complexity of post-quantum cryptography (PQC) in embedded systems, categorizing PQC algorithms into families based on their underlying mathematical problems: lattice-based, code-based, hash-based and multivariate / isogeny-based schemes. Each family presents distinct computational, memory, and energy profiles, making them suitable for different use cases. To address these challenges, this paper discusses optimization strategies such as pipelining, parallelization, and high-level synthesis (HLS), which can improve the performance and energy efficiency of PQC implementations. Finally, a detailed complexity analysis of CRYSTALS-Kyber and McEliece, comparing their key generation, encryption, and decryption processes in terms of computational complexity, has been conducted.

As intravehicular communication systems become increasingly complex and high-dimensional within the evolving paradigm of the Internet of Vehicles (loV), the need for resilient and intelligent cybersecurity mechanisms is rendered more critical. In this paper, a hybrid deep neural architecture is proposed, integrating Convolutional Neural Networks (CNNs) with autoencoder frameworks to extract both spatial feature hierarchies and latent structural patterns from multivariate vehicular telemetry. Unlike conventional approaches in the literature that rely on abstracted or non-vehicular datasets, the proposed method is trained and evaluated on the CICIoV2024 dataset-collected from a production-grade 2019 Ford vehicle under five distinct adversarial scenarios, including spoofing and Denial of Service (DoS) attacks. Through extensive experimentation, the effectiveness of the model is validated, achieving a classification accuracy of 98.88%, a mean precision of 93. 00%, recall of 93. 83%, and a Fl score of 92. 26%.

As intravehicular communication systems become increasingly complex and high-dimensional within the evolving paradigm of the Internet of Vehicles (IoV), the need for resilient and intelligent cybersecurity mechanisms is rendered more critical. In this paper, a hybrid deep neural architecture is proposed, integrating Convolutional Neural Networks (CNNs) with autoencoder frameworks to extract both spatial feature hierarchies and latent structural patterns from multivariate vehicular telemetry. Unlike conventional approaches in the literature that rely on abstracted or non-vehicular datasets, the proposed method is trained and evaluated on the CICIoV2024 dataset– collected from a production-grade 2019 Ford vehicle under five distinct adversarial scenarios, including spoofing and Denial of Service (DoS) attacks. Through extensive experimentation, the effectiveness of the model is validated, achieving a classification accuracy of 98.88%, a mean precision of 93. 00%, recall of 93. 83%, and a F1 score of 92. 26%.

In Photovoltaic Systems, detecting partial shading is critical for optimizing energy output and ensuring system reliability. This paper presents a novel method for partial shading detection in photovoltaic systems, leveraging transfer learning to improve accuracy and efficiency. By utilizing a pre-trained InceptionV3 model, discriminative features are extracted from time series signals. To align with the architectural requirements of InceptionV3, these time series signals are transformed into 2D pixel-mapped images. The proposed model is rigorously validated using both balanced and unbalanced scenarios on the Grid-connected PV System Faults (GPVS-Faults) dataset, achieving remarkable accuracies of 96.73% and 94.59% in the respective scenarios. This approach represents a significant advancement in accurately identifying partial shading, thus enhancing the performance and reliability of solar energy systems.

This paper investigates the communication performance of a sleeve dipole antenna used in the Rashid rover, part of the Emirates Lunar Mission, at a frequency of 2.42 GHz. The proposed antenna serves as the primary antenna in the Rashid rover, and its communication performance is evaluated through various scenarios using a simulated model. Initially, a stand-alone antenna was designed, and the results showed that the sleeve dipole antenna provides an omnidirectional radiation pattern and reasonable gain at 2.42 GHz. Subsequently, the model was developed using a simplified model of the lunar ground surface in which the performance of the antenna was evaluated. Finally, the antenna's performance was illustrated in the context of its location, which is close to the metallic mast of the rover which substantially affects antenna parameters. It was also observed that although the electrical properties of the lunar regolith had no significant effect on crucial antenna parameters such as operating frequency and bandwidth; placing the antenna closer to the lunar regolith causes modification in the pattern which results in a moderate increase in gain.

Cyber-Physical Systems (CPS) have been growing in the evolution of interaction with the physical world. CPS can control and manages applications of the physical world around us. However, most traditional CPS-based systems have been designed and developed within the centralized system, which can violate security, trust, and privacy (STP). Blockchain is a potential solution to realizing CPS. It can provide data security and privacy through block hash generation and transaction validation schemes. Blockchain applications can enhance the performance of CPS through the peer-to-peer (P2P) communication mechanism. In this paper, we have described several challenges of CPS applications (i.e., smart grids and connected vehicles) and provided blockchain-based solutions to address STP challenges. The benefits of blockchain in CPS have been discussed in this paper. We also proposed a blockchain-enabled CPS and discussed the integration process of blockchain in several components of CPSs. The proposed solutions enhance the performance of CPS concerning security, privacy, and trust management.

Person re-identification (PRe-ID) is a crucial task in security, surveillance, and retail analysis, which involves identifying an individual across multiple cameras and views. However, it is a challenging task due to changes in illumination, background, and viewpoint. Efficient feature extraction and metric learning algorithms are essential for a successful PRe-ID system. This paper proposes a novel approach for PRe-ID, which combines a Convolutional Neural Network (CNN) based feature extraction method with Cross-view Quadratic Discriminant Analysis (XQDA) for metric learning. Additionally, a matching algorithm that employs Mahalanobis distance and a score normalization process to address inconsistencies between camera scores is implemented. The proposed approach is tested on four challenging datasets, including VIPeR, GRID, CUHK01, and PRID450S. The proposed approach has demonstrated its effectiveness through promising results obtained from the four challenging datasets.

This paper proposes an improved version of the Lifelong Topic Model (LTM) called the HC-LTM. The traditional LTM is known to be biased in the domain selection process and does not fully consider the contextual information of target words when determining similarity. The HC-LTM addresses these issues by combining Word2vec cosine similarity and Hellinger distance between topics to identify similar words and topics, leading to better selection and more effective knowledge acquisition during iterative learning. Additionally, the problem of repetitive calculation of cosine distance is resolved by pre-loading the similarity matrix of word vectors and using Hellinger distance to calculate topic similarity accelerates the convergence of the model. The experimental results on the Amazon product review dataset demonstrate the effectiveness of the HC-LTM model, with a 49% improvement in topic consistency and a 44.57% reduction in time compared to the LTM model.

The end-users’ acceptance of electronic government applications is crucial for the effective delivery of public services. This study intends to investigate the factors that influence the end-users' acceptance of smart-government services.This paper identifies key determinants of the end-users’ acceptance of smart government services in the UAE to develop a theoretical model which is tested empirically, using the partial least squares structural equation modelling. This study examines the relationship among the factors that influence the adoption of smart government applications, along with the moderation effects of gender, age, and experience of the end-users on this linkage. The paper reveals that performance expectancy is the strongest factor influencing adoption of smart government, followed by trust in government, effort expectancy, and social influence. The Multi Group Analysis is used to test the moderation effects of the gender, age, and smart service use experience of the end-users on the relationship between the factors that influence smart-government service adoption.

The concept behind the Internet of Things (IoT) is taking everything and connecting to the internet so that all devices would be able to send and receive data online. Internet of Vehicles (IoV) is a key component of smart city which is an outcome of IoT. Nowadays the concept of IoT has plaid an important role in our daily life in different sectors like healthcare, agriculture, smart home, wearable, green computing, smart city applications, etc. The emerging IoV is facing a lack of rigor in data processing, limitation of anonymity, privacy, scalability, security challenges. Due to vulnerability IoV devices must face malicious hackers. Nowadays with the help of blockchain (BC) technology energy system become more intelligent, eco-friendly, transparent, energy efficient. This paper highlights two major challenges i.e. scalability and security issues. The flavor of edge computing (EC) considered here to deal with the scalability issue. A BC is a public, shared database that records transactions between two parties that confirms owners through cryptography. After a transaction is validated and cryptographically verified generates “block” on the BC and transactions are ordered chronologically and cannot be altered. Implementing BC and smart contracts technologies will bring security features for IoV. It plays a role to implement the rules and policies to govern the IoV information and transactions and keep them into the BC to secure the data and for future uses.

With the exponential increase in a number of web pages daily, it makes it very difficult for a search engine to list relevant web pages. In this paper, we propose a machine learning-based classification model that can learn the best features in each web page and helps in search engine listing. The existing methods for listing have lots of drawbacks like interfacing the normal operations of the website and crawling lots of useless information. Our proposed algorithm provides an optimal classification for websites which has a large number of web pages such as Wikipedia by just considering core information like link text, side information, and header text. We implemented our algorithm with standard benchmark datasets, and the results show that our algorithm outperforms the existing algorithms.

In this paper, several indoor positioning systems are reviewed and a deep neural network (DNN) algorithm based on Scaled Conjugate Gradient (SCG) algorithm is proposed. In the proposed indoor positioning system, Received Signal Strength (RSS) is used as a fingerprint to identify the indoor location in terms of Building and Floor. The performance of the system is evaluated and compared against other machine learning based positioning systems. The accuracy of the proposed DNN is 99% when tested using a standard dataset.

This paper discusses how ad-hoc collaboration boosts the operation of a set of messengers. This discussion continues the research we earlier initiated in theMESSENGER project, which develops data management mechanisms for UDDI registries of Web services using mobile users and software agents. In the current operation mode of messengers, descriptions of Web services are first, collected from UDDI registries and later on, distributed to other UDDI registries. This distribution mode of Web services descriptions does not foster the tremendous opportunities that both wireless technologies and mobile devices offer. When mobile devices are in the vicinity of each other, they can form a mobile ad-hoc network, which enables the exchange of data between these devices without any preexisting communication infrastructure. By authorizing messengers to engage in collaboration, collecting additional descriptions of Web services from other messengers can happen, too.

This work presents an approach for the dynamic management of the content of several Universal Description, Discovery, and Integration (UDDI) registries. By content, it is meant the announcements of Web services that providers post on various distributed UDDI registries. Unlike other initiatives in the field of Web services, our concerns are the following: availability of several UDDI registries, no predefined communication infrastructure between the UDDI registries, and no centralized component to coordinate the UDDI registries. The solution presented in this paper integrates users and software agents into what we call messengers. When a user is in the vicinity of an UDDI registry, his software agent, which resides in his mobile device, interacts with that registry. The objective is to submit the details on Web services that are stored in the mobile device.

The main theme of the paper is the automatic recognition of Arabic printed text using artificial neural networks in addition to conventional techniques. This approach has a number of advantages: it combines rule based (structural) and classification tests; feature extraction is inexpensive; and execution time is independent of character font and size. The technique can be divided into three major steps: The first step is preprocessing in which the original image is transformed into a binary image utilizing a 300 dpi scanner and then forming the connected component. Second, global features of the input Arabic word are then extracted such as number of subwords, number of peaks within the subword, number and position of the complementary character, etc. Finally, an artificial neural network is used for character classification. The algorithm was implemented on a powerful MS-DOS microcomputer and written in C.