This paper presents a compact, multiband, flexible metasurface (flexi-meta) antenna structure consisting of metasurface antenna elements designed exclusively for 5G sub-6 GHz applications. The proposed flexi-meta antenna is realized by first creating a double-negative (DNG) metamaterial unit cell on a flexible (0.4 mm thick) FR-4 substrate. Then, an array of these DNG unit cells is suitably placed inside the rectangular ring-slotted patch using the Theory of characteristic modes (TCM), to achieve optimal performance in the sub-6 GHz band. The resulting flexi-meta antenna is compact with an overall volume of 300 mm 3 ([Formula: see text]). It exhibits a wide bandwidth of 1100 MHz (3.15–4.25 GHz), a peak gain of 5.5 dBi, a return loss of [Formula: see text] dB, and a radiation efficiency of 85%. These characteristics distinguish the flexi-meta antenna as cost effective, compact, conformal, high-performance contender for 5G sub-6GHz and IoT applications.

Social media is the most popular platforms for opinion expression. Sentiment analysis is the process of acquiring information about things, events, and their characteristics out of people's views, assessments, and feelings. Opinion mining is an alternative term for sentiment analysis. In this paper, Enhancing Opinion Mining of Twitter Data with a Deep Convolutional Spiking Neural Network and Balancing Composite Motion Optimization (OMTD-DCSNN-BCMO) is proposed. Initially, the twitter data is obtained from Stanford Sentient Treebank (SST-2) dataset. Then, the data is fed to preprocessing. The pre-processing output is provided to extract the Radiomic features depending on Residual Exemplars Local Binary Pattern (RELBP). The extracted output is provided into the feature selection for choosing ideal features using Piranha foraging Optimization Algorithm. The selected features are provided into Deep Convolutional Spiking Neural Network (DCSNN) for classifying twitter data as negative, positive and neutral. Then, the DCSNN approach is optimized using Balancing Composite Motion Optimization (BCMO) for better performance. The efficacy of proposed technique is examined using performance metrics and method attains 23.32%, 26.07% and 28.51% higher accuracy and 21.92%, 15.03% and 19.15% lesser error rate are evaluated with existing approaches.

In the evolving landscape of intelligent transportation systems, ensuring robust cybersecurity for in-vehicle communication is critical. This paper proposes a hybrid learning framework for intrusion detection in Controller Area Network (CAN)-based autonomous vehicles by integrating a Decision Transformer with a Light Gradient Boosting Machine (LightGBM) classifier. The system incorporates contextual information from raw CAN frames and multimodal sensor data, enabling the construction of temporal trajectories that reflect historical driving behavior. These trajectories are encoded using a transformer-based temporal model to capture dynamic patterns, followed by lightweight boosted tree classification for efficient and interpretable decision-making. To enable real-time inference, the model is implemented on a Zynq UltraScale+ FPGA using hardware modules such as FIFO buffers, digital filters, tanh scalers, and sliding window controllers. The implementation was trained using a Python environment and evaluated on the OCSLab CAN-intrusion dataset comprising over 100,000 frames across multiple attack types, including DoS, Fuzzy, and Impersonation. The proposed system achieves a True Positive Rate of 71.50%, an Attack Detection Rate of 72.00%, and an overall accuracy of 80.05%, while maintaining a low False Positive Rate of 3.50%. Compared to conventional CNN and Rainbow DQN-based models, it demonstrates superior performance in detection, decision latency (under 10 ms), and computational efficiency. This hybrid model presents a scalable, cost-effective, and real-time capable solution for enhancing vehicular cybersecurity, making it highly suitable for deployment in safety-critical autonomous driving environments.

To meet the demand for high-precision biological signal detection in wearable biomedical devices, this paper proposes the design of a fourth-order discrete-time sigma–delta analog-to-digital converter (ADC) employing a multi-stage noise shaping (MASH) 2-2 architecture. In the first-stage sub-modulator, the first integrator adopts a double-sampling structure, which improves noise shaping performance markedly. A novel common-mode feedback (CMFB) circuit has also been designed, which addresses the phase-dependent loading issue in traditional switched-capacitor networks. Notably, a dynamically activated nonuniform quantization mode is innovatively proposed in the second stage. It integrates 2.5-bit coarse quantization with 2-bit fine quantization in specific intervals, enabling high-precision detection of the small-amplitude quantization errors from the first-stage sub-modulator. In addition, the comparator in this stage integrates feed-forward summation capability and retains input offset voltage storage (IOS) technology to reduce the offset voltage. Meanwhile, the designs of dynamically activated nonuniform quantizer and comparator with integrated summation function have reduced the power consumption of the modulator by 19%. In a 180-nm CMOS process, the modulator occupies a layout area of 0.57[Formula: see text]mm 2 and simulation results indicate a peak signal-to-noise-and-distortion ratio (SNDR) of 113.2[Formula: see text]dB. After processing by the cascaded integrator comb (CIC) decimation filter, the ADC achieves a peak SNDR of 115.6[Formula: see text]dB, an effective number of bits (ENOB) of 18.91 bits and a power consumption of 4.36[Formula: see text]mW under a 1.8[Formula: see text]V supply. These performance metrics correspond to a state-of-the-art figure-of-merit (FoM) of 175.1[Formula: see text]dB.

Renewable energy source (RES) production has increased nowadays due to the depletion of fossil fuel reserves and the rise in greenhouse emissions. Incorporating RES into the grid may produce additional problems in power quality, system stability, harmonics and reliability owing to their sporadic nature. The RES is capable of operating in both ON and OFF electrical grid modes since ON-grid power systems are linked to the utility grid, while off-grid systems are not. However, because of the environmental variability, it can be challenging to obtain optimal voltage and extract maximum RES power. It is crucial to derive an optimal solution to obtain the maximum power from RES. Therefore, this paper proposes a bat algorithm for tracking (BAT) with a quantum neural network (QNN) for extracting maximum power from a PV panel. The Bat Optimization Algorithm (BOA) is used to select optimal parameters for QNN. The suggested method is designed via MATLAB/Simulink, and various parameters are considered, in which total harmonic distortion (THD) and efficiency analysis of the proposed method with an ant lion optimized cuckoo search (ALO-CS) algorithm are compared. Simulation and hardware implementation results demonstrate superior performance compared to the ALO-CS method. The proposed approach achieves a peak efficiency of 93.6% in simulation and 93.4% in hardware, with convergence times of 0.1[Formula: see text]s and 0.16[Formula: see text]s, respectively. Harmonic distortion is significantly reduced, with THD values of 0.43% in simulation and 0.7% in hardware using a current control technique. These results confirm that the QNN-BOA method provides faster tracking, higher efficiency and better power quality than conventional maximum power point tracking (MPPT) techniques, making it highly suitable for hybrid RES applications.

Nowadays, the Internet of Things (IoT) is growing rapidly. IoT needs to handle efficient information to handle intelligent applications. There are many applications for Wireless Sensor Networks (WSNs), which act as an important interface between the physical world and the IoT. The continuous object boundary detection is one of the most widely used WSN applications in industry. Traditional WSNs are utilized to track leakage from continuous objects require costly long-term maintenance. To address this issue, sparse WSNs are employed. However, the inaccuracy of sparse networks poses a significant problem when using information about continuous objects to arrange retreat paths for individuals. In this paper, a Robust Sensing-Hole Detection Integrating with Rotation-Invariant Coordinate Convolutional Neural Networks in Sparse Wireless Sensor Network (RSHD-RICCNN-SWSN) is proposed. Initially, fill sensing holes by identifying their virtual points and calculating their variation rates. Then Rotation-Invariant Coordinate Convolutional Neural Networks(RICCNN) is used for detecting the boundary of continuous objects. Finally, Giza Pyramid Construction Optimization Algorithm (GPCOA) is employed for optimal path selection. The proposed RSHD-RICCNN-SWSN method is implemented in MATLAB. The performance measures including Space Significance, Data Significance, Network Lifetime, Energy Consumption, Success Rate, Time Complexity, Detection Accuracy, Network Throughput, and Scalability is evaluated. The proposed RSHD-RICCNN-SWSN method attains 20.58%, 26.73% and 24.62% higher Network Lifetime, 20.98%, 26.66% and 16.32% higher Data Significance, 10.78%, 20.47%, and 12.86% lower Energy Consumption when compared with existing models: Localization and detection of multiple attacks in wireless sensor networks using artificial neural network (LDMA-WSN-ANN), Genetic algorithm for cross-layer-based energy hole minimization in wireless sensor networks (GACL-EHM-WSN), Q-learning dependent vegetation evolution for numerical optimization and wireless sensor network coverage optimization (QLVE-WSN-NOC)respectively.

In a Decentralized Identifier (DID), the Holder does not depend on a third party but controls self-identity information and is authenticated by the Verifier. Therefore, the Verifier can request verification data for access control from the Verifiable Data Registry (VDR) and credentials to authenticate the Holder. Data access control should be used when requesting data access such that only authorized Verifiers can access it. Consequently, studies on secure and efficient data access control have been conducted, and among them a scheme using Ciphertext Policy Attribute-based Encryption (CP-ABE) is underway. However, when the CP-ABE scheme is applied to the DID, the Holder’s extended Self-Sovereign Identity (SSI), which proves that the Holder has approved access to the Holder’s data stored in the VDR, is not ensured. Furthermore, the VDR does not verify the Verifier’s data access rights, resulting in unauthorized verification and illegal access to data by the user. And issue infringement of the Holder’s privacy, where Verifiers can infer the Holder by sharing and connecting the same DID-based Verifiable Presentations (VPs) of the Holder. Also, it leads to overheads in the amount of computation and search time for encryption/decryption. Therefore, in this paper, we propose a data access control for secure authentication by solving the security vulnerabilities of CP-ABE and using a CP-ABE-based dual Verifiable Credential (VC) scheme in DID.