Search Results (316)

This work provides a structured synthesis of visual cryptography, a secret sharing technique that enables image reconstruction only when a specific number of shares are combined, with decryption performed visually by overlaying the shares. Although conceptually simple and distinctive in its reliance on the human visual system rather than complex computation, this method has been predominantly studied from theoretical and construction-oriented perspectives. The work consolidates fundamental concepts, mathematical foundations, operational principles, and security considerations of selected schemes, providing a common context for analyzing their characteristics. Particular attention is given to known attack vectors, including information leakage from individual shares and integrity violations caused by forged shares, together with corresponding mitigation approaches reported in the literature. In addition to this synthesis, the work presents an experimental investigation of the $\varphi$ correlation coefficient and its behavior for genuine and forged shares. Experiments conducted on a dataset of 100 images show that genuine–genuine share pairs consistently exhibit higher mean $\varphi$ correlation values than genuine–forged pairs, although the observed values depend on the underlying scheme. While these results suggest that $\varphi$ correlation may provide useful information for share authenticity analysis, the experiment used the same forged target image throughout the dataset, limiting the variation of the forged samples and potentially making the observed differences partly dependent on the selected target image. Finally, three Proof-of-Concept application scenarios demonstrate possible integrations of visual cryptography into QR code security, physical document verification, and IoT access control, illustrating its potential use in practical security systems. Full article

Industry 5.0 demands that engineering graduates develop competencies that integrate technical knowledge with human-centricity, sustainability and resilience. However, engineering education has not always advanced at the same pace, and effective educational designs that operationalise these principles within applied learning contexts remain scarce. This paper presents Innovation Day, a four-hour challenge-based learning experience that operationalises Industry 5.0 principles through an artificial intelligence (AI)-supported cyber–physical logistics challenge in management engineering master’s programmemes. The activity engaged 47 students organised into 14 teams across two master’s degrees at the Universitat Politècnica de València. Students progressively developed and integrated data structuring, QR-based identification, sequencing and storage assignment logic, computer vision and collaborative robotics within a single supervised automation workflow. All 14 teams completed the first three phases, while completion rates reached 71% in Phase 4 and 64% in Phase 5, reflecting the increasing demands of system integration. Results also show that generative AI acted as a pedagogical tool, enabling students with limited programming experience to develop functional Python applications while maintaining human validation and accountability. The experience demonstrates that complex Industry 5.0 scenarios can be translated into manageable, transferable educational activities through phased challenge design, functional validation gates and structured AI-supported learning. Full article

In the contemporary digital era, image watermarking is essential for protecting intellectual property due to the widespread unauthorized distribution of digital content. In this work, a robust and efficient image watermarking scheme for copyright protection is proposed. The method integrates wavelet packet decomposition (WPD) with an ensemble of bagged tree classifiers, forming the BT-WPD framework. In the proposed approach, wavelet packet coefficients extracted from each color channel are reorganized into structured batches that capture spatial frequency characteristics, enabling effective watermark embedding in the WPD domain guided by the bagged tree ensemble model. Experimental results demonstrate that the proposed method achieves high imperceptibility, with a peak signal-to-noise ratio (PSNR) exceeding 60 dB, while maintaining strong robustness against various image processing attacks. The method also exhibits low computational complexity during watermark extraction, making it suitable for practical applications. Furthermore, the framework is extended to support Quick Response (QR) code watermark embedding, demonstrating enhanced robustness and versatility for copyright protection in digital media systems. Full article

Indoor navigation remains challenging for students with visual impairments because GPS is unavailable indoors and building layouts are often complex. This paper presents a wearable marker-assisted navigation system integrating QR code localization, SSD MobileNet V3 obstacle detection, TFmini-S LiDAR ranging, A*-based dynamic route planning, and audio feedback on a Raspberry Pi 5. The main contribution is an analytical framework relating marker spacing to predicted localization uncertainty and defining a latency budget for obstacle warnings. A confidence-weighted sensor-fusion method is developed analytically but was not implemented in the evaluated prototype, in which the QR code, camera, and LiDAR channels operated independently. The proposed fusion method and the simulated multi-floor planning extension require further experimental validation. Controlled tests produced a mean positioning error below 1.2 m, a LiDAR ranging MAE of 8.3 cm, and an object-detection throughput of 6–9 FPS. A pilot field evaluation covered nine routes totalling 901 m across two buildings and included one participant with self-reported vision loss of approximately 95%. All route trials were completed, although some required researcher assistance. The system remains a proof of concept and has not yet been evaluated against a baseline or with a sufficiently large target-user sample. Full article

Addressing the critical challenge of signal degradation in biosensor cardiac monitoring, this paper introduces an efficient hybrid dilated–long short-term memory (LSTM) with residual learning (HDLR), which is a novel architecture engineered for a high-fidelity electrocardiogram (ECG) denoising signal. The proposed HDLR model synergistically integrates with dilated convolutions to expand the receptive field for multi-scale feature extraction. This is an LSTM-based backbone used to resolve long term temporal dependencies with residual learning paths to stabilize gradient flow and accelerate convergence. The proposed HDLR architecture integrates three core functional components with dilated convolutional layers utilized for local temporal feature extraction, where varying dilation rates expand the receptive field to capture both local waveform patterns and broader morphological structures without increasing computational complexity. Experimental results demonstrate a significant leap in performance, with the HDLR model achieving a mean squared error (MSE) of 0.002176, a signal-to-noise ratio (SNR) of 14.4420 dB, and a Matthews correlation coefficient (MCC) of 0.9822. Beyond quantitative metrics, the proposed HDLR architecture exhibits exceptional robustness in preserving cardiac morphology, specifically the P-wave and QRS complex of the ECG signal under stochastic noise conditions. These findings underscore the HDLR model’s potential as a backbone for next generation, real time diagnostic systems in intelligent healthcare. Full article

Introduction: There are no data in the literature regarding changes in cardiac troponin levels following radiofrequency ablation (RFA) in patients undergoing ablation for left ventricular summit-derived premature ventricular complexes (LVS-PVCs). In this study, we aimed to investigate changes in high-sensitivity cardiac troponin I (hs-cTnI) levels after RFA in patients with LVS-PVCs and to evaluate the clinical utility of hs-cTnI in this setting. Method: In this retrospective cohort study, 109 patients with LVS-PVCs who underwent RFA between 2017 and 2025 were included. In addition to routine evaluations, hs-cTnI levels were measured 24 h after the procedure in all patients. Long-term procedural success was assessed at 6 months using 24 h Holter electrocardiography. Patients were divided into two groups according to procedural outcome (successful vs. unsuccessful RFA). Results: In patients with LVS-PVCs, the long-term procedural success rate of RFA was 72.5% (n = 79). Compared with patients with unsuccessful long-term outcomes, those with successful RFA had significantly higher hs-cTnI levels, longer QRS-duration, a higher prevalence of RBBB morphology, more frequent ablation at the left coronary cusp (supravalvular/subvalvular), coronary sinus sites (GCV/AIV), and multiple ablation sites, as well as higher maximum RFA power. In contrast, the maximum deflection index and the presence of a pattern break in lead V2 were significantly lower in patients with long-term procedural success. In logistic regression analysis, hs-cTnI level, maximum RFA power, and ablation within the GCV/AIV were independently associated with RFA success (OR = 1.133, 95% CI: 1.043–1.231, p < 0.001; OR = 1.446, 95% CI: 1.176–1.773, p < 0.001; and OR = 3.281, 95% CI: 1.325–21.219, p = 0.002, respectively). ROC curve analysis demonstrated that hs-cTnI and maximum RFA power thresholds of 700 ng/L and 40 W, respectively, predicted RFA success with acceptable sensitivity and specificity. Conclusions: In patients with LVS-PVCs, hs-cTnI levels increase after RFA. Higher hs-cTnI levels are independently associated with procedural success. Measurement of hs-cTnI at 24 h after RFA may serve as an objective and noninvasive marker of procedural success in patients with LVS-PVCs. Full article

In healthcare facilities, patient wayfinding challenges—especially in complex, multi-unit or campus-scale healthcare environments—extend the patient journey and increase front-desk workload. This work investigates the integration of a Digital Twin-orchestrated, IoT-enabled architecture that links digital scheduling to in-clinic guidance through standardized tokens. The proposed approach relies on (i) an administrative mapping layer that binds unique QR identifiers to cabinets, specialties, clinicians, and human-readable location labels, (ii) an appointment confirmation workflow that issues a confirmation code and delivers an e-mail package including a QR token and an RFC 5545-compliant (Internet Calendaring and Scheduling Core Object Specification) attachment, and (iii) a kiosk-like model (embedded, single-board computer with camera-based QR scanning) that resolves tokens via REST endpoints and presents deterministic guidance using a finite-state-machine workflow with explicit fallback from appointment resolution to cabinet-level QR mapping. An optional biometric module is included for recurrent visits via a database linkage layer, enforced by a single-owner rule for the serial sensor interface to prevent concurrency faults. Scenario-based validation confirms end-to-end operability and robustness, with time–motion quantification scheduled for future on-site evaluation. Full article

Integrating IT systems in the automotive industry remains a technically complex and costly process, particularly in environments based on legacy ERP and WMS platforms. Existing research discusses QR codes and process automation separately. However, little attention is paid to non-intrusive integration architectures that combine standardized identification and intelligent automation. This study develops and implements a universal logistics integration model based on GS1-compliant QR codes and JSON data structures combined with intelligent process automation (IPA). A case study from an automotive company is presented. Analysis indicates that the main loss factors include misidentification of assets, manual data entry, and lack of feedback on delivery status. The architecture proposed in this manuscript consists of three modules: INTELOGBOT (2.07), IPABOT (1.79), and APIBOT (1.79). A structured QR-JSON identifier schema was also designed to ensure platform-independent data exchange. This eliminated manual data re-entry and enabled real-time inventory and delivery synchronization. Furthermore, it also automated logistics documentation and reduced identification errors in inbound and outbound operations. The research contribution consists of developing a solution that enables the connection of QR codes with intelligent IPA-based bots, providing a repeatable framework for advanced automation of logistics processes in complex supply chains. Full article

Wearable electrocardiogram (ECG) monitoring enables continuous cardiovascular assessment, yet signals acquired in ambulatory environments are inevitably corrupted by baseline wander, electrode motion artifacts, and muscle interference, which obscure diagnostically critical waveform features. Existing deep learning denoisers rely on heuristic attention mechanisms and time-domain-only losses, lacking principled control over what information the network retains or discards. To address this limitation, we propose EFIB-Net, an information bottleneck-guided multi-resolution network for robust ECG denoising. The framework introduces two complementary components: an efficient frequency-guided attention module that derives temporal attention weights directly from the energy distribution of parallel multi-resolution convolutional branches, requiring only four learnable parameters while providing physically interpretable feature selection that naturally highlights QRS complexes, and a variational information bottleneck constraint at the encoder–decoder bottleneck that forces the latent representation to retain only reconstruction-relevant information and discard noise, guided by a spectral–temporal composite loss. To the best of our knowledge, we are among the first to explicitly introduce the information bottleneck principle into deep-learning-based ECG signal denoising. Experiments on the MIT-BIH Arrhythmia Database show that EFIB-Net outperforms ten traditional and deep learning baselines across four standard metrics—signal-to-noise ratio (SNR), root mean square error, percentage root-mean-square difference, and correlation coefficient; at an input SNR of −5 dB it reaches $8.12$ dB output SNR, surpassing the strongest attention-based competitor by 1.77 dB ($p<0.01$) while using only 0.45 M parameters and 10.8 ms inference latency per segment; downstream evaluation further demonstrates that the denoised signals achieve 99.18% R-peak detection sensitivity and 91.26% heartbeat classification F1-score, both within approximately one percentage point of the clean-signal upper bound, making it practical for real-time cardiac monitoring on resource-constrained wearable devices. Zero-shot cross-database evaluation on the QT Database further confirms generalizability, with only 0.54 dB degradation without retraining. Full article

The high prevalence of cardiovascular diseases and the extensive application wearable electrocardiogram (ECG) devices for long-term monitoring have posed significant challenges for the transmission, storage, and real-time processing of massive amounts of ECG data. Consequently, efficient ECG compression and reconstruction have become a research priority in remote ECG monitoring. Traditional compressed sensing is complex and has high computational overhead, while single deep learning models cannot simultaneously extract local waveforms and model temporal dependencies. To address these shortcomings in the reconstruction process, this paper presents a CNN-LSTM-Attention hybrid model. This model utilizes a convolutional neural network (CNN) to capture local ECG waveform features, employs a long short-term memory (LSTM) network to learn long-term temporal dependencies, and introduces an attention mechanism to weight and fuse key diagnostic features, enabling accurate focus on key components including the QRS complex and ST segment. Experimental results on the MIT-BIH Arrhythmia dataset demonstrate that across the full compression range of 0.1–0.9, the proposed model achieves favorable comprehensive performance. Its PRD is stabilized at 10–12%, the SNR stays above 20 dB, and the RMSE is mostly lower than 0.25 mV. In terms of reconstruction accuracy and stability, our model outperforms the single CNN and CNN-LSTM models by a large margin. Full article

Ischemic Heart Disease (IHD) is the leading cause of cardiovascular mortality worldwide. Early detection of ischemic changes using electrocardiogram (ECG) signals is vital for timely intervention and enhanced clinical outcomes. However, the diagnosis of IHD varies significantly between men and women. Women often present with atypical symptoms, and their cardiovascular risk is frequently underestimated, which leads to delayed diagnosis. Also, existing approaches face challenges in subtle early-stage abnormalities, single-lead ECG presentation, and the limited interpretability of deep learning models. These cause significant challenges to the accurate diagnosis of IHD. To address these, this study proposes a gender-aware framework, Gender-Aware Morphology Encoder Network (GAMENet), for early ischemic heart disease detection using 12-lead ECG signals with clinical metadata. A novel GAMENet is developed using the PTB-XL database. The Adaptive Morphology Deviation Encoder (AMDE) through Morphology Segment Extraction (MSEG-R) using R-Peak anchoring, isolates clinically relevant waveform components (P-wave, QRS complex, ST-segment, and T-wave) from the preprocessed ECG signals. The feature vector of morphology features is passed through dense layers with dropout regularization and a SoftMax classifier. Statistical and comparative analysis ensures that the proposed framework enables accurate IHD classification and improved interpretability. Full article

This paper presents the RaDICAL monostatic passive radar framework for target detection and localization using a sparse uniform circular array (SUCA), multifrequency dither, and dictionary-based waveform processing. Rather than forming conventional spatial images or relying on explicit Doppler/TDOA/FDOA estimation, the proposed method encodes target geometry directly into a composite receiver waveform and performs localization through hypothesis testing using a library of predicted waveform responses. A SUCA-based signal model is developed for both point and extended targets, and detection/localization formulated as a waveform-domain dictionary matching problem using normalized complex correlation and QR-domain processing. A reproducible MATLAB-based Monte Carlo study evaluates waveform separability, probability of detection versus input SNR, receiver operating characteristic (ROC) behavior, localization performance, and receiver power balance. The results demonstrate that multifrequency dither produces distinctive composite waveforms with strong hypothesis separability and stable waveform domain recognition performance. ROC analysis and detection simulations showed reliable target detection at input SNR levels on the order of −10 to 0 dB, consistent with the coherent processing gain achieved through waveform-domain correlation processing. The corresponding power-balance analysis indicates that reliable detection and localization are feasible using modest illuminator EIRP and compact receiver dimensions. These results support the feasibility of compact reference-free waveform domain passive sensing for joint target detection and localization. Full article

Background and Objectives: Premature ventricular contractions are among the most common arrhythmias encountered in clinical practice. However, this disorder can be associated with arrhythmia-induced cardiomyopathy or be the first sign of primary myocardial diseases. Certain morphologies of premature ventricular contractions are associated with a higher risk for sudden arrhythmia and cardiac dysfunction in the adult population. There is data on the clinical value and significance of the contraction morphology in adults, but there is a lack of such data for children. Materials and Methods: This observational prospective study of pediatric outpatients with premature ventricular contractions was conducted at Vilnius University Hospital Santaros Clinics. Inclusion criteria comprised children aged 3–17 years with more than 5% premature ventricular contractions over 24 h. Exclusion criteria included previously diagnosed congenital heart defects and cardiomyopathies, channelopathies, or the presence of any acute condition. The electrocardiographic morphology and measurements were assessed, analyzed, and described in this study. Results: The electrocardiograms of 80 patients were analyzed according to the ECG-estimated morphology of the arrhythmia complex, arrhythmic QRS complex duration, ratio with the normal QRS complex, and maximum deflection index in V5–V6 derivations. Cardiac MRI abnormalities (8 of 30 MRI studies) was reliably associated with a PVC duration of >150 ms and the maximal amount of extrasystoles per 24 h, with a median amount of 29.6%. A long postcoupling interval (>0.9 s) was associated with PVC progression. Conclusions: In this exploratory pediatric cohort, wider PVC QRS duration and higher maximal PVC burden were associated with ventricular MRI abnormalities, while longer postcoupling interval was associated with PVC progression. Full article

Precise respiration assessment is crucial for heart rate variability (HRV) interpretation as respiratory components—particularly respiratory sinus arrhythmia (RSA)—provide essential information on vagally mediated regulation. Conventional single-lead electrocardiogram-derived respiration (EDR) methods measure the amplitude modulation of the QRS-waveform caused by respiratory chest movements. This causes a displacement of the electrical heart axis in relation to the ECG lead axis, typically within the 2D frontal plane of the Einthoven electrode montage. Another approach is based on heartbeat acceleration and deceleration during respective inspiration and expiration causing RR interval modulation. However, interval-based methods depend on the complexity of sympathovagal factors that affect RSA. The present feasibility study accounts for the 3D rotational movement of the electrical heart axis during the respiratory cycle and avoids non-respiratory neuromodulatory confounds. The beat-to-beat cardiac rotation was extracted from Frank-XYZ coordinates reconstructed via a four-electrode EASI device. In a pilot study with data from 19 healthy adults performing acoustically paced breathing (6–18 bpm), three surrogates (${\mathrm{RR-Interval}}_{\mathrm{EDR}}$, ${\mathrm{R-Amplitude}}_{\mathrm{EDR}}$, ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$) were compared using a unified Python 3.11.13 pipeline (3D VCG R-peak detection, multivariate Mahalanobis artifact correction, wavelet-based analysis) against a synthetic reference derived from the instructed breathing schedule. The results demonstrated a consistently lower estimation error and higher reference-based signal-to-noise ratio (refSNR), measuring spectral alignment with the paced-breathing trajectory for ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ and achieving a mean refSNR of 6.01 dB (vs. 4.62 dB for ${\mathrm{RR-Interval}}_{\mathrm{EDR}}$ and 3.20 dB for ${\mathrm{R-Amplitude}}_{\mathrm{EDR}}$) and a mean absolute estimation error of 0.016 Hz (vs. 0.050 Hz and 0.032 Hz, respectively). Notably, ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ and ${\mathrm{R-Amplitude}}_{\mathrm{EDR}}$ performance slightly improved at higher heart rates, consistent with the interpretation that higher cardiac sampling density benefits spectral resolution for chest movement-based methods, whereas ${\mathrm{RR-Interval}}_{\mathrm{EDR}}$ showed no significant heart rate dependence. Furthermore, ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ was compared with the EDR results obtained by applying the Kubios-HRV Premium software (version 3.5.0). Kubios-EDR yielded higher precision at elevated breathing frequencies, whereas ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ outperformed Kubios-EDR at breathing rates below 10 bpm—a range that is particularly relevant for vagally activating slow breathing protocols or treatments. Future work should validate this method using a direct respiration measurement under spontaneous natural breathing conditions. Full article

Permanent magnet synchronous generators (PMSGs) are inevitably subject to aperiodic and periodic disturbances due to complex operating conditions and internal coupling effects. To improve speed regulation under such disturbances, this paper develops a hierarchical control framework that integrates a parameter-decoupled extended state observer (DESO) with quasi-resonant control. A novel parameter decoupling method enables independent tuning of the observer bandwidth and controller parameters, while the quasi-resonant control module specifically targets periodic torque ripples caused by the tower shadow effect. Simulation results under stochastic wind conditions confirm that the proposed QR-DESO significantly outperforms conventional methods, reducing the speed tracking root mean square error (RMSE) by $61.8\%$ and the total harmonic distortion (THD) to $0.17\%$. The system also exhibits strong robustness against $\pm 20\%$ parameter mismatches, validating its effectiveness for offshore wind power applications. Full article