Existing modification-based linguistic steganography (MLS) often suffers from semantic distortion, compromising imperceptibility and security. To address this issue, we propose Low-Distortion Linguistic Steganography(LDStega), which enhances semantic fidelity via semantic compensation substitutions. LDStega utilises an augmented masked language model (MLM) with a dynamic masking strategy to generate contextually coherent candidate words. To ensure substitution integrity, we introduce a BART-based evaluation to quantitatively assess contextual appropriateness. Furthermore, a semantic compensation strategy is proposed to refine subsequent word choices based on semantic fidelity feedback rather than secret messages, effectively mitigating collocation mismatches and unintended distortions. Extensive experiments demonstrate that LDStega outperforms baseline methods in text quality, semantic similarity, and resistance to steganalysis.
Energy efficiency (EE) is a critical metric in wireless communication systems. It measures the balance between data transmission and energy consumption. An optimal EE is key objective due to demand for higher data rates and reliable connections. Modern communication systems use advanced techniques such as Massive multiple-input multiple-output (MIMO), orthogonal frequency-division multiplexing (OFDM) and hybrid methods to improve efficiency. The present work focuses on power, two block resource algorithm (TBRA) and signal-to-noise ratio (SNR). Here, the transmitter section used a block structure to transmit data symbols. The resource block is divided into sub channels and sub symbols for efficient data transmission. The proposed method is evaluated against dynamic resource optimisation, DRL-Based Resource Allocation, and Federated DRL-Based D2D scheduling. The proposed TBRA demonstrates superior energy efficiency across multiple performance metrics. Additionally, focused on the factors influences maximises the energy efficiency in wireless systems. Evaluated the impact of SNR, power and resource allocation on EE. Compare EE across different transmission massive MIMO, MIMO-OFDM and hybrid approaches.
Existing face deepfake detection methods mainly rely on extracting specific forgery patterns (such as facial features, noise characteristics, and frequency domain information) to enhance the performance of detection models. However, these learning strategies, which depend on specific forgery patterns, limit the model's generalisation ability when facing diverse forgery types, making it difficult to effectively identify fake images with unknown forgery patterns. To address this issue, a novel deepfake detection framework FARL is proposed based on adaptive feature aggregation and reconstruction learning, which enhances the model's sensitivity to forgery traces, thereby significantly improving detection performance. Specifically, a cross feature aggregation encoder (CFAE) is introduced to enhance the model's sensitivity to forgery traces through an attention refinement block and a cross fusion strategy. Additionally, a block feature aggregation decoder (BFAD) is implemented to aggregate high-resolution feature maps from encoder blocks and low-resolution feature maps from the previous decoder block. Finally, the differences between the original image and the reconstructed image are utilised as a guide. Experimental results on several benchmark datasets demonstrate the effectiveness of the proposed method for deepfake detection.
This paper provides a comprehensive review of the evolution, standardisation, applications, and advancements of power line communication (PLC) technologies. It presents a historical overview of key standards, from EN 50065 to the latest IEEE 1901c amendment, alongside an examination of significant patents and licensing efforts. The analysis highlights the diverse roles of PLC in smart grids, industrial automation, electric vehicle infrastructure, and broadband access. A central contribution of this work is its focus on the emerging integration of Artificial Intelligence (AI) into PLC systems, a topic not yet extensively synthesised in the literature. We show how AI augments PLC by enabling adaptive fault detection, intelligent security, noise mitigation, and performance optimisation under variable conditions. By consolidating global research and practical implementations, this review identifies a unique gap at the intersection of PLC and AI, offering insights into how their convergence can enhance versatility, resilience, and scalability. Finally, the study outlines future research avenues, including hybrid communication architectures, sustainable designs, and AI-driven frameworks for secure and scalable PLC innovations.
Secret sharing, as a crucial method for privacy protection, has seen rapid development in fields such as cloud computing and the Internet of Things in recent years. As a very popular information cover at present, QR code is used far more frequently than traditional images, so the application of secret sharing in QR code has highly promising. However, because the QR code is often presented in paper form, it is easy to be stained or damaged; At the same time, stego-QR codes may be tampered with by attackers during transmission. These issues will inevitably lead to bit errors in stego-QR code, and the existing research schemes are unable to recover the secret losslessly. To address this, this paper proposes a QR code secret sharing scheme based on Extended Hamming Code. This scheme not only ensures high embedding capacity but also implements error correction capability of stego-QR codes, which can accurately detect and correct bit errors in the stego-QR code, and then recover the secret. Both theoretical analysis and experimental results demonstrate the effectiveness and practicality of this scheme.
This research paper, presents a coplanar waveguide (CPW) feed Circular Slotted Microstrip Patch Antenna (CS-MPA) with miniaturised ground structure, which operates at the n78 band (3.2-3.8 GHz) and resonates at LTE-42 Band (3.55 GHz) is presented. The antenna structure (single-element) designed is of 55 & times; 30 & times; 1.6 mm(3) on FR-4 substrate material with 1.6 mm thickness, dielectric constant (epsilon value) of substrate is 4.6 and the loss tangent value is of 0.001. The antenna offers reflection coefficient value of less than -10 dB over the entire n78 band of interest and VSWR value lies between 1 and 2. The Gain and directivity of the designed antenna is 3.154 dBi and 5.266 dBi at 3.55 GHz respectively. The overall efficiency of the antenna is 61.485% at 3.55 GHz frequency. The orthogonally placed 4 element MIMO antennas with the overall dimensions of 100 & times; 100 & times; 1.6 mm(3) exhibits better isolation between the adjacent antenna elements.
A computer-based framework leveraging deep learning was developed for automated skin disease diagnosis, addressing the inaccuracies and inconsistencies of traditional manual methods. The system employs a two-stage process. First, an Adaptive Refined UNetV4 (ARUNetV4) performs disease segmentation by focusing on fine-grained lesion details while suppressing noise. The ARUNetV4's hyperparameters are optimised using the enhanced random variable-based red panda optimisation (ERV-RPO) algorithm. In the second stage, the segmented images are classified using a hybrid Vision Transformer with Residual DenseNet (ViT-RDNet). This model combines ViT's global contextual understanding with RDNet's local feature extraction to overcome visual similarities between different diseases. The framework demonstrated superior performance against existing models, achieving 96% accuracy on Dataset-1 for classification and 95.04% accuracy on Dataset-2 for segmentation.
High-performance and reliable multimedia communication is critical for modern Internet of Things (IoT) applications such as smart healthcare, wearables, homes, and industrial automation. Cross-layer protocol designs have emerged to improve network performance by enabling coordination across protocol layers. The Internet of Medical Things (IoMT) protocol applies cross-layer optimisation to reduce energy usage and latency but faces scalability and real-time issues in dense networks due to increased computational complexity. To address this, enhancements like directional antennas and the dual sensing MAC (DSDMAC) protocol were introduced at the medium access control (MAC) layer to improve channel access and reduce hidden terminal issues, forming the basis for the MAC cross layer communication protocol (MCROSS). Building on this, the optimised node clustering protocol (ONCP) introduces a modified PSO-based clustering method for intelligent selection of cluster heads and relay nodes. It uses a fitness function that considers multiple parameters (packet forwarding rate (PFR), residual or remaining energy rate (RER), congestion rate (CR)), resulting in improved energy efficiency, load balancing, and reliability. Comparative analysis confirms ONCP's superior performance across diverse metrics.
