Advancements in multimodal biometrics, which integrate multiple biometric traits, promise to enhance the accuracy and robustness of identification systems. This study focuses on improving multimodal biometric identification by using fingerprint and finger vein images as the primary traits. We utilized the “NUPT-FPV” dataset, which contains a substantial number of finger vein and fingerprint images, which significantly aided our research. Convolutional neural networks (CNNs), renowned for their efficacy in computer vision tasks, are used in our model to extract distinct discriminative features. Specifically, we incorporate three popular CNN architectures: ResNet, VGGNet, and DenseNet. We explore three fusion strategies used in security applications: early fusion, late fusion, and score-level fusion. Early fusion integrates raw images at the input layer of a single CNN, combining information at the initial stages. Late fusion, in contrast, merges features after individual learning from each CNN model. Score-level fusion employs weighted aggregation to combine scores from each modality, leveraging the complementary information they provide. We also use contrast limited adaptive histogram equalization (CLAHE) to enhance fingerprint contrast and vein pattern features, improving feature visibility and extraction. Our evaluation metrics include accuracy, equal error rate (EER), and ROC curves. The fusion of CNN architectures and enhancement methods shows promising performance in identifying multimodal biometrics, aiming to increase identification accuracy. The proposed model offers a reliable authentication system using multiple biometrics to verify identity.

In this communication, a 4-port Multiple-Input-Multiple-Output (MIMO) antenna is analyzed and investigated for vehicle-to-everything (V2X) communication centered at 5.9 GHz. The proposed optimized single antenna consists of a tapered radiating antenna with a defective ground structure fed with stepped impedance transmission line feed. The proposed 4-port MIMO antenna has a dimension of 96×64×0.8 mm3 printed on the FR4 substrate with a relative permittivity of 4.4 and loss tangent of 0.02. To obtain the proposed single antenna unit, parametric analysis, and evolution stages have been investigated and discussed. The impedance bandwidth of the proposed antenna is 5.66 - 6.00 GHz with a peak gain of 7.85 dB and radiation efficiency of 99%. In addition, machine learning techniques such as XG (Extreme Gradient) Boost, Random Forest, and Deep Neural Networks (DNN) were employed in the optimization process to predict and fine-tune the antenna's design parameters. The stacking ensemble method, combining these models, was used to improve the accuracy of the antenna performance prediction. By leveraging machine learning, the final design was achieved more efficiently, significantly reducing the simulation time and enabling more precise parameter tuning for optimal performance. Further, to validate the MIMO antenna characteristics, different diversity parameters have been calculated such as ECC (Envelope Correlation Coefficient), DG (Diversity Gain), CCL (Channel Capacity Loss), and TARC (Total Active Reflection Coefficient). The fabricated antenna is modeled, and measured findings are found to be in coherence with simulated findings.

In the era of rapidly expanding Internet of Things (IoT) ecosystems, ensuring reliable, secure, and real-time anomaly detection remains a pressing challenge due to the high dimensionality, dynamic behavior, and noise-prone nature of sensor data streams. This paper presents a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) for spatial feature extraction and attention-enhanced Recurrent Neural Networks (RNNs) for temporal sequence modeling. The architecture employs adaptive thresholding and an incremental retraining strategy to maintain responsiveness under evolving data distributions. Robust preprocessing pipelines, including noise filtering, missing data imputation, and domain-specific rule validation, ensure data quality prior to inference. Extensive evaluation across three real-world IoT datasets demonstrates the proposed model’s superiority over state-of-the-art baselines such as Swin-Transformer, Gated GNN, and ConvLSTM, achieving up to 4.6% higher F1-score, sub-6 ms inference latency, and resilience to noise and network delays with less than 4% performance degradation. Furthermore, integrated interpretability modules—such as attention heatmaps and SHAP-based feature attribution—facilitate root cause analysis for operational trust. These results position the proposed approach as a scalable, efficient, and explainable solution for real-time anomaly detection in large-scale IoT environments.