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.

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.

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.

We consider the problem of scheduling the transmission of packets in an input-queued switch. In order to achieve maximum throughput, scheduling algorithms usually employ the queue length as a parameter for determining the priority to serve a given queue. In this work, we propose a novel scheme to optimize the performance of a preexisting scheduler. Our main idea is to assist the scheduling decision, considering "messagesâ rather than queue lengths. Such messages are obtained by running an iterative parallel algorithm, inspired by a rigorous belief-propagation approach. We demonstrate that belief-propagation-assisted scheduling is able to boost the performance of a given scheduler, reaching almost optimal throughput, even under critical traffic scenarios.