International Journal of Electrical and Computer Engineering Research

Post-Quantum Cryptography in Europe: Risks, Strategy and Portugal as a National Case Study in the NIS2 Transition

2026-05-25T10:46:49+03:00

With Shor’s and Grover’s algorithms compromising the mathematical core of legacy RSA and ECC, the active threat of ‘Harvest Now, Decrypt Later’ (HNDL) campaigns is already accelerating the rollout of Post-Quantum Cryptography (PQC). This paper examines the gap between EU compliance mandates, specifically NIS2 Directive and the Cyber Resilience Act, and the actual engineering of quantum-safe systems. We push 'hybrid cryptography’ as the immediate standard, layering NIST primitives like ML-KEM and ML-DSA over classical protocols to enforce redundancy in constrained environments. We use the NIST IR 8547 framework to organise migration into discovery, prioritisation, and modernisation. Using Portugal as a national case study, this paper examines how EU-level PQC governance is translated into national implementation through the CNCS, QNRC, and PTQCI frameworks. Ultimately, success depends on building PQC expertise in Portugal while rebuilding cryptographic foundations for the quantum era.

Experimental Validation of a Low-Cost IoT-Based Residential Electrical Monitoring System using ESP32 and PZEM-004T

2026-05-25T10:22:30+03:00

The increasing demand for residential electricity and the limited availability of detailed consumption information highlight the need for accessible and reliable monitoring solutions. In many residential settings, users lack visibility of real-time electrical behavior, which restricts their ability to identify inefficiencies or abnormal operating conditions. This paper presents the design and experimental validation of a low-cost IoT-based system for real-time monitoring of electrical parameters in residential environments. The proposed system integrates an ESP32 microcontroller with a PZEM-004T V3.0 measurement module to acquire six electrical variables: voltage, current, active power, energy consumption, frequency, and power factor. Data are transmitted to a cloud-based platform for real-time visualization and storage, enabling continuous monitoring and analysis of electrical consumption patterns. Experimental validation was conducted under real residential operating conditions using inductive, resistive, and electronic household loads. Measurements obtained from the proposed system were compared against a calibrated commercial reference instrument to evaluate measurement consistency and system stability under different electrical consumption profiles. The results show mean absolute errors of 0.31 V for voltage, 0.008 A for current, and 1.16 W for active power, with low percentage deviations under typical residential operating conditions. The findings indicate that the proposed system provides reliable performance for residential monitoring applications, offering a balance between cost, functionality, and implementation simplicity. This makes it suitable for energy awareness, educational use, and preliminary energy auditing in household environments, particularly in contexts where low-cost solutions are required.

LTNIN: Lightweight Transformer Network for Inertial Navigation

2026-06-04T11:06:32+03:00

Pedestrian dead reckoning is an important task for smartphones and smart wearable devices. Traditional gait model-based methods make it difficult to match multiple motion patterns of different users. The performance of inertial odometry based on deep learning is impressive.At present, most of the data-driven pedestrian dead reckoning backbone networks use Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTMs), Residual Network(ResNet) and Temporal Convolutional Network(TCN) and related variants. In recent years, Transformer architecture has greatly progressed in large models and multi-scenes. However, the Transformer has many parameters and is not friendly to mobile edge devices. Given the demand for edge devices for lightweight models and the time series characteristics of inertial sensor data, this paper designs a lightweight Transformer inertial odometry network based on LightViT for edge devices. It uses group convolution to extract the characteristics of IMU data while reducing network parameters and fuses IMU data time and space information through a multi-head self-attention mechanism and bidirectional attention multi-layer perceptron. It is verified on three mainstream data sets (RONIN, OXIOD, RIDI). Compared with the ResNet network, which has the best effect in RONIN, ATE is reduced by 8% on average. When the network parameters are reduced by 76.6%, FLOPs are reduced by 77%, and the inference time is 12% faster. With comparable accuracy to ResNet, the network reduces parameters by 87%, FLOPs by 86%, and accelerates inference by 76%. It provides a new reference for future backbone network selection in resource-constrained pedestrian dead reckoning systems.

Unsupervised Clustering for the Chronological Analysis of Digitized Paintings

2026-06-04T11:00:00+03:00

In this study, unsupervised clustering techniques are applied to digitized paintings of two famous Pointillist artists, Paul Signac and Georges Seurat. That way we temp to reveal natural division of each artist oeuvre into different time periods and moreover to unlock the features that mostly contribute to the shaping of clusters and thus to conclude the evolution of the artistic styles adopted by the painter over time. A large set of engineered features is used and the feature ranking process identifies the most important features: run-length features, fractal dimension, and statistical features derived from hue histograms. Interpretation of the analysis of the most important features shaping the clusters in terms of painters’ style evolution is consistent with evidence of art experts.